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

4 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


  • 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


Dive into the research topics of 'DNN-Based FES control for gait rehabilitation of hemiplegic patients'. Together they form a unique fingerprint.

Cite this