Hybrid feature-time series neural network for predicting ACL forces in martial artists with resistive braces after reconstruction
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.
Authors
- Dongyue Li (ORCID: https://orcid.org/0009-0007-2062-3407)
- Hang Yang (ORCID: https://orcid.org/0000-0001-6027-1337)
- Hao Li (ORCID: https://orcid.org/0000-0002-4019-3420)
Institutions
- Guangdong University of Foreign Studies (CN)
- Shanghai University of Sport (CN)
- Guangzhou Sport University (CN)
- Guangzhou University (CN)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2025-05-09
- DOI
- https://doi.org/10.3389/fbioe.2025.1579472
- Citations
- 1
- Primary Topic
- Knee injuries and reconstruction techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.01