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240 x 159–Pantone 450 C & 613 C

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,,,,,,,,uman sensory perception of clothing involves a series of complex interactive processes, ,,,,,,,,,including physical responses to external stimuli, neurophysiological processes for decoding stimuli through the biosensory and nervous systems inside the body, neural responses to psychological sensations, and psychological processes for formulating preferences and making adaptive feedback reactions. Clothing biosensory engineering is a systematic and integrative way of translating consumers’ biological and sensory responses, and psychological feelings and preferences about clothing, into the perceptual elements of design. It enables a link between scientific experimentation and commercial application for the development of economic solutions to practical technical problems. Clothing biosensory engineering quantifies the decision-making processes through which physics, mathematics, neurophysiological and engineering techniques are applied to convert resources to meet various sensory requirements – visual/thermal/mechanical. It includes theoretical and experimental observations, computer simulations, test methods, illustrations and examples of actual product development. Dr Li is a Professor at The Institute of Textiles and Clothing at The Hong Kong Polytechnic University, Hong Kong. He is a Fellow of The Textile Institute, a member of several professional bodies and has been involved with more than 350 scientific publications and patents. Dr Wong is a Postdoctoral Fellow at The School of Nursing, The Hong Kong Polytechnic University, Hong Kong.

CRC Press LLC 6000 Broken Sound Parkway, NW Suite 300 Boca Raton FL 33487 USA CRC order number WP9095 ISBN-10: 0-8493-9095-8

Li and Wong

Woodhead Publishing Ltd Abington Hall Abington Cambridge CB1 6AH England www.woodheadpublishing.com ISBN-13: 978-1-85573-925-3 ISBN-10: 1-85573-925-9

Clothing biosensory engineering

H

Radiation

Interaction between skin and clothing

Skin

Clothes

Convection

Conduction

Clothing biosensory engineering Edited by Y. Li and A. S. W. Wong


Clothing biosensory engineering


Related titles: Effect of mechanical and physical properties on fabric hand (ISBN-13: 978-1-85573-918-5; ISBN-10: 1-85573-918-6) This new multiauthored collection addresses the effect of mechanical and physical properties on the way a fabric feels. It begins with concepts and understanding of fabric hand, covering development of fabric hand evaluation, the effect of fibre, yarn and fabric factors as well as finishing on hand, including the mechanical and physical properties. The latter part of the book goes on to cover statistical methods in evaluating hand and a comparison of hand evaluation in different cultures. Friction in textiles (ISBN-13: 978-1-85573-920-8; ISBN-10: 1-85573-920-8) The action of friction in fibres and textiles plays major roles in product performance, from the generation of the fibre through to the way a garment responds to wear. This book addresses both the beneficial and detrimental processes of friction, with chapters on fibre structure, measurement techniques, static electrification, shrink proofing and felting, surface modification treatments, friction in fabrics and fibres and its role in textile processing. Clothing appearance and fit; Science and technology (ISBN-13: 978-1-85573-745-7; ISBN-10: 1-85573-745-0) This comprehensive book provides a critical appreciation of the technological developments and scientific understanding related to clothing appearance and fit. It bridges recent active research and development in beauty and fashion design, with garment evaluation technology, drape and human anthropometrics and sizing. It includes many industrial standards, techniques and practices that will make it an essential reference for researchers, academics, professionals and students in clothing and textile academia and industry. Biomechanical engineering of textiles and clothing (ISBN-13: 978-1-84569-052-6; ISBN-10: 1-84569-052-4) Many modern consumers are demanding apparel products with higher added values in terms of functional performance to satisfy various aspects of their biological and psychological needs during wear. Naturally, engineering apparel products for biological and psychological health has become an integrated part of the concept of bioengineering. Biomechanical engineering of textiles and clothing addresses the issues of designing and producing textiles and clothing for optimum interaction with the body. It covers fundamental theories, principles and models behind design and engineering for the body’s biomechanics. Contact problems arising between textiles/clothing and the body are discussed along with the mechanics of fibres, yarns, textiles and clothing. Material properties will also be covered in relation to mechanical performance. Details of these books and a complete list of Woodhead’s titles can be obtained by: • •

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Clothing biosensory engineering Edited by Y. Li and A.S.W. Wong

Cambridge England


Published by Woodhead Publishing Limited in association with The Textile Institute Woodhead Publishing Limited, Abington Hall, Abington Cambridge CB1 6AH, England www.woodheadpublishing.com Published in North America by CRC Press LLC, 6000 Broken Sound Parkway, NW, Suite 300, Boca Raton, FL 33487, USA First published 2006, Woodhead Publishing Limited and CRC Press LLC Š 2006, Woodhead Publishing Limited The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing Limited. The consent of Woodhead Publishing Limited does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing Limited for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress. Woodhead Publishing ISBN-13: 978-1-85573-925-3 (book) Woodhead Publishing ISBN-10: 1-85573-925-9 (book) Woodhead Publishing ISBN-13: 978-1-84569-146-2 (e-book) Woodhead Publishing ISBN-10: 1-84569-146-6 (e-book) CRC Press ISBN-10: 0-8493-9095-8 CRC Press order number: WP9095 The publishers’ policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp which is processed using acid-free and elementary chlorine-free practices. Furthermore, the publishers ensure that the text paper and cover board used have met acceptable environmental accreditation standards. Typeset by SNP Best-set Typesetter Ltd., Hong Kong Printed by TJ International Limited, Padstow, Cornwall, England


Contents

Editor contact details Preface 1

Introduction to clothing biosensory engineering

xiii xv 1

YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong

1.1 1.2 1.3 1.4 1.5 1.6

Consumer trends Definition of sensory comfort Human–clothing–environment system Clothing biosensory engineering Acknowledgements References

1 3 4 6 7 7

2

Psychology and sensory comfort

9

YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10

Perception of comfort Psychological research techniques Comfort sensory descriptors Psychophysics Scales of measurement Scales to measure direct responses Wear trial techniques Conclusion Acknowledgements References

9 9 11 13 15 16 22 24 24 24 v


vi

Contents

3

Neurophysiology of sensory perceptions

28

YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8

Senses and comfort Neurophysiological basis of sensory perceptions Perception of sensations related to mechanical stimuli Perception of thermal and moisture sensations Perception of texture Perception of fabric hand Acknowledgements References

28 29 32 42 48 50 54 54

4

Physiology of thermal comfort

60

YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University

4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8

Introduction Thermal comfort Thermophysiology of the human body Thermoregulation of the human body Dynamic thermal interaction between the body and clothing Nomenclature Acknowledgements References

60 61 64 66 68 70 71 71

5

Physics of thermal comfort

74

YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11

Introduction Heat and moisture transfer Dynamic heat and moisture transfer in fabric Moisture exchange between fiber and air Boundary conditions Physical properties of fibers and fabrics Method of solution Moisture sorption of wool fabrics Behavior of fabrics made from different fibers Acknowledgements References

74 74 76 79 83 84 84 86 86 90 90


Contents 6

Thermal and moisture sensations

vii 93

YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8

Introduction Coolness to the touch Warmth to the touch Dampness sensation Clamminess and moisture buffering during exercise Environmental buffering Acknowledgements References

93 93 99 104 107 110 112 112

7

Tactile sensations

116

YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong

7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9

Introduction Fabric prickliness Fabric itchiness Fabric stiffness Fabric softness Fabric smoothness, roughness and scratchiness Garment fit and pressure comfort Acknowledgements References

116 116 120 121 127 131 141 147 147

8

Dimensions of sensory comfort perceptions

151

YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong

8.1 8.2 8.3 8.4 8.5 8.6 8.7

Individual sensations involved in the perception of sensory comfort Identification of sensory factors Thermal–wet comfort Tactile comfort Clothing pressure comfort Acknowledgements References

151 152 159 160 161 164 164


viii

Contents

9

Overall comfort perception and preferences

167

ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong and YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

9.1 9.2

9.5 9.6

Introduction Influences of different factors toward overall comfort perception Calculation of subjective preference on clothing Relationship between overall comfort perception and preference Acknowledgements References

10

Prediction of clothing sensory comfort

9.3 9.4

167 168 169 171 175 175 178

ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong and YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

10.1 10.2 10.3 10.4 10.5 10.6

10.9 10.10

Introduction Prediction of fabric hand Predictability of sensory comfort Predictability of subjective preferences Prediction of sensory factors Prediction of clothing sensory comfort on the basis of subjective measurements Prediction of clothing sensory comfort on the basis of fabric physical properties Application of hybrid models in the prediction of clothing sensory comfort Acknowledgements References

185 187 187

11

Thermal properties

189

10.7 10.8

178 178 180 181 183 183 184

J.Y. HU, YI LI and K.W. YEUNG, Institute of Textiles and Clothing, The Hong Polytechnic University, Hong Kong

11.1 11.2 11.3 11.4 11.5 11.6 11.7

Introduction Heat production and heat loss Thermal comfort Thermal insulation Thermal conductivity Cool/warm feeling Thermal manikin

189 189 191 192 194 194 198


Contents

ix

11.8 11.9 11.10

Other apparatus for fabric thermal functional evaluation Acknowledgements References

201 203 203

12

Water vapor transfer

206

J.Y. HU, YI LI and K.W. YEUNG, Institute of Textiles and Clothing, The Hong Polytechnic University, Hong Kong

12.1 12.2 12.3 12.4 12.5

206 206 208 213

12.6 12.7 12.8

Introduction Moisture phase changes Water vapor transfer Water vapor transfer with temperature gradient Comparison of water vapor transfer with and without temperature gradient Conclusion Acknowledgements References

13

Liquid moisture transfer

218

214 215 215 215

J.Y. HU, YI LI and K.W. YEUNG, Institute of Textiles and Clothing, The Hong Polytechnic University, Hong Kong

13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9

Introduction Absorbency Wettability Waterproof Contact angle Moisture management Experimental Acknowledgements References

218 218 220 221 223 223 229 233 233

14

Coupled heat and moisture transfer

235

S.X. WANG and YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

14.1 14.2 14.3 14.4 14.5 14.6 14.7 14.8

Introduction Simulation of coupled heat and moisture transfer Moisture transfer with temperature gradient The role of phase change material (PCM) fabrics Effect of PCM fabrics Measuring the thermal and moisture buffering effects of fabrics Acknowledgements References

235 235 238 238 245 248 250 250


x

Contents

15

Air permeability

252

J.Y. HU, YI LI and K.W. YEUNG, Institute of Textiles and Clothing, The Hong Polytechnic University, Hong Kong

15.1 15.2 15.3 15.4 15.5

Introduction Measurement of air permeability Humidity-dependent air permeability Acknowledgements References

252 254 258 259 259

16

Mechanical tactile properties

261

J.Y. HU, YI LI and K.W. YEUNG, Institute of Textiles and Clothing, The Hong Polytechnic University, Hong Kong

16.1 16.2 16.3 16.4 16.5 16.6 16.7 16.8

Introduction Objective measurement of fabric tactile properties Measurement of subjective tactile sensations Results of FSTT and KES testing systems Relationships between subjective sensations and objective measurements Conclusion Acknowledgements References

261 264 267 267 277 282 282 282

17

In vivo physiological measurements

285

J.Y. HU, YI LI and K.W. YEUNG, Institute of Textiles and Clothing, The Hong Polytechnic University, Hong Kong

17.1 17.2 17.3 17.4 17.5 17.6 17.7 17.8 17.9 17.10 17.11 18

Introduction Temperature measurements Humidity measurements Pressure measurements Sweating rate and skin wetness measurements Skin blood flow measurements Oxygen consumption measurements Heart rate measurements Conclusion Acknowledgements References

285 286 292 293 294 294 296 297 298 299 299

Application of clothing biosensory engineering

301

YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong


Contents

xi

18.1 18.2 18.3 18.4 18.5 18.6 18.7 18.8

Introduction Area of application Consumer research New product development Consumer subjective evaluation Test methods and instruments Acknowledgements References

301 301 302 304 307 307 309 309

19

Mechanical and thermal sensory engineering design

311

YI LI and ZHANG WANG, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and XIN ZHANG, Xian University of Engineering Science & Technology, Xian, China

19.1 19.2

311

19.4 19.5 19.6 19.7 19.8 19.9 19.10 19.11

Introduction Functional requirements of a mechanical sensory engineering design system Fundamental work in the development of mechanical engineering design system (MEDS) Structural organization of the system Example Thermal sensory engineering design Thermal sensory engineering design process and system Application of TSEDS Conclusion Acknowledgements References

20

Sensory comfort of denim product

335

19.3

313 316 325 327 330 330 331 332 332 332

YI L. KWOK, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong and XIN ZHANG, Fashion College, Xian University of Engineering Science and Technology, Xian, China

20.1 20.2 20.3 20.4 20.5 20.6

Introduction Human subjective perception on denim products Study of sensory comfort in denim jeans Conclusion Acknowledgements References

335 336 337 348 348 348


xii 21

Contents Sensory comfort of tennis wear

350

ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, YI L. KWOK and YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

21.1 21.2 21.3 21.4 21.5 21.6 22

Introduction Experimental Results and findings Conclusion Acknowledgements References

350 353 354 363 364 364

Sensory comfort of aerobic wear

366

ANTHONY S.W. WONG, School of Nursing, The Hong Kong Polytechnic University, Hong Kong and YI LI, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

22.1 22.2 22.3 22.4 22.5 22.6 22.7 22.8 22.9

Introduction Experimental Result Sensory comfort during exercise Fabric properties and overall comfort performance Comfort performance of different brands Conclusion Acknowledgements References

366 369 370 374 376 378 378 378 379

Index

381


Editor contact details

Professor Yi Li Institute of Textiles and Clothing The Hong Kong Polytechnic University Hung Hom, Kowloon Hong Kong Email: tcliyi@inet.polyu.edu.hk Dr Anthony S.W. Wong School of Nursing The Hong Kong Polytechnic University Hung Hom, Kowloon Hong Kong Email: hsaswong@inet.polyu.edu.hk

xiii


Preface

This book is an updated and expanded version of the research monograph entitled The Science of Clothing Comfort, published in the Textile Progress series by The Textile Institute in 2001. The text is divided into three major parts. After an introductory chapter, the first part of the book discusses ways of understanding the human sensory perception of clothing. It includes nine chapters covering such topics as psychology (Chapter 2), neurophysiology (Chapter 3), thermophysiology (Chapter 4), the physics of thermal and moisture sensations (Chapters 5, 6 and 7), and ways of assessing comfort perception and preferences (Chapters 8, 9 and 10). The second part of the book focuses on test methods that have been developed to characterise the sensory properties of clothing. There are seven chapters which cover topics including measuring thermal properties (Chapter 11), water vapour transfer (Chapter 12), liquid moisture transfer (Chapter 13), coupled heat and moisture transfer (Chapter 14), air permeability (Chapter 15), mechanical tactile properties (Chapter 16) and in vivo physiological measurements (Chapter 17). The final part of the book explores how to apply the concepts of clothing biosensory engineering to the design and development of clothing. Subjects covered in these five chapters include introducing the key issues (Chapter 18), the application of new computer assisted design (CAD) technology (Chapter 19), and three case studies which cover the comfort performance of denim (Chapter 20), tennis wear (Chapter 21) and aerobic wear (Chapter 22).

xv


1 Introduction to clothing biosensory engineering YI LI 1 AND ANTHONY S.W. WONG 2 1 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong 2 School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

1.1

Consumer trends

Modern consumers are interested in clothing that not only looks good, but also feels good. Silverman17 reported that, according to a major consumer survey conducted by the International Research Institute on social change, 81% of consumers (80 and 83% of women and men, respectively) selected comfort as the top attribute they seek in apparel, followed by easy care and durability. In a survey of the importance of ten different attributes (brand, colour, comfort, design, durability, easy care, fabric, fashion, fit and price) of tight-fit garments, Wong and Li21 found that comfort and garment fit were the two most important attributes. On the other hand, a garment’s brand and fashion were rated as the two least important attributes. Suppliers of both natural and synthetic fibers have identified an increasing trend for consumers to involve more than just vision in their purchasing decisions – touch, smell, intuition and emotion are increasingly involved. As a result, greater importance is being attributed to the shopping and wearing experience. Interest is growing in fabrics that feel better and comfort is being reinforced as a key parameter in clothing.1 Fiber suppliers have identified comfort as one of the key attributes in consumers’ perception of the desirability of apparel products in all markets. However, retailers and processors feel some uncertainty with regard to consumer requirements. Under the heading comfort, the problem of prickle, especially in knitwear, is universally recognized, and light-weight, fluid fabrics are seen as highly desirable. Consumers find luxurious softness somewhat less appealing than processors and retailers do. Also, it has been shown that consumers’ requirements on comfort are changing along with products and wear situations. The underlying patterns and reasons behind this still await scientific investigation. 1


2

Clothing biosensory engineering

In the new century, the apparel market was highly competitive. To meet and even exceed consumers’ needs and expectations it becomes essential for enterprises in the textile and clothing industry to succeed in the market place. Synthetic fiber manufacturers have made a successful comeback through sportswear, where they have focused on comfort, movement and performance. By addressing consumers’ needs and using the growing interface between sportswear and fashion, synthetic fiber manufacturers have taken half of the fiber consumption market. Clearly, understanding and satisfying consumers’ needs and demands in relation to apparel products is crucial for the long-term survival of any enterprise in such a competitive market place. Eisner2 stated that Canadian apparel manufacturers have changed a lot in the past 20 years due to molded apparel, computerized manufacturing technology, microdenier fabrics and increased consumer demand for comfort. The number of apparel items with the Woolmark Total Easy Care label increased to two million in 2000, a 233% increase, indicating that apparel retailers are responding to greater consumer demand for apparel with increased comfort, high performance and low maintenance requirements.11 Comfort is a fundamental and universal need for consumers. Fujiwara et al.6 examined consumer perception of apparel quality. Results of cluster analysis showed that there are six basic clusters – workmanship in sewing, physiological comfort, usefulness, physical and chemical properties, suitability to individual preference and fashion or branding. Of these, the first four clusters formed a higher order cluster that expressed the intrinsic attributes of the apparel and the last two expressed the extrinsic attributes of the apparel. Kwok et al.12 investigated the major criteria which consumers coming from different age, gender, occupational, educational and income groups consider when purchasing denim apparel for their children. Results showed that comfort and fabric quality are the most important criteria in the selection of children’s denim apparel. Moisture comfort and pressure comfort are the most important considerations for denim apparel purchases in both summer and winter. As consumers, everything we do can be considered as an effort to improve our level of comfort in life. Clothing and textile products are the essential materials that we use every day to obtain physiological and psychological comfort and, more fundamentally, to ensure that the physical conditions around our bodies are suitable to sustain life. Therefore, research on clothing comfort has fundamental meaning for the survival of human beings and the improvement of our quality of life. From the viewpoint of business management of textile enterprises, clothing comfort research has substantial financial implications due to the effort to satisfy the needs and demands of consumers in order to obtain sustainable competitive advantages in modern consumer markets.


Introduction to clothing biosensory engineering

1.2

3

Definition of sensory comfort

Comfort, however, is a very complex and nebulous subject and one that is very difficult to define. Fourt and Hollies4 surveyed literature and found that comfort involves thermal and non-thermal components and is related to wear situations such as working conditions, non-critical and critical conditions. The physiological responses of the human body to a given combination of clothing and environmental conditions are predictable when the system reaches steady state. It can be calculated from knowledge of easily measured factors such as the thermal and moisture resistance of clothing, the climate conditions and the level of physical activity. This is the traditional area for clothing comfort study, and one in which a large amount of research has been published and applied to solve practical problems. For instance, the thermal insulation value clo has been widely used for designing and classifying military uniforms10 and calculating thermal comfort indices for indoor air conditioning.3,7,8 As clothing is in direct contact with the human body, it interacts with the body continuously and dynamically during wear, which stimulates mechanical, thermal and visual sensations. This has been termed sensory comfort, and it is a relatively new area in clothing comfort research. Smith19 defined comfort as well-being and freedom from pain. Slater18 defined comfort as ‘a pleasant state of physiological, psychological, and physical harmony between a human being and the environment’. Slater identified the importance of environment to comfort and defined three types of comfort: physiological, psychological and physical. Physiological comfort is related to the human body’s ability to maintain life, psychological comfort to the mind’s ability to keep itself functioning satisfactorily with external help, and physical comfort to the effect of the external environment on the body. Fris5 stated that apparel comfort results from a balanced process of heat exchange between the wearer’s environment and apparel, specifically the ability of apparel to convey heat and moisture from the skin to the environment. Li13 stated that comfort depends on subjective perceptions of visual, thermal and tactile sensations, psychological processes, body–apparel interaction and external environmental effect. Tarafder and Chatterjee20 stated that comfort is a subjective quality that comprises a range of physiological, psychological and physical variables. It has long been recognized that it is difficult to describe comfort positively, but discomfort can be easily described with terms such as prickle, itch, hot and cold. Therefore, a widely accepted definition for comfort is ‘freedom from pain and from discomfort as a neutral state’.9 Further, comfort has been considered to be both psychological and physiological and has a number of aspects.


4

Clothing biosensory engineering

Thermophysiological comfort – attainment of a comfortable thermal and wetness state, involves transport of heat and moisture through a fabric; • Sensorial comfort – the elicitation of various neural sensations when a textile comes into contact with skin; • Body movement comfort – ability of a textile to allow freedom of movement, reduced burden, and body shaping, as required; • Aesthetic appeal – subjective perception of clothing to the eye, hand, ear and nose, which contributes to overall feelings of well being and pleasantness on the part of the wearer.9 In all these definitions, there are a number of essential components. 1. Comfort is related to subjective perception of various sensations. 2. Comfort involves many aspects of human senses such as visual (aesthetic comfort), thermal (cold and warm), pain (prickle and itch) and touch (smooth, rough, soft and stiff). 3. The subjective perceptions involve a psychological process in which all relevant sensory perceptions are formulated, weighed, combined and evaluated against past experiences and present desires to form an overall assessment of comfort status. 4. The body–clothing interactions (both thermal and mechanical) play important roles in determining the comfort status of a wearer. 5. External environments (physical, social and cultural) have great impact on the comfort status of a wearer. This suggests that comfort is multidimensional and complex. Subjective perception of comfort involves complicated processes, in which a large number of stimuli from clothing and external environments communicate to the brain through multichannels of sensory responses to form subjective perceptions.

1.3

Human–clothing–environment system

Clothing is an integral part of human life and it has a number of functions: adornment, status, protection and modesty. Clothing, by means of latest fashion and aesthetic appeal, can provide a wearer with adornment that gives him or her the mental comfort of looking their best. Well-fitting and luxurious clothing can enhance the status of a wearer, giving them a feeling of satisfaction. Clothing can also provide the function of modesty by giving the wearer the mental comfort of having their body covered properly, in order to meet the standards of society and cover any physical flaws. However, the primary role of clothing is a layer or layers of barriers that protect the body against unsuitable physical environments. The protection provides a


Introduction to clothing biosensory engineering

5

number of functions: maintaining the correct thermal environment which is essential for the body to survive; protecting the body from injury from abrasion, radiation, wind, electricity, chemical and microbiological toxic substances. These traditionally classified functions of clothing indicate clearly that clothing plays very important roles at the interface between the human body and its surrounding environments in determining the subjective perception of the comfort status of a wearer. To understand how the subjective perception of comfort is derived, we can consider the human–clothing interaction as an open system as shown in Fig. 1.1. The human body is always in a state of dynamic interaction with its surrounding environment in physical, sensory, psychological and informational terms. In this system, there are a number of processes occurring interactively which determine the comfort status of a wearer. 1. Physical processes in clothing and surrounding environments – e.g. the heat and moisture transport in clothing, the mechanical interactions between clothing and the body and reflection and absorption of light by clothing, which provide the physical stimuli (or signals) to the body.

Moisture vapor

Radiation Interaction between skin and clothing

Skin Convection

Clothes

Conduction

1.1 Human clothing system.


6

Clothing biosensory engineering

2. Physiological processes in the body – e.g. the thermal balance of the body, its thermoregulatory responses and dynamic interactions with clothing and environment, which determine the physiological status of the body and its survival at critical conditions. 3. Neurophysiological processes – i.e. the neurophysiological mechanisms of the sensory reception system of the body in the skin, eyes and other organs, by which sensory signals are formulated from the interactions of the body with clothing and surrounding environments. 4. Psychological processes – i.e. the processes by which the brain forms a subjective perception of sensory sensations from the neurophysiological sensory signals and then formulates subjective overall perception and preferences by evaluating and weighing various sensory perceptions against past experiences and internal desires. These four types of processes take place concurrently. The physical processes in the environment and clothing follow the laws of physics, which determine the physical conditions for the survival and comfort of the body. The thermoregulatory responses of the body and the sensory responses of skin nerve endings follow the laws of physiology. The thermoregulatory and sensory systems respond to the physical stimuli from clothing and environment to ensure appropriate physiological conditions are being met for the survival of the body and to inform the brain of various physical conditions that influence the comfort status. The psychological processes are the most complex and those which we least understand. The brain needs to formulate subjective perceptions from the sensory signals from the nerve endings, in order to evaluate and weigh these sensory perceptions against past experiences, internal desires and external influences. Through these processes, the brain formulates a subjective perception of overall comfort status, judgements and preferences. On the other hand, the psychological power of the brain can influence the physiological status of the body through various means such as sweating, blood flow justification and shivering. These physiological changes will alter the physical processes in the clothing and external environment. These four types of processes interact with each other dynamically to determine the comfort status of a wearer at any specific moment. Therefore, comfort status is the subjective perception and judgement of a wearer on the basis of integration of all of these physical, physiological, neurophysiological and psychological processes and factors.

1.4

Clothing biosensory engineering

Clothing biosensory engineering is defined as the application of a systematic and integrative way of seeking the means to translate consumers’


Introduction to clothing biosensory engineering

7

biological sensory responses, psychological feelings and preferences about a product into perceptual elements of design. Biosensory engineering design is an iterative decision-making process in which physics, mathematics, neurophysiology and engineering techniques are applied to convert resources optimally to meet a specific and/or a combination of various sensory requirements from visual, thermal to mechanical sensations. It is a link between scientific experimentation and commercial applications to develop economical solutions to practical technical problems. Clothing biosensory engineering can be considered as an extension of the concepts of Kansei Engineering15,16 and Sensory Engineering,22 with the emphasis on the integrative application of the sciences behind human sensory perceptions. Human sensory perception of clothing involves a series of complex interactive processes, including (1) the physical processes of generating various external physical stimuli; (2) the neurophysiological processes of receiving, encoding, transporting and decoding the stimuli by relevant biosensors and nervous systems residing inside the human body; (3) the neuropsychological processes involved in the formulation of psychological sensations from the neurophysiological signals; and (4) the psychological processes of making judgements, formulating preferences and taking behavioral and/or psychological adaptive feedback actions.13,14

1.5

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the projects A174 and A188.

1.6

References

1. Anon., Fibres of the Nineties. Textile View Magazine, 1991(11): p. 1–11. 2. Eisner, M., Manufacturing Has Been A-Changing! Canadian Apparel Manufacturer, 1997. 21(1): p. 27–29. 3. Fanger, P.O., Thermal Comfort. 1970: Copenhagen, Danish Technical Press. 4. Fourt, L. and N.R.S. Hollies, Clothing: Comfort and Function. 1970: New York, Martin Dekker Inc. 5. Fris, M.M. Thermal comfort in clothes of different textile fabrics, in Joint International Conference of the Fiber Society. 1997. University of Mulhouse, Mulhouse, France. 6. Fujiwara, Y., C. Park, and Y. Tokoro, Consumer Perceptions of Apparel Quality. Part 1: Structure of Apparel Quality Perceived by Female College Students. Journal of the Textile Machinery Society of Japan, 1994. 47(2): p. 46 –51. 7. Gagge, A.P., A. Fobeletes, and L.G. Berglund, A Standard Predictive Index of Human Response to the Thermal Environment. ASHRAE Transactions, 1986. 92:2B: p. 709–731.


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8. Gagge, A.P., J.A.J. Stolwijk, and Y. Nishi, An Effective Temperature Scale Based on a Simple Model of Human Physiological Regulatory Response. ASHRAE Transactions, 1971. 77: p. 247–262. 9. Hatch, K.L., Textile Science. 1993: New York, West Publishing Company. 10. Hollies, N.R.S. and R.F. Goldman, Clothing Comfort: Interaction of Thermal, Ventilation, Construction and Assessment Factors. 1977: Ann Arbor, MI, Ann Arbor Science Publishers Inc. 11. Kettlewell, R., Woolmark Easy Care Goes from Strength to Strength. Wool Record, 2002. 160(3687): p. 68–69. 12. Kwok, Y.L., Y. Li, B.S.H. Wong, and W.S.S. Wu, Perceptual Requirements of Hong Kong Consumers on Children’s Denim Wear. Journal of the Textile Institute, 1998. 89(3): p. 96–108. 13. Li, Y., The Science of Clothing Comfort, J.M. Layton, Ed., (Textile Progress, 31 (112)). 2001: Manchester, The Textile Institute. 14. Li,Y. Sensory Engineering Design of Textile and Apparel Products, in Proceedings of the Textile Institute 82nd World Conference. 2002. Cairo, Egypt. 15. Nagamachi, M., Kansei Engineering. Sen-i Gakkaishi, 1994. 50: p. 468–472. 16. Shimizu, H., U. Totsuka, and Y. Shimizu, Dynamic Behavior of Clothing Pressure on the Body in Slacks. Part 2. Effects of Material and Ease on Clothing Pressure. Sen-i Gakkaishi, 1990. 46(6): p. 237–243. 17. Silverman, D., Consumers choose comfort as their no. one priority, in Daily News Record. 1999. p. 2. 18. Slater, K., Human Comfort. Vol. III. 1985: Springfield, IL, Charles C. Thomas. 19. Smith, J.E., Comfort of Clothing. Textiles Magazine, 1993. 22(1): p. 18–20. 20. Tarafder, N. and S.M. Chatterjee, Techniques of Measurement of Fabric Comfort. Textile Trends India, 1994. 37(5): p. 33–39. 21. Wong, A.S.W. and Y. Li. Clothing Sensory Comfort and Brand Preference, in Proceedings of the 4th IFFTI International Conference. 2002. Hong Kong. p. 1131–1135. 22. Woo, J.L. and M.W. Suh. Sensory Engineering at the Interface between Kansei Engineering and Fabric Objective Measurement, in Proceedings of the 6th Asian Textile Conference. 2001. Hong Kong.


2 Psychology and sensory comfort 1

2.1

YI LI 1 AND ANTHONY S.W. WONG 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong

Perception of comfort

Comfort is a psychological feeling or judgement on the part of a person wearing clothing under certain environmental conditions. Pontrelli44 developed a Comfort’s Gestalt in which the variables influencing the comfort status of a wearer were listed comprehensively and classified into three groups: physical variables of the environment and clothing; psychophysiological parameters of the wearer, and psychological filter of the brain. The gestalt indicates that the comfort status of a wearer depends on all of these variables and their interactions. Section 1.3 discusses the processes involved in the formation of subjective perception. Figure 2.1 illustrates the processes by which subjective perception of overall comfort is formulated. The physical processes provide the signals or stimuli to the sensory organs in the human body, which receive the signals, produce neurophysiological impulses, send them to the brain, and take actions to adjust sweating rate, blood flow and sometimes heat production by shivering. The brain will process the sensory signals to formulate subjective perception of various individual sensations, and further evaluate and weigh them against past experience and desires. This perception is influenced by many factors such as physical environment, social and cultural surroundings and state of being. Bornais3 pointed out that thermophysiological, tactile and psychological stimuli affect the degree of comfort in apparel. The psychology of comfort is the study of how the brain receives individual sensory sensations and evaluates and weighs these sensations in order to formulate the subjective perception of the overall comfort and preferences which become our wear experience and influence our further purchase decisions.

2.2

Psychological research techniques

Human perception of clothing and the external environment involves all the relevant senses, which together form a series of concepts that we use to 9


10

Clothing biosensory engineering

Physical processes

Visual stimuli – color, light, reflection, ... Thermal stimuli – heat and moisture transfer

Clothing

and

Environment

Pressure stimuli – mechanical behavior Tactile stimuli – mechanical behavior

Physiological processes

Sensory responses of nerve endings to stimuli – thermal, pressure, pain, ... Thermoregulatory responses – sweating, blood flow control, shivering

Body

and

Skin

Psychological processes

Perception of sensory sensations Evaluate/weigh various sensations

Brain

Overall perception

Comfort/Discomfort

2.1 Flow chart of subjective perception of comfort.

express these perceptions to each other. To understand the psychological processes, we need to measure these perceptions in subjective ways. A subjective measure is the direct assessment of a person’s opinion, which is the only factor of interest in carrying out the measurements. Since there are no physical instruments to measure what a wearer is thinking or feeling objectively, the only way to obtain the subjective perception is to use psychological scaling. With psychological scaling, the process of making judgements is based on the scales of individual words or language that we collect from experience and share with peers throughout life. Slater47 pointed out a number of problems with subjective measurements. Firstly, measurements rely completely on the honesty of the human subjects. Secondly, there exist wide variations in subjective opinions in human subjects, which means that a large number of measurements are required to obtain satisfactory precision. Thirdly, there are great difficulties in carrying out statistical analysis of the subjective data, because subjective answers are not real numbers and mental calibration used by one respondent may not be the same as that used by another. Finally, there are inconsistencies in subjective data as the opinion of individual respondents is influenced by a


Psychology and sensory comfort

11

large number of psychological, physiological, social and environmental factors.47 Despite the difficulties involved, scientific psychology to study the behavior of humans has been developing for over 100 years.17 A great deal of work has been done in the field of psychological scaling, and psychological laws, experimental techniques and mathematical methods have been developed to handle the data from subjective responses.9 Many researchers have applied these psychological scaling techniques to study clothing comfort. Hollies22 summarized six essential elements in psychological scaling: • • • • • •

commonly recognized attributes to measure; language (terms) to describe the attributes; assignment of a scale to indicate the level of attributes; a rating panel to apply the rating scale to attribute measurement; appropriate data handling; comparison of results from psychological scaling and objective measurement of the same attributes.

This indicates that the psychology of clothing comfort involves a number of research techniques which will be discussed in detail in the following sections.

2.3

Comfort sensory descriptors

Sensations generated from clothing depend largely on the various combinations of human activities and environmental conditions experienced during day-to-day living. Researchers have identified many commonly recognized attributes of clothing related to comfort involving thermal, moisture, tactile, hand and aesthetic experiences. This type of identification has greater degree of input from experts. On the other hand, it is important to know whether there are some commonly recognized comfort attributes of clothing among ordinary consumers and, if so, what they are. This can be seen from the processes by which sensory descriptors were obtained. In a study of fabric hand, Howorth and Oliver25,26 asked 25 participants to rank 27 fabrics and describe the reasons. Twenty-one descriptive terms and their frequency of use were obtained. Through factor analysis, they derived seven descriptors for fabric hand: smoothness, softness, coarseness, thickness, weight, warmth and stiffness. In developing the methodology for evaluation of fabric handle, Kawabata and Niwa29 generated sensory descriptors by letting a panel of expert judges (the Hand Evaluation and Standardization Committee) select fabrics and asking them the reasons for their decisions. They identified the terms KOSHI (stiffness), NUMERI (smoothness) and SHARI (crispness) as ‘primary-hand’ expressions.


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Clothing biosensory engineering

Hollies20 found that strong sensations were experienced when mild or heavy sweating occurred and during modest degrees of warming or chilling following the inception of sweating. By repeating experiments, Hollies et al. obtained a list of sensory descriptors generated by asking the participants to describe the sensations they experienced. The list of sensations included the descriptors snug, loose, heavy, lightweight, stiff, staticky, nonabsorbent, cold, clammy, damp, clingy, picky, rough and scratchy.24 Each participant had the option to use whichever descriptors they wished and could put in additional descriptors as experienced. These sensory descriptors were repeatedly produced by participants in wear trials conducted over many years.20–24 Wong et al.56 asked 22 professional athletes to rate ten individual sensations (clammy, clingy, damp, sticky, heavy, prickly, scratchy, fit, breathable and thermal) and overall clothing comfort before, during and after a 90-minute wear trial. They then used the rating of these ten individual sensations to predict overall comfort using artificial neural networks. For evaluating men’s winter suiting fabrics, David et al.7 generated lists of ‘bipolar descriptors’ through discussion with each judge. The descriptors, collated and listed from all the judges, were then associated with the ‘Standard Definitions of Terms Relating to Textiles’. However, individual judges had the choice to use their own list of descriptors. For each judge, an individual list of 14 bipolar descriptors was produced. After eliminating the pairs of words whose contribution to analyzing the data from subjective evaluation was not useful, seven pairs of descriptors were identified: coarse– fine, stiff–pliable, rough–smooth, harsh–soft, cool–warm, hard–soft and rustly–quiet. In 1988, Li34 carried out an investigation of the psychological sensory responses to clothing of consumers living in different countries. A survey was conducted in three countries: Britain, China and the USA. Twenty-six sensory descriptors were selected: snug, loose, stiff, lightweight, staticky, non-absorbent, sticky, heavy, cold, damp, clammy, clingy, picky, rough, scratchy, cool, hot, soft, warm, wet, prickly, itchy, chill, sultry, tickling and raggy. A total of 465 observations was observed. Using analysis of variance and non-parametric analysis of differences, it was found that the ratings of most of the sensory descriptors were significantly different between three types of clothing: summer wear, winter wear and sportswear at p < 0.01 level. Differences in the ratings of most sensory descriptors were significantly different between Chinese and British respondents for summer wear, but not for winter wear and sportswear. No significant differences in ratings of the sensory descriptors were found between male and female respondents. In a study of the behavior of different sensory descriptors, Wong and Li54 asked 28 female subjects to rate nine individual sensations (clammy, sticky,


Psychology and sensory comfort

13

breathable, damp, heavy, prickly, scratchy, tight and cool) and overall comfort every five minutes during a 20-minute running wear trial. Results showed that overall clothing comfort and two individual sensations (breathable and cool) had decreased considerably. Meanwhile, the remaining sensations had increased over time. In work on Personal Construction theory, Kelly31 suggested that human participants have the ability to be specific, and can draw on an internal concept of a particular type of garment from their memory and generate specific criteria to describe that garment. On the basis of this theory, Fritz14 argued that consumers have their own internal scales and concepts in evaluating fabric quality. Consumers themselves know best and they are capable of making objective quantitative and repeatable assessments of their sensations. Researchers should try to discover what consumers desire in terms of product performance. Therefore, sensory descriptors should be derived from consumers instead of experts or researchers. Fritz reported the usage of a repertory semantic differential grid to define product attributes using descriptive adjectives by focus group study. For example, the polar pairs of descriptors for toweling fabrics include soft–harsh, smooth–rough, cool–hot, light–heavy, fine–coarse, crisp–limp, clammy–absorbent, natural–synthetic, sheer–bulky, clingy–flowing, crushable–resilient, lacy–plain, drapable–rigid, scratchy–silky and stiff-soft. Fritz14 described the procedures as: (1) groups of 10–40 participants are organized; (2) the group is presented with the concept of a product to be investigated; (3) each participant is encouraged to write down as many descriptors relating to the product as possible; (4) group discussions are made to generate further descriptors; (5) a commonly agreed list of descriptors is achieved in the group for inclusion in the semantic grid; (6) the precise meaning of each polar pair is refined, clarified and agreed upon within the group. The sensory descriptors in various independent studies have produced commonly recognized attributes related to clothing comfort and the language to describe them. However, there are difficulties in interpreting clothing sensory descriptors from one language to another with exactly the same meaning. Despite this, it is obvious that there are a number of dimensions in these sensory descriptors to describe our sensory experiences that are related to thermal, mechanical and fabric surface stimuli, implying that the study of human comfort sensations has universal implications.

2.4

Psychophysics

In 1860, Fechner first used the term psychophysics to describe the mathematical relationship between the conscious experience of a sensation and


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Clothing biosensory engineering

an external physical stimulus. His philosophy was that, if we knew the mathematical form of the psychophysical relation between a physical variable and its corresponding sensation, we could measure mental attributes by measuring their physical correlates. Therefore, psychophysics is about the measurement of the strength of internal sensations, which can be broadly defined as the quantification of sensory experience. This has two aspects: (1) the assessment of human powers of signal detection and sensory discrimination; and (2) the calibration of subjectively perceived intensities and other parameters of stimulation. In 1834, Ernst Weber proposed that the threshold (i.e. just noticeable difference) of stimulus (∆S) is proportional to the magnitude of stimulus Sp. This is known as Weber’s law and can be expressed as: ∆Sp Sp

=K

[2.1]

where K is a constant indicating the power of a human being to detect signals and to discriminate sensations. This law holds for many stimulus attributes down to about the absolute threshold, which is the smallest magnitude of stimulus that can be perceived.32 Fechner in 1860 proposed using ‘just noticeable difference’ as a unit to measure internal sensation. Fechner assumed that sensation Rs increases as the logarithm of the physical stimulus magnitude Sp, which is called Fechner’s law and can be expressed as: Rs = k log Sp

[2.2]

where k is a constant determined by the stimulus threshold, which represents the lowest physical value eliciting a sensation, and the differential threshold providing a subjective unit of sensory intensity. This law proposes that sensation increases in arithmetic steps as the physical stimulus is increased in logarithmic steps.11 Fechner’s law is internally related to Weber’s law. If Weber’s law applies to the stimulus attribute in question and the thresholds in sensation are equal, then sensation increases as the logarithm of the physical stimulus magnitude. In 1953 Stevens developed a method of magnitude estimation as an experimental procedure to investigate the relationship between subjectively perceived intensity and physical stimulus strength. This method was applied to a very large number of different stimulus attributes. The results from each attribute conform roughly to an equation of the form: Rs = aSpb

[2.3]

where a is a scale factor and b an exponent characteristic of the attribute.


Psychology and sensory comfort

15

This equation is known as Stevens’ power law.32 These psychophysical laws indicate that there is an essential distinction between the physical stimulus and the sensation that one experiences. Weber’s law and Fechner’s law play some fundamental role in sensory discrimination in terms of the ability to distinguish one stimulus from another, but fail to provide a basis for measuring sensation. Stevens’ law proposes a power relation between physical stimulus magnitude and internal sensation, which provides a ‘direct’ measurement of sensation in sensory judgement processes.32

2.5

Scales of measurement

Psychological scaling is a type of measurement that consists of assigning numbers to characteristics of objects or events according to rules to reflect some aspects of reality. In social sciences and marketing research, psychological scaling has been widely used to obtain consumers’ opinions and study their attitudes and preferences. The term number here does not always correspond to the ‘real’ numbers that are obtained from objective measurement in physical means. The numbers cannot necessarily be added, subtracted, divided or multiplied. They are used as symbols to represent certain characteristics of the object. The nature of the meaning of the numbers depends on the nature of the characteristics and the rules specifying how the numbers are assigned to the characteristics to be measured. These rules are arbitrary, rather than being a result of undeniable natural law.52 The rules governing how to assign numbers constitute the essential criteria for defining each scale. There are four types of numbers or scales of measurements: nominal, ordinal, interval and ratio. Moving from nominal to ratio scales, the rule becomes more restrictive and the kinds of arithmetic operations for which the numbers can be used are increased. Nominal scales consist of numbers used to categorize objects. A nominal number serves as a label for a class category. For instance, we can assign 0 to male and 1 to female. The number 1 does not imply a superior position to the number 0. The rule for nominal scale is that all members of a class have the same number and no two classes have the same number. The only arithmetic operation which can be performed on nominal data is the count in each category. Nominal numbers cannot meaningfully be added, subtracted, multiplied and divided. Ordinal scales comprise numbers or other symbols used to rank objects according to certain characteristics and their relative position within these characteristics. Ordinal data indicate the relative position of objects having a certain characteristic but not the magnitude of the differences between the objects. A mode or median may be used, but not or mean. Non-parametric statistics can be applied to ordinal data.


16

Clothing biosensory engineering

Interval scales consist of numbers used to rank objects in such a way that numerically equal distances on the scale represent equal distances in the characteristics being measured. However, both zero and the unit of measurement are not fixed and are arbitrary. Therefore, interval data can indicate both the relative position of objects and the magnitudes of differences between the objects with respect to the characteristics being measured. An entire range of statistics can be applied to interval scales. Ratio scales represent numbers used to rank objects such that numerically equal distances on the scale represent equal distances in the characteristics being measured and have a meaningful zero. Like interval scales, entire ranges of statistics can be applied to ratio data. In clothing comfort research, all the four types of psychological scales have been applied. Nominal scales have been used to code characteristics such as gender, age and place of living. Ordinal scales have been used to obtain the rankings of fabrics or garments under consideration. The most frequently used scales are the interval scales, which have been widely used to obtain the perception of various attributes of clothing, and which will be discussed in detail in the following section. Ratio scales are mainly applicable to the data generated from physical instruments.

2.6

Scales to measure direct responses

Most of the psychological scaling involved in clothing comfort research can be regarded as measurement of attitudes. Tull and Hawkins52 defined an attitude as an enduring organization of cognitive, affective and behavioral components and processes with respect to some aspect of the individual’s world. The three components include: • • •

a person’s beliefs or information about the object (a cognitive component); a person’s feeling of like or dislike concerning the objects (an affective component); a person’s action tendencies or predisposition towards the object (a behavioral component).

To directly measure an individual’s attitude or a component of it, attitude scales can be used on which the individual is required to explicitly state his or her attitude. Attitude scales consist of a rating scale or a group of rating scales that measure single dimensions of attitude components. In using rating scales, a respondent is required to place an attribute of the object being rated at some point along a numerically valued continuum or in one of the numerically ordered categories. The researcher can design rating scales to focus on different aspects such as the overall attitude towards an object, the degree to which an object contains a particular


Psychology and sensory comfort

17

attribute, one’s feeling toward an attribute and the importance attached to an attribute. There are two major types of rating scales: non-comparative and comparative. With a non-comparative rating scale, the respondent is not provided with a standard to use in assigning the rating. The non-comparative rating scale has two major forms: graphic and itemized. The latter are most frequently used and are the basic building blocks for the more complex attitude scales. In using itemized rating scales, the respondent is required to select one from a limited number of categories that are ordered in terms of their scale positions. In developing methodology for studying human perception of clothing, Hollies used a number of itemized rating scales for the sensations derived from participants. In studying thermal comfort, John McGinnis of the United States Army Laboratories in Natick devised an intensity scale with 13 points. These scales appear to be of the interval type. It is doubtful whether the interval between each of these points is exactly equal. However, it seems that the data from such scales can be treated as if they were in fact equal intervals, as the results of most standard statistical techniques are not affected greatly by small deviations from the interval requirements.6,51 If there is a concern, most ordinal data can be transformed into workable interval data.8 Tull and Hawkins et al.52 listed a number of issues in constructing and using itemized rating scales. • The nature and degree of verbal category descriptions affect the responses. • The number of categories can be created in many ways, depending on the purpose and nature of the investigation. Five categories can be used when several scales are to be summed for one score. Up to nine categories can be used when interested and knowledgeable respondents are comparing attributes across objects. • The decision to use a balanced or unbalanced set of categories depends on the type of information desired and the assumed distribution of attitudes in the population studied. A balanced scale provides an equal number of favorable and unfavorable categories. This type should be used unless it is known that the respondent’s attitudes are unbalanced. • The use of an odd or even number of categories is a relevant issue when balanced scales are being constructed. If an odd number of scale items are used, the middle item is generally designated as a neutral point. Normally, odd number categories should be used if respondents could feel neutral.


18

Clothing biosensory engineering

• The use of forced versus unforced scales is another important issue. A forced scale requires the respondent to indicate an attitude to the item. In such a situation, a respondent may often mark the midpoint of the scale when he or she has no attitude to, or knowledge of, the item. If a sufficient proportion of the respondents act in such way, utilization of the midpoint will distort measures of central tendency and variance. Therefore, unforced scales should be used unless it can be assumed that all respondents have knowledge of the item. Hollies et al.24 used unforced scales in his study of consumers’ perception on clothing comfort, while Kawabata and Niwa29 utilized forced scales in his study of fabric handle assessment by textile experts. In studying the moisturizing buffering of hygroscopic fabrics, Li et al.36 used noncomparative unbalanced and forced rating scales. The scales were chosen based on the fact that in the testing conditions, subjects were unlikely to select cold on the thermal scale and that the rating scale for clammy sensation would need only to have neutral to extreme points. Also, thermal and clammy sensations are such fundamental perceptions that subjects can be certain to know the items well, especially after training. In the non-comparative scales discussed above, different respondents may apply different standards or reference points when they evaluate objects without direct reference to specific standards. When asked to rate the overall comfort or handle of a garment, some respondents may compare it to their impression of what is normal for that type of garment, others to a similar garment that they own, and still others to their impression of the average same type of garments. Therefore, when we want to ensure that all respondents are approaching the rating task from the same known reference point, comparative rating scales should be used. Non-comparative rating scales (both graphic and itemized) can be converted to comparative scales simply by introducing a comparison point. The usage of comparative graphic and itemized rating scales and the issues discussed can apply to comparative scales. In studying the physical mechanism of dampness perception, Plante et al.43 used a comparative unbalanced rating scale with specific ‘dry’ and ‘very damp’ fabrics as references. In determining consumers’ preferences and their ability to discriminate among products, pairwise measurement methods can be used. There are various ways to conduct pairwise measurements, including: paired comparison, double-paired comparison, consistent preference discrimination test, triangle discrimination and triangle preference tests and response latency. Paired comparison is most frequently used in clothing comfort research. The use of this technique involves presenting the respondents with two objects at a time and requiring the selection of one of the two according to some criterion. Each respondent must compare all possible pairs of objects,


Psychology and sensory comfort

19

n(n − 1)/2, where n is the number of objects being studied. For each attribute of interest, a comparison needs to be conducted. Due to the large number of tests involved, paired comparisons are generally limited to one attribute such as overall preference or a couple of products on multiple attributes. The outputs from paired comparison can be analyzed in a number of ways. A simple visual inspection can reveal the preferences for one product over another that can be used for judging the rank order among a number of products. The data can also be converted into an interval scale through the application of Thurstone’s law of comparative judgement.19 Many researchers reported the use of paired comparisons in studying the comfort attributes of textile products. Fuzek and Ammons16 applied the paired comparison technique to obtain subjective evaluation of the comfort performance of T-shirts. Li et al.37,38 used the paired comparison technique together with non-comparative rating scales to obtain the overall preference of consumers towards T-shirts made from eight types of fibers through handling and wearing experience. The preference output was converted into an interval scale and used to study its relationships with various sensations, physiological responses and fabric physical properties. Schneider et al.45,46 utilized the paired comparison method to study the coolness to the touch of hygroscopic fibers. Wong et al.54,55 applied paired comparison to obtain subjective perception of clothing comfort and preference in active tight-fit sportswear. The ranking scores obtained through paired comparisons are essentially ordinal data, which are effective in obtaining the preferences of consumers in comparing a series of products. However, they do not provide the magnitude of the perceived differences between samples, nor their relative positions in a context of all possible relevant samples beyond a particular experiment. Rank order rating scale, with which respondents are required to rank a set of objects according to some criterion, is another scale widely used to measure preference for comfort attributes. Like paired comparison, this method is purely comparative in nature and its outputs are applicable only within the product range being studied. The rank order method forces respondents to discriminate among the relevant objects in a manner close to the actual shopping environment. It is less time-consuming than paired comparisons. The instructions for ranking are easily understood by most individuals. The major shortcoming of this technique is that it produces only ordinal data, with which the number of statistical analyses permissible is limited. In Hollies’ wear trials, the Wilcoxon Sign-Rank Test was used to detect significant differences in ranking.24 On the basis of rating scales, more complex attitude scales can be constructed to measure more aspects of an individual’s attitude toward some object. The responses from respondents to various scales can be summed


20

Clothing biosensory engineering

to provide a single attitude score for the individual. More commonly, the responses to each scale item or subgroup items may be examined independently. Hollies et al.24 developed a comprehensive attitude scale to obtain various sensory responses from respondents in wear trials. Normally, the sensory responses of subjects were analyzed individually. Li et al.37 applied similar multi-attitude scales to study the comfort performance of sportswear made from different fibers. The responses to various sensory items were first analyzed individually, then the relationships among the sensory responses were investigated by factor analysis and clustering analysis.33 Hyun et al.27 described an expanded model for textile comfort studies using human perception analysis (HPA), which is a sensory evaluation technique using peer language to visualize human responses to stimulation. The perceptions and sensations of humans can be detected by using specially selected descriptors in a rating sheet. The rating sheet, with five rating periods, comprises four sections: comfort descriptors, a McGinnis thermal scale, comments on uncomfortable locations and additional sensations. The 48 comfort descriptors are placed in alphabetical order on the rating sheet and they are grouped into seven major categories. The technique was proven effective in quantifying subjective measurements in garment comfort studies. In studying fabric hand properties, both Elder10 and Mackay et al.40 applied the magnitudeâ&#x20AC;&#x201C;estimation technique. This technique requires subjects to estimate the magnitude of an attribute of fabrics by comparing them with a standard fabric or with their own experience. Their estimations are recorded by assigning a number to, or marking a position on, a line for each fabric sample. The scales are open. Subjects can use scores that seem appropriate to them. The magnitudeâ&#x20AC;&#x201C;estimation technique was also used by Sweeney and Branson49 in studying the psychophysical mechanism of dampness perception. Tarafder and Chatterjee50 used the magnitudeâ&#x20AC;&#x201C;estimation technique for assessing moisture sensation. Another frequently used attitude scale in sensory research is the semantic differential scale. Semantic differential scales were developed by Osgood et al.42 in studying the meaning of language. Kelly30 developed a similar technique called a repertory grid. Semantic differential scales consist of a series of bipolar rating scales, each of which is made of word pairs that may be opposites or one extreme and one neutral pole. The bipolar words are bound on a number of itemized, five to seven point rating scales at each end. In the case of opposites, the center is neutral between the two extremes. Respondents are instructed to mark the blank that best indicates how accurately one or the other term describes or fits the attitude object. Semantic differential rating scales can have any number of scale points, with six to seven being most common. Friedman et al.13 recommended that the more favorable adjective or phrase be randomly assigned to the left and


Psychology and sensory comfort

21

right side of the scale. Due to widespread use, semantic differential scales have been improved in many forms such as: the upgraded semantic differential (or graphic position scale),12 numerical comparative scale,18 and Stapel scale (a simplified version of the semantic differential scale). Chen et al.5 used a 99-point polar-word scale to evaluate subjective overall hand and primary sensory factors of two groups of weft-knitted fabrics. Results showed that the hand of single knits was strongly related to the perception of softness and lightness. The hand of double knits was influenced by the perception of slickness and tightness. Jacobsen et al.28 used a semantic differential method of attitude measurement to determine consumer preference for the tactile qualities of hand knitting yarns in the ball and fabric states. Na and Kim41 studied the relationships between the hand of woven silk neckwear fabrics and consumer purchasing preferences. Male and female students evaluated fabric samples with a semantic differential scale of hand and sensibility adjectives, including modern (flat and cool), classic (flat and warm), character (rough and cool), and natural (rough and warm). Fabrics awarded a high purchasing preference exhibited a soft or flat touch and a modern or classic sensibility. A widely accepted assumption is that the data resulting from semantic differential scales are interval in nature. There are a number of ways to analyze semantic differential data. Two approaches, aggregate analysis and profile analysis, are widely used. For aggregate analysis, the larger numbers are consistently assigned to the blanks nearer the more favorable terms and the scores across all adjective pairs are summed for each individual. The individual or group of individuals can be compared to other individuals on the basis of total scores. Different objects can be also compared for the same group of individuals. Aggregate analysis is most effective for predicting overall preferences. In profile analysis, the mean or median is calculated for each adjective pair for an object by a specified group of respondents. This profile can then be compared with the profiles of other objects, other groups or the ‘ideal’ version of the object. Profile analysis is useful to isolate strong attributes of products.52 A number of researchers used semantic differential scales in clothing comfort study. Winakor et al.53 and Chen et al.5 used 99-point scales with bipolar pairs to study fabric sensory properties. During judging, a participant is asked to assign a number between 1 and 99 with 1 representing complete agreement with the left-hand descriptor, 99 representing complete agreement with the right-hand descriptor and 50 representing uncertainty. The scores were transformed in considering the non-uniform distribution of sensitivity along the scale. Fritz14,15 applied semantic differential scales to study the handle of fabrics. Seven-point scales were used with 3 at the two ends indicating ‘extremely’ and 0 at the middle point indicating ‘neither both’. Profile analysis was


22

Clothing biosensory engineering

applied to compare ideal underslip fabrics with the perceptions from male and female respondents. Byrne et al.4 applied semantic differential scales to investigate the sensory perceptions of consumers on fiber types for different end-uses. Profile analysis was applied to compare the responses from Australian and British students. Aggregate analysis was used to sum the differences between the ideal and each fiber type as a measure of the extent to which the fiber deviates from the ideal. By using the 99-point polar-word scale, Chen et al.5 found that surface friction and weight were associated with the hand ranking of single knits. Fabric surface roughness and bending hysteresis were physical properties correlated with the hand ranking of double knits. Bishop2 pointed out that in the context of fabric objective measurement, bipolar descriptors do not have any added value over single descriptors, but have a number of disadvantages. Bipolar descriptors may complicate the process of descriptor generation, impose unnecessary correlation between descriptors, represent positive and negative fabric attributes in the minds of respondents, and introduce an element of liking/disliking in an assessment. From these discussions, we can see that various attitude scaling techniques have been applied to measure sensory perception of clothing comfort. Besides these scales, there are numerous less well-known methods and modified versions of the popular scales. In comparing various scaling methods, the results have generally been equivalent across the techniques.1 Selection of a scale technique depends upon a number of issues: the required information, the characteristics of the respondents, the means of administration and the cost involved. Generally speaking, multiple measures are more effective than any single technique. The sum of several items gives a more accurate measurement than a single measurement.52

2.7

Wear trial techniques

Perceptions of sensory comfort of clothing may involve various sensory channels from all the five senses, visual, auditory, smell, taste and touch, but are mainly associated with skin sensory systems. Many comfort sensations can only be generated under certain wear situations with the existence of relevant physical stimuli. The physical stimuli such as heat, moisture and mechanical stimulation from fabric to the skin can often be generated only under specific combinations of physiological states (e.g. sweating rate), fabric materials, garment fitness and environmental conditions (e.g. temperature, humidity and air velocity). A large quantity of research work has been published on fabric sensory properties through hand.2 Human beings often use their hands to obtain tactile information as well as to manipulate


Psychology and sensory comfort

23

objects. However, Stevens reported that much of the tactile sensation comes from parts of the body other than the hands.48 This suggests that the perception of comfort performance of clothing has to be studied in wear situations. Therefore, wear trial is an important technique for clothing comfort research. On the basis of his research work on human perception and clothing comfort, Hollies et al.24 developed a wear trial experimental technique to characterize the sensory comfort of clothing. The technique included a number of components: (1) generate sensory descriptors with respondents; (2) select testing conditions to maximize the opportunities for perception of various sensations; (3) design attitude scales in the form of rating sheets to obtain various sensory responses on particular garments; (4) conduct a wear trial in controlled environmental chambers according to predetermined protocol; (5) collect data and analyze and interpret the results. The typical microclimate and exercise protocol used in Hollies’ work requires subjects to exercise in an antechamber for ten minutes at 150– 180 kg cal/m2 h at 30–33 °C. This technique has been extensively applied to evaluate various apparel products by Hollies and his colleagues.20,21,23,24 Li et al.37 adopted the principle of this technique and designed a multi-attitude scale and wear trial protocol to study physiological responses, sensory perceptions and preferences of consumers towards sportswear made from eight types of fibers. Subjective preferences and sensory responses were obtained by exposing subjects to periods of exercise (30 minutes) and rest in two environmental conditions: hot (32 °C, 45% RH) and cold (14 °C and 32% RH). The air velocity was 0.25 m/s. At every five-minute interval during exercise, measurements were made of tympanic membrane and skin temperatures, heart rate and energy expenditure. Body sweat loss and sweat absorption of the garments were also recorded. Every ten minutes, subjective responses to 19 sensory descriptors were recorded on a scale from 1 (no sensation) to 5 (totally). The sensory descriptors included snug, loose, heavy, lightweight, soft, stiff, staticky, sticky, non-absorbent, cold, clammy, damp, hot, clingy, sultry, prickly, rough, scratchy, itchy. Overall preferences between the two garments tested in each trial were recorded after handling at the beginning of the trial and again after wear at the end of the experiment. This trial applied multiple scaling techniques. The overall preferences through wearing and handling were obtained by paired comparison design, while subjective sensory responses were obtained through a comprehensive attitude scale similar to Hollies’ rating sheet shown in Fig. 2.3. Researchers in CSIRO Division of Wool Technology adopted Hollies’ technique and set up two climatic chambers to study the comfort performance and attributes of wool apparel products. A large number of wear trials


24

Clothing biosensory engineering

have been conducted in the chambers to study the comfort features of wool garments under various wear situations. For instance, the moisture buffering effect of clothing during exercise was reported in 1992,36 the thermoregulatory responses of the body during intensive exercise when wearing hygroscopic fibers in 1993,35 the perception of dampness in 199539 and the perception fabric coolness in 1996.51 Wong et al.55 investigated the psychological perception of clothing sensory comfort in eight tight-fit garments. By using paired comparison, 28 female subjects were required to run on a treadmill for 20 minutes in a controlled chamber with temperature and relative humidity of 29 ± 2 °C and 85 ± 3%, respectively. They were also required to rate nine individual sensations (clammy, sticky, breathable, damp, heavy, prickly, scratchy, tight and cool) and overall clothing comfort on a seven point scale.

2.8

Conclusion

Psychologically, perception of clothing comfort can be divided into a number of processes: how the brain (1) receives individual sensory sensations such as clamminess, hotness, prickliness and air-tightness; (2) evaluates and weighs these sensations (to identify the importance of individual perceived sensations); and (3) formulates a subjective perception of overall comfort. Finally, a judgement is made on status of comfort and preferences by integrating all the information from previous processes.

2.9

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through project A188.

2.10

References

1. Barnard, N.R. and A.S.C. Ehrenberg, Robust Measures of Consumer Brand Beliefs. Journal of Marketing Research, 1990(11): p. 477–484. 2. Bishop, D.P., Fabrics: Sensory and Mechanical Properties. Textile Progress, 1996. 26(3): p. 1–64. 3. Bornais, P., Analysis and Characteristics of Comfort in Clothing. Canadian Textile Journal, 1997. 114(4): p. 12–14. 4. Byrne, M.S., A.P.W. Gardner, and A.M. Fritz, Fibre Types and End-uses: a Perceptual Study. Journal of the Textile Institute, 1993. 84(2): p. 275–288. 5. Chen, P.L., R.L. Barker, G.W. Smith, and B. Scruggs, Handle of Weft Knit Fabrics. Textile Research Journal, 1992. 62(4): p. 200–211. 6. Crask, M.R. and R.J. Fox, An Exploration of the Internal Properties of Three Commonly Used Research Scales. Journal of the Market Research Society, 1987. 29(10): p. 317–339.


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7. David, H.G., A.E. Stearn, and E.F. Denby, The Subjective Assessment of Handle, in Proceedings of The Third Japan–Australia Symposium on Objective Measurement: Applications to Product Design and Process Control. 1985. Kyoto, Japan. p. 527–536. 8. Dowling, G.R. and D.F. Midgly, Using Rank Values as an Interval Scale. Psychology and Marketing Research, 1991: p. 37–41. 9. Dunn-Ranking, P., Scaling Methods. 1983, London: Lawrence Erlbaum Associates Publishers. 10. Elder, H.M., Fabric Stiffness. Journal of the Textile Institute, 1984. 75(4): p. 307–311. 11. Engen, T., Psychophysics, in Sensory Systems II: Senses Other than Vision, J.M. Wolfe, Ed., 1988: Boston, MA, A Pro Scientia Viva Title, p. 104–106. 12. Evans, R.H., The Upgraded Semantic Differential: a Further Test. Journal of the Market Research Society, 1980. 2: p. 477–481. 13. Friedman, H.H., L.L. Friedman, and B. Gluck, The effects of Scale-Checking Style on Responses to a Semantic Differential Scale. Journal of the Market Research Society, 1988. 30: p. 477–481. 14. Fritz, A.M., Sensory Assessment Assessed. Textile Asia, 1990. 21(5): p. 144–147. 15. Fritz, A.M., New Way to Measure Fabric Handle. Textile Asia, 1992. 23(7): p. 69–72. 16. Fuzek, J.F. and R.L. Ammons, Techniques for the Subjective Assessment of Comfort in Fabrics and Garments, in Clothing Comfort, N.R.S. Hollies and R.F. Goldman, Eds, 1977: Ann Arbor, MI, Ann Arbor Science Publishers Inc., p. 121–130. 17. Geldard, F.A., The Human Senses. 1972: New York, John Wiley & Sons Inc. 18. Golden, L.L., G. Albaum, and M. Zimmer, The Numerical Comparative Scale. Journal of Retailing, 1987. 63(Winter): p. 393–410. 19. Green, P.E., D.S. Tull, and G. Albaum, Research for Marketing Decisions. 1988: Englewood Cliffs, NJ, Prentice-Hall, Inc. 292–298. 20. Hollies, N.R.S., Investigation of the Factors Influencing Comfort in Cotton Apparel Fabrics. 1965: New Orleans, US Department of Agriculture. 21. Hollies, N.R.S., The Comfort Characteristics of Next-to-skin Garments, Including Shirts, in Proceedings of the 3rd Shirley International Seminar. 1971. Manchester, UK. 22. Hollies, N.R.S., Psychological Scaling in Comfort Assessment, in Clothing Comfort, N.R.S. Hollies and R.F. Goldman, Eds, 1977: Ann Arbor, MI, Ann Arbor Science Publishers Inc., p. 107–120. 23. Hollies, N.R.S., Improved Comfort Polyester, Part 4: Analysis of the Four Wear Trials. Textile Research Journal, 1984. 54: p. 544–548. 24. Hollies, N.R.S., A.G. Custer, C.J. Morin, and M.E. Howard, A Human Perception Analysis Approach to Clothing Comfort. Textile Research Journal, 1979. 49(10): p. 557–564. 25. Howorth, W.S., The Handle of Suiting, Lingerie and Dress Fabrics. Journal of the Textile Institute, 1964. 55: p. T251–260. 26. Howorth, W.S. and P.H. Oliver, The Application of Multiple Factor Analysis to the Assessment of Fabric Handle. Journal of the Textile Institute, 1958. 49: p. 540. 27. Hyun, S.O., N.R.S. Hollies, and S.M. Spivak, Skin Sensations Perceived in Apparel Wear: Part 1: Development of a New Perception Language. Journal of the Textile Institute, 1991. 82(3): p. 389–397.


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28. Jacobsen, M., R. Postle, A. Fritz, and R. Dhingra, Psychophysical Evaluation of the Tactile Qualities of Hand Knitting Yarns. Textile Research Journal, 1992. 62(10): p. 557–566. 29. Kawabata, S. and M. Niwa, Fabric Performance in Clothing and Clothing Manufacture. Journal of the Textile Society, 1991. 80(1): p. 19–50. 30. Kelly, G.A., The Psychology of Personal Constructs. 1955: New York, Norton. 31. Kelly, G.A., Man’s Construction of His Alternatives, in Assessment of Human Motives, G. Lindzey, Ed., 1958: New York, Holt, Rinehart & Winston. 32. Laming, D., Psychophysics, in Sensation and Perception, R.L. Gregory and A.M. Colman, Eds, 1996: London, Longman. p. 97–123. 33. Li, Y., Dimension of Sensory Perceptions on Next-to-Skin Wear in a Cold Environment. Journal of China Textile University, 1998. 15(3): p. 50–53. 34. Li, Y., Dimension of Sensory Perceptions on Next-to-Skin Wear in a Cold Environment. Journal of China Textile University, 1998. 15(3): p. 50–53. 35. Li, Y. and B.V. Holcombe. Fibre Hygroscopicity and Thermoregulatory Responses During Exercise, in Proceedings of the 2nd Asian Textile Conference. 1993. Seoul, Korea. 36. Li,Y., B.V. Holcombe, and F.Apcar, Moisture Buffering Behaviour of Hygroscopic Fabric During Wear. Textile Research Journal, 1992. 62(11): p. 619–627. 37. Li, Y., J.H. Keighley, and I.F.G. Hampton, Physiological Responses and Psychological Sensations in Wearer Trials with Knitted Sportswear. Ergonomics, 1988. 31(11): p. 1709–1721. 38. Li, Y., J.H. Keighley, J.E. McIntyre, and I.F.G. Hampton, Predictability Between Objective Physical Factors of Fabrics and Subjective Preference Votes for Derived Garments. Journal of the Textile Institute, 1991. 82(3): p. 277–284. 39. Li, Y., Plante, A. M., Holcombe, B. V., The Physical Mechanisms of the Perception of Dampness in Fabrics. Journal of Thermal Biology, 1993. 18(5/6): p. 417–419. 40. Mackay, C., S.C. Anand, and D.P. Bishop, Effects of Laundering on the Sensory and Mechanical Properties of 1 × 1 Rib Knitwear Fabrics. Experimental Procedures and Fabric Dimensional Properties. Textile Research Journal, 1996. 66(3): p. 151–157. 41. Na, Y. and C. Kim, Quantifying the Handle and Sensibility of Woven Silk Fabrics. Textile Research Journal, 2001. 71(8): p. 739–742. 42. Osgood, C.E., G.J. Suci, and P.H. Tannenbaum, The Measurement of Meaning. 1957: Urbana, IL, University of Illinois. 43. Plante, A.M., B.V. Holcombe, and L.G. Stephens, Fibre Hygroscopicity and Perception of Dampness, Part I: Subjective Trials. Textile Research Journal, 1995. 65(5): p. 292–298. 44. Pontrelli, G.J., Partial Analysis of Comfort’s Gestalt, in Clothing Comfort, N.R.S. Hollies and R.F. Goldman, Eds, 1977: Ann Arbor, MI, Ann Arbor Science Publisher, Inc. p. 71–80. 45. Schneider, A.M. and B.V. Holcombe, Coolness of ‘Cool Wool’ Fabrics. in Proceedings of the 8th International Wool Textile Research Conference. 1990. 5: p. 215–224. 46. Schneider, A.M., B.V. Holcombe, and L.G. Stephens, Enhancement of Coolness to the Touch by Hygroscopic Fibers. Part 1: Subjective Trials. Textile Research Journal, 1996. 66(8): p. 515–520. 47. Slater, K., The Assessment of Comfort. Journal of the Textile Institute, 1986. 77(3): p. 157–171.


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48. Stevens, J.C., Perceived Roughness as a Function of Body Locus. Perception and Psychophysics, 1990. 47: p. 298–304. 49. Sweeney, M.M. and D.H. Branson, Sensorial Comfort. Part 2. A Magnitude Estimation Approach For Assessing Moisture Sensation. Textile Research Journal, 1990. 60(8): p. 447–452. 50. Tarafder, N. and S.M. Chatterjee, Techniques of Measurement of Fabric Comfort. Textile Trends India, 1994. 37(5): p. 33–39. 51. Traylor, M., Ordinal and Interval Scaling. Journal of the Market Research Society, 1983. 25(10): p. 297–303. 52. Tull, D.S. and D.I. Hawkins, Marketing Research: Measurement and Method. 1993: New York, Macmillan Publishing Company. 53. Winakor, G., C.J. Kim, and L. Wolins, Fabric Hand: Tactile Sensory Assessment. Textile Research Journal, 1980. 50: p. 601–610. 54. Wong, A.S.W. and Y. Li., Clothing Sensory Comfort and Brand Preference, in Proceedings of the 4th IFFTI International Conference. 2002. Hong Kong. p. 1131–1135. 55. Wong, A.S.W., Y. Li, and P.K.W. Yeung. Comfort Perceptions and Preferences of Young Female Adult for Tight-fit Sportswear, in Proceedings of the Textile Institute 82nd World Conference. 2002. Cairo, Egypt. 56. Wong, A.S.W., Y. Li, and P.K.W. Yeung, Neural Network Predictions of Human Psychological Perceptions of Clothing Sensory Comfort. Textile Research Journal, 2003. 73(1): p. 31–37.


3 Neurophysiology of sensory perceptions 1

3.1

YI LI 1 AND ANTHONY S.W. WONG 2 Institue of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 2 School of Nursing, Thu Hong Kong Polytechnic University, Hong Kong

Senses and comfort

As discussed in Chapter 1, clothing functions as the interface between the body and the environment, interacting with both and influencing the comfort perceptions of the wearer. In daily wear, the body, clothing and environment all contribute to the comfort status and satisfaction of the wearer. These three elements together provide multisensory experiences, which consist of all the information acquired through the senses: sight, touch, kinesthetics, hearing, taste and smell. Visual perception is probably the most important factor influencing aesthetic and other sensory comfort of clothing. Kinesthetics is the perception of body movement through the nerve endings that register the stretch or contraction of the muscle. Clothing can influence kinesthetic perception by applying restriction and pressure to muscle movement; for example, tight-fitting Lycra cycling shorts can enhance the kinesthetic perception in the muscles of the wearerâ&#x20AC;&#x2122;s legs. Smell can be an important factor in comfort related to the body. Favorable smells from clothing may enhance the comfort perception of the wearer, while unfavorable smells may cause feelings of discomfort. Occasionally, the sound generated from clothing influences the comfort sensory perception of the wearer. For instance, the sound of electric discharge while taking off a synthetic garment can enhance the discomfort perception caused by the electric insults to the skin. Taste is probably the least important factor influencing clothing comfort. Covering the majority of our body for most of our daily life, clothing comes into contact dynamically and frequently with almost all of the skin. This produces various mechanical, thermal, chemical or electrical stimuli. Therefore, the major contributor to the sensory comfort of clothing is touch, which can be defined as the variety of sensations evoked by the stimulation from various external stimuli to the skin. The sensations perceived from these stimuli influence the overall state of comfort. The types of sensations perceived from clothes depend on how the fabric interacts with the skin 28


Neurophysiology of sensory perceptions

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and which sensory receptors are triggered. To obtain a clear understanding of these processes, we need to understand the mechanisms of how our skin sensory system works.

3.2

Neurophysiological basis of sensory perceptions

3.2.1 Skin stimuli and skin sensory system Human skin has two layers: the epidermis and the dermis. The epidermis is the outer layer, consisting of several layers of dead cells on top of a single living cell. The dermis is the inner layer, containing most of the nerve endings in the skin. In addition, sweat glands, hair follicles and fine muscle filaments are located here. Below the dermis, there are layers of connective tissue and fat cells.11,39 There are two types as nerve endings: corpuscular and non-corpuscular (or free nerve) endings. Corpuscular nerve endings have small bodies or swellings on the dendrites, including the Pacinian corpuscles, Meissner corpuscles, Merkle disks and Ruffini endings, which are particularly responsive to touch stimuli. The free nerve endings in subcutaneous fat are associated with pain, and those projecting into the epidermis may be associated with cold or pain.11

3.2.2 Transduction The fundamental function of the sensory receptors is to transduce various external stimuli into the standard code by which nervous systems work. The central question is how the sensory receptors convert the stimuli into nerve action potentials. It has been found that the common feature of the transduction is the generation of current flows within the receptor, recorded as a potential change that is proportional to the intensity of the applied stimulus. The current flow sets up nerve action potentials at a spike initiation site; these then travel centrally along the afferent nerve fiber.44

3.2.3 Sensory receptors Human skin is the interface between a human body and its environment. It is richly innervated and contains specialized sensory receptors to detect various external stimuli. There are three major stimuli: (1) mechanical contact with external objects; (2) temperature changes due to heat flow to or from the body surface; (3) damaging traumatic and chemical insults. In responding to these stimuli, the skin receptors produce the sensations of touch, warmth, cold or pain.44


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Mechanoreceptors There are two groups of mechanoreceptors: (1) encapsulated receptors, including the Pacinian corpuscles, Meissner corpuscles, Krause endings and Ruffini endings that are all innervated by fast-conducting myelinated fibers; (2) receptors having an organized and distinctive morphology such as the hair follicle receptors and Merkel discs. Each mechanoreceptor has a distinctive range of properties that enable it to receive and respond to a particular parameter of a mechanical stimulus. The Pacinian corpuscles detect and respond to high frequencies of displacement up to 1500 Hz, the Meissner corpuscles and the hair follicles to middle range frequencies (20–200 Hz), and the Merkel cells and Ruffini endings to steadily maintained deformation of the skin.44 Toma and Nakajima88 investigated the responsiveness of mechanoreceptors in the glabrous skin of the hand. Thirteen single afferent activities were recorded from four kinds of mechanoreceptors. Both fastadapting (FA) and slow-adapting (SA) units were sensitive to the vibratory stimuli. The relationship between the most sensitive frequency and applied pressure to the skin was analyzed as a tuning curve. FA-type I (FAI) was sensitive to vibratory stimuli at 30–40 Hz and the frequency which entrained one-to-one discharge at lower pressure was between 10 and 80 Hz. FA-type II (FAII) sensitivity was augmented sharply over 60–100 Hz. SA-type I (SAI) and SA-type II (SAII) responsiveness was almost the same, and characteristic sensitivity to the vibratory stimuli was revealed under 15 Hz. Thermoreceptors Another group of sensory receptors detects the temperature of the skin, responding to both constant and fluctuating skin temperatures. In responding to constant temperatures, the receptors discharge impulses continuously to indicate the temperature of the skin. They are very sensitive to changes in the skin temperature. There are two types of thermoreceptors: cold and warm. The cold receptors have peak sensitivity around 25–30 °C and are excited by dynamic downshifts in temperature. The warm receptors have peak sensitivity around 39–40 °C and are sensitive to increase in skin temperature.44 Kozyreva55 found that the function of the skin’s cold receptors could change during the life of an individual organism. Some parameters of the static and dynamic activity, as well as concentration of the cold receptors, function with both adaptation of the organism to cold and a rise of the noradrenaline concentration in blood. Nociceptors Nociceptors are another group of sensory receptors that respond to noxious stimuli, such as heating the skin, strong pressure or contact with sharp or


Neurophysiology of sensory perceptions

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damaging objects. The receptors have relatively high thresholds to function as warning devices that enable the organism to take protective action. There are two major types of nociceptors. The first type is A fibers that have myelinated axons conducting between 10 and 40 m/sec and are best adapted to respond to mechanical stimuli. The second type responds to a diversity of stimuli: high (>42 째C) or low (<10 째C) temperatures, pain-producing chemicals and high-intensity mechanical stimuli. They are small A or C fibers, either non-myelinated or thinly myelinated. These nociceptors have enhanced sensitivity in flamed tissues and may be excited by normally innocuous stimuli.44 A great deal of research on nociceptors has been carried out. Klement and Arndt53 studied the pain-evoking and pain-modulating properties of adenosine at venous and paravascular nociceptors in humans. Later, they studied the role of nociceptors of cutaneous veins in the mediation of cold pain in humans.54 Yarnitsky et al.94 studied the effects of the rate of rising temperature stimuli applied to the skin on (1) the unitary receptor threshold and frequency response, often single C nociceptors, and (2) magnitude and reaction times of evoked pain in 15-healthy human volunteers. Schmelz et al.80 investigated the sensitization of insensitive branches of C nociceptors in human skin. Eighteen cutaneous mechanosensitive C nociceptors were recorded from the peroneal nerves of healthy human subjects. Results showed that there are insensitive branches in human mechanosensitive cutaneous C nociceptors that can be detected by transcutaneous electrical stimulation. Recent studies in conscious humans using direct recording from single nerve fibers in peripheral nerves have confirmed that isolated activation of an individual sensory receptor can result in distinct sensory perceptions. Meissner corpuscles detect touch, Merkel receptors generate pressure and nociceptors evoke pain. The encoding of specific sensory information is started by these sensory receptors in the skin. The central nervous system makes its further analysis through neural pathways by transferring the information to the brain.

3.2.4 Neural pathways and responses The neural signals from the nerve endings are passed to the brain to formulate sensation. The pathways to the brain depend on two major principles: the types of nerve fibers and the place where the pathway terminates in the cortex. Different types of nerve endings carry different types of information to the brain. The nerve fibers can be classified in a number of ways: (1) by the types of stimuli that excite them; (2) by the way they respond to stimuli (slow- or fast-adapting); and (3) by their receptive field (large ill-defined or small well-defined). The receptive field refers to the region of the skin that, when stimulated, causes responses in a particular


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neural fiber. By these classification criteria, the nerve endings responding to mechanical deformation in glabrous skin have been classified into four types: (1) rapid-adapting fibers with small and well-defined receptive fields; (2) slow-adapting fibers with similar receptive fields; (3) slow-adapting fibers with large, ill-defined receptive fields; and (4) rapidly-adapting fibers with similar receptive fields.11 The second principle is that the location of a nerve ending determines where its information goes to the brain, regardless of the type of fiber it represents. There are 31 pairs of nerves, through which all the sensory information from skin is passed on to the spinal cord. Through the dorsal roots, the nerve endings enter into the back portion of the spinal cord. For the head region, there are four cranial nerves collecting cutaneous information. The information gets to the brain by two main pathways: medical and spinothalamic. The medical pathway, which is both rapid and large, receives inputs from large, myelinated, fast-conducting Aβ fibers terminating in corpuscular endings. Fibers are responsive to touch, temperature and movement. However, the spinothalamic pathway, which is a slow pathway made up of many short fibers, carries information on temperature and pain. At the brain stem, the spinothalamic pathway is divided into two branches: paleospinothalamic and neospinothalamic. The paleospinothalamic branch, which specializes in dull and burning pain signaling, receives input from small, unmyelinated, slow-conducting C fibers that terminate in free nerve endings in the skin. The neospinothalamic branch, which specializes in sharp and prickling pain signaling, receives most input from small, myelinated, slow-conducting Aδ fibers that terminate in free nerve endings in the skin. It also receives input from Aβ fibers.11 There is a regular relationship between where a stimulus is applied to the skin and where neural activity occurs in the somatosensory cortex. Renfield and Rasmussen created a classic map of the relationship, which was obtained by electrically stimulating the somatosensory cortex of patients during brain operations. As a point on the cortex was stimulated, the patients pointed to where they felt the sensation.

3.3

Perception of sensations related to mechanical stimuli

3.3.1 Dynamics of wear sensation The contact between the skin and clothing has a number of features: (1) the area of contact is large and encompasses cross-over regions having varying degrees of sensitivity; (2) the body very often changes its physiological state, such as skin temperature, sweating rate and humidity at the skin’s surface, which generates various new thermal stimuli; and (3) the


Neurophysiology of sensory perceptions

33

body is frequently in movement, causing clothing to move away from and be close to the skin, which often induces new mechanical stimuli. These mechanical and thermal stimuli trigger responses from various sensory receptors and formulate various perceptions â&#x20AC;&#x201C; touch, tactile, thermal, moisture and more complex synthetic sensations â&#x20AC;&#x201C; which affect the comfort status of the wearer.

3.3.2 Perception of touch and pressure Any point on the surface of a human body can evoke the sensation of touch. However, the sensitivity varies from one region to another. Average absolute thresholds for different regions of the female skin have been obtained by applying a hair to the surface of the skin with different amounts of force, and are expressed as the amount of force applied to the hair. The higher the bar, the greater the force needed to trigger the sensory receptor and the lower the sensitivity. Obviously, the absolute thresholds vary considerably over the body surface. The threshold for touch sensation depends on both frequency of vibration of a stimulus and skin temperature. Each touch sensation seems to be located at a particular place on the skin and is directly related to the amount of neural presentation in each area of the touch cortex.11 Johansson et al.46 investigated the relationship between the physical magnitude and the subjective perception of applied pressure in order to determine discomfort and pain thresholds. The subjective pressure level was made on three points: the finger, the palm and the thenar area. The slopes of the linear functions (log magnitude estimates as a function of log pressure) were 0.66, 0.78 and 0.76 for the finger, palm and thenar points, respectively. The discomfort threshold was 38% of the pain-pressure threshold at the finger point, 40% at the palm and 22% at the thenar point. Dellon et al.19 examined the relationships between the skin hardness of the human index and little finger pulp and the perception of pressure in 25 adults. Pressure perception was measured with the Pressure-Specified Sensory Device for both static and moving touch stimuli and for two-point discrimination. The mean hardness of the fingertip pulp was 12.5 Âą 0.6 gm/ mm2. There was no statistically significant difference in hardness between the dominant and non-dominant, right and left, index and little, or male and female fingertips. Skin hardness was independent of age for this population. The highest correlation, which was r = 0.46, was found between the cutaneous pressure threshold for one-point static touch and skin hardness. This degree of correlation is significant at the p < 0.02 level, 21%, which demonstrates that it leaves 79% of the variability unexplained. These results suggest a physical interaction between mechanoreceptors and dermis that is only partially explained by the hardness of the skin.


34

Clothing biosensory engineering

Greenspan and McGillis34 examined the thresholds for the perception of pressure, sharpness and mechanically evoked cutaneous pain. Examination of individual subjects’ thresholds over time revealed that 27% showed significant increases in pain threshold over the 15 days of testing. In contrast, only 6% of subjects showed significant increases in sharpness or pressure thresholds over the same period. Thus, whereas most subjects exhibited stable pain thresholds, approximately one-quarter showed significant increases in pain threshold over time. Human pressure perception was investigated by Dellon et al.20 in 1994. Pressure perceptions for static one- and two-point discrimination (s1PD, s2PD) and moving one- and two-point discrimination (m1PD, m2PD) were recorded. The mean ± sd were 0.13 ± 0.06, 0.24 ± 0.12, 0.22 ± 0.10 and 0.26 ± 0.13 gm/mm2 for s1PD, s2PD, m1PD and m2PD, respectively, on the index finger and 0.07 ± 0.05, 0.16 ± 0.12, 0.17 ± 0.07 and 0.21 ± 0.14 gm/mm2 for s1PD, s2PD, m1PD and m2PD, respectively, for the little finger. The little finger was significantly more sensitive than the index finger (p < 0.001). In the process of fabric–skin contact and mechanical interaction during wear, clothing will exert pressure on, and dynamic mechanical stimulation to, the skin, which will trigger various mechanoreceptors and generate a variety of touch sensations. Amano et al.1 reported a study on the fluctuation of clothing pressure during wear by using spectrum analysis. They observed that the positions of a subject rather than the shape of the clothes affected the amplitude of the fluctuation. Under static conditions, clothing pressure fluctuated with a peak at 0.2–0.4 Hz due to respiration. In the lower frequency range of the peak, the pressure spectrum changed with the clothes and the positions. They believed that the fluctuation in the clothing pressure was related to the comfort of clothing. Sukigara and Ishibashi85 measured the pressure between the fabric surface and the subject’s hand during subjective assessment of fabric roughness. They found that an increase in pressure tends to produce stronger ‘knobbiness’ and ‘roughness’ evaluations. Momota et al.74 studied the clothing pressure caused by Japanese women’s high socks. They measured clothing pressure and sensory responses on subjects wearing women’s high socks. After examining the relation between the clothing pressure and the sensory evaluation of the subjects, they concluded that, in order to design comfortable high socks that are not loose and don’t wrinkle, it is necessary to ensure that: (1) the length of the socks is long enough to cover the upper calf; (2) the stitches and patterns at the ankle can follow leg motion easily; and (3) the pressure at the lower leg is within the range 5–10 mmHg at a standing position of rest. Homota et al.42 reported a study of the clothing pressure wearing Japanese men’s socks by measuring the sock pressure on a model foot and on human subjects. They found that measured sock pressure on human subjects was


Neurophysiology of sensory perceptions

35

lower than the pressure measured on the model foot. Subjects reported that the socks felt comfortable at a pressure of around 10 mmHg at the top and 5–10 mmHg at the ankle. In 1993, Makabe et al.71 measured clothing pressures in the covered area of the waist for corset and waistband and recorded the sensory responses of subjects to the clothing pressure. They observed that the pressure at the waist is a function of the covering area, respiration and the ability of samples to follow bodily movement. Subjects reported their perceptions on pressure at the waist line as: (1) no sense or no discomfort when pressure was 0–15 gf/cm; (2) negligible or slight discomfort when pressure was 15– 25 gf/cm; and (3) extreme discomfort when pressure exceeded 25 gf/cm. Shimizu et al.82 measured the clothing pressure on the body in a brassière under static and dynamic conditions. They reported that the static pressure in the standing position was high at the shoulder, the side and the back. Two main zones of pressure during movement were found at the shoulder and the back. Also, the pressure under static conditions was lower than that during movement. Makabe et al.72 also studied clothing pressure from the brassière. They observed that wearer preferences were related to pressure distribution. Shimizu et al.83 investigated the dynamic behavior of clothing on the knee of a person wearing slacks for three typical sequences of motion, with nine samples of slacks made of three kinds of fabrics, with three different degrees of ease. They identified the high-pressure region by measurement of the dynamic pressure distribution over the knee surface and found that the change in clothing pressure was a function of time.

3.3.3 Perception of prickle, itch and inflammation Many consumers perceive wool as unsatisfactory for apparel fabrics because of the prickle or skin irritation that it causes,6 especially when fabric containing wool fibers is used for underwear garments. Prickle is usually described as the sensation of many gentle pinpricks. Traditionally, the prickle sensation associated with wool was considered to be associated with the skin’s allergic response. The degree of discomfort caused by prickle varies from person to person and with the wear situation. Prolonged irritation that evokes the action of scratching the affected area can lead to skin inflammation. As these sensations have significant impact on the comfort experience of consumers in practical wear situations, substantial research has been carried out to study the mechanisms involved in fabric prickle sensations. In 1984, Westerman et al.91 studied the relationship between sensations of prickle and itch and human cutaneous small nerves. Skin sensations were tested on the forearms of 12 volunteers, in whom anoxia nerve blocks of the forearm were produced by inflating a blood pressure cuff to 270 mmHg


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Clothing biosensory engineering

on the upper forearm. Touch sensations were lost after about 20 minutes, but pain, temperature and fabric-evoked prickle sensation remained until about 40 minutes. This result indicated that prickle sensations are associated with small nerve fibers. Davis18 conducted psychophysical experiments, concerning the mechanisms underlying the perception of cold pain in humans. A peltier-type stimulator (20 × 25 mm) was used to deliver stimuli to sites on the thenar eminence (glabrous skin) and volar forearm (hairy skin) of each arm. Each trial consisted of a 90 seconds, 2 °C stimulus that was preceded and followed by a 35 °C stimulus. Nine subjects rated the overall evoked pain intensity (four trials/skin type) and the prickle component (four trials/skin type). Typically, subjects perceived the cold-evoked pain as prickly, cold/freezing and achy. The pain intensity and quality was similar for glabrous and hairy skin sites within individual subjects. Pain intensity gradually rose to a plateau by ∼60 seconds into each trial. The prickle component differed amongst subjects due to its variable time course. Subjects consistently reported an intense, brief jab of prickle at both hairy and glabrous sites during the rewarming phase. Garnsworthy et al.27,28 investigated the mechanisms of fabric prickle. They used invasive experiments both on animals50 and human subjects to record electrical activity of individual nerve fibers for determining which nerve fibers were responsible for the sensation of prickle. They reported that the neurophysiological basis for fabric-evoked prickle is not caused by skin allergic reaction, nor by any chemical released from wool. The causes of fabric prickle have been identified as the mechanical stimulation of fabric to the skin that induces low-grade activity in a group of pain nerves as shown in Fig. 3.1. As a fabric begins to contact the skin, the protruding fibers from the fabric will initially take all the force. As the body of the fabric moves closer to the skin, the forces increase and the protruding fibers bend. When the forces from the individual fibers reach a certain level, large shear forces in the skin are generated and pain nerve endings are activated. The nerve endings are identified as a group of pain receptors termed nociceptors (both Aδ and polymodal C). When the nociceptors are activated, they cause a low rate of discharge from nociceptors over a wide area of skin. The critical buckling load that can trigger these pain receptors is around 0.75 mg or more at their point of contact with the skin. Garnsworthy et al.27 investigated the psychophysical relationship between the sensation of prickle magnitude with the measure of the physical stimulus of prickle by comparing the average forearm prickle test ratings with the Teflon impression method of objective measurement of prickle. An 11.4 cm2 area of Teflon, impressed with the test fabrics, was examined within ten minutes of imprinting. Three categories of fibers in the test fabric (<75 mg, 75–175 mg and >175 mg) were counted by one of the authors who


Neurophysiology of sensory perceptions

37

Fabric

Skin

3.1 Diagrammatic presentation of the mechanisms of fabric-evoked prickle sensation.

was unaware of which fabric he was evaluating. Thirteen fabrics were evaluated in this way and used for subjective assessment of prickle sensation by 55 subjects. The mean subjective perception rating of prickliness was plotted against the prickle stimulus intensity (mean number of fiber ends exerting loads >75 mg/10 cm2), as shown in Fig. 3.2.27 The data were analyzed using Stevens’ psychophysical power law. The fitted equation was: Rp = 0.54Sp0.66 where Rp is the subjective sensation magnitude of prickle and Sp is the prickle stimulus intensity (number of protruding fabric hairs with buckling loads >75 mg). The correlation coefficient of the equation was 0.91. These authors reported a number of findings that are important for understanding fabric prickle properties. •

It has been found that summation of responses from the pain group of nerves is necessary for the initiation of pain sensation.92 Prickle from fabrics could not be perceived if the density of high load-bearing fiber ends is less than three per 10 cm2 of the fabric, or the skin contact area is below 5 cm2.26 • Sensitivity to fabric prickle is influenced by a number of factors: • males had a higher threshold and more variations in sensitivity to prickle than females. • prickle sensitivity decreased progressively with age as the skin is known to harden as age increases, and it decreased with hardness of the outermost skin layer.


