Workshop Organizer

Biography: Roozbeh Jafari (Texas A&M University) is the Tim and Amy Leach Professor of Biomedical Engineering, Computer Science and Engineering and Electrical and Computer Engineering at Texas A&M University. He received his Ph.D. in Computer Science from UCLA, under the supervision of Prof. Majid Sarrafzadeh and completed a postdoctoral fellowship at UC-Berkeley. His research interest lies in the area of wearable computer design and signal processing. He has raised more than $86M for research with $23M directed towards his lab. His research has been funded by the NSF, NIH, DoD (TATRC), DTRA, DIU, AFRL, AFOSR, DARPA, SRC and industry (Texas Instruments, Tektronix, Samsung & Telecom Italia). He has published over 200 papers in refereed journals and conferences. He has served as the general chair and technical program committee chair for several flagship conferences in the areas of wearable computers. Dr. Jafari is the recipient of the NSF CAREER award (2012), IEEE Real-Time & Embedded Technology & Applications Symposium best paper award (2011), Andrew P. Sage best transactions paper award (2014), ACM Transactions on Embedded Computing Systems best paper award (2019), and the outstanding engineering contribution award from the College of Engineering at Texas A&M (2019). He has been named Texas A&M Presidential Fellow (2019). He serves on the editorial board for the IEEE Transactions on Biomedical Circuits and Systems, IEEE Sensors Journal, IEEE Internet of Things Journal, IEEE Journal of Biomedical and Health Informatics, IEEE Open Journal of Engineering in Medicine and Biology and ACM Transactions on Computing for Healthcare. He is currently the chair of the IEEE Wearable Biomedical Sensors and Systems Technical Committee (elected) as well the IEEE Applied Signal Processing Technical Committee (elected). He serves on scientific panels for funding agencies frequently, served as a standing member of the NIH Biomedical Computing and Health Informatics (BCHI) study section (2017-2021), and is the inaugural chair of the NIH Clinical Informatics and Digital Health (CIDH) study section (2020-2022). He is a Fellow of the American Institute for Medical and Biological Engineering (AIMBE).
Talk Title: Digital Medicine for Cardiovascular Health
Talk Abstract: The bold vision of pervasive physiological monitoring, through proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities for precision medicine outside clinics and in ambulatory settings. Although significant progress has been made, several unmet needs remain; Lack of availability of advanced wearable sensing paradigms, noise and missingness in wearable data and labels in ambulatory settings, the unknown circumstances surrounding data capture in wearable paradigms, heterogeneity of the users both in terms of physiological and behavioral states, and often limited view into the user’s physiological state prevent extraction of actionable information. This seminar presents several topics that coherently articulate on the vision and the opportunities of digital medicine for cardiovascular health. The seminar covers three pillars of digital medicine, i) sensing, ii) signal processing and iii) context aware analytics and personalization as it pertains to cardiovascular health. We will introduce several novel sensing paradigms using bio-impedance that leverage various types of electrodes and electronic tattoos enabling blood pressure measurement with clinical grade accuracy. We will discuss the notion of particle filters that provide a generalizable and robust paradigm for reducing the impact of noise. Finally, we will discuss the notion of digital twin leveraging machine learning/AI, that will enhance the ability to extract actionable information in the context of several real-world applications. Digital medicine and wearables will play a significant role in the future of medicine outside clinics. The future directions present opportunities both in short-term translational research efforts with direct influence on clinical practice as well as long-term foundational development of theories and computational frameworks combining human physiology, physics, computer science, engineering, and medicine, all aimed at impacting the health and wellbeing of our communities.

