Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters

• URL: http://arxiv.org/abs/2603.02499v1
• Date: Tue, 03 Mar 2026 01:04:00 GMT
• Title: Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters
• Authors: Akila Pemasiri, Ethan Goan, Glen Lichtwark, Robert Schuster, Luke Kelly, Clinton Fookes,
• Abstract summary: This paper presents a biomechanically interpretable framework for gait analysis using 3D human reconstruction from video data.<n>Results indicate strong agreement with marker-based measurements, with considerable improvements when compared with pose-estimation methods alone.
• Score: 19.48195924418134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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