Thursday, December 01, 2016
The Use of Wearable Sensors and Systems in Rehabilitation Medicine
There is a growing interest in the application of wearable technologies to monitor older adults and subjects with chronic conditions in the field of rehabilitation. The motivation for the use of wearable sensors and systems is due to the benefits that could be associated with long-term, frequent, and objective monitoring of individuals in their home and community settings. In this talk, I will review wearable systems and analytic methods that are currently used in the field of rehabilitation research by introducing a number of on-going projects that involve patients with stroke, Parkinson's disease, and knee osteoarthritis. More specifically, for the Parkinson project, I will provide a high-level overview of our recent study that focuses on remote monitoring of motor fluctuation in patients’ home and community settings using wearable motion sensors. For the stroke study, I will introduce a machine-learning based method that will combine sensor data and pre-treatment clinical data to estimate the post-treatment functional level. For the knee osteoarthritis study, I will introduce our recent development of a flexible joint sensor that can efficiently monitor ambulatory joint kinematics.
Sunghoon Ivan Lee
Assistant Professor, CS Department
University of Massachusetts, Amherst
Sunghoon Ivan Lee is an assistant professor of computer science at University of Massachusetts, Amherst. Professor Lee's research interests are in Mobile & Personalized Health, focusing on developing wearable sensors and data analytic methodologies to understand the health conditions associated with neurological, neuromuscular, or muscular skeleton disorders such as stroke, Parkinson's disease, traumatic brain injuries, osteoarthritis, etc. With a primary focus on evolution, his specific research interests include 1) designing and implementing novel sensors and remote monitoring systems that are motivated by practical medical needs, 2) constructing appropriate clinical trials, and 3) analyzing the obtained data to quantify patients' conditions and validate the systems' clinical efficacy.
Prior to joining the University of Massachusetts, Professor Lee was a postdoctoral research fellow in the Department of Physical Medicine and Rehabilitation at Harvard Medical School. He received his Ph.D. in Computer Science from UCLA in 2014 with the Outstanding Doctoral Research Award. He received his M.S. degrees in Electrical Engineering and Computer Science from UCLA in 2010 and 2012, respectively. His work received several paper awards including the Best Demo Award from the ACM MobiSys, the Best Demo Honorable Mention at IEEE SECON, and a Featured Article of the Issue at IEEE JBHI.