Direct Clinical Joint Angle Extraction from Parametric Body Model Rotation Matrices
2026-07-20 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors found a way to measure joint angles from simple body models created by analyzing single smartphone videos, without needing complicated extra steps like inverse kinematics or special fitting. Their method gets joint angle errors of about 4.5 degrees, similar to more complex systems but much faster and simpler. It works with different body models by just adjusting a small calibration table, and can run in real time from a live video stream. This makes movement analysis easier and more accessible for clinics, at-home use, and remote rehabilitation without needing extra equipment or detailed personal data.
joint anglesrotation matricesparametric body modelinverse kinematicssingle-camera videocalibration tablereal-time processingtelerehabilitationmodel scaling
Authors
Joey Páolo Kardolus, Daan Hendriks, Jaap Jansen
Abstract
Quantitative joint angles are rarely available in routine care because the tools are slow, costly, or confined to a laboratory. We show that clinical joint angles can be read directly from the per-segment rotation matrices a parametric body model already produces, with no inverse-kinematics or musculoskeletal-model fitting step. On the OpenCap LabValidation cohort, using the GEM-X body-model estimator on single-smartphone video, our pooled mean absolute error is 4.50 degrees over the fifteen joint angles that match the OpenCap Monocular reference set, the same accuracy range as OpenCap Monocular's 4.8 degrees on the same cohort and reference standard, from a much simpler pipeline. The step that connects a body model to clinical angles is a small calibration table rather than an optimisation, so the same procedure transfers unchanged to other body models: repeating it on SAM 3D Body, changing only the table, gives 4.66 degrees, statistically indistinguishable from GEM-X, and runs in real time from a live single-camera stream. The method needs no per-recording inputs beyond the video itself: no participant height, no camera-intrinsics database, no per-subject model scaling. This broadens where movement analysis is practical, from in-clinic and at-home recording to telerehabilitation and large-scale decentralised studies.