SubGaitNet: A Decision-Oriented and Interpretable AI Framework for Robust GRF-Based Gait Assessment in Neurological and Musculoskeletal Care

Ground reaction force (GRF)-based gait analysis provides objective, non-invasive evidence for neurological and musculoskeletal assessment, but its translation into medical AI decision support is limited by heterogeneous sensing devices, variable-length recordings, acquisition noise, sensor failures, and restricted access to high-cost gait laboratories. We propose SubGaitNet, a decision-oriented and interpretable AI framework designed to address four clinically relevant challenges in GRF-based medical AI: signal-length variability, sensing noise, long-range gait-phase dependency, and pathological frame-to-frame variability. SubGaitNet integrates GRF temporal slicing, multi-scale deep residual shrinkage, masked Transformer modeling, and a Sub-LSTM branch for adjacent-frame variability modeling. In subject-independent evaluation on two public clinical gait datasets, SubGaitNet achieved an AUC of 0.979 for Parkinson’s disease (PD) screening and an ACC of 0.940/F1-score of 0.910 for Hoehn & Yahr severity assessment using wearable pressure insoles. On the GaitRec force-plate dataset, SubGaitNet achieved ACC values of 0.951 and 0.918 for four-class and five-class musculoskeletal impairment assessment, respectively. Additional analyses showed stable bootstrap confidence intervals, calibrated PD screening probabilities (Brier score = 0.059; expected calibration error = 0.051), positive decision-curve net benefit across clinically relevant thresholds, and ordinally plausible H&Y errors. Robustness tests under simulated sensor failure, noise perturbation, and reduced-channel inputs supported the model’s stability under clinically plausible sensing uncertainty and accessibility constraints. SHAP explanations highlighted biomechanically meaningful hindfoot and forefoot regions. Overall, SubGaitNet provides a reusable, interpretable, and decision-support-oriented AI methodology for GRF-based gait health assessment, while prospective clinician-in-the-loop validation remains necessary before clinical deployment.