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Clothing biosensory engineering •

pain nerve endings are very close to the surface in hairy skin but not in glabrous skin, which explains why prickle cannot be felt with the fingers. • prickle sensitivity increased with the moisture content of the skin, as water can soften the stratum corneum and allow the protruding fibers to penetrate more readily. • Prickle sensitivity increased with ambient temperature in the range 12–32 °C at constant relative humidity (60–65%) as the skin moisture content increased due to perspiration in hot and humid conditions.26 Itch is a sensation that has been identified as being the result of activation of some superficial skin pain receptors.92 The pain receptors responsible for 8

7

Sensation magnitude of prickle (Sp)

6

5

4

3

2

Fabric series A 1 Fabric series B

0 0

10

20

30

40

50

Prickle stimulus intensity (Ip)

3.2 The psychophysical relationship between prickle sensation and prickle stimulus intensity.

60


Neurophysiology of sensory perceptions

39

itch may be of a different type to those responsible for the prickling sensation. Twycross et al.89 had proposed a clinical classification of four categories of itch, on the basis of advances in understanding of the peripheral and central origins of itch. Pruritoceptive itch originates in the skin and is transmitted by C nerve fibres. Neuropathic itch arises because of disease located at any point along the afferent pathway. Neurogenic itch originates centrally but with evidence of neural pathway. Psychogenic itch is the delusional state of parasitophobia. Skin inflammation (reddening), which occurs in a small proportion of the population, is a consequence of prickle and itch resulting from mechanical stimulation of skin pain receptors by prickly fabrics, most likely through a mechanism termed axon reflex.63,64 When some pain nerves in the skin are excited, vasoactive agents are released near the nerve endings. These substances dilate surface blood capillaries and cause reddening beginning in the vicinity of activated pain nerve endings, and then spreading to larger areas of skin. The agents also promote pain nerve excitation and make the condition progressively worsen with itchiness and enhanced prickle sensitivity. A rapid flare response will usually be generated by this mechanism when a painful stimulus damages the skin. This is not an allergic response, rather an irritant response. Inflammation may occur rapidly (in minutes) or slowly (in hours). It can be relieved quickly after the fabric is removed from the skin, unless the fabricâ&#x20AC;&#x201C;skin contact is too long and has produced a severe reaction. Until now, various instrumental methods such as transepidermal water loss and capacitance have provided the means for analyzing various biophysical properties of human skin and changes in these properties caused by exposure to irritants. However, these methods do not directly measure skin inflammation. A recently introduced skin surface tape sampling procedure has been shown to detect changes in skin surface cytokine recovery that correlate with inflammatory skin changes associated with chemical irritant exposure or existing dermatitis.79

3.3.4 Roughness and scratchiness The sensations of roughness and scratchiness, which are related to surface geometry, are perceived when a fabric moves across the skin. These sensations are due to the activity of sensory cells that are situated in skin tissues; so, one can say that the surfaces of textile fabrics have a major effect on skin sensory receptors. Tactile sensitivity is based on two types of skin receptors, situated both around the hair follicle and in the skin portion without hair. These skin receptors are represented through free ends and the Meissner corpuscles and Merkel discs. The aspects of sensory/tactile comfort, like any other components of clothing comfort, can be analyzed and evaluated in two major ways: subjectively and objectively.12 In the field of percep-


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tion, extensive research has been carried out to study the physical and neural bases of roughness perception. To manipulate relevant physical features systematically and independently, two types of stimuli were created: (1) surfaces with regularly spaced ridges (gratings) and (2) surfaces with raised dots. By using regularly spaced ridges, Lederman et al.57,58,60,62 identified two major factors relevant to roughness perception: (1) the spacing between neighboring ridges and (2) the amount of force applied. It was also observed that perceived roughness for a given surface was comparable whether the finger moved over the surface or the surface moved over a stationary finger when the applied forces were the same. This result suggested that the perception of roughness is only related to the cutaneous mechanoreceptors, and that it is independent of kinesthetic input. Darian-Smith and Oke14 investigated the responses of primate cutaneous mechanoreceptors to systematically applied grating stimuli. They found that the stimulus temporal frequency (cycles/second), defined as the scanning velocity divided by the spatial period of the grating, was the primary factor related to the responses of all types of mechanoreceptors. They reported two important findings: (1) the spatial period of a stimulus cannot be related to the firing rate of individual mechanoreceptors; (2) each type of mechanoreceptor has a particular sensitive range of temporal frequency: slowadapting (20–60 Hz), rapidly-adapting (60–200 Hz) and Pacinian corpuscle (100–300 Hz). Goodwin et al.30–32 reported a series of studies on the mechanoreceptor’s responses to gratings stroked across their receptive fields. They reported: (1) that the response rate of all mechanoreceptors increased with the increase in groove width from 0.18–2.0 mm; (2) a small increase in ridge response rate with increasing ridge width; (3) that the response rate decreased with increased velocity. Greenspan and Bolanowski33 summarized the psychophysical results from these findings. As no responses from individual types of mechanoreceptors match the perceived roughness, it seems that the perception of roughness is not a function of any individual mechanoreceptor. Connor et al. conducted a series of studies on the perception of roughness and dot spacing patterns. The major findings from these studies are the following. 1. Perceived roughness was correlated with the variability in mechanoreceptor response rates, particularly of slow-adapting afferent units.9 2. Roughness magnitude increased with larger dot separations.10 3. The between-fiber spatial variation in firing rates of slow-adapting afferent units provided a very good match with the shape of psychophysical function.10 4. Perceived roughness was best related to differences in discharging rate over distances of 1–2 mm on the skin.10


Neurophysiology of sensory perceptions

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5. Perceived roughness followed the trend of the slow-adapting mechanoreceptors, not the rapid-adapting ones.47 These research outcomes provide the evidence that the perception of roughness is related to the spatial variations in responses between mechanoreceptors. The surfaces of textile products are very complex. Their surface spatial dot patterns and forces from protruding fiber ends vary considerably across different types of weave structure and fibers. LaMotte56 studied the responses of mechanoreceptors to stroking the skin with nylon fabrics. He observed that response from rapid-adapting nerve fiber (Meissner) is related to the skin indentation, the weave pattern, the density of the fabric, and the rate of movement across the skin. Perception of roughness has been examined or investigated over many years. Gwosdow et al.35 reported that subjective perceptions of roughness increased with levels of skin wetness and were correlated with the frictional force required to pull fabrics across the forearms of subjects. Behmann2 made an attempt to relate subjectively perceived roughness to textile construction parameters. He reported that the irritation produced was provided by the roughness spacing. The roughness of woven and knitted fabric was found to be a function of the yarn diameter. Sukigara and Ishibashi85 evaluated the surface roughness of polyester crepe fabrics in the grey and finished states subjectively and objectively. Twenty female subjects were used. They found that the finished fabrics were perceived as being more ‘knobby’ and ‘rough’ than the grey fabrics. Roughness perception increased with the pressure between the fabric surface and the subject’s hand. In 1995, Wilson and Laing93 examined the extent of the influence of wool fiber on the tactile characteristics of homogeneous fabrics with standardized fiber content, yarn structure, dye and finishing treatment. Significant differences in the ranking of roughness and prickliness were found among these fabrics. The perception of roughness and prickliness was observed when fiber diameter was 34 µm. Chapman et al.7 examined the central neural mechanisms contributing to the perception of tactile roughness. They suggested that the perception of roughness could be based upon a simple intensive or rate code, but it is not meant to detract from the importance of Johnson’s spatial variation code in the neural representation of tactile form. Furthermore, they proposed that the relatively lower level ability of humans to scale surface roughness could be explained by a simpler code based on mean firing rate. Scratchiness is another sensory descriptor widely used by consumers to describe their experience of discomfort sensation with clothing. It has been found that scratchiness is highly related to the sensation of roughness in both consumer surveys67 and the sensory responses of subjects in wear trials.65 Mehrtens and McAlister73 studied the subjective perception of scratchiness through wear trials under hot humid conditions by using


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garments made of fabrics with identical structural features but different fibers: orlon, nylon, rayon staple fibers and cotton. They found that scratchiness perception decreased as the filament flexural rigidity and friction decreased.

3.4

Perception of thermal and moisture sensations

3.4.1 Functions of thermal sensitivity Thermal sensations have long been recognized as an important aspect of comfort. Thermal senses, which tell us about our thermal state, both internally and externally, are indispensable to body temperature regulation and thereby to personal survival. Thermal sensitivity has three functions: 1. body temperature regulation by either instrumental behavior (such as changing clothing) or autonomic behavior such as vasoconstriction, vasodilatation, sweating and shivering; 2. avoidance of local damage to the skin from burning by cold or hot temperatures; 3. monitoring temperature whilst touching an object.38 The influence of thermal sensitivity on thermoregulation and overall thermal comfort will be discussed in the next section. This section will focus on the transient cold and warm sensations as the perceptions influencing the comfort status of the wearer.

3.4.2 Thermoreceptors There are several types of thermoreceptors, some of which are located in the hypothalamus, spinal cord and gut. Their main role is to monitor body temperature and they are a part of the autonomic function. Near the body surface, a variety of receptors respond to temperature, including ‘warm’ and ‘cold’ receptors, nociceptors and SA mechanoreceptors.84 Hensel40 defined the general properties of specific cutaneous thermoreceptors as: (1) having a static discharge at constant temperature (T); (2) dynamically responding to temperature changes (dT/dt) with either a positive temperature coefficient (warm receptors) or a negative temperature coefficient (cold receptors); (3) not being excited by mechanical stimuli; and (4) being active in the non-painful or innocuous temperature ranges. Thermoreceptors can be classified into cold receptors and warm receptors according to their dynamic responses. For static temperatures, cold receptors respond to temperatures from below −5 to 43 °C with a peak at about 25–27 °C.40 Also, they respond to very high temperatures above 45 °C,


Neurophysiology of sensory perceptions

43

which are related to the paradoxical cold sensation that human beings experience when skin is exposed to very high temperatures. Warm receptors start to discharge at constant temperatures beginning at 30 °C. They increase their activity when the temperature rises and reach peaks between 45 °C and 47 °C in human hands, and between 41 °C and 43 °C for other populations of warm fibers in human skin.40 For temperature changes, a warm receptor responds with an overshoot of its discharge on sudden warming and a transient inhabitation on cooling. Conversely, a cold receptor responds with an inhabitation on warming and an overshooting on cooling. In the skin of human subjects, cold receptors have been identified as being the radial nerve in hairy skin and warm receptors as being the radial nerve in hand dorsum by Hensel40 using electrophysiological methods. In describing thermoreceptors, Spray84 stated: The actual sensory receptors are apparently mitochondria-rich conical or bulbous projections of nerve fibers into the cytoplasm of basal epithelial cells. These projections are branched unmyelinated processes from afferent fibers that are commonly dichotomized into the A delta category of myelinated axons for cold receptors and C fibers for warm receptors. Specific thermoreceptor axons are smaller than those of temperature sensitive mechanoreceptors, which are recruited into the afferent responses at higher stimulus strength; . . .

Regarding the neural pathway, Spray84 described: Primary thermal afferent from skin receptors have somata in representative sensory ganglia and project to the marginal zone of the spinal cord, synapsing on rostral brain stem and thalamic nuclei and projecting to somatosensory cortex. Integration at the first level of synaptic interaction is apparently considerable; the brain stem areas involve several midbrain raphe nuclei; thalamic areas of thermoreceptor representation include VB and NPT. Relay to the hypothalamus, where there is integration with information from deep body thermoreceptors, is apparently from midbrain raphe nuclei parallel to the thalamic projection.

In addition to these thermoreceptors, there are nociceptors which respond to noxious temperature; there are called ‘heat receptors’ or ‘nociceptors’ and are associated with pain. The SA mechanoreceptors respond to temperature, which may reflect the psychological phenomenon that we perceive simultaneously thermal and mechanical stimulation of the skin.

3.4.3 Fabric thermal sensations Thermal sensations result from the responses of thermoreceptors to constant temperatures and transient transfer of heat from the skin (a cooling process) or to the skin (a warm process), after the fabric is placed on the skin. On the basis of Hensel’s work, Ring and de Dear78 described this


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Clothing biosensory engineering

neurophysiological response mathematically. The impulse frequency of a cold receptor or a warm receptor is a function of static temperature and the rate of temperature change: Q ( y, t ) = K sTsk ( y, t ) + K d

∂Tsk ( y, t ) ∂t

[3.1]

where Q(y, t) is the pulse output response of a thermoreceptor as a function of time t and the depth y of a thermoreceptor in the skin in impulses/s, Ks is the static differential sensitivity related to the steady state temperature, Kd is the dynamic differential sensitivity associated with temperature change and Tsk(y, t) is the temperature in the skin at thermoreceptor depth y as a function of time. The first term of equation 3.1: KsTsk(y, t) represents the static discharge of the cutaneous thermoreceptors at constant temperature and the second term: Kd

∂Tsk ( y, t ) ∂t

gives the dynamic response of thermoreceptors to temperature change. Ks and Kd were derived from the slope of the static and dynamic frequency curves (∆F/∆T), i.e. the change in frequency (∆F) for a small change in temperature (∆T). Hensel and Kenshalo41 reported that a maximum static differential sensitivity (Ks) of −1 s−1 °C−1 and a maximum dynamic differential sensitivity (Kd) of −50 °C−1 were found for the cold receptors in a cat’s nose. Later, Kenshalo52 reported that the average differential sensitivity of a cold fiber population in a monkey’s skin was around −21 °C−1. Hensel and Kenshalo41 found that the highest static differential sensitivity of human warm fibers was about 4 °C−1. The average maximum static frequency from a warm receptor from a cat’s nose was 36 s−1. The highest dynamic frequency was 200 s−1. The dynamic differential sensitivity was found to be 70 °C−1. These findings defined the existence of warm and cold receptors and provided the neurophysiological basis for the thermal perceptions. However, we still cannot state that the responses from these receptors determine the subjective perceptions of thermal sensations such as warmth and coolness. For static temperature sensations, Dykes21 found that the average discharge frequency of cold fibers in a monkey’s hand correlated well with static cold sensations in humans in the range 34–25 °C. For dynamic temperature sensations, it was found that the instantaneous frequency was not a satisfactory indicator and that the number of impulses integrated over a certain period time must be considered.40 Kenshalo52 proposed a ‘central integrator’ that has a long time constant of decay. The time constant was defined in such


Neurophysiology of sensory perceptions

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way that the amount of decay is equal to the difference between the total impulses produced by fast and slow temperature changes. Hensel40 described a concept of ‘central threshold’ based on the fact that individual cold and warm receptors may be excited without conscious thermal sensations. The threshold of cold sensation responds to an instantaneous frequency of 80 s−1 or to a total number of 120 impulses in a single cold fiber. The threshold of warm sensation seems to correspond to an average instantaneous frequency of 9 s−1 or to a total number of 28 impulses in a human warm fiber. The total number of impulses from a human single warm receptor at the threshold of conscious warm sensation is a function of the rate of temperature change.40 These works suggested that the peak frequency and the cumulative impulses during the period of time following stimulus onset could be responsible for the way in which thermal responses are perceived. On the basis of these observations, Ring and de Dear78 proposed that the intensity of thermal sensations (termed psychosensory intensity – PSI) is proportional to the cumulative total impulses from stimulus onset at the thermoreceptor until such time as the receptor firing rate has decayed to within one impulse per second of the post-stimulus steady state. Li et al.66 investigated the mechanisms of fabric coolness to the touch. The temperature responses of skin to the contact of four fabrics were graphed and the resulting temperature curves used as boundary conditions at the skin surface for insertion in the thermoreceptor response model developed by Ring and De Dear.78 The responses from the cold receptors as each of the four test fabrics was brought into contact with the skin were predicted from the model.66 The integrals of the thermoreceptor frequency output curves (PSI values) were found to be 37.8 for wool, 29.4 for cotton, 20.3 for wool/polyester blend and 8.3 for polyester. The rank order of the impulse frequency of the cold receptors corresponds with the coolness ranking observed in the subjective trials conducted by Schneider et al.81 Results from the subjective perception trials were compared with the difference in predicted PSI values between the pairs of fabrics. Obviously, there was a good correspondence between the magnitude of the subjective differences and the predicted difference in coolness intensity, indicating that the integral value of the output frequency from the skin cold receptor is a good predictor of fabric coolness. Imada and Hirata45 studied the effects of skin cooling on the regional differences of thermoregulatory responses wearing a water-perfused suit. A female subject was kept in the rest supine position in a climatic chamber with temperature, relative humidity and air velocity of 30.0 °C, 50% and 0.2 m/s, respectively. The tests were repeated three times under the same experimental conditions. The temperature of the suit was set at 33 °C and


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Clothing biosensory engineering

maintained for 30 minutes; it was then lowered to 24 °C in 3 °C increments, at intervals of 30 minutes. On the torso, the change in thermal sensation at a given decreased skin temperature was greater, but the change in skin blood flow at a given decreased skin temperature was smaller than that on the extremities. These results suggest that the degree of variation in thermal sensitivity and in skin blood flow as skin temperature was lowered differed substantially between sites at the trunk and at the extremities. In an experiment on thermal radiation from fabrics on the human body, Nakajima et al.75 examined skin temperature change and the thermal sensations of an examinee wearing a sweater with a plastic heater at the back. They found that the skin temperature as perceived by thermal sensation was almost fixed at 33–35 °C, even if the radiant heat strength differed.

3.4.4 Dampness sensations Moisture in clothing has been widely acknowledged as one of the most important factors contributing to discomfort during wear. Nielsen and Endrusick76 observed that the sensation of humidity is correlated with skin wetness. They recommended the use of the subjective sensations of wetness of skin and clothing as a sensitive tool to evaluate the thermal function of garments. Sweeney and Branson86 used a psychophysical approach to study the assessment of moisture sensation in clothing. They applied the method of constant stimuli to obtain the absolute and difference thresholds of moisture sensation at the upper back area of volunteers. Subjects were trained to respond to the sensation of moisture, not temperature. By adding water to fabric swatches as stimuli, they found that the absolute threshold of moisture sensation was 0.024 ml and the difference threshold was 0.039 ml. Also, the relationship between the proportion of moisture detection and stimulus intensity exhibited a linear function. In a further work, Sweeney and Branson87 employed a magnitude estimation method to study the relationship between moisture stimulus intensity and moisture sensation. Wetted fabric swatches were applied to the upper back to obtain moisture sensation, as before, by using one type of fabric with seven moisture levels. During the experiment, Stevens’ method of magnitude estimation was used to instruct subjects to estimate their perception of moisture. Thirteen subjects were tested. Stevens’ power law was applied to hid the relationship between the moisture magnitude (Rs = 31.62Sp0.53) and the moisture stimulus (Sp) and a square of correlation coefficient of r2 = 0.96 was obtained.87 Li et al.68 and Plante et al.77 reported a series of studies on the perception of fabric dampness using a sliding interval scale ranging from ‘definitely dry’ to ‘very damp’. Fabrics with different levels of hygroscopicity were


Neurophysiology of sensory perceptions

47

studied at five levels of moisture content and different levels of ambient relative humidity. The relationships between dampness perception and fabric moisture content also exhibited a power function. In a further study, it was found that the perception of dampness depended not only on fabric moisture content, but also on fiber hygroscopicity and ambient relative humidity. With the same water content above equilibrium regain, the perception of fabric dampness decreases with fiber hygroscopicity at low relative humidity and seems to decrease with relative humidity, particularly with lower hygroscopic fibers.69 This casts a doubt on whether fabric moisture content is the actual stimulus for moisture sensation. Furthermore, at the time of writing, the neurophysiological basis of moisture perception is still not clear. Clark and Edholm8 stated in a monograph that the general consensus of opinion is that there are no specific moisture detectors in human beings. Kenins et al.51 conducted a series of experiments to study the mechanisms of human perception of air humidity by using the human forearm and hand. Their conclusions were that the experimental results provided little evidence to support the assumption of a specific humidity detector in humans and that humidity might be perceived through some indirect mechanisms. Bentley3 reported that a close-fitting garment applying even pressure with a cold temperature could produce a feeling of wetness in the absence of moisture. This finding suggested that dampness sensation may be a synthetic sensation, which consists of a number of components such as fabric temperature, pressure and distribution of pressure during the contact between skin and fabric. This hypothesis is supported by the observations that dry fabrics (equilibrated to test conditions) were never rated as ‘definitely dry’ by subjects. The perceived dampness with these dry fabrics increased with fiber hygroscopicity, which is consistent with the observation on fabric coolness perception. These observations gave rise to an investigation of the physical mechanisms of fabric dampness perception. Li et al.68,69 conducted a series of physiological experiments and mathematically simulated the heat and moisture processes in fabrics and between skin and fabric during fabric–skin contact. They identified that the skin temperature drop during the contact plays a key role in the perception of dampness. Ha et al.36 compared the thermophysiological responses and clothing microclimate under the influences of different underwear materials during walking and recovery in the cold. Seven adult females were required to walk on a motor-driven treadmill with a 6 km/h speed for 30 minutes followed by 60 minutes recovery in a climatic chamber at an ambient air temperature of 2 °C, a relative humidity of 65% and an air velocity of 0.14 m/s. Two kinds of underwear were used: two layers of cotton underwear with two-piece long-sleeved shirt and long-legged trousers (C), two layers


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of polypropylene underwear with two-piece long-sleeved shirt and longlegged trousers (P). In addition, the subject put on a two-piece ski suit of 100% polyester including 100% polyester padding. Objective parameters including rectal temperature, skin temperatures, clothing microclimate (temperature, humidity), metabolic heat production and heart rate were measured. Furthermore, subjective ratings on thermal sensation, sweating/ shivering sensation, clothing wetness sensation and skin wetness sensation for the whole body were recorded. Results showed that the degree of skin wetness sensation and clothing wetness sensation for whole body was significantly higher in P during walking and recovery. Thus, it was concluded that two kinds of underwear with different properties of moisture could influence not only clothing microclimate but also physiological parameters like skin temperatures and metabolic heat production differently in the cold. Later, Ha et al.37 investigated the combined effects of fabric air permeability and moisture absorption on clothing microclimate and subjective sensation during intermittent exercise at 27 째C. Subjects were required to exercise for 10 minutes on a cycle ergometer at an intensity of 30% maximal oxygen uptake and then rest for 10 minutes. Experiments were conducted at an ambient temperature of 27 째C, a relative humidity of 50% and an air velocity of 0.14 m/s. This sequence was repeated four times. Three kinds of clothing ensembles were investigated: (1) polyester clothing with low moisture absorption and low air permeability (A); (2) polyester clothing with low moisture absorption and high air permeability (B); and (3) cotton clothing with high moisture absorption and high air permeability (C). Positive relationships between subjective sensation and forearm sweat rates were significantly confirmed in all three kinds of clothing; however, the subjective discomfort seemed to be reduced more effectively in C than in A and B for the same sweat rate.

3.5

Perception of texture

According to Fiore and Kimle,25 texture is the uniformity and variation in the surface of an object, which can be the description of actual or implied features of surfaces. The texture of a surface can be described in many ways, such as smooth, rough, shiny or dull. Obviously, texture is a complex synthetic sensation that covers many aspects of the sensory features of a surface, including visual, auditory and various tactile perceptions. In normal situations, people often see a garment or fabric when they touch or wear it, and hear the sound of the contact and friction between the skin and fabric or between different parts of clothing material. All the information gained from the visual, tactile and auditory signals provides us with the overall perception of a surface texture.


Neurophysiology of sensory perceptions

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Perceptions of tactile texture are derived from the activation of sensory receptors in the skin, not only in the hands but all over the body surface, during the contact between skin and clothing. Texture sensation may involve all the three types of sensory receptors: pain, touch and temperature. Touch is most likely the main component of texture and roughness is an important aspect. Therefore, a large number of research papers have been published on the neurophysiological basis of roughness perception as discussed in the previous section. Lederman59 reviewed the variables involved in tactual texture perception. He noted that the judgement of texture is dependent on the spatial distribution of the texture elements from moment to moment and on the rate that the fingers travel over the texture elements. Visual perception is a very important sensory channel in assessment of the texture of a fabric or garment. In certain dimensions, vision plays a dominant role in the perception of texture. For instance, vision seems dominant over touch for identification of two-dimensional forms, spatial features and color.39 On the other hand, we learn how to recognize different textures and make assumptions about how they feel through past experience of how different surfaces and textures look and feel. Sometimes, intersensory conflict may occur when the visual perception is distorted or the stored impression may not apply in a particular situation. For instance, visual perception of a snakeâ&#x20AC;&#x2122;s skin may imply it is slick and slimy but, when touched, the surface is dry and smooth. Normally, the senses operate in cooperative manner. There may be a division of labor between vision and touch. Vision often provides guidance for the hand, with which touch is used to gain information about surface characteristics such as texture, hardness or softness, or thermal property.39 Brown et al.5 reported a study on the effect of sensory interaction on descriptions of fabrics. In examining how sensory interaction may affect perceptual responses to fabrics, 169 subjects were asked to sort 24 fabrics into groups based upon how they felt the fabrics. Subjects were divided into two groups: (1) those who touched and viewed the fabrics (sensory interaction group); (2) those who only touched the fabrics (sensory isolation group). Content analysis of subjectsâ&#x20AC;&#x2122; written explanations of their sorting methods categorized their descriptive terminology according to texture, fabric traits, fabric name, fiber content, fabric weight, end-use, appearance, extended inferences and affective responses. The authors found significant differences between the terminology of the sensory interaction and sensory isolation groups. Whereas subjects in the sensory interaction group were more likely to use terms classified as end-use and appearance, subjects from the sensory isolation group were more likely to use terms classified as texture and fiber content. Dallas et al.13 reported their investigation on sensory perceptions of fabric in relation to gender differences. Male and female subjects sorted 60 fabric


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samples into groups based on tactile only or visual and tactile sensory interaction. The descriptive terms and phrases used by the test subjects when explaining why they grouped particular fabrics together were recorded and analyzed. They observed that the adjectives used were primarily related to fabric hand and texture or fiber/fabric structure and that, although males and females possess a common set of descriptive terms for textiles, they differ in the frequency and variety of their sensory interaction and perceptual responses to fabrics. In 1995, Jung and Naruse48 used the ranking method and the pairedcomparison method to evaluate the surface qualities of silk fabrics. Eight types of silk fabrics with the same material, density and yarn counts but different weaves were used. The fabrics were evaluated visually with respect to luster, quality of texture and attractiveness, using white and black samples. It was observed that luster and attractiveness perception was considerably affected by color, but surface roughness and thickness were not.

3.6

Perception of fabric hand

Fabric hand (or handle), which describes the way a fabric feels when touched by a human hand, is an important aspect of fabric texture. Fabric hand properties have been extensively studied in the areas of subjective sensory descriptors, psychophysics, fabric mechanics, objective and subjective assessment methods. Fabric hand is a complex synthetic sensation that consists of many dimensions, which are obtained through the active manipulation of a fabric by a human hand. To understand the mechanism of fabric hand, it is necessary to explore the neural sensory characteristics of the hands. Hands are the essential tools to explore and manipulate the external world, a role in which their sensory function is very significant. The activities of exploration and manipulation are based on the effective integration of movement and sensory perceptions. The hand has several essential sensory mechanisms, including muscle sense and kinesthesia. It has been found that the mechanoreceptors in the glabrous skin play a key role. Human hands are equipped with a large number of nerve endings with about 17 000 units that are sensitive to non-noxious mechanical deformation of the skin in the glabrous skin of one hand. A sensory unit is made up of a nerve cell with a long axon running in the peripheral nerve and the associated organ(s) in the skin or subcutaneous tissues. These units constitute the sensory basis for the ability of spatial and temporal discrimination in the skin area of the hand.90 In these sensory units, there are two types of sensory receptors; I and II. Each type of sensory receptor has a fast and a slow adapting speed with distinctly different response characteristics.90 The type I receptors have very high densities at the fingertips90 and a number of features.


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1. They have small receptive fields with distinct borders to form a finegrain system allowing accurate location of stimuli. 2. The two-point discrimination at the fingertip is close to the theoretical limit. 3. The distinct receptive field of the sensory units has a counterpart in the receptive space of the mind, i.e. an individual afferent unit may be accurately represented within the brain. 4. For the fingertips that is represented at the macula of the somatosensory system, the threshold is set by the sensitivity of the skin receptors, implying that the brain extracts information out of a neural quantum in the peripheral nerve. 5. In other regions of the hand, the threshold is set by mechanisms within the brain.90 6. A single impulse from a Meissner unit may produce a sensation of touch, whereas 10–20 impulses from a Merkel unit may be required to produce a sensation. Fast- and slow-adapting type II receptors are located in the subcutaneous tissue and below the basal layer of the epidermis, respectively. The end organs associated with the fast-adapting receptors are Pacinian corpuscles and Golgi-Mazzoni bodies, while Ruffini endings are associated with the slow-adapting receptors. Both types of receptor are uniformly distributed in whole glabrous skin ∼25 cm2. There is a fundamental difference between the perception of touch when wearing a garment and that when handling a fabric. When wearing a garment, touch is passive – the wearer does not move with the intention of getting information about the clothing; information is essentially imposed on the skin. In the process of handling a fabric, touch is active – the observer is moving the hands with the intention of obtaining objective information about the fabric. According to Gibson,29 fabric handle is active touch. Heller and Schiff39 also discussed the differences between active and passive touch, and they distinguished synthetic touch from analytic touch. Synthetic touch is used to obtain an overall impression by resting a hand, while analytic touch is aimed at gaining exhaustive information about an object’s features. Katz49 classified active touch into four categories: (1) gliding touch – short back-and-forth motion of the hand to gain information about surface variations or texture; (2) sweeping touch – using one or more fingers scanning across an object’s surface to obtain information regarding contours, edges and geometrical relationships of parts; (3) grasping – like gliding or sweeping and using the thumb to get information about two or more surfaces simultaneously; finally (4) kinematic grasping – comprehensive exploration of an object’s features.


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Gibson29 pointed out that, during active touch, even though exploratory movements seem very variable, they are not aimless. The aim of the everchanging movement is to isolate and enhance the component of stimulation that specifies the features of the object, and the nature of the component is to detect and isolate invariance in changing stimulation. Following this argument, Davidson15–17 outlined the objectives of active touch as: (1) to link accurate and inaccurate methods of scanning to specific attributes under investigation; (2) to derive the characteristics of effective scanning methods, which are determined by how well the scanning strategy focuses on the relevant stimulus attribute and how well it can be encoded. In an attempt to identify the features of active touch, Davidson et al. studied the scanning strategy by having subjects make matching judgements of replicas of three-dimensional free-form shapes. By videotaping the scanning activities, they found four types of exploratory movements: (1) global search – independent use of fingers to examine several stimuli simultaneously; (2) detailed search – coordinated use of fingers to focus on a single aspect of the stimulus; (3) palmar search – pressing the palm down onto the top of the stimulus; and (4) tracing – moving fingertips along the stimulus contours. They found that, to begin with, subjects used detailed and global strategies much more and they then used other strategies more as the inspection time increased.17 Lederman and Klatzky61 conducted a series of experiments to examine ‘exploratory procedures’ – defined as a stereotyped pattern having certain invariant characteristics used to obtain information about a specific object property. By comparing the performance of subjects who used an exploratory procedure spontaneously with that of subjects who used the one under instruction, they found that the procedures chosen by subjects themselves produced the optimal performance to obtain the information desired. Four types of exploratory procedures were identified: (1) ‘pressure’ – using the hand to exert force against the object for hardness; (2) ‘lateral motion’ – sideways movement between skin and object for texture; (3) ‘enclosure’ – simultaneous contact by parts of the hand to mold it to the object for global shape; and (4) ‘contour following’ – smooth and repeated hand movement along a contour segment for exact shape. On evaluation of fabric hand, Bishop4 pointed out that human subjects usually know instinctively how to manipulate the fabric to obtain the required information when they are asked to assess fabrics against particular attribute descriptors familiar to them. However, their instincts vary considerably among subjects. They usually use different methods to assess the given attributes and may assess different features against the same descriptor. Therefore, he thinks that it is necessary to discuss both the meaning of the descriptor and the handling method with subjects.


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Elder et al.23 reported a detailed description of procedures for evaluating fabric softness, in which both the method of applying pressure on a fabric sample and the amount of force to be applied were specified clearly. This methodology was adopted by Mackay70 to train consumers for evaluating ten different fabric attributes. Bishop4 reviewed the fabric-handling techniques in the literature and agreed with the statement by Elder et al.:23 ‘little detail appears in the literature to the instructions given to judges with regard to the method to be used to handle samples. Generally speaking, it has to be assumed that in other cases judges were allowed to handle fabric samples “as they saw it.” ’ This approach adopted by textile researchers may have good underlying reasons according to Gibson’s and Davidson’s arguments and the observations from Lederman’s experiments, provided that the invariants (features) under consideration are clearly explained to the subjects. However, it seems that fundamental information on the relationships between the sensory unit distribution, hand movement and the features of the fabric under assessment is still missing. The psychophysical relationships of subjective perception of softness and stiffness with fabric compression, percentage compressibility, flexural rigidity, coercive couple and drape coefficient were studied by Elder et al. using Stevens’ power law.22–24 They found that the significance of Stevens’ power law relationship varies with different combinations of perception and objective properties and with different types of fabrics. The relationship is significant between softness and compression for both woven and non-woven fabrics. It is significant between subjective softness and percentage compressibility for woven fabrics, but not for non-woven fabrics. The relationship is significant between stiffness and coercive couple for the non-woven fabrics but not for woven fabrics, and it is significant between stiffness and drape coefficient for woven fabrics. These observations indicated that the psychophysical relationships between subjective handle perception and fabric objective properties are complex in nature and related to the features of different types of fabrics. Hu et al.43 studied the psychophysical relationships between the subjective perception of primary hand (stiffness, smoothness, fullness and softness) and various fabric mechanical properties (bending, shearing, tensile, compression, surface, thickness and weight) measured on the Kawabata evaluation system (KES). The subjective primary hand values were obtained by asking subjects to rank 39 fabrics against the linear scale of Japanese Hand Evaluation and Standardization Committee (HESC) standards. They related individual subjective hand perceptions to the sum of contributions from a number of fabric mechanical properties by using a multiple regression method. Four models were compared: linear function, the mixed linear and log–linear function used by Kawabata, Weber–Fechner law and Stevens’


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law. They observed that Stevens’ law has the smallest deviations among the four models. Bishop4 pointed out that in comparison with the correlation coefficients of the psychophysical equations from Elder’s work, the equations obtained seem disappointing. He suggested that the use of psychophysical laws (Stevens’ or Weber-Fechner law) is not appropriate provided that the subjective hand values are represented by the judges’ estimates of their own responses to the fabric stimuli. These results provided a good estimation of the possible psychophysical relationships between subjective hand perception and fabric mechanical properties. However, they do not give us a sound understanding of the neurophysiological and psychophysical mechanisms involved in fabric hand perception. Further research needs to be carried out to answer a number of fundamental questions. • • • •

How do the active touch movements of the hand generate various types of mechanical stimuli to the touch receptors? How do the four types of mechanoreceptors respond to the mechanical stimuli? How are the neurophysiological responses from the receptors coded and transferred to the brain? How does the brain process the information and formulate various subjective hand perceptions?

By answering these questions, a sound understanding of hand perception might be established.

3.7

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A188.

3.8

References

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4 Physiology of thermal comfort 1

4.1

YI LI 1 AND ANTHONY S.W. WONG 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong

Introduction

One of the fundamental functions of clothing is to keep the human body in an environment where it can maintain its thermal balance and comfort. During the course of biological development, the human body has lost much of its ability to control heat loss and maintain thermal balance. Therefore, clothing is needed to protect the body against climatic influence and to assist its own thermal control functions under various combinations of environmental conditions and physical activities. By this means, the bodyâ&#x20AC;&#x2122;s thermal balance is achieved and a microclimate, which is perceived to be comfortable, is created next to the skin. In other words, an important task of clothing is to support the bodyâ&#x20AC;&#x2122;s thermoregulatory system to keep its temperature within a median range, even if the external environment and physical activities change in a much broader range. An understanding of the role of clothing in the thermal balance of the human body and thermal comfort under steady state conditions has been developed since the 1970s and has been widely used in the clothing industry and in the heating-ventilating industry. The human body is rarely in a thermal steady state, but is continually exposed to transients in physical activity and environmental conditions. Ruckman et al.36 stated that thermophysiological comfort is critical to outdoor apparel worn in various environmental conditions. Unlike environmental conditions and human physiology, the apparel system can be altered to provide maximum comfort to the wearer. Hygroscopic fibers such as wool and cotton absorb moisture vapor from ambient air when humidity rises and release heat. Similarly, when the humidity falls, moisture is released and heat is taken up by the fibers. Under transient conditions, this sorption behavior of fibers can play an important role in the heat exchange between the human body and the environment and in thermal comfort perceptions. The aim of this chapter is to study human perception of thermal comfort from different aspects. The concept of thermal comfort is described. The 60


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relationship between thermoregulatory and human subjective perception is explained through a series of experimental findings. Mathematical expressions of the thermoregulatory system of the human body are also described in detail. This chapter also looks at the dynamic thermal interaction between the human body and different types of clothing.

4.2

Thermal comfort

The thermal comfort of man depends on the combinations of clothing, climate and physical activity. Yaglou and Miller43 defined ‘effective temperature’ as an index of warm perception when a human body is exposed to various temperatures, humidity and air movements. The scale of effective temperature was determined by the temperature of still, saturated air, which was felt to be as warm as the given conditions. For instance, any ambient condition has an effective temperature of 60 °F when it is perceived to be as warm as still air at 60 °F saturated with water vapor. Effective temperature was adopted by the American Society of Heat and Ventilating Engineers as the operating scale for establishing comfort charts for secondary clothed individuals exposed to various levels of temperature, relative humidity and wind velocity. Rohles35 derived an equation by using multiple regression to predict thermal sensations after an exposure of three hours: Y = 0.1509Tab + 0.01Ha − 8.3719

[4.1]

where Y is the thermal sensation on the scale of 1 = cold, 2 = cool, 3 = slightly cool, 4 = comfortable, 5 = slightly warm, 6 = warm and 7 = hot, Tab is the dry bulb temperature in degrees F and Ha is the relative humidity as a percentage. Gagge et al.15 studied the sensory comfort and thermal sensations of resting–sitting unclothed subjects and compared them with the associated physiological responses under steady state and transient conditions of 12–48 °C. During exposure to steady cold and warm environments, thermal comfort and neutral temperature sensations lay between 28 and 30 °C, at which no physiological temperature regulatory effort is needed. Discomfort perception was related to lowering the average skin temperature in cold environments and increased sweating in hot environments. The same conclusion was drawn for transient changes when the subjects were exposed to an environment changing from comfortable to uncomfortable, neutral to cold, and neutral to warm. Thermal discomfort was found to be an excellent stimulus for behavioral activity by men. Thermal sensation gave man an early anticipatory drive for conscious action to change his body’s microclimate rather than depending on natural but short-term thermal protection through sweating, vasodilatation and


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vasoconstriction and shivering. In 1969, Gagge et al.17 reported a study on comfort and thermal sensations and associated physiological responses during exercise at various ambient temperatures. The authors concluded that after 30–40 minutes of steady exercise, temperature sensations from cool to hot were mainly correlated with skin and ambient temperatures; warm discomfort was related to skin sweating and skin conductance. During steady state exercise, perception of temperature was dominated by sensory mechanisms in the skin, while warm discomfort was mainly determined by thermoregulatory mechanisms. The comfort and thermal sensations during thermal transients caused by the rise in metabolic rate at the start of exercise were correlated with the initial rise in mean body temperature. Hensel21 and Carterete and Friedman2,3 pointed to the physiological basis of thermal comfort and the difference between thermal comfort and temperature sensations. Temperature sensations are mainly derived from cutaneous thermoreceptors, which are used to judge the thermal state of objects or environment. Thermal comfort and discomfort reflect a general state of the thermoregulatory system, which is the integration of afferent signals from both cutaneous and internal thermoreceptors. Therefore, the measurements of temperature sensations and thermal comfort need to be distinguished. McNall et al.31 used two separate scales to study thermal comfort and thermal sensations. Chung and Tong4 studied thermal comfort of Chinese people in Hong Kong to obtain the optimum thermal conditions for buildings. They exposed 134 college-age Chinese subjects wearing 0.6 clo standard clothing and under sedentary activity to several different thermal conditions for three hours. The neutral temperature of young Hong Kong Chinese people was found to be 24.9 °C. This is not significantly different from previous studies with Danish and American subjects. Using the probit technique for analysing subjective responses to thermal sensation, the neutral zone was found to be between 22.2 and 25.2 °C. Fanger8 developed a mathematical model to define the neutral thermal comfort zone of man in different combinations of clothing and at different activity levels. Mean skin temperature and sweat secretion rates were used as physical measures of comfort. Based on Fanger’s work, the American Society of Heating, Refrigerating and Air Conditioning Engineers developed generalized comfort charts for predicting comfort acceptance under different combinations of clothing insulation, metabolic level, air temperature and wet bulb temperature (or radiant temperature). Fanger presented an international standard dealing with thermal comfort, ISO 7730, and discussed the philosophy and the scientific basis behind it.10 The standard was intended to specify conditions that are predicted to be acceptable in thermal comfort for a given percentage of the population. In the standard, thermal comfort was defined as the condition of mind that


Physiology of thermal comfort

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expresses satisfaction with the thermal environment. Dissatisfaction, which may be caused by warm or cool discomfort for the body in general, is expressed by the PMV and PPD indices. PMV index is the ‘predicted mean vote’, which is used to predict the thermal sensation for the body as a whole on a seven-point scale from cold to hot. PPD index is the ‘predicted percentage of dissatisfaction’. The ISO standard recommended PMV within the range of −0.5 < PMV < 0.5, meaning that PPD should be lower than 10%. When PMV is equal to 0, the optimal operative temperature, which is a function of activity and clothing, is achieved. The operative temperature can be defined as uniform temperature of an enclosure, in which an occupant would exchange the same amount of heat by radiation and convection as in the actual non-uniform environment. For normal practical applications, the operative temperature is roughly equal to the mean value of the mean radiant and air temperatures. Fanger found that the optimal operative temperature, which satisfies most people at given clothing and activity, ranged between 18 and 22 °C. Furthermore, the metabolic rate can be estimated from physical activities. For example, the metabolic rates for resting (lying down), standing and running at 10 km/hr are 0.8, 1.4 and 8 met, respectively. The thermal insulation of clothing (clo) can also be estimated from the type of clothing. The clo values for a male body with no clothes, underpants or suit are 0.0, 0.1 and 1.0, respectively. Using the metabolic rate and clothing clo values, the optimal effective temperature and its tolerance limit can be estimated. Gagge et al.16 developed an environmental temperature scale based on use of the heat exchange equations during the passive state as a rational starting point and the effect of physiological regulatory controls. A temperature scale called humid operative temperature (Toh) was defined as the temperature of an imaginary environment to which the body loses the same heat by radiation, convection and evaporation as in the actual environment. A new ‘effective temperature’ scale was constructed for a sedentary normally clothed (0.6 clo) subject, on the basis of loci of constant wettedness caused by regulatory sweating. Further, in 1973 Gagge12 defined three rational temperature indices: standard operative (Tso), standard humid operative (Tsoh), and standard effective temperatures (SET), in terms of average skin temperature, skin wettedness and the associated heat transfer coefficients. Generally speaking, Tso is an index of thermal stress caused by the environment; Tsoh is an index of thermal strain caused by Tso. SET is an index temperature, describing the dry bulb temperature of the standard environment at 50% RH that causes the same heat exchange for the same Tso, skin wettedness and average skin temperature. In 1986, Gagge et al.13 proposed another new index PMV* which simply replaced the operative temperature To in Fanger’s comfort equation with SET. Gagge pointed out that Fanger’s PMV is primarily based on heat load;


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it is not sensitive to changes in relative humidity or vapor pressure, nor to the vapor permeability of clothing worn. By defining PMV* with SET instead of To, the new PMV* is able to respond to both thermal stress associated with heat load and heat strain associated with changing humidity of the environment and vapor permeability of clothing worn. In 1999, Hamdi et al.18 applied fuzzy logic to estimate thermal comfort level depending on the state of air temperature, mean radiant temperature, relative humidity, air velocity, activity level of occupants and their clothing insulation. The fuzzy comfort model is deduced on the basis of learning Fanger’s PMV equation. Unlike Fanger’s PMV, the new fuzzy PMV calculation does not require an iterative solution and can be easily adjusted depending on the specific thermal sensation of users. These characteristics make it an attractive index for feedback control of heating, ventilation and air conditioning (HVAC) systems. The simulation results show that the new fuzzy PMV is as accurate as Fanger’s PMV. In 2002, Olesen and Neilsen34 proposed a new version of EN ISO 7730.

4.3

Thermophysiology of the human body

The human body has the ability to regulate its internal temperature with a certain level of accuracy under changes in external and internal conditions. The temperature regulation works through biological mechanisms – specific central and peripheral nervous systems continuously detect the temperature fluctuations in the body and attempt to keep them in balance by means of biological actions. Over the years, much research related to thermoregulatory and subjective perception has been carried out.7,22,27 Hensel21 described physiological temperature regulation as a complex system containing multiple sensors, multiple feedback loops and multiple outputs. Figure 4.121 shows Hensel’s model of autonomic temperature regulation in man. The control variable is an integrated value of multiple temperatures such as the central nervous temperature (Tcn), the extra-central deep body temperature (Ter) and the skin temperature (Tsk). Hensel defined the ‘weighted mean body temperature’ (Tnb) as the controlled variable for practical purposes: Tnb = a Ti + (1 − a) Tsk

a<1

[4.2]

Values of a were proposed between 0.87 and 0.9 by measuring Ti in the oesophagus. The rating ratio was assumed to be the relative contribution from Tsk and Ti in a linear control function. The references (or set temperatures) for different control actions such as metabolism, vasomotion and sweating might be different. The heatdissipation mechanisms such as sweating driven by warm receptors may have a higher set temperature than heat-production mechanisms driven by


Physiology of thermal comfort

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Disturbance Co

ntrol actio

n

s

Multiple reference? Controller

Metabolism Vasomotion Sweating

Controlled system

Controlled variable f (Tcn,Tdb,Tsk)

CNS

(Tcn)

Deep body Skin

(Tdb) (Tsk)

Feedback elements (thermoreceptors)

4.1 Schematic diagram of autonomic temperature regulation in man.

cold receptors. Therefore, there is a zone of thermal neutrality in which no thermal regulation occurs. Hensel classified the thermal regulation mechanisms into three categories: autonomic regulation, behavior regulation and technical regulation. The autonomic regulation responds to thermal disturbances from internal heat generated by exercise and environmental heat or cold. Thermoreceptors receive signals from the thermal disturbances and transfer them to the central nervous system via afferent nervous pathways. The receptors can respond not only to temperature but also much more effectively to temperature change, as shown in equation (3.1). This means that rapid external cooling or warming may lead to a transient opposite change of internal temperature. Behavior thermoregulation in humans is related to conscious thermal sensations and emotional feelings of thermal comfort and discomfort. Behavior thermoregulation in response to heat and cold modifies the need for autonomic thermoregulatory responses. Hensel21 summarized various autonomic and behavioral components of temperature regulation. Technical thermoregulation can be considered as the extension of the human regulatory system through technical inventions. Temperature regulation is shifted


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from the body to the environment using artificial sensors, controllers and effectors.

4.4

Thermoregulation of the human body

The thermoregulatory system of the human body has been described mathematically by various authors.14,19,20,32,39,41 To illustrate the fundamental principles, a simple model developed by Gagge et al. is discussed in this section. In 1971, Gagge et al. developed a two-node model for describing the thermoregulatory system of the human body.16 This model was further modified and updated.6 As shown in Fig. 4.2, the model assumes that the human body has two concentric shells: skin and core. The skin is represented by a thin shell with mass msk, and the body interior by a central core with mass mcr. The sum of msk + mcr is the total body mass (m). Further, the model consists of two systems incorporated in the body: one passive, the other controlling for regulating body temperature. The heat balance of the body between it and its surroundings is described by the passive system, which can be expressed by the following equations:

Eres

Work

Skin

R

R

M

Tcr

Enw

TCcr Km Vbf

C

Md

Ts

k

TC sk

k

Cs

Eo

Mt

(Tab, Cab, Va)

4.2 The two-node model of thermoregulation of human body.


Physiology of thermal comfort S = M − W − (R + C + Edif + Ersw + Ecomf) − (Eres + Cres)

67 [4.3]

The heat balance of the body core requires: Scr = M − Eres − Cres − W − (Kmin + cblVbl)(Tcr − Tsk)

[4.4]

The heat balance for the skin shell can be described by: Ssk = (Kmin + cbl Vbl)(Tcr − Tsk) − Esk − (R + C)

[4.5]

S = Scr + Ssk The rates of change of temperature of the skin and core can be described as: (°C s−1) dTsk = Ssk A/TCsk

[4.6]

dTcr = Scr A/TCcr

[4.7]

dTmb = æ dTsk + ( 1 − æ) dTcr

[4.8]

The controlling system has three mechanisms: the skin blood flow, sweating and shivering. The skin blood flow is adjusted to change skin temperature for reducing or increasing heat loss, which is assumed to be controlled by temperature signals from the skin and the central core: Vbl = (6.3 + 200 warmcr)/(1.0 + 0.1 coldsk)

[4.9]

Sweating is an effective mechanism to release extra heat from the body, which is determined by sweat glands that produce the regulatory sweating. The glands are controlled by the mean body temperature signal and the skin temperature signal. The regulatory sweating rate (regsw) in g/m2 hr was identified as: regsw = 170 warmb e(warms/10.7)

[4.10]

The metabolic heat of the body due to shivering can be adjusted by the following equation: M = M + 19.4 coldsk coldcr

[4.11]

The variables (warmcr, warmsk, coldsk, coldcr) are functions of the temperature and temperature changes at the skin and the center core, and of the whole body mass described by equations (4.6)–(4.8). Detailed description of these equations was reported in Gagge et al.13 This model has been developed for deriving a standard predictive index of human responses to the thermal environment under isotherm conditions, in which the heat and moisture transfer behavior of clothing was described by the intrinsic insulation of the clothing (clo value) and the intrinsic vapor resistance through the clothing. These criteria were developed to describe the heat and moisture transfer behavior of clothing under constant moisture and temperature gradients.


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In 2002, Jones24 discussed the capabilities and limitations of thermal models for use in thermal comfort standards. He looked at different thermal models developed by Fanger,9 Gagge et al.,13 Wissler,42 Smith,38 Jones and Ogawa,23 Lotens30 and Fu.11 He concluded that heat balance models are powerful tools and offer a practical means to incorporate the numerous possible combinations of variables into a standard. However, there are some limitations including: 1. the accuracy of the physical simulation and the input of the model; 2. the accuracy with which comfort perceptions can be related to the physiological variables simulated in the thermal model.

4.5

Dynamic thermal interaction between the body and clothing

David (1964) studied the thermal insulation of wool clothing under transient conditions and found that the insulation could increase to 50–70% above normal due to moisture sorption by wool.5 Stuart et al. (1989) investigated the heat released by dried wool garments exposed to a lowtemperature and high-humidity environment. They observed that sufficient sorption heat was released during the transients for subjects to perceive the heat as an increase in warmth.40 de Dear et al. (1989) carried out a series of experiments to study the impact of step changes of air humidity on thermal comfort using both a thermal manikin and human subjects. From the thermal manikin experiments, they found that 37–42% of the heat involved during absorption or desorption of moisture by wool garments resulting from the humidity change influenced the sensible heat balance of the wearer. From the measurement of temperatures at the skin surface, they observed significant changes in skin temperature, especially when wearing wool garments.6 Therefore, Gagge’s model is valid for describing the thermal comfort state of a clothed man under steady environmental conditions, but not valid for transient conditions. Shitzer and Chato (1985) studied the heat and mass transfer of the clothing–air–skin system. They considered the heat and mass transfer in the system as a steady state problem in a one-dimensional model composed of five layers (ambient air, fabric, airspace, skin and body core). For the body, their model assumed constant physical properties, temperature-dependent skin thermal conductivity and no energy penetration to the body core. For the fabric, their model considered the fibers as always in equilibrium with the adjacent air. This work represented a significant development in the study of simultaneous heat and mass transport through the skin–fabric system.37 In an attempt to describe this dynamic behavior, Jones et al. (1990) reported a model of the transient response of clothing systems, which took


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account of the sorption behavior of fibers with the assumption that the fibers are always in equilibrium with the surrounding air. They compared the prediction of heat loss by the model with experimental data from thermal manikin tests and found reasonable agreement.25 Further, they combined this clothing model with Gagge’s two-node model to investigate the interactions between the body and clothing.26 Data to confirm the validity of this combined model have not yet been reported. Li and Holcombe29 developed a mathematical model by interfacing the model for a naked body (equations (4.3)–(4.11)) with a heat and moisture transfer model of a fabric in which the complex moisture sorption processes were taken into account (see Sections 5.3–5.9). The boundary conditions between the skin and clothing can be described by the following equations: heat:

Mt = hti (Tsk − Tfi)

mass:

Md = hci (Csk − Cfi ) + Lsk

[4.12] ∂Csk ∂t

[4.13]

This model has been used to describe mathematically the dynamic heat and moisture transfer behavior of the body–clothing–environment system under transient conditions. With specification of the physical activity and ambient conditions, the model is able to predict the thermoregulatory responses of the body, together with the temperature and moisture profiles in the clothing. Li and Holcombe reported a series of experimental measurements with garments made from fibers with different levels of hygroscopicity. The experimental results were compared with the model predictions for an ambient condition with changing humidity.29 A small change in core temperature and greater changes in skin and fabric temperature were predicted by the model. Compared with the experimental results, the predictions agreed well with the measurements, although the measured temperatures showed greater variations than those predicted. The predicted relative humidity showed good agreement with the measurements. Similar changing trends were observed in both the predictions and experimental results, although the measured relative humidity in the microclimate appeared lower than that predicted. Theoretical predictions and physiological measurements were also carried out for polyester garments. Essentially, good agreement in temperature and relative humidity profiles was observed between theoretical predictions and experimental results. These results showed that the model is able to predict the transient heat and moisture transport behavior of garments made from highly and weakly hygroscopic fibers in dynamic wear situations. With good prediction of the corresponding dynamic skin temperature responses of the body, the model can also be used to show how the transient behavior of clothing interacts with the skin and influences the thermoregulatory


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responses of the body. Further, the model has been used to predict the dynamic heat and moisture transfer of clothing and skin temperature changes when human subjects wear highly and weakly hygroscopic fibers and are exposed to large environmental changes such as raining and walking from indoor to outdoor ambient conditions. The theoretical predictions that were made before the actual experiment were confirmed by physiological measurements made in the wear trial.28 Bakkevig and Nielsen1 investigated the significance of wet underwear and compared any influence of fibre-type material and textile construction of underwear on thermoregulatory responses and thermal comfort of humans during rest in the cold. Morooka et al.33 examined the effects of water absorption and hygroscopicity on human physiology and comfort during simulated summer environments. Results indicated that heart rate (HR) and apparel humidity (H) were closely related to humid sensation (HS) and comfort sensation (CS). The correlation between H, HS and CS was higher, indicating that H is a critical factor in thermal comfort of pantyhose. An increase in water absorption of non-hygroscopic pantyhose caused an increase in H and mean skin temperature. During perspiration simulation tests, pantyhose with higher hygroscopicity had a higher moisture transfer rate and total heat loss.

4.6

Nomenclature

A a

human body surface area (m2) weighting ratio presenting the relative effect of Ts and Ti in a linear control function convective heat loss from the body (W m−2) water vapor concentration in the ambient air (kg m−3) heat lost by convection from the lungs during respiration (W m−2) water vapor concentration at the skin surface (kg m−3) specific heat of blood cbl = 1.163 (kJ kg−1 K−1) evaporative heat loss by sweating occurring in a state of comfort (W m−2) heat lost by water vapor diffusing through the skin layer (W m−2) heat lost by moisture evaporation from the lungs during respiration (W m−2) heat lost by sweat evaporation during body temperature regulation (W m−2) convective mass transfer coefficient in the clothing microclimate (m s−1) combined heat transfer coefficient in the microclimate (W m−2 K−1)

C Cab Cres Csk cbl Ecomf Edif Eres Ersw hci hti


Physiology of thermal comfort Kmin Lsk M Md Mt R S Scr Ssk Tcr Tfi Ti Tmb Ts Tsk TCcr TCsk t Vbl W

71

minimum thermal conductance of the skin tissue in the absence of skin blood flow Kmin = 5.28 (W m−2 K−1) thickness of the outermost layer of the skin (m) metabolic heat production (W m−2) moisture flow from the skin (= pmEsk/λ) heat flow from the skin (= ph(R + C)) radiant heat loss from the skin (W m−2) heat storage of the body (W m−2) heat storage of the core of the body (W m−2) heat storage of the skin of the body (W m−2) temperature of the body core (°C) temperature at the inner surface of the fabric (°C) internal body temperature (°C) mean body temperature (°C) average skin temperature (°C) temperature of the skin (°C) thermal capacity of the body core (W hr K−1) thermal capacity of the skin (W hr K−1) real time (s) rate of skin blood flow (litre hr−1 m−2) work accomplished (W m−2)

Greek symbols æ the actual ratio of skin to total body mass

4.7

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A188.

4.8

References

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27. Katsuura, T., M.E. Tachibana, C.F. Lee, A. Okada, and Y. Kikuchi, Comparative Studies on Thermoregulatory Responses to Heat Between Japanese Brazilians and Japanese. The Annals Of Physiological Anthropology, 1992. 11(2): p. 105–111. 28. Li, Y. The Buffering Effect of Hygroscopic Clothing Against Rain, in Proceedings of the The 4th Asian Textile Conference. 1997. Taipei, Taiwan. 29. Li, Y. and B.V. Holcombe, Mathematical Simulation of Heat and Moisture Transfer in a Human-Clothing-Environment System. Textile Research Journal, 1998. 68(6): p. 389–397. 30. Lotens, W., Heat Transfer From Humans Wearing Clothing, PhD Thesis. 1993: Delft University of Technology. 31. McNall, P.E., J. Jaax, F.H. Rohles, and W.E. Springer, Thermal Comfort (Thermal Neutral) Conditions for Three Levels of Activity. ASHRAE Transactions, 1967. 73(1). 32. Mitchell, D., A.R. Atkins, and C.H. Wyndham, Mathematical and Physical Models of Thermoregulation, in Essays on Temperature Regulation, J. Bligh and R.E. Moore, Eds, 1972: Amsterdam and London, North-Holland Publication, p. 37–54. 33. Morooka, H., R. Hirata, H. Morooka, J. Deguchi, S. Hiraga, and E. Satoh, Improvement of Superior Pantyhose in Thermal Comfort. Part 1. Effects of Hygroscopicity or Water Absorption on Wearing Performance of Pantyhose in Summer. Journal of the Textile Machinery Society of Japan, 1999. 52(1): p. T1–8. 34. Olesen, B.W. and R. Neilsen, Thermal Insulation of Clothing Measured on a Movable Thermal Manikin and Human Subjects, Report. 1983: Technical University of Denmark. 35. Rohles, F.H., The Measurement and Prediction of Thermal Comfort. ASHRAE Transactions, 1967. 118(2209): p. 98–114. 36. Ruckman, J.E., H.S. Choi, and R. Murray, Engineering of Clothing Systems for Improved Thermophysiological Comfort. International Journal of Clothing Science and Technology, 1997. 9(6): p. 54–55. 37. Shitzer, A. and J.C. Chato, Thermal Interaction with Garments, in Heat Transfer in Medicine and Biology: Analysis and Applications, A. Shitzer and R.C. Eberhart, Eds, 1985: New York, Plenum Press, p. 375–394. 38. Smith, C., A Transient Three-Dimensional Model of the Human Thermal System, PhD Thesis. 1991: Kansas State University. 39. Stolwijk, J.A.J. and J.D. Hardy, Control of Body Temperature, in Handbook of Physiology, D.H.K. Lee, Ed., 1977: Bethesda, MD, American Physiology Society, p. 45–69. 40. Stuart, I., A. Schneider, and T. Turner, Perception of the Heat of Sorption of Wool. Textile Research Journal, 1989. 59(6): p. 324–329. 41. Wenner, J., Mathematical Treatment of Structure and Function of the Human Thermoregulatory System. Biological Cybernetics, 1977. 25: p. 93–101. 42. Wissler, E.H., Mathematical Simulation of Human Thermal Behaviour Using Whole Body Models, in Heat Transfer in Medicine and Biology, A. Shitzer and R.C. Eberhart, Eds, 1985: New York, Plenum Press, Vol. 1, p. 325–373. 43. Yaglou, C.P. and W.E. Miller, Effective Temperature With Clothing. Trans. Amer. Soc. Heat and Vent. Egn Ass., 1925. 31: p. 89.


5 Physics of thermal comfort 1

5.1

YI LI 1 AND ANTHONY S.W. WONG 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong

Introduction

In the previous chapters, the psychological, neurophysiological and thermophysiological aspects of comfort have been discussed. The psychological perceptions are formulated on the basis of the neurophysiological signals that are derived from various sensory nerve endings. Thermophysiological regulatory responses are also triggered by the signals from thermal receptors located throughout the body. The signals from the nerve endings are generated essentially by various physical stimuli from external environments, especially from clothing covering most of our body. The physical processes that generate those stimuli include heat transfer by conduction, convection and radiation, moisture transfer by diffusion, sorption, wicking and evaporation, and the mechanical interactions in the form of pressure, friction and dynamic irregular contact. Comprehensive research has been carried out to study the mechanisms of heat and moisture transfer processes in clothing since the 1970s. However, the mechanisms of the mechanical interaction between clothing and the body have not been investigated so thoroughly. This chapter focuses on the heat and moisture transfer processes.

5.2

Heat and moisture transfer

Heat and moisture transfer behavior of clothing has long been recognized as critical for human survival. A great amount of work has been done in this area. In 1970, Fourt and Hollies8 carried out a comprehensive survey on the literature on clothing comfort and function with special emphasis on thermal comfort. Seven years later, Slater38 carried out an extensive review of the comfort properties of textiles, including the measurement of thermal resistance of textiles, water vapor transmission, liquid-moisture transmission and air permeability. 74


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Hollies and Goldman15 reviewed the criteria in evaluating the thermal comfort performance of clothing, using a number of equations to describe the heat and moisture transfer: convective heat loss Hc:

Hc = kc · A · (Tsk − Tab)

[5.1]

evaporative heat loss He:

He = ke · A · (Psk − Pab)

[5.2]

mean radiant temperature MRT:

MRT = 1 + 2.22 V ⋅ (Tg − Tab ) [5.3] + Tab

adjusted dry bulb temperature ATdb: ATdb = (Tab + MRT)/2

[5.4]

where kc is a coefficient for convective heat transfer, which involves not only the still air layer around the body but also the thermal characteristics of the clothing worn, A is the surface area of the body, Tsk is the mean weighted skin temperature of the surface of the body and Tab is the dry bulb temperature. ke is the evaporative coefficient, which is determined by Lewis relationship: ke = 2.2 kc. Psk is the saturated vapor pressure of water at skin temperature and Pab is the ambient vapor pressure. ke has a modified index, called the permeability index im, which was proposed by Woodcock in 1962.45 im is a perfect vapor barrier (i.e. permeability to moisture vapor transfer is zero). It is equal to one if a clothed and 100% sweat-wetted man were swung by the heels at sufficient velocity that he achieved the full evaporative potential of a ventilated wet bulb thermometer. V is the wind velocity, Tg is the globe temperature and ATdb is a combined coefficient for clothing thermal insulation, incorporating both convective and radiative heat transfers. The most widely used unit in the US is the clo, which was proposed by Gagge and his colleagues at the Pierce Foundation in 1941.9 One clo was defined as the intrinsic insulation of the typical business suit worn in those days and is equal to 0.155 °C m2/watt. Mecheels and Umbach32 reviewed the psychrometric range of clothing systems. They pointed out that the thermal properties of a clothing system are determined by its resistance to heat transfer Rc and its resistance to moisture transfer Re, where: Rc = A · (Ts − Ta)/Hc Re = A · (Ps − Pa)/He

in 155 °C m2/watt 2

in 155 mmHg m /watt

[5.5] [5.6]

and im = 0.45 (Rc clothing/Re clothing). They pointed out that through these two values of resistance, the minimum ambient temperature and the maximum ambient temperature could be determined. The minimum ambient temperature was defined as the temperature at which the thermoregulation system of the human body is within


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cold range, i.e. the moisture concentration near the skin is close to that of the environment so that the moisture flow can be neglected. The maximum ambient temperature is the temperature at which the human thermoregulation system reaches the upper temperature limit range, where the wearer of a clothing system must prevent his core temperature, under certain comfort conditions, from rising by making use of evaporative cooling. The difference between the maximum and minimum ambient temperatures is called the psychrometric range of the clothing system. The resistance to heat and moisture transfer and the psychrometric range can be measured using a thermal manikin and skin model, which were developed by the Hohenstein Institute. The parameters are dependent on clothing design and the manner of wear, textile materials and wind velocity. Breckenridge1 surveyed the literature on the effects of body motion on convective and evaporative heat exchanges of clothing. The thermal insulation of clothing is dependent on a number of factors: thickness and number of layers, fit and drape of the garments, fiber density, flexibility of layers and adequacy of closures. Thermal insulation values and the evaporative potential (im) of military clothing assemblies were routinely measured by using a standing and life-size copper manikin in the Military Ergonomics Division at US Army Research Institute of Environmental Medicine (USARIEM). Sensitive balance was used to monitor a subject’s weight loss during activity for the estimation of evaporative heat loss. For measuring the ‘pumping’ coefficients associated with body motion, Mecheels29–31 reported a walking manikin at the Hohenstein Institute. All these research publications regarded the heat and moisture transport processes as being independent, which is largely applicable for wear situations under various steady states. During humidity transients, the heat and moisture transport processes are coupled. The thermal insulation of clothing is influenced by the moisture sorption of the textile fibers. Therefore, the measurement methods and criteria may not be appropriate for the evaluation of the thermal comfort of clothing under dynamic wear conditions. Since the 1990s, the dynamic heat and moisture transport behavior of clothing and its influence on the thermal and moisture perceptions have become the main focus of research in the field. The following sections focus on reviewing the major progress in this area.

5.3

Dynamic heat and moisture transfer in fabric

The coupled heat and moisture transfer in textile fabrics has been widely recognized as being very important for understanding the dynamic thermal comfort of clothing during wear. In 1939, Henry proposed a mechanism for the transient diffusion of moisture and heat into an assembly of


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textile fibers13 and he further described a model in 1948.14 In the model, Henry developed a system of differential equations to describe the processes involved. Two of the equations involve conservation of mass and energy. The third equation relates fiber moisture content to the adjacent air. As shown in Fig. 5.1, in a small element of fabric with a unit area and thickness that is packed with fibers exposed to moisture gradient and temperature gradient, water vapor diffuses through the interfiber spaces and is absorbed or desorbed by the fibers. To simplify mathematical description of the process, a number of assumptions were made: (1) the volume changes of the fibers due to changing moisture content can be neglected; (2) moisture transport through fibers can be ignored as the diffusion coefficient of water through fibers is negligible compared with that through air; (3) the orientation of fibers in the fabric plays a minimum role in the water vapor transport process as the diameters of fibers are small and water vapor can travel much more rapidly in the air than in the fibers; (4) instantaneous thermal equilibrium between the fibers and the gas in the interfiber space is achieved during the process, as most textile fibers are of very small diameter and have a very large surface/volume ratio. Sorption/desorption

Fabric

Heat

Moisture

5.1 Coupled heat and moisture transfer in a fabric.


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On the basis of these assumptions, a mass balance equation, which considers the moisture accumulation by both the air and the fibers and the moisture transport through the air space, can be written as: ε

∂C D ε ∂ 2C a ∂Ca + (1 − ε ) f = a ⋅ ∂t ∂t τ ∂x 2

[5.7]

where Ca is the water vapor concentration in the air filling the interfiber void space (kg m−3), Cf is the water vapor concentration in the fiber (kg m−3), t is the real time (s), ε is the porosity of the fabric, Da is the diffusion coefficient of water vapor in air (m2 s−1), τ is the effective tortuosity of the fabric and x is the distance from inner surface of fabric (m). In this equation, the first term on the left-hand side describes the accumulation of water vapor in the interfiber space. The second term describes the accumulation of absorbed water in the fibers, while the moisture transport through the interfiber air space is described by the term on the righthand side. A second equation for the conservation of heat energy can be derived by considering changes in the heat content of the volume element that arise from a number of processes: conduction into or out of the element, change in phase of the water vapor (sorption or desorption) and temperature changes of the fibers and of the air in the interfiber space. The equation for energy conservation can be written as: Cv

∂T ∂C ∂2T −λ f = K 2 ∂t ∂t ∂x

[5.8]

where T is the temperature of the fabric (°C), λ is the heat of sorption of water vapor by fibres (kJ kg−1), K is the thermal conductivity of the fabric (W m−1 K−1) and CV is the volumetric heat capacity of the fabric (kJ m−3 K−1). In this equation, Cv and λ are dependent on the concentration of water absorbed by the fibers. These two equations are not linear and contain three unknowns (Cf, T and Ca). A third equation that describes the relationship between Ca and Cf, i.e. the water exchange between the fiber and its surrounding air, is needed in order to progress to a solution. Li et al.17 and Luo et al.27 developed a dynamic model, which considers evaporation/condensation and movement of water, sorption/desorption of fiber and effect of atmosphere on mass transfer in porous media, to simulate heat and mass transfer in hygroscopic porous materials. Water in fabrics is considered to be present in three forms: (1) liquid water in the void space between fibers; (2) bound water in the fibers; and (3) vapor. It is assumed that the heat and mass transport mechanisms include movement of liquid water due to the capillarity and atmospheric pressure gradient, diffusion of vapor within interfibers due to the partial pressure gradient


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of vapor and total gas pressure gradient, diffusion of vapor into fiber and evaporation and condensation of water. Results showed that the temperature rise in fabric was purely due to the heat released during the moisturesorption process. By comparing the theoretical prediction and experimental measurement,24 models show the ability to predict the simultaneous heat transfer that is coupled in the moisture-diffusion process during humidity transients.

5.4

Moisture exchange between fiber and air

The moisture exchange between fiber and its adjacent air is a complex process, depending on whether the moisture is present as liquid on the fiber surface, or as vapor stored internally. This is best illustrated from the drying behavior of fabrics.

5.4.1 The drying behavior of fabrics Lyons and Vollers28 analyzed the drying process of textile materials. They found that the drying process has three distinct stages. In the first stage, a wet fabric adjusts its temperature and moisture flows to the surrounding environment. The second stage is a ‘constant drying rate’ period, in which the drying rate remains constant as the rates of heat transfer and vaporization reach equilibrium. Liquid moisture moves within the fabric to maintain a saturation condition at the surface. The third stage is a ‘declined drying rate’, during which moisture flow to the surface is insufficient to maintain saturation and the plane of evaporation moves into the fabric. Fibers begin to desorb moisture until equilibrium is reached between the fabric and environment. Li et al.25 stated that, when fabric water content is above the fiber saturation moisture content, the drying rate of both fabrics is constant and approximately the same, because the drying process is determined by a surface evaporation process for both fabrics. When fabric water content is above the fiber saturation moisture content, the drying rate of both fabrics is constant and approximately the same, because the drying process is determined by a surface evaporation process for both fabrics. When fabric water content decreased below the saturation moisture content, the drying rate declined as liquid water at the fiber surface had evaporated and the water absorbed within the fibers was released. This drying process continues until equilibrium with the ambient conditions is reached. The difference between wool and polyester fabric is that the constant rate period by evaporation is prolonged for polyester as its saturation moisture content is below 1%. Meanwhile, the ‘declined period’ is prolonged with wool, as wool has much higher saturation water content (up to 36%).


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When the water content of the fabrics is above their saturation regain, the temperatures of both fabrics are approximately the same and below the ambient temperature, because at this stage the dominant process is evaporation of free water for both fabrics. As fabric water content approaches equilibrium regain, the temperatures begin to rise until all excess moisture is evaporated and equilibrium is achieved with the surroundings. The temperature change of wool fabric behaves distinctly from polyester fabric during the drying process. The wool fabric shows a longer transition in temperature from wet to dry than the polyester fabric. This reflects the greater moisture-sorption capacity of wool and its influence on the heat and moisture exchange between fabric and environment. These observations suggest that two separate equations need to be developed to describe the moisture exchange between fiber and its surrounding air. These are discussed in the following two sections. The liquid transport in a fabric influences the mass transfer (i.e. conservation equation) but does not involve energy exchange. However, it determines the dynamic distribution of liquid in the fabric, which in turn determines the rate of water evaporation. Experimental investigations have shown that the liquid transfer has a significant impact on the heat transport processes and the thermal comfort and tactile comfort performance of clothing.18 Gibson10 developed a set of complex mathematical equations to describe the multiphase heat and mass transfer in hygroscopic porous media with applications to clothing materials. However, solutions for these equations have not been reported.

5.4.2 Evaporation and condensation Crank4 described the evaporation–condensation process mathematically, as shown in equation (5.9). This equation applies when the fabric water content is above the saturation regain of the fiber, that is, liquid water is present in capillaries within the fabric structure or at the fiber surface. The exchange of water is an evaporation–condensation process: ∂Cf = hcf Sv (Cfs − Ca ) ∂t

[5.9]

where Cfs is the water concentration in the fiber surface (kg/m3), Ca is the water concentration in the adjacent air (kg/m3), hcf is the mass transfer coefficient at the fiber surface (m/s) and Sv is the specific volume of the fabric (m−1).

5.4.3 Moisture sorption and desorption When the fabric water content is below the saturation regain of the fiber, the exchange of water can be considered as a sorption or desorption process.


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David and Nordon5 developed an experimental relationship between the rate of change of water content of the fibers and the absolute difference between the relative humidity (H) of the air and fiber. The rate equation was given as: 1 ∂Cf ⋅ = ( Ha − H f ) χ ε ∂t

[5.10]

χ = k1(1 − exp[k2|Ha − Hf|])

[5.11]

where

k1 and k2 are parameters that are adjustable according to experimental results. David and Nordon incorporated several features omitted by Henry and developed a solution of the equations by finite difference methods, which provided space–time relationships for moisture concentration and temperature within the air–fiber mass. The authors stated that the model did not consider the sorption–desorption kinetics of the fibers and that proper boundary conditions need to be specified in evaluating the coupled heat and moisture transfer processes in clothing during wear. In 1986, Farnworth7 reported a numerical model of the combined heat and water vapor transport in clothing, in which the mass of absorbed water was assumed to be directly proportional to the relative humidity. Also, the three forms of water (as vapor, liquid and absorbed water) were in equilibrium with each other locally. Therefore, the model did not take into account the complexity of the moisture-sorption isotherm and the sorption kinetics of textile fibers. Wehner44 investigated the influence of moisture sorption by fibers on the moisture flux through the air spaces of a fabric. He developed two mathematical models to describe the processes. In the first model, diffusion within the fiber is considered to be rapid. The fiber moisture content is always in equilibrium with the air at the fiber surface. Hence, the dominant mass transfer resistance for the sorption process is assumed to be the diffusion of water molecules through the air to the fiber surface. In the second model, the sorption kinetics of the fiber is assumed to be Fickian diffusion and the dominant mass transfer resistance is molecular diffusion of water molecules within the fiber interior. Therefore, the fiber moisture content lags behind the changes in the moisture content of the air at the fiber surface. In these models, the interaction between moisture absorption and heat of sorption was ignored. Downes and Mackay6 and Watt42,43 studied the kinetics of the uptake of water vapor by wool and found that it was a two-stage process. The first stage obeys Fick’s laws of diffusion with a concentration-dependent diffusion coefficient until absorption to quasi-equilibrium. The second stage is much slower than the first and is accompanied by structural changes within


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the fibers. The relative contributions of the two stages to the total uptake depend on the initial regain of the fiber and the stage of absorption. On the basis of these findings, Li and Holcombe21 assumed that the water vapor uptake rate of the fiber consists of two components associated with the two stages of sorption identified. The first stage is represented by Fickian diffusion and the second-stage sorption follows an exponential relationship. Thus, the water exchange equation can be written as: ∂Cf = (1 − p) R1 + pR2 ∂t

[5.12]

where R1 and R2 are the first-stage sorption rate and the second-stage sorption rate, respectively and p is the proportion of uptake occurring during the second stage. The first stage sorption rate R1 can be obtained by considering the sorption–desorption process as Fickian diffusion. Crank4 shows that the radial diffusion of moisture in a cylindrical medium is governed by the following relationship: R1 =

∂Cf 1 ∂ ( rDf ∂Cf ) = ⋅ ⋅ ∂t ∂r r ∂r

[5.13]

where r is the radial distance from the centre of the fiber (m) and Df is the diffusion coefficient of water vapor in the fiber (m2 s−1). In this model, the moisture content at the fiber surface is assumed to be in instantaneous equilibrium with the moisture content of the adjacent air. Hence: Cf(x,Rf,t) = f {Ca(x,t)} = ρWc(Hf)

[5.14]

where Rf is the mean radius of the fibers (m), Wc is the fractional water content at the fiber surface, Hf is the fractional relative humidity of the adjacent air, f is the moisture-sorption isotherm and ρ is the density of fibers (kg/m3). The relationship between Wc and Hf can be determined from the sorption isotherms of textile fibers. According to the experimental data presented by Watt,42,43 Li and Holcombe21 specified the proportion of uptake during the second stage ( p) as: p = 0.0

when Wc < 0.185, t < tq

[5.15]

p = 0.5

when Wc ≥ 0.185, t < tq

[5.16]

p = 1.0

when t ≥ tq

[5.17]

where tq is the time to reach quasi-equilibrium (tq ≈ 540 seconds, according to Watt).


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The second-stage sorption rate (R2) relates the local temperature, humidity and sorption history of the fiber at each point in the fabric, which was assumed to take the following form: R2 = s1 sign(Ha − Hf)exp(s2/|Ha − Hf|)

[5.18]

where s1 and s2 are constants which may be determined by experiment.

5.5

Boundary conditions

To solve these equations, initial condition and boundary conditions must be specified. The initial condition is determined by the history of the thermal and moisture environment of the fabric. For instance, when a fabric is equilibrated to a given environment, the temperature and moisture content can be regarded as uniform throughout the fabric at known values: T(x, 0) = T0

[5.19]

Ca(x, 0) = Ca0

[5.20]

Cf(x, 0) = f(Ha0, T0)

[5.21]

David and Nordon5,33 studied the situation where the fabric boundaries are exposed to an air stream of new moisture content Cab and temperature Tb. By assuming that the rate of moisture and heat diffusion is sufficiently rapid for the bulk moisture content and that the temperature of the air stream and at the surface of the fabric are equal, they specified the pertinent boundary conditions as: T(0, t) = T(L, t) = Tb

[5.22]

Ca(0, t) = Ca(L, t) = Cab

[5.23]

Nordon and David numerically solved equations (5.7), (5.8), (5.10) and (5.11) with boundary conditions (5.22) and (5.23) by the finite difference method. Those boundary conditions cannot be achieved in practice because a boundary layer of air exists and limits the transfer of heat and moisture between the fabric and the air stream. Li and Holcombe21 used a set of equations to describe this layer of air, which takes into account the convective nature of the boundary conditions: Da

∂Ca = hc (Ca − Cab ) ∂x x = 0

[5.24]

Da

∂Ca = − hc (Ca − Cab ) ∂x x = 0

[5.25]

K

∂T = ht (Ta − Tb ) ∂x x = 0

[5.26]


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K

∂T = − ht (Ta − Tb ) ∂x x = 0

[5.27]

where hc is the convective mass transfer coefficient at the outer boundary air layer (m s−1) and ht is the combined (convection and radiation) heat transfer coefficient at outer boundary air layer (W m−2 K−1). In simulating the heat and moisture processes during fabric–skin contact, Li et al. used various equations to specify the boundary conditions between fabric and skin to investigate fabric coolness to the touch,23 fabric dampness perception,22 moisture buffering behavior of hygroscopic clothing22 and the interaction between thermoregulatory responses and hygroscopic clothing during humidity transients.19

5.6

Physical properties of fibers and fabrics

To solve these equations, a number of numerical values of relevant fiber and fabric properties need to be specified. Li et al.23 summarized the numerical values for the thermal and moisture-sorption characteristics of fibers and fabrics on the basis of previous research work. David and Nordon5 reported the relationship between fiber moisture content and the relative humidity at the fiber surface for wool. The relationships for cotton and polyester were determined from the data published by Rae and Rollo36 and Urquhart.39 Rae and Rollo also reported the relationships between heat of sorption and fiber moisture content.36 Schneider37 studied the relationships between fabric thermal conductivity and moisture content, which were used to obtain the equations in Li et al.’s study.23 The relationships between fabric volumetric heat capacity and fiber moisture content were obtained by proportion based on the specific heat of the dry fibers and that of water (4.184 kJ/kg K).40 For the wool/polyester blend fabric, the polyester was treated as non-absorbent, and each of these variables and relationships was determined for a fabric containing wool present in the proportion represented in the blend. The value of the diffusion coefficient of water vapor in air used was 2.49 × 10−5 (m/s).40

5.7

Method of solution

5.7.1 Moisture diffusion into the fiber To solve the equations, the process of moisture diffusion into the fibers needs to be understood. Details of the method used to determine R1 are beyond the scope of this chapter and a full description was reported by Li and Holcombe in 1991.20 Briefly, moisture is considered to diffuse radially into a cylindrical medium (fiber) with a constant diffusion coefficient (Df),


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and at the fiber surface the moisture content is in equilibrium with the moisture content of the adjacent air. Initially, the moisture content is uniform, and the boundary condition at the center of the fiber is one of symmetry: Cf(x, r, 0) = C0

[5.28]

∂Cf =0 ∂r x = 0

[5.29]

and

The moisture content at the fiber surface (r = Rf) is in equilibrium with the moisture content of the adjacent air, so that: Cf(x, Rf, 0) = f(Ca(x, t)) = Φ(x, t)

[5.30]

where f is the moisture sorption isotherm of the fiber. An analytical solution to equation (5.28) was derived using Crank’s solution.4 This approach was demonstrated to be able to describe the dynamic heat and moisture transfer of wool fabric with good accuracy in comparison with experimental results. However, Crank’s solution is truncated, which suggests that the corresponding algorithm needs restriction of the time step. Therefore, long computation time is required to achieve acceptable accuracy. Li and Luo24 improved the model further by applying direct numerical solution to equation (5.12) without using Crank’s solution. More importantly, the two-stage moisture sorption of wool is described uniformly by equation (5.12). The first stage is considered as a Fickian diffusion process with a moisture-dependent diffusion coefficient. The second stage is also expressed as the Fickian diffusion equation with a diffusion coefficient that is time dependent, which corresponds to the nature of structural changes during moisture sorption in the fibers. This model is able to clearly illustrate the moisture and heat transport processes by two-dimensional space and time diagrams.

5.7.2 Numerical solution to the main equations In 1967, Nordon and David reported a numerical solution to equations (5.7), (5.8), (5.10) and (5.11) of their model by using the Crank–Nicholson implicit finite difference technique. Li and Holcombe20 also used the finite difference technique to obtain solutions for equations (5.7, 5.8, 5.9, 5.12, 5.13, 5.18) of the two-stage model. With the solutions, the profiles and weaves of temperature and moisture transfer can be obtained. The model can be applied to either a sorption–desorption process or an evaporation– condensation process, depending on the initial and the new boundary condi-


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tions. The calculations are performed stepwise in small time intervals to follow the development of T, Ca and Cf.

5.8

Moisture sorption of wool fabrics

Li and Holcombe21 studied the dynamic heat and moisture transport processes during moisture sorption. In the experiment, fabrics were equilibrated in a cell controlled at 20 °C and 0% relative humidity. Then, the relative humidity in the cell was changed to 99%. The water content changes during sorption were obtained by weighing the fabric continuously. The temperature changes in the fabric were also recorded by inserting thermocouple wire into the surface of fabric samples. The experimental results of both temperature and water content change were compared with the predictions from three models: Nordon–David model, Fickian diffusion model and the two-stage sorption model. The Nordon–David model can predict the trend of the water vapor uptake in general, but does not fit the experimental observations very well. The prediction from the Fickian diffusion model follows the first 540 seconds of actual sorption closely, but its predicted subsequent rate of water uptake is too high in comparison with the experimental observation. This result agrees with Watt’s findings that for single wool fibers the first stage absorption to a quasi-equilibrium obeys Fick’s laws of diffusion and that the time to reach the quasi-equilibrium is about 540 seconds. It was found that the two-stage sorption model had the best agreement with the experimental observation overall. The authors also compared the observed simultaneous temperature changes with the predictions from the model. The simultaneous fabric surface temperature changes were the result of heat released during the water vapor sorption by the fibers in the fabric. All the predicted temperature curves by the three models showed similar trends to the experimental curve: a very rapid initial temperature rise at the fabric surface resulting from a small increase in water vapor uptake, followed by gradual decreases associated with the continuing water vapor uptake. The predicted temperature change by the two-stage model showed the best fit with the experimental observations.

5.9

Behavior of fabrics made from different fibers

Further work was carried out by Li and Luo24 to investigate the dynamic moisture diffusion into hygroscopic fabrics made from different fibers. Four fabrics made from wool, cotton, porous acrylic and polypropylene were tested by using the same experimental set-up. The differences among fabrics with different levels of hygroscopicity in dynamic moisture transfer mecha-


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nisms were investigated by using different mathematical models to describe the coupled heat and moisture transfer in the fabrics. Experimental results and theoretical predictions are compared. The characteristics of the fabrics examined were shown in Table 5.1. The wool fabric had significantly greater water vapor sorption in total than the other fabrics. Wool fabric also had the highest initial sorption rate, followed by cotton, porous acrylic and polypropylene fabrics. Differences in water vapor uptake between fabrics increase with sorption time and are in the order of their respective levels of hygroscopicity. With regard to temperature changes at the surface of the test fabrics during sorption of water vapor, wool showed the highest initial temperature increase at the fabric surface, followed by cotton, porous acrylic and polypropylene. Theoretical predictions of water vapor uptake and surface temperature change during the dynamic moisture transfer processes for the fabrics used in the experiment are also presented in these two figures (Figs. 5.7 and 5.8). The features of the experiment are described well by the models. The twostage sorption model gives a good description of the water vapor transfer process in highly hygroscopic fabrics such as wool and cotton, and the Fickian diffusion model can describe the water vapor transfer in weakly hygroscopic fabrics such as acrylic and polypropylene. This is a clear indication of the differences in sorption mechanisms between highly hygroscopic and weakly hygroscopic fabrics. These results demonstrate that strongly hygroscopic and weakly hygroscopic fabrics have significant difference in dynamic moisture transfer behavior during environmental moisture transients. Highly hygroscopic fabrics such as wool and cotton show greater mass and energy exchange with the environment than weakly hygroscopic fabrics such as porous acrylic and polypropylene.

Table 5.1 Basic characteristics of fabric samples Fiber type

Fiber diameter (mm)

Yarn count (Tex)

Weight (g/m2)

Thickness (mm)

Fabric structure

Wool

20.6

20.4

272

2.96

Cotton

13.3

19.7

275

2.19

Porous acrylic

18.4

21.3

287

2.14

Polypropylene

20.0

18.3

279

2.42

Double jersey Double jersey Double jersey Double jersey


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Wang et al.41 simulated the subjective perceptions of fabric coolness and dampness, mathematically integrated the fabric heat and moisture transfer model, neurophysiological mechanisms and psychoneurophysiological relationships, and compared predicted sensations of coolness and dampness of knit fabrics with subjective perceptions measured in a psychological experiment. Relationships between the neurophysiological responses of thermoreceptors and the psychological perceptions followed Stevensâ&#x20AC;&#x2122; power function. Experimental data confirmed model predictions of coolness and dampness to the touch. Li et al.26 used a mathematical model, incorporated with liquid diffusion behavior and combined into an energy conservation equation, mass conservation equations of water vapor and liquid water, which clarified the coupling mechanism of heat transfer and liquid moisture diffusion in porous textiles. Computations using different fabric thicknesses and porosities quantified the interactions between heat transfer and moisture transfer. Predictions of temperature changes during moisture transfer agreed well with experimental measurements. Fabric thickness and porosity were important to heat transfer, which affected moisture transport processes. Pause35 developed a test method for measuring the heat and moisture transfer and absorption of knit spacer fabrics and measured the thermal comfort of a polyester spacer fabric weighing 690 g/m2 with a thickness of 5.7 mm, a density of 120.3 kg/m3 and a relative compressibility of 2.5%; a polyester spacer fabric weighing 550 g/m2 with a thickness of 7.3 mm, a density of 75.8 kg/m3 and a relative compressibility of 6.3%; a polyester spacer fabric weighing 530 g/m2 with a thickness of 7.2 mm, a density of 73.2 kg/m3, and a relative compressibility of 4.9%; and a polyester spacer fabric weighing 200 g/m2 with a thickness of 6.2 mm, a density of 32.2 kg/m3 and a relative compressibility of 5.8%. The knit spacer fabrics achieved better heat and moisture transfer than did polyester foam. The heat absorption of the fabrics and the foam were similar. Moisture absorption was greater for the spacer fabrics. A mathematical model of the mechanisms of moisture diffusion into hygroscopic fabrics during humidity transients showed that highly hygroscopic fibers had a two-stage moisture diffusion process consisting of a fast Fickian diffusion with a concentration-dependent diffusion coefficient and a slow diffusion with a time-dependent diffusion coefficient.24 Weakly hygroscopic fibers showed a single Fickian diffusion with a constant diffusion coefficient. Moisture diffusion into fabric from air was a fast process for all fabrics studied. Moisture diffusion into fibers coupled with the heat transfer process depended on fiber moisture-absorbing ability. The strength of the coupling effect depended on such fiber properties as the moisturesorption isotherms, water diffusion coefficient, fiber diameter and heat of sorption.


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Bumbarger et al.2 stated that water-retaining polymers evenly suspend water throughout a fibrous batting and provide distributed cooling and heat absorption for increased wearer comfort. Combining the polymer-infiltrated batting with an exterior woven fabric shell and a conductive microporous lining fabric resulted in protective clothing that transferred heat away from a wearerâ&#x20AC;&#x2122;s body and permitted water vapor transfer. Radiant testing measuring calorimeter temperature rise, thermocouple temperature response and moisture transfer compared protective clothing containing the polymer with conventional composite fabric protective clothing. The polymercontaining protective clothing showed improved thermal insulation properties and no evidence of excessive heat transfer due to elevated water vapor temperatures. Coutant3 stated that knowledge of the physical laws of heat transfer allows outdoor apparel designers to use technical textiles appropriately in their designs and layering systems. Heat loss occurs through radiation, conduction, convection and evaporation. New weatherproof and breathable fabrics have different values for moisture vapor transmission rates (MVTRs), but they are difficult to compare due to differences in testing methods. Many designers omit layer factors in estimating MVTRs, using only the published rate for the outer shell material. Extreme imperviousness to weather results in higher moisture accumulation within the apparel. Trapped water replaces insulating air and increases conductive heat loss. Appropriate outerwear designs use venting and layering systems to optimize weather resistance and maximize breathability. Park and Baik34 developed a mathematical model for heat and mass transfer analysis of fabric in a tentering machine. The transient fabric temperatures calculated by the model agreed well with experimental values measured by Beard. Researchers used the finite element method to solve variations in temperature and moisture content distribution. The model examined the effects of certain operational parameters, such as temperature and humidity in the tentering machine, initial moisture content of the fabric and heat and mass transfer coefficients. Optimization of the drying conditions in the tentering machine led to reductions in energy consumption. Gillum and Armijo11 developed a mathematical model that predicts seed cotton moisture content using air temperature, air mass flow and seed cotton mass flow to account for heat transfer from the conveying air, heat added to the room and the seed cotton, heat added to the moisture in the seed cotton to increase water temperature and heat added to vaporize the moisture in the seed cotton. Model calibration involved two seed cotton conditioning rates, two mix point temperatures and two levels of moisture content. The simplistic model did not split the seed cotton into lint and seed components, but considered seed cotton as a whole. The model has a satisfactory r 2 of 0.80 when regressed against actual seed moisture content, but


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does not perform well at moisture contents of less than 6%, a level at which most cotton gins do not operate. Hatch et al.12 measured heat transfer through a specially selected set of jersey knit fabrics using a modified Kawabata Thermolab apparatus housed in a controlled environmental chamber. Analytical models were used to compute thermal comfort limits on the basis of experimental values and predetermined estimates of human metabolic activity. Results show that structural features such as thickness, bulk density and air volume fraction are the most important controllers of thermal dissipation in the presence of moisture diffusion. Li17 evaluated the process of moisture diffusion into hygroscopic fabric by measuring the moisture take-up and temperature changes of wool, cotton, porous acrylic and polypropylene fabrics under humidity transience. The degree of fiber hygroscopicity determined the strength of the coupling effect between moisture diffusion and heat transfer. Under humidity transience of 0–99%, woolen fabric had the highest moisture take-up rate and temperature increase, followed by cotton, acrylic and polypropylene. Theoretical analysis determined that moisture sorption capacity, diameter, water vapor diffusion coefficient and density and heat of sorption controlled the coupling effect between moisture diffusion and heat transfer.

5.10

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A188.

