Abstract
OBJECTIVE: This study developed a hybrid neural network integrating multi-modal data to predict anterior cruciate ligament (ACL) forces during rehabilitation in martial artists using a novel resistive knee brace after ACL reconstruction. The goal was to leverage time-series biomechanical parameters and static clinical features to optimize postoperative recovery strategies. METHODS: A prospective cohort of 44 martial artists post-ACL reconstruction was randomized into an experimental group (EG, n = 22) using a resistive brace and a control group (CG, n = 22) using a traditional brace. Baseline demographics (height, weight), joint range of motion (ROM), and muscle strength were measured preoperatively (T0) and at 15 days (T1), 30 days (T2), and 60 days (T3) postoperatively. High-resolution kinematic and kinetic data were collected at T3, while ACL forces were computed at T3 using OpenSim musculoskeletal modeling. A feature-embedded temporal convolutional neural network (TCN) fused time-series gait data (T3) with static features (T0-T3) to predict ACL forces. RESULTS: The hybrid TCN model achieved superior ACL force prediction accuracy, with a mean R (2) = 0.63 (EG), R (2) = 0.58 (CG), and R (2) = 0.62 (combined cohort) in three-fold cross-validation. Comparative analyses demonstrated significant advantages over standalone TCN (R (2) = 0.54) and long short-term memory (R (2) = 0.51) models. CONCLUSION: The integration of temporal biomechanical data and static clinical features enables accurate ACL force prediction, particularly for patients using resistive braces. This approach provides a novel tool to personalize rehabilitation protocols and validates the efficacy of resistive braces in modulating ACL loads, supporting their clinical adoption for athletes recovering from ACL injuries.