Abstract
Human movement science has long been split between costly, lab-bound optoelectronic capture rigs and broadly accessible but biomechanically shallow computer vision tools. Marker-based systems require specialist operators and substantial capital investment, blocking meaningful deployment at clinical, athletic, or consumer scale. Markerless alternatives like MediaPipe and OpenPose recover 2D and 3D joint keypoints from ordinary camera footage but cannot recover the muscle forces and internal contact mechanics that drive joint wear and soft-tissue injury.
This paper presents a unified, end-to-end monocular visual-to-neural-control pipeline that maps raw single-camera smartphone footage directly into real-time internal forces, joint moments, and muscle activations at near-laboratory fidelity. The architecture integrates PromptHMR for spatial-semantic mesh reconstruction, GaitDynamics for diffusion-based generative kinetic synthesis, MuscleMimic and MuJoCo Warp for GPU-parallelized musculoskeletal simulation, low-dimensional muscle synergy priors for neurophysiological action-space constraints, SVK hyperelastic models for soft-tissue compliance at wearable interfaces, and a CNN-BiGRU-Attention predictor for real-time Knee Contact Force estimation.
The document also includes a complete Product Requirements Document (PRD) and 12-month engineering roadmap for the BioMotion-AI Enterprise SDK, targeting sub-16.6 ms latency on consumer mobile devices, with applications spanning longitudinal ACL rehabilitation tracking, early osteoarthritis screening, and assistive wearable robotics integration.
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Kanagat, S. P. (2026). Monocular Musculoskeletal Biomechanics: A Unified End-to-End Visual-to-Neural-Control Pipeline for Real-Time Clinical Dynamics. Zenodo. https://doi.org/10.5281/zenodo.20415895