5.11

References

1. Breckenridge, J.R., Effects of Body Motion on Convective and Evaporative Heat Exchanges Through Various Design of Clothing, in Clothing Comfort: Interaction of Thermal, Ventilation, Construction and Assessment Factors, N.R.S. Hollies and R.F. Goldman, Eds, 1977: Ann Arbor, MI, Ann Arbor Science Publishers Inc. p. 153–166. 2. Bumbarger, S., J. Allen, and J.O. Stull, The Use of Water-Retaining Polymer Strands in Protective Clothing Composites for Enhanced Wearing Comfort and Thermal Protection, in Proceedings of the Second International Conference on Safety and Protective Fabrics: A Technical Focus on Textile and Material Development for Personal Protection. 2000. Arlington, VA. p. 13. 3. Coutant, A.A., Examining the Physical Laws of Heat Transfer. World Sports Activewear, 1998. 4(3): p. 27–30. 4. Crank, J., The Mathematics of Diffusion. 1975: Oxford, Clarendon Press. 5. David, H.G. and P. Nordon, Case Studies of Coupled Heat and Moisture Diffusion in Wool Beds. Textile Research Journal, 1969. 39: p. 166–172. 6. Downes, J.G. and B.H. Mackay, Sorption Kinetics of Water Vapor in Wool Fibers. Journal of Polymer Science, 1958. 28: p. 45–67.


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7. Farnworth, B., Numerical Model of the Combined Diffusion of Heat and Water Vapor Through Clothing. Textile Research Journal, 1986. 56(11): p. 653–665. 8. Fourt, L. and N.R.S. Hollies, Clothing: Comfort and Function. 1970: New York, Martin Dekker Inc. 9. Gagge, A.P.A., A.C. Burton, and H.C. Bazett, A Practical System of Units for the Description of the Heat Exchange of Man with his Environment. Science, 1941. 94: p. 428–430. 10. Gibson, P., Governing Equations for Multiphase Heat and Mass Transfer in Hygroscopic Porous Media With Applications to Clothing Materials. 1994: Natick, MA, United Army Natick Research Development and Engineering Center. 11. Gillum, M.N. and C.B. Armijo, Modeling Seed Cotton Moisture from Air Temperature Drop and Mass Flows, in Beltwide Cotton Production Conference. 1997. New Orleans. 12. Hatch, K.L., R. Barker, N.L. Markee, H.I. Maibach, S.S. Woo, and P. Radhakrishnaiah, In Vivo Cutaneous and Perceived Comfort Response to Fabric. Part 3. Water Content and Blood Flow in Human Skin Under Garments Worn By Exercising Subjects in a Hot, Humid Environment. Textile Research Journal, 1990. 60(9): p. 510–519. 13. Henry, P.S.H., Diffusion in Absorbing Media. Proc. Roy. Soc., 1939. 171 A: p. 215–241. 14. Henry, P.S.H., The Diffusion of Moisture and Heat through Textiles. Discussions of the Faraday Society, 1948. 3: p. 243–257. 15. Hollies, N.R.S. and R.F. Goldman, Clothing Comfort: Interaction of Thermal, Ventilation, Construction and Assessment Factors. 1977: Ann Arbor, MI, Ann Arbor Science Publishers Inc. 16. Li, F., Y. Li, Y. Liu, and Z. Luo, Numerical Simulation of Coupled Heat and Mass Transfer in Hygroscopic Porous Materials Considering the Influence of Atmospheric Pressure. Numerical Heat Transfer, 2004. Part B(45): p. 249–262. 17. Li, Y., Fabric Wetting Factors. Textile Asia, 1999. 30(6): p. 39–41. 18. Li, Y., Liquid Transport and Active Sportswear. 1996: Sydney, CSIRO Division of Wool Technology. 19. Li, Y., The Buffering Effect of Hygroscopic Clothing Against Rain, in Proceedings of the 4th Asian Textile Conference. 1997. Taipei, Taiwan. 20. Li, Y. and B.V. Holcombe, A Kinetic Approach Modeling the Coupled Diffusion of Moisture and Heat in Wool Fabrics. 1991: Sydney, CSIRO Division of Wool Technology. 21. Li, Y. and B.V. Holcombe, A Two-stage Sorption Model of the Coupled Diffusion and Heat in Wool Fabrics. Textile Research Journal, 1992. 62(4): p. 211–217. 22. Li, Y., B.V. Holcombe, and F. Apcar, Moisture Buffering Behavior of Hygroscopic Fabric During Wear. Textile Research Journal, 1992. 62(11): p. 619–627. 23. Li, Y., B.V. Holcombe, and R.D. Dear, Enhancement of Coolness to the Touch by Hygroscopic Fibers, Part II: Physical Mechansims. Textile Research Journal, 1996. 66(9): p. 587–595. 24. Li, Y. and Z.X. Luo, Physical Mechanisms of Moisture Diffusion into Hygroscopic Fabrics during Humidity Transients. Journal of the Textile Institute, 2000. 91(2): p. 302–316. 25. Li, Y., A.M. Plante, and B.V. Holcombe, Fiber Hygroscopicity and Perceptions of Dampness. Part 2: Physical Mechanisms. Textile Research Journal, 1995. 65(6): p. 316–324.


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26. Li, Y., Q. Zhu, and K.W. Yeung, Influence of Thickness and Porosity on Coupled Heat and Liquid Moisture Transfer in Porous Textiles. Textile Research Journal, 2002. 72(5): p. 435–446. 27. Luo, Z.X., J. Fan, and Y. Li, Effect of the Environmental Atmosphere on Heat, Water and Gas Transfer within Hygroscopic Fabrics. Journal of Computational and Applied Mathematics, 2004. 163: p. 199–210. 28. Lyons, D.W. and C.T. Vollers, The Drying of Fibrous Materials. Textile Research Journal, 1971. 41(8): p. 661–668. 29. Mecheels, J., The Measurement of the Functional Effect of Clothing on the Human Body. Melliand Textilberichte International, 1971. 52(7): p. 843–849. 30. Mecheels, J., The Measurement of the Functional Effect of Clothing on the Human Body. Melliand Textilberichte International, 1971. 52(8): p. 967–974. 31. Mecheels, J., The Measurement of the Functional Effect of Clothing on the Human Body. Melliand Textilberichte International, 1971. 52(10): p. 1215–1221. 32. Mecheels, J.H. and K.H. Umbach, The Psychrometric Range of Clothing Systems, in Clothing Comfort: Interaction of Thermal, Ventilation, Construction and Assessment Factors, N.R.S. Hollies and R.F. Goldman, Eds, 1977: Ann Arbor, MI, p. 133–151. Ann Arbor Science. 33. Nordon, P. and H.G. David, Coupled Diffusion of Moisture and Heat in Hygroscopic Textile Materials. International Journal of Heat Mass Transfer, 1967. 10: p. 853–866. 34. Park, S.I. and D.H. Baik, Heat and Mass Transfer Analysis of Fabric in the Tenter Frame. Textile Research Journal, 1997. 67(5): p. 311–316. 35. Pause, B., Thermo-Physiological Comfort Provided by Knitted Spacer Fabrics. Melliand Textilberichte, 2002. 83(3): p. 134–136. 36. Rae, A. and B. Rollo, The WIRA Textile Data Book. 1973: Leeds, UK, WIRA. 37. Schneider, A.M., Heat Transfer Through Moist Fabrics. 1987, University of New South Wales: Sydney, Australia. 38. Slater, K., Comfort Properties of Textiles. Textile Progress, 1977. 9(4): p. 1–91. 39. Urquhart, A.R. and A.M. Williams, The Moisture Relations of Cotton: the Effect of Temperature on the Absorption of Water by Soda-boiled Cotton. Journal of the Textile Institute, 1924. 15: p. T559–T572. 40. Wagman, P., American Institute of Physics Handbook, 2nd edn, 1975: American Institute of Physics. 41. Wang, Z., Y. Li, and Y.L. Kwok, Mathematical Simulation of the Perception of Fabric Thermal and Moisture Sensations. Textile Research Journal, 2002. 72(4): p. 327–334. 42. Watt, I.C., Kinetic Studies of the Wool–Water System, Part I. The Influence of Water Concentration. Textile Research Journal, 1960. 30: p. 443–450. 43. Watt, I.C., Kinetic Study of the Wool–Water System. Part II. The Mechanisms of Two-stage Absorption. Textile Research Journal, 1960. 30: p. 644–651. 44. Wehner, J.A., Moisture Transport Through Fiber Networks. Doctoral dissertation. 1987: Princeton University. 45. Woodcock, A.H., Moisture Transfer in Textile Systems, Part I. Textile Research Journal, 1962. 32: p. 628–633.


6 Thermal and moisture sensations 1

6.1

YI LI 1 AND ANTHONY S.W. WONG 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong

Introduction

In modern living conditions, many workplaces and modes of transport are climate controlled, meaning that the demand for warm clothing has been substantially reduced. Consumers are now more conscious of the sensory perceptions of the garments that they are wearing. In summer, the perception of coolness during dynamic momentary contact is a favorable sensation demanded by wearers. In Chinese history, cotton, linen and silk fabrics were recognized and used as comfortable materials for summer wear due to their coolness and lightweight properties. In an attempt to expand the market for wool and change the ‘warm fiber’ image, Woolmark CompanyTM, also known as International Wool Secretariat, has in recent years produced and promoted lightweight wool fabrics (called ‘cool wool’), which are made of tight-twist-type yarns. In winter, the warmth of textiles provides comfort and favorable sensations to the wearer. When wet, wearers like to feel dry and warm. During exercise, they like garments to be ‘breathable’ (i.e. not clammy) and able to transfer the extra heat away from the body, thereby reducing heat stress. When exposed to sudden and undesirable environmental changes, they like garments which protect them. All these thermal and moisture-related sensations contribute to the overall perception of the comfort experience during wear.

6.2

Coolness to the touch

A fabric’s coolness to the touch is a skin sensation related to the transient heat and moisture transfer between fabric and skin. This sensation has a significant impact on the perception of comfort in warm and hot environments. Thermophysiological comfort when wearing clothing is determined by the sensation of warmth or coolness during contact with the fabric as well as by the loss of water vapor through the clothing, which regulates the 93


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heat balance of the body.45 Schneider et al.38,40 reported that a smooth lightweight wool fabric was perceived to be cooler than less hygroscopic fabrics that were matched in construction, such as polyester, during contact with skin in warm, humid environments. In the investigation, pairs of fabrics matched in construction and surface features but made from different types of fibers were evaluated subjectively. The fabric samples were placed across the skin of the inner forearm of the test subjects. Evaluation took place at ambient temperatures of 20 and 28 °C, and relative humidity in the range 10â&#x20AC;&#x201C;90%. The wool fabric was consistently rated cooler than the polyester fabric for all the climate conditions tested. Schneider et al.38,40 also observed that the amount of moisture desorbed from the wool fabric was significantly higher than that from the polyester fabric and that the skin temperature decreased faster and recovered more slowly after contact with the wool fabric compared with the polyester fabric. They proposed a mechanism by which the coolness to the touch of smooth lightweight fabrics was enhanced by fiber hygroscopicity due to desorption of a very small quantity of water from the fibers. Li et al.27 applied the coupled heat and moisture transfer model, discussed in Chapter 5, to describe the dynamic heat and moisture exchange between skin and fabric during contact. An experiment was carried out to measure the surface temperatures of the skin and fabric during the contact by using thermocouples. The fabric temperatures measured for wool and polyester fabrics were compared with the predicted temperatures from the model. In both measured and predicted temperatures, the polyester fabric had a significantly higher rate of temperature rise than the wool fabric. Meanwhile, the simultaneous skin temperature changes were also compared. During contact, the skin temperature had a greater initial drop and smaller recovery with the wool fabric than with the polyester fabric as shown in the measured and predicted temperature curves. The difference in fabric and skin temperatures between the wool and polyester fabrics was found to be related to the moisture desorption by the fibers at fabric surface. Predicted water content change at the inner surface of the wool decreased much more quickly than polyester fabric. A similar difference in water content change between wool and polyester was observed in the experimental result.38 In these experiments, the fabrics tested were equilibrated to ambient conditions before being momentarily brought into contact with the skin. During practical wear, garments are not in equilibrium with the surrounding environment, but somewhere in-between skin and ambient conditions, as the fabric comes into contact with the skin and moves away intermittently. The mathematical model was also applied to a wear condition, in which the fabric was initially 5 mm away from the skin and assumed to be in equilibrium with the local environment at that point, then brought into


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contact with the skin for 30 seconds and moved 5 mm away from the skin for 30 seconds periodically. The predicted changes in the temperature and water content at the inner surface of wool and polyester fabrics behaved differently. It was concluded that coolness to the touch could be enhanced by the hygroscopicity of the fibers under practical wear conditions. Li et al.26 further investigated the mechanisms involved. The model describing the coupled heat and moisture transfer in fabric (Chapter 5) was interfaced with a model of skin thermoreceptors responding to skin temperature change (Chapter 3). The authors found that the change in skin temperature was related to the moisture sorption at the fabric surface when in contact with the skin. The responses from the thermoreceptors depend on the skin temperature and its rate of change, which determines the subjective differentiation in coolness to the touch (Chapter 3). The moisture-desorption rate is a rough linear function of fiber hygroscopicity and the diffusion coefficient of water vapor in fibers. Fiber hygroscopicity was defined as the average slope of moisture sorption isotherm in the range 10–80% relative humidity. The typical values of hygroscopicity for textile fibers vary from 0.006%/% for polyester to 0.205%/% for wool. The moisture desorption rate increases almost linearly with fiber hygroscopicity and the diffusion coefficient, suggesting that fabric coolness to the touch is a function of fiber hygroscopicity and water diffusion coefficient. The authors also studied the relationship of desorption rate with fiber diameter and fiber hygroscopicity using the model. Under the same climatic conditions, 28 °C and 70% RH, with water vapor diffusion coefficient fixed at 5.8 × 10−13 m2/s, the desorption rate had a negative non-linear function with fiber diameter. Below about 15 µm, the desorption rate increased considerably with further decrease in diameter. It is clear that, by reducing fiber diameter, fabric coolness to the touch can be enhanced. However, the impact of diameter reduction is much smaller than that of increase in hygroscopicity. Comparing the desorption rate of an ultra-fine and weakly hygroscopic synthetic fabric with that of normal cotton and wool fabrics, the performance of coolness to the touch should still strongly favor the natural fibers. The sensation of coolness to the touch is most necessary in warm, humid conditions. Schneider et al.40 showed that fabric coolness associated with fiber hygroscopicity was, in general, positively related to relative humidity. Li et al.26 used the model to show the influence of the temperature and relative humidity of the air surrounding the fabric on the desorption rate at the moment of maximum skin temperature change. The desorption rate has a positive relationship with relative humidity over the humidity range from 20–80%. However, it shows a negative relationship at ambient temperatures. These results indicate the relative contributions of a number of key


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Outer layer

Core

Outer layer

6.1 Three-layer fabric model.

parameters to fabric coolness to the touch. Fiber hygroscopicity has been shown to be the most significant factor influencing the moisture-desorption rate among the properties of fibers investigated. The moisture-desorption rate during contact is influenced to a lesser extent by other parameters such as water vapor diffusion coefficient, fiber diameter, ambient relative humidity and temperature. These publications have shown that the moisture-sorption behavior of textile fibers has a significant impact on fabric coolness to the touch during fabricâ&#x20AC;&#x201C;skin contact. The heat and mass transfer process, which is influenced by fiber properties such as hygroscopicity and diameter, determines the temperature change on the skin surface, and hence the sensory coolness response. On the other hand, the heat and moisture transfer process between skin and fabric is also largely influenced by the state of fabricâ&#x20AC;&#x201C;skin contact, which is determined largely by the surface features of the skin and fabric. Schneider and Holcombe39 studied the fabric properties that influenced coolness to the touch. They developed a three-layer model of fabric structure, as shown in Fig. 6.1. A fabric was considered as having three layers: a dense core and two outer layers consisting of a predominance of air with a small number of projecting fibers. They showed that the thickness of the outer layers had a negative influence on the rate of subjective coolness perception and temperature drop at the skin surface during fabricâ&#x20AC;&#x201C;skin contact. Li and Brown23 investigated the relationships between subjective perception of coolness to the touch and fabric properties. A range of 20 fabrics


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from micropolyester lightweight fabric to wool fleece fabric was tested. A number of statistical tools were used to identify the relative contributions of various fiber properties and fabric structural features to the perception of coolness. It was found that the subjective perception of coolness was negatively related to fabric porosity, fiber diameter and fabric hairiness, but positively related to fiber hygroscopicity. Among these parameters, fiber hygroscopicity, fabric porosity and fabric hairiness are the most important contributors. With matching construction, a wool fabric could be 1.5 times cooler than a polyester fabric. When the fabric porosity decreased from 0.95 to 0.65, a wool fabric could increase its coolness to the touch by 55%. If a wool fabric changed its surface hairiness from 80 count to zero count, the fabric coolness could be increased by 84%. Kawabata and Yoneda20,51,52 reported a series of theoretical studies on the heat transfer process that occurs when a fabric is brought into contact with the skin. They developed a device to measure the coolness–warmth to the touch of fabrics. The maximum heat flux during contact was proposed as a predictor of the warm–cool feel. Hes et al.15 also reported an apparatus to measure the heat flow and the thermal contact properties such as heat capacity, thermal conductivity and thermal diffusivity, which were considered to be related to the warmth of textiles. Li and Brown23 compared the relationship between subjective perception of fabric coolness and measured criteria such as the maximum heat flux during contact and psychosensory intensity (PSI). It was found that the PSI was more closely correlated with the subjective perception than the maximum heat flux. Also, a device was developed to test the contribution of moisture desorption to the subjective coolness ratings. Mazzuchetti and Demichelis31 evaluated the use of Fabric Assurance by Simple Testing, known as FAST, machines to predict fabric hand properties and correlated fabric mechanical properties with feelings of coolness and dryness from touching pure wool and wool/animal hair blend men’s summer suiting fabrics. By applying the Weber–Fechner equation to derive predicted hand values, they found that the physical properties that affected hand values are associated with the initial fabric modulus, fabric formability and buckling values. Wang et al.48 applied mathematical models, which describe the physical mechanisms of heat and moisture transfer in fabrics, to simulate the perception of thermal and moisture sensations. These two sensations have been shown to be related to skin and fabric temperature changes, which are associated with changes in moisture content and temperature in the fabrics. Therefore, eight related equations, including heat balance, mass balance and evaporation/condensation process, were used to describe the physical process.


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Heat balance: Cv

∂T ∂2T − (1 − ε ) λΓ = Kfab 2 ∂t ∂x

[6.1]

where Cv is the volumetric heat capacity of the fabric (kJ m−3 °C−1), T is the temperature of the fabric (°C), ε is the porosity of the fabric, λ is heat of sorption or desorption of water vapor by fibers (kJ kg−1), Kfab is thermal conductivity of the fabric (W m−1 °C−1), x is the real distance (m) and t is real time (s). Mass balance: ε

∂Ca D ε ∂ 2C a + (1 − ε ) Γ = a ∂t τ ∂x 2

[6.2]

where Ca is the water vapor concentration in the air filling the interfiber voids (kg m−3), Da is the diffusion coefficient of water vapor in the air of the fabric (m2 s−1) and τ is the effective tortuosity of the fabric. Water vapor sorption/adsorption rate of the fibers: Γ=

∂C ∂Cf 1 ∂  rDf ( x, t ) f  =  r ∂r ∂r  ∂t

[6.3]

where Cf is the water vapor concentration in the fibers of the fabric (kg m−3), r is the radial coordinate of the fiber (m) and Df is the diffusion coefficient of water vapor in the fiber of the fabric (m2 s−1). Evaporation/condensation process: Γ=

∂Cf = hcf Sv (Cfs − Ca ) ∂t

[6.4]

where hcf is the mass transfer coefficient at the fiber surface (m s−1), Sv is the specific surface/volume ratio of the fabric (m−1) and Cfs is the water concentration in the fiber surface (kg m−3). Boundary conditions at the skin–fabric interface: Mt =

Ksk ∂T (Tsk − T ) + LskCvsk sk Lsk ∂t

[6.5]

Md =

Dsk ∂C (Csk − Cfs ) + Lsk sk Lsk ∂t

[6.6]

and

where ksk is the skin thermal conductivity (W m−1 °C−1), Lsk is the skin thickness (m), Tsk is the skin temperature (°C), Cvsk is the skin volumetric heat capacity (kJ m−3 °C−1), Dsk is the skin water diffusion coefficient (m2 s−1) and Csk is the skin water concentration (kg m−3). Boundary conditions at the fabric–external environment interface:


Thermal and moisture sensations

Kfab

∂T ∂x

Da ε

∂Ca ∂x

x= L

= − ht (T − Tab )

99

[6.7]

and

x= L

= − hc (Ca − Cab )

[6.8]

where ht is the convective mass transfer coefficient (kJ m−2 °C−1), Tab is the ambient air temperature (°C) and hc is the convective mass transfer coefficient (m s−1). The mathematical simulation process can be divided into three steps: (1) experimental, (2) prediction and (3) validation. Subjects’ neurophysiological impulses together with skin temperature and its changes were recorded. Meanwhile, subjective perception of coolness and dampness sensations were also recorded in the experimental process (dotted lines). The skin temperature and its changes were then computed by solving equations (6.1–6.8) numerically, calibrated by the experimental data, using the psychoneurophysiological relationship obtained from above, then prediction of coolness and dampness perception to fabric content was conducted. Finally, the predicted result was validated by a new set of experimental results. Figures 6.2 and 6.3 illustrate the relationship between the experimental and predicted sensations of coolness and dampness, respectively. Results show that this mathematical simulation, based on heat and moisture transfer in fabrics, neurophysiological mechanisms of the thermoreceptors and psychophysical relationships, is able to predict the perception of coolness and dampness sensations to the touch with reasonable accuracy.

6.3

Warmth to the touch

Warmth and cold sensations are known to derive from separate warm and cold cutaneous thermoreceptors in the form of differentiated afferent nerves. The firing rate of warm-sensing nerves increases as the temperature increases; the firing rate of cold-sensing nerves increases if the temperature is reduced.1 The warmth of clothing is related to three relatively independent but associated aspects: the thermal insulation of clothing under steady state and under transient conditions, and the warm sensation during fabric–skin contact. The thermal insulation of clothing under steady state has been extensively studied and given practical application since the 1980s. It is discussed in Section 5.1. The sensation of warmth to the touch is the opposite to the concept of coolness to the touch. The same physical mechanism applies to both warmth and coolness to the touch, as discussed in Section 6.2.


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95

y = 0.962x r ≅ 0.79

Experimental

80

65

50

35

20 20

40

60

80

100

Predicted

6.2 Experimental against predicted coolness sensation.

Thermal insulation under transient condition is related to the concept of sorption of heat, which influences the thermal insulation value of the garments and the thermal sensation of the wearer in dynamic wear situations. David8,9 investigated the thermal buffering of clothing by conducting a series of experiments. He found that 30–50% of the total heat sorption released was effective in reducing the heat loss through the fabric. Olesen and Neilson33 measured the thermal insulation of clothing under transient humidity conditions by using a movable thermal manikin and human subjects. They observed significant differences in heat exchange between hygroscopic and non-hygroscopic fibers. Similarly, de Dear et al.10 studied the thermal responses of wool clothing during humidity transients by moving a manikin dressed in wool from ambient conditions of 25 °C and 20% RH to 25 °C and 80% RH. They found that 34–42% of the total sorption of heat generated was effective in influencing a sensible heat balance for the wearer. From the human subjects, they observed significant differ-


Thermal and moisture sensations

101

120

100

y = 1.012x r â&#x2030;&#x2026; 0.79

Experimental

80

60

40

20 20

40

60

80

100

Predicted

6.3 Experimental against predicted dampness sensation.

ences between wool and polyester in skin temperature changes and subjective thermal perceptions of thermal sensation, in which wool was perceived as being warmer than polyester during humidity transients. Stuart et al.42 used wool mittens to study the perception of heat sorption. They found that the sorption of heat by hygroscopic fabrics when moved from a dry atmosphere to a moist one is sufficient to influence wearersâ&#x20AC;&#x2122; perception of warmth. Mackeprang et al.30 reported a similar study on heat sorption in hygroscopic clothing. By conducting wear trials, they observed that the heat changes due to moisture sorption in humidity transients were sufficiently large to be readily perceived. Harju13 investigated cold and warmth perception of two healthy adult groups, young (aged between 20 and 30) and elderly (aged between 55 and 65), at three different body areas. A total of 48 adults (male and female), 24 in each group, participated. Perception thresholds (method of limits), perceived intensity (free-number magnitude estimation), and perceived quality (verbal descriptors) were assessed for cold and warmth at the thenar,


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the upper arm, the knee and the foot. Inter-individual comparison of perceived-intensity scales for cold and warmth was achieved by a Master Scaling procedure4 utilizing the thenar as a reference area. Perception thresholds showed gender difference for cold at the thenar and an interaction effect of age and gender for heat-pain tolerance at the upper arm. In contrast, perceived intensity of cold and warmth showed multiple effects of age, gender and specific body area (also for the nociceptive channels). For instance, at the knee, elderly women’s perceived intensity for stimulation in the nociceptive range was elevated for both cold and warmth, as compared to young women and men and elderly men. Conversely, at the upper arm, elderly women’s perceived intensity for the corresponding nociceptive range was lowered, as compared to young women and young men and to elderly men. At the foot, both elderly women’s and men’s perceived intensity of cold and warmth was lowered, as compared to young women and men. Overall, the perceived quality of perceptions did not differ between groups. Green and Cruz12 stated that although more acute in some areas of the body than in others, temperature sensitivity is assumed to be present throughout the skin. Only when very small stimuli have been used (e.g. approximately 1 mm2) has sensitivity to warming or cooling appeared discontinuous. They reported the discovery of patches of skin several square centimeters in area within which heating cannot be detected until skin temperature exceeds the thresholds of C heat-sensitive nociceptors (> 41 °C). These warmth-insensitive fields (≥ 5 cm2), which appear to lack lowthreshold warm fibers, were also found to have reduced responsiveness to non-painful heating and significantly higher heat-pain thresholds compared to surrounding areas of skin. The existence of such sites corroborates reports that warm fibers are rare in human cutaneous nerves and confirms the classical theory that cutaneous innervation by the warmth sense is punctate and sparse. The insensitive areas also provide unique opportunities for assessing the contribution of the low-threshold warmth system to perception of heat and heat pain, and their existence in healthy young adults contraindicates use of warmth sensitivity in neurological assessments of C-fiber function. Riu et al.35 examined human thresholds for skin sensations of warmth at five different frequencies, 2.45, 7.5, 10, 35 and 94 GHz over an area of 237 cm2 on the back of 16 male adults. The thresholds for energy detection decrease as the frequency increases. This suggests that less incident power is needed to produce a perceptible sensation at higher frequencies. The experimental data fit a linear regression in log–log coordinates, which can be described by the following expression: Pth ≈

100 f 0.68

[6.9]


Thermal and moisture sensations

103

where Pth and f are the threshold incident power (mW/cm2) and frequency (GHz), respectively. By solving the one-dimensional bioheat equation:11 ρcp

∂T − k ∇ 2T + V ( x, t ) T = Q ( x, t ) ∂t

[6.10]

where T is the temperature (K) difference between tissue and blood, ρ is the tissue density (g/cm3), cp is the tissue-specific heat (J/g K), k is the thermal conductivity (W/cm K), V(x,t) is the blood perfusion parameter (W/cm3 K) and Q is the power deposited in the body (W/cm3). The authors calculated the temperature increase at the skin surface or at a depth of 175 µm at incident power levels corresponding to the observed thresholds using the direct solution of the following electromagnetic wave equation: Q ( x, t ) =

2 I o Λ −2 x δ e U (t ) δ

[6.11]

where Io is the incident wave intensity (W/cm2), Λ is the power transmission coefficient between air and the tissue, δ is the field penetration depth (depth where the electric field falls by a factor of e) and U(t) is the unit step function. Temperature increases at the skin surface and at a depth of 0.175 mm were calculated after three and ten seconds of irradiation at different frequencies. The thermal analysis suggests that the thresholds correspond to a localized temperature increase of about 0.07 °C at and near the surface of the skin. They also found that, even at the highest frequency of irradiation, the depth at which the temperature receptors are located is not a relevant parameter, as long as it is within 0.3 mm of the surface. Over the time range of the simulation, the results of the thermal model are insensitive to blood flow, but sensitive to thermal conduction; and this sensitivity increases strongly with frequency. The authors concluded that the effect of thermal conduction on surface temperature rise becomes a dominant factor at microwave frequencies over 10 GHz. Casey et al.5 investigated the somatotopic organization of the modulation of warmth sensation and heat pain by different forms of cutaneous stimuli on the hands of 14 subjects, seven male and seven female. This study was divided into two phases; Phase 1, in which thermal and electrical conditioning stimuli were tested and Phase 2, in which cold and vibratory conditioning stimuli were employed. Four male and three female subjects participated in Phase 1 of the study. The remaining subjects participated in Phase 2 of the study. Descriptive information of both phases of the study is described in Table 6.1. The suppressive effect of vibration is marginally significant (p = 0.023) at test temperatures of 41 (first set) and 47 °C (fourth set).


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Table 6.1 Description of Phases 1 and 2 Phase

Parameter

Description

1

Thermal

• •

• Electrical

• • •

• 2

Cold

• • •

• Vibratory

• • • •

6.4

Applied to the glabrous skin of the fingerpads of digits 1, 2, 4 and 5 Contact thermode set at 30 °C and increase at 10 °C/sec until target temperatures (36, 40, 43, 45, 47 and 50 and 51 °C) maintain for 5 seconds The visual analogue scale device was used to determine the intensity of noxious thermal conditioning, which averaged 48 ± 1.3 °C Each digit was tested 3–5 times at each test temperature Applied to the glabrous skin of the fingerpads of digits 1, 2, 4 and 5 10-msec pulses delivered at a frequency of 40 Hz The visual analogue scale device was used to determine the intensity of noxious electrical conditioning, which averaged 66 ± 12 V Each digit was tested 3–5 times at each test temperature Conditioning stimuli were always applied to digit 2 and test stimuli to digits 1 and 5 Thermode temperature was set at 41, 43, 45, 47 or 50 °C Applied at a temperature of 15 °C and allowed to return to a resting temperature of 30 °C after each test stimulus Each test temperature was applied 10 times to each digit (1 and 5) Conditioning stimuli were always applied to digit 2 and test stimuli to digits 1 and 5 Thermode temperature was set at 41, 43, 45, 47, or 50 °C Delivered at an amplitude of 3.5 µm and a frequency of 120 Hz Each test temperature was applied ten times to each digit (1 and 5)

Dampness sensation

Moisture in clothing has been widely recognized as one of the most important factors contributing to discomfort sensations. Nielson and Endrusick32 studied the influence of subjects’ physical activities and clothing structure on various thermal and moisture sensations. They identified that the skin wetness contributed to the sensation of humidity, and that the sensation of dampness was related to the amount of sweat accumulated in clothing. The


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subjective sensations of skin and clothing wetness were recommended as a sensitive criterion for evaluation of the thermal function of clothing. In studying the perception approach to clothing comfort, Hollies et al.17 found that sweating sensation could be generated artificially by adding 10–20% of water into clothing. Scheurell et al.37 compared the results from subjective perception of moisture with the measurements on ‘dynamic surface wetness of fabric’ by using a clothing hygrometer, and found that the two were closely correlated. Hong et al.19 also reported that the dynamic surface wetness was influenced by fiber types. These findings indicate that moisture in clothing contributes significantly to the comfort perceptions during actual wear conditions. Hock et al.16 investigated the thermal and moisture sensations experienced by the skin. They reported that a chilling sensation was produced when damp fabrics were placed on the forearm, which was correlated with the temperature drop at an artificial ‘skin’ in contact with the moist fabrics. Also, the temperature drop was influenced significantly by the degree of the fabric–skin contact that was associated with fabric construction and surface hairiness. Li et al.28 reported significant differences in subjective perception of fabric dampness between wool and polyester fabrics at various levels of moisture content. Plante et al.34 further showed that perception of fabric dampness was a function of fabric moisture content, fiber hygroscopicity and ambient relative humidity. These findings indicated that the process of dampness perception involves complex mechanisms, which may be related to the dynamic heat and moisture processes in fabrics. Therefore, a series of studies was carried out by Li et al.28,29 to investigate the physical mechanisms in dampness perception. The mathematical model described in Chapter 5 was used to study the dynamic heat and moisture transfer processes in detail during the course of perception. A physiological trial was also carried out to obtain the skin temperature changes during the contact between moist fabrics and the skin. The subjective rating scores on fabric dampness perception were found to be a function of skin temperature drop (see Fig. 3.3). The skin and fabric temperature changes were shown to be related to the changes in moisture content in the fabrics. The rates of moisture changes were significantly different between wool and polyester fabrics. This is related to the mechanisms of moisture changes between the fibers and their surrounding air, described by equations (5.9) and (5.12). At 4% of excess water above equilibrium regain, polyester fabric had a total moisture content of about 5%, which was well above the saturation regain of the fiber. The moisture exchange process was a surface evaporation following equation (5.9). At the same excess water content, the total water content of wool fabric in the test condition was about 11%, which was well below its saturation


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regain. Thus, the moisture exchange process was largely a desorption process following equation (5.12), in which moisture desorbs from within the fibers and then diffuses through the fabric to the surroundings. This means that the actual heat and moisture transport processes in the fibers and fabric determine the heat exchange between the skin and fabric, which in turn determines the magnitude of the physical stimulus and influences the subjective perception of dampness. As fiber hygroscopicity is a critical factor in determining the coupled heat and moisture transfer behavior in fabric, it has a significant impact on the skin temperature drop during the contact. By using the model, the authors showed that the skin temperature drop during a fabricâ&#x20AC;&#x201C;skin contact increases progressively with an increasing level of excess moisture, reaching a maximum magnitude when the constituting fiber reaches its saturation moisture content. Comparing fabrics with different degrees of hygroscopicity, the skin temperature drop increases more slowly with the level of excess moisture as the degree of fiber hygroscopicity increases. Ambient conditions such as temperature and relative humidity influence the skin temperature drop significantly. The skin temperature decreases as ambient temperature increases because of the decrease in temperature difference prior to the contact. However, ambient temperature has a negligible influence on the differences of the skin temperature drop among different types of fibers, because ambient temperature mainly influences the dry heat transfer process, not the moisture exchange process. Ambient relative humidity, on the other hand, has significant impact on both the skin temperature drop of all fibers and the differences between the fibers. When ambient relative humidity increases, the difference in moisture concentration between the fabric and environment decreases, resulting in a smaller temperature gradient between the skin and fabric and hence small skin temperature drop during the skinâ&#x20AC;&#x201C;fabric contact. The differences between fibers are much greater when ambient relative humidity is low. While the relative humidity approaches saturation, the differences between fibers become negligible. Lau et al.21 investigated nine individual sensations (stiffness, statickiness, stickiness, non-absorbency, dampness, clinginess, prickliness, roughness and itchiness) and overall discomfort sensations of people wearing polo shirts with different garment treatments. In wear trials A and B, five and four subjects participated. Subjects were asked to compare four polo shirts with and four without wrinkle-free treatments in wear trial A. Remaining subjects were asked to compare two (conventional and hydrophilic) wrinklefree treatments in wear trial B. Subjects were asked to evaluate the above nine sensations and overall discomfort before and after an hour of exercise when wearing these garments. There is a significant (p < 0.05) difference in


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perceptions of stiffness and roughness and stickiness and clinginess for subjects wearing garments A and B respectively. The ratings of nine individual sensations and overall discomfort before and after exercise in wear trial B were analyzed and no significant difference was found before and after exercise in perceptions of selected sensations when subjects were wearing garments E and F. Results indicated that overall discomfort is largely determined by tactile (including itchiness and prickliness) and moisture (including dampness and clinginess) sensations before and after exercise, respectively.

6.5

Clamminess and moisture buffering during exercise

Yamakawa and Isaji50 stated that clamminess is related to heat loss of a damp sample fabric and the surface characteristics quantified in the coefficient of dynamic friction. In 1939, Cassie et al.7 first postulated the concept of moisture buffering of clothing. Cassie6 restated the hypothesis that the sorption of moisture by hygroscopic clothing would evolute heat, which would have a significant impact on the heat balance and thermal perceptions of the wearer experiencing a sudden change from a warm, dry atmosphere to a cold, damp one. Hygroscopic fibers have the ability to absorb a considerable amount of moisture from the surrounding atmosphere. When humidity transients, hygroscopic fibers can absorb or desorb moisture from or to the adjacent air, which can delay the moisture change in the clothing microclimate. Theoretically, this process often acts as a buffer against sudden humidity changes in favor of the wearer. However, the effectiveness of such buffering varies widely, as reported in the literature. Rodwell et al.36 investigated the physiological impact of sorption heat in clothing. They did not find significant impact of sorption heat in the simulated damp-cold winter conditions. Umbach46 studied the buffering effects of synthetic and cotton underwear. Wear trials were conducted at ambient conditions of 27 째C and 50% RH with subjects walking at 7 km/h. No significant differences were found in moisture buffering behavior between underwear made from the two fibers. Therefore, they concluded that there was no evidence indicating that the moisture-sorption ability of fibers has a decisive role in the comfort of clothing worn next-to-the skin. Similar results were obtained from various wear trials conducted by Andreen et al.,2 Vokac et al.,47 Holmer18 and Hatch et al.14 Various physiological parameters were measured in those trials, such as sweating rate, stratum corneum water content, water evaporation from the skin, energy output, microclimate temperature and humidity. No significant differences


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were found between fabrics made from highly hygroscopic fibers such as wool and those made from weakly hygroscopic fibers such as polyester and polypropylene. On the other hand, a number of research publications reported significant differences in moisture buffering between hygroscopic and non-hygroscopic fibers. Spencer-Smith41 illustrated the buffering effect of hygroscopic clothing by using a simple, approximate, graphic explanation. By using a simulated sweating device, Scheurell et al.37 examined the response of fabrics to humidity and temperature gradients. The authors reported that a fabric property, identified as the dynamic surface wetness, correlated with the comfort perception during wear. In a study of the influence of cotton, polyester and blend fabrics on dynamic surface wetness and moisture transfer through textiles, Hong et al.19 found that, with all-cotton fabrics, the moisture build-up at the inner fabric surface facing the sweating skin was the slowest, followed by cotton/ polyester blends and all-polyester fabric. The ranking order corresponded with the level of fiber hygroscopicity. Wehner et al.49 developed an apparatus to simultaneously measure the moisture sorption by a fabric and the moisture flux through a fabric during humidity transients. By comparing fabrics with different levels of hygroscopicity, the authors found that the duration of the transient behavior depended strongly on the moisture-sorption capacity of the fabric. The moisture flux across an insert porous barrier can reach steady state within seconds, while the non-steady condition could last for more than an hour when a wool fabric was exposed to a humidity gradient. During the transient period, the total amount of moisture removed from a high-humidity environment was greater with a highly hygroscopic fabric such as cotton than with a weakly hygroscopic fabric such as polyester. In a comparison between wool and polyamide clothing, Behmann3 conducted a wear trial using one subject repeatedly, during which the subject was walking on a treadmill at 2.2 km/h under conditions of 30 째C, 30% and 60% RH. The author reported that the perception of sweating and clinging sensations was delayed when the subject wore a wool garment. Tokura et al.43 conducted a wear trial in a hot environment (33 째C and 60% RH) to study the effect of moisture absorbency of fibers on sweating rates of secondary subjects. It was found that, when wearing polyester, subjects lost a greater amount of sweat and the humidity in the microclimate was significantly higher than when wearing cotton. Li et al.25 compared wool with polyester by carrying out wear trials at 28 째C and 30% RH. Subjects were walking on a treadmill at 5.6 km/h for up to 40 minutes. There was no difference in upper body sweat loss during exercise when wearing wool or polyester garments. However, wool garments took up significantly more sweat than polyester garments.


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Correspondingly, subjects reported feeling less clammy and warmer when wearing wool than when wearing polyester during the period between 10 and 30 minutes of exercise. The coupled heat and moisture transfer model was applied to this wear situation by specifying boundary conditions that describe the wear situations. The model predicted that the relative humidity at the microclimate would be lower and the skin temperature higher during the period between ten and 30 minutes of exercise. The authors further applied the model to analyze the buffering effect of hygroscopic clothing under previously reported wear trials, and used it to explain the contradictory results reported in the literature. The model indicated that the moisture buffering by hygroscopic fibers could be effective during a certain period after exercise. The length of the buffering period and magnitude of delay of humidity rise depend on the ability of the fabric to remove moisture relative to the speed of moisture build-up in the clothing microclimate, which is related to ambient conditions, clothing material and style and exercise intensity of the subjects. Therefore, the apparent contradiction in the clothing buffering effect reported in the literature can be largely attributed to the differences in the climatic and exercise conditions used. In a study of the influence of fiber hygroscopicity on the thermoregulatory responses during exercise, Li et al.24 compared wool with polyester by conducting a wear trial at 20 째C and 35% RH. Subjects exercised by pedalling on a cycle ergometer at a load adjusted to achieve a heart rate of approximately 70% of the age-weighted maximum for each individual. It was found that the relative humidity measured in the microclimate of clothing rose more quickly when wearing polyester than when wearing wool. The difference between the two was significant in the period between ten and 20 minutes after exercise. From the corresponding temperatures measured at the skin and fabric surface, the authors observed significant differences when wearing wool and polyester. After sweating, the temperature of the garment and the corresponding skin temperature rose significantly more when wearing wool than when wearing polyester. The difference is statistically significant for the first 20 minutes after sweating starts, following which the difference between the two gradually diminishes. On the other hand, the mean core temperature showed no significant differences when wearing wool and polyester, even though the core temperature seems to rise more slowly when wearing wool. The authors calculated the dry and latent heat fluxes at the outer surface of the garment according to the measured temperature and moisture gradients. No differences in dry and latent fluxes were observed before sweating. After sweating, the dry heat flux at the outer surface of the garment was significantly higher when wearing wool than when wearing polyester.


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No significant difference in latent heat flux was found between wool and polyester. The total heat flux at the outer surface of the garment after sweating was about 13% greater with wool than with polyester until the end of the exercise period, which was significant at the 0.01 level. These results indicate that fiber hygroscopicity has a significant impact on the thermal response of the body and the heat balance of the body– clothing system during the transient period of exercise. When sweating starts, highly hygroscopic fibers absorb considerable amounts of sweat and their temperature rises due to sorption of the heat released. The elevated fabric temperature interacts with the body, stimulating higher skin temperature and raising sweat rate. Some of the sweat is further absorbed by the fabric, adding to the release of sorption heat and increasing the dry heat loss at the outer surface of the garment. Hence, the body is able to shed more heat during exercise. The sorption of moisture and the released sorption heat by weakly hygroscopic fibers such as polyester are very low. Most of the sweat in the garment was present as liquid and had less influence on the dry heat loss at the outer surface of the garments. Therefore, the role of clothing made from weakly hygroscopic fibers is more passive and its contribution to heat loss during exercise is smaller. Tuchida et al.44 examined the wearing sensations of wet fabrics by wear trials. Four arm covers consisting of cotton, wool, polyester and acrylic fibers were used as specimens. These wear trials were conducted under three sets of ambient temperature; 10 °C, 20 °C and 30 °C. The relative humidity was set at 65% for all temperature conditions. The most significant differences of wearing sensations are found between the dry and wet fabrics at an ambient temperature of 20 °C. The subjective estimate of coolness is obtained at an ambient temperature of 10 °C. The subjective estimate of touch and humidity is obtained at 30 °C. It was found that a wet wool fabric is better than other textile fabrics in the subjective estimation of warmth and humidity.

6.6

Environmental buffering

The buffering effect of hygroscopic clothing has a significant impact on the thermal balance and comfort of the wearer during the humidity transients due to environmental changes. Often, a wearer experiences various sudden and large changes in the external thermal environment. For instance, a wearer may be exposed to differences in temperature greater than 10 °C and humidity greater than 30% RH when walking from an air-conditioned indoor environment to a hot and humid summer outdoor environment. The difference in temperature between an air-conditioned indoor environment and outdoor winter environment in the cold regions can be greater than


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111

20 °C. Clothing is an extremely important barrier to protect the body against such sudden environmental changes. An investigation of the buffering effect of clothing against rain was reported by Li.22 The author analyzed the physical process of a rain droplet contacting the surface of a garment and identified that there are significant differences in temperature and humidity between the dry clothing surface and rain droplets. The difference is a function of ambient relative humidity and temperature – the lower the ambient relative humidity, the greater the temperature difference. Due to these differences, when rain droplets impact on a garment, the temperature at the clothing outer surface can decrease and relative humidity will rise to 100%. By specifying such dynamic changes as the boundary condition at a clothing surface, the magnitude of the buffering effect for a single-layer garment made from wool and polyester was estimated by using the model simulating the heat and moisture transport processes in clothing and its interaction with the thermoregulatory system (Chapters 4 and 5). A prediction was made of the temperature change at the skin surface when a person wearing wool or polyester garments in an ambient temperature of 25 °C with relative humidity varying from 30 to 90% was caught in the rain. The expectation was that the skin temperature changes with wool would be smaller than with polyester. As the ambient humidity increases, the changes in temperature become smaller with either fabric; in addition, the difference between skin temperature changes when wool is worn and those when polyester is worn decreases. These predictions suggest that there are differences in buffering against rain chill between wool and polyester fibers, which represent the extremes of hygroscopicity. For other textile fibers such as cotton and acrylic whose hygroscopicity falls between these extremes, the effectiveness of buffering is expected to be related to the extent of their hygroscopicity. The more hygroscopic the fiber, the stronger the buffering effect. In order to test these predictions, a series of physiological wear trials were carried out using jumpers made from wool and acrylic. The temperature profile from skin surface, inner clothing surface and outer clothing surface was measured. At the onset of rain, the temperature at the outer surface of jumpers, where the raindrops hit directly, fell by approximately 3–4 °C within one minute. With wool, the temperature drop tended to be smaller than with acrylic. At the inner surface, the difference between wool and acrylic was more obvious. With acrylic, a rapid temperature drop (about 1.2 °C within a minute) was observed, while with wool the temperature decreased gradually over ten minutes. These differences were extended to the skin surface level. When a subject was wearing acrylic, the skin temperature decreased more than when he was wearing wool (significant at the 98% level).


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The corresponding relative humidity at the skin surface and at the inner surface of both jumpers was also measured. At both surfaces, the relative humidity increased rapidly during the first minute of rain, then continued to rise slowly. The differences in the humidity increase between wool and acrylic were significant at the 99% level after rain. When wearing acrylic, the increase in humidity was greater and faster than when wearing wool. These experimental results confirmed the predictions that highly hygroscopic fibers such as wool could buffer the body against the sudden changes in temperature and humidity more effectively. Further, a series of psychological wear trials were conducted to test whether the buffering effect was perceivable or not. Subjective ratings on warmth, dampness and comfort over 20 subjects were obtained. Significant differences in the subjective ratings were found between wool and acrylic jumpers. Perception of warmth decreased and perception of dampness increased considerably as rain started, and slowly recovered after raining ceased. The drop in perception of warmth and increase in dampness sensation are considerably greater for acrylic jumpers than for wool jumpers, indicating that the impact of rain on thermal, moisture and comfort perception was perceivable and wool jumpers had a stronger buffering effect than acrylic jumpers. Simultaneously, the overall perception of comfort decreased quickly during the rain period and continued to decrease afterwards. Significant differences were observed between wool and acrylic. When wearing acrylic jumpers, the overall comfort ratings declined more rapidly during the rain period than when wearing wool jumpers, indicating that the buffering effect of wool against temperature and moisture change had an impact on perception of overall comfort of the subjects.

6.7

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A174.

6.8

References

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39. Schneider, A.M. and B.V. Holcombe, Properties Influencing Coolness to the Touch of Fabrics. Textile Research Journal, 1991. 61(8): p. 488–494. 40. Schneider, A.M., B.V. Holcombe, and L.G. Stephens, Enhancement of Coolness to the Touch by Hygroscopic Fibers. Part 1: Subjective Trials. Textile Research Journal, 1996. 66(8): p. 515–520. 41. Spencer Smith, J.L., Physical Basis of Clothing Comfort. V. The Behaviour of Clothing in Transient Conditions. Clothing Research Journal, 1978. 6(1): p. 21–30. 42. Stuart, I., A. Schneider, and T. Turner, Perception of the Heat of Sorption of Wool. Textile Research Journal, 1989. 59(6): p. 324–329. 43. Tokura, H., Y. Yamashita, and S. Tmioka, The Effect of Moisture and Water Absorbency of Fibers on the Sweating Rates of Sedentary Man in Hot Ambient, in Objective Specification of of Fabric Quality, Mechanical Properties and Performance, S. Kawabata, R. Postle, and M. Niwa, Eds, 1982: Osaka, Textile Machinery Society of Japan, p. 407–418. 44. Tuchida, K., A. Adachi, and T. Harada, Wear Sensations of Wet Fabrics. Journal of the Textile Machinery Society of Japan, 1986. 39(9): p. 320–327. 45. Tzanov, T., R. Betcheva, I. Hardalov, and L. Hes. Thermophysiological Comfort of Silicone Softeners Treated, Woven Textile Materials, in Proceedings of the Texsci’98. 2001. 1: p. 191–192. 46. Umbach, K.H., Hautnahe Synthetics Mit Gutem Trageomfort. Chemiefasern/ Textilind, 1980. 30/82: p. 628–636. 47. Vokac, Z., V. Kopke, and P. Keul, Physiological Responses and Thermal, Humidity, and Comfort Sensations in Wear Trials With Cotton and Polypropylene Vests. Textile Research Journal, 1976. 46: p. 30–38. 48. Wang, Z., Y. Li, and Y.L. Kwok, Mathematical Simulation of the Perception of Fabric Thermal and Moisture Sensations. Textile Research Journal, 2002. 72(4): p. 327–334. 49. Wehner, J.A., B. Miller, and L. Rebenfeld, Dynamics of Water Vapor Transmission Through Fabric Barriers. Textile Research Journal, 1988. 58(10): p. 581–592. 50. Yamakawa, M. and S. Isaji, Factors Affecting Clamminess. Journal of the Textile Machinery Society of Japan, 1984. 37(12): p. 492–500. 51. Yoneda, M. and S. Kawabata, Analysis of Transient Heat Conduction and its Applications Part I: the Fundamental Analysis and Applications to Thermal Conductivity and Thermal Diffusivity Measurements. Journal of the Textile Machinery Society of Japan, 1983. 29(4): p. 73–83. 52. Yoneda, M. and S. Kawabata, Analysis of Transient Heat Conduction and its Applications, Part II: Theoretical Analysis of the Relationship Between Warm/ Cool Feeling and Transient Heat Conduction in Skin. Journal of the Textile Machinery Society of Japan, 1985. 31(4): p. 79–85.


7 Tactile sensations 1

7.1

YI LI 1 AND ANTHONY S.W. WONG 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong

Introduction

During wear, clothing can have a powerful impact on comfort sensations. Besides playing a major role in natural changes between the human body and the external medium (heat changes or humidity transfer), it generates certain tactile sensations that can be pleasant or (very) unpleasant.8 Bornais5 stated that sensory tactile stimuli include softness, resistance to movement, stiffness and itchiness. Unlike the area of clothing thermal and moisture comfort, the theoretical framework for the physical mechanisms of clothing tactile and pressure comfort has not been fully developed. However, a considerable volume of research outcomes has been reported on relationships between fiber-to-fabric properties and various tactile and pressure sensations.

7.2

Fabric prickliness

Fabric-evoked prickle has been identified as one of the most irritating discomfort sensations for clothing wear next-to-skin. The neurophysiological basis of fabric prickle perception has been well established as discussed in Section 3.2.2. Garnsworthy et al.15 identified a special type of pain nerve responsible for prickle sensation, which is triggered by a threshold of force of about 0.75 mN. Individual protruding fiber ends from a fabric surface are responsible for triggering the pain nerve endings during contact with the skin. Summation of responses from a group of pain nerves seems necessary for the perception of prickle sensations. In subjective tests, Garnsworthy et al.16 observed that most of the subjects could not perceive prickle from fabrics containing a density of high-load-bearing fiber ends that is less than three per 10 cm2. The intensity of fabric prickle perception is a function of the density of high-load-bearing fiber ends at the fabric surface and the area of contact between the fabric and the skin. This indicates that both fiber mechanical properties and fabric surface features are important factors determining fabric-evoked sensations. 116


Tactile sensations

117

Matsudaira et al.37 compared three techniques for objective measurement of fabric prickle: low-pressure compression testing, laser-counting of protruding fibers and a modified audio-pickup method. A KES-FB compression tester was modified to measure the relationship between pressure and fabric thickness at the initial compression stage, in which the protruding fibers are bent and compressed. A laser hairiness meter developed at WRONZ was used to count the fibers protruding from fabric surfaces. The sensitivity of the instrument was found to be inadequate for the detection of all fabric surface hairs with a bias in favor of the coarser and stiffer hairs. Further, an audio-pickup head was modified to detect the protruding fibers from a fabric surface.37 They found that the modified audio-pickup technique was the most effective and the measured mean force per contact correlated well with the subjective perception of fabric prickle. During the test, the fabric surface was traversed under a stationary audio stylus, from which signals were obtained from the contact between the stylus and a protruding fiber. In developing a calibration of the stylus signal, the authors used two classical models – a loaded cantilever and a Euler column – to calculate the pointing force and the critical buckling load.37 The critical buckling load PE of the protruding fiber ends has been identified as the stimulus responsible for triggering the pain receptors, which can be expressed as: PE = π2 (EI/4l 2)

[7.1]

where E is the Young’s modulus of the fiber, I is the moment of inertia (I = πd4/64 in the case of a circular rod) and, l is the length of the protruding fiber ends. This equation clearly suggests that fiber Young’s modulus, fiber diameters and fiber lengths are the key factors determining fabric prickliness. Veitch and Naylor55 studied the mechanics of fiber buckling in the fabric prickling process and concluded that the short fiber protruding ends obey Euler’s simple buckling theory. By assuming a nominal length of 2 mm, Garnsworthy et al.16 and Naylor42 calculated that a fiber with diameter of 30 µm gives a buckling load of 0.75 mN. Using a forearm test method, the authors evaluated the prickliness of a set of wool knitted fabric samples covering a range of fiber diameters from 19 to 30 µm. By analyzing the relationships between subjective prickle perception and fabric features such as fiber diameters, treatment and finishing, they derived a multiple linear regression equation (7.2): MPE = −3.65 + 2.83 (diameter) − 0.60 (treatment) − 0.25 (finish)

[7.2]

where MPE was the mean prickle estimates. The treatment was coded 0 for untreated fabric and 1 for Kroy/silicone treated fabric. The finish was coded


118

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as 0 for steam relaxation and 1 for an aqueous scour. This equation suggested that subjective perception increased with fiber diameters and decreased with reduction of fiber–fiber friction by antiprickle treatment and finishing processes. Matsudaira et al.36 investigated the effects of finishing on fabric prickle. They found that, for fabric surfaces containing 35 µm wool fibers, the decreases and increases in prickle due to successive finishing processes were appreciable. By blending acrylic fibers with different diameters, Naylor42 studied the effect on prickle of the composition and shape of the diameter distribution of the coarse edge fibers. The author observed that the prickle of single jersey knitted fabric correlated with the percentage of fibers with diameters greater than a threshold value, which is close to 30 µm. Also, wool and acrylic fabrics with similar diameter distributions were shown to have the same level of prickle. In a further study of fabric-evoked prickle, Naylor et al.44 used a range of worsted spun single jersey knitted wool fabrics made from wool with different fiber diameter distributions. The authors reported that the prickle sensation of these fabrics could be predicted from the density of coarse fiber ends per unit area of fabric. The percentage of fiber ends with a diameter greater than 32 µm was identified as the key factor. Also, the variations in diameter distribution in individual fleeces, especially the percentage of coarse fiber ends, influenced fabric prickle significantly. Kennis26 reported a study of the influence of some parameters on prickle evoked by woven fabrics. A series of weft sateen fabrics were made from two types of wool: 18.2 µm and 23 µm. Fabrics were manufactured into high and low cover factors with weight around 250 g/m2. All the fabrics were scoured, centrifuged, hung to dry, cropped and blown for two minutes. The three most influential factors were identified as fiber diameter, fabric cover factor and finishing. The interactions between the factors were not significant. In an investigation of the influence of fabric mechanical properties on the discomfort sensations in wear trials, Li and Keighley33 found that fabric prickle sensation was positively correlated with fiber diameters, fabric thickness at low loading and fabric surface roughness. In 1997, Naylor and Phillips43 evaluated fabric-evoked prickle in worsted spun single 12-gauge jersey fabrics through a wear trial, which involved the evaluation of the acceptability of sleeves worn against bare arms by 18 experienced adult judges and an unskilled group of 52 school children aged 9–12 years. The experienced adult judges evaluated the samples in an environmentally controlled room, where temperature and humidity were set at 22 ± 1°C and 65 ± 5%, respectively, whereas the children evaluated the samples in local school environment. Results, as in Table 7.1, show that the finer wool sleeves were very comfortable and wearable. However, the sample rapidly became uncomfortable when the fiber diameter distribution exceeded about 3% of fiber ends greater than 32 µm. Based on known


Tactile sensations

119

diameter characteristics of commercially available tops, the trials showed that the acceptability for next-to-skin wear changed quickly from approximately 90% for 20.5 µm wool to 50% for 23.5 µm wool. Furthermore, applying the percentage of fiber ends greater than 32 µm in Table 7.1 (scaled to 65 mm hauteur) and the children’s test data to the Sirolan–Laserscan equation, gives the result shown in Fig. 7.1. This result can be used as a predictive tool for estimating the acceptability of single jersey fabrics made from different commercially available wools. In the investigation of the relationship between fabric objective measurement and subjective assessments of fabric prickle and softness, Bishop et Table 7.1 Result of overall acceptability of prickle and comfort of sleeves Fabric

Hauteur (mm)

GRN 1045 DQN 2110 DQN 2110 DQN 2110 DQN 2110

52 76 78 66 84

Diameter characteristics

% of people finding sleeve comfortable

Bulk measurements

Fiber end measurements

µ

CVD (%)

%, >30 µm

µ

%, >32 µm

Adults

Children

17.0 21.5 21.4 22.4 24.4

19 16 21 28 23

0.4 1.7 4.8 10.6 13.6

16.5 21.1 21.0 21.1 23.1

0.4 0.9 2.8 5.2 6.5

97 64 25* 0* 0*

99 90 71 22* 36*

* Significantly uncomfortable at the 5% level. 100

Percentage comfortable

90 80 70 60 50 40 30 20 20

21

22

23

24

Top mean diameter (µm)

7.1 Predicted skin comfort of 21-gauge fully fashioned knitwear.

25


120

Clothing biosensory engineering

al.4 found that objective measurement of buckling load and buckling energy of free fiber ends correlated with mean fiber diameter and with subjective assessments of fabric prickle and softness. In the evaluation of wool shirt comfort using a wear trial and forearm test, Wang et al.56â&#x20AC;&#x201C;58 found that increasing the environmental temperature decreased the comfort of the woolen fabrics due to the increased prickly feel. The forearm tests indicated that increasing the wool quality number decreased the level of prickle. Lightweight woolen fabrics manufactured from 70 s quality and coarser yarns resulted in the highest level of prickle. The amount of prickle correlated with the mean diameter of the fabric surface fibers. Increasing mean diameter increased fabric prickle. Critical fiber diameter for worsted woolen woven fabrics was 26 Âľm for both interior and outdoor fabrics.

7.3

Fabric itchiness

Similar to fabric prickle, itch is also found to result from activation of some superficial pain receptors.59 A prickling fabric usually has the quality of an itching sensation.16 In a number of psychological wear trials, it was found that the perception of itchiness in clothing was highly correlated with the perception of prickliness. Both sensations were classified as tactile sensory factors.32 Therefore, it can be expected that the factors influencing fabric prickle would affect fabric itchiness as well. Comparing the subjective ratings of itchiness obtained from wear trials with the mechanical properties measured objectively, Li31 observed that perception of itchiness correlated with fiber diameter, fabric thickness at low and high pressures and fabric surface roughness. In 2002, Lau et al.30 studied the comfort levels of people wearing polo shirts with and without wrinkle-free treatments before and after exercise. Six types of garment were investigated. All samples were 100% cotton single knitted jersey. The treatment for the different samples is shown in Table 7.2. The wear trial was divided into two parts; A and B. Part A compared the subjective perception of different sensations when subjects were wearing treated and non-treated garments (A, B, C and D). In part B, subjects were asked to compare garments E and F, in which the left and right bodice were treated differently in term of wrinkle-free treatment, as shown in Table 7.2. Five and four young male subjects participated in parts A and B of the wear trial, respectively. Table 7.3 shows the results from the wear trials. No significant difference was found in discomfort sensations between wrinkle-free treated fabrics and untreated fabrics on the right bodice in wear trial A. Similar results were also found for wear trial B. Results also showed no difference in wearer comfort between fabric treatments with different water-absorption rates after exercise. Tactile sensations such


Tactile sensations

121

Table 7.2 Description of garment samples in the wear trial Garment

Left bodice

Right bodice

Treatment type

Level

owf (%)

Treatment type

Level

owf (%)

A B C

– – –

– – –

– – –

Low High Low

5 10 5

D

High

10

E F

Hydrophilic Conventional

– –

10 10

Conventional Conventional Newly developed Newly developed Conventional Hydrophilic

– –

10 10

as itchiness and prickliness determined wearer comfort before exercise. Moisture-related sensations such as dampness and clinginess determined wearer comfort after exercise.

7.4

Fabric stiffness

Elder et al.13 studied the stiffness of woven and non-woven fabrics by using both subjective assessment and objective measurement. Magnitude estimation was used to obtain the subjective responses. Objective measurements of fabric stiffness were carried out using a Shirley cantilever, Cusick Drapemeter and Shirley Cyclic Bending Tester. The authors observed that the flexural rigidity obtained by bending-hysteresis measurements on the Shirley Cyclic Bending Tester correlated well with the subjective estimation of fabric stiffness. Applying Stevens’ law to the data, the authors found a logarithmic–linear relationship between the subjective stiffness estimation and the flexural rigidity. Further, Elder11 reported a study to verify their methodology and conclusion by using another set of woven fabrics and knitted fabrics. The author found that the agreement among three objective measurements, bending length, flexural rigidity and drape coefficient, was good and that these measurements were highly correlated with the subjective ratings. One year later, Elder et al.14 described a psychological scale for fabric stiffness. In this paper, the authors selected the drape coefficient measured using the Cusick Drapemeter as the objective measure of stiffness instead of the flexural rigidity measured by the cantilever. The main reason for using the drape coefficient was that the criterion could give an integrated measure as good as a human being can give. The cantilever was rejected because the measurements were directional and had greater variability.


Stiffness Statickiness Stickiness Nonabsorbency Dampness Clinginess Prickliness Roughness Itchiness Overall discomfort

Discomfort sensation

– 3.20 2.40 2.80 2.60 2.60

– 3.00 2.60 3.00 2.80 3.00

– 3.20 2.60 2.60 2.60 2.60

3.00 3.00 3.20 –

– 3.20 3.00 3.20 2.60 2.60

3.40 3.00 3.20 – 3.00 3.40 3.20 4.00* 3.00 3.60

3.80* 3.00 3.00 2.80 2.80 3.80* 3.00 3.20 3.40 3.60

3.40 3.00 3.80* 3.00 3.00 2.60 3.20 3.00 3.20 3.40

2.60 3.00 3.00 3.20

C

1 = right much more. 2 = right more. 3 = same. 4 = left more. 5 = left much more. * Significant difference (based on the Z-test at 95% confidence level).

2.80 3.00 3.00 –

3.40 3.00 3.40 –

B

2.80 2.80 3.00 3.00 3.00 3.10

3.20 3.00 2.60 2.70

D

– 3.00 3.25 3.00 3.25 3.25

3.00 3.00 2.75 – – 2.88 3.13 3.13 3.13 3.13

3.13 3.00 2.75 – 2.50 2.50 3.00 3.25 3.00 2.50

3.00 3.00 2.25 2.75

3.50 3.50 3.00 2.75 3.00 3.00

3.25 3.00 3.50 3.00

1st set of 2nd set of samples samplesa

After exercise

3.00 3.00 3.00 3.00 3.00 2.75

3.13 3.00 2.88 2.88

Mean for 2 sets of samples

1 = Conventional wrinkle-free treatment is much more uncomfortable than newly developed wrinkle-free treatment. 2 = Conventional wrinkle-free treatment is more uncomfortable than newly developed wrinkle-free treatment. 3 = Conventional wrinkle-free treatment is as uncomfortable than newly developed wrinkle-free treatment. 4 = Newly developed wrinkle-free treatment is more uncomfortable than conventional wrinkle-free treatment. 5 = Newly developed wrinkle-free treatment is much more uncomfortable than conventional wrinkle-free treatment. a After conversion.

– 2.75 3.00 3.25 3.00 3.00

3.25 3.00 2.75 –

1st set of 2nd set of Mean for samples samplesa 2 sets of samples

A

D

A

C

Before exercise

After exercise

Before exercise

B

Wear trial B

Wear trial A

Table 7.3 Comparison of average differences in wear trials A and B


Tactile sensations

123

Again, a good logarithmic–linear relationship between the subjective stiffness estimation and the drape coefficient was obtained. Fabric stiffness has been recognized as one of the primary hand expressions used by Kawabata and Niwa25 in their fabric hand evaluation system, called KOSHI in Japanese. Using a stepwise-regression method, Hu et al.19 found that fabric stiffness (called HV1) was related to a number of objective measured parameters by KES-F instruments: WC (energy in compression fabric under 5 kPa), B (bending rigidity), MIU (coefficient of steel-fabric friction), MMD (mean deviation of MIU) and LC (linearity of compression thickness curve). In 1996, Bishop3 made a comprehensive summarization on the objectively measurable physical properties associated with stiffness commonly used in the literature, including bending stiffness, thickness, areal density, shear stiffness/hysteresis and compressibility. Comparing the subjective sensory responses from wear trials and the mechanical properties measured objectively, Li31 found that subjective ratings of garment stiffness are related to three types of mechanical properties: (1) fiber diameter and tensile breaking load; (2) fabric compression properties such as thickness at low and high pressure, the energy of the compression–thickness curve, the slope of the compression–thickness curve and the resilience of the compression–thickness curve; (3) fabric frictional properties such as the mean of the friction coefficient and the mean deviation of friction coefficient. Raychaudhuri and Das51 developed a mathematical model for the physical testing of fabric stiffness by cantilever method in 2000. The purpose was to express the stiffness in terms of the physical parameters like strip dimensions and mass per unit length. The mathematical modeling of a bent cloth strip is shown in Fig. 7.2. The neutral axis of the cloth strip is illustrated by the circular arc, AB, with radius, r. The chord length of AB, C, is used to measure stiffness as it is proportional to cloth stiffness. The authors stated that the proportional bending length, C, also known as the chord length, can be determined by the following three equations: BD = y = C sin θ

[7.3]

θ is set at 41.5°, which is the instrument at a convenient value. C=

L sin θ θ

[7.4]

y=

1.5 ml 4 Ywt 3

[7.5]

where m is the mass per unit length of the strip, which acts as uniform load, l is the length of fabric overhanging the platform, w is width and t is the thickness.


124

Clothing biosensory engineering L L L–X A

D θ P Q

E Y F

r

B

r

O

7.2 Schematic diagram for the mathematical modeling of bending of clothing strip.

In term of physics, effective Young’s modulus is also defined to correlate the stiffness with physical parameters of the fabric: Y=

1.5 ml 4 ywt 3

[7.6]

It is applied to the experimental results on fabrics with four different combinations of cotton–polyester content for both new and used conditions. Values of stiffness calculated using equations (7.4) and (7.6) are shown in Table 7.4. Results show that there are no significant changes in effective Young’s modulus. This is due to the fact that there was a decrease in load on the strip after washing, instead of an increase in stiffness. Pandita and Verpoest47 compared different stiffness prediction models including Krenchel,29 Voigt,41 Reuss,41 Inclusion and Kregers’28 weight average model (WAM). The mathematical expression of the models is shown below (7.7 to 7.11): Krenchel:

EK = a1111EfVf + Em(1 − Vf)

[7.7]


100

33

20

0

B

C

D

Cotton

100

80

67

0

Polyester

Fabric content (%)

A

Sample

Used

New

Used

New

Used

New

Used

New

Condition

Warp Weft Warp Weft

Warp Weft Warp Weft

Warp Weft Warp Weft

Warp Weft Warp Weft

Direction

Table 7.4 Comparison of stiffness parameters at two different conditions

2.56 2.62 2.31 2.67

4.14 3.20 3.62 3.04

3.22 3.08 2.34 2.53

3.09 2.65 3.20 2.89

Equation (7.4), Proportional bending length (cm)

15.73 16.90 15.27 19.36

98.10 45.20 83.42 49.48

23.17 20.32 10.16 12.87

9.46 5.96 9.83 7.24

Equation (7.6), effective Young’s modulus (×103 dynes cm−2)

Tactile sensations 125


126

Clothing biosensory engineering

where EK is the Young’s modulus of composite, a1111 is the fourth-order tensor, Ef is the Young’s modules of fiber, Em is the Young’s modulus of matrix and Vf is the fiber content. Voigt:

EV = Vy〈Ey〉 + (1 − Vy)Em

[7.8]

where V signifies Voigt model, Vy is the volume fraction of yarn, Ey is the Young’s modulus of yarn and 〈Ey〉 is the average value of Ey recalculated with respect to the yarn orientation toward the loading direction. Ruess:

SR = Vy〈Sy〉 = (1 − Vy)Sm

[7.9]

where SR is the compliance of the Reuss model, 〈Sy〉 is an orientation averaging over the yarn compliance and Sm is the compliance matrix. Inclusion:

E = Em + Vy 〈EyA〉 − Em〈A〉

[7.10]

where Ey is the elastic stiffness tensors of the reinforced yarn, Em is the elastic stiffness tensors of the reinforcement matrix, Vy is the volume fraction of the yarn phase and is related the matrix Vm by Vy + Vm = 1 and A is the strain concentration tensor. Weight average:

¯ = α(EV) + [(1 − α)(SR)−1] E

[7.11]

where α is the weight factor obtained from experimental measurements, EV is the average stress from Voigt model and SR is the isostress from Reuss model. Figure 7.3 compares the accuracy of the five models prediction against experimental results. By using a statistical t-test, the accuracies of the WAM and the Inclusion model were found to be similar at the 99% confidence intervals. The disadvantage of using the WAM to predict tensile stiffness is that at least one experiment is required in order to determine the weight factor, α. Therefore, the Inclusion model is preferred. Kalidindi and Abusafieh22 conducted an experimental and modeling study on three-dimensional braided graphite–epoxy composites and the relationship between the microstructural variable and the above composites, respectively. In the experimental study, the volume fractions and braid angles of the composite sample were within the range 0.2–0.45 and 0°–30°, respectively. In the modeling study, predictions obtained from isostrain, isostress and weighted average models were compared with experimental elastic moduli. Figures 7.4a and 7.4b compare different predicted results in longitudinal and transverse modulus, respectively. Figure 7.4a shows that little difference is found between isostrain and isostress predictions when the braid angle, β, is greater than 50°. Figure 7.4b shows that predictions generated from isostrain and isostress models are very similar in the transverse modulus.


Tactile sensations

127

120

100

Accuracy, %

80

60

40

20

0 Reuss

Voigt

Krenchel

Inclusion

WAM

Micromechanical model

7.3 Comparison of five micromechanical model accuracies.

7.5

Fabric softness

Fabric softness is one of the terms most often used by consumers to describe clothing comfort performance. Fabric softness has multiple meanings, which can be related to compression and/or to smoothness and flexibility of fabrics, depending on the fabrics being handled and their end-uses. Peirce48 considered softness as the opposite of stiffness measured by bending length. Later, Howorth18 took softness as the opposite of firmness or hardness measured by thickness tests. Elder et al.12 accepted the definition of softness as ‘ease of yielding to pressure’ and conducted subjective finger-pressure assessments of fabric softness using the magnitude estimation method. Meanwhile, objective measurements of compression were carried out to objectively measure fabric softness using an InstronTM Tensile Tester fitted with a compression load cell. The authors found that the relationship between the subjective assessment of fabric softness and objective measurement of compression followed Stevens’ law of logarithmic–linear relation. The perception of softness is highly correlated with fabric compression, which was defined as the decrease in intrinsic thickness with an appropriate increase in pressure. The intrinsic thickness is the thickness of the space occupied by a fabric sub-


128

Clothing biosensory engineering

A Vf = 0.5 120

α = 0.8

Longitudinal modulus (GPa)

Isostrain 100 Weighted average 80

60 Isostress

40

20 0 0

10

20

40

30

50

60

β

7.4a Comparison of isostrain, isostress and weighted average models predictions in longitudinal modulus.

B

Vf = 0.5 α = 0.8

Transverse modulus (GPa)

9.5

9.0

Isostrain

8.5

8.0

Weighted average

7.5

Isostress

7.0 6.5 0

10

20

40

30

50

60

β

7.4b Comparison of isostrain, isostress and weighted average models predictions in transverse modulus.


Tactile sensations

129

jected to barely perceptible pressure. Further, the subjective softness assessment correlated with fabric thickness for woven and non-woven fabrics, and correlated with fabric density and specific volume for woven fabrics but not for non-woven fabrics. In Kawabata’s hand evaluation system, fabric softness was not considered as one of the primary hand values. Corresponding to ‘NUMERI’, softness was defined as a mixed feeling coming from a combination of smooth, supple and soft feelings. The typical fabric for this definition is one woven from cashmere fibers. In the dimension of ‘FUKURAMI’, softness is related to the feeling from a combination of bulky, rich and well-formed impressions. A springy property in compression and thickness, together with a warm feeling, is also associated with softness. Corresponding to ‘SOFUTOSA’, softness is a feeling coming from higher ‘NUMERI’ and ‘FUKURAMI’ and weaker ‘KOSHI’ (stiffness). Bishop reviewed literature and summarized the physical properties associated with softness as bending, compression and tensile properties, shear stiffness and hysteresis, areal density and friction.3 FUKURAMI, which was interpreted as fullness and softness in English, is one of the primary hands defined by Kawabata and Niwa25. Hu et al.19 found that FUKURAMI is closely related to fabric thickness at low pressure (T0), coefficient of steel–fabric friction (MIU), geometric roughness (SMD) and energy in compressing fabric (WC). Li31 observed that subjective perception of garment softness during wear correlated with fabric compression properties (thickness at low and high pressures, resilience and energy of the compression–thickness curve), fabric tensile properties (the maximum elongation and linearity of the load– elongation curve), fiber diameter and breaking load. These reflect the three aspects of fabric softness identified by previous researchers: compression, flexibility and smoothness. In 2000, Peykamian and Rust49 used linear and non-linear models and yarn parameters such as CV%, hairiness and surface softness to classify the softness of knitted fabrics (T-shirts) for comparison with human subjective evaluations. All classification rates were verified with a leave-one-out crossvalidation technique. Comparison of actual and predicted data, which was generated by using the linear model, is shown in Fig. 7.5. The results showed 20% misclassification when using a linear model to sort samples into two classes (low and high). When sorting into three classes, the misclassification was 30%. When sorting T-shirt softness into three classes using a tree modeling technique, the surface response average (SRA) and maximum peakto-valley height (Ry), it is possible to match the human data at a 65% rate. When using surface response parameters and measured yarn properties to sort T-shirt softness into three classes, with tree modeling it is possible to classify 91% of the samples accurately based on the human data.


Clothing biosensory engineering

10 9 8 7 6 5

Actual softness

Actual softness

130

4 3 2 1 0 0

1

2

3

4

5

6

7

8

10 9 8 7 6 5 4 3 2 1 0

9 10

0

Predicted softness

1

2

3

4

5

6

7

8

9 10

Predicted softness

7.5 Actual vs predicted softness in 2- and 3-class categories linear model. Table 7.5 Mechanical properties for softness Kawabata

Cotton

Cotton/polyester

Properties

Symbol

kc

ks

kc

ks

Tensile energy Tensile resilience Tensile strain Slope measured between φ = 0.5° and 2.5° Hysteresis of Fs at φ = 0.5° degree Hysteresis of Fs at φ = 5° degree Bending hysteresis Compressive resilience Compressibility

WT RT EMT G

17.94 – 10.90 1.23

18.48 – 11.78 1.11

12.89 53.93 7.32 1.75

10.87 57.12 6.24 1.28

2HG

3.59

3.18

4.08

2.51

2HG5

6.08

5.50

7.28

4.83

2HB RC EMC

0.11 – –

0.10 – –

– 34.18 51.57

– 38.51 56.69

Kc = KES-FB values for the control fabric. Ks = KES-FB values for the fabric treated with the softener C1.

Chen et al. (2000)7 used the method of fuzzy comprehensive evaluation to solve the problem of grading fabric softness. Fabric mechanical properties such as fabric softness were measured with the KES-FB instruments. Summarized KES-FB results are shown in Table 7.5. This indicates that the pure cotton fabric was coarser, more viscoelastic and more wrinkly than the 50/50 cotton/polyester after washing. Cotton fabric was not retained by RT, RC and EMC; this may be due to the fact that the main effect of the softener was outweighed by the increased measurement deviation. On the other hand, cotton fabric was more sensitive to the softener in 2HB. Data was


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12 y = 0.8x + 1.2 r = 0.80

Kawabata grade

10 8 6 4 2 0 0

2

4

6

8

10

12

Fuzzy grade

7.6 Plot of Kawabata grade against fuzzy grade in cotton.

then used to develop a fuzzy model to grade fabric softness. To compare the Kawabata softness and fuzzy evaluation methods, subjective judgement on fabric softness was also included. Figure 7.6 shows that the correlation between Kawabata and fuzzy grade is strong for pure cotton fabric, which has a r-value of 0.8. However, it was weak for the 50/50 cotton/polyester fabric (r â&#x2030;&#x2C6; 0.24). The average correlation between subjective and fuzzy grade is approximately equal to 0.54. The above results suggest that the fuzzy model can be modified in order to improve grading precision, so that objective grading of fabric softness can best approach human subjective rankings.

7.6

Fabric smoothness, roughness and scratchiness

As a fabric is moving across the skin, displacement of skin is increased and the perception of fabric roughness or smoothness is evoked. The friction and mechanical interaction between fabric and skin during contact are the key factors determining the perception of roughness, smoothness and scratchiness. It has been identified that roughness and scratchiness are important tactile sensations determining the comfort performance of nextto-skin wear. The friction between skin and fabric is smaller with fabrics with a smooth surface than fabrics with rougher surfaces. Moisture at the skin surface can alter the intensity of perceived fabric roughness. As moisture content increases, the friction and displacement of skin increases, which triggers more touch receptors. Therefore, a fabric that is perceived to be comfortable at low-humidity conditions may be perceived to be uncomfortable at higher humidity or sweating conditions.


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Behmann2 reported a study on the perception of roughness and textile construction parameters. The roughness was defined as irregularities in the surface, which can be described geometrically by the size of the roughness elements, or mechanically by the friction coefficients. A roughness model was reproduced as shown in Fig. 7.7. A series of subjective perception trials were conducted using single nylon filament woven and knitted fabrics. The authors concluded that the perception of roughness was determined by the roughness spacing. Further experiments were carried out to study the influence of practical textile parameters on roughness perception by using woven and knitted fabrics made from nylon yarns of different diameters. As shown in Fig. 7.8, the roughness perception decreased logarithmically with yarn diameter and, at the same yarn diameter, the knitted fabric was perceived as ‘rougher’. Comparing the subjective sensory responses from wear trials with objective measured mechanical properties, Li31 found that perception of roughness correlated with fabric surface roughness (maximum force, mean surface roughness coefficient and deviation of surface roughness coefficient), compression properties (fabric thickness at high and low pressures and energy of the compression–thickness curve), fiber diameter and fiber tensile prop1 × 75 µmφ

1 × 150 µmφ

1 × 150 µmφ

1 × 150 µmφ

55 × 18.4 µmφ = 150 µmφ

300

7.7 Roughness model of fabrics.

Distance (µm)


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133

1 0.9

Subjective rating of roughness

0.8 0.7 0.6 0.5 0.4 0.3 0.2

Kulier R/L

0.1

Leinwand 1/1

0 0

50

100

150

200

250

300

350

400

450

500

550

600

Yarn diameter (µm)

7.8 Subjective perception of roughness as a function of the yarn diameter.

erties (breaking load and breaking elongation) and fabric tensile properties (maximum tensile elongation, elongation recovery load). Similarly, subjective perception of scratchiness is related to fabric tensile properties (maximum tensile elongation, energy of tensile load–elongation curve and slope of tensile load–elongation curve), fabric surface roughness (maximum roughness force, mean surface roughness coefficient and deviation of surface roughness coefficient), fabric compression properties (thickness at low and high pressure, linearity of the compression curve, energy of the compression–thickness curve and slope of the compression–thickness curve). Ito et al.21 studied the wear performance of girdles and identified pressure and hand (smoothness and softness) as the important comfort properties. Changes in pressure related to standing and moving during wear correlated with the biaxial extension and stress relaxation properties of the fabrics used to make the girdles. Summarizing the findings from literature, Bishop3 showed that fabric roughness (smoothness) is associated with a number of physical properties objectively measured, such as roughness, friction, prickle, shear and bending


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stiffness, thickness and areal density. In Kawabata’s KES system, fabric smoothness is an important primary hand, called ‘NUMERI’, which was defined as a mixed feeling coming from a combination of smooth, supple and soft sensations. The typical fabric was identified as a woven fabric made from cashmere. Hu et al.19 reported that fabric smoothness is related to fabric thickness at low pressure (T0), geometric roughness (SMD), bending rigidity (B), linearity of compression thickness curve (LC), energy in extending fabric to 5 N/cm (WT), fabric mass per unit area (W), hysteresis of bending moment (2HB) and energy in compression fabric under 5 kPa (WC). Ajayi1 reported a study of fabric smoothness using a friction measurement on a flat horizontal platform attached to an InstronTM load cell. He found that the number of peaks in the stick–slip traces per 5 cm sled traverse was linearly related to the courses/5 cm in knitted fabrics and threads/5 cm in woven fabrics. Ramgulam et al.50 compared the method of measuring fabric surface roughness using a laser sensor with conventional contact method (KES tester). Relatively good correlation between the two methods was obtained with r = 0.801. Wilson and Laing60 reported a study on the effect of wool fiber parameters on the tactile characteristics of woven fabrics. The fabrics were made from various wool fibers but constructed from standardized yarn structure, dye and finishing treatment. Twenty-four female and 20 male subjects were used. Significant differences were found in the rankings of roughness and prickliness among the fabrics. Fabrics with a fiber diameter of less than 23 µm and fiber bulk greater than 32 cm3/g were perceived smooth and less prickly. With a fiber diameter greater than 34 µm and fiber bulk less than 21 cm3/g, fabrics were perceived to be rougher and pricklier. Kang et al.23,24 used an objective evaluation method to characterize fabric surface waviness as a function of wrinkles and seam pucker. Two CCD cameras captured fabric surface images at different angles. A laser calibrated the cameras. Computer software based on fractal geometry used the stereovision data to generate a topographical representation of fabric smoothness. Results showed that the correlation from the double logarithm plot between the unit cell length and other occupied cells in a given space was 0.999, which indicates that fractal geometry evaluated the fabric surface with high accuracy. Repeated experiments showed that subjective AATCC wrinkle and seam pucker grades did not have regular intervals, as shown in Figs. 7.9a23 and 7.9b23. The data derived from the fractal analysis were quantitative and provided more reliable predictions. Mukhopadhyay et al.40 used different KESs to determine the effects of blend proportion on the comfort properties of polyester/viscose blend plain and twill suiting fabrics. The KES-F testing machine determined fabric lowstress mechanical properties, the KES-FS testing machine determined fabric


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135

A 2120

Fractal dimension

2100

2080

2060

2040 Z

2020 Y 2000

X 1

2

3

Cube 4

5

Subjective AATCC wrinkle grade

7.9a Fractal dimensions of fabric wrinkle specimens. B 2300

Fractal dimension

2250

2200

2150

2100 Z

2050 Y 2000

X 1

2

3

Cube 4

5

Subjective AATCC pucker grade

7.9b Fractal dimensions of seam pucker specimens.


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Clothing biosensory engineering

thermal insulation properties, and it also calculated fabric hand. Increasing polyester content increased fabric hand but decreased fabric smoothness, softness and fullness. Increasing polyester content decreased the total hand value of twill fabrics. Woven fabrics showed optimal total hand value with a 75/25 polyester/viscose blend. Increasing polyester content increased thermal insulation and water vapor resistance. In 2002, Okur46 examined the frictional resistance of 21 plain knit cotton fabrics with different yarn properties and fabric settings. Frictional measurements entailed pulling a fabric-covered sled over a rectangular sample of an identical fabric mounted under slight tension on a horizontal platform. Yarn type (combed or carded), yarn linear density, twist factor and fabric setting parameters affected fabric frictional properties. The frictional resistance of the fabrics knitted with carded yarns was higher than that of fabrics knitted with combed yarns. Protruding fibers on the fabric surface were the most important factor affecting fabric surface smoothness and frictional properties. In 2000, Sharma et al.52 compared the tensile, bending, shearing, compressional and surface properties of mulberry and tassar silk fabrics using Kawabata’s system. Fabric hand evaluation was also carried out. Summarized results are shown in Table 7.6. Mulberry silk fabrics exhibited superior shear stiffness, bending rigidity, geometrical roughness, draping behavior and hand but lower compressional resilience. Tassar silk fabrics exhibited higher mean deviation of coefficient of friction and geometrical roughness, but lower smoothness values. Fabric hand values of men’s summer shirting and ladies’ summer suit were also compared, as shown in Figs 7.10a and 7.10b, respectively. The pattern of individual samples in Fig. 7.10a is quite different and this suggests that each sample has different surface properties. It is clear that the stiffness of sample 3, men’s summer shirting, is significantly greater than other samples and the total hand value of sample 3 is also lower than others, but the differences are relatively lower than those for stiffness. On the other hand, the pattern of individual ladies’ summer suit samples, as shown in Figure 7.10b, is similar and this suggests that each sample has similar surface properties. In 2002, Xin et al.61 examined the resistance properties to abrasion in a laboratory on a Random Tumble Pilling Tester for 30 minutes. Nine judges evaluated the abraded fabrics subjectively according to texture isotropy, texture regularity, particle size, particle shape, surface roughness, evenness, texture contrast and density. A mathematical model of subjective evaluation determined that the most important property of polar fleece fabric evaluation was surface roughness. Objective fractal analysis could not evaluate the surface roughness of polar fleece fabrics because statistical features of the surfaces cannot be represented by a single value of fractal dimension. Researchers therefore applied an extended morphological


G 2HG 2HG5

LC WC RC T W

B 2HB

MIU MMD SMD

EM LT WT RT

Shear

Compressional

Bending

Surface

Tensile

0.53 0.81 0.11 90.04

0.12 0.02 6.12

0.15 0.06

0.95 0.01 95.62 0.22 4.38

0.36 0.38 1.34

1

Sample

0.43 0.83 0.09 89.79

0.13 0.03 3.96

0.08 0.03

0.76 0.01 93.52 0.19 3.24

3.57 8.88 10.15

2

0.47 0.72 0.08 85.22

0.11 0.02 5.86

0.40 0.37

0.84 0.01 85.90 0.26 6.21

8.85 23.44 23.55

3

1.11 0.75 0.21 72.74

0.14 0.01 3.25

0.02 0.02

1.05 0.01 72.26 0.17 4.43

0.43 0.58 1.93

4

0.71 0.89 0.16 72.27

0.14 0.01 3.21

0.02 0.02

0.81 0.01 59.70 0.18 4.18

0.37 0.55 1.44

5

0.94 0.87 0.19 72.44

0.15 0.01 5.86

0.03 0.02

0.74 0.01 84.94 0.25 5.51

0.32 0.43 0.93

6

0.70 0.81 0.14 80.42

0.13 0.02 4.71

0.12 0.09

0.86 0.01 81.99 0.21 4.66

2.32 5.71 6.56

Mean

0.276 0.066 0.054 8.860

0.015 0.007 1.386

0.145 0.142

0.120 0.002 13.669 0.038 1.049

3.447 9.313 9.037

s.d.

Shear

: G = shear stiffness (gf cm/deg), 2HG = hysteresis of shear force at 0.5° shear angle (gf/cm), 2HG5 = hysteresis of shear force at 5° shear angle (gf/cm). Compressional: LC = linearity of compression, WC = compressional energy (gf cm/cm3), RC = compressional resilience (%), T = fabric thickness (mm), W = fabric weight (mg/cm2). Bending : B = bending rigidity (gfcm2/cm), 2HB = hysteresis of bending moment (gf/cm). Surface : MIU = coefficient of friction, MMD = mean deviation of MIU, SMD = geometrical roughness (µm). Tensile : EM = strain at 500 gf/cm of tensile load, LT = linearity of load/extension curve, WT = tensile energy (gf cm/cm2), RT = tensile resilience (%).

Symbol

Properties

Table 7.6 Kawabata test result of six fabric samples


138

Clothing biosensory engineering Koshi (stiffness) 15

A

12 9 6

Total hand value

Shari (crispness) 3 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5

0

Sample 6 Hari (anti-drape stiffness)

Fukurami (fullness and softness)

7.10a Fabric hand value for menâ&#x20AC;&#x2122;s summer shirting. Koshi (stiffness) 12

B

9 6 3 Total hand value

0

Number (smoothness) Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6

Fukurami (fullness and softness)

7.10b Fabric hand value for ladiesâ&#x20AC;&#x2122; summer suit.

fractal method to analyze fleece fabric appearance. A fractal vector derived from different scales described surface roughness. The objective and subjective data correlated well. Brucherseifer and Scheibner6 developed a test method for fabric and seam hand evaluation based on surface frictional data and compared the method with data from sensory tests. Researchers tested woven polyester


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139

fabrics with seams comprising a 0.08 mm diameter polyester monofilament sewing thread and a 14 Ă&#x2014; 2 tex polyester ply sewing thread. The test used a weighting of 0.05 to evaluate fabric surface roughness determined by the hand test. The visual impression relative to fabric roughness and flexural strength received a weighting of 0.25. The frictional force data and coefficients of friction derived from those data correlated with the results of the sensory hand tests. MacIntyre et al.34 used the Kawabata surface tester to evaluate the surface properties of 18 knit fabrics used in manufactured pressure apparel for medical purposes. Twelve, three, two and one of the fabrics were warp knit Powernet, warp knit Sleeknit and weft knit 1 Ă&#x2014; 1 rib, plain single weft knitted fabrics, respectively. Figure 7.11a compares the coefficient of friction of these types of fabric for both face and reverse surface. Powernet has a relatively lower coefficient of friction value than the other two types of fabric on both surfaces, face and reverse. This suggests that Powernet fabric has the least friction compared with other fabrics on both measured surfaces. Figure 7.11b shows that Powernet and weft-knitted fabrics have similar mean deviation of friction values. Figure 7.11c compares the surface roughness of these fabrics. Results show that face surface is rougher than reverse surface for these three types of fabric. Sleeknit fabric has the lowest surface roughness value on both surfaces. Based on these results, it can be concluded that the reverse surface properties of warp-knitted fabric make it more tolerable next to the skin than the face surface.

A 0.45 0.4

Coefficient of friction

0.35 0.3 0.25 0.2 0.15 Powernet

0.1

Sleeknit 0.05

Weft knit

0 Face

Reverse Surface

7.11a Coefficient of friction.


140

Clothing biosensory engineering

B 0.07

Mean deviation of friction

0.06 0.05 0.04 0.03 0.02

Powernet Sleeknit

0.01

Weft knit

0 Reverse

Face Surface

7.11b Mean deviation of friction.

C

18 16

Surface roughness, Âľm

14 12 10 8 6 Powernet

4

Sleeknit 2

Weft knit

0 Face

Reverse Surface

7.11c Surface roughness.

In 1997, Xu62 investigated the surface roughness of a carpet, which is the degree of irregularity in the terrain of a carpet surface that reflects the effects on appearance of such mechanical wear factors as pilling, untwisting, tuft matting and crushing. The author explored the application of one image analysis technique, fractal dimension, to the quantification of carpet surface roughness. The application of an imaging system, comprising a CCD camera,


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141

a frame grabber and a computer with reticular cell counting algorithm, revealed that fractal dimensions of a carpet surface correspond to visual evaluations of surface wear. Changes in fractal dimensions consistently corresponded to changes in carpet appearance, indicating that fractal dimensions are an effective descriptor of carpet roughness. In 1990, Behmann2 stated that the subjective perception of the roughness of textiles was related to the textile construction parameters. A total of 14 men and 16 women were required to subjectively evaluate the single nylon filaments of 75 and 150 µm and a plain weave fabric. Experimental results showed that the perception of roughness was found to be correlated to the spacing of observed irregularities on the yarn or fabric surface. Yarns of various diameters were consequently evaluated to determine if roughness perceptions could be correlated with practical textile parameters. The tests determined that the thickness of woven fabrics and the diameter of yarns can be correlated to subjective roughness evaluations. In 1994, Sukigara and Ishibashi54 examined the relationship between machine tested physical characteristics and tactile evaluations of the comfort of lingerie by consumers using bipolar word pairs correlated with comfort and discomfort. A panel of consumers evaluated various lingerie fabrics using 15 pairs of adjectives: soft/hard, smooth/rough, cool/hot, light/heavy, fine/coarse, crisp/limp, clammy/absorbent, natural/synthetic, sheer/bulky, clingy/flowing, crushable/resilient, lacy/plain, drapable/rigid, scratchy/silky and stiff/soft. The physical characteristics of the lingerie fabrics were measured by the KES-F system. Results indicated that the frequencies of three bipolar word pairs – soft/hard, smooth/rough and drapable/rigid – were significantly correlated with KES-F measurements of heat, water and air transmission properties.

