A multi-channel EMG-driven FES solution for stroke rehabilitation

Yu Zhou*, Yinfeng Fang, Jia Zeng, Kairu Li, Honghai Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Functional electrical stimulation (FES) has been applied to stroke rehabilitation for many years. However, users are usually involved in open-loop fixed cycle FES systems in clinical, which is easy to cause muscle fatigue and reduce rehabilitation efficacy. This paper proposes a multi-surface EMG-driven FES integration solution for enhancing upper-limb stroke rehabilitation. This wireless portable system consists of sEMG data acquisition module and FES module, the former is used to capture sEMG signals, the latter of multi-channel FES output can be driven by the sEMG. Preliminary experiments proved that the system has outperformed existing similar systems and that sEMG can be effectively employed to achieve different FES intensity, demonstrating the potential for active stroke rehabilitation.

Original languageEnglish
Title of host publicationIntelligent Robotics and Applications
Subtitle of host publication11th International Conference, ICIRA 2018, Proceedings, Part 1
EditorsZhiyong Chen, Alexandre Mendes, Yamin Yan, Shifeng Chen
PublisherSpringer Verlag
Pages235-243
Number of pages9
ISBN (Electronic)978-3-319-97586-3
ISBN (Print)978-3-319-97585-6
DOIs
Publication statusPublished - Sep 2018
Event11th International Conference on Intelligent Robotics and Applications - Australia, Newcastle, Australia
Duration: 9 Aug 201811 Aug 2018
http://www.icira2018.org

Publication series

NameLecture Notes in Computer Science
Volume10984
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Conference on Intelligent Robotics and Applications
Abbreviated titleICIRA 2018
CountryAustralia
CityNewcastle
Period9/08/1811/08/18
Internet address

Keywords

  • Functional electrical stimulation (FES)
  • Integration system
  • Stroke rehabilitation
  • Surface electromyography (sEMG)

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