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3D Digital Twin Visualization of Multiclass GRF-Based Gait Disorder Classification

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

Automated gait analysis requires accurate classification and interpretable outputs. We propose an integrated framework for classifying healthy gait and multiple musculoskeletal impairment groups using bilateral ground reaction force (GRF) and center-of-pressure (COP) signals. The signals were normalized over the stance phase and standardized using training-set statistics. The model achieved a validation accuracy of 99.00\% and a test accuracy of 90.07\% under a session-level split. Class-specific $ε$-LRP identified positive and negative contributions across both sides, multiple signal componen

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