Muscle-driven hand simulations emphasize the critical role of the extensor mechanism

Biomechanical simulations of complex hand motions remain scarce, due to challenges that span computation and data acquisition. Using a computer vision-based motion capture approach, a 23-degree of freedom musculoskeletal model, and direct collocation optimization, we performed muscle-driven simulations to track hand kinematics from 7 participants performing American Sign Language gestures. While proximal joints were tracked accurately, interphalangeal joint tracking was significantly worse, with a consistent flexion bias. Modifications to finger extensor muscle paths that incorporated the dual-inserting nature of the extensors improved accuracy, suggesting better representation of extensor force distribution across distal joints may be necessary for accurate hand simulations.