7.7

Garment fit and pressure comfort

Consumers have the inherent desire to dress comfortably with attractive garment appearance, which requires reducing the garment restraint imposed on the body and increasing the ability of the fabric to ‘give’. This means that a garment needs to be cut neatly in appearance and should be able to maintain a reserve of comfort for the wearer’s dynamic movements. Kirk and Ibrahim27 reported a study on the relationship between fabric extensibility and anthropometric requirements of garments. In analyzing the anthropometric kinematics, the authors identified that there are three essential components involved in meeting the skin strain requirements: garment fit, garment slip and fabric stretch. Garment fit provides the space allowance for skin strain, which is affected by the ratio of garment size to body size and the nature of garment design. Garment slip, which is determined mainly by the coefficient of friction between skin and fabric and


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between different layers of garments, is another mechanism to accommodate skin strain. Fabric stretch, as an important factor in pressure comfort, is dependent largely on fabric elastic characteristics and elastic recovery properties. Whether a garment slips or stretches is dependent on the balance of the tensile force in the fabric and the frictional forces between skin and fabric. If a fabric has a low resistance to stretch and high friction against the skin or other fabric, it tends to stretch rather than slip. The opposite occurs if the fabric has low friction and high tensile resistance. If a fabric has high friction and stretching resistance, high clothing pressure is likely to be exerted on the body, which would result in discomfort sensations. The authors identified the critical strain areas of the body as the knee, the seat, the back and the elbows. Maximum local skin strain was measured by drawing a series of lines on the skin at regular intervals and measuring changes in skin dimensions that took place with critical body movement. Table 7.727 summarizes the measurements of skin strain at the identified areas. The results showed that the skin had a high level of two-way stretch and the differences in the percentages of skin stretch were small between men and women. The authors further studied the relationship between actual fabric horizontal stretch in wear and available fabric stretch measured at Table 7.7 Skin strains at various critical body areas Body element

Body movement

Local skin strain (%) Horizontal

Vertical

Men

Women

Men

Women

21 29

19 28

41 49

43 52

Knee

Stand → Sit Stand → Deep bend

Elbow

Straight → Full bend

24

25

50

51

Seat

Stand → Sit: Overall (hip to hip) Local crotch Local buttocks Stand → Bend: Overall Local crotch Local buttocks

20 42 – 21 41 –

15 35 – 17 37 –

27 – 39 27 – 45

27 – 40 27 – 45

Local skin strain (%) Back

Straight → Forward raised arm Elbows on table Elbows bending Shoe trying

33 28 16 47

31 28 16 47


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143

the seat of various garments while the subjects were in a sitting position.27 The skin strain was considerably higher than the actual garment stretch, indicating that garment fit and garment slip played an important role in accommodating the skin strain. Meanwhile, there was a direct relationship between available fabric stretch and actual stretch. The higher the available stretch was, the higher the stretch in use. Also, the relationship between available and actual stretch varied with different types of garments, indicating the influence of the relative ratio between garment size and body size and also the effect of body contacts points. Kirk and Ibrahim also investigated the relationship between the pressure on the body and fabric stretch level. The pressure (P) was calculated according the following equation: P = TH/γH + TV/γV

[7.12]

where T is the tensile stress measure on the Instron at the same level of strain and γ is the radius of curvature of the relevant body parts. H and V indicate horizontal and vertical directions, respectively. Further, the authors studied consumer preference on stretch level in terms of comfort. It was found that higher stretch with lower power was always preferred, and that wearers’ preferences for stretch were in a range from 25–45%, depending on the end-use. Also, the direction of stretch relative to the body had a significant impact on comfort. The results of this comfort study are summarized in Table 7.8. Denton10 pointed out that there are four mechanical factors relating to comfort, which are weight, ease of movement, stretch and ventilation. The average weight of a man’s jacket, together with the contents of the pockets, was reported to be about 1.5 kg. This may contribute to discomfort perceptions as a relatively small area of the body is usually supporting them. The pressure generated by the weight on the skin may be above the comfort level. Ease of movement is largely dependent on garment design and the relative size between body and clothing. Loose fitting allows freedom of movement but may not be desirable in many situations. Tight fitting may be suitable for certain end-uses; however, it can exert pressure on localized areas of the body surface and cause discomfort. Ventilation normally occurs through openings at the legs, arms and neck, which is influenced by garment fit and styling. Stretch fabric, which can expand and contract without buckling or wrinkling, is another way to fit the body shape. Denton estimated the pressure discomfort threshold by stretching a band of elastic material around a part of the body, and making a judgement of the level of comfort. It was found that if a band was slightly uncomfortable initially, it became acceptable after a period, whereas when the band was very uncomfortable initially, it became intolerable as time passed. The pressure threshold of discomfort was found


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Table 7.8 Comfort preferences on fabric stretch End-use

Men’s suit jackets Men’s suit slacks Men’s slacks-casual Men’s shorts Men’s shirt-sleeves Men’s shirt-body Men’s undershorts Women’s slacks Women’s shorts Women’s tensioned slacks Skirts Dresses Slips

Direction of stretch H H, H H H, H H, H, H V H H H

V*

V* V* V*

Preferred at least 75% of time Stretch level (%)

Direction

30 30 30 30 25 25 25 35 35 45 25 30 30

H H H H V H H H H V H H H

H = horizontal and V = vertical * End used where direction of stretch was compared

to be around 70 g/cm2, which was close to the average capillary blood pressure of 80 g/cm2 near the skin surface. Growther17 studied the comfort and fit of 100% cotton-denim jeans. The author pointed out that classic jeans were characterized by body-hugging or tight fitting, which may result not only in sculptured form, but also in possible body malfunction in the long term. Other medical reports suggested that tight clothing could act as an effective tourniquet when the body assumed a sitting or crouching position, leading to thrombosis.9 This research suggested that the physical requirements of the wearers need to be satisfied through the design or development of apparel products. On the basis of a series of experiments, Growther concluded that the inherent properties of the fabric construction might be utilized to reduce skin strain and enhance body-contouring.17 This may be achieved by adjusting the angle of the backrise seam to one nearer to the true bias to meet skin-stretch demands in the local hip-to-hip, local-crotch and buttocks locations. Also, the front and back panels of the jeans can be pre-shaped to reduce restraint and provide local accommodation to body contours. Much research related to clothing pressure has been conducted in the 1990s. For instance, Mitsuno et al.39 employed a hydrostatic pressurebalanced method to quantitatively determine the comfort of waistbands. With a waistband reduction rate of 5%, the average pressure developed under natural respiratory conditions was 17.5 ± 2.1 mmHg. Under various body conditions (position, movement and respiration), the waistband pres-


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145

sure might become greater than 35 mmHg – a point at which brassières and girdles become uncomfortable. The coefficients among several parameters, such as the ratio of perfect fit length to body size length, the thickness of subcutaneous fat, the amount of subcutaneous fat, waistband pressure and the radius of curvature of five different regions on the body, were calculated at the waistline to evaluate the most favorable conditions for a perfectly fitted waistband. In 1992, Makabe et al.35 investigated the pressure exerted by blouses when the arm moves. The blouses tested differed in ease, the angle of the sleeve and the side length. The relationship between pressure and fit which optimized comfort when designing patterns was determined. Subjects experienced only slight discomfort when the pressure was between 0 and 20 mmHg. When the pressure exceeded 30 mmHg, the subjects experienced strong discomfort. Patterns that are designed for comfort have to provide for ease of arm movements. In 1993, Shimizu et al.53 conducted static and dynamic measurements of pressure on the body from a brassière. The static pressures on subjects in a standing position were highest at the shoulder, sides and back. The ratios of static pressures in poses other than standing were consistently larger at the shoulder and the back. For subjects in motion, dynamic analyses showed that pressure peaks centered on the shoulder and back, although varying pressures were obtained from different dynamic motions regardless of position. Pressures measured during dynamic motion were generally greater than those obtained from static positions. At the cup of the brassière, changes in pressure were small. It was concluded that, in order to evaluate comfort accurately, dynamic measurements of apparel pressure should be considered along with those of brassière pressure. Inamura et al.20 studied the relationship between the physical properties of fabrics used in the manufacture and comfort of girdles in 1995. The comfort test used the semantic differential method, which divided subjects into two classes based on their preferences in girdles (hard versus soft and a high level of pressure versus a low level of pressure). They found that comfort correlated with fabric’s tensile properties in the wale direction and with the fabric’s shear properties. The ability of the girdle to shape the body is a result of the shear properties in soft girdles and of the tensile properties in hard girdles. In 1998, Nishimatsu et al.45 studied comfort of men’s socks manufactured in Japan. In addition, they also used factor analysis to study six tactile adjectives (tight at the toe, tight at the heel, tight at the top, comfortable, cramped inside and good fit) that defined this property. The sock samples were made of different combinations of fabrics on the top and bottom area, including plain knit cotton and textured nylon, polyester/wool and nylon, wool and fully textured yarn and wool and textured nylon. Researchers used an


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Clothing biosensory engineering

elastic optical fiber to measure objective sock pressure and then examine the correlation between the subjective evaluation of comfort and the objective sock pressure measurements. Results showed that pressure at the top of the sock was an important indicator of comfort, with an increase in pressure resulting in a decrease in the perception of comfort. In 2001, Matsuoka et al.38 employed information theory technique and investigated the effects of shape on the wearing comfort of casual socks. Evaluation of the wearing comfort of socks with different top and leg lengths used such adjectives as easy to wear, good fit all over, good fit at ankle, good fit at calf, pressure all over, pressure at ankle, pressure at top, pressure at calf, stretch all over, stretch at ankle, stretch at calf, elastic all over, elastic at ankle, elastic at calf, easy to bend all over, easy to bend at ankle, slipping down from the top, and slipping down from the calf. Top lengths ranged from 2.2–15.9 cm and leg lengths ranged from 12.8–26.7 cm. You et al.63 investigated the relationships between subjective pressure sensation and objective pressure measurements, for knitted garments of different sizes and fabrics with different extensibilities. Eighteen female subjects aged between 18 and 25 participated in the garment pressure wear trial. The physical measurements of the subjects are shown in Table 7.9. Each subject was asked to wear a pair of tight-fitting shorts and perform two sets of postures, the stand and leg raise (the angle between thigh and shank was 90°), in a controlled room, with temperature and humidity of 20 °C and 60%, respectively. A basic description of selected tight-fitting shorts fabric is shown in Table 7.10. During the wear trial, subjects were required to evaluate pressure comfort at certain body locations on a scale of 1 to 10. At the same time, physical pressure measurements were also recorded by using pressure sensors. The relationship between subjective rating and objective measurement was investigated using Fechner’s logarithmic law. Results showed that fabric is not a main factor in explaining the relationship between subjective rating and objective measurement, although grouping the collected data by posture has a powerful predictive

Table 7.9 Summary of subjects’ physical constitution

Mean Standard deviation

Height (cm)

Anterior waist height (cm)

Knee height (cm)

Waist girth (cm)

Hip girth (cm)

Thigh base girth (cm)

Maximum shank girth (cm)

162.2 1.57

102.5 1.36

45.4 1.60

63.5 1.10

88.9 1.37

51.2 1.41

34.3 1.21


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Table 7.10 Description of fabric used in the experiment Fabric ID

Fabric weave

Fiber content (%)

a b c

Double jersey Double jersey Tricot

Dacron (100) Cotton (95), Spandex (5) Nylon (80), Spandex (20)

potential. Based on the r-value 0.92 for standing and 0.78 for raise-a-leg, the relationships found between the subjective rating and the objective measurement suggested that objective pressure measuring had high predictive power with regard to subjective pressure sensation only under these conditions. Wearing pressure comfort has a negative correlation with feelings of fetter, scratch, weight and pressure and a poor correlation with feelings of softness and smoothness.

7.8

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A174.

7.9

References

1. Ajayi, J.O., Fabric Smoothness Friction and Handle. Textile Research Journal, 1992. 62(1): p. 52–59. 2. Behmann, F.W., Tests on the Roughness of Textile Surfaces. Melliand Textilberichte, 1990. 71(6): p. 438–440. 3. Bishop, D.P., Fabrics: Sensory and Mechanical Properties. Textile Progress, 1996. 26(3): p. 1–63. 4. Bishop, D., E. Heine, and B. Hollfelder, Reducing Wool Prickle by Enzyme Processing, in The Fiber Society Joint International Conference University of Mulhouse. 1997. Mulhouse, France, p. 282–284. 5. Bornais, P., Analysis and Characteristics of Comfort in Clothing. Canadian Textile Journal, 1997. 114(4): p. 12–14. 6. Brucherseifer, S. and W. Scheibner, Assessing the Handle of Narrow Textiles. Melliand Textilberichte, 2002. 83(3): p. 163–165. 7. Chen, Y., B. Collier, P. Hu, and D. Quebedeaux, Objective Evaluation of Fabric Softness. Textile Research Journal, 2000. 70(5): p. 443–448. 8. Curteza, A., D. Farima, and I. Hritcu. A Subjective Evaluation Method for Tactile Sensations of Roughness and Prickliness. in Texsci2000. 2000. Liberec, Czech Republic. 9. Davidson, T.K., Pantie-girdle Syndrome. British Medical Journal, 1972. 2(5): p. 407. 10. Denton, M.J. Fit, Stretch and Comfort, in 3rd Shirley International Seminar: Textiles for Comfort. 1970. Manchester, England: The Cotton Silk and Manmade Fibres Research Association.


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11. Elder, H.M., Fabric Stiffness. Journal of the Textile Institute, 1984. 75(4): p. 307–311. 12. Elder, H.M., S. Fisher, K. Armstrong, and G. Hutchison, Fabric Softness, Handle, and Compression. Journal of the Textile Institute, 1984. 75(1): p. 37–46. 13. Elder, H.M., S. Fisher, K. Armstrong, and G. Hutchison, Fabric Stiffness, Handle and Flexion. Journal of the Textile Institute, 1984. 75: p. 99–106. 14. Elder, H.M., S. Fisher, G. Hutchison, and S. Beattie, A Psychological Scale for Fabric Stiffness. Journal of the Textile Institute, 1985. 76(6): p. 442–449. 15. Garnsworthy, R.K., R.L. Gully, P. Kenins, R.J. Mayfield, and R.A. Westerman, Identification of the Physical Stimulus and the Neural Basis of Fabric-evoked Prickle. Journal of Neurophysiology, 1988. 59(4): p. 1083–1097. 16. Garnsworthy, R.K., R.L. Gully, R.P. Kandiah, P. Kennis, R.J. Mayfield, and R.A. Westerman, Understanding the Cause of Prickle and Itch from the Skin Contact of Fabrics. Australian Textile, 1988. 8(4): p. 26–29. 17. Growther, E.M., Comfort and Fit in 100% Cotton-Denim Jeans. Journal of the Textile Institute, 1985. 76(5): p. 323–338. 18. Howorth, W.S., The Handle of Suiting, Lingerie and Dress Fabrics. Journal of the Textile Institute, 1964. 55: p. T251–260. 19. Hu, J., W. Chen, and A. Newton, A Psychophysical Model For Objective Fabric Hand Evaluation: An Application of Steven’s Law. Journal of the Textile Institute, 1993. 84(3): p. 354–363. 20. Inamura, A., M. Nakanishi, and M. Niwa, Relationship Between Wearing Comfort and Physical Properties of Girdles. Japan Research Association Textile End-uses, 1995. 36(1): p. 109. 21. Ito, N., M. Inoue, M. Nakanishi, and M. Niwa, The Relation among the Biaxial Extension Properties of Girdle. Sen-i Seihin Shohi Kagaku, 1995. 36(1): p. 102–109. 22. Kalidindi, S.R. and A. Abusafieh, Longitudinal and Transverse Moduli and Strengths of Low Angle 3D Braided Composites. Journal of Composite Materials, 1996. 30(8): p. 885–905. 23. Kang, T.J., S.H. Cho, and S.M. Kim, A New Method for the Objective Evaluation of Fabric Surface Waviness. AATCC Review, 2002. 2(2): p. 38–41. 24. Kang, T.J. and J.Y. Lee, New Method for the Objective Evaluation of the Surface Ruggedness of Fabric. AATCC Book of Papers, 1999: p. 349–357. 25. Kawabata, S. and M. Niwa, Fabric Performance in Clothing and Clothing Manufacture. Journal of the Textile Institute, 1989. 80(1): p. 19–50. 26. Kenins, P., The Cause of Prickle and the Effect of Some Fabric Construction Parameters on Prickle Sensations. Wool Technology and Sheep Breeding, 1992. 40(2): p. 19–24. 27. Kirk, W.J. and S.M. Ibrahim, Fundamental Relationship of Fabric Extensibility to Anthropometric Requirements and Garment Performance. Textile Research Journal, 1966. 57: p. 37–47. 28. Kregers, A.F. and G.A. Teters, Use of Averaging Methods to Determine the Viscoelastic Properties of Spatially Reinforced Composites. Mechanics of composite Materials, 1979. 4: p. 377–383. 29. Krenchel, H., Ed., Fibre Reinforcement. 1964: Copenhagen, Akademif Forlag. 30. Lau, L., J. Fan, T. Siu, and L.Y.C. Siu, Comfort Sensations of Polo Shirts With and Without Wrinkle-Free Treatment. Textile Research Journal, 2002. 72(11): p. 949–953.


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31. Li, Y., The Objective Assessment of Comfort of Knitted Sportswear in Relation to Psycho-physiological Sensory Studies, in Department of Textile Industries. 1988. The University of Leeds: Leeds. p. 213. 32. Li, Y., The Science of Clothing Comfort, J.M. Layton, Ed. (Textile Progress, 31 (1/2)) 2001, Manchester: Textile Institute International. 33. Li, Y. and J.H. Keighley. Relations Between Fibre, Yarn, Fabric Mechanical Properties and Subjective Sensory Responses in Wear Trials, in Proceedings of The 3rd International Conference on Ergonomics. 1988. Helsinki, Finland. 34. MacIntyre, L., M. Baird, and P. Weedall. Elastic Fabrics for Use in Pressure Garments – Comfort Properties, in Medical Textile 99. 1999. Bolton, UK. 35. Makabe, H., H. Momota, T. Mitsuno, and K. Ueda, Relation Between Changes in the Motion of the Upper Limb and Apparel Pattern. Part 3. Pressure in Blouses. Sen-i Seihin Shohi Kagaku, 1992. 33(12): p. 649–660. 36. Matsudaira, M., J.D. Watt, and G.A. Carnaby, Measurement of the Surface Prickle of Fabrics Part 2: Some Effects of Finishing on Fabric Prickle. Journal of the Textile Institute, 1990. 81(3): p. 300–309. 37. Matsudaira, M., J.D. Watt, and G.A. Carnaby, Measurement of the Surface Prickle of Fabrics Part 1: The Evaluation of Potential Objective Methods. Journal of the Textile Institute, 1990. 81(3): p. 288–299. 38. Matsuoka, T., N. Toyonori, H. Tomohiko, K. Masayoshi, T. Eiji, and K. Mikiya, Influence of Sock’s Shape for Weaving Comfort of Casual Socks. Sen-i Gakkaishi, 2001. 57(11): p. 334–337. 39. Mitsuno, T., H. Makabe, H. Momota, and K. Ueda, Waistband Pressure and its Sensory Evaluation. Sen-i Gakkaishi, 1991. 47(6): p. 282–290. 40. Mukhopadhyay, A., I.C. Sharma, and M. Sharma, Evaluation of Comfort Properties of Polyester–Viscose Suiting Fabrics. Indian Journal of Fibre and Textile Research, 2002. 27(1): p. 72–76. 41. Mura, T., Micromechanics of Defects in Solids. 1982: Den Haag, Martinus Nijhoff Publishers. 42. Naylor, G.R.S., The Role of Coarse Fibers in Fabric Prickle Using Blended Acrylic Fibers of Different Diameters. Wool Technology and Sheep Breed, 1992. 40(1): p. 14–18. 43. Naylor, G.R.S. and D.G. Phillips, Fabric-Evoked Prickle in Worsted Spun Single Jersey Fabrics. Part III: Wear Trial Studies of Absolute Fabric Acceptability. Textile Research Journal, 1997. 67(6): p. 413–416. 44. Naylor, G.R.S., D.G. Phillips, C.J. Veitch, M. Dolling, and D.J. Marland, FabricEvoked Prickle in Worsted Spun Single Jersey Fabrics. Part I. The Role of Fiber End Diameter Characteristics. Textile Research Journal, 1997. 67(4): p. 288– 295. 45. Nishimatsu, T., K. Ohmura, S. Sekiguchi, E. Toba, and K. Shoh, Comfort Pressure Evaluation of Men’s Socks Using an Elastic Optical Fiber. Textile Research Journal, 1998. 68(6): p. 435–440. 46. Okur, A., Frictional Properties of Plain-Knitted Cotton Fabrics. Textile Asia, 2002. 33(8): p. 32–34. 47. Pandita, S.D. and I. Verpoest, Prediction of Tensile Stiffness of Weft Knitted Fabric Composites Based on X-ray Tomography Images. Composites Science and Technology, 2002. 63(2): p. 311–325. 48. Peirce, F.T., The Handle of Cloth as a Measureable Quantity. Journal of the Textile Institute, 1930. 21: p. T377.


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49. Peykamian, S. and J.P. Rust, Fabric Softness Classification Using Linear and Nonlinear Models. Textile Research Journal, 2000. 70(3): p. 201–204. 50. Ramgulam, R.B., J. Amirbayat, and I. Porat, Measurement of Fabric Roughness by a Non-contact Method. Journal of the Textile Institute, 1993. 84(1): p. 99– 106. 51. Raychaudhuri, B.D.S. and S.P. Das, Physical Testing and Mathematical Modelling for Stiffness Characteristics of Fabrics Using Cotton–Polyester Blends. Man Made Textiles in India, 2000. 43(9): p. 427–430. 52. Sharma, I.C., A. Mukhopadhyay, P.K. Sinha, and R.K. Boruah, Comfort Properties of Mulberry and Tassar Silk Fabrics. Indian Journal of Fibre & Textile Research, 2000. 25(1): p. 52–58. 53. Shimizu, Y., K. Sasaki, K. Watanabe, A. Konda, Y. Kato, and H. Shimizu, Dynamic Measurement of Clothing Pressure on the Body in a Brassière. Sen-i Gakkaishi, 1993. 49(1): p. 57–62. 54. Sukigara, S. and T. Ishibashi, Analysis of Frictional Properties Related to Surface Roughness of Crepe Fabric. Sen-i Gakkaishi, 1994. 50(8): p. 349–356. 55. Veitch, C.J. and G.R.S. Naylor, The Mechanics of Fiber Buckling in Relation to Fabric-evoked Prickle. Wool Technology and Sheep Breed, 1992. 40(1): p. 31–34. 56. Wang, G., R. Postle, and W. Zhang, The Prickle of Worsted Woven Wool Fabrics. Textile Asia, 2003. 34(3): p. 25–28. 57. Wang, G., R. Postle, W. Zhang, and D. Phillips, Evaluation of Wool Shirt Comfort using Wear Trials and the Forearm Test, in Proceedings of the 10th International Wool Textile Research Conference. 2000. Aachen, Germany, p. 10. 58. Wang, G., W. Zhang, R. Postle, and D. Phillips, Evaluating Wool Shirt Comfort with Wear Trials and the Forearm Test. Textile Research Journal, 2003. 73(2): p. 113–119. 59. Willis, W.D., The Pain System: The Neural Basis of Nociceptive Transmission in the Mammalian Nervous System, in Pain and Headache, P.L. Gildenberg, Ed., 1985: Basel, Karger, p. 7–77. 60. Wilson, C.A. and R.M. Laing, The Effect of Wool Fiber Variables on Tactile Characteristics of Homogeneous Woven Fabrics. Clothing and Textiles Research Journal, 1995. 13(3): p. 208–212. 61. Xin, B., J. Hu, and H. Yan, Objective Evaluation of Polar Fleece Fabric Appearance. Textile Asia, 2002. 33(6): p. 36–40. 62. Xu, B., Quantifying Surface Roughness of Carpets by Fractal Dimension. Clothing and Textiles Research Journal, 1997. 15(3): p. 155–161. 63. You, F., J.M. Wang, X.N. Luo, Y. Li, and X. Zhang, Garment’s Pressure Sensation (2): the Psychophysical Mechanism for the Sensation. International Journal of Clothing Science and Technology, 2002. 14(5): p. 317–327.


8 Dimensions of sensory comfort perceptions 1

8.1

YI LI 1 AND ANTHONY S.W. WONG 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong

Individual sensations involved in the perception of sensory comfort

Sensations generated from clothing depend largely on the various combinations of human activities and environmental conditions experienced during day-to-day living. Researchers have identified many commonly recognized attributes of clothing related to comfort involving thermal, moisture, tactile, hand and aesthetic experiences. This type of identification has greater reliance on input from experts. However, it is important to know whether there are some commonly recognized clothing comfort attributes among ordinary consumers and, if so, what they are. This can be viewed from the perspective of how sensory descriptors were obtained. Kelly18 worked on personal construction theory and suggested that human participants have the ability to be specific and draw on an internal concept of a particular type of garment from memory, and then generate specific criteria to describe the garment. On the basis of this theory, Fritz9 argued that consumers have their own internal scales and concepts in evaluating fabric quality. Consumers themselves know best and they are capable of making objective quantitative and repeatable assessments of their sensations. Researchers should try to discover what consumers desire from the performance of products. Therefore, sensory descriptors should be derived from consumers instead of experts or researchers. Fritz reported the usage of a repertory semantic differential grid to define product attributes using descriptive adjectives by focus group study. For example, the polar pairs of descriptors for toweling fabrics include soft–harsh, smooth–rough, cool–hot, light–heavy, fine–coarse, crisp–limp, clammy–absorbent, natural–synthetic, sheer–bulky, clingy–flowing, crushable–resilient, lacy–plain, drapable–rigid, scratchy–silky and stiff–soft. In developing the methodology for evaluation of fabric handle, Kawabata and Niwa generated sensory descriptors by letting a panel of expert judges (the Hand Evaluation and Standardization Committee) select fabrics and 151


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asking them the reasons for their decisions. They identified terms KOSHI (stiffness), NUMERI (smoothness) and SHARI (crispness) as ‘primary hand’ expressions.17 In a study of fabric hand, Howorth and Oliver asked 25 participants to rank 27 fabrics and describe their reasons. Twenty-one descriptive terms and their frequency of use were obtained. Through factor analysis, they derived seven descriptors for fabric hand: smoothness, softness, coarseness, thickness, weight, warmth and stiffness.15,16 In the study of perception of different sensations during wear situations, Hollies10 found that strong sensations were experienced when mild or heavy sweating occurred, and during modest excursions of warming or chilling following the inception of sweating. By repeating experiments, Hollies et al. obtained a list of sensory descriptors that were generated by asking the participants to describe the sensations they experienced. The list of sensations included the following descriptors: snug, loose, heavy, lightweight, stiff, staticky, non-absorbent, cold, clammy, damp, clingy, picky, rough and scratchy.14 Each participant had the option to use any of the descriptors and could put in additional descriptors as experienced. These sensory descriptors were repeatedly produced by participants in wear trials conducted over many years.10–14 For evaluating men’s winter suiting fabrics, David et al.6 generated lists of ‘bipolar descriptors’ from discussions with each judge. The descriptors from all the judges were collated and listed and then associated with the ‘Standard Definitions of Terms Relating to Textiles’. However, each individual judge had the choice to use his/her own list of descriptors. For each judge, an individual list of 14 bipolar descriptors was produced. After eliminating the pairs of words that did not make a useful contribution to analyzing the data from subjective evaluation, seven pairs of descriptors were identified: coarse–fine, stiff–pliable, rough–smooth, harsh–soft, cool–warm, hard–soft and rustly–quiet. From the sensory descriptors obtained from various independent studies, there are commonly recognized attributes related to clothing comfort and languages to describe them. These sensations may be expressed in different languages and there are difficulties in interpreting the sensory descriptors from one language to another with exactly the same meaning. However, it is obvious that there are a number of dimensions in these sensory descriptors to describe sensory experiences that are related to thermal, mechanical and fabric surface stimuli, implying that the study of human comfort sensations has universal implications.

8.2

Identification of sensory factors

Data analysis is extremely important in comfort sensory study. Traditionally, it has been performed with univariate statistical tools such as analysis of


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variance and correlation analysis. Since the 1990s, multivariate techniques have become available, and these are more appropriate for increasing the understanding of complex data due to greatly increased computing capacities. Development in this area has been very fast. The common principle of the various techniques is to extract central or common information from large data volumes and to present it in understandable and simplified forms. In 1956, Miller claimed that humans could handle only 5–9 independent phenomena simultaneously in their consciousness.23 However, Martens pointed out that, on the analysis of sensory profiling data, most often 1–3 dimensions contain the essential information in the data.22 These contribute to an assumption that the complex human sensory perceptions are reduced to 5–9 independent dimensions in human consciousness. These are called latent variables or latent phenomena. In statistical terms, latent variables are the projections or linear combinations of several variables in the data. This implies that statistical analysis may reflect the process of human perception of a product. Each perceived dimension is a combination of contributions from various product attributes.25 Stein and Meredith argued from the view of physiology of perception that, although some modality-specific characteristics may be largely preserved as the brain sorts out the inputs from many cues, others are certainly altered. The brain does not perceive the world as a series of independent sensory experiences; rather there is an interweaving of different sensory impressions through which sensory components are subtly altered and integrated with one another. The product of these integrative processes is perception.26 In 1996, Risvik25 pointed out that human minds never perceive a product as a sum of its attributes. Human minds may focus on key attributes, aggregate attributes into concepts, perceive holistic forms or make up an iterative process with a mixture of the above for consultation when some decision is to be made. When a product is perceived, our brain may contain fewer attributes/concepts than a complete sensory profile, which implies that some form of aggregation of information is taking place in our information processing. The author further suggested that words for sensory profiling have a number of features. • Words describing product attributes have different levels of complexity that cannot be defined. • Complex words may be related to several aspects of product perceptions, which can be defined as a fuzzy latent structure, on the basis of the nature of the words and their utilization in the language. • In sensory profiling of a product, it is not unusual to have 15–20 words describing its attributes, most of which are interacting and overlapping.


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The majority of the words offer only slightly different perspectives on the understanding of the product. Therefore, the most important role of data analysis is to be able to handle these problems and to utilize the information for interpretation in the analysis. In studying fabric softness and stiffness, Elder et al.7,8 found that subjective perception of fabric attributes, such as stiffness and softness, may be derived from a combination of fabric physical properties rather from a single physical property. This combination may vary with fabric construction and end-uses. From these perspectives, multivariate statistical methods have been widely used to study the perception of products in the food industry and to describe consumers’ perceptions of brands in marketing research. These multivariate statistical analyses have a number of objectives: • • • •

to identify the number of dimensions that respondents use to distinguish different products; to reveal the nature or characteristics of these dimensions; to locate products on these dimensions as consumers perceive them; to determine the ideal or preferred location of a product on each of the dimensions.

There are number of statistical tools that can be used to achieve these objectives, including clustering analysis, principal component analysis (PCA), factor analysis, discriminate analysis and correspondence analysis. Bishop1 reviewed the entire philosophy of objective fabric measurement as a means of assessing the sensory and mechanical properties of fabric since its initial concept development in Japan. Bishop provided a comparison of objective fabric measurement and its relationship to other methods of evaluating similar fabric properties such as Weber–Fechner and Stevens’ Power Laws, linear and transformed linear regression models, rank correlation, multiple factor analysis, PCA, canonical correlation, weighted Euclidean distance and fuzzy comprehensive evaluation. The scope for the future application and development of objective fabric measurement includes garment appearance prediction, purchase control, process and quality control and compatibility of fabrics and interlinings assessment. In 1968, Yoshida32 conducted a series of experiments designed to discover the dimensions of tactual impressions by using stimulus samples differing in size, shape and texture. Through factor analysis, he found that 70% of the variance could be accounted for by three dimensions: (1) heaviness– coldness, (2) wetness–smoothness and (3) hardness. In 1998, Li20 reported an investigation on the psychological sensory responses to clothing of consumers living in different countries. A survey was conducted in three countries: Britain, China and the USA. Twenty-six sensory descriptors were selected: snug, loose, stiff, lightweight, staticky,


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non-absorbent, sticky, heavy, cold, damp, clammy, clingy, picky, rough, scratchy, cool, hot, soft, warm, wet, prickly, itchy, chilly, sultry, tickling and raggy. The sensory responses to these descriptors were analyzed by using oblique principal component cluster analysis. A typical result from the analysis is shown in Table 8.1, in which the variables in each cluster are listed. Two squared correlation coefficients are listed for each cluster. The column labeled ‘own cluster’ gives the squared correlation with its own cluster component; the larger the squared correlation, the closer the association. The column labeled ‘next closest’ contains the next highest squared correlation of the variable with a cluster component. This value is low if the clusters are well separated. The column headed ‘1 − R2 ratio’ gives the ratio of one minus the ‘own cluster’ r 2 to one minus the ‘next closest’ r 2. A small ‘1 − r 2 ratio’ indicates good clustering. By comparing these results from Table 8.1 with those for summer wear and sportswear, the general pattern of the cluster analysis showed that the 26 sensory descriptors could be classified into four clusters. The basic components of the four clusters are: Table 8.1 Cluster pattern Cluster

Variable

r 2 – own cluster

r 2 – next closest

1 − R 2 ratio

1

Prickly Ticking Rough Raggy Scratchy Itchy Picky Heavy

0.65 0.62 0.59 0.52 0.44 0.44 0.42 0.20

0.21 0.23 0.19 0.13 0.15 0.16 0.10 0.06

0.44 0.50 0.50 0.55 0.65 0.66 0.65 0.85

2

Clammy Sticky Sultry Non-absorbent Damp Clingy

0.53 0.52 0.42 0.40 0.39 0.36

0.17 0.12 0.10 0.06 0.08 0.12

0.56 0.54 0.64 0.64 0.66 0.73

3

Hot Soft Snug Warm Loose Lightweight

0.51 0.47 0.36 0.32 0.26 0.25

0.10 0.01 0.05 0.01 0.05 0.04

0.54 0.54 0.67 0.68 0.78 0.79

4

Cold Chill Wet Stiff Staticky Cool

0.54 0.53 0.36 0.30 0.29 0.27

0.16 0.18 0.25 0.13 0.12 0.06

0.54 0.57 0.85 0.80 0.81 0.78


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cluster 1 – tactile sensations: prickly, tickling, rough, raggy, scratchy, itchy, picky, staticky; • cluster 2 – moisture sensations: clammy, damp, wet, sticky, sultry, nonabsorbent, clingy; • cluster 3 – pressure (body-fit) sensations: snug, loose, lightweight, heavy, soft, stiff; • cluster 4 – thermal sensations: cold, chilly, cool, warm and hot. Cluster 1 of tactile sensations was the most stable group, in which the components of the cluster were relatively well defined and did not change much with the types of clothing. However, some sensations from other clusters such as heavy, stiff, clingy and clammy joined in when they became more closely associated with this cluster in certain wear conditions. Cluster 2 of moisture sensations is also relatively stable. Its components were relatively well clustered and did not change much with the types of clothing. However, it showed interaction with thermal sensations hot and chilly in sportswear, and interacted with tactile sensations in summer wear. Cluster 3 of pressure sensations and Cluster 4 of thermal sensations were not stable. Their components were not clearly clustered and changed their membership frequently. The pressure sensations can be synthetic sensations, showing interaction with tactile and thermal sensations. The perception of thermal sensations is heavily dependent on wear situations and interacts strongly with moisture sensations. For instance, hot sensations are associated with the pressure cluster for winter wear but with the moisture cluster for summer wear and sportswear, which is a reasonable change of association. From our daily experience, a hot sensation is frequently perceived with snug and soft clothing in winter, but is perceived more frequently together with the moisture sensations damp, wet, sultry and sticky in summer and sportswear situations. By non-parametric clustering analysis of the data reported by Hollies et al. over years,10–14 the authors found that the sensory descriptors used by them can be grouped into two clusters: cluster 1 (scratchy, rough, prickly, stiff, heavy, lightweight, loose and snug) and cluster 2 (damp, clammy, sticky, clingy and non-absorbent). These two clusters mainly correspond to the tactile and moisture clusters. Li et al.20 also applied factor analysis and clustering analysis to the sensory data obtained from wear trials under controlled physical activities and environmental condition (temperature and relative humidity were 32 °C and 45%, respectively). Table 8.2 shows the results from factor analysis on the sensory data obtained in the hot environment. Three factors whose eigenroots (sums of the squares of the factor loading) exceeded 0.89 were extracted. Each factor is interpreted by studying the factor loading of each


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Table 8.2 Varimax rotated factor matrix Variable

Factor 1

Factor 2

Factor 3

Sultry Damp Sticky Hot Clingy Non-absorbent Clammy Prickly Itchy Scratchy Rough Lightweight Soft Loose Snug Staticky Cold Heavy Stiff

0.808 0.798 0.778 0.740 0.729 0.567 0.529 – – – – – – −0.224 – 0.282 – 0.261 –

– – – 0.206 – – – 0.872 0.868 0.841 0.816 – −0.215 – – 0.283 – – 0.260

– – – – – – – – −0.244 – – 0.749 0.693 0.517 0.479 – – −0.302 −0.293

column. The values of factor loading between 0.200 and −0.200 were regarded as insignificant. By comparing the results from both factor analysis and variable clustering analysis, it was found that the 19 sensory descriptors could be grouped into three factors: • • •

factor 1 – thermal and moisture sensations: sultry, damp, clammy, clingy, hot, cold and non-absorbent; factor 2 – tactile sensations: prickly, scratchy, rough, itchy and staticky; factor 3 – pressure sensations: snug, loose, heavy, lightweight, soft and stiff.

Similar patterns of relationships among the psychological responses to the 19 sensory descriptors were found in another set of sensory data obtained in a cold environment (temperature and relative humidity were 14 °C and 32%, respectively).19 These analyses illustrated a pattern of relationships among the sensory responses, which is somewhat vague but stable and consistent with different statistical methods, with different wear conditions (hot and cold) and with the psychological perceptual data collected from a large number of independent studies. From these results, we can conceptualize that the comfort of clothing has three latent independent


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sensory factors (dimensions): thermalâ&#x20AC;&#x201C;wet comfort, tactile comfort and pressure comfort. In 1993, Byrne et al. conducted a consumer perceptual study on fiber types and end-uses in Britain and Australia.2 The sensory descriptors were generated by consumers in the form of semantic differential grids. The fibers studied were silk, cotton, polyester and nylon. The end-uses were sport shirts and underslips. Principal component analysis was applied to the sensory data collected. They found that two factors accounted for 90% of the total variance in the data for sport shirts. The trials contributing to the two factors are shown in Table 8.3. They also applied principal analysis to the sensory data obtained for underslips. Three factors that accounted for 94% of the variance were found as shown in Table 8.4. Comparing these results with those reported by Li et al., component 1 appears to correspond to the pressure comfort factor, component 2 to the thermalâ&#x20AC;&#x201C;wet comfort factor and component 3 to the tactile comfort factor. This further confirms the observation that there are three major latent dimensions in clothing comfort sensory perceptions. From these findings, it seems reasonable to accept the assumption that the complex sensory human

Table 8.3 Distribution of sensations Factor 1

Factor 2

Synthetic/Natural Comfortable/Itchy Harsh/Soft Cool/Clammy Clammy/Fresh Dense/Loose Sweaty/Absorbent

Flimsy/Substantial Limp/Crisp Thin/Thick

Table 8.4 Distribution of sensations within 3 factors Factor 1

Factor 2

Factor 3

Soft/Harsh Smooth/Roughness Light/Heavy Fine/Coarse Soft/Stiff Sheer/Bulky Drapable/Rigid Clingy/Flowing

Clammy/Absorbent Cool/Hot Crisp/Limp Natural/Synthetic

Scratchy/Silky


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perceptions on clothing comfort can be reduced to around three independent dimensions or latent variables. Wong and Li29 conducted a study on the psychological requirement of professional cycling athletes in 1999. Twenty-two professional athletes were asked to evaluate the performance of four different garments by rating ten individual sensations (clammy, clingy, damp, sticky, heavy, prickly, scratchy, fit, breathability and thermal) in a cycling wear trial. In order to derive a general view of the ten sensations classification, cluster analysis was applied and five clusters formed. Furthermore, with the application of statistical factor analysis, ten sensations can be abstracted into five sensory factors namely, MOIS (clammy, clingy, damp, sticky and heavy), TACT (prickly, scratchy and heavy), FIT (fit), BREA (breathability) and THER (thermal) by using factor analysis with rotation. Later on, Wong et al.31 compared the influence of using three (moisture, tactile and thermal-fit) and five (moisture, tactile, breathable, thermal and fit) sensory factors to describe and predict clothing comfort. Results showed that clothing comfort was better described with three sensory factors than with five, as the three-factors model has a higher level of predictability than the five-factors model. Based on the studies mentioned above, it is reasonable to say that thermal–wet, tactile and pressure comfort are the three main dimensions in clothing comfort. The following sections describe the research that relates to each comfort factor in more detail.

8.3

Thermal–wet comfort

Thermal–wet comfort is mainly related to the sensations involving temperature and moisture, such as sultry, clingy, hot, damp, clammy, cold, nonabsorbent and sticky. This factor responds mainly with the thermal receptors in skin and relates to the transport properties of clothing such as heat transfer, moisture transfer and air permeability. In consumer surveys of sensory perceptions of apparel conducted in a number of countries, it was found that thermal–wet, tactile and body-fit criteria are most important. Furthermore, tactile comfort (prickliness, itchiness, roughness, smoothness and softness), thermal/wet comfort (coolness to the touch, perception of dampness, breathability, moisture buffering, and buffering against environmental changes), body-fit, hand and appearance affect consumer sensory perceptions of wool.21 Sukigara and Niwa28 subjectively and objectively evaluated the effect of moisture in polyester, silk and cotton fibers on the sensation of dampness in lingerie fabrics. When fabrics had a high water content, test subjects felt a strong sensation of stickiness. Measuring the friction between wet fabrics and artificial skin and the rate of moisture transmission provided objective parameters for evaluating the sensation of clamminess. Results indicated


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that three factors influenced subjective impressions of dampness and wetness: tactile sensation, thermal sensation and water vapor sensation. Wong et al.30 also applied factor analysis to the ratings of nine individual sensations (clammy, sticky, breathable, damp, heavy, prickly, scratchy, tight and cool) in their study of the thermal comfort perception. Results showed that the above sensations can be abstracted into three main sensory factors. Factor 1 consisted of six sensations (clammy, sticky, breathable, damp, heavy and cool), which corresponds to thermal–wet comfort factors. Factors 2 and 3 correspond to tactile (prickly, scratchy and heavy) and pressure (tight) comfort respectively. Heavy sensation appears in both factor 1 and 2, suggesting that both factors were influenced by fabric weight. The total percentage of variance explained by these three dimensions is 81.9%. Thermal–wet comfort explains 39.4% of the total variance of perception of clothing comfort, followed by tactile comfort and pressure comfort with 28.8 and 13.6%, respectively.

8.4

Tactile comfort

Tactile comfort is associated with the sensations involving direct skin–fabric mechanical interactions such as prickly, scratchy, itchy, rough and staticky. This factor responds largely with the pain receptors in the skin and relates mainly to the surface characteristics of the fabric, including the diameter of fiber ends and its density, and the smoothness of the fabric surface. Sukigara et al.27 used statistical analysis to determine the relationship between machine-tested physical characteristics and tactile evaluations of the comfort of lingerie by consumers using bipolar word pairs correlated with comfort and discomfort. A panel of consumers evaluated various lingerie fabrics using 15 pairs of adjectives: soft/hard, smooth/rough, cool/hot, light/ heavy, fine/coarse, crisp/limp, clammy/absorbent, natural/synthetic, sheer/ bulky, clingy/flowing, crushable/resilient, lacy/plain, drapable/rigid, scratchy/ silky and stiff/soft. The physical characteristics of the lingerie fabrics were measured by the KES-F system. Results indicated that the frequencies of three bipolar word pairs soft/hard, smooth/rough and drapable/rigid are significantly correlated with KES-F measurements of heat, water and air transmission properties. Curteza et al.5 stated that aspects of tactile comfort, like any other components of clothing comfort, can be analyzed and evaluated in two ways: subjective and objective. Subjective evaluation of tactile sensations is a traditional technique that highlights many general problems, such as the differences in psychosensory perception between people. This type of evaluation generally includes two kinds of estimations of the perceived sensations: through tactile sensation (handling) and during wear. With this in mind, a testing method can be suggested; a method by which the perceived sensations during skin–fabric contact can be evaluated in a


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more appropriate way. So, subjective evaluations implicate two classes of variables: textile materials or clothes, as stimuli with certain physical properties and experts or consumers as evaluators with certain features. The methods of analysis also imply many research strategies which can be summarized as: evaluators, different ways people feel (different perception), terms used to describe the sensations (descriptors) and evaluation scales. The objective is to find a simple and efficient method for subjective evaluation of tactile sensations (soft, smooth–rough), by solving the problems related to: the groups of people who make the evaluation – experts or consumers; the means of evaluation – description or numerical; the evaluation scale; the means of interpretation.

8.5

Clothing pressure comfort

Pressure comfort is more complex and involves a number of synthetic sensations such as snug, loose, heavy, lightweight, soft and stiff. This factor mainly corresponds to the pressure receptors in skin and may come from a combination of a number of simple sensory responses. Fabric bulk mechanical behavior and overall fitness of garment to the body may be responsible for this dimension of comfort. Fabric handle properties are also highly related to this factor. In the study of subjective clothing pressure comfort perception, Nishimatsu et al.24 used factor analysis to study six tactile adjectives (tight at the toe, tight at the heel, tight at the top, comfortable, cramped inside and good fit) that defined this property. A system measured sock pressure with an elastic optical fiber to examine the correlation between the subjective evaluation of comfort and the sock pressure measurements. In the tops and feet, the sock samples were plain knit cotton and textured nylon, polyester/wool and nylon, wool and fully textured yarn and wool and textured nylon. Pressure at the top of the sock was an important indicator of comfort, with an increase in pressure resulting in a decrease in comfort. Chen and Zhao4 pointed out that an accurate men’s suit comfort analysis with clothing pressure is required in order to pursue men’s suit wear comfort. However, it is difficult to find the ideal people as test subjects. To solve this problem, the authors used six different dummies, D1 to D6, designed for resuscitation practice in order to obtain the clothing pressure in both standing posture and movement patterns. The basic description of the dummies, including measured parameters and material used, is shown in Table 8.5. Three out of ten subjects, considered as ‘best’, were chosen to conduct this experiment. Grey incidence analysis theory was used to relate the clothing pressure measured from the subjects and dummies. The authors concluded that D1 and D6 were relatively suitable for measuring clothing pressure synthetically with the normal standing posture. D1 was also suitable for


cm cm cm cm cm cm cm cm cm cm ° cm cm cm cm

Abdominal circumference Anterior chest arc Armscye circumference Biacromial arc Bideltoid breadth Chest breadth Chest circumference Chest depth Interscye Neckbase circumference Shoulder slope (right) Upper arm circumference Waist back length Waist breadth Waist depth

84.0 37.0 43.0 44.0 15.0 32.2 93.5 21.7 40.0 40.0 21.0 28.2 38.5 28.3 21.5

84.0 40.0 43.0 44.5 15.0 34.0 99.0 23.5 41.0 42.0 19.0 30.5 40.0 28.5 22.0

84.0 40.0 43.0 44.5 15.0 34.0 99.0 23.3 41.0 42.0 19.0 30.5 40.0 28.5 22.0

80.0 38.5 43.0 45.0 15.0 34.5 92.0 22.3 41.0 41.0 22.0 28.2 41.5 27.0 20.4

D4

+ Wood, cloth and others. 夹 Thick sponge, polyurethane, styrene foam, wood, elasticity cloth, others. ⴱ Thick sponge, styrene foam, wood, elasticity cloth, others. ✹ Improvement by China marketing, thin sponge, styrene foam, wood, cloth, others. 䊏 Cotton, polyester, cloth, steel rod, others mobile. 䊐 Sponge, polyurethane, wood, cloth, others mobile. 䊐 Sponge, wood, cloth, others mobile.

Unit

Arm

Parameter

+

Body 䊐

D3

D2

Dummies D1

Materials used for

Table 8.5 Basic description including measured parameters

80.0 39.0 43.0 46.0 15.0 35.0 92.0 21.7 42.0 40.0 22.0 28.2 41.5 27.0 21.0

D5

80.0 39.0 43.0 46.0 15.0 35.0 92.0 21.7 42.0 40.0 22.0 25.2 41.5 27.0 21.0

D6

82.0 38.9 43.0 45.0 15.0 34.1 94.6 22.4 41.2 40.8 20.8 28.5 40.5 27.7 21.3

Mean

2.19 1.11 0.00 0.84 0.00 1.04 3.47 0.84 0.75 0.98 1.47 1.96 1.22 0.79 0.63

S.D.

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measuring clothing pressure synthetically with the posture of flexing the upper limbs at 90 °. Chan and Fan3 reported the relationship between the subjective tightness sensation and the clothing pressure of girdles. Nine girdle samples from three different brands were selected for this experiment. Six female subjects aged between 21 and 29 participated in the experiment. Subjects were required to evaluate the clothing pressure level on a scale from 1 (very tight) to 7 (very loose) at ten body locations, which are shown in Figure 8.1. The subjective tightness sensation is a measure of the effectiveness of girdles, since too loose means the girdle is not effective in shaping the body and too tight means it is not comfortable and may have detrimental physiological effects. Figure 8.2 shows the optimum pressure distribution of girdles. Left and right pelvis have relatively higher optimum pressure (â&#x2030;&#x2C6;12 mmHg) than the rest of the measured body locations (<9 mmHg). Based on this experimental investigation, the effect of clothing pressure on the tightness sensation is better understood, and the optimum pressure distribution of girdles, which is an important criterion for product development and evaluation of girdles, is proposed. You et al.33,34 applied statistical factor analysis with rotation to identify the interrelationship among seven independent sensations (pressure comfort, pressure sensation, fetter, heavy, scratchy, smoothness and softness). Results showed that these sensations can be abstracted in five factors. Factor 1 consisted of pressure comfort, pressure sensation and fetter with

1 6

2

7

3 4

8

5

9

10

1 Front waist 2 Right pelvis 3 Right front stomach 4 Center front stomach 5 Right front lower

6 Left pelvis 7 Left front stomach 8 Left front lower 9 Left hip

8.1 Ten measured body locations.

10 Right hip


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Body location

Front stomach Left front lower Right front lower Right front stomach Left front stomach Right side Left side 0

2

4

6

8

10

12

14

Optimum pressure (mmHg)

8.2 Optimum pressure (mmHg) at different body locations.

factor loadings of −0.842, 0.920 and 0.767, respectively. Factor 1 mainly reflects the fit of the garment. Sensations scratchy (0.872), heavy (0.898), soft (0.939) and smoothness (0.918) formed the remaining four factors. The cumulative percentage of variance explained by these factors was 93.1, in which 49.1 was contributed by factor 1. This chapter summarizes the interrelationship amongst a wide range of independent sensations. The main sensory dimensions, which consist of thermal–wet comfort, tactile comfort and clothing pressure comfort, were identified. Their percentages of variances, which can be used to describe the importance of these sensory dimensions, were also reported.

8.6

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A174.

8.7

References

1. Bishop, D.P., Fabrics: sensory and mechanical properties. Textile Progress, 1996. 26(3): p. 1–64. 2. Byrne, M.S., A.P.W. Gardner, and A.M. Fritz, Type and End-uses: a Perceptual Study. Journal of the Textile Institute, 1993. 84(2): p. 275–288.


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3. Chan, A.P. and J. Fan, Effect of Clothing Pressure on the Tightness Sensation of Girdles. International Journal of Clothing Science and Technology, 2002. 14(2): p. 100–110. 4. Chen, D. and Q. Zhao, A Study on Clothing Pressure for Men’s Suit Comfort Evaluation. International Journal of Clothing Science and Technology, 2003. 15(5): p. 320–334. 5. Curteza, A., D. Farima, and I. Hritcu. A Subjective Evaluation Method for Tactile Sensations of Roughness and Prickliness, in Texsci2000. 2000. Liberec, Czech Republic. 6. David, H.G., A.E. Stearn, and E.F. Denby. The Subjective Assessment of Handle, in The Third Japan–Australia Symposium on Objective Measurement:Applications to Product Design and Process Control. 1985. Kyoto, Japan: Textile Machinery Society of Japan. 7. Elder, H.M., Fabric Stiffness. Journal of the Textile Institute, 1984. 75(4): p. 307–311. 8. Elder, H.M., S. Fisher, K. Armstrong, and G. Hutchison, Fabric Softness, Handle, and Compression. Journal of the Textile Institute, 1984. 75(1): p. 37–46. 9. Fritz, A.M., Sensory Assessment Assessed. Textile Asia, 1990. 21(5): p. 144–147. 10. Hollies, N.R.S., Investigation of the Factors Influencing COMFORT in Cotton Apparel Fabrics. 1965: New Orleans, US Department of Agriculture. 11. Hollies, N.R.S., The Comfort Characteristics of Next-to-skin Garments, Including Shirts, in 3rd Shirley International Seminar. 1971. Manchester, UK. 12. Hollies, N.R.S., Psychological Scaling in Comfort Assessment, in Clothing Comfort, N.R.S. Hollies and R.F. Goldman, Eds, 1977; Ann Arbor, MI, Ann Arbor Science Publishers, Inc, p. 107–120. 13. Hollies, N.R.S., Improved Comfort Polyester, Part 4: Analysis of the Four Wear Trials. Textile Research Journal, 1984. 54: p. 544–548. 14. Hollies, N.R.S., A.G. Custer, C.J. Morin, and M.E. Howard, A Human Perception Analysis Approach to Clothing Comfort. Textile Research Journal, 1979. 49(10): p. 557–564. 15. Howorth, W.S., The Handle of Suiting, Lingerie and Dress Fabrics. Journal of the Textile Institute, 1964. 55: p. T251–260. 16. Howorth, W.S. and P.H. Oliver, The Application of Multiple Factor Analysis to the Assessment of Fabric Handle. Journal of the Textile Institute, 1958. 49: p. 540. 17. Kawabata, S. and M. Niwa, Fabric Performance in Clothing and Clothing Manufacture. Journal of the Textile Institute, 1989. 80(1): p. 19–50. 18. Kelly, G.A., Man’s Construction of His Alternatives, in Assessment of Human Motives, G. Lindzey, Ed., 1958: New York, Holt, Rinehart & Winston. 19. Li, Y., Dimensions of Sensory Perceptions in a Cold Condition. Journal of China Textile University, 1998. 15(3): p. 50–53. 20. Li, Y.A., Wool Sensory Properties and Product Development. Textile Asia, 1998. 29(5): p. 35–40. 21. Li, Y., The Science of Clothing Comfort, J.M. Layton, Ed., (Textile Progress, 31(1/2)) 2001, Manchester: Textile Institute International. 22. Martens, H., Determining Sensory Quality of Vegetables, A Multivariate Study, Dr. Agric Thesis. 1986: Agricultural University of Norway. 23. Miller, G.A., The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. The Psychological Review, 1956. 63(2): p. 81–97.


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24. Nishimatsu, T., K. Ohmura, S. Sekiguchi, and E. Toba, Comfort Pressure Evaluation of Men’s Socks Using an Elastic Optical Fiber. Textile Research Journal, 1998. 68(6): p. 435–440. 25. Risvik, E., Understanding Latent Phenomena, in Multivariate Analysis of Data in Sensory Science, T. Naes and E. Risvik, Eds, 1996: Amsterdam, Elsevier. 26. Stein, B.E. and M.A. Meredith, The Merging of the Senses. 1993: London, The MIT Press. 27. Sukigara, S., T. Fujimoto, and M. Niwa, Sensorial Comfort/Discomfort of Lingerie Based on Hand Assessment and Objective Evaluation. Sen-i Gakkaishi, 1993. 49(6): p. 294–305. 28. Sukigara, S. and M. Niwa, Analysis of ‘Wet’ Sensation for Lingerie Fabrics. International Journal of Clothing Science and Technology, 1997. 9(2–3): p. 214– 219. 29. Wong, A.S.W. and Y. Li. Psychological Requirement of Professional Athlete on Active Sportswear, in Proceedings of the 5th Asian Textile Conference. 1999. Kyoto, Japan. 30. Wong, A.S.W., Y. Li, and P.K.W. Yeung. The Influence of Thermal Comfort Perception on Consumer’s Preferences to Sportswear, in Proceedings of the 10th International Conference on Environmental Ergonomics. 2002. Fukuoka, Japan. 31. Wong, A.S.W., Y. Li, P.K.W. Yeung, and P.W.H. Lee, Statistical Simulation of Psychological Perception of Clothing Sensory Comfort. Journal of the Textile Institute, 2003. 93(1): p. 108–119. 32. Yoshida, M., The Dimensions of Tactual Impressions (1). Japanese Psychological Research, 1968. 10: p. 123–157. 33. You, F., J.M. Wang, X.N. Luo, Y. Li, and X. Zhang, Garment’s Pressure Sensation (1): Subjective Assessment and Predictability for the Sensation. International Journal of Clothing Science and Technology, 2002. 14(5): p. 307–316. 34. You, F., J.M. Wang, X.N. Luo, Y. Li, and X. Zhang, Garment’s Pressure Sensation (2): the Psychophysical Mechanism for the Sensation. International Journal of Clothing Science and Technology, 2002. 14(5): p. 317–327.


9 Overall comfort perception and preferences ANTHONY S.W. WO NG 1 AND YI LI 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 1

9.1

Introduction

Consumers’ perceptions of physical comfort, psychological comfort and appearance are important in today’s apparel industries. Connell et al.4 evaluated subjective and objective instruments to identify and analyze the underlying preferences that contribute to good fit in the opinion of female consumers. Results showed that both body shape and fit preference had a significant relationship with body cathexis. Respondents, from four focus groups, with a fit preference for one garment tended to select the same fit for other garments. Stipe30 stated that, according to the ‘Consumer Color Preference Study’ conducted by Pantone Incorporated in 1995, although consumers said that color was important, comfort of fit and fabrication remained the most important characteristic of any apparel item purchased. Paek27 carried out a survey on how women perceive the comfort properties of skin contact wear fabrics (sleepwear, underwear and blouses) and the types of fibres and fabric structures (knitted or woven) they preferred. Fujiwara et al.11 conducted a study to determine the structure of perceived clothing quality from the consumer’s perspective. Eighty-eight evaluative attributes used to assess clothing quality, which could be used to measure the structure, were evaluated by 155 female college students to judge mutual similarities of meaning. Cluster analysis was performed on an 88 × 88 matrix based on these similarities and six basic clusters were formed: (1) workmanship in sewing, (2) physiological comfort, (3) usefulness, (4) physical and chemical properties, (5) suitability to individual preference and (6) fashionability or brand. The first four clusters formed a higher order cluster that expressed the intrinsic attributes of the apparel. The last two clusters expressed the extrinsic attributes of the apparel. Kosuge et al.16 evaluated the sociopsychological attitudes of female clerks, college students and their mothers in Tokyo and its environs on underwear and outerwear styles. The results of factor analysis demonstrated the criteria this group of people used to select underwear and outerwear. The selection 167


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of underwear depended on factors such as design, function, cost, convention, comfort and individual preference. Factors such as fashion, versatility, psychological comfort, self-expression, individual preference, cost and fabric type controlled the selection of outerwear. In underwear, mothers emphasized function, whereas college students and clerks emphasized cost. In outerwear, students and clerks emphasized fashion, whereas mothers emphasized function, cost and fabric type. The above studies indicate that clothing comfort and personal preference have a very significant impact. However, consumers’ preference on apparel is not based purely on comfort. In other words, more comfortable clothing does not necessarily mean that more people prefer to buy or wear it. This chapter focuses on factors that influence the perception of clothing, techniques for measuring consumers’ preference and the relationship between consumers’ overall comfort perception and their preference.

9.2

Influences of different factors toward overall comfort perception

As mentioned in earlier chapters, comfort perception is a holistic concept, which is a state of multiple interaction with the surrounding environment in physical, psychological and physiological factors.19 Denton6 stated that, after a period of wear, the length of which depends on the type of material used in making the garment, on the level of the body activity, and on environmental factors such as temperature and humidity, other comfort factors such as warmth, coolness and clamminess begin to play an important part in determining the overall comfort sensation. Li19,20 investigated clothing comfort during hot and cold environment. In order to investigate how individual sensations influence the perception of overall comfort in tightfit cycling wear, Wong and Li38 applied statistical factor analysis to identify the pattern or similarity amongst ten individual sensations (clammy, clingy, damp, sticky, heavy, prickly, scratchy, fit, breath and thermal). The results showed that these ten individual sensations can be abstracted into five sensory factors – MOIS, TACT, FIT, BREA and THER – which relate to moisture, tactile, fitness, breathability and thermal sensations, respectively. These factors contributed around 90% of the total percentage of variance, in which 37% was contributed by MOIS, followed by TACT with 22%. FIT, BREA and THER each contributed 10% of the total variance. This suggests that moisture-related sensations have the greatest influence toward overall comfort, followed by tactile-related sensations. Later on, Wong et al.39 abstracted these sensations into three sensory factors, thermal–wet comfort, tactile comfort and pressure comfort, which contribute 40, 29, and 14% of the total percentage of variance, respectively. They also found that demographical data such as the subject’s age had no significant (p > 0.05) influ-


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169

ence on perception of thermal–wet comfort. However, scores of thermal–wet comfort were significantly different (p < 0.05) between the two genders.

9.3

Calculation of subjective preference on clothing

In determining consumers’ preferences and their ability to discriminate among products, pairwise measurement methods (including paired comparison, double-paired comparison, consistent preference discrimination test, triangle discrimination and triangle preference tests and response latency) can be used. Many researchers used paired comparisons in studying the comfort attributes of textile products. Fuzek and Ammons12 applied the paired comparison technique to obtain subjective evaluation of comfort performance of T-shirts. Li et al.22,24 used the same technique together with non-comparative rating scales to obtain the overall preference of consumers towards T-shirts made from eight types of fibers through handling and wearing experience. The preference output was converted into interval scale and used to study its relationships with various sensations and physiological responses and fabric physical properties. Schneider et al.28,29 utilized the paired comparison method to study the coolness to the touch of hygroscopic fibers. The ranking scores obtained through paired comparisons are essentially ordinal data, which is effective in obtaining the preferences of consumers in comparing a series of products. However, they cannot provide the magnitude of the perceived differences between samples, nor their relative positions in a context of all possible relevant samples beyond a particular experiment. Rank order rating scale is another scale widely used to measure preference for comfort attributes, in which respondents are required to rank a set of objects according to some criterion. The advantage of this technique is that it is less time-consuming than paired comparisons and easily understood by most individuals. However, the major disadvantage of this technique is that it produces only ordinal data, with which the number of statistical analyses permissible is limited. In Hollies’13 wear trials, the Wilcoxon Sign-Rank Test was used to detect significant differences in ranking. On the basis of rating scales, more complex attitude scales can be constructed to measure more aspects of an individual’s attitude toward some objects. Hollies et al.13 developed a comprehensive attitude scale14 to obtain various sensory responses from respondents in wear trials. Normally, the sensory responses of subjects were analyzed individually. Li et al.23 applied similar multi-attitude scales to study the comfort performance of sportswear made from different fibers. The responses to various sensory items were first analyzed individually, then the relationships among the sensory responses were investigated by factor analysis and clustering analysis. In studying fabric hand properties, both Elder et al.7 and Mackay25


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applied a magnitude-estimation technique. Sweeney et al.31,32 also used the magnitude-estimation technique to study the psychophysical mechanism of dampness perception. Another frequently used attitude scale in sensory research is the semantic differential scale which was developed by Osgood et al.26 in studying the meaning of language. Kelly15 developed a similar technique called a repertory grid. Semantic differential rating scales can have any number of scale points, with six to seven being most common. Friedman et al.8 recommended that the more favorable adjective or phrase be randomly assigned to the left and right side of the scale. A number of researchers used semantic differential scales in clothing comfort study. Winakor et al.36 and Chen et al.3 used 99-point scales with bipolar pairs to study fabric sensory properties. The scores were transformed in considering the non-uniform distribution of sensitivity along the scale. Fritz9,10 applied semantic differential scales to study the handle of fabrics. Byrne et al.2 applied the same method to investigate the sensory perceptions of consumers on fiber types for different end-uses. Bishop1 pointed out that, in the context of fabric objective measurement, bipolar descriptors do not have any added value over single descriptors, but have a number of disadvantages. Bipolar descriptors may complicate the process of descriptor generation, impose unnecessary correlation between descriptors, represent positive and negative fabric attributes in the minds of respondents and introduce an element of liking/ disliking into an assessment. In the study of subjective preference on tightfit sportswear conducted by Wong and Li,37 two types of sportswear were given to each subject to evaluate by hand and wear trial. At the end of each evaluation, the subjects were required to select one of the two items as their preferred sportswear, which would have a score of 1. The remaining sportswear was given a score of 0. The preference score was calculated on the basis of the number count on subject preferences. Therefore, the sportswear with the highest preference score is the most preferred sportswear amongst the subjects. Each subjectâ&#x20AC;&#x2122;s preference score was calculated and used as an indication to describe subject preferences on sportswear. In the hand preference study, fabric N88P had the highest preference scores of 15, which covered approximately 71% of the subjects. It was followed by E95C and R95C with the preference scores of 14 (67%) and 13 (62%), respectively. P98L2 and N95C were the least preferred garments in the hand evaluation with preference scores of 5 (24%) and 6 (29%), respectively. This may be explained by the fabric structure as both of the garments were made of rib knitted fabrics. By comparing the preference scores, which were obtained from wear trials of the different garments, N88P had the highest preference score of 14, followed by N85L15 and C98L2 with preference scores of 13 and 12, respectively. However, P98L2 was the least popular garment with a preference score of 7.


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Selection of a scale technique depends upon a number of issues: the required information, the characteristics of the respondents, means of administration and the cost involved. Generally speaking, multiple measures are more effective than any single technique. The sum of several items gives a more accurate measurement than a single measurement.34

9.4

Relationship between overall comfort perception and preference

Understanding how consumers perceive clothing and formulate their preferences is of extremely compelling interest to both researchers and manufacturers. The overall sensory perception and preferences of a wearer with regard to the clothing he or she wears are the result of a complex combination of sensory factors, which come from the integration of inputs from various individual sensory modalities such as thermal, moisture, clothing pressure and pain sensations. The individual sensory modalities are related to different mechanicalâ&#x20AC;&#x201C;physical attributes of the garments. The sensory perceptions are also influenced by the psychological and physiological state of the individual wearers and the external environment. The process of integration is critical for developing an understanding of the psychological picture of clothing comfort. Subjective preference includes integrated sensory impressions based on the past experience of the wearer, together with psychological desires and physiological status, all combined to form a final assessment of clothing. The integrated sensory impressions are highly related to the sensory factors that are derived from the latent pattern in various sensations. The relative contributions of the sensory factors to subjective preference may be different under different wear situations, since the psychological and physiological requirements of a wearer to clothing are dependent on specific combinations of the physical activities of the individual and external environmental conditions. One of the common techniques used to analyze the relationships between two sets of variables is canonical correlation, where each set can contain several variables. A statistical program (CANCORR) from the SAS software package was used to perform the analysis. Given two sets of variables, CANCORR finds a linear combination from each set, called a canonical variable, such that the correlation between the two canonical variables is maximized. This correlation between the two canonical variables is the first canonical correlation. The coefficients of the linear combinations are canonical coefficients or canonical weights. CANCORR continues by finding a second set of canonical variables, unrelated to the first pair, which produces the second highest correlation coefficient. The process of constructing canonical variables continues until the number of pairs of canonical variables is equal to the number of variables in the smaller group. Each canonical variable is


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not correlated with all the other canonical variables of either set except for the one corresponding canonical variable in the opposite set.5,33,35 Canonical redundancy analysis5 is a technique to examine how well the original variables can be predicted from the canonical variables. Redundancy is the proportion of variance extracted by a canonical variable multiplied by the proportion of shared variance between the canonical variable and the corresponding canonical variable of the other set. The square of the canonical correlation coefficient is a measure of the overlap between the two canonical variables. The total redundancy of the canonical variables for the original variables is the sum of the redundancy of the individual variable and is called â&#x20AC;&#x2DC;cumulative proportionâ&#x20AC;&#x2122;. The squared multiple correlation is the sum of squared correlation coefficients of each factor with the first m canonical variables of the opposite set, where m varies from 1 to the number of canonical correlation coefficients. These squared multiple correlation coefficients indicate the predictive power of the canonical variables for each of the original variables of the other set. Li18 investigated the inter-relationships and predictability between the sensory factors and the subjective preference votes by canonical correlation analysis. The study is based on measurements of sensory responses and subjective preference votes on eight kinds of knitted T-shirts in a psychophysiological wearer trial, and three sensory factors were derived. Two significant canonical correlation coefficients were found, suggesting that psychological sensations comprise two independent sensory factors that are significantly related to the subjective preference votes. The first canonical correlation (0.939) indicates that the overall preference votes after wearing are very closely related to tactile and pressure comfort factors. Spearman correlation analysis was applied to the psychological responses to the 19 sensory descriptors obtained from the wearer trials and the subjective preference votes. It was found that the overall preference votes after wearing were significantly correlated only with sensory descriptors prickly, rough, scratch, itchy, heavy, stiff and soft. The second canonical correlation (0.580) suggests that the preference votes by handling are mainly related to the pressure comfort factor. It was found that the preference votes by handling were significantly correlated with sensory descriptors heavy, stiff and lightweight in the Spearman correlation analysis. The canonical redundancy analysis showed that the canonical variables of sensory factors are reasonably good predictors of the subjective vote canonical variables, with a cumulative redundancy of 0.788. The canonical variables of the subjective preference votes, on the other hand, are not such good predictors for the sensory factor canonical variables, with a cumulative redundancy of 0.512. This suggests that sensory factors can predict subjective preference votes quite well, but not vice versa, indicating that consumers make their prefer-


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ence judgements largely on the basis of their sensory perceptions. This interpretation is confirmed by the squared multiple correlation coefficients, which indicate that the first two canonical variables for the sensory factors have very good predictive power for the overall preference votes after wearing, but only moderate power for the handling votes. However, the first two canonical variables for the subjective preference votes have good predictive power for the tactile comfort factor, fairly good predictive power for the pressure comfort factor, but are almost useless for the thermal–wet comfort factor. These results indicate that the preference judgement by handling is different from the preference judgment by wearing. During the handling process, only a part of the sensory features of clothing can be perceived through hands, which is mainly related to the pressure sensations. In an attempt to test the validity of these findings, the same approach was applied to the sensory data obtained from another series of wear trials, which were conducted under a cold environmental condition (14 °C and 32% RH), using similar garments.17 The results from canonical correlation analysis of the data from the cold environment agree well with those from the hot environment. Two canonical correlation coefficients were obtained: 0.973 and 0.561. The first indicated the close relationship between tactile comfort factor and preference vote by wearing, and the second between pressure comfort factor with preference vote by handling. Also, canonical redundancy analysis showed that the canonical variables had relatively good predictive power for the subjective preference votes. The reverse was not true. Again, the squared multiple correlation coefficients showed good relationships of the preference votes with the tactile and pressure comfort factors, but not with the thermal–wet comfort factor. This suggests that under both hot and cold environmental conditions, consumers made their preferences based on the same integrated sensory impressions (factors) for next-to-skin garments. Correlation between preference score and overall clothing comfort was studied by Wong and Li.37 Twenty-eight female subjects participated in this wear trial. The mean ± standard deviation of their age, height and weight were 24.6 ± 5.6 years, 165.2 ± 4.2 cm and 47.1 ± 4.1 kg, respectively. Each subject was required to evaluate six of the eight randomly selected garments at three wear trials, in which only two garments were evaluated at each trial. The evaluation included overall comfort sensation (before, during and after exercise) and personal preference toward the garments (at the end of each wear trial). Comfort score was recorded on a seven-point scale, whereas the preference score was calculated on the basis of the counts of preferred wear. Results showed that the correlation coefficient between preference score and overall clothing comfort rating was 0.68, suggesting that more comfortable clothing does not necessarily mean that more people chose it. Furthermore, 7% of the subjects changed their


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mind on garment preference after the brand was revealed. Figure 9.1 illustrates the preference score before and after the brand was revealed. The preference scores of two unbranded garments, N85L15 and P98L2, decreased after the brand name was revealed. However, the preference scores of branded garment, E95C, increased after the brand name was revealed. This indicates that brand is a factor that influences choice of garment. In these studies, the overall preferences were obtained using garments of the same style and color, as the investigation was focusing on the sensory perceptions. However, this is a limitation of the study. The components of aesthetic comfort, cost and store or wear atmosphere were absent when the respondents were making their judgements. Therefore, the outcomes of the study cannot be directly applied to the processes by which consumers make purchase decisions. Besides, the conclusions can only be applied to the test conditions or similar wear situations. For different wear conditions, the relative contributions of different sensory factors change.21 This chapter points out the influence of sensory factors, derived from various individual sensations, on overall comfort perception, on the basis of different studies. The importance of these sensory factors was also reviewed. Methodologies such as paired comparison and rank order rating scale and their use in calculating and deriving subjective preference were discussed in detail. The relationship between overall comfort and personal preference was also studied. Results indicate that personal preference on clothing is not judged purely on the level of comfort perception; other factors such as brand name should also be taken into consideration. 16 Before

14

After

Preference score

12 10 8 6 4 2 0 N88P

C98L2

N85L15

R95C

P98L2

E95C

A92Np

N95C

Garment

9.1 Preference score of garment before and after the brand being revealed.


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9.5

175

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A174.

9.6

References

1. Bishop, D.P., Fabrics: sensory and mechanical properties. Textile Progress, 1996. 26(3): p. 1–64. 2. Byrne, M.S., A.P.W. Gardner, and A.M. Fritz, Fibre Types and End-uses: a Perceptual Study. Journal of the Textile Institute, 1993. 84(2): p. 275–288. 3. Chen, P.L., R.L. Barker, G.W. Smith, and B. Scruggs, Handle of Weft Knit Fabrics. Textile Research Journal, 1992. 62(4): p. 200–211. 4. Connell, L.J., E.L. Brannon, P.V. Ulrich, A.B. Presley, M. Grasso, J.H. Early, and S. Gray, Understanding Fitting Preferences of Female Consumers: Development of an Expert System to Enhance Accurate Sizing Selection. 2001: National Textile Center, p. 73–74. 5. Cooley, W.W. and P.R. Lohnes, Multivariate Data Analysis. 1971: London, John Wiley & Sons Inc., p. 23–78. 6. Denton, M.J., Fit, Stretch, and Comfort, in Textiles. 1972. 1(1): p. 12–17. 7. Elder, H.M., S. Fisher, K. Armstong, and G. Hutchison, Fabric Softness, Handle, and Compression. Journal of the Textile Institute, 1984. 75(1): p. 37–46. 8. Friedman, H.H., L.L. Friedman, and B. Gluck, The Effects of Scale-Checking Style on Responses to a Semantic Differential Scale. Journal of the Marketing Research Society, 1988. 30: p. 477–481. 9. Fritz, A.M., Sensory Assessment Assessed. Textile Asia, 1990. 21(5): p. 144–147. 10. Fritz, A.M., New Way to Measure Fabric Handle. Textile Asia, 1992. 23(7): p. 69–72. 11. Fujiwara, Y., C. Park, and Y. Tokoro, Consumer Perceptions of Apparel Quality. Part 1: Structure of Apparel Quality Perceived by Female College Students. Journal of the Textile Machinery Society of Japan, 1994. 47(2): p. 46–51. 12. Fuzek, J.F. and R.L. Ammons, Techniques for the Subjective Assessment of Comfort in Fabrics and Garments, in Clothing Comfort, N.R.S. Hollies and R.F. Goldman, Eds, 1977: Ann Arbor, MI, Ann Arbor Science Publishers, Inc., p. 121–130. 13. Hollies, N.R.S., Psychological Scaling in Comfort Assessment, in Clothing Comfort, N.R.S. Hollies and R.F. Goldman, Eds, 1977; Ann Arbor, MI, Ann Arbor Science Publishers, Inc., p. 107–120. 14. Hollies, N.R.S., A.G. Custer, C.J. Morin, and M.E. Howard, A Human Perception Analysis Approach to Clothing Comfort. Textile Research Journal, 1979. 49(10): p. 557–564. 15. Kelly, G.A., The Psychology of Personal Constructs. 1955: New York, Norton. 16. Kosuge, K., K. Inoue, H. Tomita, M. Sugiyama, and S. Kobayashi, Sociopsychological Attitudes of College Students, Their Mothers, and Women Clerks in Tokyo and its Environs on Styles of Underwear and Outerwear. Sen-i Seihin Shohi Kagaku, 1993. 34(12): p. 640–651.


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17. Li, Y., Predictability Between Human Subjective Preference and Sensory Factors Towards Clothing During Exercise in a Hot Environment, in The 26th Textile Research Symposium. 1997. Mt Fuji, Japan. 18. Li, Y., Predictability Between Human Subjective Preferences and Sensory Factors Towards Clothing During Exercise in a Cold Environment. Journal of China Textile University, 1997. 14(3): p. 55–60. 19. Li, Y., Clothing Comfort and its Application. Textile Asia, 1998. 29(7): p. 29–33. 20. Li, Y., Dimensions of Sensory Perceptions in a Cold Condition. Journal of China Textile University, 1998. 15(3): p. 50–53. 21. Li, Y., Wool Sensory Properties and Product Development, in Proceedings of The 2nd China International Wool Conference. 1998. Xi’an, China. 22. Li, Y. and J.H. Keighley, Relations Between Fibre, Yarn, Fabric Mechanical Properties and Subjective Sensory Responses in Wear Trials, in Proceedings of The 3rd International Conference on Ergonomics. 1988. Helsinki, Finland. 23. Li, Y., J.H. Keighley, and I.F.G. Hampton, Physiological Responses and Psychological Sensations in Wearer Trials with Knitted Sportswear. Ergonomics, 1988. 31(11): p. 1709–1721. 24. Li, Y., J.H. Keighley, J.E. McIntyre, and I.F.G. Hampton, Predictability Between Objective Physical Factors of Fabrics and Subjective Preference Votes for Derived Garments. Journal of The Textile Institute, 1991. 82(3): p. 277–284. 25. Mackay, C., Effect of Laundering on the Sensory and Mechanical Properties of 1 × 1 Rib Knitwear Fabrics, 1992: Bolton, England, Bolton Institute of Higher Education. 26. Osgood, C.E., G.J. Suci, and P.H. Tannenbaum, The Measurement of Meaning. 1957: Urbana, IL, University of Illinois. 27. Paek, S.L., Consumer Preference for Skin Contact Wear Fabrics. Textile Research Journal, 1983. 53(4): p. 264–265. 28. Schneider, A.M. and B.V. Holcombe, Coolness of ‘Cool Wool’ Fabrics, in Proceedings of the 8th International Wool Textile Research Conference. 1990. Christchurch, New Zealand. 29. Schneider, A.M., B.V. Holcombe, and L.G. Stephens, Enhancement of Coolness to the Touch by Hygroscopic Fibers. Part 1: Subjective Trials. Textile Research Journal, 1996. 66(8): p. 515–520. 30. Stipe, M., Consumer Color Preferences Shifting, Pantone Survey Finds. Knitting Times, 1996. 65(9): p. 81–82. 31. Sweeney, M.M. and D.H. Branson, Sensorial Comfort. Part 1. A Psychophysical Method For Assessing Moisture Sensation in Clothing. Textile Research Journal, 1990. 60(7): p. 371–377. 32. Sweeney, M.M. and D.H. Branson, Sensorial Comfort. Part 2. A Magnitude Estimation Approach For Assessing Moisture Sensation. Textile Research Journal, 1990. 60(8): p. 447–452. 33. Tatsuaka, M.M., Multivariate Analysis: Techniques for Educational and Psychological Research. 1971: London, John Wiley & Sons Inc., p. 181–193. 34. Tull, D.S. and D.I. Hawkins, Marketing Research: Measurement and Method. 1993: New York, Macmillan Publishing Company. 35. Van der Burg, E. and G. Dijksterhuis, Generalised Canonical Analysis of Individual Sensory Profiles and Instrumental Data, in Multivariate Analysis of Data in Sensory Science, T. Naes and E. Risvik, Eds, 1996: New York, Elsevier, p. 221–258.


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36. Winakor, G., C.J. Kim, and L. Wolins, Fabric Hand: Tactile Sensory Assesment. Textile Research Journal, 1980. 50: p. 601â&#x20AC;&#x201C;610. 37. Wong, A.S.W. and Y. Li, Clothing Sensory Comfort and Brand Preference, in Proceedings of the IFFTI International Conference. 2002. Hong Kong. 38. Wong, A.S.W. and Y. Li, Psychological Requirement of Professional Athlete on Active Sportswear, in Proceedings of the 5th Asian Textile Conference. 1999. Kyoto, Japan. 39. Wong, A.S.W., Y. Li, and P.K.W. Yeung, The Influence of Thermal Comfort Perception on Consumerâ&#x20AC;&#x2122;s Preferences to Sportswear, in Proceedings of the 10th International Conference on Environmental Ergonomics. 2002. Fukuoka, Japan.


10 Prediction of clothing sensory comfort ANTHONY S.W. WO NG 1 AND YI LI 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 1

10.1

Introduction

Subjective perception of clothing comfort on the part of a wearer is determined by sophisticated psychological and physiological processes, which in turn are evoked by various physical stimuli. These physical stimuli are determined by a number of physical processes that are dependent on the relevant fiberâ&#x20AC;&#x201C;fabricâ&#x20AC;&#x201C;clothing physical properties and structural features. Therefore, it seems desirable and logical to develop methods to predict the comfort performance of clothing objectively. Considerable research work has been carried out to measure fabric properties and predict some aspects of clothing comfort performance using various approaches. This chapter mainly focuses on studying different kinds of prediction methods or models, which are used to predict or simulate how human perceptions relate to clothing, including fabric hand, sensory factors (dimensions) during wear, overall clothing comfort and subjective preference toward clothing. Predictions are derived on the basis of either fabric objective measurements or subjective ratings. Linear regression, neural networks and fuzzy logic are used.

10.2

Prediction of fabric hand

Fabric hand, which is an important characteristic to the textile industry, is influenced by different fiber and yarn parameters including flexural rigidity, friction, count, twist, CV%, hairiness, stiffness and softness.10 Over the years, much research has been carried out in order to predict fabric hand on the basis of fabric properties. A short literature review on the existing yarn parameters as well as fabric hand evaluation and prediction techniques have been conducted by Peykamian and Rust.10 Yan et al.18 predicted fabric hand from the mechanical properties of woven fabric. Hu et al.2 made a psychophysical explanation for the process of fabric hand evaluation, which 178


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is a basis of the selection of Stevensâ&#x20AC;&#x2122; law. Peykamian and Rust10 dealt primarily with predicting the softness of knitted T-shirts from yarn quality parameters. Using the surface profile, a novel yarn surface analysis parameter called the surface response average (SRA) was developed, along with a model for the fiber/stylus tip interaction. In their study, ten T-shirts produced from ten different yarn samples were ranked based on their softness by a panel of judges. Results show that there was no significant correlation between standard roughness parameters and fabric softness. The correlation between hand and SRA is about â&#x2C6;&#x2019;0.6, which suggests that a higher SRA corresponds to a softer fabric. Niwa et al.9 investigated an objective method of evaluating the tactile comfort of blankets by a method of connecting the mechanical parameters of blankets to subjective evaluation. A preliminary investigation of the two methods is as follows. 1. Transformation equations for the fabric hand of suiting, KN-101-W for primary hand values and KN-301-W for THV, are applied, with the mean and standard deviation applied to these equations replaced with new values for the blanket population. 2. A new prediction equation is constructed for deriving THV directly from the mechanical parameters and thermal properties. A comparison of the prediction accuracy is of the two methods shows that the new prediction equation is a little higher than the transformation equations. Kawabata and Niwa4 introduced a method for the objective measurement of fabric hand. However, the method may be applied to other materials that interact with the human senses. The mechanical parameters used in this analysis have been applied not only to the objective hand evaluation system but also to many other fields including tailoring process control and the prediction of comfort weaving properties based on these parameters. Wong et al.17 applied stepwise regression to a series of fabric mechanical properties, which were measured using the Kawabata Evaluation System, to predict the hand touch feeling of discomfort. Results showed that the hand touch feeling of discomfort was best related with fabric surface friction (MIU). The regression model is shown in equation 10.1: hand discomfort = 5.65 (MIU) + 34.51

[10.1]

The values found for both r2 and adjusted r2 were above 0.6, suggesting that the goodness-of-fit of the model is reasonable. The ANOVA table indicates that the regression model is significant at p < 0.05. The above fabric hand studies indicate that fabric hand can be predicted on the basis of fabric mechanical properties. However, the performance of these prediction models is not very good in terms of accuracy. Therefore, further studies are needed in order to get better results, for example, adding new elements into the model.


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10.3

Predictability of sensory comfort

Vollrath and Martin11 compared the subjective judgement of skin contact comfort in the back–shoulder–neck area with fabric properties such as weight, thickness, density, fiber fineness, friction coefficient and compressibility. No fundamental relationship was found between fabric weight, thickness or density and the subjective skin contact sensation. No correlation was observed between fiber fineness, friction coefficient or compressibility and comfort statements. Drape and bending rigidity were weakly correlated, but surface roughness values obtained by an electromechanical method were strongly correlated. The authors concluded that tests with reliable and sensitive test personnel could not be replaced by laboratoryapparatus tests. In an attempt to establish the relationship between subjective comfort perceptions and fabric physical properties, Li5 carried out a series of psychophysiological wear trials using T-shirts made from eight types of fibre. In the wear trials, subjective ratings on 19 sensory descriptors were recorded under two environmental conditions, from which three fundamental sensory factors were identified, namely thermal–wet, tactile and pressure comfort.6,7 By using canonical correlation analysis, Li et al.5 studied the connections between the ten physical factors and the three psychological sensory factors. It was found that the sensory factors were significantly related to the corresponding dimensions of physical properties of fabrics. The tactile comfort factor was mainly related to fabric roughness and fullness, fabric stiffness and wettability. The pressure comfort factor was closely correlated to fabric stiffness, fabric permeability and fiber tensile stiffness, while the thermal–wet comfort factor was related to fabric wettability, fabric roughness and fullness and fabric water evaporation propensity. The canonical redundancy analysis showed that the canonical variables of the physical properties of fabrics were reasonably good predictors for the psychological sensory factors with a cumulative redundancy of over 0.71. The sensory factors, however, were poor predictors of the physical properties of fabrics with a cumulative canonical redundancy of less than 0.376. Therefore, it appeared that objective measurements of a wide range of fabric physical properties could be used to predict the sensory comfort of clothing worn next-to-the-skin reasonably well. Wong et al.13 studied the predictability of clothing comfort on the basis of statistical simulation models, which consist of three and five sensory factors. Ten individual sensations (clammy, clingy, damp, sticky, heavy, prickly, scratchy, fit, breathable and thermal) were abstracted into three and five sensory factors using factor analysis. Results indicate that, in the five-factor model, sensations clammy, clingy, damp and sticky, formed the


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first factor, which related to moisture comfort and contributed around 41% of total variance of overall comfort. Prickly and scratchy formed the second factor, which related to tactile comfort and contributed about 20% of overall comfort variance. The remaining sensations fit, breathable and thermal were classified as three individual factors, each contributing around 10% of the total comfort variance. In the three-factors model, the breathable sensation was grouped into the moisture comfort, while sensations of thermal and fit form another factor, thermalâ&#x20AC;&#x201C;fit comfort. The three and five-factors models explained around 74 and 90% of the overall variance, respectively. A linear predictable model for overall comfort was developed using the three and five-factors models and the relative contributions of individual factors as weights. The predicted comfort score was compared to the actual comfort score rated by the subjects. The relationship between simulation result and actual comfort score have a good agreement, with r = 0.893 at p < 0.001. Wong et al.14 applied the feed-forward back-propagation network, which is one of the structures in the artificial neural network (ANN), to investigate the predictability of clothing sensory comfort from psychological sensory perceptions. A series of wear trials was conducted, in which ten sensory perceptions and overall clothing comfort were rated by 22 professional athletes in a controlled laboratory. They were asked to wear four different garments in each trial and rate the above sensation during a 90-minute exercising period. The scores were then inputted into five different feedforward back-propagation neural networks models, which consisted of six different numbers of hidden and output transfer neurones. Results showed that good correlation was found between predicted and actual comfort ratings with significance at p < 0.001 with all five models, indicating the overall comfort performance is predictable by using neural networks, particularly, using the models with log sigmoid hidden neurones and pure linear output neurones. Models with a single log sigmoid hidden layer with 15 neurones or three hidden layers, each with ten log sigmoid hidden neurones are able to produce better predictions than the other models for this particular data set in the study. Based on the findings from the above studies, it can be said that overall clothing comfort can be predicted using sensory factors, which are abstracted from individual sensations. These prediction models are able to generate reasonably good predicted results according to the correlation between predicted and actual comfort ratings with their significances.

10.4

Predictability of subjective preferences 8

Li et al. applied canonical correlation and redundancy analysis to investigate the predictability of subjective preferences using the objective


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physical factors of fabrics. Three significant canonical correlation coefficients were obtained, indicating that there were three dimensions of the objective physical factors that are related to the subjective preference votes. The first canonical correlation showed that the subjective wearing preference votes were closely associated with fabric roughness and fullness, fabric wettability and fabric perpendicular deformability. The second suggested that the handling preference votes were mainly related to fabric stiffness, fabric perpendicular deformability and yarn stiffness, as well as to fabric wettability. The third indicated that the wearing preference votes were also related to fabric wettability, fabric roughness and fullness and tensile stiffness of fibres. Using canonical redundancy analysis, it was found that the objective factors of fabrics had great predictive power for the subjective preference votes, with a cumulative proportion of variance 0.983. This was further confirmed by the squared multiple correlation, which showed that the first three canonical variables of the objective physical factors of fabrics had very good predictive power for all three subjective preference votes. The authors concluded that objective laboratory measurements of physical properties of fabrics showed good ability to predict the subjective preferences for clothing, provided that enough information about the physical behavior of the fabric is obtained. Ishii3 studied the technique of using a conjoint measurement model together with an image attribute and described its application to the prediction of consumer preference. To incorporate consumersâ&#x20AC;&#x2122; information into the prediction model as prior knowledge, the consumer attribute was calculated using multivariate analysis of variance. Constructive elements of handle and those combinations were used as the product attribute of the prediction model, and they were defined as image attributes. As the applications, a one-piece dress and necktie of monochrome polka dot handle were chosen. Finally, the selected conjoint measurement models were eight kinds of one-piece dress model and one kind of necktie model. The main results obtained are as follows. 1. By using multivariate analysis of variance, four effects: age, purchase area, sex and the interaction between purchase area and sex, were extracted. 2. An appropriate conjoint measurement model was composed of two kinds of image attribute and those four levels. 3. By examining the utility of the selected prediction models, the agreement rate of the one-piece dress was high. Therefore, the prediction of handle preference by the conjoint measurement model, which incorporates the prior knowledge of consumers, is useful for merchandising of apparel products.


Prediction of clothing sensory comfort

10.5

183

Prediction of sensory factors

Wong et al.16 applied the neural network to predict sensory factors from fabric physical factors, which were abstracted using factor analysis, as part of their hybrid model prediction process. This process is divided into two sections: training and testing. Neural network has the ability to learn the relationship between fabric physical factors and sensory comfort factors. Therefore, part of the collected data (scores of fabric physical factors and sensory factors) was used in the training process. When the training process was completed, the neural network began to predict a series of sensory factor scores on the basis of the remaining fabric physical factors’ scores. The relationship between predicted and experimental sensory thermal– wet comfort factor scores was investigated and a significant linear relationship was found between them with r = 0.89, p < 0.05. The same methodology is applied to derive predictive models for tactile comfort and pressure comfort. Significant relationships are observed between predicted and experimental tactile comfort and pressure comfort factor scores with rvalues of 0.79 (p < 0.05) and 0.77 (p < 0.05), respectively.

10.6

Prediction of clothing sensory comfort on the basis of subjective measurements

Young adults’ psychological perception of clothing comfort in tightfit sportswear has been investigated by Wong et al.13,14 through hand evaluation and a running trial. These investigations not only identify the element required in order to fulfil the requirement of psychological sensory perceptions and preferences towards tightfit sportswear; they also indicate the contribution of subjective sensory perceptions towards the determination of clothing comfort during wear, which is critical for the development of predictive models on clothing comfort performance. A model, as shown in equation (10.2), was constructed to simulate the overall clothing comfort on the basis of assuming a linear relationship between comfort rating and factor scores with percentages of variance as the weighting coefficients: SCF = −0.416FM − 0.207FT − 0.119FTF

[10.2]

where SCF is the simulated comfort factor score, FM is the factor score of moisture comfort, FT is the factor score of tactile comfort and, FTF is the factor score of thermal–fit comfort. In order to examine the validity of the model, equation (10.2) was applied to this completely new set of data. The predicted factor comfort scores were then plotted against actual comfort scores, giving a satisfactory result with r 2 = 0.64, significant at p = 0.05. This suggests that the model is essentially valid. In order to improve the accuracy


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of the prediction on the basis of the above assumption, the factor scores of thermal–wet comfort, tactile comfort and pressure comfort sensory factors and their contributions from this study were used to derive a second equation (10.3): SCF = −0.615FTW − 0.114FT − 0.089FP

[10.3]

The predicted comfort scores based on equation (10.3) were plotted and compared with those generated from equation (10.2). The result suggested that the prediction using equation (10.3) has a better linear relationship with actual comfort than that using equation (10.2). However, there was no significant difference between two sets of predictions at 0.05 levels. This confirms that the linear model is able to give a reliable prediction on overall comfort from sensory factors.

