Multi modal fusion of medical imaging and biomechanical data using attention based swin-unet and LSTM for sports injury prediction
By integrating CT imaging and biomechanical data within a Swin Unet based framework, this study offers a precise and personalized approach to sports injury prediction. The inclusion of real-time monitoring further enhances the practical value of the model, supporting early intervention and improving athlete safety and training efficiency.
Authors
- Siyuan Li (ORCID: https://orcid.org/0000-0002-5041-6499)
- Kamran Amjad
- Ziyu Hou
- Husnain Mushtaq
Institutions
- Hunan University of Science and Technology (CN)
- Central South University (CN)
- Wuhan College (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-12-12
- DOI
- https://doi.org/10.3389/fphys.2025.1687895
- Citations
- 2
- Primary Topic
- Sports injuries and prevention
- Type
- article
- Field-Weighted Citation Impact
- 3.44
Funders
- Hunan University
- Hunan University of Science and Technology