insights into the participants’ interactions as to how many elements along the way they can remember or which intended viewpoints really have been consumed. Future studies ought to consider the ecological validity and its importance for real-world trials. A potential might be the development of 3D reference models for more flexible gaze analyses and more realistic result graphics. Merging real-life eye movements into 2D reference images or reducing it to statistics will not define the future of this meaningful method. The result graphics mentioned above are being developed and the next steps will be presented in early 2017. Being able to see and understand what catches a person’s eye and the response will be a guide to better design.
2.
Suitability of physiological sensors for multisensory comfort assessment
Trevor Keeling, Etienne Roesch, Derek Clements-Croome BuroHappold Engineering, London W1T 1PD School of Built Environment, University of Reading, Whiteknights, Reading RG6 6AY, UK Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Whiteknights, Reading RG6 6AH, UK trevorkeeling888@gmail.com Keywords: physiological sensors, multisensory comfort, acoustics, wearable sensors. Summary: We present the results from a study that tests the suitability of wearable sensors for multisensory comfort assessment. Participants’ heart rates and skin conductivity are measured while they work at their desk in their own office. This is combined with measurement of background light levels, temperature, CO2 and sound levels. Introduction: To define multisensory environmental experience singular environmental measures must be combined (Clements-Croome, 2013a). Most multisensory studies aim to understand the interplay of the senses by assessing their effect on a common outcome, often combining measurement of background levels (Cao et al., 2012; Huang et al., 2012) using a bottom up approach to predict their effect on a common outcome such as environmental satisfaction. In this way, these approaches all account for multisensory experience by weighting of the single senses and summing these into a single one-dimensional outcome. Typically through a bottom up, multiple linear regression type approach (Gadotti & Albatici, 2016). An alternative approach is to contrast the qualitative differences of the senses (Pallasmaa, 2005). Pallasmaa (2005) suggests that it is not possible to combine and sum different sensory experiences, rather it is the balance and contrast of the different senses that makes an experience what it is. For instance he describes vision as detached and abstract and a snapshot in time, whereas sound is intimate, situated and can only be comprehended in terms of its unfolding in time. For him, understanding the contrasting,