10.7

Prediction of clothing sensory comfort on the basis of fabric physical properties

In the prediction of clothing comfort using a stepwise regression method, the not so highly related fabric properties were excluded in the model development. Six regression models, each with a different number of fabric properties, were generated. However, only the one with meaningful model structure and high r 2 and adjusted r 2 values was selected, as shown in equation (10.4): Overall comfort = −6.44 (MIU) + 2.24 (WC) + 2.44 (MWRL) + 1.15 (SMD) + 55.35

[10.4]

where MIU is the frictional coefficient, WC is the compressional energy, MWRL is the maximum wetted radius (lower) and SMD is the geometrical roughness. Regression model (10.4) consists of frictional coefficient, compressional energy, maximum wetted radius at the lower fabric surface and geometrical roughness. These physical properties can be represented as the dimensions of moisture, tactile and pressure in relation to clothing comfort, which indicates the major dimensions in the perception of clothing comfort. From previous findings, moisture and thermal sensations can be abstracted into a single sensory factor. The limitation of the model is that air permeability, thermal conductivity and thermal insulation were the only three objective measurements related to breathable and thermal sensations. These two types of objective measurements may not be good enough to describe the human perception of thermal comfort. Furthermore, in the investigation of the interrelationship between nine individual sensations, it was found that breathable and cool (thermal) were abstracted into the thermal–wet comfort factor. This suggests that


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they were correlated. The r2 and adjusted r2 values were 0.974 and 0.940, respectively. Predictions generated from the model involving frictional coefficient, compressional energy, outer maximum wetted radius (lower) and geometrical roughness had a stronger linear relationship with the experimental overall clothing comfort score than other models’ predictions. The experimental garment overall comfort rating was highly correlated with the calculated garment overall comfort rating. A good linear relationship was found between the calculated and the experimental result, with r ≈ 0.987. Based on the above study, fabric physical properties can be used to predict subjective judgement of hand evaluation, garment pressure comfort, sensory factors and overall clothing comfort by using a statistical method. However, there are some weaknesses in this method. In the development of the regression model, a full set of 33 fabric physical properties’ measurements cannot be used, as it cannot cope with a large volume of information from subjective judgements on a same piece of garment. Therefore, the fabric physical properties (independent variables) are selected subjectively in order to reduce the number of independent variables. Furthermore, the mean values of fabric physical properties’ measurements and subjects’ ratings have to be used to match the two data sets in the model development process. The consequence of this is that a large volume of information from the subjective evaluation of individual subjects is lost. The size of the output of the data is also trimmed down from 168 to 8, as there are only eight garments in the trial, which limits the reliability of the models.

10.8

Application of hybrid models in the prediction of clothing sensory comfort

Doke and Shanmugam1 stated that artificial intelligence (AI) systems are one of the options available in the textile industry to integrate elements such as production, quality, cost, information, statistical process control, just-in-time manufacturing and computer integrated manufacturing. Expert systems and artificial neural networks (ANN) are introduced and future potential for the Indian textile industry assessed. The principles of AI are explained and it is shown that AI can be subdivided into several different disciplines, each unique but capable of being intermixed. They are: expert systems, neural networks, fuzzy logic, genetic algorithms and natural language. Expert systems are examined together with the two ways in which they can be built. They are widely used with varied applications noted. Artificial neural networks are defined and the biological model explored. Back-propagation neural networks are looked at more closely, with network architecture and its parameters described. ANN applications cover cotton


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grading, yarn CSP prediction, yarn grading, fabric colourfastness grading, fabric comfort and fabric inspection systems. Wong et al.12,15,16 integrated mathematics, statistics, neural network and fuzzy logic into different hybrid models to predict psychological sensory perception on the basis of fabric physical properties. The predictive hybrid models are defined by the number of stages required to predict psychological sensory perception from fabric physical properties. The four theoretical hybrid models consist of different combinations of linear modeling (LM), traditional statistics (TS), neural network (NN) and fuzzy logic (FL). Amongst these hybrid models, two four-stage models, TS-TS-NN-FL and TS-TS-NN-NN, are able to derive reasonable and reliable prediction of overall comfort ratings. The hybrid model with the application of fuzzy logic has better performance than the one with neural network. Model TS-TSNN-LM has the poorest performance in the prediction of overall comfort rating, suggesting that the linear model cannot handle the fuzziness of subjective judgement and dynamic learning process in the simulation process. In the proposed hybrid model prediction system, overall clothing comfort can be predicted from fabric physical properties by going through four different stages. The development and validation of the four-stage hybrid models are discussed and described in detail. Nevertheless, there are other alternative ways to predict overall clothing comfort from fabric physical properties. One way is to reduce the stage number in the proposed hybrid models. This can be done by predicting overall clothing comfort directly from fabric physical properties or predicting sensory factors from fabric physical properties instead of physical factors. As these two approaches consist of one and three stages in the prediction process, they will be called S1 and S3 hybrid models respectively. The same data set was applied to these models in order to test their performances. Comparing the performances of the eight hybrid models, which are developed on the basis of three different theoretical routes, showed that of TS-TS-NN-FL and TS-TS-NN-NN performed better than TS-NN-FL and TS-NN-NN, respectively. Both TS-TS-NN-LM and TS-NN-LM hybrid models have relatively poor performances. The result also indicates predicted overall comfort directly from fabric physical properties (S1 models) using neural network (NN) or traditional statistics (TS). However, this may not be a good idea, as the correlation between NN predicted and experimental comfort ratings is either poor or there are too few data points in the TS model. These results show that the TS-TS-NN-FL hybrid model has the best performance in the prediction of overall clothing comfort amongst the models. This may be explained by its prediction process, which is very similar to the human perception process, as they both consist of summation (fabric physical properties and sensory perceptions), selflearning technique and fuzzy reasoning.


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It can be concluded that it is possible to predict human perception of clothing comfort on the basis of fabric physical properties, which include transfer and mechanical properties. Perception of clothing comfort is a complex process involving a combination of different stages, and the requirements at each stage are different. Therefore, it is reasonable to use different methodologies at each stage in order to achieve good prediction. The advantage of using an artificial hybrid model is that it integrates various methods into a system that is able to handle linearity or non-linearity in the prediction process.

10.9

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A174.

10.10 References 1. Doke, S.S. and N. Shanmugam, Artificial Intelligence and its Applications in Textiles. Asian Textile Journal, 2002. 11(7): p. 49–54. 2. Hu, J., W. Chen, and A. Newton, A Psychophysical Model For Objective Fabric Hand Evaluation: An Application of Stevens’ Law. Journal of the Textile Institute, 1993. 84(3): p. 354–363. 3. Ishii, M., Prediction Models of Polka Dot’s Preference That Incorporate Consumer Attribute as a Prior Knowledge. Journal of the Japan Research Association for Textile End-Uses, 2000. 41(6): p. 28–36. 4. Kawabata, S. and M. Niwa, Objective Measurement of Fabric Hand. 1995: New York, Marcel-Dekker, p. 329–354. 5. Li, Y., The Objective Assessment of Comfort of Knitted Sportswear in Relation to Psycho-physiological Sensory Studies. 1988: Department of Textile Industries, The University of Leeds, Leeds. p. 213. 6. Li, Y., Dimension of Sensory Perceptions on Next-to-Skin Wear in a Cold Environment. Journal of China Textile University, 1998. 15(3): p. 50–53. 7. Li, Y., Clothing Comfort and Its Application. Textile Asia, 1998. 29(7): p. 29–35. 8. Li, Y., J.H. Keighley, J.E. McIntyre, and I.F.G. Hampton, Predictability Between Objective Physical Factors of Fabrics and Subjective Preference Votes for Derived Garments. Journal of the Textile Institute, 1991. 82(3): p. 277–284. 9. Niwa, M., M. Inoue, and S. Kawabata, The Objective Evaluation of Blanket Hand and Durability: A Preliminary Investigation. International Journal of Clothing Science and Technology, 1999. 11(2–3): p. 90–104. 10. Peykamian, S. and J.P. Rust, Yarn Quality Indexing Using a Mechanical Stylus. Textile Research Journal, 1999. 69(6): p. 394–400. 11. Vollrath, L. and H. Martin, Relationships Between the Sensory Judgement of the Skin Contact Behaviour of Fabrics and Laboratory Testing of Properties. Textiltechnik, 1983(4): p. 225–231. 12. Wong, A.S.W. and Y. Li. Hybrid Models to Predict Clothing Sensory Comfort From Fabric Physical Properties, in The Fiber Society 2003 Spring Symposium


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– Advanced Flexible Materials and Structures: Engineering with Fibers. 2003. Loughborough, UK: The Fiber Society, UK. Wong, A.S.W., Y. Li, and P.K.W. Yeung, Statistical Simulation of Psychological Perception of Clothing Sensory Comfort. Journal of the Textile Institute, 2002. 93(1): p. 108–119. Wong, A.S.W., Y. Li, and P.K.W. Yeung, Neural Network Predictions of Human Psychological Perceptions of Clothing Sensory Comfort. Textile Research Journal, 2003. 73(1): p. 31–37. Wong, A.S.W., Y. Li, and P.K.W. Yeung, Performances of Artificial Intelligence Hybrid Models in Prediction of Clothing Comfort From Fabric Physical Properties. Sen-i Gakkaishi, 2003. 59(11): p. 429–436. Wong, A.S.W., Y. Li, and P.K.W. Yeung, Predicting Clothing Sensory Comfort With Artificial Intelligence Hybrid Models. Textile Research Journal, 2004. 74(1): p. 13–19. Wong, A.S.W., Y. Li, and P.K.W. Yeung, Psychological Sensory Perceptions and Preferences of Young Adults Towards Tight-fit Sportswear. Submitted to The Journal of Textile Institute, 2004. Yan, H., S. Zhao, S. Yang, and N. Pan. A New Approach to the Prediction of Fabric Hand From the Mechanical Properties of Woven Fabrics, in The 3rd Japan/Australia Joint Symposium on Objective Measurement Applications to Product Design and Process Control. 1985. Osaka, Japan, Elsevier Science Ltd.


11 Thermal properties J.Y. HU, YI LI AND K.W. YEUNG Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

11.1

Introduction

Clothing is an integral part of human life and as such it has a number of functions: adornment, status, protection and modesty. In addition to representing the wearer’s social status, meeting the latest fashion and being aesthetically appealing, it also improves the wearer’s quality of life. Particularly with respect to thermal physiology and comfort, clothing functions to resist heat and moisture transfer between skin and the environment and maintain the human body’s thermal balance and thermal comfort in appropriate thermal environments. In this way, it can protect against extreme heat and cold, but at the same time it hampers the loss of superfluous heat during physical effort. Therefore, clothing is needed to protect the body against climatic influence and to assist its own thermal control functions under various combinations of environmental conditions and physical activities. In other words, an important task of clothing is to support the body’s thermoregulatory system to keep its temperature within a median range, even if the external environment and physical activities change in a relatively wide range.23

11.2

Heat production and heat loss

Mammals, including man, are homoeotherms. Their body temperatures are relatively constant even though environmental conditions vary. The body temperature is controlled by balancing heat production against heat loss. Most heat produced in the body is generated in the deep organs such as the liver, brain, heart and skeletal muscles in the form of one of three separate mechanisms: basal heat production, shivering and voluntary exercise.6 This heat is then transferred from the deep organs and tissues to the skin, where it is lost into the surroundings.11 The metabolism of food through the normal enzymatic and metabolic processes of 189


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life constantly produces heat. At the basal or resting metabolic rate, heat is produced at about 50 kcal/m2 of body surface area per hour or about 70–100 kcal/hr for the average adult. Shivering is the body’s involuntary defense against the cold when the core temperature falls to about 37 °C and this pattern of muscle activity increases metabolic heat production to about 500 kcal/hr. Voluntary exercise is the most efficient defense against the cold that the body possesses. Heat derived from exercise can range from 300 kcal/hr for moderate work to 1000 kcal/hr for strenuous exercise. Indeed, skiing uphill at maximum speed can produce as much as 1100 kcal/ hr. Only a well-conditioned athlete can tolerate expenditures of 700– 800 kcal/hr for a long duration.11 deDear7 summarized the energy consumption cost of selected activities including resting at different postures, walking at different speeds and driving different vehicles. The heat loss depends on the temperature and water vapor pressure gradients between the skin and the environment, as well as on the thermal and water vapor resistance values of the clothing.26,27 Heat energy can occur in one of following three mechanisms or in a combination of them.11 1. Conduction – conduction is the direct transfer of heat from the human body surface to the surrounding environment (or from the environment). Conduction is most important in immersion hypothermia, because water conducts heat nearly 30 times more rapidly than air and has a specific heat capacity 1000 times that of dry air. 2. Radiation – radiation is the gain or loss of heat through energy emission from the skin surface. The most familiar example of radiation is the warm sun rays on an otherwise cold winter day. Radiation will not cool when the surrounding temperatures are greater than the temperature of the radiating body. 3. Convection – convection is the block movement of air or water from around the body. Still air or water next to the body is warmed. Movement strips this warmed layer away, necessitating the warming of a new layer of water or air. Convection thus carries away heat far more quickly than simple evaporation or conduction. The requirements for heat balance vary with the climate; in hot climates the problem is one of heat dissipation whereas in cold climates it is one of heat conservation. Especially, under hot conditions, where the environment temperature is higher than the skin temperature, the only means for the body to lose heat is through evaporation of sweat. Changing liquid water into vapor requires large amounts of heat energy. It is known that one calorie will raise the temperature of one gram of water one degree Celsius; however, it takes 2424 J (580 calories) to evaporate one gram of water at body temperature around 33 °C. Therefore, the thermal comfort of a person


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is entirely dependent on the combinations of clothing, climate, and physical activity.31

11.3

Thermal comfort

Hensel13 pointed out that thermal comfort reflects a state of the thermoregulatory system, which is the integration of afferent signals from both cutaneous and internal thermoreceptors and is different from temperature sensation, which is mainly derived from skin thermoreceptors. According to ASHRAE standard 55–66 (later adapted by ISO 7730),17 thermal comfort is defined as ‘That condition of mind which expresses satisfaction with the thermal environment’. Thermal comfort depends on how well the clothing transmits heat and evaporated sweat from the skin into the environment. The human body is constantly exchanging heat with the thermal environment. Heat exchange is dependent on the temperature difference. An individual’s perception of thermal comfort depends on maintaining a balance between heat produced by the body and heat loss. So net heat generated by body (Q) is: Q=H−R−E

[11.1]

where H is the heat generated by the body – depending on metabolic rate, R is the heat lost through respiration (two components – exhaust air (warmer than air taken in and more moist) and latent heat of evaporation) and E is the heat lost through evaporation from skin (sweat). To maintain a balance, this value of Q must be lost by radiation and convection from the clothing, i.e. Q = H − R − E = Qr + Qc

[11.2]

where Qr is heat lost by radiation and Qc is heat lost by convection. If H becomes too large for heat loss, then the body will overheat and it increases evaporation from the skin to compensate for this. If it becomes cold, the body responds by shivering, which causes the skin to roughen to increase the surface air resistance, and hence resistance to heat transfer increases. Several environmental factors affect the sensation of thermal comfort: 1. 2. 3. 4. 5. 6.

the the the the the the

air temperature mean radiant temperature relative humidity level of clothing activity level air velocity.


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In general, clothing plays an important role in the maintenance of heat balance as it modifies the heat transfer between skin surface and the surrounding environment. However, no one clothing system is suitable for all occasions: a clothing system that is suitable for one climate is usually completely unsuitable for another. The main fabric properties that important in maintaining thermal comfort are:31 • insulation • wind proofing • moisture vapor permeability • waterproofing. Several terms are used to quantify heat transfer. These include thermal insulation, thermal conductivity, thermal resistance and the comfortassociated term, clo, which is a unit used to measure thermal insulation in clothing. One clo represents the insulation required to keep a resting person warm in an indoor room at 70 °F (21.1 °C).

11.4

Thermal insulation

As discussed above, thermal properties of fabrics are the most important features of textiles. For instance, thermal insulation determines the elementary function of garments. Most of the studies hitherto carried out are devoted to measurements of static thermal properties such as thermal conductivity, thermal resistance and thermal diffusion. Thermal insulation is a very important factor in estimating apparel comfort for the user. Thermal insulation properties are determined not only by the physical parameters of fabrics but also by structural parameters such as weave and drape. Relevant standards for thermal insulation evaluation are summarized in Table 11.1. ISO standard 1107920 proposed a standard testing method for evaluating the thermal stress on humans in cold environments; ISO 773017 disclosed the method for neutral environments and ISO 7933 that for heat environments. In these testing standards, thermal insulation is a necessary thermal characteristic of the clothing ensemble. ISO 9920 provides a method for determining the thermal insulation of clothing.19 In this standard, the thermal insulation (resistance to dry heat loss from the body) of a clothing ensemble is expressed as the basic clothing insulation, Icl, expressed in square meter degrees Celsius per watt (m2 °C/W), which is the insulation from the skin to the clothing surface: I cl =

Tsk − Tcl H

[11.3]


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Table 11.1 Relevant standards for thermal insulation evaluation Standard

Description

ISO 7345 ISO 9288

Thermal Insulation – Physical Quantities and Definitions Thermal Insulation – Heat Transfer by Radiation – Physical Quantities and Definitions Thermal Insulation – Mass Transfer – Physical Quantities and Definitions Ergonomics of the Thermal Environment – Estimation of the Thermal Insulation and Evaporative Resistance of a Clothing Ensemble Evaluation of Cold Environments – Determination of Required Clothing Insulation (IREQ) Moderate Thermal Environments – Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort Ergonomics of the Thermal Environment – Analytical Determination and Interpretation of Heat Stress Using Calculation of the Predicted Heat Strain Thermal Insulation – Determination of Steady-state Thermal Resistance and Related Properties – Guarded Hot Plate Apparatus, first edition Standard Test Method For Measuring the Thermal Insulation of Clothing Using a Heated Manikin Standard Test Method For Measuring Thermal Insulation of Sleeping Bags Using a Heated Manikin

ISO 9346 ISO 9920

ISO 11079 ISO 7730

ISO 7933

ISO 8302

ASTM F1291 ASTM F1720

where H is the dry heat loss per square meter of skin area, in watts per square meter, Tsk is the mean skin temperature, in degrees Celsius and Tcl is the mean surface temperature of the clothed person, in degrees Celsius. Please also note that in this testing method the mean surface temperature of the clothed person is not only influenced by clothing surface temperature but also by the skin temperature of the unclothed parts of the body. In this standard a large database of clothing insulation values which have been measured on copper manikins is included. In ISO 8302, an absolute or primary testing method of using a guarded hot plate to determine the steady-state heat transfer through flat specimen is introduced.18 There are two types of apparatus for measuring fabric thermal insulation with one or two specimen(s). The principle of the apparatus is that the guarded hot plate establishes a unidirectional uniform density of heat flow-rate at steady-state conditions; thermal insulation capacity can be calculated by measuring the temperature difference between the specimen’s two surfaces.


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11.5

Thermal conductivity

Thermal conductivity is a property of materials used to describe the thermal transfer behavior of the heat flow through a fabric due to a combination of conduction and radiation where the convection within a fabric is negligible. The conduction loss can be determined by the thickness of the fabric and its thermal conductivity. As defined by ASTM, thermal conductivity is the time rate of unidirectional heat transfer per unit area, in the steady state, between parallel planes separated by unit distance, per unit difference of temperature of the planes.2 Another relevant concept is thermal conductance (C), also defined by ASTM as the time rate of heat flux through a unit area of a body induced by unit temperature difference between the body surfaces.2 Normally, thermal conductivity can be expressed in equation (11.4) k=

Q A ∆T ∆L

[11.4]

where Q is the amount of heat passing through a cross-section, A, and causing a temperature difference, ∆T, over a distance of ∆L. Q/A is therefore the heat flux which is causing the thermal gradient, ∆T/∆L. The measurement of thermal conductivity, therefore, always involves the measurement of the heat flux and temperature difference. The difficulty of the measurement is always associated with the heat flux measurement. Guarded hot plate, as described in ISO 8302, is a widely used and versatile method for measuring the thermal conductivity of textiles. A flat, electrically heated metering section, surrounded on all lateral sides by a guard heater section controlled through differential thermocouples, supplies the planar heat source introduced over the hot face of the specimens. The most common measurement configuration is the conventional, symmetrically arranged guarded hot plate where the heater assembly is sandwiched between two specimens. In the single-sided configuration, the heat flow passes through one specimen and the back of the main heater acts as a guard plane creating an adiabatic environment. Another widely used simple method is directly using a heat flow meter as described in ASTM C 518. Some thermal conductivity evaluation standards are summarized in Table 11.2.

11.6

Cool/warm feeling

Thermal sensations, which represent both the internal and external thermal state of the body, have been recognized as an important aspect of comfort and are involved in body temperature regulation. The human ‘thermostat’ is located in the hypothalamus.32 Like a furnace’s thermostat, it controls a feedback loop to increase the metabolic output or increase/decrease the


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Table 11.2 Thermal conductivity relevant standards Standard

Description

ASTM E 1530

Standard Test Method For Evaluating the Resistance to Thermal Transmission of Materials By the Guarded Heat Flow-meter Technique Standard Test Method for Thermal Transmittance of Textile Materials E(1998) R(1998) Standard Test Method for Steady-state Thermal Transmission Properties by Means of the Heat Flow-meter Apparatus Cork and Cork Products – Determination of Thermal Conductivity – Hot Plate Method, first edition

ASTM D 1518 ASTM C 518 ISO 2582

sweating–cooling systems. The temperature sensors for the body’s thermostat are located in the skin and in the core. Cool blood flow to the neck and brain in particular will activate the sensors and cause an increase in muscle activity, shivering. Skin temperature sensors can also increase shivering and piloerection (goosebumps) with cold exposure. Warm blood flow to the head will cause an increase in sweating and vasodilation, activation of the cooling system. Hensel13 proposed the term thermoreception to describe the thermal sensation and the corresponding physiological response of the human body. Actually, thermoreception is performed by thermoreceptors, which monitor body temperature changes and are involved in the automatic function of temperature regulation in the human body. The receptor cells, which monitor body temperature changes, have a role in the automatic function of temperature regulation of the human body. In responding to constant temperatures, the receptors discharge impulses continuously to indicate the temperature of the skin. They are very sensitive to changes in the skin temperature. There are two types of thermoreceptors: cold and warm. The cold receptors have peak sensitivity around 25–30 °C and are excited by dynamic downshifts in temperature. The warm receptors have peak sensitivity around 39–40 °C and are sensitive to increases in skin temperature. In the same literature, Hensel also defined the general properties of thermoreceptors as: (1) having a static discharge at constant temperature (T); (2) dynamically responding to temperature changes (dT/dt); (3) not being excited by mechanical stimuli; and (4) being active in the non-painful or innocuous temperature ranges. Based on Hensel’s research, Ring and deDear proposed a mathematical model to describe how thermoreceptors respond to the heat flux and temperature profiles in the skin. The impulse frequency of a cold receptor or a warm receptor is a function of static temperature and the rate of temperature change:30


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∂Tsk ( y, t ) ∂t

[11.5]

where Q(y, t) is the pulse output response of the thermoreceptors as a function of time t and the depth y of the thermoreceptors below the skin surface in impulses/sec, which can be used as the quantitative measurement of temperature sensation. Tsk(y, t) is the temperature on the skin at the thermoreceptor depth y as a function of time, Ks is the proportionality constant for the static component of the response related to the steady-state temperature, Ts, and Kd is the proportionality constant for the dynamic component. Normally Ks = −2 and Kd = −62. According to the experimental data, Wang et al.34 developed another equation to calculate the impulses generated by thermoreceptors as: Q ( y, t ) = KsTsk ( y, t ) + kd

∂Tsk ( y, t ) +C ∂t

[11.6]

where Ks = −0.72, Kd = −50 and C = 28.1. The integrals of the thermoreceptor frequency output curves, known as the psychosensory intensity (PSI value), were calculated to describe the intensity of the thermal sensations and then stated as equation (11.5): t

PSI = ∫ Q ( y, t ) dt

[11.7]

0

Skin temperature may vary wildly depending on the area measured. For example, a comfortable temperature reading on the toes could be 25 °C while one on the forehead is 34 °C. Even temperatures within a small region may show significant variation due to airflow. How these temperatures are perceived by the body depends on the range and the context of the temperatures. Table 11.3 summarizes typical responses to skin temperatures. In general, the receptors in the skin are much more sensitive to changes in temperature. Thus, momentary contact with a surface that is warmer than

Table 11.3 Skin temperature sensation Skin temp. (°C)

State

45 43–41 41–39 39–35 37–35 34–33 33–15 15–5

Tissue damage Threshold of burning pain Threshold of transient pain Hot Initial sense of warm Neutral Increasingly cold Intolerably cold


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the skin will elicit a sensation that seems much hotter than would be felt with more constant contact.33 In an attempt to investigate the relationship between subjective comfort perceptions and objective physical stimulus, much research has been carried out in the past few years. Kawabata et al. reported a method to objectively measure and predict fabric thermal perception.21 Li carried out a series of psychophysiological wear trials using T-shirts made from eight types of fiber.22 In his wear trial, subjective ratings on 19 sensory descriptors were recorded under two environmental conditions, from which three fundamental sensory factors were identified, namely thermal–wet, tactile and pressure comfort. Based on the above discussion, the most effective way to determine the skin warm/cool feeling objectively is the measurement of skin mean temperature and the temperature change rates on the skin surface. A wide range of instruments can be employed to determine the skin surface temperature with scatter or continuous data recording using either a contact or a non-contact method. As an example, two type-T thermocouples were fixed on the inner surface of a subject’s forearm. During the trial, the temperatures began to be recorded before the fabric was placed on the forearm and then recording continued for 15 seconds with 100 Hz sampling frequency during the fabric–skin contact. The typical skin surface temperature change curve is as shown in Fig. 11.1. From this, the temperature change rate curve displayed in Fig. 11.2 was obtained. Furthermore, the thermal receptor impulse frequency curve was also obtained according to equation (11.5). The relationship between impulse and time is shown in Fig. 11.3. Finally, based on equation (11.7), the PSI value was calculated to describe the intensity of the thermal sensations.

Temperature at skin surface (deg C)

29

28.5

28

27.5

27 0.7

2.7

4.7

6.7

Time (sec)

11.1 Typical skin surface temperature change during fabric–skin contact.


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Clothing biosensory engineering 0.5

0

dT/dt

0.7

2.7

4.7

6.7

–0.5

–1

–1.5 Time (sec)

11.2

Typical temperature change rate during fabric–skin contact.

50

Impulse

0

–50

0.7

2.7

4.7

6.7

–100 Time (sec)

11.3 Typical Q(y, t) value during the period of temperature during the evaluation process.

11.7

Thermal manikin

Since the first one-segment copper thermal manikin in the world was made for the US army in the early 1940s, more than 100 different thermal manikins have been employed for research and product development worldwide.4,14 Holmér14 reviewed thermal manikin development history and


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summarized the milestones. Interest in using thermal manikins in research and measurement standards is steadily growing, and several international testing standards have been developed in the field of the thermal comfort evaluation as summarized in Table 11.4. A complete understanding of human heat exchange does not require only convective, conductive and irradiative heat losses to be measured. Sweat evaporation is also a main mechanism of heat loss and must be taken into account. Basically, there are three different principles â&#x20AC;&#x201C; constant surface temperature, constant heat flux and comfort equation â&#x20AC;&#x201C; of regulation of surface temperature and heat loss from the heated sensors tested. The advantages and disadvantages of these principles are discussed by Mats Bohn and Holmer.25 To simulate sweating on non-perspiring manikins, many workers put underwear made of highly absorbent fabrics on the manikin, and supplied water to the underwear by sprinklers or water pipes.3,8 To date, thermal/sweating manikins are widely used in large-scale textiles and clothing research laboratories all over the world for analyzing the thermal interface of the human body and its environment. Normally, the thermal manikin is made from metal or fabric, e.g. copper, plastic or water/ windproof fabric, with an independent controllable heating/sweating subsystem, data measurement and analyzing subsystems. With the development of computer and computation technologies, visual realization models have become more and more important and are now widely applied in the field of thermal comfort estimation. Li et al. developed a computer-based model

Table 11.4 Thermal manikin relevant standards Standard

Description

ASTM F 1291

Standard Test Method for Measuring the Thermal Insulation of Clothing Using a Heated Manikin Standard Test Method for Measuring Thermal Insulation of Sleeping Bags using a Heated Manikin Estimation of the Thermal Characteristics of Clothing Thermal Manikin for Measuring the Resultant Basic Thermal Insulation Protective Clothing Against Cold Protective Gloves Against Cold (Thermal Hand Model) Evaluation of the Thermal Climate in Vehicles, Parts 1 and 2 Evaluating Thermal Environments by Using a Thermal Manikin with Controlled Skin Surface Temperature Measurement of Thermal Comfort and Local Discomfort by a Thermal Manikin

ASTM F 1720 ISO 7920 EN-ISO 15831 ENV 342 EN 511 ISO 14505 ASHRAE 3739 ASHRAE HI-02-17-4


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for studies of heat moisture transfer in clothing systems.24 Buxton et al.5 in the UK are also developing a similar model that allows the use of human body data from whole-body scanners and motion patterns derived from real recordings. Some manikins are briefly introduced below. NCSU’s Coppelius-type sweating manikin was developed for the Center for Research on Textile Protection and Comfort through a technology exchange agreement with the Technical Research Center of Finland VTT, Laboratory of Plastics and Fiber Technology.29 The size 40 manikin is housed in a climatic chamber with 18 individually controlled body sections. A computer-controlled sweating system with 187 individually controlled sweating glands can produce sweating over the whole body with the exception of the head, hands and feet by setting the desired ‘sweating’ rate. Prosthetic joints at the knees, hips, elbows and shoulders permit movement and different postures. Water is supplied from a reservoir, placed on a balance near the ceiling in the chamber. A microvalve system in the manikin distributes the water to the 187 sweat glands, and the computer system allows individual control of each sweat gland. The operator controls the water supplied to each of the simulated sweat glands by setting the desired ‘sweating’ rate. Individual ‘sweat glands’ are calibrated with software controlled routing. Based on the calibrated values, the software algorithm time proportions the valve openings to supply the desired water delivery rate. The valve supplying each sweat gland is opened after a precision balance has established the weight of the supply water. When the sweat gland is closed, the water supply is reweighed. The time-dependent weight change determines the ‘sweating’ rate for each individual sweat gland. The condensed water on the dressed manikin is recorded by measuring the change in the weight of the clothed manikin during the test. This measurement is made from the output of the sensitive balance from which the manikin is suspended. Test garments are weighed before and immediately after the test. This is done to estimate the amount of moisture condensation in the individual clothing layers. Moisture condensation in the skin material of the manikin is calculated as the total weight change subtracted by the moisture condensed in the clothing. Another similar thermal sweating manikin, located in the office of transportation in USA, has a specially designed sweating system, which consists of a water delivery system and sweating skin layer on the surface of the manikin.29 The sweating skin will be made out of a porous plastic material with the lower portion of the layer comprising a hydrophilic material and the upper layer comprising a hydrophobic material. A water line will be connected to each segment of the manikin and injected into the lower hydrophilic layer. The hydrophilic layer will then rapidly wick the water throughout the segment to evenly distribute the sweat. The water will then


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be forced through the hydrophobic layer with pressure in order to sweat at a rate determined by the human thermal model. In the Hong Kong Polytechnic University, a fabric manikin was developed in 2001. The specification of this manikin is as follows.9 1. The height of the man-like manikin is 1.65 m and the surface area is 1.66 m2. 2. ‘Skin’ has a soft feel and the body is flexible. 3. Core temperature is controlled at 37 °C 4. The human body’s insensible perspiration is around 30 g/hr. When sweating, the perspiration can reach 1000 g/hr. 5. The skin temperature distribution can be adjusted by altering the pumps’ output and opening the valves.

11.8

Other apparatus for fabric thermal functional evaluation

11.8.1 Sweating cylinder The sweating cylinder was designed to measure simultaneously heat and moisture transfer in textile systems in the Technical Research Center of Finland.26 The basic concept of the cylinder is that it produces heat and moisture in a way similar to the human body. The cylinder wall is electrically heated to a surface temperature corresponding to the skin temperature (normally 35 °C). Water is supplied to the surface, where it evaporates and leaves the cylinder as water vapor. Heat is lost from the surface in the form of convection and radiation (dry-heat loss) as well as through evaporation of supplied water (evaporative-heat loss). The amount of water supplied can be chosen within certain limits; the maximum value at room temperature and without test materials was approximately 300 g/m2/hr. The thermal resistance of the textile materials is determined from the heat input and temperature values. The thermal resistance of the textile layers Rcyltext is: Rcyltext =

Ts − Ttext A ( °Cm 2 W −1 ) P

[11.8]

The total thermal resistance Rcyltotal is: Rcyltotal =

Ts − Ta A ( °Cm 2 W −1 ) P

[11.9]

where Ts is the cylinder surface temperature (°C), Ttext is the textile surface temperature (°C), Ta is the environment temperature (°C), P is the power input and A is the test area.


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11.8.2 Thermal/sweating plate The thermal/sweating plate has been used for years to determine the thermal and moisture resistance properties of fabrics. The applications and description of sweating hot plates can be found in Goldman,12 Kawabata et al.21 Holmer et al.15 and Adams et al.1 There are several types of skin model employed in clothing comfort research. Firstly, Kawabata reported the application of hot plate technology for the measurement of fabric warm and cool feelings. The thermal lab is only used to evaluate the warm or cool feeling produced upon touching a fabric. Thermal conductivity is measured in the steady state. A damp paper was put on the hot plate to simulate human skin.21 A representative sweating hot plate is described in the Farnworth’s paper.10 This sweating hot plate is designed to maintain a constant surface temperature of 35 °C and consists of a circular-shaped inner plate, a guard ring plate and a base plate. The sides of the inner plate are separated from the guard ring plate by a 1 mm air gap and the bottom of the inner plate is separated from the base plate by 50 mm of foam insulation. The guard ring plate and the base plate prevent heat flow away from the inner plate in the lateral and downward directions, respectively. Electrical heaters, connected to DC power supplies, are used to maintain the inner plate at a constant temperature of 35 °C, which is determined by a thermistor. All three plates are located inside a heated box to eliminate further heat flow away from the inner plate in any direction other than that upward from the plate surface. Typical structure and detailed description of a sweating hot plate can be found in ISO 11902:1993(E).16 Another newly developed apparatus is the Dynamic Sweating Hot Plate, which was developed by NCSU and can be used to measure the thermal capacity of fabrics under different humidity conditions.29 The system consists of five distinct parts:28 1. 2. 3. 4. 5.

an Amico-Aire unit a guarded hot plate a diffusion cell a computer interface unit and a data acquisition program.

The Amico-Aire unit controls temperature in the range 5–70 °C and humidity in the range 10–100% RH. Air velocity in the chamber is controlled at 50 cm/s. A guarded hot plate, maintained at 35 °C, is used as a heat source. The diffusion cell consists of a water container, three Gore-tex membrane layers, a shutter arrangement and several humidity and temperature sensors. A 40 cm3 measured supply of preheated water was used as the moisture source.


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The Gore-tex membranes control the mass flow of moisture delivered to the fabric test specimen. The shutter mechanism is used to set temperature and moisture vapor gradients and to control the test time interval. This is done to simulate a dynamic sweat pulse produced in human sweating. The analog output of temperature and relative humidity in the microclimate air gap is measured using a General Eastern Model 850 transmitter connected to the humidity and temperature sensors. The accuracy of temperature measured is ±5 °C. Humidity is measured to an accuracy of ±2% RH in the range 15–99% RH.

11.9

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the projects G-V987, A188, and ITF project ITS-023-03.

11.10 References 1. Adams, W.C., G. Mack, W.G. Langhans, and E.R. Nadel, Effects of Varied Air Velocity on Sweating and Evaporative Rates During Exercise. J. Appl. Physiol., 1992. 73: p. 2668–2674. 2. ASTM, ASTM D123 Standard Terminology Relating to Textiles. 2003: West Conshohocken, PA, ASTM International. 3. Awano, M., N. Kobayashi, and K. Ishikawa, Applying Humidity Gradient Method to Estimation of Water Evaporation of Wet Fabrics. Sen-i Gakkaishi, 1987. 43(8): p. 431–437. 4. Belding, H.S., Protection Against Dry Cold, in Physiology of Heat Regulation and the Science of Clothing. 1949: Dept. of the Army, Office of the Quartermaster General, Military Planning Division, Research and Development Branch, Environmental Protection Section. 5. Buxton, A.C., G. Michel, W. Zang, and H.A.M. Daanen. Recent Developments of the Virtual Manikin, in Proceedings of the 4th International Meeting on Thermal Manikins. 2001. EMPA, Switzerland. 6. Cortili, G., P. Mognoni, and F. Saibene, Work Tolerance and Physiological Responses to Thermal Environment Wearing Protective NBC Clothing. Ergonomics, 1996. 39(4): p. 620–633. 7. deDear, R., http://atmos.es.mq.edu.au/~rdedear/pmv/, 2005. 8. Dozen, Y., Y. Aratani, T. Saitoh, K. Tsuchida, K. Harada, and S. Takenishi, A Model of Sweating Thermal Manikin. Journal of the Textile Machinery Society of Japan (English Edition). 1991. 37(4): p. 101–112. 9. Fan, J. and Y.S. Chen, Measurement of Clothing Thermal Insulation and Moisture Vapour Resistance Using a Novel Perspiring Fabric Thermal Manikin. Measurement Science & Technology, 2002. 13(7): p. 1115–1123. 10. Farnworth, B., Comments on ‘Dynamic Surface Wetness of Fabrics in Relation to Clothing Comfort’. Textile Research Journal, 1986. 56(7): p. 462–463.


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11. Fergenbaum, J., Heat and Temperature: Fundamentals of Medical Physiology, Dowden, Ed., 1980: New York, Hutchinson and Ross. 12. Goldman, R.F., Evaluating the Effects of Clothing on the Wearer, in Bioengineering, Thermal Physiology, and Comfort, K. Cena and J.A. Clark, Eds, 1981: New York, Elsevier Scientific Publishing Company, p. 41–56. 13. Hensel, H., Thermoreception and Temperature Regulation. 1981: London, Academic Press. 14. Holmer, I., Thermal Manikin History and Applications. Eur. J. Appl. Physiol., 2004. 92: p. 614–618. 15. Holmer I., H. Nilsson, S. Rissanen, K. Hirata, and J. Smolander, Quantification of Heat Balance During Work in Three Types of Asbestos-protective Clothing. Int. Arch. Occup. Environ. Health, 1992. 64: p. 243–249. 16. ISO, ISO 11902:1993(E); ‘Textiles – Physiological Effects – Measurement of Thermal and Water-vapor Resistance Under Steady-state Conditions (Sweating Guarded-hotplate Test). 1993: Geneva, Switzerland, ISO. 17. ISO, ISO 7730 Moderate Thermal Environments – Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort, Second Edition. 1995: Geneva, Switzerland: ISO. 18. ISO, ISO 8302 Thermal Insulation – Determination of Steady-State Thermal Resistance and Related Properties – Guarded Hot Plate Apparatus. 1991: Geneva, Switzerland, ISO. 19. ISO, BS EN IS0 9920 Ergonomics of the Thermal Environment – Estimation of the Thermal Resistance of a Clothing Ensemble. 1995: London, British Standards Institution. 20. ISO, DD ENV ISO 11079 Evaluation of Cold Environments. Determination of Required Clothing Insulation (IREQ). 1999: London, British Standards Institution. 21. Kawabata, S., M. Niwa, and H. Sakaguchi, Application of the New Thermal Tester Thermo Labo to the Evaluation of Clothing Comfort. 1985: Osaka, Textile Machinery Society of Japan, p. 96–98. 22. Li, Y., Objective Assessment of Comfort of Knitted Sportswear in Relation to Psychosphysiological Sensory Studies. 1988: Department of Textile Industries, The University of Leeds, Leeds, p. 213. 23. Li, Y., The Science of Clothing Comfort, J.M. Layton, Ed., (Textile Progress, 31(1/2)). 2001: Manchester, The Textile Institute. 24. Li, Y., N.E., X. Luo, and Z. Luo, Integrated CAD for Functional Textiles and Apparel. in Ergonomics of Protective Clothing. K. Kuklane and I. Holmer, Eds, 2000: Stockholm, National Institute for Working Life, p. 8–11. 25. Mats Bohm, O.N., and I. Ingvar Holmer. Factors Affecting the Equivalent Temperature Measured with Thermal Manikins in Proceedings of the Third International Meeting on Thermal Manikin Testing. 1999: Stockholm, Sweden, National Institute for Working Life, p. 45–53. 26. Meinander, H., Introduction of a New Test Method for Measuring Heat and Moisture Transmission Through Clothing Materials and its Application on Winter Workwear, in Technical Research Centre of Finland. 1985: Finland. 27. Meinander, H., Measuring Heat and Water Vapour Transmission in Functional Clothing. Textile Technology International, 1994: p. 193–196. 28. Micro Climate Analysis Laboratory, NCSU, http://www.tx.ncsu.edu/tpacc/ comfort/dynamic.html, 2005.


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29. NCSU, http://www.tx.ncsu.edu/tpacc/comfort/sweating_manikin.html. 30. Ring, K., and R. deDear, A Model for Heat Diffusion through the Skin: Thermoreceptor Responses and the Thermal Sensations. Indoor Air â&#x20AC;&#x201C; International Journal of Indoor Air Quality and Climate, 1991. (4): p. 448â&#x20AC;&#x201C;456. 31. Saville, B.P., Physical Testing of Textiles. 1999: Cambridge, Woodhead Publishing Limited. 32. Stewart, C., http://www.storysmith.net/Articles/Thermal%20regulation.pdf, 1994. 33. Starner, T. and Y. Maguire, A Heat Dissipation Tutorial for Wearable Computers, in Wearable Computers. 1998. Pittsburgh, PA. 34. Wang, Z., Y. Li, K.Y. Yeung, and Y.L. Kwok, Mathematical Simulation of Thermal and Moisture Sensations of Knitted Fabrics. 2000: Hong Kong, ITC.


12 Water vapor transfer J.Y. HU, YI LI AND K.W. YEUNG Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

12.1

Introduction

Water vapor transfer is one of the key fabric physical properties in todayâ&#x20AC;&#x2122;s active wear as the loss of water vapor in clothing is fundamental for the heat balance of the body and for comfort.20 Very often, outerwear apparel is made of water-resistant yet breathable fabrics, and these fabrics normally have multilayer structures of various polymeric materials.4 It is difficult for outdoor apparel manufacturers to interpret the technical information provided by fabric suppliers concerning fabric â&#x20AC;&#x2DC;breathabilityâ&#x20AC;&#x2122; properties because different methods and test conditions are used. In addition, fabrics with hydrophilic components change their properties under different humidity conditions.14 The aim of this chapter is to examine water vapor transfer and its role in textiles. A theoretical study of heat and moisture exchange of liquid water with water vapor, moisture diffusion into fiber and liquid water and vapor transfer within the inter-fiber void space is presented, together with a study of water vapor transfer from different aspects or areas.

12.2

Moisture phase changes

Vaporization of liquid below its boiling point is called evaporation, and it occurs at any temperature when the surface of liquid is exposed to an unconfined space. On the water surface a saturated air boundary layer with a temperature equal to that of the water surface is formed due to the irregular movement of water molecules. If the water vapor concentration of the boundary layer is above that of the air around it, then water vapor molecules going into the boundary are fewer than those leaving the boundary. This is the mechanism of evaporation. When the liquid water diffuses to the region where the relative humidity is below 100%, evaporation takes place; the opposite process is condensation. The wet exchange mass rate of unit volume of porous textiles can be expressed as follows: 206


Water vapor transfer Q1 = εshl↔gSv(Ca*(T) − Ca)

207 [12.1]

where εs is the volume fraction of gas, hl↔g is the exchange coefficient between the liquid water and gas in m/s, Sv denotes the specific volume of fabric in 1/m3, Ca*(T) is the saturated water vapor concentration and is solely determined by the liquid water temperature T in kg/m3 and Ca is the water vapor concentration in gas in kg/m3. The latent exchange energy can be expressed as: L = λ(Q1)

[12.2]

where λ is evaporation heat of water [J/kg].

12.2.1 Moisture diffusion into fiber Moisture diffusion into a fiber was first proposed by Henry and then further developed by Nordon, David, Li, Holcombe and Luo. It can be expressed as the Fickian Law:10

(

∂Cf 1 ∂ ∂C = rDf ( wc ) f ∂t r ∂r ∂r

)

[12.3]

where Df(wc, t) is the diffusion coefficient. It is a function of the water content of the fibers. Cf is the moisture concentration in the fiber. The boundary condition around the fiber is determined by assuming that the moisture concentration at the fiber surface is instantaneously in equilibrium with the surrounding air. Hence, the moisture concentration at the fiber surface is determined by the relative humidity (RH) of the surrounding air, i.e.: Cf(Rf) = f (RH, T)

[12.4]

where f is a non-linear function, which has been determined experimentally for different fibers.

12.2.2 Liquid water and vapor transfer within void space of inter-fibers As the pore sizes in fibrous materials are generally small, the diffusion of flux is largely governed by Darcy’s law, which can be used to describe the mass transfer under total atmospheric pressure gradient: ε sVs = −

KK rs ∂ps µ s ∂x

ε l Vl = −

KK rl ∂ps ∂pc − µl ∂x ∂x

(

[12.5]

)

[12.6]


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where ps is the pressure of gas, pc is capillary pressure and µs, µl are the dynamic viscosity of gas and liquid water, respectively. Extending the results of reference,14 we can obtain: K=

3ε sin 2 βdc 2 80

[12.7]

where K is the intrinsical permeability, β is the average angle of the capillaries in fabric, dc is effective radius of the pore in fabrics and ε is the porosity of fabrics. The relative permeability of liquid water is given by: Krl = (εl/ε)3

[12.8]

and the relative permeability of the gas by: Krs = 1 − Krl

[12.9]

The relation of capillary pressure pc and the volume fraction of liquid water εl is: ∂pc 2 σ cos φε ∂ε l =− ∂x ∂x ε 2l dc

[12.10]

where σ is the surface tension and φ is the contact angle.

12.3

Water vapor transfer

The water vapor permeability of a given material plays an important role in evaluating the physiological wearing comfort of clothing systems or in determining the performance characteristics of textile materials used in technical applications.17 Water vapor condensation occurs when air space size is very small, say 5 mm. In such cases, water vapor permeability has more effect on heat and mass transfer than air permeability. Vaporpermeable fabrics have a relatively lower amount of condensation heat flux than vapor-impermeable fabrics. In other words, such a fabric can keep the temperature and the amount of absolute water vapor concentration in the microclimate low – high values of these cause sensations of humidity and heat stress in the wearer.8 The effect of air and vapor permeability of material on heat and mass transfer in clothing systems by experimental and theoretical models was investigated by Fukazawa et al.8 The numerical analysis shows that the velocity of convective flow in a microclimate is closely related to the air permeability of the fabric. However, the convective flow rate in a microclimate under air-impermeable clothing is faster than that under permeable clothing. Furthermore, for garments without air and vapor permeability the only pathway available is through the openings. This indicates that thermal insulation and water vapor resistance of clothing systems are determined


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not only by fabric properties but also by construction factors such as the condition of upper and lower openings and the size of the microclimate air space. Wang et al.21 investigated the influence of waterproof fabrics on heat and moisture transfer through a clothing system. The water vapor permeability (WVP) for the outside layer was derived from published experimental data and was considered to be a factor influencing the mass transfer coefficient at the boundary exposed to the environment. The analysis was carried out using a mathematical model that describes the coupled heat and moisture transfer in porous textiles. The numerical simulation reveals a significant influence of waterproof fabrics on the dynamic moisture content distribution in fabrics/fibers, moisture vapor condensations and the liquid transfer behavior inside the clothing. This research has shown the potential usage of mathematical simulation in evaluating the performance of different waterproof and breathable fabrics in clothing systems, as well as its potential in the development of functional apparel products. Li et al.13 conducted a theoretical investigation of the coupling mechanism of heat transfer and liquid moisture diffusion in porous textiles using a mathematical model developed earlier. In the model, an equation describing liquid diffusion behavior was incorporated into an energy conservation equation and mass conservation equations of water vapor and liquid water transfer; these included vapor diffusion, evaporation and sorption of moisture by fibers. A series of computations with systematic variations of fabric thickness and porosity was carried out in order to investigate the interactions between heat transfer and moisture transfer. Meanwhile, experiments were conducted to validate the model for fabrics with different degrees of hygroscopicity and thickness. Predictions of temperature changes during moisture transients were compared with experimental measurements and good agreement was observed between the two, indicating that the model was satisfactory. Analysis of the computational and experimental results showed that the heat transfer process, which is influenced by fabric thickness and porosity, significantly impacts moisture transport processes. In the investigation of the influence of semi-permeable functional films (SPFF) on the surface vapor pressure and temperature between two fabric layers, it was seen that they affected vapor pressure and temperature changes on both surfaces of the assembly under simulated body-clothing conditions. Kim12 found that the physical characteristics of films have a great influence on the level of transient, comfort-related variables including changes of inner surface vapor pressure and temperature. Mixed cotton and polyester layers of a film reveal that the film can negatively affect moisture sorption of the assembly, not only by blocking the air spaces for moisture diffusion but, more importantly, by ineffective heat dissipation from the cotton inner fabric for continued moisture sorption. Using film and fabric


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assemblies, this study reinforces the interdependence of moisture sorption and temperature barrier effects during dynamic moisture transfer through clothing assemblies. Fiber hygroscopicity of the layered fabrics and the physical properties of breathable waterproof film are more important than the hygroscopicity of film in determining the comfort performance of multilayer functional clothing in transient conditions. Wehner et al.22 developed two mathematical models for the interaction between moisture sorption by the fibers and moisture flux through the air spaces. Many methods are used for measuring the parameters of water vapor transfer through textiles. However, these vary in effectiveness and are difficult to correlate with one another. Pause17 reported and discussed the mechanism of water vapor transfer in textiles and common methods used to determine its parameters. McCullough et al.14 studied and measured the water vapor permeability and evaporative resistance of 26 different waterproof, windproof and breathable shell fabrics using five standard test methods. The water vapor transmission rate (WVTR) was measured using the ASTM E 96 upright and inverted cup tests with water, the JIS L 1099 desiccant inverted cup test and the new ASTM F 2298 standard using the dynamic moisture permeation cell (DMPC). The evaporative resistance was measured using the ISO 11092 sweating hot plate test. The WVTRs were consistently highest when measured with the desiccant inverted cup, followed by the inverted cup, DMPC and upright cup. The upright cup was significantly correlated with the DMPC (0.97) and the desiccant inverted cup was correlated with the sweating hot plate (â&#x2C6;&#x2019;0.91). Jena and Gupta10 measured air permeability and water vapor permeability of naphion membranes using specially built instruments, a PMI diffusion permeameter and a water vapor transmission analyzer. In their study, air permeability was almost zero and water vapor permeability was zero during an incubation period. The permeability increased and became quite high and then gradually decreased with time after the incubation period. This behaviour has been attributed to the contributions of chemical and mechanical forces to the net flux through the membrane. They also presented a model to explain absorption and transport of water vapor through naphion membranes. Wehner et al.22 used experimental equipment that permits simultaneous measurement of moisture sorption by a fabric and moisture flux through a fabric during the transient period after the fabric had been exposed to a humidity gradient, to study of the dynamics of water vapor transmission through a fabric barrier. Results indicated that moisture flux occurs chiefly through the fabric air spaces; the fibers act as a moisture source or sink. Murata et al.15 conducted an experiment on an external-flow model using a nitrogen purging method. The membrane area, water vapor flow rate and temperature range were set at 39.5 m2, 1000 L/hr and 15â&#x20AC;&#x201C;35 °C, respectively.


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211

Transfer rate of water vapor (mol/hr)

2.5

35 °C

2 30 °C 1.5 25 °C 1

20 °C 15 °C

0.5

0 0

5

10

15

20

Mass flow rate of nitrogen sweep gas (normal liter per minute)

12.1 Water vapor transfer rate with nitrogen purging method.

The experimental result is shown in Fig. 12.1. There is a linear relationship between transfer rate of water vapor and mass flow rate of nitrogen sweep gas. Furthermore, when the mass flow rate of nitrogen sweep gas reaches 20, water vapor transfer rates are approximately 0.6 and 2.5 mol/hr at temperatures of 15 and 35 °C, respectively. Fukazawa et al.6 conducted a series of experiments on the combined heat and water vapor transfer through fabric at simulated high altitudes. A considerable water vapor concentration difference is imposed between the water vapor source and the environment so that condensation takes place in the specimen. By considering the combined heat and mass transfer between the specimen and the environment, they derived a new analytical expression for the rate of condensation in textile materials. Although the amount of condensation in textiles did not correlate well with the primitive parameters such as temperature or water vapor concentration differences, good agreement was obtained between the condensation mass flux measured experimentally and that calculated by means of the derived expression. Ren and Ruckman18 conducted a series of experiments in order to find out whether condensation occurring on the inner surface of a waterproof breathable fabric or moisture content within such a fabric has the greater effect on water vapor transfer rate using two different types of waterproof breathable fabrics, porous polyurethane and hydrophilic laminated, under isothermal and non-isothermal conditions. It was found that moisture


212

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content and condensation have effects on water vapor transfer in both fabrics; higher moisture content and larger amounts of condensation increase the water vapor transfer. The water vapor transfer rate of polyurethane laminated fabrics was greater under isothermal conditions while the water vapor transfer rate of hydrophilic laminated fabrics was greater under non-isothermal conditions, especially when a fabric contained more moisture in either form. It was also found that the way moisture content and condensation affect the rate of water vapor transfer is different; the effect of moisture content is greater than that of condensation on water vapor transfer under both isothermal and non-isothermal conditions. Rossi et al.19 analyzed the water vapor transfer and moisture accumulation in the layers of different four-layer combinations at several moderately cold temperatures with a sweating arm that simulates the thermophysiological behavior of a man’s arm. The result shows that the sample permeability and condensation rates are strongly dependent on the outside climate and the hydrophilicity of the outer layers. The differences in effective water vapor resistance between the ensembles are small in a climate with temperature and relative humidity of 20 °C and 65%, respectively, but become larger with decreasing outside temperatures. The formation of condensation is the smallest for samples with a hydrophilic membrane laminated on the hydrophilic inner side. Hydrophilic layers placed underneath the outer shell generally absorb more moisture than similar hydrophobic layers, revealing probable liquid moisture transfer from the outer shell to the inner layers of the combinations. The rate at which these breathable fabrics are able to transmit water vapor is most often measured under standard textile testing conditions of 20 °C and 65% relative humidity, but these tests are often little better than useless in predicting a fabric’s performance under ‘real’ conditions.4 Therefore, Finn et al.4 studied the working mechanisms of water vapor transmission through outerwear apparel fabrics, at four different ambient temperatures of 6, 10, 15 and 20 °C with the relative humidity held at a constant 65%. In order to simulate a sweating body, water was heated to 33 °C, and the rate at which the vapor was transmitted through various breathable fabrics was measured. Investigations related to water vapor permeability of commercially available fabrics have been conducted. Fukazawa et al.5 measured the water vapor permeability resistances of a commercial polytetrafluoroethylene (PTFE) membrane and some general polyester (PET) textiles at simulated altitude conditions, where temperature and pressure were both controlled simultaneously. The experiment was carried out using a simple apparatus in which the effect of the air layer could be eliminated on either side of the test specimen. The WVP resistance of the PTFE membrane was not appreciably influenced by either the ambient temperature or the atmospheric


Water vapor transfer

213

pressure and was found to be almost constant with a value of approximately 4 sec/m. However, the resistances of some PET textiles decreased with the increasing simulated altitude. They were roughly proportional to the inverse of the diffusion coefficient of water vapor in the air. The reasons for this difference are discussed in terms of the pore sizes of the specimens and the intermolecular mean free path of the water vapor. Thus, in the case of normal textile materials, it was concluded that the water vapor resistance of clothing systems decreases with increasing altitude. Osczevski16 measured the water vapor diffusion resistance of the hydrophilic component of Gore-Tex II at temperatures down to −24 °C. The study aimed to simulate diffusion of water vapor through a clothing shell from a coating of ice on its inner surface. The permeability of the samples decreased exponentially with decreasing temperature; this variation is a combined effect of temperature and relative humidity. At sub-zero temperatures, water vapor transfer through this material is only a few percent of its room temperature value. Other hydrophilic films used in outdoor clothing are likely to be similarly affected. Studies related to water vapor transfer and human subjective sensory perception have also been conducted. Bartels and Umbach1 investigated the physiological impact of waterproof textiles on the wearers of protective clothing. Wear trials with test subjects in a climatic chamber involve ambient temperatures of +20, 0 and −20 °C. The physiological function of breathable materials was compared to water vapor impermeable constructions. The results showed that water vapor permeable constructions provide a clear benefit to wearers at the three tested temperatures. Moisture accumulation in both breathable protective garments and whole clothing systems was much smaller than in non-breathable ones. There is no indication of a temperature dependency of the water vapor resistance of hydrophilic membrane laminates, but results show that, especially at ambient temperatures far below the freezing point, such breathable foul weather protective textiles still offer a great benefit to wearers. Zielinski and Przybysz23 investigated the transmission of water vapor through four types of apparel fabrics which were made of four different types of fiber including polyester, wool and two types of cotton, by two different methods. The weave and weight of these fabrics were different. The studies were carried out at a specific temperature and relative humidity and the microclimate created was examined. The results that were used to determine the comfort levels of fabric and garments generated from these two methods were similar.

12.4

Water vapor transfer with temperature gradient

Bendkowska2 investigated woven fabrics with a microporous polyurethane coating and knitted and woven fabrics with a microporous PTFE membrane


214

Clothing biosensory engineering

in the temperature range 5–25 °C. In the investigation, the effect on wearer discomfort was discussed and the relationship between water vapor transmission and temperature was established. Moisture permeability through ten-layered fabrics under temperature gradient conditions was compared with moisture permeability under an isothermal condition.11 In the temperature gradient condition, a vessel cup temperature was held at 32 °C and the experimental conditions were performed at various environmental humidities with a temperature lower than 32 °C. In the isothermal condition, the vessel cup and the environmental conditions were set at the same temperature. Moisture permeability, humidity on the inside and outside surfaces of ten-layered fabrics and a diffusion coefficient of a moisture vapor in fabrics were compared with the temperature gradient and the isothermal conditions. Gretton et al.9 examined moisture vapor transport through waterproof breathable samples and clothing systems under a temperature gradient using a simple research method. Results were compared with those obtained from isothermal tests. The transport properties were identified as being dependent on the temperature gradient across the waterproof breathable layer, the humidity of the clothing microclimate and the interaction between water vapor and the clothing layers. The transport properties of hydrophilic polymers and clothing systems incorporating hydrophilic polymers, especially those with low transmission rates in the isothermal tests, improved by considerably greater amounts than those incorporating microporous polymers when a temperature gradient was applied.

12.5

Comparison of water vapor transfer with and without temperature gradient

Fukazawa et al.7 developed a new apparatus to measure the water vapor permeability resistance of textiles with and without temperature differences imposed on both sides of a specimen. Water vapor resistance was measured for a combination of temperature and pressure that simulated elevated altitudes. The effect of temperature on water vapor resistance was small, while that of pressure was significant, that is, water vapor resistance decreased with increasing simulated altitude due to an increase in the water vapor diffusion coefficient with increasing altitude. The amount of condensation in the specimen tended to increase with increasing simulated altitude. Moreover, water vapor resistance decreased appreciably due to increased condensation in the specimen. In addition, the reduced resistance further enhanced the amount of condensation. These results indicate that decreased water vapor resistance enhances condensation in clothes and thus may cause further discomfort and a drop in body temperature at high altitudes.


Water vapor transfer

215

To clarify the principles and mechanisms of water vapor transfer by diffusion in waterproof breathable fabrics for clothing, Ruckman20 carried out a series of experiments using a simple glass dish under steady-state conditions, with and without a temperature gradient in the climatic chamber. It was found that both vapor pressure and natural convection within the air gap affected water vapor transfer. The rates of water vapor transfer were ranked: microfiber fabrics, cotton ventiles, PTFE-laminated fabrics, poromeric polyurethane laminated fabrics, hydrophilic laminated fabrics and polyurethane-coated fabrics. In the presence of a temperature gradient, condensation was also found to be a major factor, especially at air temperatures below 0 °C. Condensation occurred least on the inner surface of PTFE-laminated fabrics, poromeric polurethane-laminated fabrics, poromeric polyurethane-laminated fabrics and polyurethane-coated fabrics. In order to assess water vapor transfer by diffusion, experiments were carried out under steady-state conditions with and without a temperature gradient. Realistic atmospheric conditions were simulated using a precipitator to create rainy conditions and an electric fan to create windy conditions. Experiments involving simultaneous heat and water vapor transfer were carried out to study the condensation problem further. These suggested that the currently accepted system of constructing breathable waterproof clothing should be reconsidered.3

12.6

Conclusion

Water vapor permeability is an important element in active sportswear. Based on the research work shown above, there are a number of factors that influence the water vapor permeability of a fabric apart from its properties: • • • •

condition of upper and lower opening size of microclimate air space change of inner surface vapour pressure and temperature hydrophilicity of the outer layers.

12.7

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the projects G-V987, A188 and ITF project ITS-023-03.

12.8

References

1. Bartels, V.T. and K.H. Umbach, Water Vapor Transport Through Protective Textiles at Low Temperatures. Textile Research Journal, 2002. 72(10): p. 899–905.


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2. Bendkowska, W., Water Vapour Transmission Through Waterproof Breathable Apparel Fabrics in the Case of a Temperature Gradient. Przeglad Wlokienniczy, 2003(2): p. 12–15. 3. Ea, J.Y., Water-vapour Transfer in Breathable Fabrics for Clothing. 1988: Leeds, Leeds University. 4. Finn, J.T., A.J.G. Sagar, and S.K. Mukhopadhyay, Effects of Imposing a Temperature Gradient on Moisture Vapor Transfer Through Water Resistant Breathable Fabrics. Textile Research Journal, 2000. 70(5): p. 460–466. 5. Fukazawa, T., H. Kawamura, and T. Tamura, Water Vapour Transfer Through Microporous Membranes and Polyester Textiles at Combinations of Temperature and Pressure that Simulate Elevated Altitudes. Journal of the Textile Institute, 2000. 91(3): p. 434–447. 6. Fukazawa, T., H. Kawamura, Y. Tochihara, and T. Tamura, Experiment and Analysis of Combined Heat and Water Vapor Transfer Through Clothes With Condensation. Textile Research Journal, 2003. 73(9): p. 774–780. 7. Fukazawa, T., H. Kawamura, Y. Tochihara, and T. Tamura, Water Vapor Transport Through Textiles and Condensation in Clothes at High Altitudes – Combined Influence of Temperature and Pressure Simulating Altitude. Textile Research Journal, 2003. 73(8): p. 657–663. 8. Fukazawa, T., Y. Satsumoto, M. Takeuchi, and K. Ishikawa, Effect of Air Permeability and Water Vapor Permeability Upon Simultaneous Heat and Mass Transfer in Simulated Clothing Systems. Sen-i Gakkaishi, 1998. 54(9): p. 443–451. 9. Gretton, J.C., D.B. Brook, H.M. Dyson, and S.C. Harlock, Moisture Vapor Transport Through Waterproof Breathable Fabrics and Clothing Systems Under a Temperature Gradient. Textile Research Journal. 1998. 68(12): p. 936–941. 10. Jena, A. and K. Gupta, Characterization of Water Vapour Permeable Membranes. Desalination, 2002. 149: p. 471–476. 11. Kanetsuna, H. and T. Takenaka, A Moisture Permeability Through a Fabric Under Temperature Gradient Conditions. Sen-i Gakkaishi, 2000. 56(11): p. 544– 549. 12. Kim, J.O., Dynamic Moisture Vapor Transfer Through Textiles Part III: Effect of Film Characteristics on Microclimate Moisture and Temperature Changes. Textile Research Journal, 1999. 69(3): p. 193–202. 13. Li, Y., Q.Y. Zhu, and K.W. Yeung, Influence of Thickness and Porosity on Coupled Heat and Liquid Moisture Transfer in Porous Textiles. Textile Research Journal, 2002. 72(5): p. 435–446. 14. McCullough, E.A., M. Kwon, and H. Shim, A Comparison of Standard Methods for Measuring Water Vapour Permeability of Fabrics. Measurement Science and Technology, 2003. 14: p. 1402–1408. 15. Murata, H., Y. Tomita, M. Miyashita, K. Sakai, M. Toda, and T. Ohmi, Mass Transfer of Water Vapor in a Hollow Fiber for Degassing Processes. American Institute of Chemical Engineers Journal, 1999. 45(4): p. 681–690. 16. Osczevski, R.J., Water Vapor Transfer Through a Hydrophilic Film at Subzero Temperature. Textile Research Journal, 1996. 66(1): p. 24–29. 17. Pause, B., Measuring the Water Vapor Permeability of Coated Fabrics and Laminates. Journal of Coated Fabrics, 1996. 4(25): p. 311–320.


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18. Ren, Y.J. and J.E. Ruckman, Water Vapour Transfer in Wet Waterproof Breathable Fabrics. Journal of Industrial Textiles, 2003. 32(3): p. 165–175. 19. Rossi, R.M., R. Gross, and H. May, Water Vapor Transfer and Condensation Effects in Multilayer Textile Combinations. Textile Research Journal, 2004. 74(1): p. 1–6. 20. Ruckman, J.E., Water Vapour Transfer in Waterproof Breathable Fabrics: Part 1: Under Steady-state Conditions. International Journal of Clothing Science and Technology, 1997. 9(1): p. 10–22. 21. Wang, Z., Y. Li, Y.L. Kwok, and C.Y. Yeung, Influence of Waterproof Fabrics on Coupled Heat and Moisture Transfer in a Clothing System. Sen-i Gakkaishi, 2003. 59(5): p. 187–197. 22. Wehner, J.A., B. Miller, and L. Rebenfeld, Dynamics of Water-Vapor Transmission Through Fabric Barriers. Textile Research Journal, 1988. 58(10): p. 581–592. 23. Zielinski, J. and A. Przybysz, Water Vapour Transfer Through Multilayer Fabrics. Przeglad Wlokienniczy, 2003(7): p. 5–7.


13 Liquid moisture transfer J.Y. HU, YI LI AND K.W. YEUNG Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

13.1

Introduction

The primary cooling mechanism of the human body is evaporation of perspiration, with water vapor carrying heat away from the body as it evaporates out of the skin’s pores. In the garment–skin microclimate environment, the absorption of sweat by garment and its transportation through and across the fabric where it is evaporated are claimed by some researchers to aid clothing comfort perception.14,15,19,22 The manner of the moisture absorbed at the fabric inner surface, transported between the two sides and evaporated at the outer surface significantly influences the wearer’s comfort sensation, as the moisture is a much better heat conductor than air. Therefore, it is necessary to determine the fabric physical properties of moisture transfer before design of high value-added garments or products. For example, sportswear or casual wear needs the ability to transfer sweat from the skin’s surface to the outer side of the garment to evaporate and then to maintain the dry sensation during high-level physical exercise with a heavy sweating rate. The same concepts are used to describe the properties of moisture on or in the fabric.

13.2

Absorbency

Absorbency is used to describe the ability of a fabric to take in moisture – a very important property, which affects many other characteristics such as skin comfort, static build-up, shrinkage, water repellency and wrinkle recovery. Defined in ASTM D123–01, absorption is a process in which one material (the absorbent) takes in or absorbs another (the absorbate).10 The AATCC test method 79–2000 describes absorbency as an important factor that determines the suitability of a fabric for a particular use and is defined as the propensity of a material to take in and retain a liquid, usually water, in the pores and interstices of the material.5 Normally, the property 218


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219

of fabric absorbency is important when fabric is to be dyed, where the completeness and uniformity of the dyeing are dependent upon the absorbency, or when the fabric is used for special functional garments or products. The relative testing standards for absorbency are summarized in Table 13.1. AATCC Test Method 79–2000, ‘Absorbency of Bleached Textiles’ proposed a standard test method, which can be applied to determine the suitability of a fabric for a particular use, as in the case of gauze or toweling. Wettability or absorbency of the textile fabrics or yarns can be determined by this method. The principle of this method is: a drop of water is allowed to fall from a fixed height onto the taut surface of a test specimen; the time required for the water drop to disappear is measured and recorded as wetting time; the average of five readings is taken; the shorter the average time, the more absorbent the textile.5 ASTM D 4772–97, ‘Standard test method for surface water absorption of terry fabric (water flow)’ presented another method to determine the ability of terry fabric to absorb surface water and retain moisture from surfaces such as human skin, dishes and furniture. However, the limitation of ASTM D4772–97 is that it is suitable only for terry fabric, as the steep angle of the specimen on the apparatus may cause a large amount of water to run off no-pile fabrics; it is also difficult to apply on decorative terry fabric, because of the water repellence properties of its surface.8 The water flow tester can be set up according to the schematic diagram in Fig. 13.1.8 For details of installation, operation and calculation of information and requirements, refer to the standard.

Table 13.1 The relative testing standards for absorbency Developer

Document number

Title

AATCC INDA

79–2000 10.1

ASTM

ASTM D 5725–99

Absorbency of Bleached Textiles Absorption – liquid absorbency time, capacity and wicking rate (ASHRAE Handbook: Systems) Standard Test Method for Surface Wettability and Absorbency of Sheeted Materials Using an Automated Contact Angle Tester Standard Test Method for Surface Water Absorption of Terry Fabrics (Water Flow) Non-woven Absorption Rate of Sorption of Wiping Materials Demand Absorbency

ASTM D 4772–07 EDANA

IST 10.1 IST 10.2 IST 10.3


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C

B H D

3.0 ± 0.2 cm

A = Hoop F

B = Base

E

0.6 ± 0.4 cm

C = Funnel D = Valve

A E = Graduate 60°

G

F = Pour spout G = Pan H = Adjustment screw

13.1 Schematic diagram of a typical water flow tester.

13.3

Wettability

The concept of wettability is widely used in the paper industry, to describe the properties of a liquid to adhere to, or ‘wet’, a sheeted surface, or to be absorbed by that surface, or both.9 This concept is also accepted in the textile industry. The behavior of wetting between a liquid and a particular sheeted substrate mainly depends on the relationship between the surface energy (tension) of the liquid and the surface energy of the substrate. One of the major tasks during textile finishing is improved wettability,16,20 including dyeing, printing, finishing, wet coating and the design of medical dressings and diapers. In many industrial applications of high-quality textiles, finishing techniques are used to improve the functional properties, thereby maximizing finished garment quality, consistency, value and profit for the producer.18 Several standards have been developed to determine the wettability of textiles, including AATC 79–20005 (see Section 13.2) and ASTM D 5725–99. All of these standards (summarized in Table 13.2) determine the wettability by measuring the contact angle. ASTM standard D 5725–99, ‘Standard Test Method for Surface Wettability and Absorbency of Sheeted Materials Using an Automated Contact Angle Tester’,9 was developed by subcommittee D06.92 and also approved by ANSI. This test method measures the contact


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221

Table 13.2 The relative testing standards for wettability Developer

Document number

Title

AATCC

79–2000 35–2000 ASTM D 5725–99

Absorbency of Bleached Textiles Water Resistance: Rain Test Standard Test Method for Surface Wettability and Absorbency of Sheeted Materials Using an Automated Contact Angle Tester Standard Test Method for Surface Wettability of paper (Angle-of-contact method) Surface Wettability of Paper (Angle-of-contact method)

ASTM

ASTM D 724–99 TAPPI

T458cm–94

angle of a test liquid in contact with a flat specimen of a film or a paper substrate under specified testing conditions. The sorption properties of the specimen surface can be described as the rate of change of the contact angle as a function of time under the specified testing conditions. The entire process of the liquid drop being absorbed on the specimen is recorded by an image at a given frequency. The data calculated from all captured images in one sequence are represented by three check points at 0.1, 1.0 and 10 seconds.

13.4

Waterproof

The concept of fabric water resistance is defined by AATCC as follows: the ability to resist wetting and penetration by water.3 In textiles, AATCC defined water-repellency as the characteristic of fiber, yarn or fabric to resist wetting.6 Fabric treated with water-repellent properties is widely used in outdoor garments, especially for army uniform. The US Department of Defense and the UK Ministry of Defense have developed several standards to determine fabric water-repellent properties, such as ‘MIL-C-24939 CLOTH, RIPSTOP, PARA-ARAMID/PBI, WATER REPELLENT’ and ‘A-A-50531 CLOTH, POPLIN; POLYESTER AND COTTON (WATER REPELLENT)’. A summary of the standard testing methods related to fabric waterproof properties is given in Table 13.3. AATCC Test Method 22–2001, ‘Water Repellency: Spray Test’, proposed a standard test method to determine the resistance of fabrics to wetting. This method is suitable for any textile fabric whether or not it has been given a water-repellent finish, but is especially useful for measuring the water-repellent efficacy of finishes applied to fabrics, particularly plain woven fabrics. The principle of this method is: water sprayed against the taut surface of a test specimen under controlled conditions produces a


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Table 13.3 The relative testing standards for fabric waterproof properties Developer

Document number

Title

ASTM

D3781–02

Standard Performance Specification for Men’s and Boys’ Knitted Rainwear and All-Purpose, Water-Repellent Coat Fabrics Standard Performance Specification for Women’s and Girls’ Woven Rainwear and All-Purpose, Water-Repellent Coat Fabrics Fabric, Non-Woven, Water-Repellent Men’s and Boy’s Woven Rainwear and All Purpose, Water-Repellent Coat Fabrics Determination of Resistance to Surface Wetting (spray test) (ISO 4920: 1981) Water Resistance: Hydrostatic Pressure Test Water Repellency: Tumble Jar Dynamic Absorption Test Water Resistance: Impact Penetration Test Water Resistance: Rain Test Water Repellency: Spray Test

D3779–02

MODUK KSA

TS 10312 K 3815

DIN

DIN EN 24920

AATCC

127–2000 70–2000 42–2000 35–2000 22–2001

wetted pattern whose size depends on the relative repellency of the fabric. Evaluation is accomplished by comparing the wetted pattern with pictures on a standard chart.1 AATCC Test Method 35–2000, ‘Water Resistance: Rain Test’, proposed a standard test method to determine resistance of fabrics to the penetration of water by impact. This method is suitable for any textile fabric, with or without a water-resistant or water-repellent finish. It can, therefore, be used to predict the probable rain penetration resistance of fabrics and is especially suitable for measuring the penetration resistance of garment fabrics. The principle of this test method is: a test specimen, backed by a weighed blotter, is sprayed with water for five minutes under controlled conditions; the blotter is then reweighed to determine the amount of water which has leaked through the specimen during the test.2 AATCC Test Method 42–2000, ‘Water Resistance: Impact Penetration Test’, proposed a standard test method to determine the resistance of fabrics to the penetration of water by impact. This test method is suitable for any textile fabric, with or without a water-repellent finish. Therefore, the method can be used to predict the probable rain penetration resistance of fabrics. The principle of this method is: a volume of water is allowed to spray against a taut surface of a test specimen backed by a weighed blotter; the blotter is then reweighed to determine water penetration.3 AATCC Test Method 70–2000, ‘Water Repellency: Tumble Jar Dynamic Absorption Test,’ proposed a standard test method, which can be applied


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223

to any textile fabric with or without a water-resistant or water-repellent finish, by measuring the resistance of fabrics to wetting. The limitation of this method is in applying it to rain penetration resistance of fabrics, because it measures absorption of water into, but not through, the fabric. The results obtained primarily depend on the resistance to wetting or water-repellency of the fibers and yarns in the fabric, and not upon the construction of the fabric.4 AATCC Test Method 127–1998, ‘Water Resistance: Hydrostatic Pressure Test,’ proposed a standard test method that can be used to measure the resistance of a fabric to the penetration of water under hydrostatic pressure and is suitable for all types of fabrics, including those treated with a waterresistant or water-repellent finish. Water resistance depends on the repellency of the fibers and yarns, as well as on the fabric construction.6

13.5

Contact angle

The phenomenon of wetting or non-wetting of a solid by a liquid is better understood by studying what is known as the contact angle. The contact angle, which a liquid forms on a smooth, homogeneous surface, is dependent on the solid’s surface energy.11,12 Contact angle methods have been developed extensively since the 1960s. A large body of reliable data has been accumulated and a vast literature exists correlating contact angle data with surface properties of tension. It has been shown that higher energy surfaces exhibit a smaller contact angle, and offer better wettability.13,17 Consider a drop of liquid resting on a solid surface, which forms an angle. It may be considered as resting in equilibrium by balancing the three forces involved, namely, the interfacial tension between solid and liquid (SL), that between solid and vapor (SV) and that between liquid and vapor (LV).7 The angle within the liquid phase is known as the contact angle or wetting angle. It is the angle included between the tangent plane to the surface of the liquid and the tangent plane to the surface of the solid, at any point along their line of contact. The surface tension of the solid will favor spreading of the liquid, but this is opposed by the solid–liquid interfacial tension and the vector of the surface tension of the liquid in the plane of the solid surface. Some standards relating to contact angle measurement are summarized in Table 13.4.

13.6

Moisture management

The properties of moisture transfer in the fabric in multidimensions are defined as fabric moisture-management properties. Recently, functional fabrics with excellent moisture-management properties have appeared on the market, and these are widely used for sportswear, high value-added


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Table 13.4 The relative testing standards for contact angle Developer

Document number

Title

ASTM

D724–99

Standard Test Method for Surface Wettability of Paper Standard Test Method for Surface Wettability and Absorbency of Sheeted Materials Using an Automated Contact Angle Tester Standard Test Method for Hydrophobic Contamination on Glass by Contact Angle Measurement Standard Test Method for Corona-Treated Polymer Films Using Water Contact Angle Measurements Surface Wettability of Paper

D5725–99

C813–90 (1994)e1

D5946–96

TAPPI

T458

casual wear and uniforms. It is claimed that these fabrics have a rapid drying rate and the most efficient movement of moisture away from the skin as well as having excellent breathability. To describe scientifically the entire procedure of the liquid transfer in the fabric, a new testing method has been developed and is introduced in this chapter.

13.6.1 Principle of the test method and apparatus design The principle of the apparatus design is that, when moisture transports through a fabric, the contact electrical resistance of the fabric will be changed. The value of the resistance change depends on two factors: the components of the water and the water content in the fabric. When the influence of the water components is fixed, the electrical resistance measured is related to the water content in the fabric.21 The electrical resistance of a textile is usually very large when it is placed in a closed circuit as shown in Fig. 13.2. So, no electric current can be detected and the voltage on the reference resistor of 1 MΩ is almost zero. However, when the fabric is wet or contains a certain quantity of moisture, the resistor will be reduced to a level of hundreds of kΩ. Therefore, voltage change can be detected on the reference resistor of 1 MΩ. Such a method is employed to measure the change of moisture content on the two surfaces of textiles.22 If the voltage between Vss and GND is V0, resistance of fabric is Rf, resistance of the resistor (1 M here) is Rc; then V-test (V1) is: V1 =

V0 * Rc Rc + Rf

[13.1]


Liquid moisture transfer

225 GND

Vss

100 p

Textiles

1M f

f V–test

13.2 Simple model of the testing method.

and so Rf V0 = −1 Rc V1

[13.2]

where Rf is a known function of moisture content (M), as shown by equation (13.3): 1 = A⋅ M Rf

[13.3]

So M can be expressed as: M=

1 V1 ⋅ A ⋅ Rc V0 − V1

[13.4]

Thus we can see that, for a certain fabric, M is positively and linearly related to V1/(V0 − V1), which is the principle that we used to detect the moisture content in the fabric. Fabric area, A, is determined by the calibration experiment to determine the relationship between Rf and M for individual rings.22

13.6.2 Apparatus structure An apparatus has been designed to objectively and accurately measure the fabric moisture-management properties. The specimen will be held flat by the top and bottom sensor with a certain pressure. Then a certain weight (0.15 g) of predefined test solution (synthetic sweating – AATCC Test Method 15) is put into the sweat gland and introduced on to the top surface of the fabric. Meanwhile, the computer dynamically records the resistance change between each couple of proximate metal rings, which act as detec-


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tors, to detect the fabric wetted area in order to measure the size of wetted area and time it takes for the fabric to get wetted. The solution will transfer in three directions after arrival at the top surface of the fabric: 1. spreading outward on the top surface of the fabric; 2. transferring through the fabric from the top surface to the bottom surface; 3. spreading outward on the bottom surface of the fabric and then evaporating. The resistance of each couple of proximate metal rings will decrease because the solution can conduct electricity as it arrives in the area between them. The apparatus is linked to a computer and the signal is logged and can be processed immediately. Therefore, the water content on the two surfaces of the fabric can be determined by the following equations, respectively: 5

5

i=0

i=0

U t = ∑ Mti and U b = ∑ Mbi

[13.5]

where Ut is the total water content on the top fabric surface, Ub is the total water content on the bottom fabric surface and Mti and Mbi indicate the water content in the area between each couple of proximate metal rings at the top and bottom surfaces, which can be calculated by using equation (13.4) for each ring separately.

13.6.3 Experimental environment conditions To reduce the influence of environmental factors on the measurement results, the specimen should be cut to 90 × 90 mm and pre-washed by an ultrasonic cleaner. Any excessive water and wrinkle should be removed. The specimen is then placed in the condition room, where the environment is controlled at 21 ± 1 °C and RH 65 ± 2% (Ref: ASTM D1776), with at least 24 hours at ‘equilibrium regain’.

13.6.4 Indexes definition The typical changes in water content against time on the top (Ut) and bottom (Ub) surfaces of the fabric are shown in Fig. 13.3. Ut and Ub represent the surface contacts with skin and exposed to atmosphere, respectively. The water content of the top surface is much lower than that of the bottom surface, indicating that most of the liquid introduced to the top surface of the fabric is transferred quickly from the top to the bottom. From the measurement curves, a set of indexes for determining the fabric moisturemanagement properties can be derived. These are defined as below.


Liquid moisture transfer

591.2 549.5

227

Water content vs Time

Water content (%)

489.5 429.5 389.5 UT

309.5

UB

249.5 189.5 129.5 89.5 9.5 0.0

20.0

40.0

60.0

80.0

Top surface 5.074 16.8537 20.0 3.4266

Wetting time (sec) Absorption rate (%/sec) Max wetted radius (mm) Spreading speed (mm/sec) One way transport capability Description

100.0

Time(s) 120.0

Bottom surface 3.639 30.6881 15.0 3.1599 102.4627 MMT–

13.3 Typical measurement water content curves (sample A92NP).

Wetting time – WTt (top surface) and WTb (bottom surface) (sec) WTt and WTb are, respectively, the time periods in which the top and bottom surfaces of the fabric start to get wet after the test commences, which are defined as the times in seconds (s) when the slopes of the total water content at the top and bottom surfaces (i.e. Ut and Ub) become greater than tan 15 °, respectively. Wetting times can be compared with the absorbency drop test specified in AATCC 79–2000.6 Maximum absorption rate: MARt and MARb (%/sec) MARt and MARb are the maximum moisture absorption rates of the fabric top and bottom surfaces, respectively. Typically, they are the initial slopes of the water content curves. Maximum absorption rate (%/sec) is defined as MARt = Max(Slope(Ut)): MARt = 0

if

MARt < 0

[13.6]

and MARb = Max(Slope(Ub)): MARb = 0

if

MARb < 0

[13.7]


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Maximum wetted radius: MWRt and MWRb (mm) Maximum wetted radius (MWRt and MWRb) is defined as maximum wetted ring radius at the top and bottom surfaces, respectively, where the slopes of water content (Mti or Mbi) become greater than tan 15 ° for the top and bottom surfaces, respectively. Spreading speed: SSt and SSb (mm/sec) SSt and SSb are the speeds of the moisture spreading on the top and bottom fabric surfaces to reach the maximum wetted radius. Spreading speeds (SSt and SSb) are defined as: SSt =

MWR t t wrt

[13.8]

SSb =

MWR b t wrb

[13.9]

and

where twrt and twrb are the times reaching the maximum wetted rings on the top and bottom surfaces, respectively. Accumulative one-way transport capacity: OWTC OWTC is the difference of the accumulative moisture content between the two surfaces of the fabric in unit testing time period: OWTC =

∫U − ∫U b

t

T

[13.10]

where T is total testing time. Overall moisture management capacity (OMMC) This is an index to indicate the overall capability of the fabric to manage the transport of liquid moisture, which comprises three performances aspects: 1. moisture absorption rate at bottom side; 2. one-way liquid transport capability; 3. moisture drying speed at bottom side, which is represented by the maximum spreading speed. The overall moisture management capacity (OMMC) is defined as:


Liquid moisture transfer OMMC = C1 ∗ MARb + C2 ∗ OWTC + C3 ∗ SSb

229 [13.11]

where C1, C2 and C3 are the weights of the indexes of the absorption rate (MARb), one-way transport capacity (OWTC) and spreading/drying rate (SSb) (here, C1 = 0.25, C2 = 0.5 and C3 = 0.25), and are adjustable in practice according to the end-uses. The values of the coefficients used are determined on the basis of analyzing the relative importance of absorbance, one-way transport and drying speed and the correlations of the indexes with subjective moisture sensations from our results, which are obtained from our pre-experimental results analysis. The larger the OMMC, the higher the overall moisture-management capability of the fabric.

13.7

Experimental

13.7.1 Fabric Eight types of tight-fit garments, five of which were branded and purchased from department stores, were selected for this study. All the testing was carried out in a condition room, where the environment was controlled at 21 ± 1 °C and 65 ± 2% RH according to ASTM D1776. From each set of sportswear, ten pieces of specimen were cut to 90 × 90 mm and pre-washed by an ultrasonic cleaner, and ironed to remove any excessive water and wrinkle. They were then conditioned in the condition room for at least 24 hours. To simulate sweating, a special solution (synthetic sweat) was introduced onto the fabric top surface during the test, which was pre-prepared according to AATCC15. During testing, the same quantity of the solution (0.15 g) was automatically injected onto the top surface of each specimen by moisture-management tester (MMT). The basic properties of each fabric are listed in Table 13.5. Table 13.5 Fabric content and construction Fabric code

Average weight (g/m2)

Average thickness (mm)

Fabric content

Fabric construction

N88P C98L2 N85L15 R95C P98L2 E95C A92Np N95C

280.0 179.0 215.0 260.0 220.0 240.0 360.0 410.0

0.84 0.73 0.57 1.10 1.27 1.01 1.12 1.50

88% 98% 85% 95% 98% 95% 92% 94%

Plain knitted Plain knitted Plain knitted Plain knitted Rib knitted Plain knitted Plain knitted Rib knitted

polyester and 12% spandex cotton and 2% spandex nylon and 15% spandex cotton and 5% spandex polyester and 2% spandex cotton and 5% spandex nylon and 8% spandex cotton and 6% spandex


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13.7.2 Results and analysis The measurement results are summarized in Table 13.6. One-way ANOVA was also carried out to identify the significance of differences among the fabrics by using SPSS. Table 13.6 shows that the fabric A92NP has the highest liquid moisture-management capacity (OMMC = 68) and one-way transfer capacity (OWTC = 103), showing that the liquid sweat can be easily and quickly transferred from next to the skin to the outer surface to keep the skin dry. This fabric also has a relatively large spreading rate (SSb = 0.8 mm/sec) and medium wetted radius (MWRb = 10.63 mm) on the bottom surface, indicating that liquid can spread on the bottom surface and dry quickly. E95C and R95C, however, have poor moisture-management properties with very much lower wetted radius and spread rates (MWRb = 0 mm and SSb = 0 mm/sec for both fabrics) on the bottom surface, and negative one-way transport capacity (OWTC = −273.96 and −208.51 individually). This indicates that the liquid (sweat) cannot diffuse easily from next to the skin surface and will be accumulated on the top surface of the fabric. N95C and N85L15 represent another type of fabric that has lower oneway-transfer capability (OWTC = −18.13 and −10.50, respectively), and medium or low wetted radius and spread rates (MWRb = 10.0, 5.63 mm and SSb = 0.58, 0.49 mm/sec individually) on the bottom surface, indicating that the liquid (sweat) cannot easily diffuse from next to the skin to the top layer and evaporate into the environment. They are the slow-drying fabrics. C98L2 and N88P have medium one-way transfer capability (OWTC = 38.33 and 45.31 respectively), higher spread rates (SSb = 0.81 and 0.87 mm/ sec, respectively) and larger wetted area (MWRb = 12.5 and 15.00, respectively), indicating that the liquid (sweat) can transfer from surface next to the skin to the top layer and spread quickly on the bottom surface of the fabric with a large wet area, where it evaporates into the environment. These fabrics have quick-dry capabilities. P98L2 also has a good moisturemanagement capability (OMMC = 50), absorption property at both sides (MAt = 101.67 and MAb = 194.57), but is weak in one-way transfer capacity (OWTC = 2.81) and spread rate (SSb = 0.30 mm/sec) as well as wetted radius (MWRb = 6.88 mm) on the bottom surface. This indicates that the liquid (sweat) can transfer from next to the skin to the top layer but is not easily evaporated into the environment. Through one-way ANOVA, we find that there are significant differences in the liquid moisture-management properties among the fabrics in all the measured indexes at the level of p ≤ 0.01, except SSt at the p = 0.029 level.


3.74 0.56

Mean s.d.

Mean s.d.

Mean s.d.

Mean s.d.

Mean s.d.

Mean s.d.

Mean s.d.

Mean s.d.

N88P

C98L2

N85L15

R95C

P98L2

E95C

A92Np

N95C

7.51 1.98

6.33 0.98

3.17 0.20

3.15 0.13

3.46 0.25

3.03 0.11

4.98 1.23

WTt

Fabric

7.16 0.75

4.23 0.38

119.95 0.00

13.52 3.50

119.95 0.00

8.32 2.66

4.39 0.48

3.44 0.42

WTb

41.14 32.62

20.46 8.28

93.71 26.41

101.67 18.72

70.54 9.79

105.65 21.00

23.15 6.23

20.07 3.77

MARt

22.39 7.24

65.11 12.82

1.18 0.73

194.57 49.00

0.70 0.37

135.44 43.47

56.25 22.90

39.30 19.01

MARb

Table 13.6 Summary of fabric moisture-management properties

6.88 2.59

8.75 2.31

10.00 0.00

7.50 2.67

10.00 0.00

8.13 2.59

14.38 1.77

15.00 0.00

MWRt

10.00 0.00

10.63 1.77

0.00 0.00

6.88 2.59

0.00 0.00

5.63 1.77

12.50 2.67

15.00 0.00

MWRb

0.64 0.30

0.67 0.09

0.93 0.09

1.10 0.64

0.89 0.08

1.08 0.50

0.75 0.07

0.82 0.09

SSt

0.58 0.06

0.80 0.12

0.00 0.00

0.30 0.15

0.00 0.00

0.49 0.16

0.81 0.16

0.87 0.09

SSb

68.35 7.26 −3.33 9.47

103.74 9.81 −18.13 17.68

−136.69 18.46

−273.96 36.97

−104.08 3.69

−208.51 7.39

50.12 23.58

28.73 27.37

−10.50 51.86

2.81 25.43

33.43 10.68

32.70 12.98

OMMC

38.33 15.35

45.31 17.68

OWTC

Liquid moisture transfer 231


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13.7.3 Subjective perception of moisture sensations Twenty-eight females aged between 18 and 35 participated as subjects in a psychological sensory wear trial, which was conducted in an environmentally controlled chamber with temperature and humidity fixed at 29 ± 1 °C and 85 ± 2%, respectively.23 Subjects were required to run on a treadmill with a randomly selected garment for 20 minutes, two moisture sensations (clammy and damp) were rated by each subject on a seven-point scale before (time = 0), during (time = 5, 10 and 15) and at the end (time = 20) of the 20 minutes’ running period. At the end of the trial, subjects were required to rest for at least 30 minutes; this allowed them to cool down and their bodies to dry. After 30 minutes of resting, subjects repeated the trial process with another garment.23 The correlations between OMMC and clammy are not significant at 0, five and ten minutes, but become significant around 15 and 20 minutes of running. This shows that the perception of clammy is not related to the OMMC as sweat becomes significant during exercise. The correlation between OMMC and clammy is increased from |r| = 0.658 at the 15th minute to |r| = 0.776 at the 20th minute. Similarly, the correlations between OMMC and damp are not significant at 0 and five minutes, but significant at the 10th, 15th and 20th minute of the running period, indicating that, when sweating increases and is accumulated in the fabric as the trial progresses, the overall moisture-management capacity of the fabric becomes more important and plays a more important role in keeping the wearer dry. The correlation between OMMC and damp increased from |r| = 0.734 at the 10th minute to |r| = 0.892 at the 20th minute. There are no significant differences between subjective ratings of clamminess at time 0 when subjects wore garments made of different fabrics with OMMC values between −150 and 100. The same phenomenon is also observed at five and ten minutes. Similarly, there were no significant differences between the subjective ratings of dampness at 0 and five minutes when wearing garments made of fabric with the same range of OMMC values. This suggests that subjects cannot notice the differences in OMMC properties within these time periods. However, there are significant differences between subjective ratings of clamminess at 15 and 20 minutes and at ten, 15 and 20 minutes for dampness, suggesting that subjects can notice the differences in OMMC properties within these time periods. This may be explained by the fact that subjects did not sweat or did not sweat enough at the beginning of the wear trial. Therefore, no significant differences were found in the perception of moisture-related discomfort sensations at the beginning of the wear trial. As the running time continues, the relationship between OMMC and subjective ratings becomes stronger. Similar patterns are also found in the relationships between OWTC and the subjective


Liquid moisture transfer

233

ratings of clamminess and dampness. These results suggest that the influences of OMMC are not significant until subjects generate significant amounts of sweat to perceive moisture-related discomfort.

