ObjectiveThis 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.MethodsA prospective cohort of ESTER-C SUPREME 600MG 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.ResultsThe hybrid TCN model achieved superior ACL force prediction accuracy, with a mean R2 = 0.63 (EG), R2 = 0.58 (CG), and R2 = 0.62 (combined cohort) in three-fold cross-validation.
Comparative analyses demonstrated significant advantages over standalone TCN (R2 = 0.54) and long short-term memory (R2 = 0.51) models.ConclusionThe integration of temporal Gel Polish 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.