Creating the next generation of health wearables
2021 RESEARCH PORTFOLIO Energy Harvesting & Storage • Low Power Sensing Low Power Systems-on-Chip • E-Textiles • Engineered Systems
Energy Harvesting & Storage Body heat Body motion Biochemical Wireless
Low Power Sensing Physiological Biochemical Environmental Low Power Systems-on-Chip Low power electronics Low power radios Body optimized antennas E-Textiles Sensor integration Performance Manufacturability Engineered Systems Hard and soft wearables Textiles Data
Energy Harvesting & Storage 6 7 8 9 10 11 12 13 14
Flexible Thermoelectric Generators for Body Heat Harvesting Modeling of Thermoelectric Generators for Body Heat Harvesting Self-Powered Smart Insoles for Balance and Gait Detection New Mode of Mechanical-to-Electrical Energy Harvesting Printing of Stretchable Conductors Enabled by Highly Tunable Multiphase Liquid Metal Pastes Development of Sweat BioCapacitor for Self-Powered Multimodal Metabolite Sensors Ultrasonic Energy/Data Transfer for Implantables High Energy Density Lithium-Ion Capacitor for Wearable Technologies High-Performance Three-Dimensional Thin-Film Thermoelectric Generators
15 Low Power Sensing 16 17 18 19 20 21 22
TABLE OF CONTENTS
Ultra-Low Power Metal Oxide Electronic Nose Arrays for Environmental and Breath Monitoring Capacitive Micromachined Ultrasonic Transducer based Gas and Breath Sensing Multimodal Biosensing System for Electrochemical and Photonic Monitoring of Health Novel Biomaterials and Bioelectronics for Implantable Cardiovascular Therapies Ultra-Low Power PhotoPlethysmoGraphy (PPG) for Wearable Applications Potentiometric Detection of Neuropeptides for Non-Invasive Monitoring of Stress Improving the Performance and Design of Potentiometric Biosensors for the Detection of Extracellular Histones in Blood with Deep Learning Use Case: Long-Term Wound Monitoring with Multimodal Patches Zero-Power Wearable Sweat Assays and Long-term Sensing Device Interfaces Using Osmotic Pumping and Paper Microfluidics
25 Low Power Systems-on-Chip 26 27 28 29
Ultra-Low Power System on Chip (SoC) Wireless Wakeup for Energy Reduction in Plugged-In MELs (Miscellaneous Electric Loads) Novel, Flexible, High Efficiency and Multifunctional Wearable Antennas Body Worn Flexible Antenna for Applications in Communication and RF Energy Harvesting
31 E-Textiles 32 33
Transformative Textiles Designs for Self-Powered, Multi-Modal Sensing Garments Method of Automated Handling of Textiles for Improved Efficiency and Accuracy to Enable E-Textile Manufacturing Novel Textile-Based Sensors for Inner Prosthetic Socket Environment Monitoring
35 Engineered Systems 36 37
38 39 40 41
Correlated Sensing of Health and Exposure for Personalized Asthma Monitoring Health and Exposure Tracker (HET): Integration and Demonstration of a Modular Wearable Monitoring System Self-Powered Platform for Cardiovascular and Asthma Monitoring SenSE: AI-Driven, Resilient and Adaptive Monitoring of Sleep (AI-DReAMS) Bio-Electro-Photonic Microsystem Interfaces for Small Animals Cough Detection Using Wearables and Embedded Machine Learning
FROM OUR CENTER DIRECTOR: Since 2012, the NSF-funded Center for Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST) has continued to lead the way in developing flexible, self-powered wearable devices that enable continuous monitoring of both personal health and personal environment. These devices are
Our research team is multidisciplinary, multi-university, and clinically engaged, with partnerships in the medical field helping us to validate our systems. For example, we are
monitoring a variety of chronic health diseases, and the data are being generated to support physicians and patients. Recently, we also added implantable devices to provide
partnering with the University of Miami for monitoring wounds and with East Carolina University Medical School for kidney
additional continuous monitoring capabilities.
transplant monitoring. Our research is highly entrepreneurial, with spinouts representing our most successful technology transfer and commercialization avenue to date. ASSIST
The ASSIST Center is led by NC State and includes Florida International University, Penn State University, and the Universities of Michigan, North Carolina, Notre Dame, Virginia, and Utah as
launched two more companies last year, bringing its total to 10 startups. Alongside our research mission, we are also training a
partners. We maintain our technical leadership in five theme areas, i.e., energy harvesting and storage, low power sensors,
pipeline of students, from K-12 to doctoral levels, for next generation leadership in health and technology.
low power electronics, electronic textiles, and engineered systems and data analysis. This inaugural issue of the annual research brochure highlights key accomplishments in 31 projects
As we enter our tenth year, we look forward to graduating from NSF and moving towards self-sufficiency. We have added
spread among the five themes. Together, these research projects successfully drive the demonstration of multiple self-
several new research projects through new funding, and we are expanding our industry membership portfolio. We are pleased to have Medtronic, Biostrap, Triad Semiconductor,
powered wearable systems with multimodal sensing capability. We are very proud of our recent accomplishments in systems integration as well as in many cutting edge technologies. Our health and environment monitoring systems for monitoring
Vadum, and Olftech join us as our newest members. As you review this research portfolio, I invite you to reach out to us for potential collaboration opportunities.
asthma and metabolism, and our self-powered cardiac systems for vigilant ECG monitoring, provide unique capabilities. We have built ultra light and flexible wound monitoring patches.
Finally, COVID has shown that the outcomes for many infected with the virus were significantly worsened if they had chronic diseases. Join us in our mission to build always-on devices that help us manage chronic conditions and improve our health and resilience.
Several of our wearable systems are in clinical validation studies. This past year, even with COVID restrictions, we made significant accomplishments in developing novel flexible materials properties for thermoelectric generators (TEGs), which are now more flexible than ever with record-high power levels. With two recent NSF Partnerships for Innovation grants, ASSIST is now focusing on manufacturing TEGs and capacitive
Sincerely, Dr. Veena Misra Distinguished Professor, NC State University
micromachined ultrasonic transducers (CMUTs). The CMUTs are being used for sensing and ultrasound energy transfer in
Director, ASSIST Center
implantables. Our biochemical sensing portfolio includes several biomarkers as well as the ability to collect sweat and other fluids passively and at zero power.
FROM OUR INNOVATION ECOSYSTEM DIRECTOR: In the pages that follow, you’ll see research that is more than academic. It targets real-world use cases, with the goal of translating a new generation of wearable technologies from the university to the clinic, the battlefield, and the market. Achieving this requires partners and supporters from across the
Onda Vision Technologies earned their first SBIR award. We were awarded a Partnerships for Innovation grant to explore market opportunities for ASSIST's flexible thermoelectric generator (TEG) technology. We also ramped up translational prototyping, creating demonstration devices for technology showcases and conferences. Finally, we expanded discussions with member companies to advance ASSIST technologies and evaluate commercialization opportunities.
university, large and small companies, clinics, and supporting organizations. These partners create an Innovation Ecosystem where research drives impact. The impact goes beyond commercializing technologies. It includes transferring knowledge, preparing students, and fostering new ideas and opportunities from relationships.
In the coming year, we will be providing our member companies with new engagement opportunities, providing access to even more research, and driving commercialization of ASSIST technologies in new ways. We also look forward to expanding our ecosystem with new members to generate even greater impact.
Some of our most important relationships are with our industry members. ASSIST membership provides organizations access to intellectual property, preferred commercialization opportunities, engagement with faculty and students, and networking with other organizations in the same technology and application spaces.
While many of you are already part of ASSIST, if your company or organization is not yet involved but aligns with our research areas, or if you’re interested in joining the ecosystem in other ways, we’d welcome a conversation, and we invite you to join us.
In addition, our members participate in the Center’s growth. This past year, we welcomed Medtronic, Biostrap, Olftech, Triad Semiconductor, and Vadum as new member companies. We expanded the breadth of research we share with our members to include projects funded outside of ASSIST. We built new opportunities for industry member engagement within the Center, holding member company seminars, introducing member companies’ products to students and faculty, and providing access to student recruiting opportunities. With ASSIST support, member company
Sincerely, Dr. Adam Curry Innovation Ecosystem Director, ASSIST Center
ASSIST Member Companies
FROM OUR EDUCATION DIRECTOR: Over the past 10 years, ASSIST's education programs have engaged students from middle school to graduate school in a variety of innovative programs focused on technical, professional, and translational skills. At every level, we offer hands-on, immersive experiences that spark students' curiosity and propel them towards future career success. The Wearable Device Challenge Competition for Middle and High School Students challenges teams to design and build a wearable device to address an issue at the intersection of human, animal, and environmental health. This event has grown every year since its inception in 2015, with teachers from local schools near NC State, Penn State, and University of Virginia coaching teams and hosting local competitions.
Graduate students are one of the Center's greatest assets, and we are very proud to be a foundation and springboard for their future success. In addition to research, our Translational Engineering Skills Program (TESP) aims to provide graduate students with key skills for future success including: systems thinking, entrepreneurship, industry experience, mentoring and leadership, communication skills, ethics, diversity and inclusion, and more. We work closely with our innovation ecosystem to provide students opportunities to engage with industry partners. To date, the Center has graduated over 80 PhD students who have gone on to successful and impactful careers in both academia and industry.
Between 20 and 30 teams of students compete in the final event hosted at NC State in April of each year. Their unique designs demonstrate understanding of the engineering design process and as well as remarkable creativity and technical accomplishment. ASSIST's Young Scholars (YS) and Research Experiences for
Teachers (RET) programs provide immersive summer research opportunities for high school students and middle and high
Dr. Elena Veety Assistant Teaching Professor, NC State University Education Director, ASSIST Center
school teachers. To date, we have hosted more than 200 Young Scholars and teachers in our labs, giving them a unique opportunity to experience life on a college campus and in a research laboratory. In addition to conducting an independent research project, participants also design and build their own wearable device and participate in a variety of technical and professional development activities. For undergraduate students, ASSIST provides 10-week summer research opportunities as well as academic term research fellowships. These programs allow students to apply their theoretical classroom knowledge to an independent research project under the mentorship of a graduate student and faculty advisor. ASSIST also sponsors projects in Capstone Design Courses, providing senior-level engineering students practical engineering challenges focused on ASSIST's strategic technical thrusts. At the curriculum level, ASSIST has developed a multidisciplinary minor program in nano-science and technology.
