*This seminar was held in-person in Levine 307 as well as virtually…
In the realms of health and sports, quantitative analysis of human movement provides guidance for individualized performance characterization, training, and health assessment. Data collection for biomechanical analysis of human motion is typically performed using expensive, specialized hardware that requires experiments to occur in a constrained lab setting. Computer vision algorithms using inexpensive, off-the-shelf video cameras for data collection would enable observation in more natural environments with minimal cost. Towards this end, this talk reports on:
• Our effort in collecting the first publicly available Taiji-MultiModal (PSU TMM100) dataset containing 100 sequences of simplified 24-form Taiji with synchronized mocap, video, and foot-insole pressure maps.
• An unsupervised learning method for training a light-weight encoder suitable for 3D body pose classification and sequence-to-sequence temporal alignment.
• The first deep learning baseline that demonstrates reliable and repeatable mapping from either a single frame or short sequence of human pose (kinematics) into predicted foot pressure map output (dynamics), leading to image-based stability monitoring in natural environments.