Human-Centred sEMG-Based Collaborative Control for Voluntary Participation in Rehabilitation

This systematic review examined how surface electromyography-based (sEMG-based) human-machine collaborative control supports voluntary participation in rehabilitation for individuals with motor impairment. Following PRISMA guidelines, five databases were searched, and 91 studies were included in the main review, with 7 supplementary studies. Across the reviewed studies, sEMG mainly entered the rehabilitation control loop through intention-driven control, contribution regulation, and parameter regulation. The roles supported variable impedance control, admittance or compliant control, assist-as-needed control, shared control, and sliding mode control, and were further reflected in function-oriented active training, ability-adaptive training modes, and state and feedback regulation. Overall, the findings suggest that sEMG is increasingly used not only to detect movement intention but also to organize assistance, training progression, and feedback updates.

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Publication Details

Journal
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Published
2026-10-17
DOI
https://doi.org/10.1177/10711813261498713
Primary Topic
Prosthetics and Rehabilitation Robotics
Type
article
Field-Weighted Citation Impact
0.00

Funders

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article

Human-Centred sEMG-Based Collaborative Control for Voluntary Participation in Rehabilitation

Shuyang Wei, Tianjian Cui, 荆自立, Hao Fan et al.
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Prosthetics and Rehabilitation Robotics
article

Human-Centred sEMG-Based Collaborative Control for Voluntary Participation in Rehabilitation

Shuyang Wei, Tianjian Cui, 荆自立, Hao Fan, Jinlei Shi, Yueling Zhang, Siling Xu, Ke Yao, Yinuo Chen, Lin Wang, Mengcheng Wang
article en

Abstract

This systematic review examined how surface electromyography-based (sEMG-based) human-machine collaborative control supports voluntary participation in rehabilitation for individuals with motor impairment. Following PRISMA guidelines, five databases were searched, and 91 studies were included in the main review, with 7 supplementary studies. Across the reviewed studies, sEMG mainly entered the rehabilitation control loop through intention-driven control, contribution regulation, and parameter regulation. The roles supported variable impedance control, admittance or compliant control, assist-as-needed control, shared control, and sliding mode control, and were further reflected in function-oriented active training, ability-adaptive training modes, and state and feedback regulation. Overall, the findings suggest that sEMG is increasingly used not only to detect movement intention but also to organize assistance, training progression, and feedback updates.

Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Northwestern Polytechnical University (CN), Xi'an University of Technology (CN), Southwest Jiaotong University (CN), Southeast University (CN), University of California, Berkeley (US)
Fundamental Research Funds for the Central Universities
Good health and well-being
Openalex Percentile: Top 24%
Prosthetics and Rehabilitation Robotics
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