ויזואליזציה 3D של תאומים דיגיטליים לסיווג הפרעות הליכה

תקציר מקורי באנגליתarXiv:2609.12442v1 Announce Type: new 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 visualiz