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

Yu Zhou; Yinfeng Fang; Jia Zeng; Kairu Li; Honghai Liu

doi:10.1007/978-3-319-97586-3_21

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

Shanghai Jiao Tong University

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Intelligent Robotics and Applications
Subtitle of host publication	11th International Conference, ICIRA 2018, Proceedings, Part 1
Editors	Zhiyong Chen, Alexandre Mendes, Yamin Yan, Shifeng Chen
Publisher	Springer Verlag
Pages	235-243
Number of pages	9
ISBN (Electronic)	978-3-319-97586-3
ISBN (Print)	978-3-319-97585-6
DOIs	https://doi.org/10.1007/978-3-319-97586-3_21
Publication status	Published - Sept 2018
Event	11th International Conference on Intelligent Robotics and Applications - Australia, Newcastle, Australia Duration: 9 Aug 2018 → 11 Aug 2018 http://www.icira2018.org

Publication series

Name	Lecture Notes in Computer Science
Volume	10984
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Intelligent Robotics and Applications
Abbreviated title	ICIRA 2018
Country/Territory	Australia
City	Newcastle
Period	9/08/18 → 11/08/18
Internet address	http://www.icira2018.org

Keywords

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

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10.1007/978-3-319-97586-3_21

ICIRA_2018_115_final_v2
The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-97586-3_21.
Accepted author manuscript (Post-print), 1.41 MB

Cite this

Zhou, Y., Fang, Y., Zeng, J., Li, K., & Liu, H. (2018). A multi-channel EMG-driven FES solution for stroke rehabilitation. In Z. Chen, A. Mendes, Y. Yan, & S. Chen (Eds.), Intelligent Robotics and Applications: 11th International Conference, ICIRA 2018, Proceedings, Part 1 (pp. 235-243). (Lecture Notes in Computer Science; Vol. 10984). Springer Verlag. https://doi.org/10.1007/978-3-319-97586-3_21

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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.",

keywords = "Functional electrical stimulation (FES), Integration system, Stroke rehabilitation, Surface electromyography (sEMG)",

author = "Yu Zhou and Yinfeng Fang and Jia Zeng and Kairu Li and Honghai Liu",

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booktitle = "Intelligent Robotics and Applications",

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Zhou, Y, Fang, Y, Zeng, J, Li, K & Liu, H 2018, A multi-channel EMG-driven FES solution for stroke rehabilitation. in Z Chen, A Mendes, Y Yan & S Chen (eds), Intelligent Robotics and Applications: 11th International Conference, ICIRA 2018, Proceedings, Part 1. Lecture Notes in Computer Science, vol. 10984, Springer Verlag, pp. 235-243, 11th International Conference on Intelligent Robotics and Applications, Newcastle, New South Wales, Australia, 9/08/18. https://doi.org/10.1007/978-3-319-97586-3_21

A multi-channel EMG-driven FES solution for stroke rehabilitation. / Zhou, Yu; Fang, Yinfeng; Zeng, Jia et al.
Intelligent Robotics and Applications: 11th International Conference, ICIRA 2018, Proceedings, Part 1. ed. / Zhiyong Chen; Alexandre Mendes; Yamin Yan; Shifeng Chen. Springer Verlag, 2018. p. 235-243 (Lecture Notes in Computer Science; Vol. 10984).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Li, Kairu

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AB - 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.

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Zhou Y, Fang Y, Zeng J, Li K, Liu H. A multi-channel EMG-driven FES solution for stroke rehabilitation. In Chen Z, Mendes A, Yan Y, Chen S, editors, Intelligent Robotics and Applications: 11th International Conference, ICIRA 2018, Proceedings, Part 1. Springer Verlag. 2018. p. 235-243. (Lecture Notes in Computer Science). Epub 2018 Aug 3. doi: 10.1007/978-3-319-97586-3_21

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

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Keywords

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