A Lightweight Feature-Grouped Gated Fusion Network for Parkinson’s Disease Gait Screening Using Force-Plate GRFs

Parkinson’s disease (PD) is associated with characteristic gait impairment, motivating objective screening methods based on biomechanical signals. This study presents a lightweight, physics-informed framework for PD gait screening using ground reaction force (GRF) signals acquired from force plates, together with a prototype acquisition-and-analysis system for practical screening workflows. Continuous GRF recordings are segmented into complete gait cycles, from which bilateral physics-informed features are constructed, including normalized force, dynamics-derived acceleration and velocity, and friction-related descriptors. The resulting feature tensor is then standardized and used as input to a Feature-Grouped Gated Fusion Network (FGGF-Net). The proposed model separately encodes force–acceleration features and velocity–ratio features using low-order nonlinear and linear pathways, respectively, and integrates them via gated fusion with a residual baseline pathway. Under subject-wise five-fold cross-validation, FGGF-Net achieves strong subject-level performance, reaching 94.8% accuracy, 92.9% F1-score, and 0.974 AUC, while consistently outperforming representative baselines. Ablation studies further verify the effectiveness of feature grouping and gated fusion. In addition, the trained model remains compact (1.09 M parameters, 4.16 MB) and supports millisecond-level CPU inference, making the proposed framework promising for practical force-plate screening workflows.