Energy Harvesting & Storage
Flexible Thermoelectric Generators for Body Heat Harvesting Objective:
The objective of this program is to build high-performance flexible thermoelectric generators, which can conform to the human body, providing both performance and aesthetics. Thermoelectric generators (TEGs) that can convert body heat to electricity are of interest to realize selfpowered wearable sensor systems, which can provide hassle-free, long-term, continuous monitoring. Such devices can significantly improve management of chronic diseases such as cardiovascular diseases and increase patients’ quality of life.
Our group was the first to propose flexible TEGs that relied on rigid thermoelectric pellets used in commercial rigid TEGs. Our group was also the first to propose the use of liquid metal interconnects in flexible thermoelectric generators. To date, our team has developed several new elastomer composites to improve the thermal engineering of our devices, including a high thermal conductivity elastomer for EGaIn encapsulation and a low thermal conductivity elastomer between the pellets for reduced heat leakage.
Our unique patented approach employs industry standard rigid semiconductor pellets, which are embedded in a
Our latest devices provide best-in-class performance among published flexible generators and rival the performance of rigid thermoelectric modules, providing a continuous supply of energy to realize self-powered
flexible elastomer. The pellets are connected in series using Eutectic Gallium-Indium (EGaIn) liquid metal interconnects, which provide excellent flexibility, stretchability, and electrical conduction. By employing rigid pellets currently used in commercial rigid TEGs, the approach eliminates the
wearable monitoring devices.
need for new thermoelectric material development, thereby providing a low-cost-of-ownership flexible TEG option to existing TEG manufacturers.
A cross-sectional image of a flexible thermoelectric generator made with high thermal conductivity encapsulation and liquid metal interconnects (left). A data acquisition system to capture power generated by a flexible TEG under various modes of activity (right).
Principal Investigators: Dr. Mehmet Ozturk, Dr. Daryoosh Vashaee, Electrical & Computer Engineering, NC State University Dr. Michael Dickey, Chemical & Biomolecular Engineering, NC State University
Dr. Farzad Mohaddes Dr. Yasaman Sargolzaeiaval
NSF ASSIST Center NSF PFI program
Modeling of Thermoelectric Generators for Body Heat Harvesting Objective:
The objective of this program is to develop efficient analytical models to accurately predict the performance of thermoelectric generators (TEGs) on the human body under various contextual scenarios. Thermoelectric generators that can convert body heat to electricity are of interest to realize self-powered wearable sensor systems, which can provide hassle-free, long-term, continuous monitoring. Such devices can significantly improve management of chronic diseases such as asthma, cardiovascular disease, and diabetes, increasing patients’ quality of life.
Our model published in 2016 was the first 3-D analytical model for TEG simulation. These simulations were complemented by 3-D numerical simulations using TM COMSOL simulation environment. Our latest model, which is a first of its kind, includes the impact of the human thermoregulatory system and physical activity.
Impact: Our latest analytical model can not only guide design engineers in improving their device design, but also provide the ability to factor in the user’s age, sex, weight, and height. As an example, our results indicate that older individuals will generate approximately 30% less power
than younger ones, which is significant because older users will likely benefit significantly from self-powered operation enabling continuous, long-term monitoring. The modeling
Our approach includes the use of both highly-efficient analytic and 3-D numerical simulations. The analytic models that we develop provide the ability to quickly understand the impact of different design parameters on device
of the impact of physical activity is especially important for sports performance monitoring.
performance, while the 3-D numerical models provide a more in-depth understanding of the TEG operation. The models include the contributions of the device architecture, physical dimensions, thermoelectric materials, and parasitic thermal/electrical resistances. The models are also designed to take into account the impact of the human thermoregulatory system, which determines the metabolic rate and core body temperature, allowing time-dependent simulations of the TEG output.
Excellent agreement is obtained between measured and calculated output voltages of a flexible TEG during walking/running at different speeds. Calculated values include the rise in metabolic rate and core body temperature, as well as convective cooling, at different speeds.
Principal Investigator: Dr. Mehmet Ozturk, Electrical & Computer Engineering, NC State University
Dr. Farzad Mohaddes NSF ASSIST Center Dr. Yasaman Sargolzaeiaval
Self-Powered Smart Insoles for Balance and Gait Detection Objective:
This project seeks to develop a self-powered sensing array in a shoe insole for gait detection. The larger aim is to develop technologies to provide accurate, just-in-time assessment of risk of injury for conditions in which balance, gait, posture, and
We have fabricated our first piezoelectric-on-foil sensing arrays and tested them on a material testing machine. We have thus demonstrated the basic pressure sensing technology. We have demonstrated our first energy
physical activity are key predictors. Such conditions include, but are not limited to, old age, physically demanding job spaces, Parkinson’s disease, and diabetes.
harvester and tested it in a shoe. It can produce approximately 20 milliwatts under normal walking conditions. Finally, we have fabricated the silicon pyramid arrays needed for the transition to flexoelectric transducers.
Approach: Our approach is two-fold: 1) develop thin film piezoelectricon-foil pressure sensing arrays to be inserted in the shoe insole
and 2) power these via an energy harvester placed in the heel of the shoe, eventually moving to just the insole. Many
Tens of millions of people have an elevated risk of falling, including the elderly, persons with mobility disorders, and individuals with diabetes. Falls resulted in 3 million emergency room visits and 28,000 deaths in 2014 in the United States. The
smart insoles for use in clinical environments exist. However, these are expensive and power hungry, limiting their potential outside the clinic. By using piezoelectric-on-foil technology,
annual cost burden is estimated to be $50B. This work will enable long-term studies on the relationship between gait
we aim to create a very low-power sensing array that can be self-powered and therefore used for longer term studies
patterns and injuries from falls. Our ultimate goal is for the technology to be used in real-time assistive systems to predict risk of fall and warn users before falls occur.
outside the clinic and eventually as an assistive care product. The energy harvester is initially being developed with lead zirconate titanate (PZT) piezoelectric transducers. However, we will transition to a new technology, space-charge enhanced flexoelectric transducers, that are lead free, potentially very low cost (i.e., made from silicon), and have higher electromechanical coupling.
Flexoelectric transducers (left) embedded in the heel of the shoe will provide self-powered operation of the piezoelectric thin film sensing arrays (right) distributed throughout the shoe insole, enabling long-term real-time assessment of gait and balance.
Principal Investigators: Dr. Susan Trolier-McKinstry, Materials Science & Engineering, Penn State University Dr. Shad Roundy, Mechanical Engineering, University of Utah
Issak Allaire-McDonald Sujay Hosur Travis Peters
NSF ASSIST Center
New Mode of Mechanical-to-Electrical Energy Harvesting Objective: used commercially in supercapacitors to store large amounts of energy. Whereas conventional supercapacitors use rigid, porous carbon electrodes to store energy, we propose to make such electrodes out of stretchable liquid metal to generate energy because of liquid metal’s ability to change geometry. When the geometry changes (due to mechanical energy input), the capacitance changes, and charge moves through a circuit as electricity.
Mechanical energy is often wasted (i.e. not harvested) in the form of vibrations, wind, and ocean waves. We seek a new, simple approach to energy harvesting by creating a new type of “variable area soft capacitor” in which any mechanical inputs (compression, stretching, twisting, etc.) can generate electricity. The device is built entirely from soft materials, thus making it compatible with the human body and wearable devices. It also is distinguished by the use of salt water, making it compatible with harvesting energy in the ocean or in the presence of sweat.
Key Accomplishments: We have published a paper in Advanced Materials (Vallem, et al. ‘A Soft Variable-Area Electrical Double Layer Energy Harvester’) that shows how combining gallium alloys with
Approach: We convert mechanical motion to electricity using a completely new soft “variable area electrochemical
hydrogels can create a device that converts motion to electricity.
supercapacitor.” Mechanical input from stretching, deforming, or oscillating the device causes charges to move (i.e., generate electricity) in/out of a circuit. The idea is to utilize a non-toxic metal alloy with a low melting temperature as a stretchable electrode to fabricate
variable area capacitors that convert mechanical to electrical energy. When metals are placed in saltwater,
convert any mode of mechanical deformation (compression, strain, twisting, etc) to electricity. The scaling
they form a so-called “electrical double layer” at their surface (positive and negative charges). This principle is
physics suggest a path forward to increase the power output by increasing the surface area of the metal.
This energy-harvesting device is completely soft and therefore is comfortable for wearables. It also means it can
Representation of electricity generation when a soft, stretchable hydrogel supercapacitor is mechanically deformed. The opposite charges in the double layer are shown as red and blue.
Principal Investigator: Dr. Michael Dickey, Chemical & Biomolecular Engineering, NC State University
NSF ASSIST Center Nano-Bio Materials Consortium (NBMC)
Printing of Stretchable Conductors Enabled by Highly Tunable Multiphase Liquid Metal Pastes Objective: Liquid metals (LMs) are of great interest for many applications given their excellent electrical and mechanical properties that allow significant stretchability. However, their deployment is currently inhibited by manufacturing issues
(such as polymers), but non-trivial to do the opposite. The mixing of secondary fluids or solids into LM is a surprisingly non-trivial task due to the high cohesive energy density of the metal. Based on our preliminary results, we assert that the rapid surface oxidation of LMs enables a general pathway for achieving this task.
stemming from the large surface tension of LMs, which makes it difficult to pattern and adhere to surfaces. The objective of this project is to demonstrate a novel class of multiphase LM pastes whose properties can be designed through systematic incorporation of solid additives and fluid
Key Accomplishments: To date, we have demonstrated it is possible to create liquid metal pastes that can be 3D printed. We form these
micro-capsules with nanometer thin oxide shells. Introduction of these easily tunable pastes could enable large-scale adoption of the materials in stretchable electronic,
pastes using oxide or particle inclusions to modify the rheology of the otherwise Newtonian liquid.
Impact: This project will create soft metallic materials with completely new properties while retaining electrical
conductivity to make them more manufacturable. Our personal interest is tuning the rheological properties to
By incorporating solid and fluid fillers, this project seeks to render LMs easier to pattern by additive manufacturing, broaden their range of physical and chemical properties, and, by decreasing the overall metallic content, increase
enable facile printing of metallic materials at room temperature that are soft, stretchable and flexible. Yet, there are many other properties, such as adhesion, that
their economic appeal. To date, researchers have shown it is easy to distribute liquid metal droplets in other materials
can be tuned by forming foams and pastes.
This project seeks to transition from typical liquid metal printing (left) to paste-like extrusion (right) by novel materials engineering.
