DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients

被引：7

作者：

Jung, Suhun ^{[1
]}

Bong, Jae Hwan ^{[2
]}

Kim, Seung-Jong ^{[3
]}

Park, Shinsuk ^{[1
]}

机构：

[1] Korea Univ, Dept Mech Engn, Coll Engn, Seoul 02841, South Korea

[2] Sangmyung Univ, Dept Human Intelligence Robot Engn, Cheonan Si 31066, South Korea

[3] Korea Univ, Coll Med, Seoul 02841, South Korea

来源：

APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 07期

基金：

新加坡国家研究基金会;

关键词：

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;

D O I：

10.3390/app11073163

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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.

引用

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