DNN-Based FES control for gait rehabilitation of hemiplegic patients

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

doi:10.3390/app11073163

DNN-Based FES control for gait rehabilitation of hemiplegic patients

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

School of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Article number	3163
Journal	Applied Sciences (Switzerland)
Volume	11
Issue number	7
DOIs	https://doi.org/10.3390/app11073163
Publication status	Published - 2021 Apr 1

Keywords

Electromyogram
Functional electrical stimulation
Gait rehabilitation
Machine learning
Muscle fatigue

ASJC Scopus subject areas

Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/app11073163

Cite this

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abstract = "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.",

keywords = "Electromyogram, Functional electrical stimulation, Gait rehabilitation, Machine learning, Muscle fatigue",

author = "Suhun Jung and Bong, {Jae Hwan} and Kim, {Seung Jong} and Shinsuk Park",

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N2 - 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.

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DNN-Based FES control for gait rehabilitation of hemiplegic patients

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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