8 citations · 13 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Fiber Optic Sensing Glove for High Performance Dexterous Manipulation Capture
J. D. Peiffer, Taylor Niehues, Li Guan +2
Capturing hand pose during dexterous manipulation remains difficult: vision-based methods degrade under occlusion and challenging lighting, while sensorized gloves, though occlusio…
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 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…