Principal Investigator: Dr. Michael Dickey, Chemical & Biomolecular Engineering, NC State University
Febby Krisnadi Joe Vong
NSF Arizona State University
Development of Sweat BioCapacitor for Self-Powered Multimodal Metabolite Sensors Objective:
The objective of this study is to develop a self-powered multimodal metabolite sensor which is operated from bioelectrochemical energy harvested from organic compounds (glucose and lactate) existing in sweat. The
The success of this study will accelerate the realization of self-powered wearable sensors for health care and diseasemonitoring systems, which may include the addition of advanced technologies for sampling (e.g. sweat sampling
inherent challenges to harvesting energy from biochemical sources are 1) the current density and theoretical limit for generating electrochemical potential are both low, and 2) the lactate concentration in sweat is too high to be efficiently catalyzed by the conventionally-utilized enzyme for lactate oxidation. This challenge calls for the
or reversed iontophoresis-based interstitial fluid sampling).
development of technologies that fully and efficiently utilize biochemical compounds in sweat for energy harvesting and consequent powering of health sensors.
Approach: Our approach is based on an innovative bioelectrochemical device, “BioCapacitor”, and engineered direct electron transfer (DET) type engineered redox enzymes. The enzyme fuel cells in BioCapacitor are composed of engineered DET-type redox enzymes, which are able to efficiently utilize glucose and lactate in sweat, and thereby realize the Dual-BioCapacitor, which provides sufficient electricity to operate multimodal sensors and a wireless signal transmission system.
Key Accomplishments: Engineering of lactate oxidizing enzyme resulted in the construction of an ideal lactate oxidizing enzyme which is heat stable (stable up to 70°C), with high Km value (~10 mM) and DET and quasi-DET ability with electrode. Through the combination of DET-type glucose dehydrogenase and flexible thin-film multiplexed electrodes, simultaneous detection of lactate (1 – 10 mM) and glucose (0.5 – 50 mM)
Sweat collecting patch and redox enzymes for lactate and glucose (top). BioCapacitor incorporated with support electronics including a microprocessor, memory, and transmitter (middle). Charge and discharge cycles of the dual cell BioCapacitor (bottom).
was achieved in artificial sweat. Continuous operation ( ̴10 hours) of a lactate BioCapacitor was also achieved.
Principal Investigators: Dr. Michael Daniele, Electrical & Computer Engineering, Biomedical Engineering, NC State University Dr. Koji Sode, Biomedical Engineering, UNC-CH, NC State University
Postdocs and Students:
Dr. Inyoung Lee Kartheek Batchu, Kentaro Hiraka, Thy Le, David Probst
Nano-Bio Materials Consortium
Ultrasonic Energy/Data Transfer for Implantables Objective: circuits to function as an ultrasonic power receiver, a distance measurement sensor, and a transmitter for wireless data transfer to an external unit.
Implantable medical devices offer significant opportunities for sensing, stimulating, and data transfer. However, a big challenge for these implanted devices is related to their power consumption and power management, which can limit long term usage. Ultrasonic power transfer provides substantially higher power density and reaches much deeper in tissue compared to alternative sources using
Key Accomplishments: To date, our team has demonstrated the feasibility of the presented approach, which includes key concepts for an implantable intravascular ultrasound device to monitor/diagnose endoleak in endovascular aneurysm
inductive coupling or radio-frequency (RF). A key advantage of ultrasonic energy transfer over the competing RF technology is that the maximum allowed power level in
repair stent-grafts. In benchtop studies using externally biased CMUTs and off-the-shelf discrete components, we demonstrated 1) greater than 1 mW power recovery from
tissue for diagnostic ultrasound is 7.2 mW/mm2, which is about 70x higher compared to RF energy limits. Furthermore, attenuation of ultrasonic signals in tissue is far less than RF, and the wavelength of the ultrasonic energy in tissue is on
a 3 mm2 device with incident ultrasound intensity of 5 mW/mm2, which is less than the spatial-peak temporal-
the order of millimeters. These advantages translate to a small device size and excellent range in biological systems. In this project, we aim to develop a miniature ultrasonically
average ultrasound intensity (ISPTA) limit of 7.2 mW/mm2 set for diagnostic devices. 2) Ultrasonic biphasic communication concept with potential for high data rate
powered device integrated into an endo-vascular aneurysm repair (EVAR) stent-graft that could provide on-
and 3) pulse-echo ranging from sensor to EVAR structures have also been shown. We also designed and fabricated pre-charged CMUTs and integrated circuits for power transfer.
demand diagnostic information about the presence of endoleak (a condition leading to pressure buildup in the aneurysm sac), based on measurements of the aneurysm sac dimensions, and of the stent-graft inside the vessel lumen.
Impact: The results accomplished to date show the potential of CMUT-based powering, sensing, and wireless
communication for implantables in a broad range of applications ranging from cardiovascular health to neural
Our approach to implement the described implantable device relies on using a capacitive micromachined ultrasonic transducer (CMUT) with integrated electronic
sensing and stimulation.
Implantable system (left) including CMUT device (top right image detail) and integrated electronics (bottom right image detail).
Principal Investigator: Dr. Omer Oralkan, Electrical & Computer Engineering, NC State University
Other Faculty, Postdocs and Students:
Dr. Yaoyao Jia Dr. F. Yalcin Yamaner Muhammet Annayev, Linran Zhou
NSF ASSIST Center (Center-2-Centre Grant)
High Energy Density Lithium-Ion Capacitor for Wearable Technologies Objective: This project aims at developing a high energy density lithium-ion capacitor (LIC) with low self-discharge characteristics and long cycle life as a promising energy storage solution for the design of self-powered wearable technologies for continuous health monitoring. The technology improves the volumetric energy density of existing state-of-the-art capacitor technologies by ~3x. The
for use in conjunction with low power energy harvesting and sensor technologies. The technology can be used either as a standalone energy storage solution or in conjunction with batteries for continuous health monitoring wearable devices and Internet-of-Things (IoT) based systems.
long cycle life (>10,000 cycles), high energy density, and fast charging/discharging capabilities make it a suitable alternative for the lithium polymer batteries that are used in
current wearable device platforms.
cell prototype and a volumetric energy density of ~13Wh/L. The cells were capable of being charged between 2.2V and 3.8V. In comparison with a commercial 3.6V lithium-ion
The technology is based on a high capacity porous carbon cathode and a prelithiated graphite anode. The high
rechargeable (LIR) battery of similar form factor, the capacitor shows higher capacity retention at fast charging
surface area porous carbon cathode, with its unique bimodal pore size distribution, provides accessible surface
and discharging rates. Long-term stability tests under constant current conditions showed that our capacitor lasted three times longer (~550 hours) relative to batteries
area with adequate transport porosity that enables the fabrication of ultrathick electrodes that are > 0.5 mm thick. The prelithiation process, when combined with the
Currently, we have fabricated lithium-ion capacitors that have a cell capacitance of 4–5 F packaged in a 2016 coin
when charged and discharged at 8 mA. These results show that LICs can provide an energy efficient solution for fast
microstructure of graphite anode, facilitates fast charging and discharging rates as high as 10 C-rate. Additionally, the
charging or high pulsed current loading conditions. To further demonstrate the functionality of our LIC, we have
assembled capacitors show a low self-discharge rate with 90% capacity retention over 2 months, making it attractive
shown that the capacitor can be used to power a Maxim Integrated Health Sensor platform. The capacitor, when charged with a flexible solar cell powered by indoor lighting, can continuously power the sensor platform for several days.
Impact: A high energy density lithium-ion capacitor packaged in a small form factor, such as a coin cell prototype, can offer significant advantages over rechargeable and primary batteries of similar form factor in terms of current ratings, cycle life, and energy efficiency. Successful development of the technology into various form factors that include flexible pouch cells can offer the potential to fabricate cells/modules with different capacities and extend its application as a standalone energy storage device or its use in conjunction with batteries. The developed products can have major applications in wearables, Internet of Things (IoT) systems for industrial use, renewable energy sectors, and automotive industries.
Comparison of ASSIST coin-cell Capacitor (image insert) to similar form factor Li-ion battery, demonstrating higher capacity retention at fast charging/discharging rates.
Principal Investigators: Dr. Ramakrishnan Rajagopalan, Department of Engineering, Applied Materials, Penn State University Dr. Clive Randall, Materials Science & Engineering, Penn State University
NSF ASSIST Center
High-Performance Three-Dimensional Thin-Film Thermoelectric Generators Objective:
Harvesting power from body heat via thermoelectric generators (TEGs) is a promising route to realizing selfpowered wearables for continuous, long-term monitoring of health and environmental parameters. This research focuses on developing a novel thermoelectric device structure that enables high-efficiency thermoelectric generators specially designed for power generation from low temperature
A wafer-level fabrication process has been developed, and the proof-of-concept devices are currently being manufactured. Nanocomposite and heterostructure thermoelectric materials with high efficiency have been developed for device fabrication. The films are grown by a hybrid cross-beam Pulsed Laser Deposition – Molecular Beam Epitaxy (PLD-MBE) system to enable an extensive
gradients. Such generators are relevant for harvesting energy from the body, which may differ from the ambient temperature by only a few degrees.
range of growth process parameters and achieve highquality films. Heterostructured materials offer higher efficiency compared to homogeneous films and are optimized for device prototyping testing.
Approach: In contrast to conventional devices made of only dozens of millimeter-scale thermoelectric elements, the new device
consists of several thousand micro-scale elements. As a result, it can generate >1000X larger voltage from a similar
electrical power than conventional devices while reducing the area needed for thermal energy harvesting. The devices will be small enough to be seamlessly integrated
The envisioned devices will provide significantly more
temperature gradient. The project focuses on developing a wafer-scale microfabrication process on inexpensive silicon wafers. The fabrication relies on mature processes and
into wearable monitoring devices.
techniques used in microelectromechanical systems (MEMS) fabrication. The research covers both materials development and device fabrication.
Silicon wafer with multiple micro TEG chips (left). Top view of a single TEG with close-up (middle). N- and P-type materials deposited by pulsed laser deposition (right).
