Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/app11073163
Keywords
functional electrical stimulation; electromyogram; machine learning; muscle fatigue; gait rehabilitation
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Funding
- National Research Foundation of Korea (NRF) - Korean Government (MSIT) [NRF 2016R1A5A1938472]
- Sports Promotion Fund of Seoul Olympic Sports Promotion Foundation from Ministry of Culture, Sports and Tourism
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A novel machine-learning-based FES control algorithm was proposed in this study to enhance gait rehabilitation in post-stroke hemiplegic patients. The system controlled electrical stimulation via deep neural networks trained using muscle activity data from healthy people during gait, and was tested with healthy human subjects in comparison with a conventional FES control method.
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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