DNN-Based FES control for gait rehabilitation of hemiplegic patients

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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 languageEnglish
Article number3163
JournalApplied Sciences (Switzerland)
Issue number7
Publication statusPublished - 2021 Apr 1

Bibliographical note

Funding Information:
Funding: This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) (NRF 2016R1A5A1938472) and the work of S.P. was partially supported by the Sports Promotion Fund of Seoul Olympic Sports Promotion Foundation from Ministry of Culture, Sports and Tourism.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Electromyogram
  • Functional electrical stimulation
  • Gait rehabilitation
  • Machine learning
  • Muscle fatigue

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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