13.7.4 Conclusion The properties of liquid moisture transfer in fabric in multidimensions significantly influence wearing comfort perception. Several standards relevant to characterizing fabric moisture-management properties are introduced in this chapter. A new test method and tester, which can be used to characterize the moisture-management properties of fabric, are presented. With this new apparatus, liquid transfer in the fabric can be dynamically measured in three directions in one step.

13.8

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the projects G-V987, A188 and ITF project ITS-023-03.

13.9

References

1. AATCC, AATCC 22-2001 Water Repellency: Spray Test. 2001: Research Triangle Park, NC, AATCC. 2. AATCC, AATCC 35-2000 Water Resistance: Rain Test. 2000: Research Triangle Park, NC, AATCC. 3. AATCC, AATCC 42-2000 Water Resistance: Impact Penetration Test. 2000: Research Triangle Park, NC, AATCC. 4. AATCC, AATCC 70-2000 Water Repellency: Tumble Jar Dynamic Absorption Test. 2000: Research Triangle Park, NC, AATCC. 5. AATCC, AATCC 79-2000 Absorbency of Bleached Textiles. 2000, AATCC. p. 103–104: Research Triangle Park, NC, AATCC. 6. AATCC, AATCC Test Method 127–1998 Water Resistance: Hydrostatic Pressure Test. 1998: Research Triangle Park, NC, AATCC. 7. Adamson, A.W., Physical Chemistry of Surfaces, 3rd edn, 1976: New York, Wiley-Interscience. 8. ASTM, ASTM D 4772-97 Standard Test Method for Surface Water Absorption of Terry Fabric (Water Flow). 1997: West Conshohocken, PA, ASTM. 9. ASTM, ASTM D 5725-99 Standard Test Method for Surface Wettability and Absorbency of Sheeted Materials Using an Automated Contact Angle Tester. 1999: West Conshohocken, PA, ASTM. 10. ASTM, D123-01 Standard Terminology Relating to Textiles, in Annual Book of ASTM Standards. Vol 07.01. 2002: West Conshohocken, PA, ASTM. p. 9–91. 11. Bretschneider, H. and B. Seidel, Determination of free surface energy of single fibres by contact angle measurements. Textiltechnic, 1987. 37(7): p. 377–382.


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12. Buckton, G., Contact-Angle, Adsorption and Wettability – a Review with Respect to Powders. Powder Technology, 1990. 61(3): p. 237–249. 13. Collier, B.J. and H.H. Epps, Textile Testing and Analysis, B.J. Potthoff, Ed., 1998: New Jersey, Prentice-Hall, Inc. 14. Davies, S., Polyester: Its Role in Active Sportswear. World Sports Activewear, 1999. 5(3): p. 54–56. 15. D’Silva, A.P., C. Greenwood, S.C. Anand, D.H. Holmes, and N. Whatmough, Concurrent Determination of Absorption and Wickability of Fabrics: a New Test Method. Journal of the Textile Institute, 2000. 91(3): p. 383–396. 16. Filipic, Z., K. Stana Kleinschek, and T. Kreze, The Sorption Characteristics of Different Cellulose Fibres Monitored by Tensiometry. Tekstilec, 2000. 43(7–8): p. 245–250. 17. Good, R.J., Contact Angle, Wetting, and Adhesion – a Critical Review. Journal of Adhesion Science and Technology, 1992. 6(12): p. 1269–1302. 18. Hawkyard, C.J., M.R.B. Lavasani, and P. Singh, A Comparison of Manual and Automated Test Methods for Wettability. 2000, in Proceedings of the 80th World Conference of the Textile Institute. 2000. Manchester, England. 19. Holme, I., Survival 2002: Performance Garments. Textile Horizons, 2002. (May/ June): p. 7–8. 20. Hsieh, Y.L., J. Thompson, and A. Miller, Water Wetting and Retention of Cotton Assemblies as Affected by Alkaline and Bleaching Treatments. Textile Research Journal, 1996. 66(7): p. 456–464. 21. ITC Department, the Hong Kong Polytechnic, MMT User Menu. 2000: Hong Kong. p. 16. 22. Li Y., W. Xu, K.W. Yeung, and Y.L. Kwok, Moisture Management of Textiles. 2002: USPTO 6,499,338. 23. Wong, A.S.W., Y. Li, and K.W. Yeung. Comfort Perceptions and Preferences of Young Female Adults for Tight-Fit Sportswear, in Proceedings of the Textile Institute 82nd World Conference. 2002. Cairo, Egypt.


14 Coupled heat and moisture transfer S.X. WANG AND YI LI Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

14.1

Introduction

There is no doubt that coupled heat and moisture transfer in fabric is one of the most important elements in the study of clothing comfort. Therefore, over the years, a series of theoretical study and modeling research related to heat and moisture transfer in fabric has taken place. This has included coupling mechanism of heat transfer and liquid moisture diffusion in porous textiles using mathematical modeling11 and mathematical simulation of the physical mechanisms of moisture diffusion into hygroscopic fabrics during humidity transients.7 As coupled heat and moisture transfer is complex, it is necessary to use computational and mathematical modelling techniques to simulate the process in order to have a better and deeper understanding of the mechanism involved.

14.2

Simulation of coupled heat and moisture transfer

There has been considerable research effort in this area and a review of some of this work is presented. Li and Wang8 developed a one-dimensional mathematical model to describe the dynamic coupled heat and moisture transfer inside multilayered porous polymers and anisotropic materials. Comparing this model with their previous one indicated that three improvements have been made. 1. The model takes into account the dynamic complex behavior of coupled heat and liquid moisture transfer inside each layer. 2. The model provides different (inter-) boundary conditions that correspond with the contact situations between neighboring layers and the influences from waterproof fabrics. 3. The model demonstrates the combinations of different types of polymer fibers in each layer and their roles with respect to processes of heat and moisture transfer. 235


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In order to validate the developed model, in terms of dynamic temperature and moisture concentration distribution in the assembly, as well as the initial and final absolute liquid volumetric fractions at the surface of the top layer, a series of physical experiments for liquid transfer through layered fabric assemblies with different constructions was carried out. Good agreement between the calculated and experimental results was obtained. Wang et al.13 investigated the influence of waterproof fabrics on heat and moisture transfer through a clothing system. Based on the published experimental data, water vapor permeability (WVP), which is considered to be a factor influencing the mass transfer coefficient at the boundary exposed to the environment, was derived. The result of their numerical simulation revealed that waterproof fabric has a significant influence on dynamic moisture content distribution in fabrics/fibers, moisture vapor condensation and liquid transfer behavior inside the clothing. In order to simulate the complex coupled heat and moisture transfer in porous polymer materials, Wang et al.14 considered the effects of the many processes involved, such as radiation and conduction heat transfer, liquid capillary action, moisture sorption and condensation, in their one-dimensional mathematical model. The volume of fraction (VOF) technique was applied to model the dynamic distribution of moisture in two different phases, vapor and liquid. The finite volume method (FVM) was used to develop a numerical computational scheme to solve the model. The temperature change on the fabric surface derived from the computational results of the model was compared with experimental measurements and reasonable agreement between the two was found. Li and Zhu10 developed an equation to describe liquid diffusion behavior in the form of the diffusion coefficient and a numerical computational scheme to solve the coupled equations involving a fractional volume of fluid method. In the validation of the model, experimental and predicted fabric surface temperatures were compared and results showed reasonable agreement between the two. Li and Luo7 conducted a mathematical simulation to study the physical mechanisms of moisture diffusion into hygroscopic fabrics during humidity transients. Based on the mathematical model, which describes the coupled heat and moisture transfer in wool fabric, the moisture-sorption mechanisms were investigated for fabrics made from fibers with different degrees of hygroscopicity. Theoretical predictions of the moisture uptake and temperature changes under humidity transients were compared with those measured previously in a sorption-cell experiment for fabrics made from wool, cotton, acrylic fiber and polypropylene fiber. It was concluded that the physical mechanism of moisture diffusion into highly hygroscopic fibers such as wool and cotton can be described by a two-stage moisture-


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diffusion process: a fast Fickian diffusion with a concentration-dependent diffusion coefficient and a slow diffusion with a time-dependent diffusion coefficient. For weakly hygroscopic fibers such as polypropylene, the moisture-sorption process can be described by a single Fickian diffusion with a constant diffusion coefficient. Through theoretical calculations of the distributions of moisture concentration in the air of fabric void space, fiber moisture content and temperature through fabric thickness, we show that moisture diffusion into a fabric through air is a rapid process for all the fabrics studied. Meanwhile, the moisture diffusion into fibers is coupled with the heat-transfer process, which is much slower and is dependent on the ability of fibers to absorb moisture. The strength of the coupling effect is a function of a number of fiber properties, such as the moisturesorption isotherms, water-diffusion coefficient, fiber diameter and heat of sorption. Li and Luo6 developed an improved mathematical model that takes into account the water vapor sorption mechanisms of wool fiber to describe and predict the coupled heat and moisture transport in wool fabrics. The twostage moisture sorption of wool is handled by a diffusion equation with two sets of variable water vapor diffusion coefficients in the fibers. The improved model is solved numerically by the finite difference method. Predictions from the new model are compared with those from the Nordonâ&#x20AC;&#x201C;David model and the previous two-stage model, and with experimental observations. Processes of moisture transfer in the air and in fibers and their interaction with heat transfer under humidity transient conditions are illustrated by a series of 3D diagrams. Li and Holcombe4 presented an extended mathematical model, in which water vapor sorption kinetics of wool fibers was taken into account to describe the coupled heat and moisture transport in a wool fabric. The predictions from the two-stage model were compared with those from the Nordonâ&#x20AC;&#x201C;David model and a simple Fickian diffusion model, and with experimental observations on a sorption-cell. The two-stage model showed the best agreement with observations from the sorption-cell tests. Gibson and Charmchi1 integrated their numerical model, which describes coupled energy and mass transport through porous hygroscopic materials, with an existing human thermal physiology model to provide boundary conditions for a clothing model. In the human thermal control model, parameters including skin temperature, core temperature, skin heat flux, water vapor flux and liquid water accumulation at the skin surface were provided. The integrated model provides the opportunity to systematically examine a number of clothing parameters, not usually included in steadystate thermal physiology studies, and to evaluate their potential importance under various conditions of human work rates and environmental conditions.


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In conclusion, it can be seen that the process of coupled heat and moisture transfer in clothing is influenced by many factors including: fabric construction, number of fabric layers, moisture vapor condensation, liquid capillary action, moisture sorption and heat of sorption. Various mathematical experiments demonstrated that the process of coupled heat and moisture transfer in clothing can be simulated and good agreement was found between actual and predicted results.

14.3

Moisture transfer with temperature gradient

Moisture vapor transport through waterproof breathable samples and clothing systems under a temperature gradient was examined by Gretton et al.2 using a simple research method. The results were then compared with those obtained from isothermal tests. The transport properties of hydrophilic polymers and clothing systems incorporating hydrophilic polymers, especially those with low transmission rates in the isothermal tests, improved by considerably greater amounts than those incorporating microporous polymers when a temperature gradient was applied. They also identified that the transport properties were dependent on: • • •

the temperature gradient across the waterproof breathable layer; the humidity of the clothing microclimate; the interaction between water vapor and the clothing layers.

Kanetsuna and Takenaka3 compared moisture permeability of ten-layered fabrics under two different conditions: temperature gradient and isothermal. For the temperature gradient condition, the temperature of the evaporation technique vessel cup was held at 32 °C and the experiments were performed at various environmental humidities with the temperature below 32 °C. For the isothermal condition, both the temperature of the vessel cup and the environmental temperature were the same. Moisture permeability and humidity on both the inside and outside surfaces of ten-layered fabrics and a diffusion coefficient of a moisture vapour in fabrics were also compared in both conditions. Although heat and moisture transfer with convection and conduction heat transfer have been studied regularly, radiation heat transfer has been neglected. Therefore, in the mathematical study of heat and moisture transfer in rough fabric, Shi and Ni12 also considered the radiation heat transfer caused by a temperature gradient.

14.4

The role of phase change material (PCM) fabrics

Phase change material (PCM) plays an important role in the dynamic thermal and moisture buffering process. Therefore, the focus of this section is to describe the design and development of PCM clothing fabric through


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a series of experiments and computational simulations. The basic physical properties of the PCM clothing fabric are shown in Table 14.1. The dimensions of layers 1, 2, 4 and 5 are 25 cm × 25 cm (= 625 cm2), but for layer 3 the dimension is 15 cm × 15 cm (= 225 cm2). All the layers were sewn together as a one-piece fabric during the test. Two types of specially designed fabrics, as shown in Figs 14.1a and 14.1b, were selected for the experiment. The difference between the two fabrics is that the one in Fig. 14.1b has a PCM coating on the second layer and the one in Fig. 14.1a has no PCM coating. All the experiments were conducted in a climate chamber, which was temperature controlled at −15 ± 0.5 °C. In order to easily control the skin surface temperature, the bionic skin model (BSM)5 was used. The advantage of using BSM is that its surface temperature, 33 °C, can be kept at a constant perspiration state. A total of ten sensors (five each for measuring temperature and humidity) were fixed at each layer, as shown in Fig. 14.2. The heat flux and infrared sensors were fixed on the surface of the skin and on the top of the control sample, respectively. At the beginning of the experiment, the surface temperature of which was controlled at 33 °C, a sample was placed on the BSM. When the temperature of the second layer reached 27 °C and 29 °C, the power of the heating fabric was automatically switched on and off, respectively. The aim of the experiment was to investigate the effect of heating on fabric with and without PCM. Therefore, there were four sets of experiments, each repeated five times. Figures 14.3 and 14.4 illustrate the temperature changes of PCM fabric without and with heating assembly at different layers in 3-dimensions, respectively. Figure 14.5 compares the temperature changes of PCM with and without heating assembly at different layers. PCM with heating

Table 14.1 Basic physical properties of the tested fabric Layer

Content

1 2

Cotton fabric Non-woven polyester with PCM Heating fabric Non-woven polyester Waterproof breathable fabric

3 4 5

PCM = Phase change material.

Thickness (mm)

Density (kg · m−3)

Weight (kg · m−2)

Thermal conductivity (W · m−1 · K−1)

0.82 4.52

361.52 146.29

0.295 0.44

0.061 0.107

0.70 16.55 0.51

481.32 14.72 437.91

0.335 0.243 0.224

0.073 0.086 0.056


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A

Skin

Environment

33 °C –15 °C

Cotton

Non-woven Heating Non-woven Waterproof polyester polyester breathable fabric fabric

14.1a The structure of tested heating fabric without PCM.

B

PCM

Skin

Environment

33 °C –15 °C

Cotton

Non-woven Heating Non-woven Waterproof polyester breathable polyester fabric fabric

14.1b The structure of tested heating fabric with PCM.


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Skin 33 °C Layer-1

Layer-2 Layer-3

Layer-4

Layer-5

Humidity sensor Environment –15 °C

Temperature sensor

14.2 Location of the temperature and humidity sensors.

35 30 25

Temperature (°C)

20 15 10 5 0 –5 –10 0 –15 0

5

500 10

15

20

25

1000 Time (s)

Thickness (mm)

14.3 Temperature across the fabric layers without heating.


35 30

Temperature (°C)

25 20 15 10 5 0 –5 –10 0 –15 0

5

500

10

15

20

1000

25

Time (s)

Thickness (mm)

14.4 Temperature across the fabric layers with heating.

t1

35

t2 30

t3

25

PCM without heating PCM with heating

Temperature (°C)

20 15 10 5 0 0

5

10

15

–5

20

25

t4

–10

t5

–15 Thickness (mm)

14.5 Comparison of PCM temperature changes with and without heating assembly at different layers.


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assembly can keep the fabric temperature within the range 35–25 °C at a thickness of 5 mm. However, the fabric temperature dropped significantly, from 30 to 15 °C, for other fabric with thickness 5 mm. The temperature of the first layer of PCM with heating assembly is about 1.80 °C higher than the one without heating assembly. Similarly, the temperature of the second layer of the PCM with heating assembly is about 9.58 °C higher than the one without. Figures 14.6 and 14.7 illustrate the temperature changes of non-PCM fabrics without and with heating effect, respectively. The temperatures of the outer layer of PCM with and without heating assembly are about −10.86 and −11.87 °C, respectively. Therefore, there is a difference of approximately 1 °C. The mean temperatures of the third and fourth layers of PCM without heating assembly are about 17.06 and −9.31 °C, respectively. However, for the PCM with heating assembly, the mean temperatures at the third and fourth layers are about 27.21 and −7.3 °C, respectively. In general, the temperature of each layer of PCM with heating assembly is higher than PCM without heating.

40

Temperature (°C)

30

20

10

0

–10 0 –20 0

5

500 10

15

Thickness (mm)

20

25

1000 Time (s)

14.6 Temperature changes of non-PCM fabric without heating assembly.


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35 30 25

Temperature (°C)

20 15 10 5 0 –5 –10 –15 0

0 500 5

10

15

Thickness (mm)

20

25

1000 Time (s)

14.7 Temperature changes of non-PCM fabric with heating assembly.

Figure 14.8 compares the effect of heating on two non-PCM fabrics. The temperature of the first layer of non-PCM fabric with heating assembly is about 1.19 °C higher than that of the non-PCM fabric without heating assembly. The temperature of the second layer of the non-PCM fabric with heating assembly is about 12.4 °C higher than that without. The mean temperatures of the third layer and the fourth layer of non-PCM fabric without heating assembly are about 14.05 and −9.99 °C, respectively, but the counterparts for non-PCM fabric with heating assembly are about 26.95 and −7.28 °C. The temperature of the outer layer of the non-PCM fabric with heating assembly is about −11.17 °C. The temperature of the outer layer of the non-PCM fabric without heating assembly is about −12.36 °C. In general, the temperature of each layer of the PCM/non-PCM fabric with heating assembly is higher than the counterpart for PCM/non-PCM fabric without. The main reason for this is that heating fabric consumes electrical power and releases heat, keeping the temperature of the second layer in the range 27–29 °C. Therefore, the PCM with heating and the non-PCM fabric with heating assemblies are warmer than those without. At the same time, heating


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Temperature distribution t1

35

t2 30

t3

25

Temperature (°C)

20 No PCM without heating No PCM with heating

15 10 5 0 0

5

10

15

–5 –10

20

25

t4

t5

–15 Thickness (mm)

14.8 Effect of heating assembly on non-PCM fabrics.

also increases the water vapor zone and the insulation value of the assembly. Figure 14.9 compares the effect of heating on PCM and non-PCM clothing. The pattern of both cases is very similar. Clothing with PCM and heating assembly has a slightly higher temperature compared to the non-PCM with heating assembly. On average, the temperature drops from 32.0 °C at t1 to 29.5 °C at t2.

14.5

Effect of PCM fabrics

The mean and standard deviation of PBS, FH, HP and NHP in PCM and non-PCM with heating assembly are shown in Table 14.2. PBS is the electrical power in watts consumed by the BSM, FH is the working cycle period of the heating fabric in seconds, HP is the value of the heating time divided by the one-cycle period and NHP is the value of the non-heating time divided by the one-cycle period. FH and heating time are illustrated in Fig. 14.10. The BSM with PCM and heating consumed less energy than that without PCM and with heating. The electrical power consumption of BSM with PCM and with heating is about 38.03 W, whereas that of the BSM


246

Clothing biosensory engineering Temperature of different layers

33 32

Temperature (째C)

PCM with heating No PCM with heating

t1

31 30 29

t2

28 27

t3

26 25 1

0

2

3

5

4

6

7

Thickness (mm)

14.9 The effect of heating on PCM and non-PCM clothing.

Table 14.2 Mean and standard deviation of PBS, FH, HP and NHP in PCM Sample

PCM with heating Non-PCM with heating

Mean s.d. Mean s.d.

Power consumed by BSM, PBS (W)

Heating cycle period, FH (s)

Heating time per cycle period, HP (%)

Non-heating time per cycle period, NHP (%)

38.0 2.54 39.2 3.57

128.0 11.43 82.0 4.50

32.2 1.61 46.3 1.30

67.8 0.07 53.7 0.05

without PCM and with heating is around 39.17 W. The heating cycle period of the PCM with heating is about 128 seconds, which is longer than that of the non-PCM with heating at 82 seconds. The HP of the nonPCM with heating is about 46.3%, which is larger than that of the PCM


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Heating cycle 30 29.5

PCM No PCM

Temperature (°C)

29

28.5

28 Heating time

27.5

27 FH 26.5 0

20

40

60

80

100

120

140

160

Time (s)

14.10 Heating cycle of PCM and non-PCM clothing.

with heating (32.21%). In the heating condition, when the PCM layer’s temperature becomes higher than the PCM’s melting point (28 °C), the PCM melts and becomes liquid. During this process, some heat energy will be absorbed and stored. After all of the PCM has become liquid, the temperature continually increases. When the PCM layer’s temperature reaches 29 °C, the heating fabric is powered off. Then, the temperature of the PCM layer decreases after a short time. When the temperature of the PCM layer decreases below 26 °C, the liquid PCM becomes solid and releases heat energy. In this process, the PCM acts as a heat buffer, storing and releasing heat. In the temperature decreasing process, due to the PCM’s heat releasing effect, the PCM with heating assembly takes longer to decrease to 26 °C than the non-PCM with heating assembly. So, the heating cycle period of the PCM with heating assembly is longer than that of the non-PCM with heating assembly. In the heating process, some heat energy is unavoidably lost. The PCM with heating can consume less energy than the non-PCM with heating. In 30 minutes, there were about 14 cycles for the PCM with heating and 22 cycles for the non-PCM with heating. About 30.9% energy can be saved by the PCM with heating in this period.


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Based on the findings from these experiments, it can be concluded that heating fabric can significantly increase the temperatures of the different layers of the assembly, thus making the assembly warmer. In the heating condition, PCM can raise the temperature of the assembly and can save electrical energy.

14.6

Measuring the thermal and moisture buffering effects of fabrics

Clothing comfort is a very complex subjective perception, which relates to the interactions between fabrics, climatic, human physiological and psychological variables. In order to investigate textile comfort properties under the dynamic sweating condition, the BSM was invented by Li et al. in 2004.5 The BSM is an independent electronic controller which consists of four components: an infrared source, a signal condition circuit, a data acquire system (to control the water tank temperature and skin surface temperature and sweating rate of the BSM) and a 24-channel recording device for temperature, humidity, heat flux and infrared sensors output. Furthermore, the temperature of the skin surface was set at 33.0 째C. The temperatures of the thermal guard and water tank were set at 33.5 and 33.0 째C, respectively. The sweating rate was controlled at 24 g/hr. The above four parameters are controlled automatically. However, the on/off of the infrared source is controlled manually. The structure of the bionic skin model is illustrated in Figs. 14.11 and 14.12.

14.11 Structure of the bionic skin model.


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14.12 Detailed structure of the bionic skin model upper surface design.

The bionic skin model consists of upper cover (51), heat retainer (52), inlet (53) and perspiration container (54). An electronic heating pad is fixed underneath the perspiration container (54) in order to maintain the temperature of the model skin surface at 33 °C. In order to simulate insensible perspiration or sensible sweating of the human body, there are 33 holes, each has a diameter of Ό0.5 mm, distributed on the surface of the upper cover (513), which is covered by five layers of synthetic moisture management fabric (MMF). Through the uniformly distributed holes and MMF, water vapor or liquid is able to reach the fabric surface from water. The characteristic of MMF is that it has excellent liquid water one-way transfer properties and a high spreading speed on the upper surface. This bionic skin model is used to measure the dynamic thermal and moisture buffering effects of clothing assemblies, particularly those incorporating phase change models.


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14.7

Acknowledgements

The authors would like to thank the Hong Kong Research Grant Council and the Hong Kong Polytechnic University for funding this research through the project PolyU 5281/03E and Hong Kong Innovation Technology Commission ITS-023-03.

14.8

References

1. Gibson, P.W. and M. Charmchi, Coupled Heat and Mass Transfer Through Hygroscopic Porous Materials – Application to Clothing Layers. Sen-i Gakkaishi, 1997. 53(5): p. 183–194. 2. Gretton, J.C., D.B. Brook, H.M. Dyson, and S.C. Harlock, Moisture Vapor Transport Through Waterproof Breathable Fabrics and Clothing Systems Under a Temperature Gradient. Textile Research Journal, 1998. 68(12): p. 936– 941. 3. Kanetsuna, H. and T. Takenaka, A Moisture Permeability Through a Fabric Under Temperature Gradient Conditions. Sen-i Gakkaishi, 2000. 56(11): p. 544– 549. 4. Li, Y. and B.V. Holcombe, Two-stage Sorption Model of the Coupled Diffusion of Moisture and Heat in Wool Fabrics. Textile Research Journal, 1992. 62(4): p. 211–217. 5. Li, Y., J.Y. Hu, S.X. Wang, and K.W. Yeung, Thermal and Moisture Management Properties of Porous Materials. 2004, Submitted to USPTO, USA. 6. Li, Y. and Z. Luo, An Improved Mathematical Simulation of the Coupled Diffusion of Moisture and Heat in Wool Fabric. Textile Research Journal, 1999. 69(10): p. 760–768. 7. Li, Y. and Z.X. Luo, Physical Mechanisms of Moisture Diffusion into Hygroscopic Fabrics During Humidity Transients. Journal of the Textile Institute, 2000. 91(1/2): p. 302–316. 8. Li, Y. and Z. Wang, Mathematical Simulation of Dynamic Coupled Heat and Liquid Moisture Transfer in Multilayer Anisotropic Porous Polymers. Journal of Applied Polymer Science, 2004. 94(4): p. 1590–1605. 9. Li, Y., W. Xu, K.W. Yeung, and Y.L. Kwok, Moisture Management of Textiles, The Hong Kong Polytechnic University, USPTO 6,499,338, Hong Kong, 2002. 10. Li, Y. and Q. Zhu, Simultaneous Heat and Moisture Transfer with Moisture Sorption, Condensation, and Capillary Liquid Diffusion in Porous Textiles. Textile Research Journal, 2003. 73(6): p. 515–524. 11. Li, Y., Q. Zhu, and K.W. Yeung, Influence of Thickness and Porosity on Coupled Heat and Liquid Moisture Transfer in Porous Textiles. Textile Research Journal, 2002. 72(5): p. 435–446. 12. Shi, X.K. and B. Ni, Mathematical Study on Coupled Heat and Moisture Transfer Through Fabrics with the Internal Heat Radiation Heat Transfer. Fangzhi Gaoxiao Jichukexue Xuebao, 2004. 17(1): p. 52–58. 13. Wang, Z., Y. Li, Y.L. Kwok, and C.Y. Yeung, Influence of Waterproof Fabrics on Coupled Heat and Moisture Transfer in a Clothing System. Sen-i Gakkaishi, 2003. 59(5): p. 187–197.


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14. Wang, Z., Y. Li, and Z.X. Luo, Radiation and Conduction Heat Transfer Coupled with Liquid Water Transfer, Moisture Sorption, and Condensation in Porous Polymer Materials. Journal of Applied Polymer Science, 2003. 89(10): p. 2780â&#x20AC;&#x201C; 2790. 15. Xu, W., Y. Li, K.W. Yeung, and Y.L. Kwok, Treatment of Fabrics, Hong Kong Polytechnic University, USPTO 6,454,814, Hong Kong, 2002.


15 Air permeability J.Y. HU, YI LI AND K.W. YEUNG Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

15.1

Introduction

The concept of ‘air permeability’ is widely used in the textile industry to interpret the intrinsic characteristics of fabric. Outdoor garment manufacturers in particular frequently employ this technical information to describe functional performance of products. Several existing standards can be used for air permeability evaluation with different testing conditions. Air permeability is significantly influenced by a fabric’s material and structural properties, such as shape and value of the pores of the fabric and yarn, which in turn are dependent on the structural parameters of the fabric, such as fabric weave, the raw material of the yarns, the set of yarns and others. In addition, as the results of McCullough’s research show, fabrics with hydrophilic components can change their air permeability properties under different humidity conditions.13 Construction factors and finishing techniques can also have an effect upon air permeability by causing a change in the length of air flow paths through a fabric. Fabrics with different surface textures on either side can have a different air permeability depending upon the direction of air flow. For woven fabric, yarn twist is also important. As twist increases, the yarn diameter and the cover factor are decreased, and this increases air permeability. Increasing yarn twist may also allow the more circular, high-density yarns to be packed closely together in a tightly woven structure with reduced air permeability. ASTM defined the term air permeability as the rate of air flow passing perpendicularly through a known area under a prescribed air pressure differential between the two surfaces of a material. It is generally expressed in SI units as cm3/s/cm2 or in inch-pound units as ft3/min/ft2.2 The term air permeability is often used in evaluating and comparing the ‘breathability’ of various fabrics for such end-uses as raincoats, tents and uniforms. It is also closely related to the terms fabric water vapor permeability and wind resistance performance which evaluate the performance of parachutes, sail cloth, sportswear and industrial filter fabrics.5,11,16,17 252


Air permeability

253

Based on earlier research results, the clothing system needed to protect an individual in a cold environment would depend on the following main factors: (1) metabolic heat, (2) environment factors, like temperature, humidity and wind speed, and (3) fabric/garment properties, like thermal insulation, air permeability and moisture vapor transmission behaviours. The survival of a dressed human depends on the balance of heat losses due to (2) and (3) and heat generation due to metabolic heat (1). Prior research also points out that vapor transfer through clothing systems may occur due to diffusion (driven by vapor concentration gradients) and convection (driven by air pressure differences). Convective heat and mass transfer in textiles is often more important than transport due to diffusion, especially if such materials are used in conditions where a large pressure gradient is present.10 Therefore, the air penetration and vapor diffusion behaviour significantly influences the thermal comfort sensations and can even increase an individualâ&#x20AC;&#x2122;s ability to survive in critical cases. As an example, many types of functional fabrics like modern waterproof fabrics are designed for use in garments that provide protection from the weather, that is from wind, rain and loss of body heat. Therefore, in the modern textile and clothing industry, the fabric treatments such as finishes, coatings and film membranes are added to shell fabrics to reduce or prevent water and wind penetration into clothing layers. However, some of these treatments inhibit the evaporation of sweat from the body surface and its transportation through the fabric layers to the environment. If water vapor cannot escape, it may condense in the cooler outer layers of the clothing system or on the inner surface of the shell fabric. The accumulation of water vapor inside the clothing microclimate may cause discomfort to the wearer.13 Milenkovi et al.14 point out that the air permeability of a fabric can influence its comfort behaviour in several ways. 1. In the first case, a material that is permeable to air is also, in general, likely to be permeable to water, in either the vapor or the liquid phase. Thus, the moisture-vapor permeability and the liquid-moisture transmission are normally closely related to air permeability. 2. In the second case, the thermal resistance of a fabric is strongly dependent on the enclosed still air, and this factor is in turn influenced by the fabric structure, as also is the air permeability. A very open cloth can inflict serious wind chill problems on the wearer in cold climates with a breeze blowing and may thus affect survival chances in extreme cases. 3. Finally, a highly air-permeable fabric may be sheer or have a very open structure, so that aesthetic factors such as modesty, dimensional stability, drape, handle, etc. may result in discomfort of a psychological or physical nature in the wearer. Although air permeability in itself is


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Clothing biosensory engineering merely another effect, rather than a cause, associated with such manifestations of discomfort, it can nevertheless provide a convenient and readily measured way of quantifying the likely behaviour of a fabric in these other areas.

Another related term is â&#x20AC;&#x2DC;breathabilityâ&#x20AC;&#x2122;, which indicates that a fabric is actively ventilated. For the functional breathable fabric, water vapor produced by the body is required to easily diffuse through it into the surrounding environment and to prevent the penetration of liquid water from outside into the garment in order to maintain a dry feeling during wear. These are the essential requirements for maintaining comfort sensations during wear. Because perspiration is one of the main mechanisms for cooling the body, if the water vapor is unable to escape into the surrounding atmosphere easily, it will increase the relative humidity of the microclimate inside the clothing. Such increases cause damp and clammy perceptions and also a corresponding increase in thermal conductivity of the insulating air, which can affect coolness perceptions. In extreme cases hypothermia can result if the body loses heat more rapidly than it is able to produce, for example when physical activity has stopped, causing a decrease in core temperature. Unlike modern waterproof breathable fabric, prior research indicated that conventional woven fabrics display a proportional relationship between vapor permeability and air permeability.12 Wind resistance is usually assessed by measuring air permeability. This is the rate of air flow per unit area of fabric at a standard pressure difference across the faces of the fabric and can significantly influence the wearing comfort behavior. If a fabric is permeable to air, it also means that the water vapor or liquid moisture can transfer from the fabricâ&#x20AC;&#x2122;s inner surface to its outer surface and evaporate into the environment. Therefore, water vapor or liquid moisture transmission are closely related to the material air permeability and thermal comfort sensations during wear. On the other hand, the fabric thermal resistance is strongly dependent on the enclosed still air; this is also influenced by fabric structure and fabric permeability. As an example, it is generally more stressful for a worker operating in a warm or hot environment than in a neutral environment.15

15.2

Measurement of air permeability

Air permeability is normally measured on apparatus designed to force air through the test specimen, usually classified into two types. In one system, the pressure difference between the opposite faces of a test specimen is fixed and measurement is made of the resulting air flow through the material. In the other type, the rate of movement of air through the fabric is


Air permeability

255

Table 15.1 Relative testing methods and standards Developer

Document number

Title

ASTM

D 737-96

ASTM

F 2298-03

BS EN ISO

9237

ASTM

D 6476

BS

3424-16:1995

Standard Test Method for Air Permeability of Textile Fabrics Standard Test Methods for Water Vapor Diffusion Resistance and Air Flow Resistance of Clothing Materials Using the Dynamic Moisture Permeation Cell Textiles – Determination of the Permeability of Fabrics to Air (Supersedes BS 5636-90) (L) Standard Test Method for Determining Dynamic Air Permeability of Inflatable Restraint Fabrics Testing Coated Fabrics – Part 16: Method 18. Determination of Air Permeability

adjusted to a fixed value and the pressure difference that must be developed across the fabric in order to maintain this air flow is then measured.14 In the textile industry, the principle of the test to determine fabric air permeability is that air is drawn through a specified area of fabric. The rate of air flow is adjusted until a specified pressure difference between the two fabric surfaces (face and back) is achieved. The air flow is measured and the air permeability is calculated. Several relevant published standards are summarized in Table 15.1. The ASTM 737–96 procedure determines the volume rate of air flow per unit area of fabric in cubic centimetres per square centimetre per second. The British, European and International standard procedure determines the velocity of air of a standard area, pressure drop and time, in millimetres per second. The standard pressure specified in the ASTM standard procedure is 125 Pa (12.7 mm water gauge) whereas that specified in the British Standard procedure is 100 Pa for apparel fabrics and 200 Pa for industrial fabrics. Results obtained using the two procedures are, therefore, not comparable. In general, the testing environment must be set up in a standard conditioned laboratory, so that the air being drawn through the specimen is at standard conditions, i.e. 20 ± 2 °C and RH 65 ± 2% following ASTM D1776. A steady-state air permeability test apparatus consists of: •

a clamping device for securing the test specimen in a flat, tensionless state;


256

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a device to prevent air leaking from the edges of the test area, usually called a guard ring; • a pressure gauge or manometer to measure the pressure drop from one side of the specimen to the other; • an air pump to draw a steady flow of air through the clamped specimen; • a means of adjusting the rate of airflow to achieve and hold the specified pressure drop from one side of the specimen to the other; • a flow-meter to measure the actual rate of air flow through the specimen. In British Standard BS 9237, ‘Determination of Permeability of Fabrics to Air’, a standard testing method is given to test the airflow through a given area of fabric at a constant pressure drop across the fabric of 10 mm head of water. During the testing the specimen is clamped over the air inlet of the apparatus using gaskets and air is sucked through it by means of a pump as shown in Fig. 15.1.3 The air valve is adjusted to give a pressure drop across the fabric of 10 mm head of water and the air flow is then measured using a flow-meter. Several flow-meters with different ranges are usually incorporated to enable the instrument to deal with a wide range of fabrics. At least five specimens are used, each with a test area of 508 mm2 (25.4 mm diameter) and the mean air flow in millilitres per second is calculated from the five results. From this the air permeability can be calculated in millilitres per 100 mm2 per second. Then fabric air resistance can be defined as the time in seconds for 1 ml of air to pass through 100 mm2 of fabric per second under a given pressure head of 10 mm of water. A widely employed testing standard is ASTM D737–96, ‘Standard Test Method for Air Permeability of Textile Fabrics’, which gives a test method for measuring the air permeability of textile fabrics. This test method applies to most fabrics including woven fabrics, non-woven fabrics, air bag fabrics, blankets, napped fabrics, knitted fabrics, layered fabrics and pile fabrics. The fabrics may be untreated, heavily sized, coated, resin-treated or otherwise treated. BS 3424–16:1995, ‘Testing Coated Fabrics – part 16: Method 18. Determination of Air Permeability’, provides another method to determine fabric air permeability. The principle of this method is that the rate of air flow through a known area of coated fabric is adjusted so that there is a known pressure drop across the fabric. According to this standard the main components of testing apparatus include a rigidly mounted circular specimen holder, having an orifice 5 cm2, 20 cm2, 50 cm2 or 100 cm2 (i.e. 25.23 ± 0.05 mm, 50.05 ± 0.05 mm, 79.79 ± 0.05 mm or 112.84 ± 0.05 mm diameter). A suction pump draws a steady flow of air through the test specimen with


Air permeability

257

15.1 Typical apparatus for air permeability testing.

adjustable suction rate so that the pressure differential across the test specimen can be maintained at a constant 50 Pa, 100 Pa, 200 Pa, 500 Pa or 1 kPa. During the measurement, the test specimen is mounted in the circular specimen holder with the coated surface to the low-pressure side if single-faced with sufficient tension to eliminate wrinkles. The suction pump is started and the rate of flow adjusted until the required pressure differential is obtained. The air flow rate in litres per minute is then recorded. The pressure differential should be maintained for a further one minute and the air flow rate in litres per minute measured again. Finally, the fabric air permeability R can be calculated according to equation (15.1): R=

r 167 A

[15.1]

where r is the arithmetic mean of the air flow rate in L/min and A is the area of orifice of the test assembly in cm2.


258

Clothing biosensory engineering

15.3

Humidity-dependent air permeability

Another important property of a fabric is the way in which it allows water vapor to pass through it. This property is known as ‘permeability of a fabric to moisture vapor’ and is closely related to fabric air permeability behavior. Moisture-vapor permeability in fabrics is achieved or lost at either the manufacturing or the finishing stage of the production process. Although heat transmission may be critical to survival in cold weather, it is incontestable that moisture-vapor transmission is crucial to comfort in both cold and hot weather. Free movement of water to the fabric surface is essential if perspiration discomfort, causing fabric wetness with resulting freezing in winter or clamminess in summer, is to be prevented. Porous hygroscopic materials, like pure cotton woven fabrics, often exhibit humidity-dependent air permeability due to the swelling of the solid matrix as it takes up water vapor from the environment. These effects are most evident in materials such as tightly woven fabrics, low-porosity hygroscopic membranes and non-woven fiber mats. Humidity-dependent air permeability is usually evident from the volumetric flow rate versus pressure drop plot. The plot will no longer have a line of constant slope, but will show some curvature according to the relative humidity of the gas flowing through the sample. It would be very appealing to have a test method available that can determine both diffusion and convection properties, and is able to directly compare the results obtained between materials as different as air-impermeable membrane laminates, very air-permeable knitted fabrics, woven fabrics, and complicated non-woven and polymeric foam structures.7,9 ASTM F2298-03, ‘Standard Test Methods for Water Vapor Diffusion Resistance and Air Flow Resistance of Clothing Materials Using the Dynamic Moisture Permeation Cell’, proposed a device for measuring water vapor transport and air permeability of porous materials like textiles.1 The reasons for carrying out this testing are due to some functional fabrics like Gore-Tex, Sympatex, etc., having much better water vapor transport properties when they are in a humid environment than when they are in a dry environment.6 All the testing is carried out on an apparatus called a dynamic moisture permeation cell (DMPC).7 Nitrogen streams consisting of a mixture of dry nitrogen and watersaturated nitrogen are passed over the top and bottom surfaces of the sample. The relative humidity of these streams is varied by controlling the proportions of the saturated and the dry components. By knowing the temperature and water vapor concentration of the entering nitrogen flows, and by measuring the temperature, water vapor concentration and flow rates of the nitrogen leaving the cell, one may measure the fluxes of gas and


Air permeability

259

water vapor transported through the test sample. With no pressure difference across the sample, transport of water vapor proceeds by pure diffusion, driven by vapor concentration differences. If a pressure difference across the sample is present, transport of vapor and gas includes convective transport, where the gas flow through the sample carries water vapor with it, which may add to or subtract from the diffusive flux, depending on the direction of the convective gas flow.7 Results may be shown in terms of water vapor flux (g/m2/day) or resistance to the diffusion of water vapor (units of s/m). The resistance units make comparing results obtained at different environmental conditions much easier. The lower the diffusion resistance, the more water vapor gets through the material.8,9,11 With the advantage of DMPC, this apparatus also can be used to determine the following material properties, except for the steady-state airpermeation properties:1,4 1. 2. 3. 4. 5.

concentration-dependent permeability; temperature-dependent permeability; combined convection/diffusion; humidity-dependent air permeability; transient sorption/desorption.

15.4

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through projects G-V987, A188 and ITF project ITS-023-03.

15.5

References

1. ASTM, ASTM F2298-03 Standard Test Methods for Water Vapor Diffusion Resistance and Air Flow Resistance of Clothing Materials Using the Dynamic Moisture Permeation Cell. 2003: West Conshohocken, PA, ASTM. p. 10. 2. ASTM, ASTM D6476 Standard Test Method for Determining Dynamic Air Permeability of Inflatable Restraint Fabrics. 2002: West Conshohocken, PA, ASTM. 3. BSI, Textiles â&#x20AC;&#x201C; Determination of the Permeability of Fabrics to Air. BS EN ISO 9237:1995. 1995: London, British Standards Institution. 4. U.S. Army Soldier Systems Center, http://www.natick.army.mil/soldier/media/ fact/ss&t/DMPC.htm, 2004. 5. Epps, H.H., Prediction of Single-layer Fabric Air Permeability by Statistical Modeling. Journal of Testing and Evaluation, 1996. 24(1): p. 26â&#x20AC;&#x201C;31. 6. Gibson, P., http://www.verber.com/mark/outdoors/gear/breathability.pdf, 2004. 7. Gibson, P., D. Rivin, and C. Kendrick, Convection/Diffusion Test Method for Porous Materials Using the Dynamic Moisture Permeation Cell, in Final rept.


260

8.

9.

10.

11.

12.

13.

14.

15. 16.

17.

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Jun–Nov 97. 1998: MA, Army Natick Research and Development Center, p. 60. Gibson, P., D. Rivin, C. Kendrick, and H. Schreuder-Gibson, Humiditydependent Air Permeability of Textile Materials. Textile Research Journal, 1999. 69(5): p. 311–317. Gibson, P.W., Factors Influencing Steady-State Heat and Water-Vapor Transfer Measurements for Clothing Materials. Textile Research Journal, 1993. 63(12): p. 749–764. Gibson, P.W. and M. Charmchi, Coupled Heat and Mass Transfer through Hygroscopic Porous Materials – Application to Clothing Layers. Sen-i Gakkaishi, 1997. 53(5): p. 183–194. Gibson, P.W., A.E. Elsaiid, C.E. Kendrick, D. Rivin, and M. Charmchi, A Test Method to Determine the Relative Humidity Dependence of the Air Permeability of Woven Textile Fabrics. Journal of Testing and Evaluation, 1997. 25(4): p. 416–423. Holmes, D.A., Waterproof Breathable Fabrics, in Handbook of Technical Textiles, S.C. Anand and A.R. Horrocks, Eds, 2000: Cambridge, UK, Woodhead Publishing Limited p. 283–314. McCullough, E.A., M. Kwon, and H. Shim, A Comparison of Standard Methods for Measuring Water Vapour Permeability of Fabrics. Measurement and Textile Technology, 2003. 14: p. 1402–1408. Milenkovi, L., P. Kuudri. Sokolovi, and T. Nikoli, Comfort Properties of Defense Protective Clothings. Working and Living Environmental Protection, 1999. 1(4): p. 101–106. Olsuskiene, A. and R. Milasius, Dependence of Air Permeability on Various Integrated Fabric Firmness Factors. Materials Science, 2003. 9(4): p. 401–404. Oxtoby, E., Air-permeability Measurement of Open Fabrics by Using Superimposed Fabric Layers. Journal of the Textile Institute, 1970. 61(3): p. 153–156. Szosland, J., A. Babska, and E. Gasiorowska, Air-penetrability of Woven Multi-layer Composite Textiles. Fibres & Textiles in Eastern Europe, 1999. 7(1): p. 34–37.


16 Mechanical tactile properties J.Y. HU, YI LI AND K.W. YEUNG Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

16.1

Introduction

The concept of fabric hand has long been used in the textile and clothing industries as a description of fabric quality and prospective performance.23 During wear, clothing contacts and interacts with the skin of most parts of the body dynamically and continuously. The properties of the fabric hand, which describe the feeling during the contact between human skin and fabric, have been extensively studied through objective measurement and subjective sensory descriptors. Normally, fabric handle can be perceived subjectively by hand. When fabric is manipulated with the fingers, many psychological sensations such as stiffness or rigidity, softness or hardness, warm or cool, wet or dry are perceived.5 A considerable volume of research outcomes has been reported on various tactile and pressure sensations including prickliness, itchiness, stiffness, softness, smoothness, roughness, scratchiness and fitness.2,3,6,11,15,18 These fabric proprieties not only affect comfort sensations but also influence their aesthetic qualities, which may motivate consumers to make purchase decisions. Traditionally, fabric handle is judged by experts, and many problems arise due to disagreement.19,22 Quantification of tactile comfort is complex because of the range of responses that people experience when they touch and move a fabric with their hand or on their skin. However, since this property is very influential in a consumer’s decision-making process, much work has been done to quantify the factors that comprise fabric handle. Neurophysiological research found that the various sensations resulting from the skin–fabric interaction are triggered by sensory receptors. There are three categories of receptors, which cover pain, thermal and touch sensations. During fabric–skin contact, the fabric produces pressure and vibration on the skin and stimulates the touch receptors. Mechanoreceptive nerve fibers related to tactile perception have the following characteristics:8 • •

each receptor has a different frequency tuning; light stimulation at a particular frequency will cause only receptors tuned to that frequency to respond; 261


262 • •

Clothing biosensory engineering

intense stimulation at a particular frequency will cause all receptors to respond; relative stimulation across all types of receptors (across-fiber-pattern coding) underlies our perception.

Peirce18 was the first to describe the relationship between measuring fabric properties and handle. He proposed that the sensations of stiffness, hardness and roughness were based on the fabric properties, e.g. bending, flexural rigidity, thickness and compressibility. Peirce concluded that fabric stiffness was the key factor in the study of fabric handle. Lindberg et al.14 later established the relationship of properties and garment appearance of fabric by measuring fabric shear, tensile, bending and formability properties. They also developed the test procedures and experimental equipment to measure these properties. Hu et al.7 used a stepwise regression method to obtain the best-fit Stevens’ law equations containing the minimum number of independent variables (KES-F parameters) to predict fabric stiffness, smoothness, softness and fullness. Pan and his co-workers17 developed a more logical and rational mathematical process called ‘Euclidean distance’ calculation. This method has been proved to be more suitable for different textile and fabric markets. Another approach developed by Raheel21 used a fuzzy comprehensive evaluation to predict hand. The model is very useful for quality judgement, but it has not proved reliable in different markets. Due to work performed by Kawabata,9–11 and a number of other researchers,2,12,16,20 a knowledge base of objective fabric sensory values now exists. Kawabata was the first researcher to separate handle into three levels: mechanical properties, primary handle values and total handle value. He developed several apparatus to evaluate fabric handle properties. The original KES-F developed in 1972 consists of four components, which allow the measurement of properties of planes (fabrics, knitted fabrics or films). There is also a newer apparatus-combination for the evaluation of thermal properties. The FAST system, developed by CSIRO in Australia, is another set of instruments and test methods, which can assess fabric properties by measuring thickness at two predetermined loads, bending, extensibility of the fabric in the warp, weft and bias directions as well as relaxation shrinkage and expansion on individual instruments.4 Some standards and test methods relevant to fabric mechanical tactile comfort are summarized in Table 16.1. ASTM standard D123 on terminology gives the following as important terms for describing fabric handle:1 • •

flexibility – ease of bending; compressibility – ease of squeezing;


Mechanical tactile properties

263

Table 16.1 Several fabric mechanical tactile comfort measurement relative standards Developer

Document number

Title

AATCC

Evaluation procedure 5 D1388 D4032

Fabric Hand: Guidelines for the Subjective Evaluation of Standard Test Method for Stiffness of Fabrics Standard Test Method for Stiffness of Fabric by the Circular Bend Procedure Standard Test Method for Tearing Strength of Fabrics by the Tongue (Single Rip) Procedure (Constant-Rate-of-Extension Tensile Testing Machine) Standard Test Method for In-Plane Shear Properties of Polymer Matrix Composite Materials by the Rail Shear Method Standard Test Method for Determination of Compression Resistance and Recovery Properties of Highloft Non-woven Fabric Using Static Force Loading Standard Test Method for Tensile Properties of Single Textile Fibers

ASTM

D2261

D4255/ D4255M D6571

D3822

• • • • • •

extensibility – ease of stretch; resilience – ability to recover from deformation; density – mass/unit volume (refers to light or heavy perception); surface contour – divergence of the surface from the fabric plane; surface friction – resistance to slipping; thermal character – apparent difference in temperature of the fabric and skin.

ASTM D4255/D4255M described a method to determine the in-plane shear properties of high-modulus fiber-reinforced composite materials using either one of two procedures. In Procedure A, laminates clamped between two pairs of loading rails are tested. In Procedure B, laminates clamped on opposite edges with a tensile or compressive load applied to a third pair of rails in the centre are tested. The limitation of the method is that it is only suitable for continuous-fiber or discontinuous-fiber-reinforced polymer matrix composites. ASTM D6571 described a method to measure the compression resistance and recovery properties of any type of highloft non-woven fabric using a simplistic and economical applied static weight loading technique. ASTM D3822 covers the measurement of tensile properties of natural and man-made single-textile fibers of sufficient length to permit mounting test specimens in a tensile testing machine. ASTM D1388


264

Clothing biosensory engineering

covers the measurement of stiffness properties of fabrics by measuring the bending length and calculating the flexural rigidity. ASTM D4032 determines the stiffness of fabrics by using the circular bend procedure. ASTM D2261 can be used to determine the tearing strength of textile fabrics by the tongue (single rip) procedure using a recording constant-rate-ofextension-type (CRE) tensile testing machine. Although there are methods and instruments that can be used to measure fabric tactile properties, most of them only can be employed to determine fabric tactile properties on a series of separate apparatus. To meet the need for a new model of integrated intelligent fabric tactile tester, which can measure, record and analyze the thermal and mechanical properties in one step, a new instrument named a Fabric Smart Tactile Tester (FSTT) has been developed, which is able to measure the fundamental mechanical and thermal sensory signals at the same time and under the same climatic conditions.13

16.2

Objective measurement of fabric tactile properties

As shown as in Fig. 16.1, the apparatus consists of the following four principal components: (1) bottom measuring head; (2) upper measuring head; (3) head motion mechanism sub-system; and (4) aluminum casting frame. Five different kinds of transducers for measuring temperature, pressure, friction, displacement and heat flux are installed in this instrument. The block diagram of the sensors, signal acquiring, processing and judgement is shown in Fig. 16.2. All analogue signals generated during the measurement are acquired and conditioned by a data acquire card and processed by a computer. Before the measurement, a 200 mm Ă&#x2014; 200 mm fabric sample is prepared and then put on the bottom measuring plate in such a way that the sample edges cross the straight lines of the bottom plate symmetrically. The sample edges, in this arrangement, also cross the straight parts of the pressure-sensing frame surrounding the bottom measuring plate, which creates a horizontal plane with the level of the bottom measuring plate. Before the testing, the temperature between the upper and bottom measuring heads is accurately controlled at 10 °C. At the initiation, the upper measuring head sinks and fixes the sample between the free surfaces of the upper head and bottom plate. Due to the applied pressure, the whole system moves downwards and the fabric edges deform and apply pressure to the pressure-sensing frame. In a few milliseconds, the bottom plate reaches the lowest position and the sample thickness is recorded with the total pressure


Mechanical tactile properties

265

3 2

1

4

16.1 Measuring system of FSTT.

Pressure sensors Temperature sensors Friction sensor

Signal conditioning

ADDA

Personal computer

Displacement sensor Heat flux sensor

16.2 Block diagram of FIHT.

acting on the bottom plate and pressure-sensing frame. Simultaneously, the heat flow passing from the upper head through the sample towards the bottom measuring plate is measured and its time course is used for the thermal parameter evaluation.


266

Clothing biosensory engineering

Table 16.2 Indexes definition of STT Curve

Definition

Symbol

Bending and shearing

Min, max and mean of the frame pressure changes during the whole testing (N)

BSmax BSmin MSmean WBSdown

WBSdown =

t2

t4

( Sbs − St4 ) dt

t1

WBSup = Fabric compression

∫ t3

Max/min/mean for pressure changes under the bottom measuring head (N)

WFC down =

WFC up =

Heat flux changes and changing rate

t2

t1

t4

t3

( Sp − St1 ) dt

PSI up =

t2

t1

t4

t3

( I t − I mean ) dt

WFK =

ts

HFmax HFmin HFmean PSIdown PSIup

I t − I mean dt

Max/min/mean for the sensor value (N) te

FCmax FCmin FCmean WFCdown

( Sp − St4 ) dt

Max/min/mean for the sensor value (BTU/ft2.hr). According to the heat flux changing rate: PSI down =

Friction

( Sbs − St1 ) dt

FK t − FK mean dt

FKmax FKmin FKmean WFK FKPmax FKPmin

Note: WBSdown and WBSup are the work of heat at down and up stage, respectively. WFCdown and WFCup are the work of head at down and up stage, respectively. PSIdown and PSIup are the work of head at down and up stage, respectively. FKmax/FKPmin for max/min value of positive peak and FKmin/FKPmax for max/min value of negative peak.

When the sample reaches the lowest position, a step motor fixed on the upper measuring head is switched on to drive the friction-measuring disc to carry out reciprocal rotation on the sample surface. A strain gage, which is mounted on the rotate axis, is used to dynamically measure the change of torque. When the measurement is complete, the upper measuring head rises up automatically. The user interface for the data acquisition and control system will display a set of dynamic measuring curves for fabric warmth and coolness, fabric bending and shearing, friction and fabric compression. Based on the data


Mechanical tactile properties

267

from these, a set of indexes, which are summarized in Table 16.2, can be calculated.

16.3

Measurement of subjective tactile sensations

In order to compare the indexes measured by FSTT with subjective perceptions and with the results of KES system measurements, a series of tests were carried out. Fifteen subjects (11 males and four females, ranging in age from 24 to 54) participated in the subjective trial. All experiments were carried out in a conditioning room with the temperature controlled at 21 °C and RH 65 ± 2% (Ref: ASTM D1776). Before testing, all the specimens were put in the room for at least 24 hours in order to be at ‘equilibrium regain’. Five tactile comfort relative perception components, softness, smoothness, prickle, thermal, and moisture, were selected in this test. In the trial, the subject manipulated the fabric sample with his/her fingers. Then the subjective scores on softness, smoothness, prickle, thermal and moisture were recorded on five-point scales, which ranged from ‘stiff’ to ‘soft’, ‘rough’ to ‘smooth’, ‘prickly’ to ‘soft’, ‘cool’ to ‘warm’ and ‘wet’ to ‘dry’, respectively, and the corresponding numerical value was recorded by the operator. Finally, all these fabric samples were measured by the FSTT and KES system.

16.4

Results of FSTT and KES testing systems

The statistical software SPSS 11 (Statistical Package for the Social Sciences) was employed for results analysis and data processing. All the subjective perception data were standardized in the range from 0 to 5 indicating from ‘stiff’ to ‘soft’, ‘rough’ to ‘smooth’, ‘prickly’ to ‘soft’, ‘cool’ to ‘warm’ and ‘wet’ to ‘dry’, respectively. After standardizing the numerical value, the mean values of the subjective perception are summarized in two groups shown in Figs. 16.3 and 16.4, respectively. From Fig. 16.3, Fabric E has the highest score for the properties stiff, rough and prickly – the mean value of smoothness is 0.1567, softness is 0.088 and prickle is 0.2387, individually. It is then followed by Fabric F. Fabric L has the softest and smoothest properties, with mean values for smoothness of 3.2, softness 3.808 and prickle 3.4827, respectively. Figure 16.4 shows that Fabric F has the coolest perception with mean value 0.6213 and Fabric O has the warmest perception (3.812). On the other hand, Fabric L also has the highest wet perception (0.8667) and Fabric O has the highest dry perception (3.4992).


5

95% CI Mechanical sensations

4

3

2

1

Smoothness

0

Softness

Prickle

â&#x20AC;&#x201C;1 N = 151515 151515151515 151515 151515 151515 151515 151515 151515 151515 151515 151515 E F G H I J K L M N O P Fabric ID

16.3 Subjective rating of smoothness, softness and prickle sensations.

95% CI Warmth and dampness sensations

5

4

3

2

1

Thermal sensation

0

â&#x20AC;&#x201C;1 N = 1515 1515 1515 1515 1515 1515 1515 1515 1515 1515 1515 1515 E F G H I J K L M N O P

Moisture sensation

Fabric ID

16.4 Subjective rating of thermal and moisture sensations.


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269

Table 16.3 Correlation analysis between five individual sensations

Smoothness

Softness

Prickle

Thermal

Moisture

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

Smoothness

Softness

Prickle

Thermal

Moisture

1

0.631

0.689

0.199

0.077

– 180

0.000 180

0.000 180

0.008 180

0.301 180

0.631

1

0.594

0.321

0.204

0.000 180

– 180

0.000 180

0.000 180

0.006 180

0.689

0.594

1

0.207

0.076

0.000 180

0.000 180

– 180

0.005 180

0.308 180

0.199

0.321

0.207

1

0.671

0.008 180

0.000 180

0.005 180

– 180

0.000 180

0.077

0.204

0.076

0.671

1

0.301 180

0.006 180

0.308 180

0.000 180

– 180

Correlation is significant at 0.01 level (2-tailed).

An analysis of correlation between each subjective perception was carried out and is summarized in Table 16.3. From the table, we can see that some relationships do exist among the fabric tactile sensations smoothness, softness and prickle (where Pearson Correlation is equal to 0.631 and 0.689 at p < 0.001, individually) and thermal with the moisture (with Pearson Correlation >0.67 at p < 0.001). However, there are no relationships between thermal–moisture sensations and tactile sensations. In addition to the correlation analysis, linear regression analysis was employed to determine the relationship between each sensation and the results are summarized in Figs. 16.5–16.8 with r2 > 0.85. From the regression equations, a strong linear relationship can be found between the sensations of softness and prickle, with r2 > 0.96, and between thermal and moisture with r2 > 0.94. All the specimens were tested on KES-FB-1, KES-FB-2, KES-FB-3, KES-FB-4 and thermal lab individually to determine the fabric’s mechanical and thermal properties. The results are summarized in Figs. 16.9–16.14. Detailed information on the indexes, definitions and units used here can be found in Section 16.6.


270

Clothing biosensory engineering 5 y = 1.0553x – 0.039 r 2 = 0.8312

4

Softness

3

2

1

0 1

0

2

3

4

–1 Smoothness

16.5 Relationship between softness and smoothness sensations. 5 y = 0.7736x + 0.5469 r 2 = 0.8723 4

Prickle

3

2

1

0 0

1

2

3

–1 Softness

16.6 Relationship between prickle and softness sensations.

4


Mechanical tactile properties

271

5

4 y = 1.022x – 0.1887 r 2 = 0.9603 Smoothness

3

2

1

0 0

1

2

3

4

–1 Prickle

16.7 Relationship between smoothness and prickle sensations.

5 y = 0.8204x + 0.271 r 2 = 0.9399 4

Moisture

3

2

1

0 0 –1

1

2

3

4

Thermal

16.8 Relationship between moisture and thermal sensations.

5


95 % CI Shearing properties (gf/cm deg)

20

10

Shear G mean

0

Shear 2HG mean

â&#x20AC;&#x201C;10 N = 333 333 333 333 333 333 333 333 333 333 333 333 E F G H I J K L M N O P

Shear 5HG mean

Fabric ID

16.9 Fabric shearing properties.

70 60

95 % CI Fabric tensile test

50 40 30

Linearity mean

20 Tensile energy mean 10 Resilience mean 0 â&#x20AC;&#x201C;10 N = 3333 3333 3333 3333 3333 3333 3333 3333 3333 3333 3333 3333 E F G H I J K L M N O P Fabric ID

16.10 Fabric tensile properties.

Tensile strain mean


1.0

95 % CI Fabric bending properties

0.8

0.6

0.4

0.2

Bending B mean

0.0

â&#x20AC;&#x201C;0.2 N = 33 33 33 33 33 33 33 33 33 33 33 33 E F G H I J K L M N O P

Bending 2HB mean

Fabric ID

16.11 Fabric bending properties.

95 % CI Compression properties

5

4

3

2

1

Compression TO

0 N = 33 33 33 33 33 33 33 33 33 33 33 33 E F G H I J K L M N O P Fabric ID

16.12 Fabric compression properties.

Compression TM


14

95 % CI Surface SMD (Micron)

12

10

8

6

4

2 0 N=

3 E

3 F

3 G

3 H

3 I

3 J

3 K

3 L

3 M

3 N

3 O

3 P

Fabric ID

16.13 Fabric surface properties. 0.14

0.12

95 % CI Qmax (W/cm2)

0.10

0.08

0.06

0.04

0.02 N=

5 E

6 F

5 G

5 H

5 I

5 J

Fabric ID

16.14 Fabric thermal properties.

5 K

5 L

5 M

5 N

5 O

5 P


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In Fig. 16.9, Fabric E has the greatest value of shear stiffness, the mean of G is 4.40 gf/cm.deg, hysteresis of shear force at 0.5 ° of shear angle is 11.59 gf/cm and hysteresis of shear force at 5 ° of shear angle is 14.47 gf/cm. In Fig. 16.10, Fabric L has the greatest value of tensile energy and tensile strain, where WT is 28.26 gf.cm/cm2 and EM = 39.49%, respectively, Fabric H has the highest resilience with RT = 55.44%. As shown in Fig. 16.11, Fabric E has the greatest value of bending stiffness and bending hysteresis, where B is 0.56 gf.cm/cm and 2HB is 0.69 gf.cm/cm, respectively. As shown in Fig.16.12, Fabric P has the largest value of linearity of compression thickness curve, compressional energy and resilience as well as thickness with PIm equal to 0.5 gf/cm2 and 50 gf/cm2, where the mean of LC is 0.52, WC is 2.01 gf.cm/cm2, RC is 100%, TO is 4.42 mm and TM is 2.87 mm, individually. Figure 16.13 illustrates that Fabrics E and G have relatively larger variation in surface SMD than other fabrics. On average, Fabric I has the highest surface SMD value of 12.28 µm. In Fig. 16.14, Fabric E also has the highest Qmax value, with Qmax = 0.12 W/cm2. The same specimens were tested again on the FSTT, giving the objective measurement results summarized in Figs. 16.15–16.18. Figure 16.15 compares the bending properties (BSmin, BSmax and BSmean) of selected fabrics. Fabrics E and F have significantly higher BSmax and BSmean values than the other fabrics. Fabric F has the highest BSmax value, 0.92 N, followed by Fabric E with 0.81 N. This means that, while the upper measuring head is going down, the maximum pressure force on the pressure-sensing frame caused by Fabric F deforming is 0.92 N, a result which indicates that Fabric F is stiffer than Fabric E. From Fig. 16.16, we found that Fabric M has the largest compressive force value with FCmax = 1.45 N. It is followed by Fabrics K and N with FCmax = 1.42 N. Fabric O has the highest compression work where WFCdown is 2.32 N.s and next is Fabric P (2.05 N.s). Fabric M has the highest recovery work, where WFCup = 2.83 N.s and next is Fabric M (2.80 N.s). As shown in Fig. 16.17, Fabric F has the greatest value of surface friction where FKmax is 0.33 N and WFK is 2.26 N.s. This means that among the specimens, Fabric F is the roughest fabric. From Fig. 16.18, Fabric M has the biggest values of PSIup and PSIdown, 60.06 and 58.46, respectively. This means that Fabric F has the highest temperature change rate on the surface and will give a strong stimulation during fabric–skin contact. Analysis of variance was carried out to identify the influence of fabric on the objectively measured indexes. Using the GLM multivariate procedure, we obtained the significance of the influence of fabric construction on the objective measurements, which is summarized in Table 16.4. From the table, we can see that fabric constructions have significant influence on most of the FSTT measured indexes with the p level <0.05, except


2761.0 Clothing biosensory engineering

95 % CI Fabric bending properties

0.8

0.6

0.4

0.2

BSmax

0.0

BSmin

BSmean

â&#x20AC;&#x201C;0.2 N = 666 666 666 666 666 666 666 666 666 666 666 666 E F G H I J K L M N O P Fabric ID

16.15 Fabric bending properties.

1.6

1.5 1.4

95 % CI FCmax

1.3 1.2 1.1 1.0 0.9 0.8 N=

6 E

6 F

6 G

6 H

6 I

6 J

6 K

Fabric ID

16.16 Fabric compression properties.

6 L

6 M

6 N

6 O

6 P


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277

0.4

95 % CI FKmax

0.3

0.2

0.1 N=

6 E

6 F

6 G

6 H

6 I

6 J

6 K

6 L

6 M

6 N

6 O

6 P

Fabric ID

16.17 Fabric surface properties.

for the measured index FCmin (the minimum pressure force during fabric bending) and FKmean (the mean value of fabric surface friction), where p > 0.65 and p > 0.50, individually. The table also indicates that this apparatus is able to show the significant differences in fabric constructions.

16.5

Relationships between subjective sensations and objective measurements

Further, stepwise regression analysis was applied to study the relationship between the subject perceptions (softness, smoothness, prickle, thermal and moisture) and objectively measured indexes. The derived statistical relationships are summarized in Table 16.5. From the table, we can see that the subjective perceptions have a linear relationship with KES objective measurements, with all r-values in the range 0.69â&#x20AC;&#x201C;0.913. Among them, softness perception has the strongest linear relationship with r = 0.913.


278

Clothing biosensory engineering 70

95 % CI Fabric thermal properties

60

50

40

30

20 PSIdown

10 0 N=

PSIup 66 E

66 F

66 G

66 H

66 I

66 J

66 K

66 66 L M

66 66 N O

66 P

Fabric ID

16.18 Fabric thermal properties.

The subjective perception of smoothness has a strong correlation with the KES objectively measured results MMDmean, Bmean Rtwarp, LC and RTmean. The standardized coefficients for MMDmean, Bmean, Rtwarp, LC and RTmean are −0.867 ( p < 0.001), −0.339 ( p < 0.004), −0.615 ( p < 0.001), −0.309 ( p < 0.003) and 0.373 ( p < 0.02), respectively, indicating that MMDmean has the highest weight in determining subjective smoothness perception, followed by RTwarp and then Bmean and LC. The bigger the RTmean is, the stronger is the softness perception. The bigger the MMDmean, Bmean, RTwarp and LC, the weaker is the smoothness perception. Similar results can be found for the subjective sensations softness, prickle, thermal and moisture with the KES objective measurements. Softness sensation is highly correlated with Gwarp, EMwarp and WTwarp with r > 0.91. The bigger EMwarp is, the stronger is the softness perception, the bigger Gwarp and WTwarp, the weaker is the softness perception. Prickle perception is a function of HG5weft, EMmean and Bmean, with r > 0.767. HG5weft and Bmean show their negative influence and EMmean shows its positive effect in the relationship. Thermal sensation is correlated with WC and EMwarp, with r > 0.69. WC has a positive effect and EMwarp has a negative effect in the


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Table 16.4 Influence of fabric construction on the FSTT measurements Dependent variable

Type III sum of squares

df

BSmax BSmin BSmean WBSdown WBSup FCmax FCmin FCmean WFCdown WFCup HFmax HFmin HFmean PSIdown PSIup Dlta T FKmax FKmin FKmean WFK

4.42 0.00 1.01 6.04 2.19 1.85 0.01 0.43 7.01 21.07 0.33 40.76 12.61 17274.20 14476.07 2.67 0.28 0.18 0.01 12.22

11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11

Mean square 0.40 0.00 0.09 0.55 0.20 0.17 0.00 0.04 0.64 1.92 0.03 3.71 1.15 1570.38 1316.01 0.24 0.03 0.02 0.00 1.11

F

Sig.

2947.83 12.56 2083.14 6.69 99.79 119.49 0.79 102.60 2.01 4.06 4.57 502.16 231.48 486.68 194.81 8.98 41.10 26.85 0.95 371.47

0.00 0.00 0.00 0.00 0.00 0.00 0.65 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00

Table 16.5 Regression analysis of subjective sensation and KES measurements Sensation

Equations

r

Smoothness

5.594 − 113.346MMDmean − 2.643Bmean − 9.40E-02RTwarp − 1.162LC + 8.884E-02 RTmean 2.186 − 0.252 Gwarp + 0.194EMwarp − 0.152 WTwarp 2.490 − 0.106 HG5weft + 5.078E-02 EMmean − 3.117 Bmean 1.432 + 1.534 WC − 7.80E-02EMwarp 7.142 − 45.751 Qmax + 2.322 Bwarp − 6.558 MIUwarp

0.854

Softness Prickle Thermal Moisture

0.913 0.767 0.694 0.747

Significant at p < 0.001 level.

function. Moisture perception is correlated with Qmax, Bwarp and MIUwarp, with p > 0.74. The equation also showed that the moisture sensation is closely related to the maximum temperature change rate Qmax. Qmax and MIUwarp show their negative effect and Bwarp its positive effect in moisture perception.


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The same stepwise linear regression analysis was also carried out to determine the relationship between subjective sensations and FSTT objective measurements, and the results are summarized in Table 16.6. From the results, we can also see that the relationships between the subjective sensations and objective measurements can be set up with r in the range 0.525– 0.862. Like the regression analysis results of the KES system, the relationship between softness perception and objective measurements has the highest r value (0.862). From the equation listed in Table 16.6, subjective perception of smoothness is correlated with the FSTT objective measurement results WFK, HFmin, BSmean and PSIup. The standardized coefficients for WFK, HFmin, BSmean and PSIup are 0.337 ( p < 0.001), 0.914 ( p < 0.001), −0.51 ( p < 0.001) and 0.552 ( p < 0.04), respectively, indicating that HFmin has the highest weight in determining subjective smoothness perception, followed by BSmean and PSIup and then WFK. The bigger HFmin is, the stronger is the softness perception. The bigger BSmean is, the weaker is the smoothness perception. Similarly, softness sensation is correlated with PSIup and WBSup, with r > 0.86; prickle sensation is correlated with HFmin, WBSup and WFK, with r > 0.73; thermal sensation is correlated with HFmean, with r > 0.53; and moisture sensation is correlated with HFmin and FKmin, with r > 0.52. The same analysis method was also employed to determine the relationships between FSTT and KES measurements, summarized in Table 16.7. The results indicate that there are strong correlations between two systems with r in the range 0.725–0.991. As a sample equation explanation, BSmax is correlated with the indexes of KES system Gwarp, Bweft, HG2weft, HB2warp, Bwarp, EMwarp, WTwarp and RTweft, with r > 0.969. The standardized coefficients for Gwarp, Bweft, HG2weft, HB2warp, Bwarp, EMwarp, WTwarp and RTweft are 0.753 ( p < 0.001), 0.159 ( p < 0.04), −0.452 ( p < 0.007), 0.62 ( p < 0.009), −0.436 ( p < 0.042), −0.647 ( p < 0.001), 0.562

Table 16.6 Regression analysis of subjective sensation and FSTT objective measurements Sensation

Equations

r

Smoothness

2.713 + 0.986 WFK + 1.464 HFmin − 5.219 BSmean + 4.66E-02 PSIup 5.178 − 4.6E-02 PSIup − 5.251 WBSup 3.843 + 0.672 HFmin − 4.269WBSup + 0.548WFK 3.181 + 1.641 HFmean 4.338 + 0.711 HFmin + 5.528 FKmin

0.735

Softness Prickle Thermal Moisture

Significant at p < 0.001 level.

0.862 0.734 0.531 0.525


0.991 0.988 0.990 0.970

−0.257 + 0.124TO − 16.489MMDmean + 0.637HB2warp − 0.066(2HG5)weft − 5.347Qmax + 0.169Gwarp − 0.095LC

3.94 + 293.882Qmax + 1.287SMDmean − 16.344HB2warp + 443.332MMDmean − 3.075TO + 17.004HB2weft

31.165 − 13.933TO + 250.074MMDweft − 0.282WTweft + 14.531TM − 15.148Bwarp + 160.613Qmax − 0.793SMDwarp

HFmean

PSIdown

PSIup

+ 1.518Bweft − 1.006HB2weft

−0.365 + 0.178TO − 24.396MMDmean − 0.031SMDwarp − 16.046Qmax + 0.055SMDwarp − 0.018SMDweft

HFmin

1.811 − 0.204Gwarp + 0.074HG2weft − 0.28HB2warp − 0.764 MIUwarp − 2.603Qmax

0.953

0.174 + 0.191Gwarp − 0.076HG2weft + 0.451HB2weft + 0.273HB2warp − 4.96EMwarp

WBSup

FCmax

0.795

−0.851 + 2.475Bweft + 2.684 MIUweft + 0.052SMDmean

0.738 − 0.112Gwarp + 0.037HG2weft − 0.237HB2warp − 3.993MMDwarp + 0.238Bmean

0.914 0.973

0.403 + 0.092Gwarp − 2.44HG2warp − 0.139Bwarp + 0.008WTwarp − 2.432Qmax − 0.019HG5warp − 0.020EMmean − 0.318LTmean

BSmean

WBSdown

0.050 + 0.338HB2mean + 0.142TO − 0.181TM

0.989 0.708

−0.008 − 0.009Gwarp + 0.002HG5weft

BSmin

HFmax

0.969 0.725

0.483 + 0.165Gwarp + 0.447Bweft − 5.110HG2weft + 0.645HB2warp − 0.488Bwarp − 0.024EMwarp + 0.024WTwarp − 0.007RTweft

BSmax

FCmean

r

Kawabata parameters

STT

Table 16.7 Relationships between FSTT and KES measurements

Mechanical tactile properties 281


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( p < 0.001) and −0.180 (p < 0.039), individually. This indicates that Gwarp has the highest weight in determining FSTT BSmax, followed by HB2warp, EMwarp and WTwarp. HG2weft, Bwarp, EMwarp and RTweft show their negative influence in the relationship. Similarly, BSmin, BSmean, WBSdown, WBSup, FCmax, FCmean, HFmax, HFmin, HFmean, PSIdown and PSIup can be described as a function of KES measurements as listed in Table 16.7.

16.6

Conclusion

Fabric hand properties not only affect comfort sensations but also influence the aesthetic qualities that may motivate consumers to make purchase decisions. When the fabric is manipulated with the fingers, many psychological sensations are perceived, including tactical sensations and thermal– moisture sensations. Some relationships can be found between the tactile sensations, smoothness, softness and prickle, individually with r2 > 0.85. Similarly, thermal sensation is related to moisture sensation with r2 > 0.93. However, there is no relation between the two groups – these sensations are in two independent sensory dimensions. A patented apparatus called the fabric smart tactile tester (FSTT), which can measure, record and analyse the thermal and mechanical properties which are exhibited during the hand evaluation process simultaneously in one step under the same environmental conditions, has been introduced. As the analysis shows, the instrument is able to show the significant differences in fabric constructions and their influence on subjective hand perceptions. The subjective tactile and thermal–moisture perceptions can be described by FSTT objective measurements with r in the range 0.525–0.862 at p level <0.001. Furthermore, the measurements from the FSTT and KES systems for the same specimens are correlated with each other and the results of FSTT can be described by the KES measurements with linear regression equations, where r is in the range 0.725–0.991.

16.7

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the projects G-V987, A188 and ITF project ITS-023-03.

16.8

References

1. ASTM, ASTM D123–03 Standard Terminology Relating to Textiles, 2003: West Conshohocken, PA, ASTM.


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2. Curteza, A., D. Farima, and I. Hritcu, A subjective evaluation method for tactile sensations of roughness and prickliness, in Proceedings of the Texsci: 2000. 2001. Liberec, Czech Republic. p. 197–198. 3. Datta, R.K. and N.C. Patel, Fabric Feel, Hand and Appearance of Some Indian Suiting Fabrics Using Kawabata Instruments. ATIRA Communications on Textiles, 1995. 29(1): p. 22–35. 4. DeBoos, A.G. and D.H. Tester, The FAST Approach to Improved Fabric Performance, in Textile Objective Measurement and Automation in Garment Manufacture, G. Stylios and L. Cheng, Eds, 1991: Chichester, Ellis Horwood Limited, p. 55–77. 5. Hatch, K.L., Textile Science, 1993: Minneapolis/St Paul, MN, West Publishing. 6. Horiba, Y., M. Kamijo, S. Hosoya, M. Takatera, Y. Shimizu, and T. Sadoyama, Evaluation of Tactile Sensation for Wearing by Using Event Related Potential. Sen-i-Gakkaishi, 2000. 56(1): p. 87–94. 7. Hu, J.L., W.X. Chen, and A. Newton, A Psychophysical Model for Objective Hand Evluation: an Application of Steven’s Law. Journal of the Textile Institute, 1993. 84: p. 354–363. 8. Johnson, K.O., The Roles and Functions of Cutaneous Mechanoreceptors. Current Opinion in Neurobiology, 2001. 11(4): p. 455–461. 9. Kawabata, S., The Standardisation and Analysis of Handle Evaluation. S. Kawabata, Ed., 1980: Osaka, The Textile Machinery Society of Japan. 10. Kawabata, S., The Development of the Objective Measurement of Fabric Handle, Objective Specification Fabric Quality, Mechanical Properties and Performance. 1982: Osaka, The Textile Machinery Society of Japan, p. 31–59. 11. Kawabata, S. and M. Niwa, Objective Measurement of Fabric Hand. 1995: Osaka, Textile Machinery Society of Japan, p. 329–354. 12. Kim, J.O. and B.L. Slaten, Objective Assessment of Fabric Handle in Fabrics Treated with Flame Retardants. Journal of Testing and Evaluation, 1996. 24(4): p. 223–228. 13. Li, Y., J.Y. Hu, and H. Lubos, Textile Fabric Testing, The Hong Kong Polytechnic University, USPTO, 6,601,457 B2, Hong Kong, 2003. 14. Lindberg, J., L. Waestberg, and R. Svenson, Wool Fabrics as Garment Quality, Mechanical Properties and Performance, 1961: Osaka, Textile Machinery Society of Japan. 15. Mehta, R. and K.V. Narrasimham, Clothing Comfort: a Review of Related Properties. Man-made Textiles in India, 1987. 30(4): p. 327–335. 16. Paek, S.L., Subjective Assessment of Fabric Comfort by Sensory Hand. Journal of Consumer Studies and Home Economics, 1984. 8(4): p. 339–349. 17. Pan, N., K.C. Yen, S.J. Zhao, and S.R. Yang, New Approach to the Objective Evaluation of Fabric Handle from Mechanical Properties. I. Objective Measure for Total Handle. Textile Research Journal, 1988. 58(8): p. 438–444. 18. Peirce, F.T., The Handle of Cloth as a Measurable Quantity. Journal of the Textile Institute, 1930. 21: p. T377–T416. 19. Radhakrishnaiah, P., X. Meng, G. Huang, G. Buschle Diller, and W. Walsh, Mechanical Agitation of Cotton Fabrics During Enzyme Treatment and its Effect on Tactile Properties. Textile Research Journal, 1999. 69(10): p. 708–713. 20. Radhakrishnaiah, P. and S. Tejatanalert, Handle and Comfort Properties of Woven Fabrics Made From Random Blend and Cotton Covered Cotton/ Polyester Yarns. Textile Research Journal, 1993. 63(10): p. 573–579.


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21. Raheel, M., Introduction, in Developments in Textile Characterization Methods, M. Raheel, Ed., 1995: New York, Marcel Dekker, p. 1â&#x20AC;&#x201C;8. 22. Sinclair, R.J., Neural Mechanisms of Tactile Perception and Cognition, School of Medicine, Washington University, http://research.medicine.wustl.edu/ocfr/ Research.nsf/s/A51A038F11DDA2808625677D00593E41, 2002. 23. Stearn A.E, A.R.L., Postle R., and T.J. Mahar, A Statistical Analysis of Subjective and Objective Methods of Evaluating Fabric Handle. Journal of the Textile Machinery Society of Japan, 1988. 34(1): p. 13â&#x20AC;&#x201C;18.


17 In vivo physiological measurements J.Y. HU, YI LI AND K.W. YEUNG Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

17.1

Introduction

Clothing function, which influences the comfort perceptions of the wearer, can be described as the interface between the body and the environment. In an outdoor situation and undertaking a certain level of activity, the perception of comfort on the part of a clothed person is influenced by many environmental factors, such as air temperature, air humidity, solar radiation intensity, atmospheric thermal radiation and ground thermal radiation. Other factors include the wearerâ&#x20AC;&#x2122;s posture, metabolic rate and sweating rate, and the processes of moisture absorption, sweat evaporation and heat loss through conduction and convection via blood. Improvements in the scientific understanding of clothing physiology have been driven by an increase in the demand for high-performance protective clothing systems for use in space and polar exploration, underwater operations and the industrial environment. New measuring methods have facilitated this research.11 This chapter describes the methodology and apparatus of measuring certain human physiological responses including temperature, humidity, skin pressure, sweating rate, skin wetness, skin blood flow, oxygen consumption and heart rate. Research on human physiological responses related to clothing comfort perception has found that skin is an important organ with a multilayer biological structure consisting of many specialized cells and tissues, which are related to body temperature maintenance and the detection of sensory information. Amongst these responses, sweating plays an important role in thermoregulation, especially in that liquid sweat transfer in fabric can significantly influence the effect of heat loss on a clothed body. Furthermore, neurophysiological research findings have also identified that there is no sensory receptor responsible for moisture sensation, which is strongly related to temperature changes, mechanical stimuli on the skin surface or the outside environment. In 1998, Li et al.20 reported an investigation on psychological sensory responses to clothing of con285


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sumers living in different countries and identified that clothing comfort has three latent independent sensory factors: tactile, pressure and thermal– moisture. In Holmer’s12 lecture, Clothing Physiology: Past, Present, and New Perspectives, he divided the earliest serious theoretical studies of clothing physiology into four categories: (1) wear trials on humans; (2) thermal manikins; (3) mathematical models; and (4) combinations. Wear trial is one of the most widely used methods to observe subjects’ immediate responses in clothing under different climate conditions and activities. Many physiological indexes can be derived on the basis of the effects of clothing on humans. Based on a large number of wear trials, Fanger and co-workers set up a clothing comfort model, which has been further developed as an international standard.6,17 Based on the above studies, human physiological measurement plays an important role in the field of clothing comfort research and technology development. Safety and non-invasive techniques are the most important criteria for individual measurement methodology in clothing physiology research, followed by sensitivity, which is the ability of the technique to discriminate between variations in the test, implementation requirements and operator assistance, which can be described as the degree to which a subject agrees to follow the procedures of a technique.25

17.2

Temperature measurements

Temperature is an important indicator for many illness syndromes. An abnormal body temperature might indicate the presence of infection, deteriorating patient condition and disorders of the thermoregulatory function.2 The first practical thermometer was developed by Ole Christensen Rømer in 1701. He used red wine as the temperature indicator and created his own temperature scale, in which 0, 7½ and 60 represent the temperature of salt and ice mixture (≈259 K), freezing point of water (≈273 K) and boiling point of water (≈373 K). In 1744, Carl Linnaeus proposed a centigrade relative temperature scale, in which 0 and 100 represent the freezing (≈273 K) and boiling (≈373 K) point of water, respectively. The units of the centigrade temperature scale were also designated as ‘degree centigrade’, using the symbol ‘°C’.16 In the early years, the traditional method for taking a person’s temperature was a glass thermometer filled with mercury. Nowadays, electronic thermometers are most commonly used and recommended. The plastic strip thermometer is another newly developed tool to measure skin temperature. Unlike the other thermometers, the degree of temperature in a plastic strip thermometer is indicated by a different color.


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17.2.1 Thermocouple A thermocouple is a thermoelectric device which can continuously measure temperature changes through a temperature sensor, widely used in the in vivo physiological field. A typical thermocouple consists of two dissimilar metals, which are joined together at both ends and produce a small voltage proportional to the temperature. The voltage output depends on the temperature difference between two dissimilar metal wires at the junction. To measure temperature, one of the junctions must be maintained at a known (reference) temperature. This junction is known as the ‘cold junction’. It must be held at 0 °C using an ice flask or simulated electronically by adding an appropriate electromotive force (EMF). When choosing an appropriate thermocouple, its type, insulation and probe construction should be taken into consideration. Standard thermocouples are listed as below. • Type K (Chromel (Ni–Cr alloy)/Alumel (Ni–Al alloy)) is the ‘general purpose’ thermocouple. It is low cost and available in a wide variety of probes. The availability is in the range −200 °C to +1200 °C and the sensitivity is about 41 µV/°C. • Type E (Chromel/Constantan (Cu–Ni alloy)) has a high output (68 µV/°C), which makes it well suited to low temperature (cryogenic) use. • Type J (Iron/Constantan) is limited to the measurement of temperature in the range −40 to +750 °C. • Type N (Nicrosil (Ni–Cr–Si alloy)/Nisil (Ni–Si alloy)) has high stability and resistance to high-temperature oxidation, which makes it suitable for high-temperature measurements without the cost of platinum. • Types B, R and S are all noble metal thermocouples and exhibit similar characteristics. They are the most stable of all thermocouples but, due to their low sensitivity (approximately 10 µV/°C), they are usually only used for high-temperature measurement (>300 °C). The following is one example of thermocouple application. In order to measure skin surface temperature during fabric–skin contact, two type T thermocouples were placed between the inner fabric surface and the subject’s forearm surface, as shown in Fig. 17.1. The sensors should not protrude more than 0.5 mm from the skin surface. Lead wires should be bonded to the surface according to BS EN ISO 15831:2004. During the trial, the temperature began to be recorded before the fabric was placed on the forearm and then continued to record for 15 seconds with 100 Hz sampling frequency during the fabric–skin contact. Typical objective measurement results during the fabric–skin contact are shown in Fig. 17.2. This device also has wide applications in industrial, scientific, food temperature and medical research. The advantages of this device include that


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17.1 Experiment setting for measuring fabricâ&#x20AC;&#x201C;skin contact temperature.

Temperature at skin surface (deg C)

29

28.5

28

27.5

27 0.7

2.7

4.7

6.7

Time (sec)

17.2 Typical temperature change between fabric and skin during subjective evaluation process.

it costs less, is relatively small in size, is interchangeable, can measure a wide range of temperature and, most important, can record temperature changes continuously. However, the limitation of this device is that system errors of less than 1 °C can be difficult to achieve.


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17.2.2 Resistance thermometer detector (RTD) The resistance temperature detector (RTD), is a thin film device made of platinum, which is used for measuring temperature. It has great stability, accuracy and repeatability. The resistance tends to be almost linear with temperature – the higher the temperature, the larger the resistance. Unlike other thermocouples, no special extension cables or cold junction compensations are required for RTD. Furthermore, the conductor resistance is related to its temperature. Commercial platinum grades are produced which exhibit a change of resistance of 0.385 Ω/°C, according to the European Fundamental Interval. The sensor is usually made to have 100 Ω at 0 °C, which is defined in BS EN 60751:1996. The American Fundamental Interval is 0.392 Ω/°C.30 They are among the most precise temperature sensors available with resolution ± 0.1 °C. In order to determine the resistance, RTD requires a small constant current to pass through; however, this may cause self-heating. Lead wire resistance should be considered and adopting three and four wire connection strategies can result in eliminating connection lead resistance effects from the measurements.

17.2.3 Infrared temperature measurement Infrared (IR) radiation is an electromagnetic radiation with a wavelength longer than visible light but shorter than microwave radiation. By using an IR camera, we would be able to remotely detect an object’s surface temperature. The principle of an IR camera is that the amount of radiation emitted by an object increases or decreases with temperature changes, which suggests that, by detecting the amount of radiation of an object within the IR range of the electromagnetic spectrum, we should be able to detect the object’s surface temperature by looking at its color through the IR camera. This technology has a wide range of applications, in terms of in vivo physiology, and it can be used to measure human body skin temperature. The advantages of this technology include providing quick response, identifying area temperature distribution and monitoring temperature changes continuously and remotely. However, it is expensive to buy and difficult to maintain. Figures 17.3 and 17.4 illustrate the skin temperature distribution after 30 minutes of running at a speed of 4 mph followed by 80 minutes’ rest while wearing a specially designed functional T-shirt, with the left and right parts made of pure cotton moisture management and normal pure cotton fabrics, respectively. Both fabrics have the same structure. From the image in Fig. 17.3, which was taken immediately after the shirt was removed, the temperature difference between both parts was significant. From Fig. 17.4,


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17.3 IR image of back view immediately after removal of the shirt.


In vivo physiological measurements

17.4 IR image of back view after 70 seconds.

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which was taken 70 seconds after the shirt was taken off, it can still be seen that the temperature of the back at the right-hand side is lower than the left part.

17.3

Humidity measurements

There is an ever-increasing need within industry and commerce to be able to measure humidity and moisture. The term ‘humidity’ describes the moisture content in the air, and is a primary element of climate. Humidity measurements include absolute humidity, the mass of water vapor per unit volume of natural air and relative humidity (RH). Usually the term ‘RH’ means the ratio of the actual water-vapor content of the air to its total capacity at a given temperature.3 Relative humidity is most commonly used in the field for physiological humidity measurement, where it directly impacts on human comfort. To evaluate the relative humidity on the skin surface or layers of garments, the capacity of humidity sensors should have an accuracy of at least ± 5% RH and a repeatability of ± 3% as required by ISO 15831:2004. To determine the relative humidity of air, a hygrometer, which is an instrument designed to measure the moisture content of a gas, can be used. The most common type of hygrometer is the dry and wet bulb psychrometer. It consists of two identical mercury or electrical thermometers, one of which has a wet cotton or linen wick around its bulb. Evaporating water from the wick absorbs heat from the thermometer bulb, causing the thermometer reading to drop. Humidity is calculated from the ambient temperature as shown by the ‘dry bulb thermometer’ and the difference in temperature shown by the ‘wet bulb’ and dry bulb thermometers. The typical structure of a RH sensor consists of an industrially proven thermoset polymer, three-layer capacitance construction, platinum electrodes and, except for high-temperature versions, on-chip silicon integrated voltage output signal conditioning. In operation, water vapor in the active capacitor’s dielectric layer equilibrates with the surrounding gas. The porous platinum layer shields the dielectric response from external influences while the protective polymer over-layer provides mechanical protection for the platinum layer from contaminants such as dirt, dust and oils.13 The principle of an RH sensor is that the electrodes are coated or covered with a dielectric polymer, which absorbs or desorbs water vapor from the environment with changes in humidity. The resulting change in dielectric constant causes a variation in capacitance. This provides impedance that varies in relation to the humidity. Because modern relative humidity sensors, such as HIH series relative humidity sensors from Honeywell, already integrate a special amplification circuit and have output in the range 0–5 VDC, they can be easily connected to an analog logger for data recording.


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17.4

293

Pressure measurements

The term ‘pressure’ can be defined as the application of continuous force by one body on another via touching or compression. Garments are made from flat fabrics by sewing 2D patterns together with certain geometric topology and compression, and they touch and scratch the skin. The interaction behaviors between garments and the human body are very much dependent on the fabric mechanical and surface properties and significantly influence pressure comfort sensations and health. Takagi et al. in 1972 and Ogawa et al. in 1979 reported that skin pressure on specific areas of the body could cause a decrease in the sweating rate.26,36 In Sone et al’s report, the clothing skin pressure has been determined to have an inhibitory effect on the absorption of dietary carbohydrate in the small intestine, but no effect on the orocecal transit time of a meal.33 Based on their own experiment, Okura et al. suggested that the pressure applied to the body can influence the digestive response by decreasing the amount of saliva via the autonomic nervous system.28 Due to extensive studies carried out on pressure garments, compression stockings, active sports bras and other types of tight sports wear, the requirements of pressure measurements are considered to be critical indexes for researchers and designers. This section is mainly focused on discussion of the measurement methodologies to evaluate the pressure on the body caused by garments. In 1997, Junger et al. reported a method to determine the effective pressure exerted by compression stockings using a 2 mm thick, 5 mm diameter probe.18 The Samba 3000 fiber-optic pressure measurement system is suitable for pressure evaluation in physiological measurement and has a very small sensor. The diameters of the sensor and optical fiber cable are 0.42 and 0.25 mm, respectively. The RS232 output (max 500 Hz) and two analog outputs (0–10 V/Max 3000 Hz) are for data collection.21 The Samba 3000 is based on a miniature silicone sensor, which is located on the end of a fiberoptic cable, so that pressure is measured at rather than near the tip. The application of such a sensor would be limited in the case of clothing pressure measurement because a certain curvature is required to keep the tip of the optical fiber cable against the direction of pressure. Another super thin pressure sensor is the Flexiforce load sensor, which is an ultra-thin (thickness ≈ 0.208 mm) paper strip-like pressure sensor. It has satisfactory repeatability properties and a flexible printed circuit that senses contact force.37 The sensor has a two-layer structure and a 9.53 mm diameter sensing area at the end. On each layer, a conductive material (silver) is applied, followed by a layer of pressure-sensitive ink. Adhesive is then used to laminate the two layers of substrate together to form the force sensor. The active sensing area is defined by the silver circle on top


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of the pressure-sensitive ink. Silver extends from the sensing area to the connectors at the other end of the sensor, forming the conductive leads.

