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DNN-Based FES control for gait rehabilitation of hemiplegic patients
- Authors
- Jung, S.; Bong, J.H.; Kim, S-J.; Park, S.
- Issue Date
- Apr-2021
- Publisher
- MDPI AG
- Keywords
- Electromyogram; Functional electrical stimulation; Gait rehabilitation; Machine learning; Muscle fatigue
- Citation
- Applied Sciences (Switzerland), v.11, no.7
- Indexed
- SCIE
SCOPUS
- Journal Title
- Applied Sciences (Switzerland)
- Volume
- 11
- Number
- 7
- ISSN
- 2076-3417
2076-3417
- Abstract
- In this study, we proposed a novel machine-learning-based functional electrical stimulation (FES) control algorithm to enhance gait rehabilitation in post-stroke hemiplegic patients. The electrical stimulation of the muscles on the paretic side was controlled via deep neural networks, which were trained using muscle activity data from healthy people during gait. The performance of the developed system in comparison with that of a conventional FES control method was tested with healthy human subjects. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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Collections - 1. Basic Science > Department of Biomedical Engineering > 1. Journal Articles
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Related Researcher
- Kim, Seung Jong
- College of Medicine (Department of Biomedical Engineering)