5 citations · 5 across the 4 of their papers we have counts for
6 papers · 1 filter
Self-Supervised Learning of Gait-Based Biomarkers
R. James Cotton, J. D. Peiffer, Kunal Shah +5
Markerless motion capture (MMC) is revolutionizing gait analysis in clinical settings by making it more accessible, raising the question of how to extract the most clinically meani…
Markerless Motion Capture and Biomechanical Analysis Pipeline
R. James Cotton, Allison DeLillo, Anthony Cimorelli +5
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 rehabili…
Improved Trajectory Reconstruction for Markerless Pose Estimation
R. James Cotton, Anthony Cimorelli, Kunal Shah +3
Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views, and has the potential to make movement analysis easy and quick, inc…
Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions
Paweł A. Pierzchlewicz, R. James Cotton, Mohammad Bashiri +1
Due to depth ambiguities and occlusions, lifting 2D poses to 3D is a highly ill-posed problem. Well-calibrated distributions of possible poses can make these ambiguities explicit a…
Transforming Gait: Video-Based Spatiotemporal Gait Analysis
R. James Cotton, Emoonah McClerklin, Anthony Cimorelli +2
Human pose estimation from monocular video is a rapidly advancing field that offers great promise to human movement science and rehabilitation. This potential is tempered by the sm…
PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research
R. James Cotton
There has been significant progress in machine learning algorithms for human pose estimation that may provide immense value in rehabilitation and movement sciences. However, there…