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 |
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Article number | 3163 |
Journal | Applied Sciences (Switzerland) |
Volume | 11 |
Issue number | 7 |
DOIs | |
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