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Self-Supervised Learning of Gait-Based Biomarkers
We find that contrastive learning on unannotated gait data learns a representation that captures clinically meaningful information.
Markerless Motion Capture and Biomechanical Analysis Pipeline
Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis.
Improved Trajectory Reconstruction for Markerless Pose Estimation
Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views, and has the potential to make movement analysis easy and quick, including gait analysis.
Transforming Gait: Video-Based Spatiotemporal Gait Analysis
Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation.
Spatiotemporal Characterization of Gait from Monocular Videos with Transformers
Using more than 9000 monocular video from an instrumented gait analysis lab, we evaluated the performance of existing algorithms for measuring kinematics.
