Multilevel Assessment of Exercise Fatigue Utilizing Multiple Attention and Convolution Network (MACNet) Based on Surface Electromyography
MACNet achieves the highest average classification accuracy and F1-Score, significantly higher than other state-of-the-art methods like SVM, RF, MFFNet, TSCNN, LMDANet, Conformer and MSFEnet, enhancing the extraction of exercise fatigue insights from sEMG channels and time-domain features.
Authors
- Peng Zan
- G. Zhang (ORCID: https://orcid.org/0009-0008-8754-3994)
- Dingguo Zhang (ORCID: https://orcid.org/0000-0003-4803-7489)
- Banghua Yang (ORCID: https://orcid.org/0000-0002-8561-5631)
Institutions
- Shanghai University (CN)
- University of Bath (GB)
Publication Details
- Journal
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1109/tnsre.2024.3523332
- Citations
- 4
- Primary Topic
- Muscle activation and electromyography studies
- Type
- article
- Field-Weighted Citation Impact
- 0.74
Funders
- National Natural Science Foundation of China
- China Scholarship Council
- Engineering and Physical Sciences Research Council
- National Key Research and Development Program of China
- National Defense Basic Scientific Research Program of China