Principal Investigator: Dr. Daryoosh Vashaee, Electrical & Computer Engineering, NC State University
Postdocs and Students:
Dr. Jie Liu Prithu Bhatnagar
Low Power Sensing
Ultra-Low Power Metal Oxide Electronic Nose Arrays for Environmental and Breath Monitoring Objective:
An individual’s exposure to certain gases in the environment can have a direct impact on health, and volatile organic compounds (VOCs) emitted in breath can indicate the state of metabolic activity. To understand these factors and
To date, our team has fabricated several MOx sensors including n-type SnO, n-type ZnO, p-type CuO, p-type SnO, and their composites. In the area of air quality, our sensors have been able to detect ozone concentrations at a few
explore new biomarkers, we seek to build a wearable device that can monitor a person’s immediate environment as well as their breath over extended periods of time and correlate these measures with results from other health sensors.
ppb levels. These sensors have been utilized in human subject studies related to asthma, given that ozone is a known trigger for asthma attacks. Our sensors have also been used to measure breath acetone and breath ethanol under various user scenarios. Different MOx
surfaces showed differences in breath analysis for regular diet, 30-hour fasting, and for ethanol. We have also used these arrays to measure mixtures of different VOCs. The
Our unique approach utilizes multiple metal-oxide (MOx) sensors that are made using a novel monolithic process based on complimentary metal-oxide-semiconductor (CMOS) processing, microelectromechanical systems (MEMS), and atomic layer deposition. This novel route can be used to produce a large number of metal oxide surfaces in a single device, thereby providing the discernability of an electric nose (e-nose) array. This e-nose array has ultra-low power operation (<1 mW) and, through machine learning, can differentiate between a variety of gases and correlate these to air quality or metabolic state.
power consumption of these sensors is the lowest reported to date.
Impact: These results illustrate the potential of these gas sensor technologies to identify disease patterns as well as provide warnings to vulnerable individuals when exposed to poor air quality. Since these wearables are also monitoring health vitals, this allows the direct correlation of health and environment. In addition, detection of VOCs in breath directly can provide insight into the metabolic state of the body.
Wrist-worn wearable device (left) enables continuous, low-power detection of environmental gases and breath using novel MOx sensor arrays (right).
Principal Investigators: Dr. Veena Misra, Electrical & Computer Engineering, NC State University Dr. Bongmook Lee, Electrical & Computer Engineering, NC State University
Postdocs and Students:
Dr. Farzad Mohaddes Smriti Rao, Yilu Zhou
NSF ASSIST Center
Capacitive Micromachined Ultrasonic Transducer based Gas and Breath Sensing Objective: Monitoring different pollutants in the environment in real time and correlating this with physiological parameters in an individual opens the opportunity to manage wellness and disease in a personalized manner. Volatile organic compounds (VOCs) represent a large class of gases that can present a health hazard, especially in indoor environments. Furthermore, volatiles in the breath have been shown to be early indicators of disease. For this reason, a wearable device that can monitor a person’s environment as well as their breath is significant.
Key Accomplishments: To date, our team has fabricated 8-channel sensor prototypes integrated with custom-designed low-power integrated circuits as a battery-powered wireless unit. These prototypes have been shown to selectively sense volatiles such as ethanol, toluene, p-xylene, styrene, and others in ppb- to ppm-level concentrations. These sensors have also been shown to be capable of differentiating between breath samples collected at different metabolic states.
Approach: Our approach to implement the described sensor system relies on achieving high specificity and sensitivity by using a mechanically resonant mass-loading sensor coated with selective functionalization layers. The mechanical resonator
of choice is a capacitive micromachined ultrasonic transducer (CMUT), which is suitable for array
applications ranging from environmental sensing to health monitoring.
The results accomplished to date show the potential of the CMUT-based gas sensor systems in a broad range of
implementation and achieves a high quality factor enabled by a vacuum cavity on the backside of a vibrating plate structure. The array approach is especially important to achieve high selectivity by functionalizing different elements
Wearable multichannel VOC sensor transmits raw data to a cell phone (left). Specific VOC detection (center). VOC chip integrated into the wearable electronics (right).
of the array with different materials.
Principal Investigator: Dr. Omer Oralkan, Electrical & Computer Engineering, NC State University
Other Faculty, Postdocs and Students: Dr. Yalcin Yamaner Dr. Erdem Sennik Zack Coutant, Ali Biliroglu
Funding source: NSF ASSIST Center
Multimodal Biosensing System for Electrochemical and Photonic Monitoring of Health Objective:
The objective of this project is to engineer a wearable multimodal biosensor platform for continuous monitoring of glucose, lactate, pH, skin temperature, and tissue oxygenation. Integration of multimodal sensors provides more impactful physiological data, and it provides more accurate sensor data by enabling sensor-to-sensor calibration and validation.
This innovation enables real-time correction and extended use of the biosensors. Multimodal sensing will provide new correlative data streams for analyzing the user’s physiology. For example, combining lactate sensing with pulse oximetry can be applied to identification and evaluation of 1) cardiopulmonary issues or 2) performance issues like fatigue. Other use cases include chronic pulmonary disease, infectious respiratory diseases, performance monitoring and/or prediction.
Approach: The sensor system combines a multiplexed array of electrochemical sensors to measure glucose, lactate, and pH in a wearable form factor, and it incorporates these sensors with the necessary optoelectronics for simultaneous photoplethysmography or pulse oximetry.
Key Accomplishments: The integration and testing of the multimodal biosensor platform demonstrated 1) the simultaneous measurement of fluid pH and temperature to correct enzymatic measurements in real-time for the operation of enzymatic glucose, lactate, and urea sensors, 2) electrochemical sensing of metabolites in collected or actively extracted sweat samples, and 3) combined electrochemical and photonic sensing for local tissue or arterial oxygenation measurements. In benchtop measurements using standard
Conventional electrochemical sensors can be combined with optical sensors for multimodal sensing and internal calibration.
instrumentation, the electrochemical sensors are shown to have sensitivities of 26.31 μA·mM-1·cm-2 for glucose, 1.49 μA·mM-1·cm-2 for lactate, 54 mV·pH-1 for pH, and 0.002 V· °C-1 for temperature. With the custom wearable system, these values were 0.84 ± 0.03 mV·μM-1·cm-2 for glucose, 31.87 ± 9.03 mV·mM-1·cm-2 for lactate, 57.18 ± 1.43 mV·pH1 for pH, and 63.4 μV·°C-1 for temperature. The wearable system demonstrated comparable performance to the much more hardware.
Principal Investigators: Dr. Alper Bozkurt, Electrical & Computer Engineering, NC State University Dr. Michael Daniele, Electrical & Computer Engineering, Biomedical Engineering, NC State University
Kaila Peterson Tanner Songkakul
NSF ASSIST Center
Novel Biomaterials and Bioelectronics for Implantable Cardiovascular Therapies Objective:
The objective of this collaborative project with the Centre for Research in Medical Devices (CÚRAM) is to develop biomaterials for use in instrumented cardiac patches which monitor regenerative performance by integrating cardiac stem/stromal cell (CSC) patches with biodegradable circuit boards and thin-film electronics. Sustaining reliable, cytocompatible, tissue-integrating bioelectronics requires
Integration of sensing modalities with cardiac cell therapies can provide routes to therapy optimization, improved performance, and outcome prediction. Moreover, the generally demonstrated biomaterial circuit boards can be delivered in myriad applications, including implantable medical devices, transient electronics, and stretchable electronics.
novel substrate materials that 1) can perform alongside the delivered therapy (i.e., regenerative cell therapy) and 2) are compatible with bioelectronic fabrication processes.
Approach: The sensor system combines a vascularized engineered tissue construct with biocompatible elastomers for delivery of both cell therapies and bioelectronics. To do so, we fabricated cardiac regenerative cell patches and synthesized a biocompatible elastomer to perform as the system substrate. Vascularized cardiac therapeutic patch implants on porcine heart promotes tissue repair after myocardial infarction.
Key Accomplishments: We have engineered pre-vascularized CSC patches and demonstrated their efficacy in both rodent and porcine models. Specifically, patches implanted into an immunecompetent porcine model of acute myocardial infarct (MI) have 1) improved angiogenesis at the host-patch interface and in the risk region and 2) improved myocardial viability and augmented cardiac function. We have fabricated and characterized thin-films for biodegradable circuit boards using poly(octamethylene maleate (anhydride) citrate) (POMaC), a soft, elastic, biodegradable material. POMaC was formed into thin-films with both thermal and photo-curing processes. Accordingly, we have fabricated stretchable thin-films from POMaC via conventional soft and photo-lithography methods.
Chemical composition of biocompatible elastomer substrates, as-formed films, and stretchable circuit boards.
Principal Investigators: Dr. Ke Cheng, Molecular Biomedical Sciences, Biomedical Engineering, NC State University Dr. Michael Daniele, Electrical & Computer Engineering, Biomedical Engineering, NC State University Dr. Frances Ligler, Biomedical Engineering, NC State University Dr. Stefano Menegatti, Chemical & Biomolecular Engineering, NC State University Postdocs and Students: Dr. Manus Biggs, Biomedical Engineering, NUI-Galway Dr. Teng Su Dr. Carolina Vargas Funding source: Brendan Turner NSF ASSIST Center (Center-2-Centre Grant) 19
Ultra-Low Power PhotoPlethysmoGraphy (PPG) for Wearable Applications Objective: rate from the sparse PPG signal, with the whole system consuming 1.66 mW power for continuous streaming of heart rate data over the commercial off-the-shelf Bluetooth Low Energy radio of ASSIST's Health and Exposure Tracker (HET) engineered system. We were able to demonstrate a wrist-worn system as an efficient platform for future evaluation of the compressive-sensing based PPG technique through in-vivo clinical studies under the HET testbed environment.
This project focuses on developing ultra-low power and novel biophotonic techniques for wearable physiological sensing. State-of-the-art circuit designs reduce power consumption using techniques like logarithmic amplifier, heartbeat locked loop, etc. However, most of these systems were evaluated on a benchtop or on the finger. This project demonstrated that compressive sensing is one of the lowestpower consuming alternative techniques for measurements performed not only at the fingertip but also on the wrist for continuous passive data collection.
Impact: Photonic measurements, such as PPG and pulse oximetry, are the most common methods in wearable systems to
track physiology. On the other hand, these are some of the most power consuming modalities due to the necessity of
application-specific integrated circuit (ASIC) for photoplethysmography (PPG). We worked on miniaturization and integration of this novel compressive-
generating a large number of photons (i.e., generating sufficiently bright light). Although most of this light is lost due to absorption and scattering in the tissue, important
sensing-based ultra-low power PPG ASIC into a wearable wristband and evaluated its usability to track heart rate on the wrist.
hemodynamic parameters are assessed in return. An ultralow power ASIC for PPG is required for overcoming
translational barriers related to use of these systems as a part of self-powered or extended battery life operation.
