arXiv Machine Learning September 14, 2026 By Nayoung Son, Minwoo Shin

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

Read the original on arXiv Machine Learning →

The paper presents a new framework that classifies healthy gait and multiple musculoskeletal impairment groups using bilateral ground reaction force (GRF) and center-of-pressure (COP) signals. After normalizing and standardizing the signals, the model achieved 99.00% validation accuracy and 90.07% test accuracy with a session-level split. The approach includes class‑specific ε‑LRP explainability and synchronizes processed GRF signals with model predictions in a Blender‑based 3D visualization for sample‑level inspection.

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