Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters
This work addresses the need for accurate and markerless gait analysis in clinical and real-world settings, providing an incremental yet significant improvement over existing pose-estimation methods.
The authors tackled the problem of gait analysis with a markerless 3D human reconstruction framework, achieving strong agreement with reference marker-based data and outperforming pose-estimation methods alone. The framework offers a scalable and interpretable approach for accurate gait assessment.
This paper presents a biomechanically interpretable framework for gait analysis using 3D human reconstruction from video data. Unlike conventional keypoint based approaches, the proposed method extracts biomechanically meaningful markers analogous to motion capture systems and integrates them within OpenSim for joint kinematic estimation. To evaluate performance, both spatiotemporal and kinematic gait parameters were analysed against reference marker-based data. Results indicate strong agreement with marker-based measurements, with considerable improvements when compared with pose-estimation methods alone. The proposed framework offers a scalable, markerless, and interpretable approach for accurate gait assessment, supporting broader clinical and real world deployment of vision based biomechanics