The system miniaturization for a wearable form-factor was achieved with no compromise in the performance of the ASIC. The ASIC consumes 172 μW of power to extract heart
PPG block diagram (top left). ASIC (top middle) and assembled board (top right), wrist-worn system (bottom).
Principal Investigator: Dr. Alper Bozkurt, Electrical & Computer Engineering, NC State University
Dr. Parvez Ahmmed
NSF ASSIST Center
Potentiometric Detection of Neuropeptides for Non-Invasive Monitoring of Stress Objective:
Neuropeptide Y (NPY) plays a central role in a variety of emotional and physiological functions in humans. Most notably, it has been found to possess anxiolytic properties, thus forming a part of the body’s response to stress, anxiety, post-traumatic stress disorder, and drug/alcohol addiction. Clinical studies have confirmed pg/mL concentrations of NPY in sweat. The current state-of-the-art detection techniques are not suitable for point-of-care deployment. This project seeks to reliably detect NPY, a biomarker for stress found in human sweat, using gold-based potentiometric sensors.
Detection of NPY down to the targeted pg/mL range has been achieved using potentiometry. The impact of nonspecific adhesion was mitigated by using a PEG-based backfill of the aptamer-functionalized surface. We have also demonstrated detection using a field effect transistor (FET)-based approach, which can be explored for both traditional complimentary metal-oxide-semiconductor (CMOS) and non-traditional semiconductor platforms. We have also translated our functionalization protocol to gold microelectrodes on flexible substrates and achieved pMrange detection of NPY using electrochemical impedance spectroscopy.
In this project, we are developing a gold microelectrode
Reliable and sensitive detection of NPY through wearable biosensors can provide early insight into various emotional
based potentiometric sensor capable of ~pg/mL detection of NPY in artificial sweat. Selectivity for NPY is achieved via DNA aptamer immobilization. This approach is compatible
and physiological functions and enable management of stress-related responses.
with ASSIST’s Health and Exposure Tracker (HET) platform, which already performs potentiometric measurements of sweat on both rigid and flexible substrates, thus enabling wearable, non-invasive sensing.
NPY in pg/mL concentrations can be detected in sweat. Selectivity for NPY is achieved via DNA aptamer immobilization.
Principal Investigators: Dr. Ke Cheng, Molecular Biomedical Sciences, Biomedical Engineering, NC State University Dr. Spyridon Pavlidis, Electrical & Computer Engineering, NC State University Dr. Koji Sode, Biomedical Engineering, UNC-CH
Grace Maddocks Kaila Peterson Hayley Richardson
NSF ASSIST Center Nano-Bio Materials Consortium
Improving the Performance and Design of Potentiometric Biosensors for the Detection of Extracellular Histones in Blood with Deep Learning Objective:
The objective of this project is to combine potentiometric biosensors with standard machine learning techniques and state-of-the-art deep learning techniques to detect circulating blood-borne histones, which contribute to the development of Multiple Organ Dysfunction Syndrome (MODS), a potentially fatal condition in critically ill patients. This information will also be used to improve sensor sensitivity and drive design optimization.
We have demonstrated physiologically relevant nM detection of calf thymus histone (CTH) using our potentiometric sensors. We have also used surface plasmon resonance (SPR) to understand how immobilization protocols impact sensitivity, selectivity, and stability in response to regeneration. Regression analysis is being applied to these experimental data to unveil nonlinear relations on the data. These analyses have also been
performed with human histones (H4). Finite element modeling is being used to accelerate data generation.
Gold sensing electrodes are functionalized with RNA aptamers to detect extracellular histones with extended gate sensors. These devices are being evaluated in buffer, serum, and whole blood as benchmarks for point-of-care
Impact: Most demonstrations of potentiometric sensors stall during the translation from testing well-controlled laboratory solutions to operating in serum or whole blood. Our integrated approach aims to overcome this to provide
(POC) deployment. We will then leverage deep learning techniques to reveal intricate relationships and trends to compensate for the conventional losses in sensitivity observed in blood-based tests. These findings drive the optimal design of the potentiometric sensors, thus establishing design rules that can accelerate the
better sensitivity and reliability. The use of machine learning remains nascent in this field. Therefore, we will establish a standardized protocol that other researchers in the field can leverage in order to accelerate the adoption of potentiometric biosensors in new applications.
development of these sensors across the community. A major obstacle to the application of machine/deep learning techniques to biosensing is the generation of adequate training data. A multiplexed potentiometric biosensing platform, made possible by the use of the extended gate approach, and computer simulations will be developed in order to identify time- and resource-efficient approaches to algorithm training.
The system uses machine learning for modeling, prediction, and uncertainty quantification in blood samples for histone detection.
Principal Investigators: Dr. Edgar Lobaton, Electrical & Computer Engineering, NC State University Dr. Spyridon Pavlidis, Electrical & Computer Engineering, NC State University Dr. Francis Miller, Dept. of Medicine, Duke University
Hayley Richardson Jeffrey Barahona
Use Case: Long-Term Wound Monitoring with Multimodal Patches Objective:
Diabetic wounds are a leading cause of amputations, with millions of people suffering around the world. As a result of inefficient wound monitoring techniques, wound care costs exceed $15 billion per year in the United States alone. Our technology aims at developing a smart bandage for realtime wound monitoring for personalized wound care. The
Our platform has been developed on a completely flexible and skin-conformable substrate using high throughput fabrication techniques. Our design has allowed us to achieve consistent sensor performance with limited sensor calibrations. The development of the entire platform on a conformable substrate enables the placement of the
real-time monitoring aspect of our platform is meant to inform the patient’s wound care plan to mitigate complications associated with chronic conditions and decrease the risk of infection and traumatic amputation. Existing wound care techniques require direct patient
patch directly over the wound without causing any patient discomfort. We have demonstrated biocompatibility of the platform (ISO 10993, MEM elution L929 48 h) and are conducting human subject trials.
interaction and are limited to objective analyses, such as wound width and depth.
Impact: In line with our use case, development of smart bandage
technologies will promote better wound care management, improve clinical outcomes by detecting infections in a timely manner, and enhance quality of life
Our technology provides a unique platform that measures uric acid to quantify biochemical changes in the wound environment; lactic acid to evaluate the formation of bacterial biofilms; and pH/temperature to evaluate the
for patients with chronic wounds. Furthermore, the electrochemical sensing methodologies and the flexible
wound environment and quantify analyte levels.
electronics integrated within our platform can be utilized to develop a myriad of truly wearable systems, including systems for active wound healing.
Flexible, wireless, biocompatible patch, shown integrated with standard wound dressing, continuously monitors key indicators of wound health.
Principal Investigators: Dr. Alper Bozkurt, Electrical & Computer Engineering, NC State University Dr. Michael Daniele, Electrical & Computer Engineering, Biomedical Engineering, NC State University Dr. Vladimir Pozdin, Electrical & Computer Engineering, Florida International University
Postdocs and Students:
Dr. Pulak Bhushan Ziwei (Adam) Mao Tanner Songkakul
NSF ASSIST Center
Zero-Power Wearable Sweat Assays and Long-term Sensing Device Interfaces Using Osmotic Pumping and Paper Microfluidics Objective:
Our team has pioneered a unique sweat extraction technique that can operate over periods of multiple days by a novel, non-invasive osmotic-capillary method that does not require any electrical power, and which can
We focus as the first major outcome of the project on the development of simple inexpensive non-invasive skin patches for testing of sweat for cortisol levels, interleukins, other stress biomarkers, ionic balance, and toxins. This
interface with on-device sensors or benchtop assays.
platform can be used in integrated wearable systems for long-term sweat and interstitial fluid (ISF) sampling and prolonged analysis of hormones, glucose, and other ISF biomolecules.
Approach: Our technique gently extracts sweat from the skin using a hydrogel patch infused with benign solute. The solute creates an osmotic pressure gradient that pulls sweat from the sweat glands (sweat glands naturally expel sweat by
Impact: The osmotic-capillary principles that we have pioneered to interface the skin could form the basis of a stunning
osmotic principles; thus, the technique is biomimetic). The collected sweat is then transported toward sensors by
technology breakthrough in the field, as they are biomimetic, non-invasive, and do not require any electrical
simple and reliable paper-based microfluidics, which use wicking and capillarity to transport fluids without the need for external electrical power. Engineered evaporation pads placed at the end of the paper strips continually drive the
power or any active sweating. The ability to continuously harvest sweat in a non-invasive and non-irritating way for long durations enables performing a variety of bioassays in
transport of fluids for days; in contrast, state of the art
a non-invasive, user-friendly manner, and without an external power source.
paper-based assays are typically single-use devices and have a short operating time.
Demonstration of paper microfluidic sweat collection devices (left), low-power electronics integration (middle), and overall system design (right).
Principal Investigators: Dr. Michael Dickey, Chemical & Biomolecular Engineering, NC State University Dr. Orlin Velev, Chemical & Biomolecular Engineering, NC State University
Sneha Mukherjee, Tamoghna Saha, Nano-Bio Materials Consortium Tanner Songkaku, Murat Yokus
Low Power Systems-on-Chip
Ultra-Low Power System-on-Chip (SoC) Objective:
This project seeks to develop an ultra low powercore electronics platform for integrating technologies from other ASSIST themes into a unified self-powered wearable sensing system with a total power sustainable by energy harvesting. This project targets a power budget less than 50 μW while providing flexible, multi-modal capabilities for ASSIST’s selfpowered armband that monitors the wearer’s
Demonstrated new SoC with RISC-V microcontroller unit (MCU), 8 kB memory, boot ROM, and on-chip clocks, for < 1 µW power consumption. Demonstrated a new flexible analog front end (AFE) with 4 modalities (V/I/R/C), consuming: 3 nW ECG; 13 nW respiration; 9.35 µW PPG w/ LED; 16.7 µW SpO2 w/ 2 LEDs; and 57 nW gas sensing.
electrocardiogram (ECG), pulse via photoplethysmography (PPG), activity, and ozone environment. This armband, known as the self-powered adaptive platform (SAP) Gen 2,
Event-driven system operation for analog front end (AFE) of 6.4 µW (Respiration, heart rate, SpO2, pulse transit time, and ozone).
is the second device built on ASSIST’s Self-powered Adaptive Platform (SAP).
Energy-harvesting power management unit (EH-PMU) with concurrent harvesting from light, heat flux, piezoelectrics, and 4 regulated VDDs (supply voltage), using 1 inductor, requiring 100 nA IDDQ (quiescent
Approach: The multi-chip platform, centered around a system on chip (SoC), includes circuits for data collection, data storage, data processing, node control, power management, power harvesting, power delivery, and wireless communication.
current). Python system model for duty-cycled, hierarchical selfpowered systems.
