8 citations · 12 across the 16 of their papers we have counts for
6 papers · 1 filter
Differentiable Biomechanics for Markerless Motion Capture in Upper Limb Stroke Rehabilitation: A Comparison with Optical Motion Capture
Tim Unger, Arash Sal Moslehian, J. D. Peiffer +5
Marker-based Optical Motion Capture (OMC) paired with biomechanical modeling is currently considered the most precise and accurate method for measuring human movement kinematics. H…
A Causal Framework for Precision Rehabilitation
R. James Cotton, Bryant A. Seamon, Richard L. Segal +5
Precision rehabilitation offers the promise of an evidence-based approach for optimizing individual rehabilitation to improve long-term functional outcomes. Emerging techniques, in…
Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics
J. D. Peiffer, Kunal Shah, Shawana Anarwala +2
Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensor…
Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation
Paweł A. Pierzchlewicz, Caio O. da Silva, R. James Cotton +1
Single camera 3D pose estimation is an ill-defined problem due to inherent ambiguities from depth, occlusion or keypoint noise. Multi-hypothesis pose estimation accounts for this u…
Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture
R. James Cotton
Recent developments have created differentiable physics simulators designed for machine learning pipelines that can be accelerated on a GPU. While these can simulate biomechanical…
Advancing Monocular Video-Based Gait Analysis Using Motion Imitation with Physics-Based Simulation
Nikolaos Smyrnakis, Tasos Karakostas, R. James Cotton
Gait analysis from videos obtained from a smartphone would open up many clinical opportunities for detecting and quantifying gait impairments. However, existing approaches for esti…