eWEAR Symposium 2021: Session 3

Haeyoung Noh is an associate professor in the Department of Civil and Environmental Engineering (CEE). At Stanford University, Noh received her PhD and MS degrees in the CEE department and her second MS degree in Electrical Engineering. Noh earned her BS in Mechanical and Aerospace Engineering at Cornell University.  Her research introduced the new concept of “structures as sensors” to enable physical structures, e.g., buildings and vehicle frames, to be user- and environment-aware. These structures indirectly sense humans and surrounding environments from their vibrations, instead of directly measuring the sensing targets with additional dedicated sensors. This concept brought a paradigm shift in how structures themselves provide valuable information and how the structures interact with us.  Similarly, in vehicle engineering, vibrations measured inside vehicles contain information about transportation infrastructure, vehicle itself, and driver.  The result of her work has been deployed in a number of real-world applications from trains, to the Amish community, to eldercare centers, to pig farms.  Ultimately, her work aims to allow structural systems to become general sensing platforms that are more practical to deploy than dedicated sensing hardware and easier to maintain throughout the structural lifetime.

Smart structures are designed to sense, understand, and respond to various needs of human users. However, traditional monitoring approaches using dedicated sensors often result in dense sensing systems that are difficult to install and maintain in large-scale structures. This talk introduces “structures as sensors” approach that utilizes the structure itself as a sensing medium to indirectly infer occupant information. For example, we can infer occupant identity, location, and walking pattern through their activity induced building floor vibrations. This approach enables non-intrusive monitoring while significantly reducing the number and type of sensors needed to be installed and maintained. Challenges lie, however, in creating robust inference models for analyzing convoluted noisy structural response data collected from multiple structures (e.g., building responses due to human activities, HVAC systems, and outside traffic). To this end, we developed physics-guided data analytics approaches that combine statistical signal processing and machine learning with physical principles. I will present a model transfer approach for occupant tracking and characterization across multiple structures. Our system is deployed in real-world testbeds, including eldercare centers, pig farms, and campus buildings.

Alberto Salleo is a Professor of Materials Science & Engineering and Department Chair at Stanford University.  Alberto Salleo holds a Laurea degree in Chemistry from University of Rome and graduated as a Fulbright Fellow with a PhD in Materials Science from UC Berkeley in 2001.  From 2001 to 2005 Alberto was at Xerox Palo Alto Research Center as a post-doctoral research fellow and then member of the research staff.  At PARC Alberto conducted research on the fabrication and characterization of plastic-based electronics and printing of optoelectronic components for displays.  In 2005 Alberto became a professor at Stanford and has received numerous awards, such as the NSF Career Award, the 3M Untenured Faculty Award, the SPIE Early Career Award, the Tau Beta Pi Excellence in Undergraduate Teaching Award, and the W.L. Gore Award for Excellence in Teaching, Stanford’s highest teaching honor.  Alberto is Associate Editor of MRS Communications since 2011 and has published over 200 peer-reviewed articles in addition to editing 2 books.  He has been a Thomson Reuters Highly Cited Researcher since 2015, recognizing that he ranks in the top 1% cited researchers in his field. In 2020 Alberto was knighted in the Order of Merit of the Italian Republic.

Conjugated polymers are a promising materials family for flexible, wearable electrochemical sensors. I will describe a conventional photolithographic patterning process that allows to define arrays of polymer electrodes and electrochemical transistors on a flexible substrate. This process has been used to demonstrate electrochemical sensors on thin polyimide that can sense electrolytes in sweat in a form factor that can be used for wearable applications. 

An astounding 800,000 strokes occur annually in the United States, creating an immense burden for stroke survivors, their caregivers, and the healthcare system with costs of more than $100 billion per year. Currently, no therapies exist to improve recovery. Stem cell therapy has emerged as a promising clinical therapy for stroke recovery. We propose to utilize implantable stem cells, conductive polymer scaffolds, and rehabilitation made possible by wireless powered systems to create a more regenerative microenvironment for stroke repair. The goals of this project are to: 1) Determine the optimal in-vitro stimulation patterns for modulating factor expression of stem cells; 2) Achieve wireless in-vivo stimulation of animals during rehabilitative exercises to determine effect on stroke recovery.  The goals would have profound effects on stem cell biology and stroke therapeutics. Our implantable wirelessly powered system provides a unique platform to interact with the recovering brain during rehabilitation.  Understanding and reinforcing recovering circuits in the stroked brain provides a powerful tool to improve recovery and better understand neural repair.

We aim to address the problem of affect dysregulation in youth diagnosed with autism spectrum disorder (ASD).  Affect dysregulation refers to a failure to successfully manage one’s emotions, moods, or stress responses in a context-appropriate way. In response to challenges with existing non-technology-based approaches and scarcity of vibrotactile-based approaches for adolescents diagnosed with ASD, we aim to leverage our expertise in affect regulation and product design and development to help youth with ASD to regulate their affect. We will do this with the following steps: 1) Investigate placement, pattern and personalization of vibrotactile effects for this population, as well as appropriate in-lab stressors, based on the insights of positive psychology and participatory design; 2) Design a vibrotactile-based system that can be used for out-of-lab and longer-term studies that is effective for participants diagnosed with ASD; 3) Conduct a preliminary evaluation of the efficacy of vibrotactile-based approaches for affect regulation for youth diagnosed with ASD in the lab.

Dr. Matthew Smuck is the Chief of PM&R and Professor of Orthopaedic Surgery at Stanford University. His clinical work concentrates on medical and interventional management of spine disorders. He is a physician leader and current President of the Spine Intervention Society (SIS) and has served on the Executive Editorial Board of The Spine Journal and the Board of Directors of the North American Spine Society (NASS) and the Foundation for PM&R. Dr. Smuck is an award-winning researcher and pioneer of the new field of physical performance monitoring using wearable sensors. He founded and directs the Wearable Health Lab at Stanford, focused on developing methods of wearable sensor data analytics to discover digital phenotypes of mobility-limiting orthopedic and neurologic diseases, and applying these methods to improve disease detection, prevention and treatment. Dr. Smuck has authored more than 100 peer-reviewed publications. His work is recognized by numerous research society awards and publication awards, including the ISSLS Medtronic Award, the American Academy of PM&R’s President’s Citation Award, the PM&R Journal’s Best Original Research Award, the ISSLS Prize, and The Spine Journal’s Outstanding Paper Award.

Physical inactivity is considered both a cause and a consequence of musculoskeletal disease, yet the details of this relationship are poorly understood. In part this is due to reliance on imprecise self-reported measures of physical activity. However, objective measurement using inertial sensors (traditionally focused on step-counting or energy expenditure) has shown poor responsiveness to changes in disease severity, and often failed to differentiate between disease populations and controls. In this talk I will outline a multiphase approach to redefining the role of inertial sensors in musculoskeletal disease (called “physical performance monitoring”); and I will present results from several recent studies demonstrating how this new approach uncovered accurate digital biomarkers for a variety of common musculoskeletal disorders.