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DNN-Based FES control for gait rehabilitation of hemiplegic patients
- Jung, S.;
- Bong, J.H.;
- Kim, S-J.;
- Park, S.
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8초록
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.
키워드
Electromyogram; Functional electrical stimulation; Gait rehabilitation; Machine learning; Muscle fatigue; FUNCTIONAL ELECTRICAL-STIMULATION; EVENT DETECTION; NEURAL-NETWORK; JOINT TORQUE; WALKING; EMG; RECOVERY; TREADMILL; ANKLE; SIGNALS
- 제목
- DNN-Based FES control for gait rehabilitation of hemiplegic patients
- 저자
- Jung, S.; Bong, J.H.; Kim, S-J.; Park, S.
- 발행일
- 2021-04
- 유형
- Article
- 저널명
- Applied Sciences-basel
- 권
- 11
- 호
- 7
- 언어
- ENG
- 출판사
- MDPI
- 발행국가
- 스위스
- ISSN
- E 2076-3417