DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients

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 tra...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 7; p. 3163
Main Authors Jung, Suhun, Bong, Jae Hwan, Kim, Seung-Jong, Park, Shinsuk
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2021
Subjects
Ankle
Control algorithms
Control methods
Control theory
Electrical stimuli
electromyogram
Fitness equipment
functional electrical stimulation
Gait
gait rehabilitation
Learning algorithms
Machine learning
Medical research
Muscle fatigue
Muscles
Neural networks
Patients
Range of motion
Rehabilitation
Spasticity
Stimulation
Stroke
Walking
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11073163