This project plays a key role in the strategic plan of the Center, since low-power electronics are an important
component in systems able to be powered by energy harvested from the body.
This work is enabling the ASSIST SAP Gen 2 and future SAP systems to operate entirely from harvested energy, due to the low power operation and flexible functionality of the custom chips.
System on Chip (SoC) testing setup demonstrating harvesting and management of power from simultaneous thermoelectric, piezoelectric, and photovoltaic sources.
Principal Investigator: Dr. Benton Calhoun, Electrical & Computer Engineering, University of Virginia
Rishika Agarwala, Henry Bishop, Jacob Breiholz, Anjana Dissanayake, Katy Flynn, Shourya Gupta, Sumanth Kamineni, Peter Le, Shuo Li, Xinjian Liu, Natalie Ownby, Daniel Truesdell, Peng Wang
NSF ASSIST Center
Wireless Wakeup for Energy Reduction in Plugged-In MELs (Miscellaneous Electric Loads) Objective:
The overarching objective of this project is to develop a flexible wireless connectivity module that takes advantage of differentiating component capabilities to substantially reduce the power consumption of miscellaneous electric
Through this work, to date, we have demonstrated: an ultra-low power WiFi wakeup receiver with power consumption of 578 µW, vs. 80,000 µW for a commercial off-the-shelf (COTS) device
loads (MELs). MELs are plugged-in appliances and devices, which the Department of Energy (DOE) has identified to consume over 30% and 36% of electricity consumption in residential and commercial buildings, respectively.
a 5G / NB-IoT (Narrow Band - Internet of Things) wakeup receiver with 2.1 mW of power consumption (best in class by 10x) a custom integration System-on-Chip (SoC) with 0.5 - 10 µW active power and 50 nW sleep power (vs. 50 µW target and >1,000 µW for COTS) phantom energy reduction to <1 mW with wake-on-
wireless and predicted turn-on capabilities, resulting in >87% reduction in MELs standby energy.
MELs typically consume many watts in phantom power, which is continuous power consumption even when the device is not in use or even “off.” Our team is prototyping a connectivity module based on a new custom radio
Impact: This work is demonstrating substantial savings in MELs standby energy and phantom power. If adopted, this
frequency integrated circuit (RFIC) system and assessing how the connectivity module would reduce MELs power consumption across several dozen MELs appliances. The
technology could reduce nationwide power consumption by a meaningful percentage. Also, the components
system comprises a wakeup radio (to receive a wakeup signal), an always-on processor for node control, and an
developed in this work have made power reductions by orders of magnitude in standards-compatible wakeup receivers.
electrical interface to the MELs device(s). The system cuts off power to idle MELs and uses either a wireless wakeup signal or a predictive model to turn on the MELs device in time to prepare it for use.
Principal Investigators: Dr. Benton Calhoun, Electrical & Computer Engineering, University of Virginia Dr. David Wentzloff, Electrical Engineering & Computer Science, University of Michigan
Omar Abdelatty, Shourya Gupta, Shuo Li, Xinjian Liu, Trevor Odelberg
US Department of Energy
Novel, Flexible, High Efficiency and Multifunctional Wearable Antennas Objective:
Our goal is to develop advanced, ground-breaking, cuttingedge wearable antenna technologies whose custom designs outperform the available commercial off-the-shelf (COTS) antennas as well as provide new antennas with multifunctional capabilities that currently have no COTS counterparts.
Our groundbreaking wearable antenna technology mainly targets high-efficiency in a small form factor as well as advanced multifunctional capabilities which include fullduplex operation for enabling wearable systems operating in both receiving (Rx) and transmitting (Tx) modes, and reconfigurable multi-mode functionality for on- and offbody communications. We have successfully developed several transformative wearable antenna designs, such as: miniaturized capacitor-loaded metasurface-enabled antennas, a highly-flexible PDMS and silver nanowire composite circularly polarized (CP) metasurface-enabled
Approach: The design challenges include 1) effectively minimizing the degradation in antenna performance caused by human body loading, 2) developing antenna designs that are robust to deformations due to body motion and location of placement, 3) achieving efficient coupling of wearable antennas to on-body and/or off-body propagation modes,
antenna, dual-band circularly polarized antennas, a miniature yet broadband proximity-fed antenna for easy garment integration,
and 4) maintaining high radiation efficiency in a small form factor.
a large bandwidth dual-port full-duplex textile antenna (can support both Rx and Tx modes in a single antenna), a high-isolation full-duplex antenna with isolation up to 40 dB, small form-factor highly-integrated filtering antennas (filtennas), an omnidirectional dual-polarized armband textile antenna array that operates at 6 GHz and the 2.45 GHz ISM band, and a textile endfire leaky-wave antenna that can support both on-body and off-body communication modes. Furthermore, we have a portfolio of patents filed/awarded on our wearable antenna technology.
Impact: To meet the demanding requirements for present and future body-area networks, we have developed a suite of best-in-class custom designed broadband, small formfactor, low-profile and/or multifunctional wearable antennas for easy textile/garment integration. Through their optimized design, these antennas reduce the power needed to operate wireless transmitters/receivers on the body.
Flexible antenna using stretchable EGaIn liquid metal conducting lines (top), miniature broadband antenna for textile integration (center), and textile antenna wrapped around a cylinder representing an arm (bottom).
Principal Investigator: Dr. Douglas Werner, Electrical Engineering & Computer Science, Penn State University
Postdocs and Students:
Dr. Saber Soltani Connor Haney Yuhao Wu
NSF ASSIST Center
Body Worn Flexible Antenna for Applications in Communication and RF Energy Harvesting Objective: The overarching objective of this project is to demonstrate antennas, rectifiers, and circuits optimized for sensor powering, data-modulation, and data-extraction systems on textiles. This caters to the need for integrating passive and active sensor components into fabric surfaces for comfortable wearables. Indeed, integration into textiles is
Key Accomplishments: One key accomplishment of this project was to demonstrate a textile-integrated RF powered surface with near-zone based power transfer jacket and far-zone based power transfer jacket. For the near-zone scenario,
the highest form of integration towards the most ergonomic use-cases.
ergonomic use of the jacket was demonstrated while showing integration with items of daily use such as chairs. Recently, a near-zone RF powered surface was integrated
Current technologies that use textiles-based electronics are limited to antenna and passive components, and they have
with a voltage controlled oscillator based data modulation and data extraction circuit for wound sensing (via
generally utilized ink-based printing. Our objective in this project is to explore embroidery-based methods which have
monitoring uric acid levels).
lower radio frequency (RF) losses for the antenna as well as active circuits. Using this approach, we have demonstrated the first all-textile based wearable systems for wireless
Impact: The impact of this project is that, in demonstrating our
powering as well as sensor-electronics, which support not only powering but data extraction as well.
textile RF powering, data-modulation and data-extraction system, we have enabled the first completely textile RFID sensors. The success of this project provides a new way to
power and gather data from textiles-based wearables, with applications in the medical treatment industry and
The approach for achieving textile integrated antennas and circuits is to use embroidery of conductive thread on surfaces such as organza and denim. With the precision available from off-the-shelf embroidery machines, circuits in
the 2.4 GHz range are possible. RF power harvesting antennas, circuits, and data-modulation circuits, such as those based on voltage-controlled oscillators, are demonstrated, having optimized embroidery parameters.
Representations of various textile based antenna designs for RF Energy Transfer
TX LEDs on
Rectifier + Anchor shaped RX Antenna
Principal Investigators: Dr. Shekhar Bhansali, Dr. Shubhendu Bhardwaj, Dr. Vladimir Pozdin, Dr. John Volakis Electrical & Computer Engineering, Florida International University Dr. Douglas Werner, Electrical Engineering & Computer Science, Penn State University
Pulak Bhusan Pawan Gaire Dieff Vital
NSF ASSIST Center Auestech WiGl
Transformative Textiles Designs for SelfPowered, Multi-Modal Sensing Garments Objective:
The objective of this project is to apply macroscale strategies for innovative materials design and manufacturing to solve the tradeoff issues that exist between textile and device performance in the field of electronic textiles (e-textiles). The fundamental research explores mechanical burdens placed upon textiles by the incorporation of electronic materials and devices and the
Our team has determined a means for evaluating the impact of electrode location and contact pressure on the ECG sensing performance on the upper left arm. We also evaluated how the size of the armband form factor affects its ECG sensing performance. Our experimental results confirm that armbands exhibiting modeled contact pressures of 500 Pa to 1500 Pa can acquire ECG signals.
low cost means by which to resolve these burdens. Solutions sought through this effort primarily focus on commercially ready materials and processes, enabling rapid translation of
However, armband sizes exhibiting experimental contact pressures of 1297 ± 102 Pa demonstrated the best performance, with signal-to-noise ratios (SNR) comparable
the innovations to industry.
with wet electrode benchmarks. These results will be applied in an Institutional Review Board (IRB)-approved
study with law enforcement in a local municipality (Town of Cary, NC). Our team is also creating an information portal
Our team is establishing garment design methods to validate the biometric performance of wearable systems. Systematic studies are designed for testing the quality of ASSIST’s shirt and armband platforms, including a use case
for existing e-textile commercial products that incorporates teardown of the products to inspect materials and design trade-offs.
of law enforcement officer stress monitoring through biometrics analysis.
Impact: Biometric data quality of an e-textile smart garment is heavily reliant on the design strategy for fabrication of the garment and can vary from user to user. This project identifies the key factors in achieving design, comfort, and performance of smart textiles using methods that are manufacturable in the textile industry.
Arm sleeve test platform (left) and example comparison data (right) between ECG collected from the sleeve with Ag/AgCl dry electrodes and that obtained from the chest with Ag/AgCl wet electrodes.