17.5

Sweating rate and skin wetness measurements

Sweating is important for heat regulation, and it is also a major source of water and solute loss. There are two types of water loss: insensible perspiration and sweating. Insensible perspiration loss from the skin cannot be eliminated. Daily loss is about 400 ml in an adult. The heat loss can be quite significant because there is a loss of 0.58 kcal for every ml of water evaporated. The maximum rate of sweating is up to 50 ml/min or 2000 ml/hr in an acclimatized adult. This rate cannot be sustained, but losses up to 25% of total body water are possible under severe stress and could be fatal. Extensive research has been carried out to evaluate the sweating rate. In 1998, Ohhashi et al. reviewed the methods of human perspiration evaluation.27 One type of sweating rate measurement technique uses a capsule covering a specified skin area and ventilated with dry gas (air or nitrogen) at a particular flow rate with changes in humidity of the out-flowing gas being measured by a high-sensitivity hygrometer. In 1986, Kraning and his co-operator reported a new forced-evaporation-type skin capsule for measuring local sweat gland activity in humans. Sweat is evaporated from the skin surface by a stream of dry nitrogen gas; subsequent changes in capsule relative humidity and temperature are measured by a commercially available thin-film capacitance sensor and a solid-state current regulating device, respectively.19 Ten years later, Scott et al. proposed a method to determine horse sweating rate by using a sealed plastic capsule held against the coat of the horse. Dry air was blown through the capsule over the skin and the change in absolute humidity (g/m3) between air entering and leaving the capsule was used to calculate sweating rate.31 Shamsuddiny and Togawa reported a method of continuous monitoring of sweating in which deion solution was perfused at a constant flow rate through a chamber attached to the skin surface. The chamber was designed so that the electrodes were installed inside at the inlet and outlet, and a 14 mm3 channel was constructed at the bottom to wash out sweat. The 90% response time was 0.12 s.32

17.6

Skin blood flow measurements

The mean regional blood flow is fundamentally important to the understanding of many physiological processes. Ideally, a skin blood flow measurement technique should be non-invasive, direct and easy to use in in vivo physiological measurement. Capillary blood pressure (the pressure is increased until capillary blood flow stops and then slowly reduced until flow returns) is one of the primary


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techniques for measuring tissue perfusion. Mahler et al. evaluated skin blood flow using feedback control to keep the pipette pressure accurately tracking capillary pressure changes.23 The pipette is filled with highly conductive saline. When in the capillary lumen, blood entering the pipette alters the conductivity, and this change is detected and used to control the pressure of the saline in the pipette. This method can be used to indirectly describe the skin blood flow.5 Due to the major role of the skin circulation in supplying nutrients to the tissue to keep it alive, the skin oxygen content can be an indirect parameter to describe skin viability. As skin is not homogeneous, the amount of oxygen content present varies from a minimum value in the tissue cells to a maximum value in the arterial circulation; therefore, the nature of the relationship between oxygen content and skin blood flow is still under exploration. Subcutaneous injections of various substances labeled with radioactive isotopes are used to evaluate skin blood flow. The activity in the area of the injection is measured and this is related to the rate of clearance of the isotope by the local blood supply. The European Physiology Modules (EPM) facility contains a science module named ‘skin blood flow measurement’ and proposed a method to evaluate skin blood flow by injection of a radioactive tracer, 133xenon, into the skin and measurement of the emitted gamma-radiation by a cadmium-telluride detector connected to a signal processing and solid-state memory unit. The principle of this methodology is that the rate of residue wash-out is monitored by continuous detection of residual radioactivity by calibrated detectors. The wash-out rate is proportional to the blood flow through the tissue by a tissue-specific proportionality constant.4 The methods of skin temperature measurement can be used as tools for skin blood flow measurement and the value of skin temperature can also be used as a local or regional indicator of skin blood flow.35 Spence et al. pointed out that the ability of the venules to act as a reservoir for large volumes of slow-moving blood has the most significant impact on heat exchange in the cutaneous layers of the skin.34 Houdas and Ring have shown the non-linear relationship between change in blood flow and skin temperature.14 The Doppler effect was first described by the Austrian mathematician and physicist, Johann Christian Doppler (1803–1853). He found that, if a star is moving towards the observer, the frequencies of the observed light waves are slightly higher than the emitted frequencies and vice versa. The change in frequency can be used to estimate the speed of the star relative to the observer.9 The technology of Doppler ultrasound has been used for many years in the area of skin blood flow investigation and offers more accuracy and the ability to select the depth from which the measurement is to be taken.29 The time-averaged mean velocity may be measured using


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duplex scanning, and Fourier transform techniques are used to extract the necessary flow of information from the Doppler shift frequency. The laser Doppler measurement technique is very popular since it is non-invasive and easy to use. The coherent light produced by lasers may be utilized to measure Doppler frequency shifts when reflected from moving red blood cells. The main difficulty is that the volume cannot be controlled. As a matter of fact, the penetration depth of the laser light depends on the local characteristics of the skin (thickness, hydration). The result obtained is then averaged over an unknown volume that may be smaller or larger and that incorporates vessels of very diverse sizes.10 In in vivo measurement, skin blood flow will normally be determined by the Doppler technique, which involves placing a sensor on the surface of the forearm and shining an infrared light beam onto the skin. The forearm site is cleaned with alcohol, and any excess body hair that would interfere with the performance of the measurement is removed using a disposable razor (used on a single subject and then discarded in a sharps biohazard container). The light penetrates the skin to a depth of 0.5â&#x20AC;&#x201C;1.5 mm, in about a 1 mm2 area â&#x20AC;&#x201C; the amount of light reflected provides an indirect estimate of skin blood flow.

17.7

Oxygen consumption measurements

Oxygen consumption is an expression of the rate at which oxygen is used by tissues, usually given in microliters of oxygen consumed in one hour by 1 mg dry weight of tissue.15 The measurement of respiratory gas exchange and estimation of metabolic rate provide important information in studies of metabolism and nutrition.7 The most popular method of oxygen consumption measurements was usually to attach the subject to a mouthpiece and facemask and collect the expired air in a spirometer or Douglas bag, together with separate chemical analyses such as the Lloydâ&#x20AC;&#x201C;Haldane or Scholander apparatus. But Askanazi research outputs showed that such a procedure is uncomfortable for long periods of measurement for subjects and has also been shown to affect the pattern of ventilation.1 For modern measurement methodologies, the use of automated metabolic gas analysis systems or metabolic measurement carts (MMC) in exercise studies is common throughout the industrialized world. They have become essential tools for diagnosing many hospital patients, especially those with cardiorespiratory disease.22 VO2max is the maximum volume of oxygen that the body can consume during whole-body exercise, while breathing air at sea level. This volume is expressed as a rate, either liters per minute (L/min) or millilitres per kg bodyweight per minute (ml/kg/min). Because oxygen consumption is linearly related to energy expenditure, when we measure oxygen consumption we are indirectly measuring an


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individual’s maximal capacity to do work aerobically. Therefore, VO2max is generally considered to be the best single physiological indicator of an individual’s ability to sustain dynamic muscular activity and is a measure of aerobic power. Measurement of VO2max involves a progressive incremental test performed on an exercise device such as a treadmill or cycle ergometer to the point where the individual can go no further. Typically, the following apparatus is used for oxygen consumption measurement: • • • • •

open circuit spirometry apparatus – computer analysis lab timer cycle ergometer metronome heart watch monitor.

Direct measurement of oxygen consumption must be done when a person is exercising. The subject wears headgear which contains a non-rebreathing valve which is held in the mouth like a snorkel.24 Before the experiment starts, the oxygen and carbon dioxide concentrations in the room air are evaluated. During the testing, the subject wears the headgear and undertakes physical exercise on a treadmill. The room air is inhaled through the valve, and air which is exhaled goes through a tube into an MMC. The cart measures the amount of oxygen and carbon dioxide in the exhaled air, as well as the volume of air. Finally, the amount of oxygen consumed can be computed after correction for barometric pressure, humidity and temperature.24

17.8

Heart rate measurements

Each time the heart beats, it pumps out enough blood to make each artery stretch and bulge outward briefly as the blood surges into the artery. Heart rate is the number of heart beats per minute: the times per minute that the heart contracts. When resting, the adult human heart beats at about 70 bpm (males) and 75 bpm (females), but this rate varies between people. There are many ways to measure heart rate; the most convenient and widely used method is, while looking at a watch, to hold two fingers gently on an artery on your neck, or on your wrist, just so you can feel the pulses, and count the pulses during a minute of time. During in vivo clothing physiological measurement the following indexes for heart rate can be considered.8 •

Recovery heart rate – this is the heart rate that the body will decrease to after an exercise session. For example, exercise for 30 minutes at 160 bpm. Two minutes after you stop exercising, your heart rate decreases to 90 bpm. That 90 would be your recovery heart rate. It is used to evaluate your fitness level after exercise.


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Resting heart rate – this is the number of beats in one minute when at complete rest. Resting heart rate indicates basic fitness level. The more well-conditioned the body, the less effort and fewer beats per minute it takes the heart to pump blood to the body at rest. • Maximum heart rate – this is the highest number of times the heart can contract in one minute. Maximum heart rate is the most useful tool to use in determining physical exercise intensities, because it can be individually measured or predicted. Clinical tests are the most accurate method and are recommended. Predicted maximum HR can also be estimated by the following mathematical formula, but with lower accuracy: WOMEN:

226−your age = age-adjusted max heart rate

MEN:

220−your age = age-adjusted max heart rate

A heart rate monitor or ECG/EEG (electrocardiograph/electroencephalograph) can be used to obtain a more accurate heart rate measurement. The pulse rate (which in most people is identical to the heart rate) can be measured at any point on the body where an artery is close to the surface; such places include wrist, neck, elbow and groin. A pair of electrocardiograph transducers can be located at one of these places for a continuous heat rate monitor. This is particularly important during exercise where the motion of exercise often makes it hard to get a clear measurement. These methods are also useful when you wish to record heart rate changes over short time periods. Another method of measuring heart rate is to use a commercially available heart rate monitor. These are specialized wearable electrocardiographic monitors consisting of a chest strap with electrodes. The signal is transmitted to a wrist receiver for display. Heart rate monitors allow accurate measurements to be taken continuously and can be used during exercise when manual measurement would be difficult or impossible.

17.9

Conclusion

Basic in vivo physiological measurement methodologies related to clothing comfort research have progressed considerably in the past several decades. Reliable commercial devices are now available for measuring several different aspects of human physiological conditions. Prior research results also indicated that those technologies play a very important role in the area of clothing comfort research. Safety and non-invasiveness are the most important criteria for individual measurement methodology, followed by sensitivity, implementation requirements and operator assistance. The non-invasive nature of available techniques provides effective methods for monitoring the temporal effects of wearing clothing.


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17.10 Acknowledgements The authors would like to thank the Hong Kong Polytechnic University for funding this research through the projects G-V987, A188 and ITF project ITS-023-03.

17.11 References 1. Askanazi, J., P. Silverberg, R. Foster, A.I. Hyman, J. Milic-Emili, and J. Kinney, Effects of Respiratory Apparatus on Breathing Pattem. J. Appl. Physiol., 1980. 48: p. 577–580. 2. Carroll, M., An Evaluation of Temperature Measurement. Nursing Standard, 2000. 14(44): p. 39–43. 3. Columbia University Press, The Columbia Electronic Encyclopedia, http://www.answers.com/topic/humidity, 2003. 4. European Physiology Modules, Skin Blood Flow Measuring Device, http://www.spaceflight.esa.int/file.cfm?filename = utilcolepmsbfmd, 2000. 5. Fagrell, B., A. Fronck, and M.I. M, A Microscope Television System for Studying Flow Velocity in Human Skin Capillaries. Am. J. Physiol., 1977. 23(2): p. H318–321. 6. Fanger, P.O., Thermal Comfort. 1970, Copenhagen: Danish Technical Press. 7. Fellows, I.W. and A. Macdonald, An Automated Method for the Measurement of Oxygen Consumption and Carbon Dioxide Excretion in Man. Clin. Phys. Physiol. Meas., 1985. 6(4): p. 349–355. 8. Fitmed Inc, Heart Rate & Exercise Basics, http://www.heartmonitors.com/ heart_rate_basics.htm, 2005. 9. GE Healthcare, Doppler Ultrasound, http://www.amershamhealth.com/ medcyclopaedia/medical/Volume%20I/DOPPLER%20ULTRASOUND.asp, 2004. 10. Gens, F., J.-P. Remenieras, F. Patat, S. Diridollou, and M. Berson, Potential of a High-frequency Correlation Method to Study Skin Blood Flow. Skin Research and Technology, 2000. 6(1): p. 21–26. 11. Holmer, I., Recent Trends in Clothing Physiology. Scand. J. Work Environ Health, 1989. 15(Suppl 1): p. 58–65. 12. Holmer, I., Clothing Physiology: Past, Present, and New Perspectives, in Proceedings of the 9th International Conference on Environmental Ergonomics. 2000. Dortmund, Germany 2000. 13. Honeywell, Humidity Sensor Theory and Behavior, in HIH Series. 2003. 14. Houdas, Y. and E.F.J. Ring, Human Body Temperature. 1982: New York, Plenum. 15. Houghton Mifflin Company, Medical Definition of Oxygen Consumption, http://www.answers.com/topic/oxygen-consumption, 2002. 16. InterGalactic Power & Light, A Brief History of Temperature Measurement, http://home.comcast.net/~igpl/Temperature.html, 2005. 17. ISO, ISO 7730 Moderate Thermal Environments – Determination of the PMV and PPD Indices and Specification of the Conditions for Thermal Comfort (N). 1995: Geneva, ISO.


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18. Junger, M., A. Maichle, T. Klyscz, H.M. Hafner, M. Hahn, and G. Rassner, Dynamic In Vivo Skin Pressure Measurement in Quality Control of Compression Stockings. Hautarzt, 1997. 48(7): p. 471–476. 19. Kraning, K. and D. Sturgeon, Measurement of Sweating Rate with Capacitance Sensors. Ann. Biomed. Eng., 1986. 11(2): p. 131–146. 20. Li, Y., Clothing Comfort and Its Application. Textile Asia, 1998. 29(7): p. 29–35. 21. Linton Instrumentation, Micro Pressure Transducer System, http://www. lintoninst.co.uk/Samba.htm, 2002. 22. Macfarlane, D., Automated Metabolic Gas Analysis Systems: a Review. Sports Med., 2001. 31(12): p. 841–861. 23. Mahler, F., M.H. Muheim, M. lntaglietta, A. Bollinger, and M. Anlicker, Blood Pressure Fluctuations in Human Nailfold Capillaries. Am. J. Physiol., 1979. 230: p. 888–893. 24. NISMAT, NISMAT Exercise Physiology Corner: A Primer on Maximum Oxygen Consumption, http://www.nismat.org/physcor/max_o2.html, 2005. 25. O’Donnel, C.R.D. and F.T. Eggemeier, Workload Assessment Methodology, in Handbook of Perception and Human Performance, K.R. Boff, L. Kaufman, and J.P. Thomas, Eds, 1986: New York, John Wiley and Sons, p. 42-1–42-49. 26. Ogawa, T., M. Asayama, and M. Ito, Significance of Skin Pressure in Body Heat Balance. Jpn J. Physiol., 1979. 29: p. 805–816. 27. Ohhashi, T., M. Sakaguchi, and T. Tsuda, Human Perspiration Measurement. Physiological Measurement, 1998. 19(4): p. 449–461. 28. Okura, K., T. Midorikawa-Tsurutani, and H. Tokura, Effects of Skin Pressure Applied by Cuffs on Resting Salivary Secretion. J. Physiol. Anthropol. Appl. Human. Sci., 2000. 19(2): p. 107–111. 29. Payne, P.A., R.Y. Faddoul, and S.M. Jawad, A Single Channel Pulsed Doppler Ultrasound Instrument for Measurement of Skin Blood Flow, in Practical Aspects of Skin Blood Now Measurement, V.A. Spence and C.D. Sheldon, Eds, 1985: London, Biological Engineering Society. 30. Peak Sensors, Resistance Thermometers (RTDs), http://www.peaksensors.co.uk/ rtddatasheets.html, 2005. 31. Scott, C., D. Marlin, and R. Schroter, Modified Ventilated Capsule for the Measurement of Sweating Rate in the Exercising Horse. Equine Vet. J. Suppl., 1996. 22: p. 48–53. 32. Shamsuddiny, A.K.M. and T. Togawa, Continuous Monitoring of Sweating by Electrical Conductivity Measurement. Physiol. Meas., 1998. 19: p. 375–382. 33. Sone, Y., N. Kato, Y. Kojima, N. Takasu, and H. Tokura, Effects of Skin Pressure by Clothing on Digestion and Orocecal Transit Time of Food. J. Physiol. Anthropol. Appl. Human Sci., 2000. 19(3): p. 157–163. 34. Spence, V.A., P.T. McCollum, and W.F. Walker, Comparative Studies of Cutaneous Haemodynamics in Regions of Normal and Reduced Perfusions, in Practical Aspects of Skin Blood Flow Measurement, V.A. Spence and C.D. Sheldon, Eds, 1985: London, Biological Engineering Society, p. 1–10. 35. Swain, I.D. and L.J. Grant, Methods of Measuring Skin Blood Flow. Phys. Med. Biol., 1989. 34(2): p. 151–175. 36. Takagi, K., Functional Control of Living Systems. 1972: Tokyo, Chuo Koronsha. 37. Tekscan, Force Sensors, http://www.tekscan.com/medical.html, 2002.


18 Application of clothing biosensory engineering 1

18.1

YI LI 1 AND ANTHONY S.W. WONG 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong

Introduction

From the discussions in previous chapters, it is obvious that significant progress has been made in various clothing comfort research areas, and that this progress has great potential for application in the textile and clothing industry. In the modern consumer market, meeting and exceeding customer expectation is the key to success for any enterprise. As consumers demand higher comfort performance from apparel products and more technical information in order to ensure the value of goods they are buying, comfort research is becoming more and more important for individual companies within the industry. This has been demonstrated clearly from the successful comeback of the synthetic fiber industry through the development of hightech apparel products with emphasis on comfort and functional performance. Understanding consumers’ needs is essential in the clothing industry. This chapter discusses how clothing comfort research knowledge and methodology can be applied to real-world situations. The requirements of developing new products are also discussed and the methodology of evaluating consumer subjective perception is described.

18.2

Area of application

The knowledge and methodology developed in clothing comfort research can be applied in a number of ways: •

to conduct consumer research by using the techniques developed to understand what consumers want and need and to identify market gaps for new product development; to use the technical knowledge obtained to develop textile products which have unique functional features and are sure to satisfy targeted customers; to use consumer sensory evaluation as a means for new product evaluation to reduce the risk of market failure; 301


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to develop technical information and specification for promotional and marketing purposes; to formulate quality control tools by developing test methods, instruments and standards.

18.3

Consumer research

Understanding and predicting human needs and behavior are two of the most important issues for modern enterprises if they are to develop sound business strategies to meet consumers’ requirements and combat market competition. The key business strategies involved include brand management, new product development and market development. Consumer research needs to identify buyers’ product-related attitudes and find out why markets change, in order to provide good estimations of changes to sales volumes. Through such attitude studies, all the criteria affecting buyers’ decisions between brands can be identified and screened. Typically, hundreds of criteria or attributes including both functional and emotive criteria are generated. Of these 10–15 possible underlying dimensions of choices are identified, from which individual consumers use 2–5 criteria to make their purchase decisions.4 Creig4 pointed out the importance of systematic studying of product attributes and their relative importance in motivating buyers by inferential procedures. A set of functional and emotional product attributes needs to be identified to explain buyers’ brand preferences among all the choices. Through this type of analysis, a picture can be built up to show which brands are competing on what attributes and where the market share is most likely to be profitably increased with regard to changing some images over others. However, one of the most difficult tasks in social science is to obtain an understanding of consumers’ needs and wants and to predict their purchase behavior. Without good understanding of consumers, low success rates for new product launches and the waste of up to half of advertising carried out have been traditionally observed.4 Dayal6 reported that consumers are not adept at identifying what are the appealing qualities in a garment, while Greenwood9 pointed out that consumers are generally not required to describe in detail the characteristics that they seek in purchasing garments. This suggests that consumers’ vocabulary for garment attributes is underdeveloped. Creig4 argued that buyers should not be directly asked about the relative importance of product attributes. Reasonable ranking of functional importance can be derived by direct questions, but not for emotional attributes. Consumers usually under-estimate the importance of emotional attributes and over-estimate the attributes related to social and political issues.


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During the process of purchase decision making, consumers evaluate the appeal of garments as a whole.1 The process involves many individual factors and their integration, including the physical properties of the clothing, physiological characteristics of the buyers, their wearing experiences, economic and social background.7,16–18 As discussed in the previous sections on evaluation of clothing sensory comfort, a complex variety of factors stimulate various sensory perceptions. The judgement of overall appeal of a garment comes from a complicated experience involving the integration of individual sensations. Many of the sensations cannot be felt without exposure to a specific combination of physical activities, environmental conditions and the physiological–psychological status of the wearers. The traditional market research methods and consumer surveys can derive information on brands that are of a very general nature, but cannot provide more detailed understanding of the roles of physical–mechanical attributes in consumer desirability of fabrics and garments. Consumers are generally not required or are unable to pinpoint appealing properties of garments for particular end-uses without specific references. Therefore, more advanced or more specific consumer research techniques are required to obtain consumer information for product development, brand management and development of market strategies. Creig4 pointed out that using a new generation of consumer research tool, business firms could select product development, new product strategies and rational–emotional advertising messages with more accuracy. Several companies employing such techniques found that brand market share predictions achieved were within 10% of those predicted 80% of the time and that there was an 80% success rate for new products. The techniques developed over the years in clothing comfort research and consumer sensory perceptions can become powerful tools for marketing purposes. The techniques developed by Hollies12 for generating individual descriptors from human subjects for sensations experienced during wearing clothing can be used to obtain information on the product attributes desired by consumers. The wear trial techniques can be an effective means of obtaining more specific and accurate sensory responses from consumers with fresh perceptual experiences on clothing in controlled dynamic ambient temperature and humidity changes. The personal construct theory developed by Kelly13 validated these approaches theoretically on the basis that human beings have the ability to be specific and draw on the internal concept of a particular type of garment for generating specific criteria to evaluate it. The semantic differential scaling techniques, which have been developed and applied successfully in studies in fabric hand and clothing comfort by many researchers such as Winakor et al.,22 Harada,10 David et al.5 and Fritz,8 can be effective in identifying consumers’ desired product attribute profiles. The three major underlying sensory dimensions in


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consumers’ comfort perceptions, which have been identified from a large number of sensory studies,15 can be used as the criteria for evaluating clothing comfort performance during wear. The relative contributions of the three comfort dimensions to consumers’ overall preferences or sensory responses,15 which are obtained by analyzing the results from various sensory studies, can be useful guidelines for the development of apparel products for many specific combinations of physical activities and environmental conditions. Therefore, application of these techniques and their integration into the conventional marketing research methodology can become a powerful consumer research tool for any companies in the textile and clothing industry for both product development and marketing management. In summary, the application of sensory study techniques in the clothing comfort field confers two major advantages: •

using them, we can more accurately identify the specific product attributes that consumers want, the attributes on which certain brands’ competitive edges are based and the market gap that can be filled by developing and positioning new product lines; conducting focus group studies or wear trials in simulated practical wear situations, it is possible to obtain much more accurate sensory responses and preferences from consumers as they can draw their own conclusions and make judgements on the basis of current experiences instead of vague memories of past experiences.

18.4

New product development

In the modern consumer market, companies that fail to develop new products are facing great risks. Their existing products are vulnerable to various changes in the market places, such as changes in consumer needs and preferences, new technologies, shortened product life cycles and increasing competition from substitutes and foreign-domestic companies. Meanwhile, new product development is risky and entails disturbing failure rates. It has been estimated that 75–80% of new products fail at launch.3,14 Cooper and Kleinschmidt2 studied the common features of successful new products across a wide range of industries and concluded that the number one success factor is a unique superior product characteristic, such as higher quality, new features or higher values in use. New products with a high advantage succeeded 98% of the time, while new products with moderate advantage had 58% success and those with minimal advantage only 18% success. The second success factor is a well-defined product concept prior to development, where the target market, product requirements and benefits are carefully defined and assessed. Other factors include the synergy between technology and marketing, and market attractiveness. In the light of these changes, developing new products that meet the needs of new


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consumers is becoming both increasingly difficult and more important if companies are to remain competitive within the clothing market. Clothing markets in the 1990s were highly competitive. Consumers are demanding more functions, higher quality and more added value. Fashion is no longer dictated by a handful of couture designers. Instead, consumer lifestyles are having a growing impact on the direction of fashion trends. Today, consumers want to dress more casually and can do so as more and more companies institute more casual dress policies. At the same time, consumers desire garments that enable them to be comfortable and feel good in whatever activity they happen to be engaged. They want their clothing to be natural, comfortable and easy care, and demand more information about the products that they buy. With these changes, development of new products to satisfy consumers’ needs and wants has become both increasingly difficult and more important if companies are to compete in the consumer markets. To increase the success rate of new products, it is important to find an effective way to identify consumers’ specific requirements and link them with the technical attributes of products. The knowledge and methodology developed in clothing comfort research are exactly the weapons needed to achieve this objective. Psychological sensory study identifies what consumers want and desire, which is linked to the technical features (or physical stimuli) of products through psychophysical perceptual trials. Further, the physical mechanism that generates such technical features is able to provide the technical specification and guidelines to produce the desired products. Summarizing these psychological–psychophysical–physical processes, a five-level system for apparel product and development can be devised. At level 1, traditional consumer research techniques such as focus groups, personal interviews and consumer surveys are carried out to obtain market information on the products. Information obtained through these types of research is general in nature with limited accuracy, as there can be many uncontrollable variables in the environment, human respondents and market situations. At level 2, wear trials can be conducted with a limited or large number of subjects in the field at which the garments are used. These types of wear trials are mainly used in consumer evaluation, market testing or in fundamental studies for particular types of clothing, to derive more specific information on product attributes desired by the end users. This type of research is expensive and has relatively high risk as some of the confidential information may be detected by competitors. At level 3, controlled wear trials with human subjects are carried out in climatic chambers for psychological sensory study, consumer focus group study and subjective evaluation of apparel products. Very specific information on desired product attributes can be obtained from consumers’ sensory responses. This type of research can be more accurate and safe, as the variations in environmental conditions and human subjects are accu-


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rately controlled in the laboratory. Also, the psychological assessment can be used to test the predictions or results obtained from the measurements and systems of analysis from levels 4 and 5. At level 4, the entire garment system is tested on human subjects or thermal manikins to determine the thermal insulation and moisture transport properties, mechanical behavior and appearance during wear. These physiological and physical measurements are aimed at determining and evaluating the technical details of apparel products. The technical information needed includes the design and style of garments, the effect of body movement and environmental conditions, and the effect of garment assemblies from underwear and outerwear to air layers in the clothing microclimate and at the external surface of the garment. Research at this level can provide the essential technical specifications for the garment design and manufacturing processes. At level 5, fabrics are tested on a range of physical properties from heat and moisture transfer behavior, to mechanical behavior to color and surface features. These measurements are used to evaluate the features and quality of fabric materials in order to achieve the desired comfort and functional performance during wear. The information obtained here can provide detailed technical specifications for textile processing and manufacturing processes. From level 1 to level 5, the information flows from market requirements to technical specifications, from the general to the specific. Using such a system, new products can be developed according to the market demand and can satisfy the requirements of consumers on various product attributes such as comfort, easy care and appearance maintenance. On the other hand, from level 5 to level 1, information flows from being technical to being sensory and subjective. This means that, by utilizing the knowledge and understanding of the physical mechanisms, psychophysical mechanisms and psychological preferences of consumers on various sensory perceptions, predictive tools can be developed using either statistical or mathematical models for product development purposes. With these tools, manufacturers can conveniently optimize the product design according to the identified consumers’ requirements prior to actual production. This enables textile and clothing manufacturers to save money and time, and to respond to market demand quickly and with confidence. Umbach19 reported a model for clothing comfort and functional design using an example of an optimal multilayered cold protection garment with controllable ventilation. With optimal combination of fabrics and garment design, the protective clothing was able to maintain a comfort microclimate in a wide range of external environment from −20–16 °C, which was confirmed by tests using thermal manikins and human subjects in climatic chambers. Similar approaches were applied to leisure and sportswear such


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as ski-suits, motorbike and sailing suits and all-weather coats. Great market success was reported.20

18.5

Consumer subjective evaluation

In a new product development process, the expenses increase as the work progresses. The ratios of costs for one successful new product were 1, 5, 25, 31 and 156 from idea screening, concept testing, product development and test marketing to final market launch.14 The highest cost is located at the last stage – the final market launch. Therefore, it is extremely important to ensure that consumers’ perceptions and acceptance of the new products are positive before launching onto the market. To ensure this, consumer subjective evaluation of new products should be carried out at the various stages. For apparel products, this is extremely important because many sensations, that are determined by the combinations of physical activities, environmental conditions and social environment, cannot be perceived without testing the garments in actual wear situations. The sensory studies techniques described above can be utilized effectively to obtain consumers’ responses at the stages of idea generation, idea screening, concept testing and product development in the form of functional tests and consumer tests. These can be carried out at three levels: climatic chamber wear trials, limited field tests and large-scale field tests. The functional tests can be carried out in a laboratory or in the field to ensure that the products perform safely and effectively through a range of physical and physiological tests at level 4 of the system. Consumer tests can be conducted in a number of ways, from bringing consumers into a climatic chamber for simulated wear trials to conducting field wear trials. A range of sensory study techniques, such as simple ranking, paired comparison, rating scales or semantic scales, can be used to obtain consumers’ perceptions and preferences. Abundant information can be obtained through these tests, such as the perceived superiority of the products, the perceived difference between consumers’ ideal products and the developed products, the positions of the newly developed products relative to a company’s own existing products and those of its competitors, and the perceived value and price of the new products. The information is critical for companies to make important business decisions, such as Go or Drop, product modification, branding and positioning, packaging, distribution, pricing and other market launching strategies such as promotion and advertising.

18.6

Test methods and instruments

The objective test methods developed in clothing comfort research can be used to derive a quantitative measurement of the clothing comfort-related properties of fabrics and garments and to develop comfort specifications.


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Umbach20 reported that the physiological specifications developed from their measurement system had been included in some German Industry DIN standards for work and protective clothing, as shown in Table 18.1.20 For instance, the weather protective suit, which is used in building construction for protection against the influence of wet and wind, is required to have a water vapor resistance of not greater than 0.200 m2 mbar/W according to DIN 61539. This is aimed at maintaining sufficient comfort for a wearer performing moderately heavy physical activity at ambient temperature up to 20 °C. According to DIN 61537, the values of fabric thermal resistance and water vapor resistance for protective vests against cold were specified for the purpose of extending the range of use of the weather protective clothing defined by DIN 61539 down to −5 °C. These standards, based on parameters measured on clothing wear comfort performance, have been used as the technical specifications for industrial clothing and military uniforms. These techniques have also been applied to the areas of leisure, sport and everyday clothing, such as technical specifications for weather protective clothing, ski-suits, anoraks, motorbike suits, sailing suits, all-weather coats, sports underwear, such as ski underwear, football and tennis tops. When the products are designed according to the technical specifications and produced in fashionable colors, fashion and function harmonize and work together to improve the chances of market success.20 The work carried out by Umbach and his colleagues has demonstrated how effective application of the research on clothing thermophysiology and comfort has achieved significant market success. However, clothing comfort has much broader dimensions than thermophysiological. Studying the pattern of relationships among the sensory responses during wear shows that the comfort of clothing has three independent sensory factors: thermal– wet comfort, tactile comfort and pressure (body-fit) comfort.15 Further, analysis of the sensory responses obtained from the survey and wear trials indicated that the relative importance of the three factors in consumers’

Table 18.1 Some DIN standards for work and protective clothing Test standard DIN 54101 (T 1 (E)) Skin model Application standards DIN 61539 Weather protective suit DIN 61537 (E) Protective vest against cold DIN 30711 T3 Warning clothing DIN 30711 T 2 Warning clothing DIN 32763 Protective clothing against chemicals Type 2


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sensory experiences varies with different combinations of physical activities and environmental conditions.11 For winter wear, the tactile and thermal– wet comforts were perceived to be important, taking about 67% of the total variance. For summer wear and spring wear, the thermal–wet comfort was perceived to be important, followed by tactile and pressure comfort. However, these three factors account for only about 60% of the total variance. For sportswear, thermal–wet was perceived to be extremely important, accounting for 43% of the total variance, followed by tactile and pressure comfort. It should be noted that these results have a basic assumption that the style and appearance of the garments were the same or met the standards required by the consumer.11 On the other hand, consumers’ requirements for apparel products are much more than just comfort. Heller and Schiff11 studied the perceptions of importance of various attributes of clothing wear in the workplace. A number of national groups from Europe, Asia and Australia were included. The main trends among the groups were found to be similar, with the attributes of comfort, fit, style, color and quality rated highest. This suggests the need for an effective method of evaluating the overall wear performance of clothing. Vatsala et al.21 proposed a product-performance index for integral evaluation of fabric performance, which is based on giving weightings to four functions: comfort, aesthetic appeal, ease-of-care and durability. A comprehensive system for overall evaluation of apparel products can be crucial for new product success. As all the final judgements are made by consumers on the basis of their perceptions and experiences during practical wear situations, a system can be used for overall evaluation of apparel products. With such a system, the techniques developed in clothing comfort research can be used effectively to improve the quality of life for modern and future consumers. Also, utilization of the knowledge and methodology can become powerful tools for companies in the textile and clothing industry to develop product-based business strategies and achieve sustainable competitive advantages in the market place.

18.7

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A174.

18.8

References

1. ASTM, Manual on Sensory Testing Methods. 1986: West Conshohocken, PA, American Society for Testing and Materials. 2. Cooper, R.G. and E.J. Kleinschmidt, New Products: the Key Factors in Success. 1990: Chicago, American Marketing Association.


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3. Cooper, R.G. and E.J. Kleinschmidt, New Product Processes at Leading Industrial Firms. Industrial Marketing Management, 1991. 20(5): p. 137–147. 4. Creig, I., The Application of a New Generation of Consumer Research Techniques to Predicting and Maximising Market Share in the Fibre Market. Journal of the Textile Institute, 1994. 85(2): p. 231–243. 5. David, H.G., A.E. Stearn, and E.F. Denby. The Subjective Assessment of Handle, in Third Japan-Australia Symposium on Objective Measurement: Applications to Product Design and Process Control. 1985. Kyoto, Japan: Textile Machinery Society of Japan. 6. Dayal, R., Comfort Properties of Textiles, Part 1. Textile Dyer and Printer, 1980(Sept.): p. 21–24. 7. DeMartino, R.N., Comfort Properties of Polybenzimidazole Fibre. Textile Research Journal, 1984. 54(8): p. 516–521. 8. Fritz, A.M., New Way to Measure Fabric Handle. Textile Asia, 1992. 23(7): p. 69–72. 9. Greenwood, K., Making Clothing More Comfortable. Textile Month, 1971. 49(June): p. 107. 10. Harada, T., Main Factors Affecting Wear Comfort On the Field Tests. Journal of the Textile Machinery Society of Japan, 1983. 36: p. 16–21. 11. Heller, M.A. and W. Schiff, The Psychology of Touch. 1991: Hove and London, Lawrence Erlbaum Associates. 12. Hollies, N.R.S., Visual and Tactile Perceptions of Textile Quality. Journal of the Textile Institute, 1989. 80(1): p. 1–18. 13. Kelly, G.A., Man’s Construction of His Alternatives, in Assessment of Human Motives, ed. G. Lindzey, Ed., 1958: New York, Holt, Rinehart & Winston. 14. Kotler, P., Marketing Management, 8th edn, 1994: London, Prentice Hall International, Inc. 15. Li, Y., Dimension of Sensory Perceptions on Next-to-Skin Wear in a Cold Environment. Journal of China Textile University, 1998. 15(3): p. 50–53. 16. Li, Y., J.H. Keighley, and I.F.G. Hampton, Physiological Responses and Psychological Sensations in Wearer Trials with Knitted Sportswear. Ergonomics, 1988. 31(11): p. 1709–1721. 17. Lyn, J.E., Comfort, It’s More Than Meets the Eye. American Dyestuff Reporter, 1978. 67(9): p. 9. 18. Pontrelli, G.J., Partial Analysis of Comfort’s Gestalt, in Clothing Comfort, N.R.S. Hollies and R.F. Goldman, Ed., 1977: Ann Arbor, MI, Ann Arbor Science Publisher, Inc., p. 71–80. 19. Umbach, K.U., Protective Clothing Against Cold with a Wide Range of Thermophysiological Control. Melliand Textilberichte, 1981. 62(4): p. 360–364, 456–462 (English edition). 20. Umbach, K.H., Methods of Measurement for Testing Physiological Requirements of Civilian, Work and Protective Clothing and Uniforms. Melliand Textilberichte, 1987. 68: p. 857–865. 21. Vatsala, R. and V. Subramaniam, The Integral Evaluation of Fabric Performance. Journal of the Textile Institute, 1993. 84(3): p. 495–500. 22. Winakor, G., C.J. Kim, and L. Wolins, Fabric Hand: Tactile Sensory Assessment. Textile Research Journal, 1980. 50: p. 601–610.


19 Mechanical and thermal sensory engineering design YI LI 1 , ZHANG WANG 1 AND XIN ZHANG 2 1 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Kowloon, Hong Kong 2 Fashion College, Xian University of Engineering Science & Technology, China

19.1

Introduction

Engineering design is an iterative decision-making process in which the basic sciences together with mathematics and engineering are applied in order to to optimally convert resources to meet a stated objective.18 It is the link between scientific experimentation and commercial application to develop economic solutions to practical technical problems. Engineering design has been successfully applied in a number of areas such as machine manufacturing, civil engineering and bridge construction. In 1986, the concept of sensory engineering (kansei-engineering) was developed by the Mazda company in Japan as a development of human factors. Sensory means the psychological feelings or image of a product and sensory engineering refers to the quantitative translation of consumersâ&#x20AC;&#x2122; psychological feelings about a product into perceptual design elements. The technique involves determining which sensory attributes elicit particular objective responses from people and then designing a product using the attributes that elicit the desired responses. Sensory engineering has been applied with great success in the automotive industry (the Mazda Miata being a notable example) and is being extended to other product domains including the development of new fibers.9 In the textile industry, products have been designed by using â&#x20AC;&#x2DC;trial and errorâ&#x20AC;&#x2122; for thousands of years. But from the 1970s onwards, industrial and academic experts1 have recognized the importance of systematic engineering design of textiles and textile processes.6,8,10,14,17 In 1994, Hearle7 presented the concept of textile-product design using fabric mechanics as a design tool. He described the different approaches available to tackle fabric mechanics in a hierarchical way and developed the concept of a computeraided total-design system based on three frameworks: a database of information on fiber and fabric properties; a knowledge-based system using the pool of available expertise and historical data; and a deterministic suite of 311


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programs in structural mechanics. In the 1990s, Matsuo and Suresh14 proposed the concept of fiber-assembly-structure engineering (FASE) for total material design. Total material design refers to the design of a textile product starting from the conceptual design and progressing to devising the manufacturing method. The design has three stages: 1. aesthetic-effect or functional design; 2. basic structure design; 3. basic manufacturing design. In the 1990s, a series of papers by Dastoor et al.1â&#x20AC;&#x201C;3 presented the computerassisted structural design of industrial woven fabric, illustrating the possibility of creating a CAD environment to help to carry out structural design and evaluation of industrial fabric economically. The goal was to develop the engineering design of the ideal quality of suiting on the basis of fiber science, textile mechanics and objective measurement technology. Modern consumers demand clothing products with superior multifunctional and comfort performance to satisfy their physiological and psychological needs. Garment mechanical comfort, such as pressure comfort, has been identified as one of the important attributes. Denton4 pointed out that the discomfort level of clothing pressure was found to be between 20 and 40 g/cm2, depending on the individual and the part of the body concerned, which is similar to blood pressure in the capillary blood vessels near the skin surface. It was reported that the thresholds for the perception of pressure were found to be different on the front and the back of the lower leg.15 Therefore, engineering design of clothing mechanical performance demands a different kind of logical structure to that of a textile product. An important difference is that human factors (physiologic and psychology) have to be accounted for in the engineering design of clothing mechanical performance. However, there has been no CAD system for the engineering design of clothing mechanical and thermal performance, especially for 3D numerical simulation of the mechanical or thermal interaction of the body and clothing and sensory evaluation of clothing mechanical and thermal comfort, respectively. Some ideas have thrown light on engineering design systems, but these are still at the conceptual level. Not many studies are available to express a design methodology and a design process for the engineering design of clothing mechanical and thermal performance â&#x20AC;&#x201C; sensory engineering design of clothing products is thus still at the development stage. This chapter discusses the development of two types of sensory engineering design systems for textile and apparel products â&#x20AC;&#x201C; mechanical and thermal. The mechanical sensory engineering design system aims to create a completely new framework and technology for engineering design of


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clothing mechanical performance and to bring science into the design and development of textile and apparel products, a field traditionally dominated by artistic fashion design and performed through trial-and-error methods. The fundamental work in the derivation of the system, including the development of dynamic models of body–garment contact, development of an engineering database and an investigation of the psychophysical relationship between mechanical stimulation and subjective perception, is presented. This system is the first one to integrate clothing design process, clothing–body biomechanical models, numerical solutions, analysis and evaluation of clothing sensory mechanical performance into a CAD environment based on a collection of well-integrated software tools. The application of the system is illustrated through an example on the design of the mechanical performance of jeans. The thermal sensory engineering design system, which is developed on the basis of consideration of human factors, for textile and apparel product has three functional models: design, analysis and evaluation. The fundamental work including the development of physical models, engineering database and investigation of psychophysical relationships between physical stimulation and comfort perception is presented. The application of the system is illustrated through an example on the design of active sportswear for thermal sensory performance.

19.2

Functional requirements of a mechanical sensory engineering design system

19.2.1 Clothing design based on human sensory factors The study of people as a component of an engineering system is called human factors engineering. Human factors engineering is an interdisciplinary science which aims to fit the product to the person rather than the person to the product. The design is specified with the requirements of people as the starting point and as the main criterion for effectiveness. It takes knowledge from anatomy, anthropometry (the science of dimensions of humans), applied psychology, biomechanics, bioengineering and physiology. The concept of the mechanical sensory engineering design of clothing performance involves human factors in two aspects of the design process – fashion design and clothing material design – in order to achieve the desired clothing aesthetic and functional effects. The human factors mainly concern five aspects: 1. physical – such as the body size and the shape; 2. biomechanics of the human body – such as the deformation of the muscles, the skin and the soft tissue at different body parts;


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3. physiology – formulating sensory signals from the interactions of the body with the clothing and surrounding environments; 4. neurophysiology; 5. psychology – subjective perception of sensory sensation from the neurophysiological sensory signals, leading to formulated subjective overall perception and preferences. The four factors, physical, biomechanics, physiology and neurophysiology, must be considered in pattern design and material design, and the psychological factors must be taken into account in designing the aesthetic effects of clothing. Sensory engineering design of clothing mechanical performance should be based on quantitative investigations of the relationship between clothing mechanical performance and human sensory (physiological and psychological) factors. Three fundamental investigations are required if the concept is to be achieved. The first is the quantitative translation of consumers’ psychological feelings about a product into perceptual design elements that will be important attributes in the evaluation of fashion and material design. The second is the investigation of the dynamic mechanism involved in the contact interface between the human body and clothing, which bridges the relationship between human biomechanics and fashion design. The third is the induction of clothing mechanical characteristics from the dynamic analysis of features such as its deformation magnitude, stretch-recovery properties and rheological behavior during wear. These physical and mechanical characteristics are the basic information for the engineering design of clothing materials.

19.2.2 Computer aiding the iterative decision-making process Clothing design means creating new clothing by enhancing or altering existing designs to perform new functions. The clothing design task consists of selecting the style, color and materials to meet specified functional requirements for the clothing. The conventional design process is largely based on the experience and intuition of the designer, which has several disadvantages. First, a great deal of reliance is placed upon individual designers but not on the knowledge-based design model. It is difficult to require a designer to communicate with interdisciplinary professionals to achieve the developed concept, and the training of new design ‘apprentices’ is a lengthy, tedious and costly process. Second, it is difficult to achieve an iterative decision-making process in a short design time. Third, the designer does not have the means to make a parametric analysis of the clothing mechanical


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performance before it is produced. Finally, it is difficult to maintain a consistently updated information system involving multiple sets of data at several locations within an organization. A CAD system should overcome these disadvantages through the development of fundamental frameworks. It requires the development of knowledge-based design procedures to guide the user in designing clothing mechanical performance based on human sensory factors. The design begins with a product specification to identify a type of garment (e.g. jeans or bra), followed by selection of garment style and human body parameters from the human factor database and the product database. From the input parameters of the human body and the garment, the deformation characteristics of clothing should be identified, based on mechanical analysis of the dynamic contact between the human body and the garment. This will govern the next steps of selecting fabric structure and mechanical properties of the fiber–yarn–fabric. The selection is a revision of design achieved by searching or reworking some previous fabric structure, which reasonably approximates to the current design requirements. Next is a mechanical model of the body–garment, allowing a numerical simulation and analysis of the mechanical performance of garment and body to be performed. The iterative procedure has to be completed before the garment is produced, if the design is satisfactory at the simulation and evaluation stages. The iterative design cycle will be shortened through use of the CAD environment which is supported by four engineering databases and based on a quantity of fundamental research. Through the design process in the CAD system, a sample is manufactured for a wear trial. This aims to further modify the design according to subjective evaluation of the garment before formal production.

19.2.3 Analysis based on 3D virtual prototypes The design phase determines feasible style and material parameters. The analysis phase is used to compute and illustrate the mechanical performance of the dynamic contact system, such as distributions of stresses and strains in the garment, the deformation of the skin and soft tissues, and the garment pressure distributions on the skin. This analysis involves computational experiments of the design to test whether it meets the desired functional criteria of clothing. The saying ‘A picture is worth a thousand words’ remains valid. Visualization through virtual prototypes gives the designer immediate feedback on design decisions. Therefore, to analyze clothing mechanical performance based on 3D virtual prototypes requires the development of 3D mechanical models of the human body and clothing that can simulate and


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visualize the mechanical interactions between them in different wear situations. Otherwise, the predicted behavior may not match the actual behavior, resulting in poor design decisions.

19.2.4 Sensory evaluation During wear, clothing comes into contact with the skin of most parts of the body. Li12 pointed out that the contact has three features: 1. large contact areas with varying degrees of sensitivity; 2. changing physiological parameters of the body (such as skin temperature, sweating rate, and humidity at the skin surface); 3. a moving body that induces new mechanical stimuli from the contact between the body parts and clothing â&#x20AC;&#x201C; the mechanical stimuli in turn induce responses from various sensory receptors and formulate various perceptions, such as touch, pressure, prickle, itch and inflammation, which affect the mechanical comfort of the wearer. The study of the psychophysical process of perception on clothing mechanical behavior makes it possible to predict and evaluate mechanical sensory comfort of clothing from the mechanical properties and structural features of fibers, yarns and fabrics, as well as garments. Mechanical comfort should be one of the criteria used to decide which is the optimal design from among several alternatives in the design process. The designer can judge whether the product meets the comfort requirements by comparing the predictions with desirable values, such as for pressure distributions and psychological perceptions of comfort pressure. Therefore, in addition to the design and the analysis functions, the system should provide the function of sensory evaluation of mechanical comfort. Clothing comfort is a very subjective concept. Evaluation of pressure comfort must combine the predicted pressure and the sensation index from a large volume of experiments involving wear trials. Much work on psychological evaluation of garment pressure is required from which a series of psychophysical models will be developed based on the investigation of the relationships between objective stimuli and psychological perceptions and between the predictions and the objective measurements.

19.3

Fundamental work in the development of mechanical engineering design system (MEDS)

To design MEDS with the required functions, a series of fundamental developments have been made by integrating science, engineering and information technologies. The research involves a number of aspects: development


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of 3D digital models of the human body and garments; mechanical models for the human窶田lothing contact system; a clothing engineering database; and analysis as the psychophysical relationship between mechanical stimulation and psychological comfort perception. Contact mechanics is the fundamental theory for modeling the dynamic contact between the human body and garment, and numerical computation is the key to solving the contact problem. In the system, finite element methods and particle-based models have been developed.

19.3.1 Development of 3D digital models of human bodies and garments Clothing mechanical behavior is related to both fabric mechanical properties and the space allowance between the body and the garment during body movement. According to the degree of space allowance, garments can be classified into three types: 1. foundation garments, in which the garment area is less than the body area; 2. perfect fitting garments, in which the garment area is equal to the body area; 3. loose garments, in which the garment area is larger than the body area. Foundation garments are designed to apply a certain level of pressure on the appropriate body part when the body is both active and at rest. Perfect fitting garments have a figure-shaping function, but are not designed to apply pressure on the body. Because the body movement can reduce the space allowance, loose garments may also exert pressure on the body in the contact areas. Because of this the development of 3D digital models of human bodies and garments is fundamental for numerical analysis and visualization of clothing mechanical performance. However, there are difficulties in creating the 3D digital models. Currently, 3D digital models of human bodies have been developed on the basis of commercially available virtual human models. Using these human models as a starting point, a series of 3D digital garment models have been developed, which can be meshed for mechanical analysis.13,20,21

19.3.2 Development of 3D dynamical human窶田lothing contact models In general, clothing behaves mechanically in a non-linear way and is subject to multidirectional stress/strain components. In approaching the complex


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issue of clothing mechanics, it is usually assumed that the clothing material is a continuum of plate (planar when unstrained), or shell (naturally curved) or membrane (perfectly flexible plate or shell). This is due to the fact that studies have mainly focused on the macroscopic mechanical behavior that occurs under an external force or prescribed curvatures from a human body. How external forces act on an element of a garment has been described in detail by Zhang et al.21 It is important to understand the relationship between clothing mechanical performance and fabric mechanical properties in simple deformations (tension, shearing and bending compression), because the fabric properties are the basic parameters used in mechanical models of clothing. Further, the investigation of textiles’ micromechanics is a stratagem whose objective is to achieve the engineering design of clothing performance from fundamental parameters of fiber–yarn–fabric. The human body is supported by bones and is separated from the external environment by the skin. The movement of the body is accomplished by the contraction of skeletal muscles acting within a system of levers and pulleys formed by bones, tendons and ligaments.16 The subcutaneous adipose tissue sits above the muscle and bone to store fat, which varies in thickness among individuals. When the body is subject to small external forces during wear, such as garment pressure, the bone can be considered as a stiff material without deformation. The skin’s deformations are elastic. The soft tissue that consists of the adipose tissue and the skeletal muscles in passive condition can be regarded as incompressible rubber. Therefore, in the development of biomechanical human body models, the human body is assumed to have three major components: skin, soft tissue and bone. Based on the analysis of the biomechanical characteristics of human bodies, a series of biomechanical human models has been developed, including one that consists of the bone, the soft tissue and the skin20 and the body parts such as the breast.13 A geometrical model of the human body can be developed by applying anthropometrics technology or by using commercial software. The parameters of human biomaterials are estimated by using published information.19,22 The mechanical interaction between the human body and garments varies significantly for different wear situations, depending on four factors: (1) garment style; (2) garment allowance; (3) dynamic wearing processing; (4) posture of the human body. These factors have to be seen as providing different boundary conditions in the development of contact models. Based on analysis of the contact characteristics between the human body and various garments, and on the development of a biomechanical body model and a garment model, a series of contact models have been developed for


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simulation of dynamic interaction for a human body wearing sportswear,21 a bra,13 jeans21 and a girdle.20 In each case, the garment is regarded as an elastic membrane and the human body is assumed to be elastic. In the case of jeans, for example, the contact is modeled as a dynamic sliding interface as the jeans are worn from the foot to the waist of the body. A time-dependent contact system between jeans and the human body involves four objects (the garment, the skin, the soft tissue and the bone) and three contact interfaces (between the jeans and the skin, between the skin and the soft tissue, between the soft tissue and the bone), in a fixed global coordinate system x (x1, x2, x3). Before the wearing process begins, time is considered as 0, the garment occupies domain 0Ω1 and the human body occupies domains 0Ω2 for the skin, 0Ω3 for the soft tissue and 0Ω4 for the bone, respectively. When the process begins, time t > 0, the garment is occupying new domains tΩ1 and contacting the domain tΩ2 of the skin that corresponds to the domain tΩ3 of the soft tissue and tΩ4 of the bone. The mechanical performance of the four objects in the contact system within time domain (0, t) is governed by three main groups of equations, i.e. the equation of motion, the constitutive equations and the boundary conditions.21 The solutions of the contact models are complicated by (1) the dynamic balance in the space domain and in the time domain; (2) the geometric non-linearity of the garment deformation; (3) the contact non-linearity due to the unknown contacting boundaries prior to the solution of the problem. Therefore, the model is solved numerically using a finite element method in the space domain and a finite difference method in the time domain by using the explicit, non-linear finite element analysis program LS-DYNA.5 Currently, the 3D dynamic interaction between the human body and the garment has been visualized by using post-processing software based on numerical data from the solution of the models, including the deformation and stress distributions of the garment, the deformation and stress distributions of the skin and soft tissues, and the garment pressure distributions on the skin.13,20,21 In the future, visualization of mechanical sensory perceptions will be achieved by psychophysical experiments using commercial visualization software.

19.3.3 Development of a clothing engineering database To support the design, analysis and evaluation phases in a systematic design process with various types of information, it is necessary to develop an engineering database. There are several important differences between an engineering database and an administrative or a business database. First,


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engineering design is an iterative-decision process with analysis and synthesis based on knowledge of basic sciences, mathematics and engineering. Second, engineering design requires dynamic database involving two kinds of information: the design environment (rules, methods, standard elements, etc.) and data that are not previously known but defined during the design process. The volume of information increases with the progress of a design. Third, engineering design deals with a number of data types (text, numbers, equations, diagrams, graphical, photographic images, etc.). An important characteristic that must be considered in the development of a clothing engineering database is that the clothing design process communicates frequently with the information from the hierarchical construction of a fiber–yarn–fabric–garment and a 3D human body. The structural geometry of textiles is central to all aspects of the computer-aided design and prediction, which should be presented to the designer during the design process. Therefore, the clothing engineering database should support user-friendly program packages to input, store and display information on the fiber–yarn–fabric–garment quickly and effectively in time and space. In MEDS, the clothing engineering database is separated into four components: 1. human body database (HDB) – storing information about human factors (physical and biomechanical data); 2. product database (PDB) – storing the structure parameters and images of products (fiber–yarn–fabric–clothing); 3. material database (MAD) – storing the mechanical parameters used in the design and analysis models (fiber–yarn–fabric–clothing); 4. model database (MOD) – storing the geometrical models, the mechanical models and psychophysical models for different wearing situations. The items in each database are presented in Table 19.1. In the database, the fiber–yarn–fabric mechanical parameters are collated from a series of mechanical experiments, including simple fabric deformations by KES instruments, the relaxation deformation of fiber–yarn–fabric and 3D deformation of fabric bagging testing on the InstronTM tester. The major mechanical parameters taken from the experiments are stored in the material database as shown in Table 19.1. To set up a numerical database of textile properties, tension–recovery curves of fiber–yarn–fabric have been recorded in the material database by direct input of the experimental curves. The experimental curves have been recorded as images in the database, and can be recalled during the design process by searching for their identified numbers.


Product database Items

Garment commercial name Garment ID Fabric ID Fabric finishing condition Fabric structure Fabric weight Fabric thickness Fabric density Fabric cover factor Yarn count Yarn crimp Fibre components Fiber count Fiber diameter Fiber length Garment image ID Fabric image ID Yarn image ID Fiber image ID ...

Human factor database Items

Human body ID Human image ID Stature Shoulder Breast Waist Hip Arm Leg Neck Weight Skin thickness Skin tensile modulus Skin density Bone elastic modulus Bone density Soft tissue thickness Soft tissue elastic modulus Soft tissue density ...

Table 19.1 Items in each database

Fabric tensile modulus Fabric bending modulus Fabric shear modulus Fabric Poisson’s ratio Fabric frictional coefficient Fabric roughness Fabric bagging resist Fabric bagging fatigue Yarn elastic modulus Yarn viscoelastic modulus Yarn relaxation time Yarn bending modulus Yarn compressor modulus Yarn torsion modulus Yarn Poisson’s ratio Fiber elastic modulus Fiber viscoelastic modulus Fiber relaxation time Images of experimental curves ...

Material database Items

...

...

FEM-Human body–jeans FEM-Human–girdle FEM-Human–bra FEM-Human–sportswear FEM-Bagging FORTRAN-Bagging ... ... Geometrical model of body Geometrical model of garment ... Psychophysical models in various wearing situation

Model database Items

Mechanical and thermal sensory engineering design 321


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In science and engineering, the data model for a database system represents the technical environment covered by the engineering databases. There are three principal models: 1. the relational data model – all entities and dependencies between entities to be stored in the database are described structurally by its domains (names and kind of values); 2. the hierarchical data model – each entity is represented as a record and hierarchical relationships by a tree structure, whose nodes are the records; 3. the network data model – each entity is represented as a record and may be owned by more than one record, leading to a network structure. Based on analysis of the characteristics of information and the design process for clothing mechanical performance, the hierarchical data model is used in the database system. In the data model, a material object in different hierarchic levels (fiber–yarn–fabric–clothing) can be represented by abstraction of an entity. An entity has an identifiable number ID. An entity is a collection of values, each value describing a property of the entity. The identifying number ID, which includes its parent’s ID or the tree’s node it belongs to, indicates the hierarchical relationship. An identifiable number ID is specified as the unique name of an entity in the engineering database, by which all the information about it can be found. As examples of the database, Table 19.2 illustrates the records of nine pairs of denim jeans in the product database, and Table 19.3 shows their mechanical parameters recorded in the material database. For example, for garment ID 001, it can be seen that the jeans are Levi’s 3D and the material of the fabric has the identifying number 001-1. The mechanical parameters of the fabric can be searched for in the material database using the identified number 001-01, and the tension–recovery curves of the fabric in warp and weft directions, respectively, using the identifying numbers Wp001-01 and We001-01, as shown in Table 19.3.

19.3.4 Investigation of psychophysical relationship between stimulation and perception To visualize the sensory perception in the CAD environment, a series of psychophysical experiments have been carried out to study the mechanical comfort of jeans in different wear situations by conducting mechanical sensory comfort perception trials11 and objective measurement of dynamic pressure distribution.11 Further, psychophysical models of mechanical comfort have been developed in different wear situations using statistical methods and neural networks, respectively.12


Garment commercial name

Levi’s 3D Levi’s Andyrex Apple Mixed Wagner’s CK Edwin Giordano

Price

$735 $535 $338 $329.5 $148 $250 $550 $679 $190

001 002 003 004 005 006 007 008 009

Garment ID

001-01 002-01 003-01 004-01 005-01 006-01 007-01 008-01 009-01

Fabric ID

E E E E E E E E E

001-01 002-01 003-01 004-01 005-01 006-01 007-01 008-01 009-01

Fabric image ID

3 3 3 3 3 3 3 3 3

× × × × × × × × × 1 1 1 1 1 1 1 1 1

twill twill twill twill twill twill twill twill twill

Fabric structure

431 475 453 451 438 428 458 459 400

Fabric weight (g/m2)

0.88 1.04 1.12 1.03 1.06 1.04 1.07 1.09 1.08

Fabric thickness mm

Table 19.2 Records of nine pairs of denim jeans in the product database

26 27 30 27 28 25 27 28 27

ends/cm 19 20 19 19 21 20 20 20 19

picks/cm

Fabric density

25% Ly 75%Cot 100% Cotton 100% Cotton 100% Cotton 100% Cotton 100% Cotton 100% Cotton 100% Cotton 100% Cotton

Fibre composite %

6.8 6.2 7.0 6.9 7.9 6.7 7.0 6.6 9.6

warp 6.0 6.2 7.4 6.2 7.2 7.3 6.2 7.9 5.5

weft

Yarn count lltex

238.9 265.6 274.2 248.2 239 237.1 245.9 267.8 213.1

warp

191.1 192.3 172.1 187.7 189.3 184.4 191.5 177.1 199.2

weft

Fabric cover factor K

11.7 18.0 21.5 18.7 21.4 19.1 20.2 23.3 21.3

warp

7.3 6.9 8.0 7.1 9.4 10.9 7.7 7.8 8.9

weft

Yarn crimp (%)


Garment ID

001 002 003 004 005 006 007 008 009

Garment commercial name

Levi’s 3D Levi’s Andyrex Apple Mixed Wagner’s CK Edwin Giordano

001-01 002-01 003-01 004-01 005-01 006-01 007-01 008-01 009-01

Fabric ID 0.88 1.04 1.12 1.03 1.06 1.04 1.07 1.09 1.08

Thickness (mm) 492 455 405 439 413 412 427 421 370

Density (kg/m3) 50000.0 40000.0 42000.0 36000.0 40000.0 46000.0 40000.0 45000.0 24000.0

EA (kgf/m2)

Table 19.3 Records of nine pairs of denim jeans in the material database

48000.0 70000.0 80000.0 80000.0 76000.0 52000.0 96000.0 112500.0 60000.0

EB (kgf/m2) 0.38 0.48 0.38 0.45 0.38 0.44 0.42 0.40 0.40

Poison’s BSA 13179.0 16645.7 20026.4 20771.3 21974.6 25899.6 20714.0 25298.0 11431.4

GAB (kgf/m2)

Wp001-01 Wp001-02 Wp001-03 Wp001-04 Wp001-05 Wp001-06 Wp001-07 Wp001-08 Wp001-09

Curve-Wp

We001-01 We001-02 We001-03 We001-04 We001-05 We001-06 We001-07 We001-08 We001-09

Curve-We

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19.4

325

Structural organization of the system

19.4.1 Hardware and software environment To achieve the objectives, a computer-based MEDS was developed on a PC platform, by using and integrating software packages such as Visual Basic, database software, finite element software, self-designed Fortran programs and a post-processing program. There are seven working components in the system: 1. user interface; 2. design processing algorithms; 3. generation of 3D geometrical models (pre-processing software or geometrical algorithms program); 4. definition of boundary conditions of the contact system (such as LSDYNA and ANSYS); 5. mechanical modeling and solution (using software such as LS-DYNA, ANSYS and self-developed models); 6. analysis and simulation (Post GL1.0 post-processing program); 7. sensory evaluation (animation software and Visual Basic). Each working module accomplishes its purpose with suitable software. A key feature is the integration of relevant software components into the MEDS, because many of the software packages have been developed independently in different industries for their own specific purposes with little knowledge of the mechanical engineering design of textiles. Currently, the integration has been achieved by writing the programs to support the user interface and the design-processing algorithms.

19.4.2 Design processing algorithm Each of the design activities can be implemented using an independent program or a stand-alone algorithm. However, the information in the design activities is intimately interlinked with text, data and graphical communications. The system needs to create and use dynamic data structures. Currently, this is implemented by designing processing algorithms for sorting, searching or pattern matching in the design process. In the design process, some of the design steps can be implemented using independent, stand-alone algorithms, such as numerical computation of garment pressures and stress with finite element software or computing bagging performance with self-developed programs. Some of the steps are closely interlinked with rules. For example, the evaluation of mechanical comfort involves comparing the predictions with certain comfort criteria stored in the database. There is a need to create and use dynamic data structures by means of a design process algorithm for sorting, searching or


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pattern matching. Currently, Visual Basic is used for developing algorithms to implement the design procedure.

19.4.3 User interface To provide a simple and convenient interface that requires minimum analysis expertise, the user interface of MEDS has been designed with much image information and many associated choices to clearly present the status of the design environment and the progress of the design process at any time. The user interface is constructed in a PC operating system using Visual Basic. A high-resolution graphic display is used to provide the geometric information and simulation function in the system. Presentation of choices to the user utilizes pop-up menus with default suggestions for guidance. In MEDS, the design process has been patterned closely on the concept of engineering design based on human factors, such as those shown in Fig. 19.1, forcing the user in directions alien to the traditional design process. The design process contains the logical steps of specification of a designed product, definition of design parameters, modeling and solution, analysis and evaluation. Therefore, a design guide always appears in the user interface to indicate the next step to user.

Computer-aided engineering design

Product specification Marketing research

Selection of garment style Input of human body parameters Definition of clothing deformation characteristics during wearing Selection of fabric structure and color Selection of fiberâ&#x20AC;&#x201C;yarnâ&#x20AC;&#x201C;fabric mechanical properties Definition of biomechanical model of the bodyâ&#x20AC;&#x201C;garment Simulation and evaluation

Wear trial

Human factors database HDB

Product database PDB

Material database MDB

Measurement technology of human body

Subjective evaluation technology

Objective evaluation technology

Manufacture sample Production pattern Manufacture product

19.1 Design procedure in MEDS.

Models database MOD

Textile mechanics biomechanics, contact mechanics


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The structural geometry of textiles is central to all aspects of the design process. A program is designed to build up the list box with the pictures that come from the engineering database.

19.5

Example

To illustrate the use of the MEDS, the design of a pair of jeans is used as an example. It is not our goal to present a detailed design process, but to focus on the use of the functions of design, analysis and evaluation during the design process.

19.5.1 Design The first step in the process is the specification of the product to be designed. In the engineering database, previous products can be searched as a reference, then jeans can be chosen as the target product, according to associated choices of different garments with default suggestions. In the Human Model menu, a 3D digital human model can be chosen by sex. Then the physical parameters of the human body are identified by searching the existing information about the human body in the database or by inputting new parameters according to current design requirements. In the Garment menu, a pop-up list box with images of different jeans styles can be seen as shown in Fig. 19.2. A picture of jeans can be selected according to the displayed information, which includes the price, the basic parameters of materials as well as the brand. Based on the selection, a 3D digital model of the jeans is presented in the user interface as shown in Fig. 19.3, by searching the model database or by developing it using pre-processing software and the geometrical program. In the Fabric menu, a pull-down list box can be seen, which includes types of fabrics, such as woven and knitted. When woven fabric is chosen, the system shows all images of the fabrics that can be used. Using the displayed image and text information, it is easy and quick to select fabrics for different parts of the jeans.

19.5.2 Modeling simulation After the design phase, a mechanical model needs to be defined, according to the wear conditions of different garment styles (jeans or bra, etc.) and required mechanical performances (garment pressure or garment bagging, etc.). Then, input of mechanical parameters depends on the mechanical model selected. For example, the definition of parameters for the bagging model based on textile micromechanics requires input of fundamental parameters of fiberâ&#x20AC;&#x201C;yarnâ&#x20AC;&#x201C;fabric, but for the finite element model of the


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19.2 Selection of jeans styles.

bodyâ&#x20AC;&#x201C;jeans, the parameters of the human body and the fabrics used are required. In the Mechanical Parameters menu, the mechanical model of the bodyâ&#x20AC;&#x201C; jeans is selected to simulate jeans pressure performance. The system screen then shows the user interface for the definition, as shown in Fig. 19.3. The mechanical properties of the fabric can be regarded as isotropic or anisotropic by using a choice menu, because different parameters need to be inputted in different selections. The biomechanical parameters of the human body can be inputted according to default suggestions that guide the input. The mechanical boundary conditions in finite element models can be defined in the Pre-processing menu. A suitable pre-processing program can be selected to define the contact type of the model, the dynamics of the wearing process, etc. In the Solver menu, different programs can be selected to solve different mechanical models, such as a Fortran program written by the authors for solution of the one-way bagging model and commercial finite element software for solution of the finite element models. For


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19.3 Definition of mechanical parameters of the human body and the jeans.

example, LS-DYNA software is selected to solve the model for simulation of the jeans wearing process.

19.5.3 Analysis of garment mechanical performances With the numerical results of the model, the mechanical performances of the jeans can be analyzed through the Post-processing menu, in which the dynamic distributions of pressures, stresses, strains and deformations can be visualized in a 3D virtual human body and the jeans with color contour images, graphical curves and animation movies. This involves different post-processing programs for different purposes of the analysis. The Post GL1.0 programmer of the LS-DYNA software can be used to simulate the mechanical interaction between the body and the garment during wear, the distributions of garment pressure and the skin pressure due to skin deformation. An iterative design can be carried out if the design is unsatisfactory at the simulation and evaluation stages. The numerical results from the itera-


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tive design are recorded as 3D virtual images, which allow a comparison of the pressure distributions of jeans with different styles or different materials.

19.5.4 Evaluation of pressure comfort In the Evaluation menu, the sensory comfort perception of garment pressure can be visualized. A value bar of pressure comfort is shown in the interface to suggest the degree of jean pressure comfort in a wearing condition. Consequently, a judgement can be made on whether the jeans designed could meet the requirement of pressure sensory comfort by comparing the predictions with the preferred pressure perception for jeans products, which were measured in wear trials and stored in the knowledge base of the system.

19.6

Thermal sensory engineering design

Sensory engineering design of clothing thermal performance is based on quantitative investigations of the relationship between clothing thermal performance and human sensory (physiological and psychological) factors. There are three fundamental investigations required to put the concept into practice: 1. the quantitative translation of consumersâ&#x20AC;&#x2122; psychological feelings about a product into perceptual design elements that will be important attributes in the evaluation of fashion and material design; 2. the investigation of the dynamic interactions between the human body and clothing, which bridges the relationship between human thermophysiology and garment design; 3. the induction of the clothing physical performances from the dynamic analysis, such as distributions of temperature, moisture, humidity and condensations during wear. These physical and structural characteristics of clothing materials are the basic information for the engineering design of clothing materials.

19.7

Thermal sensory engineering design process and system

A CAD system, called thermal sensory engineering design system (TSEDS), is in the process of development using knowledge-based design procedures to guide the user in designing clothing thermal performance based on human sensory factors. To develop TSEDS with the required functions, a series of fundamental developments have been made by integrating science, engineering and computing technologies. The research involves develop-


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ment of 3D geometric digital models of the human body and garments, thermophysiological models of human body, physical models of heat and moisture transfer of clothing, a clothing engineering database and the psychophysical relationship between thermal and moisture stimuli and psychological thermal and moisture sensory perceptions. The model of thermophysiology and heatâ&#x20AC;&#x201C;moisture transfer in clothing is the fundamental theory for simulating the dynamic thermal interactions between the human body and clothing, and numerical computation is the key to solving the models. In system, finite difference and finite volume methods have been developed to do this. The design procedure for clothing thermal performance begins with product specification to identify a type of garment, followed by selection of garment style and human body parameters from the human factor database and the product database. From the input parameters of the human body and the garment, the deformation characteristics of clothing should be identified, based on the analysis of thermal dynamics between human body and garment. These characteristics will govern the next steps of selecting fabric structure and selecting physical properties of fibers, yarns and fabrics. The selection is a revision of design achieved by searching or reworking some previous fabric structure, which reasonably approximates to the current design requirements. Next there is a physical model of the bodyâ&#x20AC;&#x201C;clothing system, allowing a numerical simulation and analysis of the thermal performance of the garment and body. The iterative procedure has to be completed before the garment is produced, if the design is not satisfactory at the simulation and evaluation stages. The iterative design cycle will be shortened through the CAD environment that is supported by four engineering databases and based on a number of fundamental researches. Through the design process in the CAD system, a sample is manufactured for a wear trial. The aim is to further modify the design according to subjective evaluation of the garment before formal production. The system has three functional modules: a design module for defining the parameters of the human body and clothing; an analytical module for analyzing and simulating clothing thermal performance; and an evaluation module for evaluating clothing thermal comfort. One of the analytical models illustrates the dynamic processes of coupled heat and moisture exchange in a humanâ&#x20AC;&#x201C;clothing environment system.

19.8

Application of TSEDS

As an example, a simulation of a man running in a hot and humid environment was carried out in order to investigate the design of a sportswear using different types of textile materials. The heat and moisture transfer performances of the sportswear are illustrated by the changes of the distributions of temperature and humidity in the system as well as thermoregulatory


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factors such as the skin blood flow rate and sweating rate of the human body. The numerical calculation results from the simulation were further transmitted into color changes of a series of digital human models. Finally, playable animations were made so that the dynamic process could be visualized and easily understood.

19.9

Conclusion

The originality of the MEDS and TSEDS CAD systems is evident in four major aspects: 1. Consideration of human factors (neurophysiological, thermophysiological and psychological) in engineering design of clothing products; 2. Simulation and visualization of clothing thermal comfort performance before the garment is produced; 3. Integration of a range of software components into the system for computer-aided engineering design; 4. A platform with an engineering database to input, store and display information about the hierarchical physical properties and structural features of garments and human bodies. Mechanical and thermal sensory engineering design systems for textile and apparel products have been developed based on a quantity of fundamental research work. These systems have three functional models: a heuristic design model for defining the parameters of the human body and clothing; analytical models for analyzing and simulating clothing mechanical and thermal performance; and evaluation models for evaluating clothing mechanical and thermal comfort. They may be the first to integrate functional models of the design, analysis and evaluation of clothing performance into a single CAD environment. The systems provide a design methodology and a tool for fashion designers and textile scientists, engineers and product developers to design new products for mechanical and thermal performance.

19.10 Acknowledgements The authors would like to thank the Hong Kong Polytechnic University for the funding of this research through the project A188.

19.11 References 1. Dastoor, P.H., S.P. Hersh, S.K. Batra, and W.J. Rasdorf, Computer-assisted Structural Design of Industrial Woven Fabrics, Part I: Need, Scope, Background, and System Architecture. Journal of the Textile Institute, 1994. 85(2): p. 89â&#x20AC;&#x201C;109.


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2. Dastoor, P.H., S.P. Hersh, S.K. Batra, and W.J. Rasdorf, Computerassisted Structural Design of Industrial Woven Fabrics, Part II: System Operation, Heuristic Design. Journal of the Textile Institute, 1994. 85(2): p. 110–134. 3. Dastoor, P.H., S.P. Hersh, S.K. Batra, and W.J. Rasdorf, Computer-assisted Structural Design of Industrial Woven Fabrics, Part III: Modelling of Fabric Uniaxial/ Biaxial Load-Deformation. Journal of the Textile Institute, 1994. 85(2): p. 135–157. 4. Denton, M.J., Fit, Stretch and Comfort, in Proceedings of the 3rd Shirley International Seminar: Textiles for Comfort. Manchester, England 1971. 5. Hallquist, J.O., LS-DYNA Theoretical Manual. 1998: Livermore Software Technology Corporation. 6. Hearle, J.W.S., Fabric Mechanics as a Design Tool. Textile Horizons, 1994. 14(5): p. 12–16. 7. Hearle, J.W.S., Textiles for Composites: a Business Opportunity for the 21st Century. Textile Horizons, 1994. 14(6): p. 12–15. 8. Hearle, J.W.S., A. Newton, and P.J. Grigg, Computer-aided Textile Design: a Liberating Prospect. Computers in the World of Textiles. 1984, New York, NY: The Textile Institute, Manchester. 9. Hongu, T., Hi-tech Fibers. 1999, Toyko: Woodhead Publishing Limited. 10. Kawabata, S., M. Niwa, and Y. Yamashita, Recent Developments in the Evaluation Technology of Fiber and Textiles Towards the Engineered Design of Textile Performance, in Proceedings of the International Technical Symposium “100 Years of Modern Fiber Science”. 1998. Asheville, NC. 11. Kwok, Y.L., Y. Li, A.S.W. Wong, R. Liu, and D.W.C. Wong, Clothing Pressure Comfort in Women Pantyhoses. International Journal of Clothing Science and Technology, 2003. Submitted. 12. Li, Y., The Science of Clothing Comfort, J.M. Layton, Ed. (Textile Progress, (1/2)). 2001: Manchester: Textile Institute International. 13. Li, Y., X. Zhang, and K.W. Yeung, A 3D Bio-mechanical Model for Numerical Simulation of Dynamic Mechanical Interactions of Bra and Breast during Wear. Sen’i Gakkaishi, 2001. 59(1): p. 12–21. 14. Matsuo, T. and M.N. Suresh, The Design Logic of Textile Products, P.W. Harrison, Ed., Vol. 27. 1997: Manchester, The Textile Institute. 15. Nakahashi, M., H. Morooka, H. Morooka, S. Hiraga, and J. Deguchi, Effect of Clothing Pressure on Front and Back of Lower Leg on Compressive Feeling. Journal of the Japan Research Association for Textile End-uses, 1999. 40(10): p. 49–56. 16. Nigg, B.M. and W. Herzog, Biomechanics of the Musculo-skeletal System. 1994, Chichester, New York: John Wiley. 17. Postle, R., S. Kawabata, and M. Niwa, Objective Evaluation of Apparel Fabrics. 1983, Osaka: The Textile Machinery Society of Japan. 18. Woodson, T.T., Introduction to Engineering Design. 1966, New York: McGraw-Hill. 19. Yamada, H., Strength of Biological Materials. 1970, Baltimore: Williams & Wilkins. 20. Yeung, K.W., Y. Li, and X. Zhang, A 3D Bio-mechanical Human Model for Numerical Simulation of Body–Garment Dynamic Mechanical Interactions during Wear. Journal of the Textile Institute, 2001. 95: p. 59–79.


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21. Zhang, X., K.W. Yeung, and Y. Li, Numerical Simulation of 3D Dynamic Garment Pressure. Textile Research Journal, 2002. 72(3): p. 245â&#x20AC;&#x201C;252. 22. Zheng, X.Y., A study of Clothing Pressure Developed by the Girdle. Journal of the Japan Research Association for Textile End-uses, 1998. 32(9): p. 424â&#x20AC;&#x201C;438.


20 Sensory comfort of denim product YI L. KWOK 1 , ANTHONY S.W. WONG 2 , YI LI 1 AND XIN ZHANG 3 1 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong 3 Fashion College, Xian University of Engineering Science & Technology, China

20.1

Introduction

Denim is a woven cotton fabric of twill construction, with the warp yarns being dyed and the weft left undyed. It was usually made with indigo-blue or brown warp and white filling. Because the ratio of warp threads/cm to weft threads/cm is approximately 3 : 2, the dyed warp shows predominantly on the face and the undyed filling on the back.5 Denim is a heavy, strong twilled cotton cloth and can be considered as a kind of fustian (thick, strong, coarse cloth). Paul and Pardeshi10 described 24 different types of denim fabric, including heavy, soft, stretch, natural dyed, bullet hole, printed, reverse, pseudo and true generic denim along the way. The stretch properties of denim were examined by Wildhaber and Nef.13 Denim is one of the most popular materials used in today’s world of leisure fashion as ‘Today jeans wear is synonymous with lifestyle’.11 The demand for denim clothes is increasing worldwide, with the USA, Italy and Japan emerging as the largest potential markets for denim jeans.2 Barnes1 found that the trend of the West European denim jeans market remained buoyant, and Stogdon12 found that the volume of European Union (EU) denim products exported to the non-EU countries was rising, and that the biggest source of denim imports from outside the EU was the USA. Zimmermann16 discovered that the EU had the second largest denim market in the world, just behind the USA. The trend in casual denim wear was discussed by Crosby.3 When Wong et al.14 studied the features of EU denim products in 1999, they found that between 1994 and 1997 import (which grew from €1.1 to €1.5 billion with a growth rate of 6.11% per year) of denim products from non-EU countries was significantly larger than its export (decreased from €537 to €481 millions with a decrease rate of 2.91% per year). The trade deficit (export–import) had increased from €613.8 335


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millions to €1 billion with a growth rate of 10.3% per year between 1994 and 1997. The denim production, sales and trade in Hong Kong, which was the second most important garment exporter in the world and the leading textile exporter in 1998, was reported by Li et al.8 Based on the findings of the research and reports above, there is no doubt that denim apparel and jeans are one of the most popular items of casual wear in today’s clothing market. However, there is a lack of information on describing the relationship between human responses and denim wear. Therefore, this chapter tries to fill these knowledge gaps by describing; • • • •

the important attributes for an ideal pair of jeans the satisfactory level of ‘hand feel’ on the part of the wearer when touching and feeling the jeans the subjective assessment of garment pressure comfort the objective evaluation of garment pressure distribution.

20.2

Human subjective perception on denim products

As people spend quite a long time wearing jeans during a day, it is important to understand their perception of comfort and other sensations during wear. Crosby3 stated that the key themes of the casual look were comfort and softness, while younger looks focused on new denim blends and technical fabrics. Crowther4 investigated comfort and fit in 100% cotton denim jeans. Morris and Prato9 studied consumer perception of comfort, fit and tactile characteristics of denim jeans. Kwok et al.7 investigated the major criteria consumers consider when purchasing denim apparel for their children. Focus group interviews and printed surveys investigated differences between parent and child product preferences, consumer satisfaction with the available market and consumer preferences in denim fabric. Respondents included consumers from different age, gender, occupational, educational and income groups. Results indicated that comfort and fabric quality are the most important criteria in the selection of children’s denim apparel. Moisture comfort and pressure comfort are the most important considerations for denim apparel purchases in both summer and winter. Past research has already shown that there is a close relationship between moisture and thermal comfort. Therefore, it is necessary to study the effect of fabric heat transfer as well as its moisture-management properties. Kamata et al.6 experimentally studied convective heat transfer from the outer surface of a cylindrical heat source covered with a fabric. They also conducted an analytical examination of the effect of fabrics on the heat transfer coefficient. It was found that, for fabrics such as tweed or denim, which are less permeable to air, the experimental and analytical methods used in this study were suitable; also, the heat transfer coefficient was reduced by the fabric covering.


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Garment pressure is also a great concern in jeans. Zhang et al.15 developed a mechanical model for numerical simulations of 3D dynamic garment pressure during wear of denim jeans. The model was developed on the basis of dynamic contact mechanics theory, by analyzing the contact characteristics between the human body and denim jeans. The garment was regarded as an elastic shell of geometric non-linearity, the human body was assumed to be rigid and the contact between body and garment was modeled as a dynamic sliding interface. Compared with values of measured garment pressure reported in the literature, the predicted pressure was close to the magnitude of experimental measurements, indicating that the model is able to simulate garment pressure during wear with reasonable accuracy. The computational model can simulate and predict the dynamic mechanical behavior of garments during wear, such as garment deformation, pressure and internal stresses, without actually requiring the garment.

20.3

Study of sensory comfort in denim jeans

The following section summarizes some of the research work in relation to sensory comfort and preference in denim jeans.

20.3.1 A pair of ideal denim jeans In a study to identify the important attributes for the ideal pair of jeans, researchers surveyed the attitude of 153 people towards an ideal pair of jeans. They were asked to rank ten attributes (brand, colour, style, durability, fitting, price, quality, fabric thickness, comfort and aesthetic) in terms of their importance, in which 1 and 10 represent the most and least important, respectively. Results of the survey are shown in Fig. 20.1. People ranked ‘fitting’ as the most important attribute in an ideal pair of jeans. The mean ± standard deviation of ‘fitting’ ranking score is 3.1 ± 1.9. This was followed by ‘comfort’ and ‘style’ with ranking scores of 3.7 ± 2.3 and 3.8 ± 2.3, respectively. ‘Brand’ was ranked as the least important attribute in an ideal pair of jeans with a score of 8.1 ± 2.5.

20.3.2 Subjective assessment of hand feel A total of nine pairs of jeans, which were purchased in Hong Kong local department stores, were selected for this subjective assessment. The basic description of the selected jeans is shown in Table 20.1. The price of these jeans ranged between HK$140 and 740 (≈US$18–95). Thirty-six female subjects aged between 18 and 30 years of age participated in the study. Subjects were required to ‘touch and feel’ the nine pairs of denim jeans with their hands in an environmentally controlled chamber with tempera-


338

Clothing biosensory engineering 12 10 8

Ranking score

6 4 2

d an Br

ss

ilit Fa

br

ic

D

th

ur

ab

ua Q

ic kn e

y

y lit

ic e Pr

C

ol

or

ic st h

et

yl e Ae

St

fo om C

Fi tt

in

g

rt

0

Attribute

20.1 Importance ranking of ten attributes.

ture and humidity of 20 ± 3 °C and 60 ± 2%, respectively. Then they were asked to evaluate four attributes – wrinkle resistance, fabric stretchiness, fabric softness and fabric weight – on a scale of 1, 2, 3, 4, 5 and 6 which represent most satisfactory, very satisfactory, satisfactory, unsatisfactory, very unsatisfactory and most unsatisfactory, respectively. Furthermore, they were also required to rank nine pairs of jeans in terms of comfort perception. Ranking order 1 and 10 represent most comfortable and least comfortable, respectively. Table 20.2 compares the rating of four ‘fabric hand’ sensations. Overall, the subjects were satisfied (≈3) with the wrinkle resistance, stretchiness, softness and heaviness of the selected jeans. On average, I has the lowest wrinkle resistance (2.61) rating. Subjects were satisfied with Q, stretchiness (2.92). Furthermore, they were very satisfied with its softness (2.00) and heaviness (2.28). There were significant differences ( p < 0.05) between attribute ratings amongst selected jeans. This might be explained by fact that the jeans were made of different fabrics consisting of different fiber content and construction. Table 20.3 shows the analysis of variance, ANOVA, of the influence of subject age on the attribute rating. It was found that subject age had no significant influence on fabric hand attributes. However, subject age had a significant influence on different attribute ratings. Table 20.4 compares the ranking score of nine pairs of jeans. The lower ranking score was more preferred by the subjects. It is clear that Q was the most


4.31

4.75 4.53 4.51 4.38 4.28 4.58 4.59 4.00

H

I J K L M O P Q

C = Cotton and L = Lyocell.

Weight (g/cm2)

Code

1.14 1.17 1.08 1.12 1.14 1.16 1.18 1.14

0.88

Thickness (mm)

Woven Woven Woven Woven Woven Woven Woven Woven

Woven

Structure

Table 20.1 Basic description of nine selected pairs of jeans

C (75) & L (25) C (100) C (100) C (100) C (100) C (100) C (100) C (100) C (100)

Content (%)

4.36 4.00 5.18 5.05 5.47 5.37 3.37 4.41

4.49

Air permeability (KPa.s/m)

0.14 0.15 0.15 0.15 0.14 0.15 0.15 0.14

0.13

Thermal conductivity (W/m.K)

0.19 0.19 0.20 0.20 0.17 0.18 0.16 0.16

0.24

Warm/cool feeling (W/cm2)

Sensory comfort of denim product 339


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Clothing biosensory engineering

Table 20.2 Comparison of four sensations mean and standard deviation between jeans Jeans H I J K L M O P Q Total

Mean Std deviation Mean Std deviation Mean Std deviation Mean Std deviation Mean Std deviation Mean Std deviation Mean Std deviation Mean Std deviation Mean Std deviation Mean Std deviation

Wrinkle resistance

Stretchiness

Softness

Heaviness

2.81 0.951 2.61 0.728 3.03 0.910 3.25 0.649 3.17 1.000 3.61 0.871 3.42 0.841 3.69 0.822 2.81 1.091 3.15 0.941

3.67 1.014 3.36 0.990 3.47 0.878 3.25 0.732 3.17 0.878 3.25 0.806 3.61 0.728 3.92 0.732 2.92 1.052 3.40 0.911

3.22 0.989 3.08 0.770 3.39 0.838 3.31 0.856 2.81 0.856 2.92 0.841 3.97 0.878 4.25 0.806 2.00 0.793 3.22 1.042

2.94 0.860 3.61 0.645 3.58 0.554 3.47 0.560 3.69 0.668 3.33 0.828 3.50 0.941 3.83 0.878 2.28 0.882 3.36 0.885

preferred jeans with a ranking score of 1.9 ± 1.31. This was followed by M (4.1 ± 2.38) and H (4.2 ± 2.53). P and O have the highest ranking scores, 7.4 ± 1.80 and 7.3 ± 2.14, respectively. This suggests that these two pairs of jeans were the least preferred.

20.3.3 Assessment of subjective garment pressure comfort This subjective assessment of garment pressure comfort was designed on the basis of Scheffe’s Comparison Method, in which each subject evaluates only two pairs of jeans at each trial. The running order of the trial was randomized. In order to imitate the average summer weather in Hong Kong, the temperature and humidity of the chamber were set at 30 ± 3 °C and 80 ± 2%, respectively. Each subject was required to put on one pair of the randomly selected jeans and perform nine different postures, which are described in Table 20.5. Evaluation of garment pressure comfort at 12 different body locations during wear is shown in Fig. 20.2. This process was repeated for the second pairs of jeans on the same subject. (The jeans were the same as those used for hand feel.) Fig. 20.3 illustrates the overall pressure comfort at 12 body locations. On average, subjects found that the pressure comfort at most of the measured


Between groups Within groups Total

Between groups Within groups Total

Between groups Within groups Total

Between groups Within groups Total

Wrinkle resistance * Age range

Stretchiness * Age range

Softness * Age range

Heaviness * Age range

Table 20.3 Influence of age

(Combined)

(Combined)

(Combined)

(Combined)

0.854 251.896 252.750

0.814 350.062 350.877

3.591 264.248 267.840

0.314 285.970 286.284

Sum of squares

2 321 323

2 321 323

2 321 323

2 321 323

df

0.427 0.785

0.407 1.091

1.796 0.823

0.157 0.891

Mean square

0.544

0.373

2.181

0.176

F

0.581

0.689

0.115

0.838

Sig.

Sensory comfort of denim product 341


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Clothing biosensory engineering Table 20.4 Comparison of ranking between different pairs of jeans Jeans

Mean

Std deviation

H I J K L M O P Q Total

4.22 4.33 5.69 5.19 4.83 4.14 7.25 7.44 1.86 5.00

2.531 1.867 2.436 1.864 1.859 2.380 2.143 1.796 1.313 2.589

Table 20.5 Basic description of nine selected postures Posture

Description

1 2 3 4 5 6 7 8 9

Ten steps walking Sit on the floor with straight legs Kneel down on the floor Sit on a chair Cross leg sitting Raise up the knee up to 90° Sit on the floor and bend the feet Sit on the floor and bend the knee Squat down and stretch up

body locations (thigh girth, knee, waist (side and back), hip, side hip and shank) lay between very satisfied and satisfied. However, subjects found the pressure comfort at the front of the waist, pelvis and crotch were between satisfied and unsatisfied. The influence of posture on clothing pressure comfort at different body locations is shown in Fig. 20.4. Results show that each posture had its own pattern in terms of body location. Least clothing pressure was perceived at all body locations by subjects when they walked ten steps. The average ratings of ‘walked ten steps’ were between 2.1 and 2.7, in which only the rating at hip was greater than 2.5. This suggests that subjects were ‘satisfied’ and ‘very satisfied’ with the clothing pressure comfort at hip and other locations, respectively. Subjects were ‘unsatisfied’ with the clothing pressure at pelvis (3.6), center front waist (3.7) and crotch (3.8) when they sat on the floor with their knees bent. These results suggest that pressure comfort at different body locations was influenced by the type of posture, in which ‘walking’ was the posture that provided least clothing pressure discomfort to the wearers.


Sensory comfort of denim product

1 2

343

6

9

7

10

3 8 11

4 5

12

1

Front waist

6

Side waist

9

2

Pelvis

7

Side hip

10 Hip

Back waist

3

Crotch

8

Side thigh

11 Back thigh

4

Front thigh

5

Knee

12 Shank

20.2 Twelve selected body locations.

Figure 20.5 compares the clothing pressure comfort rating at different body locations with different branded jeans. No clear pattern is found amongst different jeans in terms of clothing pressure at different body locations. Jeans I had the lowest average clothing pressure comfort rating at all body locations. The rating range of Jeans I was between 1.9 and 2.7, which indicates subjects satisfaction levels were between ‘very satisfied’ and ‘satisfied’. Apart from the rating at the crotch, the satisfactory levels of other locations were closer to ‘very satisfied’ than ‘satisfied’. The average ratings of Jeans Q at all body locations also fell into the ‘very satisfied’ to ‘satisfied’ zone, but there were only four locations (side thigh, centre back waist, back thigh and shank) that were close to the ‘very satisfied’ level. However, the clothing pressure comfort rating of Jeans O fell into the ‘satisfied’ to ‘unsatisfied’ area. Subjects were ‘unsatisfied’ with the clothing pressure at the center front waist, front thigh, pelvis and crotch with the average rating of 3.5, 3.5, 3.9 and 4.0, respectively. These results suggest that the design and cut of jeans play an important role in the clothing pressure comfort perception.


