Abstract: Recent advances in wearable sensing and mobile computing have given researchers the ability to collect unprecedented amounts of data about everything from biology to behavior that can explain and improve people's health status. Day-to-day data from wearable sensors allows for better and more personalized decisions in regard to health care and management. Specifically, continuous monitoring of physiology and behavior can help us to assess disease risks, perform disease prevention and early detection of chronic conditions. However, there still exist a multitude of challenges to implement this vision of precision medicine. Wearable sensors provide large, noisy, complex data streams about the many facets of our life and health, but there is still a gaping need for computational techniques that can transform sensor data into set of useful bio-markers readily interpretable by clinicians. This talk will describe our recent work in pairing rich probabilistic models with novel wearable sensor designs to dramatically expand the scale and quality of physiological data we can obtain in the field while minimizing the burden to participants.
Bio: Emre Ertin is a research associate professor with the Department of ECE and a principal investigator with the Davis Heart and Lung Research Institute at OSU. He is the Sensor Technology Lead for the NIH Center of Excellence in Mobile Sensor Data-to-Knowledge (MD2K). Before joining Ohio State in 2003, he was with the Core Technology Group of Battelle Memorial Institute. At OSU he served as a principal investigator on NSF, AFOSR, ARO, AFRL, ARL, DARPA, and NIH funded projects on novel sensor concepts and associated data analytic techniques. EasySense, an ultra-wideband microradar sensor developed by Dr. Ertin’s research group for lung water monitoring in congestive heart failure patients is currently in pilot testing on a cohort of 150 patients at Ohio State’s Wexner Medical Center.