Dr. Jingang Yi 
Professor, Rutgers University
Friday, November 11, 2022
Tickle 500 2:15-3:15pm
Abstract: Real-time gait activity detection and pose estimation are critical for enabling many human healthcare automation and other industrial automation applications. In this talk, I will present a machine learning-enabled, wearable inertial measurement unit (IMU)-based design to provide effective and efficient activity detection and pose estimation for legged walkers. The gait event and activity detection are first built on a recurrent neural network (RNN) with long short-term memory (LSTM) cells with IMUs on lower limbs. A learned low-dimensional motion manifold is parameterized by a phase variable with a Gaussian process dynamic model. I will present two case studies to illustrate the design. The first application is related to real-time human walking gait detection and pose estimation for construction workers, and the second application is for horse limb lameness detection and pose estimation. Experimental results show that the RNN-LSTM-based approach achieves gait activity detection with 96% accuracy, the estimated human pose errors are within 8.30 degs, and the detection latency is within 20 ms using only a single IMU attached to human shank. For horse lameness detection, the design achieves 95% accuracy and the pose estimation scheme can predict the 24 limb joint angles with average errors less than 5 and 10 degs under normal and induced lameness conditions, respectively. Because the design is built on wearable inertial sensors, it can be potentially used for potential real-time applications in open field.
Bio: Professor Jingang Yi received the B.S. degree in electrical engineering from Zhejiang University in 1993, the M.Eng. degree in precision instruments from Tsinghua University in 1996, and the M.A. degree in mathematics and the Ph.D. degree in mechanical engineering from the University of California, Berkeley, in 2001 and 2002, respectively. He is currently a Full Professor in mechanical engineering and a Graduate Faculty member in electrical and computer engineering at Rutgers University. His research interests include human-robot interactions and assistive robotics, autonomous robotic and vehicle systems, dynamic systems and control, mechatronics, automation science and engineering, with applications to biomedical, transportation and civil infrastructure systems. Prof. Yi is a Fellow of American Society of Mechanical Engineers (ASME) and a Senior Member of IEEE. He has received several awards, including the 2017 Rutgers Chancellor’s Scholars, 2014 ASCE Charles Pankow Award for Innovation, the 2013 Rutgers Board of Trustees Research Fellowship for Scholarly Excellence, and the 2010 NSF CAREER Award. He serves as a Senior Editor for IEEE Robotics and Automation Letters and IEEE Transactions on Automation Science and Engineering, and an Associate Editor for International Journal of Intelligent Robotics and Applications. He also served in editorial board of IFAC journals Control Engineering Practice, Mechatronics, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Automation Science and Engineering, IEEE Robotics and Automation Letters, and ASME Journal of Dynamic Systems, Measurement and Control.