8 citations · 12 across the 16 of their papers we have counts for
17 papers
Cross-Model Distillation of a Human-Pose Foundation Model from Unannotated Infant Video for Markerless 3D Pose Estimation
R. James Cotton, Divya Joshi, Colleen Peyton
Spontaneous movement is one of the earliest windows onto an infant's neuromotor health, and structured clinical instruments that score it are validated early predictors of cerebral…
Biomechanical 3D Body: Self-Supervised Distillation of Biomechanical Pose from a 3D Body Foundation Model
R. James Cotton, J. D. Peiffer, Lucinda Williamson +2
State-of-the-art monocular body recovery methods predict mesh vertices and angles on the corresponding kinematic tree, but their outputs lack biomechanically defined joint angles t…
Markerless Motion Capture in Routine Clinical Upper Limb Assessments: Validity and Insights Beyond Ordinal Scoring
Tim Unger, Olivier Lambercy, Roger Gassert +3
The Action Research Arm Test (ARAT) is a widely-used upper limb outcome measure in neurorehabilitation, but its ordinal scoring is subjective and suffers from limited sensitivity a…
Biomechanics-aware Multi-view Markerless Motion Capture of Dexterous Hand Movements
Pouyan Firouzabadi, J. D. Peiffer, Kunal Shah +4
Markerless motion capture (MMC) techniques have been widely beneficial in biomechanical analysis of human movement; however, application to complex motions of the hand lags other m…
Markerless Motion Capture for Biomechanical Whole-Body Kinematic Estimation in Infants
Divya Joshi, J. D. Peiffer, Colleen Peyton +1
arly identification of motor impairment in infancy relies on expert visual assessment of spontaneous movement, motivating the development of automated, objective alternatives. One…
Monocular Biomechanical Tracking of Fingers with Inverse Kinematics to Foundation Models
R. James Cotton, Pouyan Firouzabadi, Wendy Murray
Accurate hand and finger tracking from video has significant clinical applications for monitoring activities of daily living and measuring range of motion, yet monocular video appr…
Monocular Markerless Motion Capture Enables Quantitative Assessment of Upper Extremity Reachable Workspace
Seth Donahue, J. D. Peiffer, R. Tyler Richardson +7
To validate a clinically accessible approach for quantifying the Upper Extremity Reachable Workspace (UERW) using a single (monocular) camera and Artificial Intelligence (AI)-drive…
Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture
Seth Donahue, Irina Djuraskovic, Kunal Shah +3
Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation and trust of multi-view markerle…
Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone
J. D. Peiffer, Kunal Shah, Irina Djuraskovic +6
Movement directly reflects neurological and musculoskeletal health, yet objective biomechanical assessment is rarely available in routine care. We introduce Portable Biomechanics L…
Biomechanical Reconstruction with Confidence Intervals from Multiview Markerless Motion Capture
R. James Cotton, Fabian Sinz
Advances in multiview markerless motion capture (MMMC) promise high-quality movement analysis for clinical practice and research. While prior validation studies show MMMC performs…
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…
Dynamic Gaussian Splatting from Markerless Motion Capture can Reconstruct Infants Movements
R. James Cotton, Colleen Peyton
Easy access to precise 3D tracking of movement could benefit many aspects of rehabilitation. A challenge to achieving this goal is that while there are many datasets and pretrained…