Biography: Paolo Bonato (Harvard University), Ph.D., serves as Director of the Motion Analysis Laboratory at Spaulding Rehabilitation Hospital, Boston MA. He is an Associate Professor in the Department of Physical Medicine and Rehabilitation at Harvard Medical School, an Adjunct Professor of Biomedical Engineering at the MGH Institute of Health Professions, an Associate Faculty Member at the Wyss Institute for Biologically Inspired Engineering, and an Adjunct Associate Professor at Boston University College of Health & Rehabilitation Sciences. He has held Adjunct Faculty positions at MIT, the University of Ireland Galway, and the University of Melbourne. His research work is focused on the development of rehabilitation technologies with special emphasis on wearable technology and robotics. Dr. Bonato served as the Founding Editor-in-Chief of Journal on NeuroEngineering and Rehabilitation and currently serves as Founding Editor-in-Chief of the IEEE Open Journal of Engineering in Medicine and Biology. He received the M.S. degree in electrical engineering from Politecnico di Torino, Turin, Italy in 1989 and the Ph.D. degree in biomedical engineering from Universita` di Roma “La Sapienza” in 1995.
Talk Title: Using Digital Health Technology to Enable Tele-Rehabilitation Interventions
Talk Abstract: This lecture will review recent advances in the application of digital health technologies to the field of tele-rehabilitation. We will show how relying on digital health technologies and on machine learning algorithms, researchers have developed approaches suitable to derive accurate estimates of clinical scores via the analysis of data collected during the performance of functional movements. Examples provided during the lecture will include techniques to assess motor impairments and functional limitations from sensor and video data. We will discuss how digital technologies can be used to collect data to generate feedback during the performance of rehabilitation exercises outside of the clinic. Finally, we will discuss how these technologies can transform the way rehabilitation interventions are designed and implemented as they enable tracking individual responses to clinical interventions.

Biography: Sujit Dey (University of California, San Diego) is a Professor in the Department of Electrical and Computer Engineering, the Director of the Center for Wireless Communications, and the Director of the Institute for the Global Entrepreneur at the University of California, San Diego. He heads the Mobile Systems Design Laboratory, developing innovative and sustainable edge computing, networking and communications, multi-modal sensor fusion, and deep learning algorithms and architectures to enable predictive, personalized health, immersive multimedia, and smart mobility applications. He has created interdisciplinary programs involving multiple UCSD schools as well as community, city, and industry partners, notably the Connected Health Program in 2016 and the Smart Transportation Innovation Program in 2018. In 2017, he was appointed as an Adjunct Professor, Rady School of Management, and the Jacobs Family Endowed Chair in Engineering Management Leadership. Dr. Dey served as the Faculty Director of the von Liebig Entrepreneurism Center from 2013-2015, and as the Chief Scientist, Mobile Networks, at Allot Communications from 2012-2013. In 2015, he co-founded igrenEnergi, providing intelligent battery technology and solutions for EV mobility services. He founded Ortiva Wireless in 2004, where he served as its founding CEO and later as CTO and Chief Technologist till its acquisition by Allot Communications in 2012. Prior to Ortiva, he served as the Chair of the Advisory Board of Zyray Wireless till its acquisition by Broadcom in 2004, and as an advisor to multiple companies including ST Microelectronics and NEC. Prior to joining UCSD in 1997, he was a Senior Research Staff Member at NEC C&C Research Laboratories in Princeton, NJ. He received his Ph.D. in Computer Science from Duke University in 1991. Dr. Dey has co-authored more than 250 publications, and a book on low-power design. He holds 18 U.S. and 2 international patents, resulting in multiple technology licensing and commercialization. He has been a recipient of nine IEEE/ACM Best Paper Awards, and has chaired multiple IEEE conferences and workshops. Dr. Dey is a Fellow of the IEEE.
Talk Title: Personalized and Precise mHealth Care using Existing Devices and AI
Talk Abstract: For the past six years, we have been developing a platform P3.ai which enables personalized and precise health care of patients using existing off-the-shelf devices and AI, offering autonomous continuous engagement with patients resulting simultaneously in better health outcomes, reach and care efficiency. This talk will describe development of the platform for two broad areas – virtual chronic care and telerehabilitation. We will first describe our work with hypertension. Utilizing data remotely collected by existing devices like smart watches/activity trackers, home BP monitors and our mobile app, P3.ai uses machine learning techniques to identify the complex relationships between BP and lifestyle factors in order to obtain precise insights about the exact causes of BP for an individual hypertension patient. It provides precise, personalized and proactive lifestyle recommendations to the patient through an interactive and engaging patient app to enable them to achieve their BP goals. It also provides a dashboard for care providers to easily track patient outcomes and provide timely notifications only when physician intervention is needed, along with recommended precise care pathways. P3.ai is a cloud-based fully autonomous system with no health coaches and minimal physician/care team interventions needed, and hence enables highly cost-efficient scalable deployments. Next, we will briefly discuss our work on developing a “virtual physical therapist” to facilitate low-cost, continuous and remote rehabilitation for patients, utilizing patient’s existing mobile device, and machine vision and machine intelligence, to enable real-time monitoring, guidance and recommendations, while letting care providers track remotely the well-being, progress and compliance of patients. We will end the talk briefly looking at P3.ai roadmap, which includes extension to other chronic conditions, like diabetes, depression, and anxiety, and post-rehabilitation and post-surgery monitoring and guidance.