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Markerless motion capture using computer vision and human pose estimation(HPE) has the potential to expand access to precise movement analysis. Thiscould greatly benefit rehabilitation by enabling more accurate tracking ofoutcomes and providing more sensitive tools for research. There are numeroussteps between obtaining videos to extracting accurate biomechanical results andlimited research to guide many critical design decisions in these pipelines. Inthis work, we analyze several of these steps including the algorithm used todetect keypoints and the keypoint set, the approach to reconstructingtrajectories for biomechanical inverse kinematics and optimizing the IKprocess. Several features we find important are: 1) using a recent algorithmtrained on many datasets that produces a dense set of biomechanically-motivatedkeypoints, 2) using an implicit representation to reconstruct smooth,anatomically constrained marker trajectories for IK, 3) iteratively optimizingthe biomechanical model to match the dense markers, 4) appropriateregularization of the IK process. Our pipeline makes it easy to obtain accuratebiomechanical estimates of movement in a rehabilitation hospital.

Markerless Motion Capture and Biomechanical Analysis Pipeline