Assisting hand gesture classification and rehabilitation assessment via sEMG and finger motion data
These results confirm that the proposed system maintains high recognition accuracy even in severely impaired subjects. The dataset presented here offers substantial value for gesture recognition research, rehabilitation assessment, and neuromuscular signal analysis.
Authors
- Fukui Wu
- Jun Li
- Haifeng Huang
- Tao Hu
- Lingfeng Zhang
- Xiuxiu Yang
- Xinran Wei
Institutions
- Tokyo University of Information Sciences (JP)
- Minzu University of China (CN)
- Intelligent Health (United Kingdom) (GB)
- The University of Tokyo (JP)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2026-01-08
- DOI
- https://doi.org/10.3389/fbioe.2025.1751763
- Primary Topic
- Hand Gesture Recognition Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00