Speaker: Xiaoxu Wu
Affiliation: Ph.D. Candidate - UCLA
Abstract: The proliferation of powerful microcomputers and the development of modern machine learning tools have enabled human daily activity monitoring systems using wearable inertial sensor like accelerometers and gyroscopes. These systems fulfilled the urgent need in health and wellness industries in helping doctors and clinicians during diagnosis, treatment and rehabilitation processes for neurological diseases like strokes and Parkinson’s.
For most current activity monitoring systems, there exists an assumption that the sensors are always securely and correctly mounted. Unfortunately, such assumption does not hold as the scale of studies increase, and it is especially challenging for subjects with neurological diseases to follow instructions about how to mount the sensors. This is because elderlies tend to be technophobic and neurological diseases are usually companied with cognitive difficulties. Such errors in sensor mounting pose can cause large amount of data loss and distortion and will affect the robustness of the systems severely.
In observance of these issues, a series of solutions for sensor orientation and position errors in human motion monitoring and activity classification will be presented. Opportunistic calibration methods to find the true sensor orientation and position will be discussed. In addition, robust monitoring systems regardless of the exact sensor pose will be proposed.
Biography: Xiaoxu Wu is currently a Ph.D. candidate in Electrical Engineering Department, University of California, Los Angeles. She received her B.S. in Communications Engineering from Fudan University, Shanghai, China in 2010 and her M.S. in Electrical Engineering from University of California, Los Angeles in 2012. Her current research focus is on robust human activity recognition with wearable inertial sensors.
Date(s) - Mar 02, 2016
9:30 am - 11:30 am
E-IV Faraday Room #67-124
420 Westwood Plaza - 6th Flr., Los Angeles CA 90095