344

Clothing biosensory engineering Unsatisfied

4.0

Pressure comfort rating

3.5

Satisfied

3.0

2.5

Very satisfied

2.0

k h

ig

th

st

h

ai

w

st

ig

th

p

hi

ai

w

an

Sh

ck

Ba

ck

ip

H

Ba

de

Si

de

Si

de

Si

gh

hi

h

tt

ee

Kn

on

tc

st

ai

tw

is

lv

ro

Fr

C

Pe

on

Fr

Clothing pressure comfort satisfactory rating

20.3 Pressure comfort rating distribution.

4.5 4.0

Walk 10 steps Sit straight Squat down and stretch up Sit on the floor (leg straight) Sit on the floor (bend knee)

Knee raise up to 90째 Bend one leg to the opposite thigh Kneel down on the floor Sit on the floor (bend knee)

3.5 3.0 2.5 2.0 1.5 Center Pelvis Crotch Front Knee Side Side Side Center Hip front thigh waist hip thigh back waist waist Body location

20.4 Pressure comfort rating location and posture.

Back Shank thigh


Sensory comfort of denim product

345

Clothing pressure comfort satisfactory rating

4.5

4.0

H

I

J

K

L

M

O

P

Q

3.5

3.0

2.5

2.0

1.5 Center Pelvis Crotch Front Knee Side Side Side Center Hip front thigh waist hip thigh back waist waist

Back Shank thigh

Body location

20.5 Pressure comfort rating location and brand.

20.3.4 Objective evaluation of garment pressure distribution The purpose of this wear trial was to identify the objective clothing pressure distribution when wearing jeans. The influence of body location and posture, which can been seen in normal daily activities, was also taken into consideration. Eight female subjects participated in the wear trial. The mean and standard deviation of subjects’ age and the length of waist, hip, thigh and knee are shown in Table 20.6. The brand and size of the selected jeans are shown in Table 20.7. During the experiment, garment pressure at nine body locations – waist (front, side and back), side hip, crotch, thigh (front, side and back) and knee – was measured using pressure sensors, which were placed between the subject’s skin and the jeans inner surfaces. The experiment was conducted in an environmentally controlled chamber with temperature and relative humidity set at 25 ± 3 °C and 65 ± 2%, respectively. Subjects were required to wear seven pairs of jeans in order to complete the wear trial. Table 20.8 summarizes the influence of jeans, posture and their interaction on garment pressure at the selected body locations. Garment pressure at front waist, back waist and front thigh was influenced by jeans and posture. Furthermore, the garment pressure at front waist was also influenced by the interaction between jeans and posture. Different types of jeans gave subjects significantly different garment pressure. Different postures


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Clothing biosensory engineering

Table 20.6 Basic description of eight subjects Subject

Age (years)

Waist girth (cm)

Hip girth (cm)

Thigh girth (cm)

Knee girth (cm)

1 2 3 4 5 6 7 8

19 21 22 22 27 23 22 21

66.7 70.5 66.7 69.2 69.7 66.7 69.2 70.5

94.9 100.0 92.3 94.9 89.7 87.2 93.6 95.1

46.9 48.7 44.9 48.7 46.2 42.8 43.6 48.7

35.9 38.5 34.6 38.5 35.9 35.1 34.6 34.6

Average s.d.

22.1 2.3

68.7 1.7

93.5 3.9

46.3 2.4

36.0 1.6

Table 20.7 Brand and size of the jeans Jeans

Waist length (cm)

Hip length (cm)

J P Q I H L K

71.8 69.2 71.8 69.2 69.2 71.8 71.8

92.3 94.9 97.4 92.3 94.9 97.4 100.0

Average s.d.

70.7 1.4

95.6 2.9

Table 20.8 Influence of jeans, posture and their interaction on objectively measured garment pressure Body location

Jeans

Posture

Jeans ± Posture

Front waist Side waist Back waist Pelvis Crotch Front thigh Side thigh Back thigh Knee

0.000 0.005 0.042 – – 0.016 – – –

0.000 – 0.012 – 0.010 0.011 – – 0.002

0.022 – – – – – – – –


Posture Standing Leg rises 90° Sitting Sitting with bending feet Squatting Sitting on the floor with bending legs

Jeans J P Q I H L K

13.3 24.1 54.3 62.8 87.2 86.4

53.1 41.1 34.7 39.6 25.5 31.4 157.4

± ± ± ± ± ±

± ± ± ± ± ± ± 10.99 24.17 82.85 92.30 153.57 132.82

49.27 37.11 35.14 39.91 24.01 30.66 227.14

Front waist

Body location

10.6 18.6 32.1 32.5 36.2 38.6

18.6 42.3 11.3 16.3 73.2 18.5 16.5 ± ± ± ± ± ±

± ± ± ± ± ± ± 9.98 25.01 106.0 107.67 95.33 111.65

20.29 120.71 11.36 17.94 185.95 15.09 15.45

Side waist

5.5 5.7 36.0 41.3 14.1 7.8

9.0 16.8 51.9 15.2 10.6 13.2 12.3 ± ± ± ± ± ±

± ± ± ± ± ± ± 5.60 4.40 122.87 107.63 43.95 7.59

12.78 28.02 176.11 28.59 16.25 19.75 18.43

Back waist

22.0 64.5 121.0 116.4 64.5 85.7

88.1 92.5 80.0 69.6 54.9 105.4 62.7 ± ± ± ± ± ±

± ± ± ± ± ± ±

Crouch

Table 20.9 Mean and standard deviation of objective garment pressure measurements

22.17 110.78 182.79 212.83 108.42 202.83

178.52 165.24 151.56 127.87 47.99 200.85 185.84

7.7 20.8 17.7 26.9 22.0 19.3

16.4 11.7 17.5 17.9 32.5 17.4 19.9

± ± ± ± ± ±

± ± ± ± ± ± ±

9.47 21.85 14.83 58.06 17.16 14.94

12.56 10.65 14.30 16.32 62.71 22.57 16.63

Front thigh

5.7 17.0 21.2 36.0 65.0 30.9

17.2 50.2 34.7 26.6 40.5 15.6 20.2

± ± ± ± ± ±

± ± ± ± ± ± ±

Knee

8.04 20.88 26.49 92.73 141.27 69.31

20.12 132.42 78.11 31.46 125.80 21.41 24.36

Sensory comfort of denim product 347


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Clothing biosensory engineering

also had a significant effect on garment pressure at the crotch and knee, which suggests that, at these body locations, some postures gave significantly greater garment pressure than others. Table 20.9 shows the mean and standard deviation (s.d.) of objective garment pressure measurements in terms of jeans and posture. On average, K, H and Q provided significantly greater garment pressure than other jeans at the front, side and back waist of the subjects. In term of posture, ‘squatting’ gave subjects significantly greater garment pressure at the front waist and knee than other body locations. On average, the crotch had the greatest perceived garment pressure during standing, leg rising at 90 ° and sitting (on a chair with or without bending feet) positions.

20.4

Conclusion

Based on this study, it can be concluded that denim fabric should be light, smooth and not too thin, in order to provide good ‘hand feel’ sensation. During normal daily activities, such as standing, walking and sitting, front waist, crotch and pelvis provided relatively greater garment pressure to the wearer than other measured body locations.

20.5

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A174.

20.6

References

1. Barnes, P., Trends in the West European Market for Denim Jeans. Textile Outlook International, 1992. 41: p. 91–108. 2. Blomquist, C., J. Yokogawa, M.C. Pavarini, M. Schmidinger, L. Dartmann, and O. Horton, Playing the Blues. Sportswear International, 2004. 191: p. 46–49. 3. Crosby, G., Comfortable Casuals in Cologne. Canadian Apparel, 1999. 23(2): p. 25–27. 4. Crowther, E.M., Comfort and Fit in 100% Cotton-denim Jeans. Journal of the Textile Institute, 1985. 76(5): p. 323–338. 5. Hemdan, A. and H.A. Abou-Taleb, Denim Jeans Evaluation. Textile Asia, 1990. 11: p. 40. 6. Kamata, Y., T. Kato, A. Ito, and N. Yahata, Convective Heat Transfer from the Human Body. II. Effect of Fabric on Heat Transfer. Sen-i Gakkaishi, 1988. 44(2): p. 78–87. 7. Kwok, Y.L., Y. Li, B.S.H. Wong, and W.S.S. Wu, Perceptual Requirements of Hong Kong Consumers on Children’s Denim Wear. Journal of the Textile Institute, 1998. 89(3): p. 96–108. 8. Li, Y., L. Yao, and R.M. Jones, Report on the Production, Sales and Trade of Denim in Hong Kong. Journal of Fashion Marketing and Management, 2001. 5(3): p. 241–259.


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9. Morris, M.A. and H.H. Prato, Consumer Perception of Comfort, Fit and Tactile Characteristics of Denim Jeans. Textile Chemist and Colorist, 1981. 13(3): p. 60–66. 10. Paul, R. and P.D. Pardeshi, Everything You Ever Wanted to Know About Denim. Textile Horizons, 2003. 5/6: p. 12–16. 11. SDI, The Light Side of Denim, in Inside Fashion. 1999. p. 14–15. 12. Stogdon, R., Denim: Trends in European Production and Trade. Textile Outlook International, 1993. 47: p. 60–76. 13. Wildhaber, J. and U. Nef, Machine Developments for Processing Elastic Yarns. DWI Reports, 2000(123): p. 427–434. 14. Wong, A.S.W., Y. Li, and L. Yao, Feature of the EU Denim Products Trading, in Proceedings of the 5th Asian Textile Conference. 1999. 1: p. 554–557. 15. Zhang, X., K.W. Yeung, and Y. Li, Numerical Simulation of 3D Dynamic Garment Pressure. Textile Research Journal, 2002. 72(3): p. 245–252. 16. Zimmermann, R., Denim: What Might the Future Hold for the World’s Hottest Textile Sector? Textile Leader, 1998. 2: p. 34–38.


21 Sensory comfort of tennis wear ANTHONY S.W. WONG 1 , Y I L. KWOK 2 AND YI LI 2 1 School of Nursing, The Hong Kong Polytechnic University, Hong Kong 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong

21.1

Introduction

Growing interest in sporting activities and the emergence of new sports has stimulated a demand for textiles that perform functions which are helpful to participants.10 Sportswear needs to fulfil four main functions: weather protection, thermal insulation, vapor permeability and stretch. The principles of clothing physiology, which is the interaction between the human body and its clothing system, with particular reference to the interplay between the body, climate and clothing, were discussed by Rupp.13 The factors that govern the physiological processes involved when clothing is being worn, including insulation, vapor transmission and convection and ventilation, were also described.13 Bardhan and Sule2 stated that protection, comfort and shape retention are the principal parameters in sportswear and leisurewear design. Kothari and Sanyal10 stated that the three main areas that fiber and fabric developers focus on are functionality, comfort and aesthetic appeal. The development and importance of high-performance sportswear and equipment in tennis, swimming and American football over recent years were studied by Susta.14 Lonsinger12 reviewed the trends in activewear including sportswear and sports-inspired clothing for casual wear in the US market. Result shows that cottonâ&#x20AC;&#x2122;s pre-eminence in the field was being challenged by synthetic fibre and high-performance fabrics. The Woolmark Co., formerly the International Wool Secretariat, launched a range of technical sportswear for the 2004 Australian Open tennis championships. The new shirts were constructed from Sportwool, which boasts superior heat and moisture transfer properties that reduce the risk of post-exercise chill. The company claimed that Sportwool can hold up to 30% of its own weight in moisture vapor before it feels wet or clammy to the touch. This property allows the skin to cool more quickly during exercise and plays a crucial role in maintaining wearer comfort.1 Gupta and Gupta7 demonstrated a huge variety of functional properties which have proven useful to both 350


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351

professional athletes and leisure sports participants, including super-lightweight, high tenacity and stretchability, body heat retention, cooling capacity, sweat absorption, quick drying, vapor permeability and anti-bacterial action. Bhat et al.3 studied the thermal properties of elastic fibers including spandex, which are mainly used in sportswear. Chung and Cho5 aimed to develop a thermally adaptable, vapor-permeable water-repellent fabric. In their study, they compared two types of water-repellent fabrics. WR-PCM is the water-repellent fabric containing octadecane, which is a type of phase change material (PCM), microcapsules. WR is the water-repellent fabric without PCM. In wear trials, the mean skin temperature and microclimate temperature of subjects wearing activewear made with WR-PCM are lower than those made with WR. However, the microclimate humidity with WR-PCM has a higher rate than that with WR. Although the subjective sensations of WR and WR-PCM are not significantly different, WR-PCM is cooler and more comfortable than WR. The results of this study indicate that octadecane-containing microcapsules in water-repellent fabrics provide a cooling effect but sacrifice water resistance. Ceken4 investigated certain physical properties of five different types of circular knitted fabrics used for active sportswear. These fabrics have different knitting structures and material blend ratios. Air permeability, abrasion resistance and bursting strength tests were applied to these fabric samples and their impact on the usage characteristics was investigated. All tests were made on fabric swatches, the measurements were repeated for ten different locations of the fabric samples and the average of these values was taken. It was observed that interlock piquĂŠ knitted fabrics have higher air-permeability values in comparison to fleece knitted fabrics and double face jersey fabrics. It was suggested that this kind of knitting structure is particularly suitable for active sportswear in hot seasons. Further, it was noted that bursting resistance was influenced by the percentage of synthetic fibers in the blend, rather than the knitting structure. Finally, abrasion test results of fabric swatches suggested better values for plated single jersey and interlock piquĂŠ knitted fabrics in comparison to raised fleece knitted fabric structures. Using a series of manikin and human subject experiments, Crow and Osczevski6 examined the interaction between water and a range of fabrics to investigate claims that synthetic fibers such as polypropylene do not pick up moisture. They found that all fiber types, when made into fabrics, pick up water, with a strong correlation between a fabricâ&#x20AC;&#x2122;s thickness and the amount of water it picks up, freely expressed in absolute terms rather than as a percentage of its mass. They also found that properties relevant to clothing on an exercising person, such as drying time and energy required to evaporate water from under and through a dry fabric or to dry a wet


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fabric, depend on the amount of water the fabric picks up, not the fiber type. The amount of water wicked from one layer to another depends on pore sizes and their corresponding volumes. Human perception of clothing comfort is an interaction between physical, physiological and psychological factors with the surrounding environment when wearing a garment. Different aspects of clothing comfort have been studied for many years. Thiry15 provides an overview of apparel comfort issues including the effect of the environment, available test methods, fabric hand, moisture management, thermal management and psychological comfort. The influence of dry (radiation, conduction and convection) and latent (perspiration) flux processes on comfort was studied in detail by Kothari and Sanyal.10 Li et al.11 studied the effects of wool fiber hygroscopicity or moisture sorption on the dynamic comfort performance of wool apparel comfort. Wool fabrics were tested against polyester fabrics and acrylic fabrics for properties including coolness to touch, dampness perception, moisture buffering, enhanced body heat loss and rain buffering. Wang et al.16 evaluated wool shirt comfort with wear trials and the forearm test. Results showed that increasing the environmental temperature decreased the comfort of the woolen fabrics due to the increased prickly feel, and it was observed that increasing the wool quality number decreased the level of prickle from the forearm test. Ishtiaque9 stated that the comfort of athletic apparel depends on optimizing interactions between fiber type; spinning, weaving or knitting parameters; fabric density and weight; finishing; fit; and manufacture. Critical functions include thermal retention, ultraviolet light resistance, cooling capacity, sweat absorption, rapid drying, antibacterial properties and relaxation without fatigue. Thicker air layers within the apparel system provide greater thermal insulation and moisture resistance. Wong and Li17 studied the relationship between clothing sensory comfort and brand preference in tight-fit aerobic wear. They found that comfort and garment fitness were two of the most important attributes in tight-fit garments. Furthermore, brand influenced the determination of preferred garments as subjects changed their mind after the garment brand was revealed. Later on, Wong et al.18 applied neural networks to predict clothing comfort in tight-fit garments. Among these and other studies of psychological perception of clothing comfort, a list of sensory descriptors such as clammy, damp, wet, prickle, scratchy, sticky and tight were used to describe clothing comfort. As each particular perception can be described in many words, depending on wearers, Wong et al.19 used statistical methods to identify the comfort dimension in tight-fit garments. However, despite all of this available research, the comfort dimensions in female tennis wear have not been reported. The aim of this chapter is to:


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1. study the thermal and moisture transport properties of fabrics used to make the selected tennis wear; 2. investigate the influence of time and type of tennis wear on human subjective perception of various sensations through a series of wear trials; 3. study the distribution of individual sensation pairs at different time periods; 4. identify the dimension of clothing comfort by using statistical factor analysis; 5. develop a prediction model for clothing comfort on the basis of different sensory factors.

21.2

Experimental

21.2.1 Participants A group of 15 females aged between 20 and 24 participated in this study. All participants were considered healthy and undertook regular exercise. Approval for the study and written consent forms, which were signed by participants, were obtained from the Ethics in Human Research Committee and participants, respectively. The procedure of the wear trial and questionnaire were explained in detail to all subjects in the laboratory prior to the wear trial taking place.

21.2.2 Tennis wear Six types of commercially available tennis wear, purchased from local department stores, were selected. The selection criteria for these items included design, brand and fabric used. A basic description of the tennis wear is given in Table 21.1. Transport properties of fabrics used in the selected clothing were measured using the Kawabata evaluation system (KES) and a moisture management tester (MMT), which was developed

Table 21.1 Basic description of tennis wear Code

Weight (g/m2)

A-95P A-100P N-70C F-60C N-100C N-88P

260 145 199 245 192 266

± ± ± ± ± ±

(28.1) (6.1) (6.2) (10.6) (6.4) (6.8)

Thickness (mm) 0.9 0.7 0.8 1.0 0.7 0.9

± ± ± ± ± ±

(0.13) (0.02) (0.01) (0.01) (0.03) (0.01)

Content 95% Polyester & 5% Elastane 100% Polyester 70% Cotton & 30% Polyester 60% Cotton & 40% Polyester 100% Cotton 88% Polyester & 12% Spandex


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by The Hong Kong Polytechnic University.8 Air permeability and thermal insulation were measured using KES. Ten moisture-related parameters – WTT; WTB; MART; MARB; MWRT; MWRB; SST; SSB; AOWT and OMM; which describe fabric wetting time, maximum absorption rate, maximum wetted radius, spreading speed, accumulative one-way transport capacity and overall moisture management – were measured using MMT. Subscripts T and B represent the top and bottom side of the fabric, respectively.

21.2.3 Wear trial Each participant was required to wear two garments in each trial and there were three trials to test the six garments in total. Subjects were required to run on a treadmill at a constant speed of 5.6 km/hr in an environmentally controlled chamber, where temperature and humidity were set at 29 ± 2 °C and 85 ± 3%, respectively. Eight pairs of sensory perceptions (dry–damp, smooth–sticky, breathable–airtight, light–heavy, soft–prickly, gloss–scratchy, loose–tight and cool–hot) and overall clothing discomfort (comfortable– uncomfortable) were evaluated by each participant at five-minute intervals from time 0–20. An example of a rating scale for dry–damp sensation is shown in Fig. 21.1. The ratings were then converted into a scale of −3 and 3. In order to reduce the chances of the subjects’ bodies cooling down and drying up, the maximum time for each subject to complete the rating at each session was one minute.

21.3

Results and findings

21.3.1 Transport properties of tennis wear fabric The mean and standard deviation (mean ± sd) of 13 transport properties are shown in Table 21.2. Fabric top and bottom surfaces represent the side that has had direct contact with the wearer’s skin and the surrounding environment, respectively. If the wetting time on both sides is short, this

Extremely Min.

Moderately

Extremely Dry

Damp

0 5th 10th 15th 20th 1

2

3

4

21.1 Rating scale of damp–dry sensations.

5

6

7


MMT Wetting time (T) Wetting time (B) Max. absorption rate (T) Max. absorption rate (B) Max. wetted radius (T) Max. wetted radius (B) Spreading speed (T) Spreading speed (B) Accumulative one-way transport capacity Overall moisture management

AP TC TI

KES Air permeability Thermal conductivity Thermal insulation 5.0 ± 0.52 7.1 ± 2.31 96.5 ± 6.28 134.9 ± 47.07 14.0 ± 3.77 14.0 ± 3.77 0.5 ± 0.19 0.5 ± 0.21 18.4 ± 99.20 43.0 ± 61.21

4.0 ± 0.17 3.8 ± 0.11 37.1 ± 7.25 82.4 ± 10.00 12.0 ± 2.47 15.0 ± 0.00 0.7 ± 0.19 0.6 ± 0.01 55.2 ± 24.72 48.3 ± 13.68

%/sec

mm

mm

mm/sec mm/sec %

MARB

MWRT

MWRB

SST SSB AOWT

OMM

0.2 ± 0.01 0.1 ± 1 × 10−3 3.0 ± 0.03

A-100P

sec sec %/sec

1.1 ± 0.45 0.1 ± 4 × 10−3 2.7 ± 0.04

A-95P

Fabric

WTT WTB MART

KPa.s/m W ¯ W

Symbol Unit

Parameter

Table 21.2 Transport properties of selected fabrics

70.03 ± 5.03

0.7 ± 0.12 0.9 ± 0.03 119.4 ± 11.35

15.0 ± 0.00

12.0 ± 2.47

40.4 ± 2.87

4.2 ± 0.67 3.8 ± 0.17 19.9 ± 4.72

0.3 ± 0.00 0.1 ± 1 × 10−3 2.9 ± 0.04

N-70C

30.2 ± 18.03

0.8 ± 0.04 0.8 ± 0.04 38.3 ± 28.4

15.0 ± 0.00

15.0 ± 0.00

43.6 ± 16.57

3.5 ± 0.13 5.0 ± 2.72 23.1 ± 4.33

0.2 ± 7 × 10−3 0.2 ± 2 × 10−3 2.9 ± 0.03

F-60C

8.3 ± 73.04

0.2 ± 0.08 0.2 ± 0.05 −31.72 ± 92.56

11.0 ± 2.01

12.0 ± 2.47

96.6 ± 110.74

4.29 ± 2.01 32.0 ± 22.68 96.1 ± 6.50

0.3 ± 0.00 0.2 ± 1 × 10−3 3.8 ± 0.04

N-100C

−3.5 ± 6.50

0.7 ± 0.13 0.6 ± 0.28 −16.9 ± 12.55

14.0 ± 4.93

14.0 ± 2.01

19.1 ± 1.82

4.5 ± 0.15 5.0 ± 0.28 21.4 ± 3.54

1.9 ± 0.13 0.2 ± 0.01 3.5 ± 0.10

N-88P


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suggests that sweat takes less time to transport from the skin to the outer environment, away from the fabric. The wetting time at the bottom is significantly longer than the top in N-100C, which was made of 100% cotton. This indicates that sweat/moisture takes a longer time to move from the bottom surface to the environment than from top to bottom. The greater the overall moisture-management value, the better the fabric moisturemanagement performance. It is clear that N-70C has the best moisturemanagement performance (mean ≅ 70) amongst the selected garments, followed by A-95P (mean ≅ 48) and A-100P (mean ≅ 43), respectively. By using statistical analysis of variance, ANOVA, the garment type has a significant (p < 0.001) influence on all objectively measured transport properties.

21.3.2 Influence of time and garment on various sensations Table 21.3, which was generated using SPSS, shows the influence of time, garment and their interaction on perception of dry–damp sensation. This indicates that the type of garment had no significant influence on perception of dry–damp, as its significance (p-value) is 0.064, which is greater than 0.05. Its interaction with time is also not significant. On the other hand, time had significant influence on perception of dry–damp, as its p-value is less than 0.05. This suggests that the selected garments provided a similar dry–damp sensation to the wearers, but the perception of dry–damp was significantly different during different periods of time in this study. Table 21.4 summarizes the influence of time, garment and their interaction on other sensations. Time had a significant influence on selected sensations, but its interaction with garments had not. Garment had no significant influence on comfort perception.

Table 21.3 Influence of time and garment on perception of dryness sensation Dependent variable: dry Source

Type III sum of squares

df

Mean square

F

Sig.

Corrected model Intercept Garment TIME Garment*Time Error Total Corrected total

356.450 8798.453 10.913 339.554 5.983 436.217 9591.120 792.667

29 1 5 4 20 420 450 449

12.291 8798.453 2.183 84.889 0.299 1.039

11.834 8471.365 2.101 81.733 0.288

0.000 0.000 0.064 0.000 0.999


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Table 21.4 Influence of time and garment on all sensations and overall discomfort Sensation

Tennis wear

Time

Interaction

Dry–damp Smooth–sticky Breathable–airtight Light–heavy Soft–prickly Gloss–scratch Loose–tight Cool–hot Comfortable–uncomfortable

– 0.000 0.007 0.000 0.000 0.000 0.000 0.000 –

0.000 0.000 0.000 0.000 0.001 0.005 0.000 0.000 0.000

– – – – – – – – –

21.3.3 Distribution of individual sensations Figures 21.2a to 21.2d illustrate the mean scores of dry–damp, smooth– sticky, breathable–airtight and cool–hot sensations at five selected time periods. In Fig. 21.2a, the scores of −3, −2 and −1 indicate that the subject felt extremely dry, dry or slightly dry during the experiment, respectively. The scores of 1, 2 and 3 indicate that the subject felt slightly damp, damp and extremely damp during the experiment, respectively. Moderate is indicated as 0. Similarly, in Fig. 21.2b, the scores of −3, −2 and −1 indicate that the subject found the garment extremely smooth, smooth or slightly smooth during the experiment, respectively. The scores of 1, 2 and 3 indicate that the subject found the garment slightly sticky, sticky and extremely sticky during the experiment, respectively. In general, the mean scores of eight pairs of selected sensations had increasing trends from time 0–20. Figure 21.2a indicates that subjects felt dry (dry–damp score ≈ −2) before they exercised. As they started to run, they felt wetter and wetter, the dry–damp score began to increase, and at the end of the exercise they felt slightly damp (dry–damp score ≈ 1). During the 20 minutes of the experimental period, the mean score of perception of smooth–sticky ranged between smooth and slightly sticky, as shown in Fig. 21.2b. For perception of breathable–airtight, the range is between slightly breathable and slightly airtight, as shown in Fig. 21.2c. For perception of cool–hot, the range is between slightly cool and hot, as in Fig. 21.2d. The distribution of the mean overall clothing comfort score is shown in Fig. 21.3. On average, subjects felt comfortable before the exercise (at time 0), and by the end of the exercise (time 20) they felt slightly uncomfortable.


358 A

Clothing biosensory engineering 3.0

Dry–damp sensation score

2.0

1.0

0.0

–1.0

–2.0

–3.0 0

5

10

15

20

Time (min)

21.2a Dry–damp sensation mean score at different time period. B

3.0

Smooth–sticky sensation score

2.0

1.0

0.0

–1.0

–2.0

–3.0 0

5

10

15

20

Time (min)

21.2b Smooth–sticky sensation mean score at different time period.


Sensory comfort of tennis wear C

3.0

Breathable–airtight sensation score

2.0

1.0

0.0

–1.0

–2.0

–3.0 0

5

10

15

20

Time (min)

21.2c Breathable–airtight sensation mean score at different time period. D

3.0

Cool–hot sensation score

2.0

1.0

0.0

–1.0

–2.0

–3.0 0

5

10

15

20

Time (min)

21.2d Cool–hot sensation mean score at different time period.

359


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Comfortable–uncomfortable sensation score

3.0

2.0

1.0

0.0

–1.0

–2.0

–3.0 0

5

10

15

20

Time (min)

21.3 Overall comfort sensation mean score at different time period.

21.3.4 Dimension of comfort in tennis wear The dimension of clothing comfort in tennis wear is derived by using statistical factor analysis and the result is summarized in Table 21.5. The factor loadings indicate the importance of variables at each factor. Factor loading less than 0.5 is considered as not significant and does not belong to that particular factor. In this study, the eight pairs of sensations can be abstracted into three factors; the first factor consists of dry–damp (0.785), smooth– sticky (0.789), breathable–airtight (0.785) and cool–hot (0.895). The second factor consists of soft–prickly (0.911) and smooth–scratchy (0.920), and the third factor consists of loose–tight (0.831). Based on the type of sensation, these three factors can be named as thermal–wet, tactile and pressure. The factor loadings of light–heavy are 0.582 and 0.594 in thermal–wet and tactile, respectively, which suggests that perception of lightness and heaviness might influence the perception of thermal–wet and tactile sensations. This finding agrees with the previous studies on clothing comfort in tight-fit sportswear. Table 21.6 shows the percentage of variance, which can be used to describe the importance, explained by thermal–wet, tactile and pressure. The total percentage of variance explained by these three factors is 81. It is clear that


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Table 21.5 Factor analysis result of eight pairs of sensations Factor

Dry–damp Smooth–sticky Breathable–airtight Light–heavy Soft–prickly Smooth–scratchy Loose–tight-fit Cool–hot

1

2

3

0.785 0.789 0.785 0.582 0.252 0.231 0.494 0.895

0.180 0.423 2.514E-02 0.594 0.911 0.920 −1.35-03 −5.98E-03

0.150 5.044E-03 0.214 0.200 −5.26E-02 −2.22E-02 0.831 −0.273

Table 21.6 Total variance explained by abstracted factors Factor

1 2 3 4 5 6 7 8

Initial eigenvalues

Rotation sums of squared loadings

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

4.269 1.482 0.723 0.511 0.344 0.291 0.242 0.137

53.358 18.529 9.038 6.391 4.305 3.635 3.029 1.715

53.358 71.887 80.925 87.316 91.621 95.257 98.285 100.000

3.355 2.241 0.877

41.942 28.019 10.964

41.942 69.961 80.925

thermal–wet is the most important factor in clothing comfort, as 42% of clothing comfort is explained by this factor. It is followed by tactile and pressure with 28 and 11%, respectively.

21.3.5 Relationship between fabric transport properties, thermal–wet and discomfort sensation The correlation between the selected transport properties and factor score of thermal–wet is also investigated. All the objective measurements have been standardized as factor scores change considerably over the complete exercising period compared with transport properties. Therefore, it would be better to investigate the relationship in five different time periods (time 0–20 for every five minutes). The results are shown in Table 21.7. The factor


0.945** 0.878*

−0.813* −0.862*

10

−0.820*

15

−0.854*

20

0.870*

Discomfort

0

Note: * and ** indicate the correlations are significant at the 0.05 and 0.01 level, respectively.

Air permeability Thermal conductivity Thermal insulation MMT Wetting time (T) Wetting time (B) Max. absorption rate (T) Max. absorption rate (B) Max. wetted radius (T) Max. wetted radius (B) Spreading speed (T) Spreading speed (B) Accumulative one-way transport capacity Overall moisture management

Thermal–wet

KES

5

0

Parameter

Time

Table 21.7 Influence of fabric transport properties on thermal–wet and discomfort perception

−0.848*

5

10

15

20


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363

score of thermal–wet mainly correlated with maximum absorption rate, both top and bottom, spreading speed and accumulative one-way transport capacity. However, perception of discomfort sensation mainly correlated with thermal conductivity and maximum absorption rate at the bottom at time 0 and 5, respectively. No sweat was produced in the beginning of the wear trial and moisture-related parameters are not significantly correlated with overall discomfort sensation. After five minutes of exercising, subjects began to sweat and moisture-related parameters become important.

21.3.6 Prediction of clothing comfort From the results above, it is clear that there are three major sensory factors that determine the comfort performance of clothing. On the basis of these observations, a model to simulate the overall comfort performance from individual sensory factors is proposed with an assumption of a linear relationship between overall comfort perception and the sensory factors, which can be expressed as: p

Y = ∑ wi xi = w1 x1 + w2 x2 + w3 x3 . . . + w p x p

[21.1]

i =1

where Y is the predicted overall comfort performance, xi is the individual sensory factor scores, wi is the percentage of variance explained by the corresponding sensory factors and p is the number of perception group. Overall, comfort performance of the garments can be found by using the three factor scores of moisture comfort, tactile comfort and thermal–fit comfort and their corresponding percentage of variance. Figure 21.4 illustrates the relationship between predicted and actual score of comfortable–uncomfortable. A good linear relationship between predicted and actual comfortable–uncomfortable scores is found. This result shows that overall comfort performance of clothing can be predicted from individual sensory factors and their relative contributions to total variance. Therefore, it can be concluded that the overall comfort in tennis wear is mainly determined by three major sensory factors: thermal–wet, tactile and pressure.

21.4

Conclusion

By conducting a series of wear trials with 15 female subjects wearing six different types of tennis wear undertaking exercise in a controlled climatic chamber, the score of subjective perception rating on eight pairs of sensations and overall clothing comfort within the 20 minutes’ wear trial were obtained. By using factor analysis, three sensory factors; thermal–wet; tactile


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y = 1.7719x – 0.4333 r = 0.785

Actual comfortable–uncomfortable score

3.0

2.0

1.0

0.0 –1.5

–1.0

–0.5

0.0

0.5

1.0

1.5

2.0

–1.0

–2.0

–3.0

–4.0 Predicted comfortable–uncomfortable score

21.4 Correlation between predicted and actual comfortable– uncomfortable score.

and pressure, were abstracted. Thermal–wet is the most important factor in explaining clothing comfort. It explains 42% of the total variance of clothing comfort and is followed by tactile and pressure, which explain 28 and 11% of the total variance, respectively. A linear predictable model for overall comfort was developed using the factor score and the relative contributions of corresponding factors as weights. Good correlation (r ≈ 0.785, p < 0.01) between the predicted and actual comfort score was found, indicating that the overall comfort performance is predictable from individual sensory factors.

21.5

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through the project A174.

21.6

References

1. Anon., The Woolmark Company: New Sportswear Range. Performance Apparel Markets, 2003. 10(7): p. 14–15.


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2. Bardhan, M.K. and A.D. Sule, Anatomy of Sportswear and Leisurewear: Scope of Spandex Fibres. Man Made Textiles in India, 2001. 44(3): p. 81–86. 3. Bhat, G., S. Chand, and S. Yakopson, Thermal Properties of Elastic Fibers. Thermochimica Acta, 2001. 367–368: p. 161–164. 4. Ceken, F., Certain Physical Properties of Knitted Structures used for Active Sportswear. Melliand International, 2004. 10(3): p. 204–206. 5. Chung, H. and G. Cho, Thermal Properties and Physiological Responses of Vapor-permeable Water-repellent Fabrics Treated with Microcapsule-containing PCMs. Textile Research Journal, 2004. 74(7): p. 571–575. 6. Crow, R.M. and R.J. Osczevski, The Interaction of Water with Fabrics. Textile Research Journal, 1998. 68(4): p. 280–288. 7. Gupta, S. and D. Gupta, Creatively Engineered Sportswear Fabrics. ATA Journal, 2003. 14(4): p. 82–83. 8. Hu, J.Y., Y. Li, and A.S.W. Wong, A New Test Method to Measure Liquid Moisture Management Properties of Textiles, in The Fiber Society 2003 Spring Symposium – Advanced Flexible Materials and Structures: Engineering with Fibers. 2003. Loughborough, United Kingdom: The Fiber Society, UK. 9. Ishtiaque, S.M., Engineering Comfort. Asian Textile Journal, 2001. 10(11): p. 36–39. 10. Kothari, V.K. and P. Sanyal, Fibres and Fabrics for Active Sportswear. Asian Textile Journal, 2003. 12(3): p. 55–61. 11. Li, Y., B.V. Holcombe, and A.M. Plante, Wool for Comfort. Textile Asia, 1994. 25(6): p. 57–60. 12. Lonsinger, K., High-performance Fabrics Get a Boost from Growing Activewear Market. Knitting Times, 1993. 62(2): p. 36–37. 13. Rupp, J., Functional Sportswear. International Textile Bulletin, 1998. 44(4): p. 14–20. 14. Susta, D., When Yarn Helps the Athlete. Tessili per Impieghi Tecnici, 2003. 10(11): p. 46–51. 15. Thiry, M.C., Feelin’ Fine: Textiles Bring a Sense of Comfort. AATCC Review, 2003. 3(3): p. 9–14. 16. Wang, G., W. Zhang, R. Postle, and D. Phillips, Evaluating Wool Shirt Comfort with Wear Trials and the Forearm Test. Textile Research Journal, 2003. 73(2): p. 113–119. 17. Wong, A.S.W. and Y. Li, Clothing Sensory Comfort and Brand Preference, in Proceedings of the 4th IFFTI International Conference. 2002. Hong Kong. p. 1131–1135. 18. Wong, A.S.W., Y. Li, and P.K.W. Yeung, Neural Network Predictions of Human Psychological Perceptions of Clothing Sensory Comfort. Textile Research Journal, 2003. 73(1): p. 31–37. 19. Wong, A.S.W., Y. Li, and P.K.W. Yeung, Statistical Simulation of Psychological Perception of Clothing Sensory Comfort. Journal of the Textile Institute, 2003. 93(1): p. 108–119.


22 Sensory comfort of aerobic wear ANTHONY S.W. WO NG 1 AND YI LI 2 School of Nursing, The Hong Kong Polytechnic University, Hong Kong 2 Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong 1

22.1

Introduction

Sportswear requires aesthetic features such as an attractive appearance and a pleasant look as well as functions that improve athletes’ performance, provide extra comfort and promote the health of the wearer.9 Today’s garments for sports and active outdoor wear are lighter, softer, more durable and faster drying. Also, they perform a multitude of functions while appealing to the fashion-conscious consumer.8 In the study of trends in sports and other performance apparel markets, Muran8 pointed out the following. 1. Women tend to spend more on performance apparel than men. 2. There is a tendency for fashion and function to merge in the industry. 3. Technological developments have led to the creation of intelligent, stimuli-sensitive fabrics for the activewear and casual wear markets – a broad range of intelligent fabrics, some of which interact with the body to provide health benefits, has hit the market. 4. Research and development will continue to take center stage as companies search for new ways to gain a competitive advantage by providing peak performance and more comfort. Factors such as (1) increased sports participation, (2) specialty clothing, (3) increased performance requirements, (4) fitness and sports dressing related to self-image and lifestyle, (5) greater consumer demand and (6) innovative materials have had a significant influence on the growth of the sportswear market since the 1980s.6 Shiotani and Hayakawa9 listed the properties, including thermal insulation, moisture-permeable waterproof properties, water repellency, ultraviolet ray shielding performance, moisture absorption, perspiration absorption, quick dryability, anti-bacterial deodorizing properties and stretchability, that should be considered in sportswear. Chaudhari2 pointed out that the desirable attributes of functional sportswear include optimum moisture and heat regulation, good water and air permeability, absence of dampness and soft and pleasant touch. 366


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Much research has been carried out in relation to active sportswear and its relation with the human body. Bardhan and Sule1 discussed the logic and anatomy of comfortable, functional sportswear with stretch ratios. Susnik and Zadnikar10 investigated the thermoregulation of aerobic dancers dressed in tracksuits made of cotton and Lycra fabrics. In their study, six female aerobic dancers were asked to wear tracksuits made from 100% cotton and 100% Lycra during a vigorous 45 minutes’ workout wear trial, in which relative humidity, air temperature and air speed were kept constant. Results showed that the mean body temperature of the dancers wearing the Lycra tracksuits was slightly lower than that of the dancers wearing cotton tracksuits at the end of the workout. Only one dancer felt comfortable in both types of tracksuit. Subjective evaluation of how much perspiration was generated was about the same for both fabrics. Dickson and Pollack3 studied the needs and preferences of 54 female in-line skaters, as consumer athletes, in Ohio and Florida, regarding their skating commitment, sport clothing interests and preferences and skating behaviors. The results showed that greater interest in achieving a unique and feminine appearance for performance enhancement and greater desire for fashion rather than comfort significantly predicted female skaters’ likelihood of buying clothing specifically for in-line skating. Additionally, a model was developed to predict the likelihood of purchasing special skating clothing on the basis of a skater’s age and commitment to skating. Feather et al.4 investigated if there were differences between black and white female athletes’ satisfaction with their bodies and fit of their clothing in order to examine the relationship between body satisfaction and clothing fit. A total of 290 female collegiate basketball players (168 white and 122 black) participated in this study. Their evaluations on body satisfaction and clothing fit were analyzed using MANOVA, Hotelling’s Tsup 2, ANOVAs and Pearson Product Moment correlations. The results showed that black females were more positive about their bodies than white females in terms of perception of their overall bodies. However, there was no significant difference in satisfaction with garment fit between athletes. Significant positive correlations were found between perceptions of body cathexis and garment fit. The relationships between active sportswear and human physiological responses have been studied. For example, the effect of sportswear from three experimental knitted fabrics and two types of environmental conditions on the stratum corneum water content, rate of water evaporation from the skin surface, rate of capillary blood flow and skin temperature on seven fit female subjects was studied by Markee et al.7 In their study, each subject was required to wear garments from 1.5 den polyester, 3.5 den polyester or 100% combed cotton while exercising and resting in a hot/humid and a hot/dry environment. Their findings include the following.


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1. Rate of evaporative water loss was slower at high humidities, when more water also accumulated in the stratum corneum during exercise. 2. Capillary blood flow is not significantly affected by environment and the difference in skin temperature is negligible. 3. With loose-fitting garments for hot environments, greater consideration should be given to openness of construction rather than to fibre content; when the fit is snug, fibre content may be of more critical importance in maintaining comfort. Wang et al.11 conducted a survey on student athletesâ&#x20AC;&#x2122; psychological and physiological evaluations of sportswear in Taiwan. Survey results indicated subjects agreed that sportswear has an important effect on their performance. A series of properties related to sportswear were considered. Subjects found ease, moisture sorption, softness, air permeability and elasticity to be important properties in sportswear, and other properties such as color, brand, appearance and price were considered as relatively unimportant. However, the subjects ranked both appearance and price as significantly more important when the survey changed the type of question to a priority order. The responses on priorities between male and female athletes were completely different. Males ranked appearance, color and brand as priorities, whereas females ranked durability, ease, softness and moisture sorption as priorities. The most important sources of discomfort were caused by perspiration on the skin because of poor moisture sorption, restriction of movement and abrasion. The males were more sensitive to discomfort than the females. Harumi et al.5 clarified the effects of the clothing pressure exerted on a trunk by a swimsuit on heart rate, skin blood flow and respiratory function, by conducting wear tests on seven female students. They found that, when a swimsuit was worn, blood pressure increased and heart rate decreased in a standing posture. Furthermore, oxygen intake and ventilation decreased during exercise. Based on these results, they concluded that, when the clothing pressure exerted on a trunk was relatively low â&#x20AC;&#x201C; lower than 1.0 kPa â&#x20AC;&#x201C; venous pump action was promoted and work efficiency became higher. However, when the clothing pressure exerted on a shoulder and inguinal region was very high, skin blood flow to peripheral parts (foot) was suppressed and the recovery of blood pressure after exercise was delayed. In order to investigate the relationship between clothing pressure comfort and tight-fit active sportswear, Zhang et al.13 developed a mechanical model for numerical simulations of 3D dynamic garment pressure during wear using a finite element method. Based on analyzing the contact characteristics between the human body and the garment, a mechanical model was developed using the theory of dynamic contact mechanics. A process of a female human model wearing a set of perfectly fitting sportswear, with the


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same style and made of either cotton denim or knitted nylon fabrics, was simulated. The garment was regarded as an elastic shell of geometric nonlinearity and the human body was assumed to be rigid. The contact between the body and the garment was modeled as a dynamic sliding interface. The predicted pressure was close to the magnitude of experimental measurements, indicating that the model was able to simulate garment pressure during wear with reasonable accuracy. Wong et al.12 simulated clothing pressure distribution in tight-fit sportswear by using a numerical computational method. Three sets of tight-fit sportswear were simulated on the basis of their mechanical properties. In addition, a 3D human body was also simulated in order to investigate its interaction with sportswear in the wearing process. Results showed that pressure increases significantly around the waist girth until it passes through the pelvis during the wearing process. Meanwhile, pressure begins to increase for other tested body locations. Based on a body contour plot, it was found that pressure distribution was not uniformly distributed and the high-pressure zone was concentrated around the waist girth at the end of the wearing process. The simulated pressure has no significant difference (p > 0.05) with the experimental pressure measurements. Furthermore, subjective pressure comfort rating was linearly correlated with the simulation, suggesting that the simulation method can provide reliable prediction in terms of pressure comfort. This chapter is based on a study of human psychological perception of comfort in aerobic wear from different aspects: fabric handle, wear trial and preference. Fabric handle covers the perception of cool, soft, smooth, prickly, scratchy, heavy and discomfort. The influence of subjects and aerobic wear was also studied. Personal preference was compared with perception of discomfort. One of the main purposes of aerobic wear is to provide comfort to the wearer during exercise. Therefore, perception of nine individual sensations (breathable, clammy, cool, damp, heavy, prickly, scratchy, sticky and tight) and overall clothing comfort during a 20-minute exercise was described. The influence of subjects, aerobic wear and time on the perception of individual sensations and overall comfort was investigated. The relationship between fabric properties and overall comfort was described using a statistical regression model. The comfort performance of different branded aerobic wear was compared.

22.2

Experimental

22.2.1 Participants Thirty-eight young adults (28 female and ten male) aged between 18 and 35 were selected to participate in a psychological sensory wear trial, which


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Table 22.1 Basic description of selected aerobic wears

Average weight (g/m2) Average thickness (mm) Content (%)

N88P

C98L2

N85L15

R95C

P98L2

E95C

A92Np

N95C

280

179

215

260

220

240

360

410

0.8

0.7

0.6

1.1

1.3

1.0

1.1

1.5

P(88) S(12)

C(88) L(12)

N(85) L(12)

C(95) S(5)

P(98) L(2)

C(95) S(5)

N(92) L(8)

C(94) S(6)

C = Cotton, L = Lycra, N = Nylon, P = Polyester and S = Spandex.

was conducted in an environmentally controlled chamber. Each participant had a regular body build, which suits small- or medium-sized clothing.

22.2.2 Aerobic wear Eight types of aerobic wear were selected for the trial. Five of them were sold in the market as sportswear and the remaining three were sponsored by one of the Hong Kong fabric manufacturers. The basic description of the aerobic wear is given in Table 22.1.

22.2.3 Hand preference and wear trial Two out of eight tight-fit items were randomly selected and given to each subject to touch and feel with their hands. Subjects were asked to rate six sensations (cool, soft, smooth, prickly, scratchy, heavy) and overall perception of discomfort (discomfort) after they completed the hand judgement for the first sportswear. The rating scales used for hand evaluation and perception of clammy sensation, as an example, are shown in Figs 22.1 and 22.2, respectively. The subjects were then asked to repeat the same procedures for the second item of sportswear in order to complete a wear trial cycle. Each subject was asked to conduct three wear trials cycle in this study.

22.3

Result

22.3.1 Influence of subjects The influence of subject age and gender on perceptions of hand feel was analyzed using multivariate ANOVA. Three age groups were used: (1) 18–21, (2) 22–28 and (3) 29–35. In the selection of subjects a convenient


Sensory comfort of aerobic wear Very Aerobic wear Cool Soft

Neutral

I II

I II

I II

I II

371

Very I II

I II

I II Warm Hard

Smooth Prickly

Rough Smooth

Scratchy Heavy

Soft Light

Discomfort

Comfort

22.1 Rating scale for hand evaluation.

Time (min) 0

Sensation Very Clammy 3

Neutral

Very

2

1

0

–1

–2

–3

3

2

1

0

–1

–2

–3

3

2

1

0

–1

–2

–3

3

2

1

0

–1

–2

–3

3

2

1

0

–1

–2

–3

Sensation Dry

5 10 15 20

22.2 Rating scale for clammy sensation.

sampling method was used. The distribution of the three groups was as follows: (1) 18 (47%); (2) 12 (31.6%); (3) 8 (21.1%). There were 28 females and ten males included. The reason for this is that the wear trial consisted of eight garments in total. By using paired comparison, 28 subjects are required. As some of the garments are sold as active tight-fit wear for females only, this reduces the number of available garments for male subjects to five. By using paired comparison, only ten male subjects are required. Therefore, there is a difference in the number of subjects between the two genders. Subject gender had a significant influence (p < 0.05) on the perception of scratchy and prickly, which suggests that these perceptions are different between male and female with average ratings of 44 and 28, respectively. This indicated that males perceived a greater degree of scratchy and prickly sensations than females in this study. Also, the interaction between age group and gender had a significant influence on the perception of cool and soft.


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Clothing biosensory engineering

Table 22.2 Comparison of the mean value of four sensations between eight aerobic wears

Cool Soft Smooth Heavy

N88P

C98L2

N85L15

R95C

P98L2

E95C

A92Np

N95C

56.1 60.6 63.6 46.0

57.6 54.6 52.0 35.4

69.2 65.2 67.7 35.4

54.0 58.7 54.8 58.0

46.0 38.4 33.3 48.0

49.5 60.1 64.7 42.4

73.8 61.9 72.2 57.1

52.4 53.2 46.0 62.0

22.3.2 Influence of aerobic wear N85L15 and P98L2 were rated as the least and the most uncomfortable sportswear, respectively. Subjects found that N85L15 was able to provide the best sensations of cool, soft and smooth amongst the sportswear types. However, R95C gave subjects the least sensations of scratchy and prickly. They also found that N85L15 and N95C were the lightest and heaviest garments, respectively. By comparing the rating of each sensation in terms of sportswear, the result shows that the ratings of cool, soft, smooth and heavy were significantly different amongst the tested sportswear. There was no significant difference among the eight different items of sportswear rating on scratchy, prickly and discomfort sensations. Table 22.2 shows that A92Np and P98L2 had the highest and lowest average scores in relation to the sensations of cool, soft and smooth amongst the eight items of sportswear, respectively. This indicates that subjects are most and least satisfied with the performances of A92Np and P98L2, respectively, in terms of providing the sensations of cool, soft and smooth. Subjects found that C98L2 and N85L15 were the lightest sportswear in the hand evaluation. However, they found N95C was the heaviest garment. The ratings of heaviness of C98L2 and N95C agree with the objective measurement as they are weighted as the lightest and heaviest garments.

22.3.3 Preference of hand evaluation In order to obtain more information relating to subjectsâ&#x20AC;&#x2122; preferences on sportswear, each subjectâ&#x20AC;&#x2122;s preference score was calculated and used as an indication to describe subject preferences on sportswear. The preference score was calculated on the basis of a count of the number of subject preferences. As mentioned in the previous section, two types of sportswear were given to each subject to evaluate by hand and wear trial. At the end of each evaluation, the subjects were required to select one of the two items of sportswear as their preferred item, which would have a score of 1. The remaining sportswear was given a score of 0. Therefore, the sportswear with


Sensory comfort of aerobic wear

373

the highest preference score is the most preferred sportswear amongst the subjects. Furthermore, the preference percentage of each sportswear, which is equal to each sportswear preference score divided by the total number of times that it has been tested, was computed. As there were 28 pairs of garments and 38 subjects, in order to ensure that the scoring chances of each possible pair of garments were equal, the preference assessment was assigned to 28 females only. Figure 22.3 illustrates that around 50% of the subjects selected R95C, C98L2 and E95C as their preferred sportswear. However, fewer than 23% of the subjects selected P98L2 and A92Np as their preferred sportswear. This may be explained by the fabric physical property and structure of the sportswear. P98L2 was made of fabrics with rib knitted construction and A92Np was the heaviest garment. However, the above findings do not provide enough information on subject preference in relation to their perceptions. Therefore, the correlation between sensory perceptions and hand evaluation preference score was investigated. Results showed that the sensation soft and smooth have significant correlation with hand 16

100

Hand evaluation preference score

80 12 70 10

60 50

8

40

6

30 4 20 2

Cumulative percentage of preference score (%)

90

14

10

0

0 R95C C98L2

E95C N98L15

N95C

N88P P98L2

A92Np

Aerobic wear

22.3 Comparison between subjectsâ&#x20AC;&#x2122; hand evaluation preferences.


374

Clothing biosensory engineering

evaluation preference, suggesting that the subjects made their preference judgements largely based on the perception of softness and smoothness of sportswear.

22.4

Sensory comfort during exercise

22.4.1 Influence of time Figures 22.4a and 22.4b illustrate the behaviour of nine sensory perceptions and overall comfort over time. The subject responses to these sensory perceptions have clear increasing or decreasing trends. Subject perception of breathable, cool and overall comfort decreased considerably. On the other hand, the remaining seven sensations (clammy, sticky, damp, heavy, prickly, scratchy and tight) all have an increasing trend. Also, by comparing the average rating taken from the beginning and at the end of the running trial, clammy, sticky, damp and tight have relatively greater changes in comparison with heavy, prickly and scratchy.

A

80

Subjective perception rating

70

60

50

40 Clammy Sticky 30

Breathable Damp Cool

20 0

5

10

15

20

Time (min)

22.4a Subjective rating of thermal moisture-related sensations at different time period.


Sensory comfort of aerobic wear B

375

80

Subjetive perception rating

70

60

50 Heavy Prickly 40 Scratchy Tight

30

Overall comfort 20 0

5

10

15

20

Time (min)

22.4b Subjective rating of other sensations and overall comfort at different time period.

22.4.2 Influence of subject The investigation indicated that subject’s age, gender and the interaction between age and gender have significant influence on the perception of clammy. The average ratings of clamminess of females and males were 24.0 and 31.0, respectively. In terms of age group, the average ratings of age groups 1 and 2 were 28.9 and 22.6, respectively. Furthermore, the average ratings of female and male aged between 22 and 28 were 19.8 and 28.2, respectively. A multivariate ANOVA was carried out to investigate how subject’s age and gender influence other sensory perceptions. The results of the analysis showed that perceptions of clammy, sticky, damp, heavy, prickly, scratchy and overall comfort are significantly different for subjects at different age groups. For instance, the ratings of sticky for the subjects in the 18–21 and 22–28 age groups were 50.0 and 42.6, respectively. This result shows that subjects belonging to the 18–21 age group perceive greater sticky sensation than subjects belonging to the 22–28 age group. Gender has a significant influence on the ratings of all sensory


376

Clothing biosensory engineering

perceptions. For instance, the perception ratings of clammy were 45.4 and 55.1 for female and male subjects, respectively. The interaction of age and gender has significant influence on the perceptions of clammy, sticky, damp and heavy, which are moisture related. The average rating of clammy perception by females belonging to the 18–21 age group was 47.0. On the other hand, the average rating of clammy perception by males belonging to the 22–28 age group was 57.2.

22.5

Fabric properties and overall comfort performance

In the prediction of clothing comfort using the stepwise regression method, the not so highly related fabric properties were excluded from the model development. Six regression models, each with a different number of fabric properties, were generated. However, only the one with a meaningful model structure and high r2 and adjusted r2 values was selected, as shown in equation (22.1): overall comfort = −6.44 (MIU) + 2.24 (WC) + 2.44 (MWRL) + 1.15 (SMD) + 55.35

[22.1]

where MIU = frictional coefficient, WC = compressional energy, MWRL = maximum wetted radius (lower) and SMD = geometrical roughness. Regression model 22.1 consists of frictional coefficient, compressional energy, maximum wetted radius at the lower fabric surface and geometrical roughness. These physical properties can be represented as the dimensions of moisture, tactile and pressure in relation to clothing comfort, which indicates the major dimensions in the perception of clothing comfort. From previous findings, moisture and thermal sensations can be abstracted into a single sensory factor. Therefore, fabric properties which describe thermal sensation might influence the maximum wetted radius. The limitation of the model is that air permeability, thermal conductivity and thermal insulation were the only three objective measurements related to breathable and thermal sensations. These two types of objective measurements may not be good enough to describe the human perception of thermal comfort. Furthermore, in the investigation of the interrelationship between nine individual sensations, it was found that breathable and cool (thermal), were abstracted into the thermal–wet comfort factor. This suggests that they were correlated. The r2 and adjusted r2 values of regression model 22.1 were 0.974 and 0.940, respectively. Figure 22.5 illustrates that predictions generated from the model involving frictional coefficient, compressional energy, outer maximum wetted radius (lower) and geometrical roughness had the strongest linear


70

Experimental overall comfort score

65 y = 1.0005x – 0.0346 r = 0.987 60

55

50

45

40 40

45

50

55

60

65

70

Predicted overall comfort score

22.5 Fitness of garment overall comfort rating from model 22.1.

relationship with the experimental overall clothing comfort score compared with other models’ predictions. The experimental garment overall comfort rating was highly correlated with the calculated garment overall comfort rating. A good linear relationship was found between the calculated and experimental results, with r ≈ 0.987. Based on the above study, fabric physical properties can be used to predict subjective judgement of hand evaluation, garment pressure comfort, sensory factors and overall clothing comfort by using statistical methods. However, there are some weaknesses in this method. In the development of the regression model, a full set of 33 fabric physical properties’ measurements cannot be used, as it cannot cope with a large volume of information of subjective judgements on the same piece of fabric. Therefore, the fabric physical properties (independent variables) are selected subjectively in order to reduce the number of independent variables. Furthermore, the mean values of fabric physical properties’ measurements and subjects’ ratings have to be used to match the two data sets in the model develop-


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Clothing biosensory engineering

ment process. The consequence of this is that a large volume of information from the subjective evaluation of individual subjects has been lost. The size of the output of the data is also trimmed down from 168 to 8, as there are only eight garments in the trial, which limits the reliability of the models.

22.6

Comfort performance of different brands

The perception ratings are significantly different (p < 0.05) among the different garments in this study. For example, the ratings of clammy perception are 54.1, 41.8 and 47.4 for garments N88P, C98L2 and N85L15. This indicates that subjects perceive greater sensation of clamminess with N88P than C98L2 and N85L15. Subjects perceive most and least prickle sensation with P98L2 (49.3) and N95C (27.4), respectively. R95C (69.0) and N95C (50.7) are rated as the loosest and tightest garments, respectively.

22.7

Conclusion

This chapter illustrates human perception of individual sensations and overall comfort in aerobic wear through a series of fabric handle and wear trial processes. Based on this study, the following findings are obtained. 1. Fabric handling: • no significant difference between the selected aerobic wear rating on scratchy, prickly and overall discomfort sensations; • males perceived significantly greater degree of scratchy and prickly sensations than females; • subjects made their preference judgements largely based on the perception of the softness and smoothness of the aerobic wear. 2. Wear trial: • time clearly has significant influence on the perception of selected sensations and overall comfort; • the perception of overall comfort is linearly related to four fabric physical properties (1) frictional coefficient; (2) compressional energy; (3) maximum wetted radius (lower) and (4) geometrical roughness; • fabric physical properties can be used to predict subjective judgement of hand evaluation, garment pressure comfort, sensory factors and overall comfort.

22.8

Acknowledgements

The authors would like to thank the Hong Kong Polytechnic University for funding this research through project A174.


Sensory comfort of aerobic wear

22.9

379

References

1. Bardhan, M.K. and A.D. Sule, Anatomy of Sportswear and Leisurewear: Scope of Spandex Fibres. Man Made Textiles in India, 2001. 44(3): p. 81–86. 2. Chaudhari, S.S., Fibres for Active Sportswear Fabrics. Textile Magazine, 2003. 44(5): p. 57–60. 3. Dickson, M.A. and A. Pollack, Clothing and Identity Among Female In-line Skaters. Clothing and Textiles Research Journal, 2000. 18(2): p. 65–72. 4. Feather, B.L., D.G. Herr, and S. Ford, Black and White Female Athletes’ Perceptions of Their Bodies and Garment Fit. Clothing and Textiles Research Journal, 1997. 15(2): p. 125–128. 5. Harumi, M., N. Miyuki, M. Hideo, and K. Kiyokazu, Effects of Clothing Pressure Exerted on a Trunk on Heart Rate, Blood Pressure, Skin Blood Flow and Respiratory Function. Journal of the Textile Machinery Society of Japan, 2001. 54(2): p. 57–62. 6. Kathiervelu, S.S., Changing Sportswear Fashion Trends. Textile Magazine, 2002. 43(12): p. 65–69. 7. Markee, N.L., K.L. Hatch, S.N. French, H.I. Maibach, and R. Wester, Effect of Exercise, Garment Fabric and Environment on Cutaneous Conditions of Human Subjects. Clothing and Textiles Research Journal, 1991. 9(4): p. 47–54. 8. Muran, L., Trends in the Market for Sports and Other Performance Apparel. Textile Outlook International, 2003(104): p. 143–156. 9. Shiotani, T. and K. Hayakawa. New Fabrics and Technologies for the Active Sportswear Market, in Proceedings of the 37th International Man-Made Fibres Congress. 1998. Dornbirn, Austria, p. 13. 10. Susnik, J. and V. Zadnikar, Thermoregulation of Aerobic Dancers Dressed in Tracksuits from Cotton Fabrics and Lycra Fabrics. Tekstilec, 1992. 35(10): p. 453–457. 11. Wang, L.C., S.S. Soong, and J.J. Chen, Sportswear, Wearers’ Assessment. Textile Asia, 1995. 26(3): p. 86–89. 12. Wong, A.S.W., Y. Li, and X. Zhang, Influence of Fabric Mechanical Property on Clothing Dynamic Pressure Distribution and Pressure Comfort on Tight-fit Sportswear. Sen-i Gakkaishi, 2004. 60(10): p. 293–299. 13. Zhang, X., K.W. Yeung, and Y. Li, Numerical Simulation of 3D Dynamic Garment Pressure. Textile Research Journal, 2002. 72(3): p. 245–252.


Index

AATCC (American Association of Textile Chemists and Colorists) standards 134–5, 218–23, 229, 263 abrasion resistance 351 absorbency 218–20 Abusafieh, A. 126 acrylic 110–12, 118, 236, 352 active touch 50–1 activity levels 191 Adams, W.C. 202 aerobic wear 366–79 comfort performance 376–8 exercise 374–6 subject influence 375–6 time influence 374–5 experimental 369–70 hand preference and wear trial 370 participants 369–70 types of 370 fabric properties 376–8 result 370–4 fabric influence 372 hand preference 372–4 subject influence 370–2 aesthetic factors 4, 253 age influence 341 air permeability 252–60, 351, 354 humidity-dependent 258–9 measurement of 254–7 velocity 191 see also fiber and air moisture exchange Ajayi, J.O. 134 algorithms, genetic 185 Amano, T. 34 American Association of Textile Chemists and Colorists (AATCC) standards 134–5, 218–23, 229, 263 American Fundamental Interval 289 American Society of Heat and Ventilating Engineers 61

American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE) 62 standards 191 American Society for Testing and Materials (ASTM) standards 194–5, 210, 218–20, 229, 252–67 Ammons, R.L. 19, 169 ‘analytic’ touch 51 Andreen, J.H. 107 ANOVA (analysis of variance) 179, 230, 338, 356, 367, 370, 375 ANSI (American National Standards Institute) 220 ANSYS system 325 application 301–10 area of 301–2 consumer evaluation 307 consumer research 302–4 new product development 304–7 test methods 308–9 Armijo, C.B. 89 Army Research Institute of Environmental Medicine, US (USARIEM) 76 Arndt, J.O. 31 artificial intelligence (AI) 185 artificial neural network (ANN) 181, 185–6 Asia 309 ASTM (American Society for Testing and Materials) standards 194–5, 210, 218–20, 229, 252–67 attitude scales 16 Australia 22, 158, 262, 309 autonomic temperature regulation 65 Baik, D.H. 89 Bakkevig, M.K. 70 Bardhan, M.K. 350, 367 Barnes, P. 335 Bartels, V.T. 213 Beard, J. Taylor 89

381


382

Index

behavior thermoregulation 65 Behmann, F.W. 108, 132, 141 bending 124, 273, 276 Bendkowska, W. 213 Bhat, G. 351 biomechanics 313 bionic skin model (BSM) 239, 245–9 Bishop, D.P. 22, 52–4, 154, 170 tactile sensation and 119, 123, 129, 133 body locations 163, 343 body movement comfort 4 body-clothing-environment-system 69 body-fit sensations 156 Bolanowski, S.J. 40 Bornais, P. 9, 116 Branson, D.H. 20, 46 BREA 159, 168 breathable garments 93, 180–1, 184, 252, 254 breathable–airtight sensation 357, 359–60 Breckenridge, J.R. 76 Britain 12, 22, 154, 158 see also United Kingdom (UK) British Standards (BS) 255–6, 287, 289 Brown, D.M. 49 Brown, G. 96–7 Brucherseifer, S. 138 buckling 143 buffering 107–10, 238–9 environmental 110–12 Bumbarger, S. 89 bursting strength tests 351 Buxton, A.C. 200 Byrne, M.S. 22, 158, 170 CAD (computer-assisted design) system 312–13, 315, 330–2 Canada 2 canonical analysis 172–3, 180 canonical correlation 154 Carterete, E.C. 62 Casey, K.L. 103 Cassie, A.B.D. 107 Ceken, F. 351 Center for Research on Textile Protection and Comfort 200 Chan, A.P. 163 Chapman, C.E. 41 Charmchi, M. 237 Chato, J.C. 68 Chatterjee, S.M. 3, 20 Chaudhari, S.S. 366 Chen, D. 161 Chen, P.L. 21–2, 170 Chen, Y. 130 China 12, 62, 93, 154 Cho, G. 351 Chung, H. 351 Chung, T.M. 62

clamminess 107–10, 371 Clark, R.P. 47 clothing database 319–22 human factors and 313–14 preferences 169–71 pressure comfort 161–4 Clothing Physiology: Past, Present, and New Perspectives (lecture) 286 clothing sensory comfort see prediction of clothing sensory comfort cluster analysis 155–6, 167 comfort senses and 28–9 sensory descriptors 11–13 thermal 191–2 comfort perception and preferences 9, 167–77 calculation of 169–71 influences 168–9 relationship between 171–4 see also dimensions of sensory comfort perceptions Comfort’s Gestalt 9 Commonwealth Scientific Industrial Research Organisation (CSIRO) 262 Division of Wool Technology 23 compressibility 262 compression properties 273, 276 computer-assisted design (CAD) 312–13, 315, 330–2 conductivity, thermal 190, 194–5 Connell, L.J. 167 Connor, C.E. 40 ‘constant drying rate’ 79 constant-rate-of-extension (CRE) 264 ‘Consumer Color Preference Study’ (Pantone Inc) 167 consumer tests 307 consumer trends 1–2 convection 190 coolness 93–100 cool–hot sensation 357, 359–60 warmth and 194–8 Cooper, R.G. 304 correspondence analysis 154 cotton 60, 93, 107–11, 125–47, 236, 258, 370 see also denim products coupled heat and moisture transfer 235–51 buffering 247–50 PCM fabrics 245–7 results 239–45 role of phase change in PCM fabrics 238–9 simulation of 235–8 temperature gradient and 238


Index thermal and moisture buffering 247–50 role of phase change in PCM fabrics 238–9 see also moisture transfer Coutant, A.A. 89 Crank, J. 80, 82 Crank-Nicholson implicit finite difference technique 85 Creig, I. 302–3 Crosby, G. 335–6 Crow, R.M. 351 Crowther, E.M. 336 Cruz, A. 102 CSIRO (Commonwealth Scientific Industrial Research Organisation) 262 Division of Wool Technology 23 Curteza, A. 160 Cusick Drapemeter 121 dacron 147 Dallas, M.J. 49 damp–dry sensations 354, 356, 358 dampness 46–8, 101, 104–7 Darcy’s law 207 Darian-Smith, I. 40 Das, S.P. 123 Dastoor, P.H. 312 database, engineering 320–2 David, H.G. 12, 68, 100, 152, 207, 303 thermal comfort and 81, 83–5 Davidson, P.N. 52–3 Davis, K.D. 36 Dayal, R. 302 de Dear, R.J. 43, 45, 68, 100, 190, 195 ‘declined drying rate’ 79 Dellon, E.S. 33–4 Demichelis, R. 97 denim products 335–49 jeans study 337–8 garment pressure 340–8 hand feel 337–40 ideal jeans 337 subjective perception 336–7 Denmark 62 density 263 Denton, M.J. 143, 168, 312 desorption, sorption and 80–3 Dickson, M.A. 367 diffusion 84–5, 207, 210 see also Fickian diffusion dimensions of sensory comfort perceptions 151–66 clothing pressure comfort 161–4 individual sensations 151–2 sensory factors development 152–9 tactile comfort 160–1 thermal–wet comfort 159–60

383

DIN (German Research Institute) standards 308 discriminate analysis 154 Doke, S.S. 185 Doppler, Johann Christian 295–6 Downes, J.G. 81 dry–damp sensation 356–8, 360 drying behavior 79–80 duplex scanning 296 Dykes, R.W. 44 dynamic moisture permeation cell (DMPC) 210, 258–9 dynamic sweating hot plate 202 Edholm, O.G. 47 Eisner, M. 2 Elder, H.M. 53–4, 121, 127, 153, 169 electrocardiograph (ECG) 298 electroencephalograph (EEG) 298 Endrusick, T.L. 46, 104 environment 5, 191, 226 buffering and 110–12 Ethics in Human Research Committee 353 Euclidean distance 262 Euler column 117 Euler’s simple buckling theory 117 Europe 309, 335 European Fundamental Interval 28 European Physiology Modules (EPM) 295 European Union (EU) 335 evaluation menu 330 evaporation and condensation 80 exercise 120, 374–6 expert systems 185 ‘exploratory procedures’ 52 extensibility 263 fabric behavior 86–90 hand properties 20, 138, 178–9, 303 handling 53, 378 influence 372 menu 327 properties 84, 184–5 stretch 142–4 Fabric Assurance by Simple Testing (FAST) 97, 262 Fabric Smart Tactile Tester (FSTT) 264–7, 275–82 factor analysis 154 Fan, J. 163 Fanger, P.O. 62–4, 68, 286 Farnworth, B. 81, 202 Feather, B.L. 367 Fechner’s law 13–15, 146 fiber -assembly-structure engineering (FASE) 312 air moisture exchange and 79–83


384

Index

drying behavior 79–80 evaporation and condensation 80 sorption and desorption 80–3 fabric properties and 84 moisture diffusion 84–5 Fick, A. 81, 86 Fickian diffusion 81–2, 85–8, 236–7 finite volume method (FVM) 236 Finland VTT, Laboratory of Plastic and Fiber Technology 200–1 Finn, J.T. 212 Fiore, A.M. 48 FIT 159, 168, 180–1 fitness of garment 377 fleece 138 flexibility 262 Flexiforce load sensor 293 Florida 367 Fortran program 328 Fourier transform techniques 296 Fourt, L. 3, 74 friction 139–40 Friedman, H.H. 20, 170 Friedman, M.P. 62 Fris, M.M. 3 Fritz, A.M. 13, 21, 151, 170, 303 FSTT (Fabric Smart Tactile Tester) 264–7, 275–82 Fu, G. 68 Fujiwara, Y. 2, 167 Fukawaza, T. 208, 211–12, 214 FUKURAMI 129 functional tests 307 Fuzek, J.F. 19, 169 fuzzy comprehensive evaluation 154 fuzzy grade 131 fuzzy logic (FL) 185–6 Gagge, A.P. 61–3, 66–9, 75 garment influence 356–7 menu 327 performance 329–30 pressure 337, 340–8, 346–7 comfort and 141–7 Garnsworthy, R.K. 36, 116–17 Germany 308 Gibson, J.J. 51–3 Gibson, P.W. 80, 237 Gillum, M.N. 89 GLM (general linear modeling) multivariate procedure 275 Goldman, R.F. 75, 202 Golgi-Mazzoni bodies 51 Goodwin, A.W. 40 Gore-Tex 213, 258 Green, B.G. 102 Greenspan, J.D. 34, 40 Greenwood, K. 302

Gretton, J.C. 214, 238 Growther, E.M. 144 Gupta, D. 350 Gupta, K. 210 Gupta, S. 350 Gwosdow, A.R. 41 Ha, M. 47–8 Hamdi, M. 64 hand evaluation 371, 373 feel 337–40 preference 370, 372–4 Hand Evaluation and Standardization Committee (HESC) 11, 53, 151 Harada, T. 303 hardware and software 325 Harju, E.-L. 101 Harumi, M. 368 Hatch, K.L. 90, 107 Hawkins, D.I. 16–17 Hayakawa, K. 366 Hearle, J.W.S. 311 heart rate 297–8 heat balance 66–7 loss 189–91 moisture phase changes and 206–8 moisture diffusion into fiber 207 void space of inter-fibers 207–8 moisture transfer and 74–9 see also coupled heat and moisture transfer heating, ventilation and air conditioning (HVAC) systems 64 heaviness 340–1 Heller, M.A. 51, 309 Henry, P.S.H. 76–7, 81, 207 Hensel, H. 42–5, 62, 64–5, 191, 195 Hes, L. 97 hierarchical data model 322 Hirata, K. 45 Hock, C.W. 105 Hohenstein Institute 76 Holcombe, B.V. 69, 82–6, 96, 207, 237 Hollies, N.R.S. 3, 74–5, 105, 303 perception and 152, 156, 169 psychology and 11–12, 17–20, 23 Holmer, I.I. 107, 198–9, 202, 286 Homota, H. 34 Hong, K. 105, 108 Hong Kong 62, 336–7, 340, 370 Hong Kong Polytechnic University 201, 354 Hotelling’s Tsup 2 367 Houdas, Y. 295 Howorth, W.S. 11, 127, 152 Hu, J.L. 53, 123, 134, 178, 262 human body database (HDB) 320 human clothing system 5


Index human model menu 327 human perception analysis (HPA) 20 human-clothing-environment system 4–6 humidity 107–9, 191, 238–59, 289–92, 303, 351 see also relative humidity (RH) hydrophilic fabrics components 252 layers 212–13 polymers 238 hygroscopic fabrics 87–88, 101, 235, 258 fibers 60, 70, 95–6, 100, 106–12, 210, 237 hygroscopicity 69, 86, 94 Hyun, S.O. 20 Ibrahim, S.M. 141, 143 Imada, N. 45 Inamura, A. 145 inclusion model 124, 126–7 inflammation 35–9 information theory technique 146 infrared (IR) radiation 289, 296 Instron Tensile Tester 127, 134, 143, 320 insulation, thermal 192–3, 354 inter-fibers 207–8 International Research Institute 1 International Wool Secretariat 93, 350 interval scales 16 Isaji, S. 107 Ishibashi, T. 34, 41, 141 Ishii, M. 182 Ishtiaque, S.M. 352 ISO (International Organization for Standardization) standards 63–4, 191–4, 210, 290 isostrain 128 isostress 128 Italy 335 itch 120–1 prickle and inflammation 35–9 Ito, N. 133 Jacobsen, M. 21 Japan 53, 123, 154, 311, 335 Jena, A. 210 JIS (Japanese Standards Association) standards 210 Johansson, C. 33 Johnson, K.O. 41 Jones, B. 68 Jones, B.W. 68–9 Jung, C. 50 Junger, M. 293 Kalidindi, S.R. 126 Kamata, Y. 336 Kanetsuna, H. 238 Kang, T.J. 134 Kansei Engineering 7

385

Katz, D. 51 Kawabata evaluation system (KES) 27, 53, 129–39, 179, 269–83, 320, 353–4 KES-F 134, 141, 160, 262 KES-FB 117, 130 KES-FS 134 Kawabata, S. 97, 151, 179, 262 psychology and 11, 18 tactile sensation and 123, 129–31, 134 thermal functions and 197, 202 Kawabata Thermob apparatus 90 Keighley, J.H. 118 Kelly, G.A. 13, 20, 151, 170 Kenins, P. 47, 118 Kenshalo, D.R. 44 KES see Kawabata evaluation system (KES) Kim, C. 21 Kim, J.O. 209 Kimle, P.A. 48 kinesthesia 28, 50 Kirk, W.J. 141, 143 Klatzky, R.L. 52 Kleinschmidt, E.J. 304 Klement, W. 31 KOSHI (stiffness) 11, 123, 129, 152 Kosuge, K. 167 Kothari, V.K. 350, 352 Kozyreva, T.V. 30 Kraning, K. 294 Krausse endings 30 Kregers, A.F. 124 Krenchel, H. 124 Krenchel model 127 Kwok, Y.L. 2, 336 Laing, R.M. 41, 134 LaMotte, R.H. 41 Lau, L. 106, 120 Lederman, S.J. 40, 49, 52–3 Lewis, A.J. 75 Li, Y. 1, 3, 45–7, 69, 180–1, 285, 336, 352 moisture transfer and 207, 209, 235–7 perception and 154, 156, 158–9, 168–70, 172–3 psychology and 12, 18–20, 23 tactile sensation and 118, 120, 123, 129, 132 thermal comfort and 78–97, 105–11, 197, 199, 316 light–heavy sensation 357, 360 Lindberg, J. 262 linear modeling (LM) 154, 186, 275 linen 93 Linnaeus, Carl 286 liquid moisture transfer 218 liquid-moisture transmission 253 Lonsinger, K. 350 loose–tight sensation 357, 360 Lotens, W. 68


386

Index

LS-DYNA system 319, 325, 329 Luo, Z.X. 78, 85–6, 207, 236–7 Lycra 28, 367, 370 Lyons, D.W. 79 McAlister, K.C. 41 McCullough, E.A. 210, 252 McGillis, S.L. 34 McGinnis, John 17 McGinnis thermal scale 20 MacIntyre, L. 139 Mackay, B.H. 81 Mackay, C. 20, 53, 169 Mackeprang, J.F. 101 McNall, P.E. 62 magnitude-estimation technique 20 Mahler, F. 295 Makabe, H. 35, 145 manikins, thermal 198–201, 306 MANOVA (multivariate analysis of variance) 367 Markee, N.L. 367 marketing 304–7 Martens, H. 153 Martin, H. 180 master scaling procedure 102 material database (MDB) 320, 323 Mats Bohn, O.N. 199 Matsudaira, M. 117–18 Matsuo, T. 312 Matsuoka, T. 146 maximum absorption rate (MAR) 227 maximum heart rate 298 maximum wetted radius (MWR) 228, 230 Mazda company 311 Mazzuchetti, G. 97 measurement scales of 15–16 direct responses 16–22 mechanical parameters menu 328 mechanical sensory engineering design 311–30 example 327–30 design 327 garment performance analysis 329–30 modeling simulation 327–9 pressure comfort 330 functional requirements 313–16 3D virtual prototypes 315–16 clothing and human factors 313–14 computers and 314–15 sensory evaluation 316 mechanical engineering design system (MEDS) 316–27, 332 3D digital models 317 3D human-clothing contact models 317–19 clothing database 319–22 stimulation and perception 322

structural organization 325–7 hardware and software 325 processing algorithm 325–6 user interface 326–7 mechanical stimuli 32–42 dynamics of wear 32–3 prickle, itch and inflammation 35–9 roughness and scratchiness 39–42 touch and pressure 33–5 mechanical tactile properties 261–84 FSTT measuring 264–6 indexes definition 266 protocol 264–6 measurement of subjective tactile sensations 267 results of FSTT system 275–8, 280–1 KES system 269–75, 280–3 subjective sensations 267–9, 278–80 mechanoreceptors 30, 261 Mecheels, J.H. 75–6 MEDS see under mechanical sensory engineering design Mehrtens, D.G. 41 Meissner corpuscles 29–31, 39, 41 Meredith, M.A. 153 Merkel discs 29–31, 39 metabolic measurement carts (MMC) 296–7 Milenkovi, L. 253 Military Ergonomics Division (US) 76 Miller, G.A. 153 Miller, W.E. 61 Mitsuno, T. 144 models 3D digital 317 3D human-clothing contact 317–19 database (MDB) 320 hybrid 185–7 simulation 327–9 MOIS 159, 168 moisture comfort 181 diffusion 84–5, 207 exchange, heat and 206–8 resistance 352 sensations 156–7, 268–9, 271 see also thermal and moisture sensations sorption 86 moisture transfer 218–34 absorbency 218–20 contact angle 223 experimental 229–31 fabric 229 results and analysis 230–1 subjective perception 232–3 heat and 74–9 moisture management 223–9


Index apparatus structure 225–6 environment conditions 226 indexes definition 226–9 test method and apparatus 224–5 waterproof 221–3 wettability 220–1 see also coupled heat and moisture transfer moisture vapor permeability 192, 253 transmission rates (MVTRs) 89 Momota, H. 34 Morooka, H. 70 Morris, M.A. 336 Mukhopadhyay, A. 134 multiple factor analysis 154 Muran, L. 366 Murata, H. 210 Na, Y. 21 Nakajima, T. 46 Nakajima, Y. 30 Naruse, N. 50 natural language 185 Naylor, G.R.S. 117–18 Nef, U. 335 Neilsen, R. 46, 64, 100, 104 network data model 322 neural networks (NN) 183, 185–6, 352 neural responses 31–2 neurophysiology of sensory perceptions 6, 28–59, 314 basis of 29–32 neural responses 31–2 sensory receptors 29–31 skin stimuli 29 transduction 29 fabric hand 50–4 mechanical stimuli and dynamics of wear 32–42 prickle, itch and inflammation 35–9 roughness and scratchiness 39–42 touch and pressure 33–5 senses and comfort 28–9 texture 48–50 thermal and moisture 42–8 dampness 46–8 fabric sensations 43–6 functions 42 thermoreceptors 42–3 Ni, B. 238 Nielsen, R. 70 Nishimatsu, T. 145, 161 nitrogen sweep gas 211 Niwa, M. 11, 18, 123, 129, 151, 159, 179 nociceptors 30–1 nominal scales 15 Nordon, P. 81, 83–5, 207 Nordon-David model 86, 237

387

North Carolina State University (NCSU) 200, 202 NUMERI (smoothness) 11, 129, 134, 152 nylon 145, 147, 370 octadecane 351 Ogawa, T. 293 Ogawa, Y. 68 Ohhashi, T. 294 Ohio 367 Oke, L. 40 Okur, A. 136 Okura, K. 193 Olesen, B.W. 64, 100 Oliver, P.H. 11, 152 one-way transport capacity (OWTC) 228, 230, 232 ordinal scales 15 Osczevski, R.J. 213, 351 Osgood, C.E. 20, 170 overall moisture management capacity (OMMC) 228, 230, 232–3 oxygen consumption 296–7 Pacinian corpuscles 29–30, 51 Paek, S.L. 167 Pandita, S.D. 124 Pantone Incorporated 167 Pardeshi, P.D. 335 Park, S.I. 89 passive touch 51 Paul, R. 335 Pause, B. 88, 210 Pearson Correlation 269 Pearson Product Moment 367 Peirce, F.T. 127, 262 perception 9 see also comfort perception and preferences; dimensions of sensory comfort perceptions; neurophysiology of sensory perceptions performance, comfort 376–8 Personal Construction theory 13 Peykamian, S. 129, 178–9 phase change material (PCM) fabrics 238–47, 351 Phillips, D.G. 118 physical factors 5, 184–5, 306, 313 physics of thermal comfort 74–92 boundary conditions 83–4 fabric behavior 86–90 fiber and air moisture exchange 79–83 drying behavior 79–80 evaporation and condensation 80 sorption and desorption 80–3 fiber and fabric properties 84 heat and moisture transfer 74–6 in fabric 76–9


388

Index

solution method 84–6 fiber moisture diffusion 84–5 numerical 85–6 wool moisture sorption 86 physiological factors 6, 306, 314 physiological measurements, in vivo 285–300 heart rate 297–8 humidity 289–92 oxygen consumption 296–7 pressure 293–4 skin blood flow 294–6 sweating and skin wetness 294 temperature 286–9 infrared (IR) radiation 289 resistance thermometer detector (RTD) 288–9 thermocouple 287–8 physiology of thermal comfort 60–73 thermal comfort 61–4 thermal interaction 68–70 thermophysiology 64–6 thermoregulation 66–8 Pierce Foundation 75 Plante, A.M. 18, 46, 105 PMI diffusion meter 210 Pollack, A. 367 polyester 94–5, 97, 101–11, 125, 130–45, 370 textiles (PRT) 212–13 polymers 238, 258 polymid clothing 108 polypropylene 108, 236–7, 351 polytetrafluoroethylene (PTFE) 212–15 Pontrelli, G.J. 9 Powerknit 139–40 Prato, H.H. 336 predicted mean vote (PMV) index 63–4 predicted percentage of dissatisfaction (PPD) index 63 prediction of clothing sensory comfort 178–88 fabric hand 178–9 fabric physical properties and 184–5 hybrid models and 185–7 predictability 180–1 sensory factors 183 subjective measurements and 183–4 subjective preferences 181–2 predictions 100–1, 119, 363 preferences scores 174 subjective 181–2 see also comfort perception and preferences pressure 293–4 comfort 141–7, 180, 184, 197, 330, 344–5 clothing 161–4 garment 337, 340–8

sensations 156–7 touch and 33–5 Pressure-Specified-Sensory-Device 33 prickle 35–9, 116–20, 268–71 ‘primary-hand’ expressions 11 principle component analysis (PCA) 154 processing algorithm 325–6 menu 328–9 product database (PDB) 320, 323 properties fabric 184–5, 376–8 softness 130 transport 354–6 prototypes, 3D virtual 315–16 Przybysz, A. 213 psychological factors 6, 314 research techniques 9–11 sensory studies 305–6 psychology and sensory comfort 9–27 comfort sensory descriptors 11–13 perception of comfort 9 psychological research techniques 9–11 psychophysics 13–15 scales of measurement 15–16 direct responses 16–22 wear trial techniques 22–4 psychophysics 13–15 perceptual trials 305–6 psychosensory intensity (PSI) 45, 97 radiation 190 Rae, A. 84 Raheel, M. 262 Ramgulam, R.B. 134 random tumble pilling tester 136 rank correlation 154 Rasmussen, D.D. 32 ratio scales 16 Raychaudhuri, B.D.S. 123 receptors 50–1 recovery heart rate 297 relational data model 322 relative humidity (RH) 100, 106, 109, 290–2 Ren, Y.J. 211 Renfield, M. 32 resilience 263 resistance thermometer detector (RTD) 288–9 resting heart rate 298 Reuss model 124, 126–7 Ring, E.F.J. 295 Ring, K. 43, 45, 195 Risvik, E. 153 Riu, P.J. 102 Rodwell, E.C. 107 Rohles, F.H. 61 Rollo, B. 84 Rømer, Ole Christensen 286


Index Rossi, R.M. 212 roughness and scratchiness 39–42, 131–41 Ruckman, J.E. 60, 211, 215 Ruffini endings 29–30, 51 Rupp, J. 350 Rust, J.P. 129, 178–9 Samba pressure measurement system 293 Sanyal, P. 350, 352 SAS software 171 sateen 118 scales of measurement 15–16 for direct responses 16–22 Scheffe’s Comparison Method 340 Scheibner, W. 138 Scheurell, D.M. 105, 108 Schiff, W. 51, 309 Schmelz, M. 31 Schneider, A.M. 19, 45, 84, 94–6, 169 Scott, C. 294 scratchiness 39–42, 131–41 seam pucker 134, 135 semantic differential scale 20–2, 145, 303 semi-permeable functional films (SPFF) 209 senses and comfort 3–4, 28–9 Sensory Engineering 7 sensory factors 152–9, 183 sensory receptors 29–31 Shamsuddiny, A.K.M. 294 Shanmugam, N. 185 SHARI (crispness) 11, 152 Sharma, I.C. 136 shearing properties 272 Shi, X.K. 238 Shimizu, Y. 35, 145 Shiotani, T. 366 Shirley cantilever 121 Shirley Cyclic Bending Tester 121 Shitzer, A. 68 silk 93, 136 Silverman, D. 1 simulation 327–9 Sirolan-Laserscan equation 119 skin blood flow 294–6 inflammation 39 stimuli 29 strains 142–3 -stretch 144 temperature 196–8, 288 wetness 294 Slater, K. 3, 10, 74 Sleeknit 139–40 Smart Tactile Tester (STT) 266 Smith, C. 68 Smith, J.E. 3 smooth–sticky sensation 357–8, 360 smoothness 131–41, 268–71 soft–prickle sensation 357, 360

389

softness 127–30, 268–70, 340–1 SOFUTOSA 129 Solver menu 328 Sone, Y. 293 sorption desorption and 80–3 wool moisture 86 Spandex 370 Spence, V.A. 295 Spencer-Smith, J.L. 108 sportswear see aerobic wear; tennis wear Sportwool 350 Spray, D.C. 43 spreading speed 228 ‘Standard Definitions of Terms Relating to Textiles’ 12, 152 standard effective temperatures (SET) 63–4 standards 199, 218–25, 229, 263 Stapel scale 21 Statistical Package for the Social Sciences (SPSS) 230, 267, 356 Stein, B.E. 153 step-wise regression method 3 Stevens’ power law 15, 46, 53–4, 88, 121, 154, 179 logarithmic linear relation 127 Stevens, S.S. 14, 23, 46 stiffness 121–7 stimulation and perception 322 Stipe, M. 167 Stogdon, R. 335 strain 142–3 stretchiness 340–1 Stuart, I. 68, 101 subject influence 370–2, 375–6 subjective sensations 267–9 Sukigara, S. 34, 41, 141, 159–60 Sule, A.D. 350, 367 Suresh, M.N. 312 surface contour and friction 263 properties 274, 277 response average (SRA) 129, 179 Susnik, J. 367 Susta, D. 350 sweating 67 cylinder 201 plate 202–3 skin wetness and 294 Sweeney, M.M. 20, 46, 170 Sympatex 258 ‘synthetic’ touch 51 synthetic underwear 107 TACT 159, 168 tactile comfort 160–1, 180–1, 184, 197 see also mechanical tactile properties tactile sensations 116–50, 156–7 garment fit and pressure comfort 141–7


390

Index

itchiness 120–1 prickliness 116–20 smoothness, roughness and scratchiness 131–41 softness 127–30 stiffness 121–7 Taiwan 368 Takagi, K. 293 Takenaka, T. 238 Tarafder, N. 3, 20 Teflon impression method 36 temperature 191, 241–7, 286–9, 303 gradient 213–15, 238 regulation 65 skin 196–8, 288 tennis wear 350–65 experimental 353–4 participants 353 types of 352–4 wear trial 354 results and findings 354–63 comfort dimension 360–1 comfort prediction 363 individual sensations 357–60 time and garment influence 356–7 transport properties 354–6 thermal–wet and discomfort 361–3 tensile properties 272 THER 159, 168 thermal and moisture sensations 42–8, 93–115 clamminess and buffering 107–10 coolness 93–9 dampness 46–8, 104–7 environmental buffering 110–12 fabric 43–6 functions 42 thermoreceptors 42–3 warmth 99–104 thermal factors character 184, 263 comfort 61–4 see also physics of thermal comfort properties 274, 277 resistance 253 sensations 156–7, 268–9, 271 thermal-fit 181 thermal–wet comfort 159–60, 180, 184, 197 thermal properties 189–205 comfort 191–2 conductivity 194 coolness and warmth 194–8 evaluation apparatus 201–3 sweating cylinder 201 sweating plate 202–3 heat production and loss 189–91 insulation 192–3 interaction 68–70

manikins 198–201 moisture sensation 374 thermal sensory engineering design 330–2 process and system (TSEDS) 330–1 application 331–2 thermocouple 287–8 thermophysiology 4, 64–6 thermoreceptors 30, 42–3, 195 thermoregulation 65–8 Thiry, M.C. 352 Thurstone’s law 19 time influence 356–7, 374–5 Togawa, T. 294 Tokura, H. 108 Tokyo 167 Toma, S. 30 Tong, W.C. 62 touch 50–1 touch and pressure 33–5 traditional statistics (TS) 186 transduction 29 transport properties 354–6, 362 Tsup 2, Hotelling’s 367 Tuchida, K. 110 Tull, D.S. 16–17 Twycross, R. 39 ultrasound 295 Umbach, K.H. 75, 107, 213, 306, 308 United Kingdom (UK) 200 Ministry of Defence 221 see also Britain United States of America (USA) 12, 62, 75, 154, 200, 335, 350 Army Laboratories 17, 76, 198 Department of Defense 221 Urquhart, A.R. 84 user interface 326–7 Vatsala, R. 309 Veitch, C.J. 117 Verpoest, I. 124 viscose 136 Visual Basic 326 visualization 315–17, 322 Voigt model 121, 124, 126–7 Vokac, Z. 107 Vollers, C.T. 79 Volrath, L. 180 volume of fraction (VOF) technique 236 Wang, G. 120, 352 Wang, L.C. 368 Wang, Z. 88, 97, 209, 235–6 warmth 99–104, 194–8 water repellent (WP) fabric 351 water vapor permeability (WVP) 209, 212, 236 transfer 206–17


Index moisture phase changes 206–8 moisture diffusion into fiber 207 void space of inter-fibers 207–8 temperature gradient and 213–14 comparisons 214–15 transmission rate (WVTR) 210 waterproofing 192, 221–3 Watt, I.C. 81–2, 86 wear, dynamics of 32–3 wear trials 120–3, 146, 303–5, 307, 351, 370, 378 techniques 22–4 Weber, Ernst 14 Weber-Fechner laws 53–4, 97, 154 Weber’s law 14–15 weft knit 139–40 Wehner, J.A. 81, 108, 210 weight average model (WAM) 124, 126–8 weighted Euclidean distance 154 Westerman, R.A. 35 wettability 220–1 wetting time 227 Wilcoxon-Sign-Rank Test 19, 169 Wildhaber, J. 335 Wilson, C.A. 41, 134 Winakor, G. 21, 170, 303 wind proofing 192 wind resistance 254 Wissler, E.H. 68 Wong, A.S.W. 1, 159–60, 335, 352, 369 perception and 168, 170, 173 prediction and 179–81, 183, 186 psychology and 12, 19, 24

Woodcock, A.H. 75 wool 60, 93–120, 134, 145, 236–7, 352 moisture sorption 86 Wool Technology 23 Woolmark Company 93, 350 Woolmark Total Easy Care label 2 wrinkle 134, 143 -free treatments 120, 122 grades 135 resistance 340, 340–1 WRONZ 117 Xenon (radioactive tracer) 295 Xin, B. 136 Xu, B. 140 Yaglou, C.P. 61 Yamakawa, M. 107 Yan, H. 178 Yarnitsky, D. 31 Yi, L. 90 Yoneda, M. 97 Yoshida, M. 154 You, F. 146, 163 Young’s modulus 117, 124, 126 Zadnikar, V. 367 Zhang, X. 318, 337, 368 Zhao, Q. 161 Zhu, Q. 236 Zielinski, J. 213 Zimmermann, R. 335

391