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

Abstract: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 $\epsilon$-LRP identified positive and negative contributions across both sides, multiple signal components, and different stance phases. Separately, the processed GRF signals and model predictions were synchronized within a Blender-based 3D visualization, enabling sample-level inspection of gait trials and classification results. The proposed framework integrates classification, explainability, and 3D visualization to improve model transparency. The source code is available in the following repository: this https URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.12442 [cs.LG]

(or arXiv:2609.12442v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.12442 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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