Principal Investigators: Dr. Jesse Jur, Textile Engineering, Chemistry, & Science, NC State University Dr. Amanda Mills, Textile Engineering, Chemistry, & Science, NC State University
Tashana Flewwellin, Isabel Hines, Beomjun Ju, Furkan Kose, Braden Li, Marissa Noon, Busra Sennik, Olivia Turschak, Vince Varju 32
NSF ASSIST Center
Method of Automated Handling of Textiles for Improved Efficiency and Accuracy to Enable E-Textile Manufacturing Objective:
Automation through fabric handling and assembly is a necessary means for bringing textile manufacturing back to the United States. This method reduces the amount of labor necessary and enables new capabilities in textile product
We have successfully automated the grasping and transferring of the part pieces to construct an Improved Outer Tactical Vest (IOTV) cummerbund. Currently this product is being constructed manually with an average of
manufacturing to lower the end product cost. The cost issue is heightened for electronic textile products, lending to an
169 seconds spent just transferring the part pieces throughout the construction process for each production
increased need for automation. Currently, textile manufacturing is highly dependent on manual construction due to the challenges in automating the handling of fabric because of its flexibility and drape. Automated handling
batch. With the insertion of automated handling, this time can be reduced, and the process to construct the IOTV cummerbund can be streamlined.
can provide more reliable and consistent construction. The
goal of this work is to use this automated handling technique to provide accurate placement of fabric, enabling new opportunities in e-textile product construction.
The implementation of automated handling in the construction of textile products will reduce the amount of labor required, allowing for shorter lead times as well as a more economically viable method for manufacturing domestically. Additionally, it will provide an avenue to integrate more advanced materials such as e-textile systems because of the improved consistency within the
Approach: We have developed an electromagnetic end-effector gripper that latches and connects to the rigid
construction from automated handling.
ferromagnetic components of the e-textile systems as a means for material handling. Instead of trying to grasp the fabric itself, we have developed a method of grasping either permanent or temporary connectors within the textile to handle and transfer different textile part pieces. Automating the handling of the textile part pieces generates higher accuracy, consistency, and speed within construction of textile products, creating opportunities for advanced materials integration for applications such as wearable technology.
Electromagnetic end-effector gripper that latches and connects to the rigid ferromagnetic components of the e-textile system.
Principal Investigator: Dr. Jesse Jur, Textile Engineering, Chemistry, & Science, NC State University
UNC General Administration ARMY DEVCOM-CCDC
Novel Textile-Based Sensors for Inner Prosthetic Socket Environment Monitoring Objective:
This project aims to develop a novel Flexible InneR-socket Sensing Technology (FIRST) seamlessly, unobtrusively, and elegantly integrated into the lower-limb prosthesis socket. FIRST is based on an electronic-fabric structure in which the fibers of the fabric act as sensory elements that can simultaneously track tactile forces, moisture/wetness,
Amputation is one of the major causes of disability. Sockets are the important prosthesis components and physical interface to integrate the prosthetic limbs mechanically with the amputee's residual limb to replace lost function. Objective monitoring of the inner socket environment (i.e. pressure, temperature, and humidity) and residual muscle
electromyography and body temperature at multiple sensing points around the residual limb. The major challenge is to develop a fundamental understanding of the coupling and interaction between multi-component fiber crosssectional architecture, fabric structure, and its electro-
activity during daily prosthesis use requires flexible, unobtrusive, multi-modal sensors that can be integrated into the socket structure without causing subject discomfort. The lack of such an inner-socket sensor technology has been a long-standing problem for
mechanical response to achieve a multimodal sensor that can be unobtrusively integrated into 'textile-based' sensory
evaluating the prosthesis socket, preventing the complications elicited by poor socket design and fit, and
devices in general. The interpretation of the data is to identify locations of skin problems to enable patient selfmanagement and allow for more objective clinical
advancing the socket technologies. Therefore, advanced socket technologies are urgently needed and will be developed under this project to significantly reduce the
evaluation to avoid the occurrence of potential skin breakdown and the resulting complications.
number of clinic visits, lower healthcare costs for amputees, and ultimately improve their quality of life.
Approach: Our collaborative research team works on melt-extruded multi-component fiber and seam-line based sensor development in which we carefully engineer the fiber crosssection, fabric structure, and electrical response. This targets a sensitive and specific multimodal response using microfabricated and, ultimately, textile-based polymeric fibers with ordered segments of conducting and insulating areas in the fiber cross-sectional structure. We aim to unobtrusively integrate these into many electronic small- or large-area textile-based sensory devices and systems of the future, especially for health monitoring.
Key Accomplishments: We manufactured arrays of multi-component fiber and seam-line based sensors connected to a wireless highspeed data recording and transmission system via textile interconnects. We tested the sensors on an in vitro artificial limb testing setup and two in vivo experiments involving an able-bodied subject donning a bent-knee adapter and a bilateral transtibial amputee participant. In all these cases, the sensor array successfully detected pressure changes
Schematic depictions of the fiber and seam-line sensor arrays, images of the integrated textile sensors, and examples of integration and testing with human subjects, demonstrating successful detection of pressure changes.
within the inner socket during weight-shifting and walking experiments.
Principal Investigators: Dr. Alper Bozkurt, Electrical & Computer Engineering, NC State University Dr. Tushar Ghosh, Textile Engineering, Chemistry, & Science, NC State University Dr. Helen Huang, Biomedical Engineering, UNC-CH
Correlated Sensing of Health and Exposure for Personalized Asthma Monitoring Objective:
This project seeks to combine physiological and environmental sensors in a low-power and portable form factor. The combination of these sensing modalities allows for a unique perspective on an asthmatic individual’s response to local environment and air quality conditions. Current solutions for monitoring ozone exposure typically rely on nearby environmental monitoring stations, leading to much less granularity in terms of personal exposure.
This project has been used to showcase the custom ozone sensor developed within the ASSIST Center as well as take a step towards personalized gas exposure tracking. These devices have been used to measure physiological responses to controlled amounts of ozone with respect to a gold standard (Shimmer3 ECG). This gold standard showed a correlation between an individual’s heart rate variability and their lung function. This project is now focusing on performing similarly structured studies to show these correlations in at-home settings over several weeks’ time.
Approach: The system comprises both a wristband and a chest patch. The chest patch is designed for physiological monitoring, including sensors for electrocardiography (ECG), pulse (via photoplethysmography, or PPG), and motion. The wristband
is designed for environmental exposure monitoring, including sensors for PPG, motion, ambient temperature
and other respiratory conditions that are triggered by environmental toxins. The eventual goal is to allow for
and humidity, ambient volume levels, ozone, and volatile organic compounds (VOCs). The sensors allowing for ozone and VOCs are custom technology to ASSIST and have been integrated into modular plug-in boards. These plug-in
predictive algorithms to assess the risk of impending exacerbations and allow for acute lifestyle alterations to mitigate these risks rather than relying on rescue inhalers.
This research has the potential to transform the treatment methodology for patients with moderate to severe asthma
boards mate with the main board of the wristband, which allows different sensors to be utilized.
Wearable device worn on the wrist (left) and a simplified diagram showing the individual components that make up the wearable device (right), .
Principal Investigators: Dr. Alper Bozkurt, Dr. James Dieffenderfer, Dr. Bongmook Lee, Dr. Veena Misra Electrical & Computer Engineering, NC State University
Dr. Tahmid Latif
NSF Smart Connected Health NSF ASSIST Center
Health and Exposure Tracker (HET): Integration and Demonstration of a Modular Wearable Monitoring System Objective: The Health and Exposure Tracker (HET) is a unique modular platform enabling evaluation of various ASSIST technologies, from electrodes and optical devices to ultra-low power electronics and sensors. This system demonstrates ASSIST's vision of correlated health and environmental exposure tracking. The HET can host physiological sensors (e.g.,
The flexible patch collects sweat via a zero-power osmotic pump connected to screen printed enzymatic glucose and lactate sensors. Glucose and lactate concentration monitoring in sweat could help assess metabolic state, enabling automated and actionable feedback to support diet management for diabetic patients.
electrocardiography (ECG), photoplethysmography (PPG), and pulse oximetry), behavioral sensors (via inertial measurement units), environmental sensors (ozone, volatile organic compounds, ambient temperature, and relative humidity), and novel biochemical sensors (lactate, glucose,
Key Accomplishments: ASSIST's HET systems are part of various clinical experiments for asthma exacerbation prediction, sweat analysis, and wound sensing. Our prototypes have technology readiness levels (TRLs) of 5 to 6 and sub-milliwatt power consumption.
and pH) for a more comprehensive and correlated analysis. No other existing systems provide the multi-sensing
capability of the HET along with an open platform into which next generation sensing devices can be integrated.
Monitoring physiological and environmental conditions could help individuals with asthma manage their exposure to irritants. Analysis of biochemical markers in sweat/wounds could be a breakthrough in wearables.
Approach: The HET comprises a modular architecture with various electrophysiological, biophotonic, inertial, potentiostatic, amperometric, and environmental sensor front ends together with a system-on-chip (microcontroller and Bluetooth Low Energy transceiver). The system can be powered by inductively rechargeable lithium batteries or energy harvesting devices such as flexible solar cells or ASSIST’s thermoelectric generators. This circuit architecture has been packaged in several form factors including wristband, chest-patch, flexible patch for various body locations, and bandage.
These capabilities could enable medical professionals to track patient health remotely and support advanced data analytics to generate automated feedback.
A wristband form factor is outfitted with an ozone sensor to monitor individual ozone exposure, accelerometers to measure activity, and PPG sensors to measure heart rate, thereby enabling correlated sensing of environmental exposure and physiological response. Chest form factors allow monitoring PPG, ECG, and cough frequency. The system is being used in studies at the University of North Carolina and an Environmental Protection Agency test chamber. The bandage form factor is a wound monitoring system which tracks uric acid levels near the wound site to determine how well wounds are healing.
Principal Investigators: Dr. Alper Bozkurt, Dr. Michael Daniele, Dr. James Dieffenderfer, Dr. Edgar Lobaton, Dr. Veena Misra, Dr. Omer Oralkan, Electrical & Computer Engineering, NC State University; Dr. Michael Dickey, Dr. Orlin Velev, Chemical & Biomecular Engineering, NC State University Dr. Michelle Hernandez, Department of Pediatrics, UNC-CH Dr. Vladimir Pozdin, Electrical & Computer Engineering, Florida International University
Postdocs and Students:
Dr. Tahmid Latif Devon Martin, Kaila Peterson Tamoghna Saha, Tanner Songkakul
NSF ASSIST Center
Self-Powered Platform for Cardiovascular and Asthma Monitoring Objective:
The objective of this project is to develop self-powered wearables that track physiological and environmental parameters, in order to improve monitoring of cardiopulmonary performance and self-management of respiratory conditions such as asthma. Self-powered wearables enable uninterrupted data collection, and environmental monitoring on the person ensures the most
We have demonstrated an ECG-monitoring shirt streaming ECG data continuously over Bluetooth to a mobile application, powered only by body heat. Due to ASSIST’s low power electronics and radio, coupled with ASSIST’s body-optimized wearable antenna, the shirt consumes only about 65 μW of average power. We have also demonstrated a self-powered armband that monitors ECG,
relevant measures of irritants to which the person is being exposed.
