Wenlong Zhang, UC Berkeley
1pm Friday, Nov 21, 2014 in ITEB 336
Our society is witnessing an unprecedent, enduring, and pervasive aging process. Aging not only contributes to integrative challenges to memory, balance, and mobility for healthy subjects, but also leads to degenerative conditions of the musculoskeletal system and the nervous system. With more people requiring walking assistance, the demand for gait physical therapy and rehabilitation has increased rapidly over the years.
Current gait rehabilitative therapy is provided by physical therapists who stimulate patients’ reflexes as well as use manual techniques to facilitate their walking. Moreover, the current assessment of the gait impairments is based on the visual observation of therapists, video analysis, physical tests, as well as self reports from patients. Therefore, current gait evaluation is quite subjective and it puts really strict requirements on physical therapists, who are already in short supply.
As the first step to build the next-generation gait rehabilitation system, a wireless human motion capture system is introduced for performing real-time gait analysis and providing visual feedback to therapists and patients during rehabilitation training. The system includes a pair of smart shoes, several inertial sensors, and a user interface. The smart shoes are employed to measure ground reaction forces and detect gait phases, and inertial sensors are used to estimate joint rotations, step lengths, and gait speeds. User interfaces are developed on a laptop as well as an iPad.
Experimental data have been collected from healthy subjects and patients with neurological impairments. A clinical test has been conducted with 24 stroke and Parkinson’s disease patients to examine the performance of the system and evaluate the possibility of in-home rehabilitation. Progress of patients in the control group (with traditional rehabilitation only) and experimental group (with traditional rehabilitation and visual feedback) is discussed based on statistical data analysis. As concluding remarks, achievements and limitations of the clinical study are summarized.
Bio: Wenlong Zhang received the B.Eng. degree (Hons.) in control science and engineering from Harbin Institute of Technology, China, in 2010, and the M.S. degree in mechanical engineering and the M.A. degree in statistics from the University of California at Berkeley (UC Berkeley) in 2012 and 2013, respectively, where he is currently working toward the Ph.D. degree in mechanical engineering. His research interests include gait analysis and rehabilitation, real-time and human-involved cyber-physical systems, and statistical data analysis. Mr. Zhang received the Best Paper Award at the IEEE Real-time System Symposium in 2013 and was among the Semi-plenary Paper Award Finalists at the ASME Dynamic Systems and Control Conference in 2012. He also received the Berkeley Fellowship for Graduate Study at UC Berkeley from 2010 to 2015.