DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients

Applied Sciences

Seung-Jong Kim¹  JaeHwan Bong²  Suhun Jung³  Shinsuk Park³ 

[1] College of Medicine, Korea University, Seoul 02841, Korea;Department of Human Intelligence Robot Engineering, Sangmyung University, Cheonan-si 31066, Korea;Department of Mechanical Engineering, College of Engineering, Korea University, Seoul 02841, Korea;

关键词: functional electrical stimulation;    electromyogram;    machine learning;    muscle fatigue;    gait rehabilitation;   

DOI  :  10.3390/app11073163

来源: DOAJ

【 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.

【 授权许可】

Unknown