PPG, and ozone levels. The armband currently uses commercial off-the-shelf electronics, rather than the custom low-power electronics we have developed for this
application, due to the custom electronics’ longer development timeline. Nonetheless, we have achieved self-
We have developed two wearables for these applications. The first is a self-powered electrocardiogram (ECG)monitoring shirt that utilizes dry ECG electrodes and a
powered operation through a combination of body heat and solar energy harvesting. Future generations utilizing
flexible thermal energy harvester to provide a comfortable ECG-monitoring solution suitable for daily wear. The second is a self-powered armband that utilizes dry ECG electrodes
ASSIST’s newest low-power electronics are anticipated to use only body heat for power.
and a combined thermal/solar energy harvester to provide
continuous monitoring of ECG, pulse (via photoplethysmography, also called PPG), and low power gas sensors that monitor ozone levels for assessing asthma risk. These apparel-based monitors integrate advancements
These wearable devices are having impact on multiple
across ASSIST’s primary research areas of energy harvesting,
cardiac performance during strenuous activity, and alerting individuals with asthma to high ozone levels. A second is
fronts. One is that they are filling performance gaps in currently-available wearables by continuously monitoring for cardiac conditions such as atrial fibrillation, tracking
low power sensors, low power electronics, e-textiles, and systems integration.
that, by increasing the functionality of ASSIST’s self-powered devices (i.e. advancing from powering ECG to powering ECG, PPG, and ozone sensors simultaneously), they drive new technical achievements in ASSIST’s research. A third is that these devices serve as platforms that can be tailored to specific use cases and performance requirements in collaborations with clinical partners or companies.
Schematic of armband with selfpowered ECG capability.
Electronics module of ECG armband with enabling functionality.
Postdocs: Dr. Tahmid Latif Dr. Amanda Mills Dr. Yasaman Sargolzaeiaval
Funding source: NSF ASSIST Center
Principal Investigators: Dr. Alper Bozkurt, Dr. Michael Dickey, Dr. James Dieffenderfer, Dr. Edgar Lobaton, Dr. Veena Misra, Dr. Mehmet Ozturk, Electrical & Computer Engineering, NC State University Dr. Michael Daniele, Biomedical Engineering, Electrical & Computer Engineering, NC State University Dr. Jesse Jur, Textile Engineering, Chemistry, and Science, NC State University Dr. Benton Calhoun, Electrical & Computer Engineering, University of Virginia Dr. David Wentzloff, Electrical Engineering & Computer Science, University of Michigan Dr. Douglas Werner, Electrical Engineering & Computer Science, Penn State University
SenSE: AI-Driven, Resilient and Adaptive Monitoring of Sleep (AI-DReAMS) Objective:
This project investigates the use of an artificial intelligencedriven, reconfigurable sleep monitoring system to transform sleep research in the clinic and at home. A sensor fusion strategy backed by artificial intelligence to ultra-miniaturize the sleep assessment instruments and explore novel sleeprelated biomarker features has the potential to enable more
This project stems from an earlier clinical study funded by National Institutes of Health to explore the use of near infrared spectroscopy and machine learning to bring a new perspective to sleep studies. The team demonstrated flexible devices to perform near infrared spectroscopy and electroencephalography in the form factors of a flexible
efficient and accurate diagnosis and treatment of sleep disorders. There is a need for combining lower cost with improved comfort and more efficient data analysis to pave the way for rapid translation, adoption, and effective deployment of sleep technologies.
bandage. The current efforts focus on constructing a reconfigurable version of the hardware platform to collect data at home and in sleep clinic studies and support the development of the proposed artificial intelligence techniques for studying sleep more efficiently.
This project integrates two parallel efforts combining innovations in hardware and data analytics: 1) enabling an
A considerable percentage of the population in the US and around the world suffers from a chronic sleep disorder. However, most of these disorders are not diagnosed or treated. There is a vital need for new wearable
adaptable and reconfigurable embedded system platform in the form factor of an adhesive patch, and 2) developing state-of-the-art machine learning techniques incorporating the data-driven models necessary for improving sleep monitoring system resilience. The hardware system fuses
technologies to increase the capacity of sleep researchers to make further advances in investigating sleep, understanding sleep pathologies, and to improve the ability of clinicians to reliably detect and treat sleep disorders. The results from this research also have the potential to positively influence the continuous-monitoring
multimodal wearable sensors, combining near infrared spectroscopy with other traditional sleep related signal sensors on skin-conformable substrates, to collect data on multiple body locations. The data analytics platform includes 1) signal processing to enable data-driven metrics
instrumentation required for other chronic conditions such as heart diseases. In addition to providing a novel, artificial intelligence-driven and reconfigurable tool design for sleep research, this effort will shed light into novel multimodal
for signal quality assessment for a given inference task, 2) inference models based on transfer learning techniques and diverse datasets for detection of sleep events and disorders using new sensing modalities, and 3) Bayesian Neural Network supported sensor selection for improving
biomarkers assessed noninvasively in wearable form factors for detection of sleep stages and disorders.
the resilience and adaptability of sleep sensor systems.
Photonic sensor and circuit for sleep studies, in an adhesive bandage form factor with wireless recharging capabilities.
Principal Investigators: Dr. Alper Bozkurt, Dr. James Dieffenderfer, Dr. Edgar Lobaton, Electrical & Computer Engineering, NC State University Dr. Michael Daniele, Electrical & Computer Engineering, Biomedical Engineering, NC State University Dr. Vladimir Pozdin, Electrical & Computer Engineering, Florida International University
Postdocs and Students:
Dr. Parvez Ahmmed Devon Martin, Kaila Peterson, Tamoghna Saha, Tanner Songkakul, Evan Williams
NSF ASSIST Center
Bio-Electro-Photonic Microsystem Interfaces for Small Animals Objective:
This project is aimed at developing a wirelessly powered injectable capsule capable of wirelessly monitoring biophotonic and bioelectrical physiological signals in small animals. This microsystem responds to the critical need for a minimally invasive class of devices for continuous recording of key physiological parameters of animals in typical environments without disturbing natural behavior.
The microsystem under development is expected to open a physiological window to improve understanding of the physiology of small animals in their natural environment. This system would be impactful for the welfare of farm, companion, working, and wildlife animals in addition to providing new bi-directional channels to communicate with them. Canine Chest Strap
Approach: Front Chest Support
This project develops two parallel physiological and behavioral sensing platforms on two different form factors: an injectable subcutaneous capsule and a wearable harness system for animals. The capsule platform provides photoplethysmography, electrocardiography, accelerometry, and thermometry measurements from under the skin. This is used to calculate heart rate, respiration rate, oxygen saturation, pulse transit time, and core body temperature. The harness system is for simple wearable applications and provides electrocardiography,
3D Printed Electrodes
RPI Zero Wireless
Battery and Charging Circuit
photoplethysmography, inertial sensing, and environmental sensing integrated into a standard dog harness and collar. Smart Collar
Key Accomplishments: The capsule system has been evaluated in a clinical setting for tracking physiological signals in rats and chickens. The collar and harness system have been deployed in the field with guide dog puppies, with the goal of improving the puppy training program outcomes. Recent efforts focus on training dogs to follow or interact with unmanned-air-
Environmental Ambient temperature, humidity, barometric pressure, light, noise Behavioral Activity level and barking Physiological Sleep and resting heart rate and respiration
vehicles, with the goal of deploying this in working dog applications such as search and rescue operations and agricultural pest detection.
Principal Investigator: Dr. Alper Bozkurt, Electrical & Computer Engineering, NC State University
Postdocs and Students: Funding source:
Dr. Parvez Ahmmed James Reynolds, Caleb Readling, Devon Martin, Brendan Thompson, Evan Williams
Cough Detection Using Wearables and Embedded Machine Learning Objective:
The objective of this project is to develop an Automatic Cough Detection Algorithm (ACDA) for wearable devices that meets clinical monitoring requirements by fusing multimodal sensor data. This ACDA should be able to extract features from a wearable and process the data in a smart device. Furthermore, we wish to ensure that privacy is
Our CNN-based ACDA achieves a sensitivity of 92.7%, a specificity of 92.3%, and an accuracy of 92.5% using a sampling frequency of just 750 Hz. A low sampling frequency allows us to preserve patients' privacy by obfuscating their speech. We have analyzed the trade-off between speech obfuscation for privacy and cough
maintained so no speech is recognizable from the features, while maintaining enough details in the signal to detect and characterize different types of coughs.
detection accuracy and realized that the 750 Hz sampling rate is optimal.
Our group has developed an ACDA that meets clinical
Cough detection can serve as an important biomarker to
monitoring requirements, was developed using publicly available data, reliably operates at a low sampling frequency, and maintains user privacy. This ACDA is
monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent
implemented using a convolutional neural network (CNN). A realization of this solution is shown in the figure, in which an
ACDAs have shown promise to meet clinical monitoring requirements, but due to the required portability of sensing
acoustic signal is filtered by the embedded device, and it is used for cough detection in the smart device. We are working on enhancing the system by incorporating multiple sensing modalities (e.g., ECG, PPG, audio, and inertial) in
technologies and the extended duration of data recording, only in recent years have they made their way to non-clinical settings. More precisely, these ACDAs operate at high sampling frequencies, which leads to high
the wearable device. By combining data from a wearable,
power consumption and computing requirements, making
we will be able to separate interfering sounds from individuals other than the main user. In addition, we will be able to correlate and fuse these measurements with the
them difficult to implement on a wearable device. Having an ACDA capable of continuous monitoring while maintaining privacy would allow for new types of diagnosis
other modalities. Part of our focus for our cough detection efforts has also been integration with the embedded
and prevention of disease spread in communities.
hardware, in which we are trying to minimize the amount of data to be transmitted and processed, thereby reducing power consumption and addressing privacy concerns.
The cough detection system uses a wearable with an integrated microphone to capture, filter, and transmit audio signals to a smart phone, which runs the Automatic Cough Detection Algorithm (ACDA) .
Principal Investigators: Dr. Edgar Lobaton, Electrical & Computer Engineering, NC State University Dr. Alper Bozkurt, Electrical & Computer Engineering, NC State University Dr. Michelle Hernandez, Department of Pediatrics, UNC-CH
Postdocs and Students:
Dr. Tahmid Latif Dr. James Dieffenderfer Jeffrey Barahona, Yuhan Chen
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