ArticleFrontiers in bioengineering and biotechnology2025
Hybrid feature-time series neural network for predicting ACL forces in martial artists with resistive braces after reconstruction.
Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Observational
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3 authors.
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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, Results: The hybrid TCN model achieved superior ACL force prediction accuracy, with a mean 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.
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