Portable sEMG-Controlled Hand Electrical Stimulation System
Contralateral controlled functional electrical stimulation (CCFES) serves as a stroke rehabilitative intervention capable of inducing bilateral hands’ engagement in restorative movements. Clinical practice has shown that CCFES significantly improves hand movement control and dexterity. Existing CCFES systems, leveraging technologies such as myoelectric sensors, flexion sensors, and Lidar, can trigger movements but lack real-time muscle status feedback and a rehabilitation movement control strategy, limiting assistance in intricate daily activities. This study introduces an innovative, portable CCFES system based on surface electromyography (sEMG). This system facilitates adaptive amplitude modulation using bilateral hand surface electromyographic signals and enables real-time transitions between rehabilitation movements according to user intent. In the domain of online gesture recognition, the system demonstrated exceptional performance, achieving an average F1 score of 0.935. Furthermore, the system possesses the capability to adaptively modulate stimulation amplitudes by evaluating the differences in sEMG signals between the participant’s hands. The conclusive “object taking” experiment substantiates the system’s potential applicability in the rehabilitation training of daily activities.
Authors
- Shuxian Zhang (ORCID: https://orcid.org/0009-0007-7405-9215)
- Longhan Xie (ORCID: https://orcid.org/0000-0002-5137-1413)
- Yumeng Cai (ORCID: https://orcid.org/0000-0003-3477-9844)
- Zhitao Chen (ORCID: https://orcid.org/0009-0002-5542-7215)
- Hao Huang (ORCID: https://orcid.org/0000-0003-3663-7997)
- Shuxin Liu (ORCID: https://orcid.org/0000-0002-3366-4466)
- Li Huang
- Yan Chen (ORCID: https://orcid.org/0000-0001-7650-3307)
Institutions
- Sun Yat-sen University (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1109/jsen.2024.3506788
- Citations
- 2
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China