Markerless Motion Capture and Biomechanical Analysis Pipeline
This work addresses the need for accessible and accurate movement analysis tools in rehabilitation, though it is incremental as it builds on existing methods with specific optimizations.
The paper tackles the challenge of designing an accurate markerless motion capture pipeline for biomechanical analysis by analyzing key steps like keypoint detection and inverse kinematics optimization, resulting in a pipeline that facilitates precise movement estimation in rehabilitation settings.
Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabilitation by enabling more accurate tracking of outcomes and providing more sensitive tools for research. There are numerous steps between obtaining videos to extracting accurate biomechanical results and limited research to guide many critical design decisions in these pipelines. In this work, we analyze several of these steps including the algorithm used to detect keypoints and the keypoint set, the approach to reconstructing trajectories for biomechanical inverse kinematics and optimizing the IK process. Several features we find important are: 1) using a recent algorithm trained on many datasets that produces a dense set of biomechanically-motivated keypoints, 2) using an implicit representation to reconstruct smooth, anatomically constrained marker trajectories for IK, 3) iteratively optimizing the biomechanical model to match the dense markers, 4) appropriate regularization of the IK process. Our pipeline makes it easy to obtain accurate biomechanical estimates of movement in a rehabilitation hospital.