TFNet: A Temporal-Frequency Domain Model for Gait Biomechanical Signal Prediction.

Precise prediction of upcoming gait signals, especially over an extended time scale like the entire gait cycle, is crucial. During this period, devices or gait retraining programs can respond to dynamic changes while considering multiple factors in the neurological and musculoskeletal systems. This enables effective adjustments, ultimately optimising outcomes based on the unique rehabilitation goals. However, current state-of-the-art models, whether driven by physical modelling or data modelling approaches, are constrained by short prediction time scales, limited accuracy, and high computational costs, which hinder their use on edge devices. We developed TFNet, a dual-stream neural network model that integrates temporal and frequency domain analyses to accurately predict biomechanical signals across the entire gait cycle. TFNet predicted lower limb joint angles and ground reaction forces with high precision, within 5 degrees and 0.1 body weight, respectively. The model demonstrated the feasibility for deployment on edge devices and adaptability to patients with gait impairments. Explainability analysis highlighted key biomechanical features throughout the gait cycle, improving interpretability and clinical relevance. These comprehensive validations demonstrate the potential of TFNet as a reliable and cost-effective solution for clinical applications aimed at restoring and enhancing gait function.