DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients

Jung, Suhun; Bong, Jae Hwan; Kim, Seung-Jong; Park, Shinsuk

doi:10.3390/app11073163

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DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients

Jung, Suhun;
Bong, Jae Hwan;
Kim, Seung-Jong;
Park, Shinsuk

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7

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8

초록

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.

키워드

functional electrical stimulation; electromyogram; machine learning; muscle fatigue; gait rehabilitation; 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, Suhun; Bong, Jae Hwan; Kim, Seung-Jong; Park, Shinsuk

DOI: 10.3390/app11073163

발행일: 2021-04

유형: Article

저널명: Applied Sciences (Switzerland)

권: 11

호: 7