Learning Dynamics from Kinematics: Estimating Foot Pressure from Video.

Human pose stability analysis is the key to understanding locomotion and control of body equilibrium, with numerous applications in the fields of kinesiology, medicine and robotics. We propose and validate a novel approach to learn dynamics from kinematics of a human body to aid stability analysis. More specifically, we propose an end-to-end deep learning architecture to regress foot pressure from a human pose derived from video. We have collected and utilized a set of long (5min +) choreographed Taiji (Tai Chi) sequences of multiple subjects with synchronized motion capture, foot pressure and video data. The derived human pose data and corresponding foot pressure maps are used jointly in training a convolutional neural network with residual architecture, named PressNET. Cross-validation results show promising performance of PressNET, significantly outperforming the baseline method under reasonable sensor noise ranges. We also show that the center of pressure locations computed from our regressed foot pressure maps are more accurate than those obtained from the baseline approach.