Antoine Motte dit Falisse

This study evaluates OpenCap's accuracy in measuring ACL injury risk factors both in and out of the lab compared to optical motion capture.

This paper develops deep learning models to detect neuromuscular diseases and quantify movement impairment using video-derived biomechanics data from smartphone recordings.

This paper develops a hybrid machine learning and musculoskeletal simulation framework to improve dynamics estimation from smartphone video during walking, achieving lower error rates than simulation-only or ML-only approaches.

Smartphone-video biomechanics (OpenCap) reproduced four timed function tests (r > 0.98) and separated facioscapulohumeral from myotonic dystrophy better than test times (balanced accuracy 82% vs 50%; 129 participants).

This paper improves motion capture accuracy by enhancing sparse keypoints with a more generalizable marker enhancer trained on a larger dataset.

This study compares hamstring mechanics during accelerative and constant speed running, finding greater muscle-tendon lengths and lengthening velocities during acceleration.