A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation
IntroductionHemiparesis is a common consequence of stroke that severely impacts the life quality of the patients. Active training is a key factor in achieving optimal neural recovery, but current systems for wrist rehabilitation present challenges in terms of portability, cost, and the potential for...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1161187/full |
| _version_ | 1797821449844555776 |
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| author | Shiqi Yang Min Li Jiale Wang Zhilei Shi Bo He Jun Xie Guanghua Xu |
| author_facet | Shiqi Yang Min Li Jiale Wang Zhilei Shi Bo He Jun Xie Guanghua Xu |
| author_sort | Shiqi Yang |
| collection | DOAJ |
| description | IntroductionHemiparesis is a common consequence of stroke that severely impacts the life quality of the patients. Active training is a key factor in achieving optimal neural recovery, but current systems for wrist rehabilitation present challenges in terms of portability, cost, and the potential for muscle fatigue during prolonged use.MethodsTo address these challenges, this paper proposes a low-cost, portable wrist rehabilitation system with a control strategy that combines surface electromyogram (sEMG) and electroencephalogram (EEG) signals to encourage patients to engage in consecutive, spontaneous rehabilitation sessions. In addition, a detection method for muscle fatigue based on the Boruta algorithm and a post-processing layer are proposed, allowing for the switch between sEMG and EEG modes when muscle fatigue occurs.ResultsThis method significantly improves accuracy of fatigue detection from 4.90 to 10.49% for four distinct wrist motions, while the Boruta algorithm selects the most essential features and stabilizes the effects of post-processing. The paper also presents an alternative control mode that employs EEG signals to maintain active control, achieving an accuracy of approximately 80% in detecting motion intention.DiscussionFor the occurrence of muscle fatigue during long term rehabilitation training, the proposed system presents a promising approach to addressing the limitations of existing wrist rehabilitation systems. |
| first_indexed | 2024-03-13T09:52:52Z |
| format | Article |
| id | doaj.art-bd66e5ecee5845c08d0cc0179f0dace8 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-03-13T09:52:52Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-bd66e5ecee5845c08d0cc0179f0dace82023-05-24T06:03:50ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182023-05-011710.3389/fnbot.2023.11611871161187A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitationShiqi YangMin LiJiale WangZhilei ShiBo HeJun XieGuanghua XuIntroductionHemiparesis is a common consequence of stroke that severely impacts the life quality of the patients. Active training is a key factor in achieving optimal neural recovery, but current systems for wrist rehabilitation present challenges in terms of portability, cost, and the potential for muscle fatigue during prolonged use.MethodsTo address these challenges, this paper proposes a low-cost, portable wrist rehabilitation system with a control strategy that combines surface electromyogram (sEMG) and electroencephalogram (EEG) signals to encourage patients to engage in consecutive, spontaneous rehabilitation sessions. In addition, a detection method for muscle fatigue based on the Boruta algorithm and a post-processing layer are proposed, allowing for the switch between sEMG and EEG modes when muscle fatigue occurs.ResultsThis method significantly improves accuracy of fatigue detection from 4.90 to 10.49% for four distinct wrist motions, while the Boruta algorithm selects the most essential features and stabilizes the effects of post-processing. The paper also presents an alternative control mode that employs EEG signals to maintain active control, achieving an accuracy of approximately 80% in detecting motion intention.DiscussionFor the occurrence of muscle fatigue during long term rehabilitation training, the proposed system presents a promising approach to addressing the limitations of existing wrist rehabilitation systems.https://www.frontiersin.org/articles/10.3389/fnbot.2023.1161187/fullbrain-machine interfacesmachine learning for robot controlrehabilitation roboticssEMGmuscle fatigue detection |
| spellingShingle | Shiqi Yang Min Li Jiale Wang Zhilei Shi Bo He Jun Xie Guanghua Xu A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation Frontiers in Neurorobotics brain-machine interfaces machine learning for robot control rehabilitation robotics sEMG muscle fatigue detection |
| title | A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation |
| title_full | A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation |
| title_fullStr | A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation |
| title_full_unstemmed | A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation |
| title_short | A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation |
| title_sort | low cost and portable wrist exoskeleton using eeg semg combined strategy for prolonged active rehabilitation |
| topic | brain-machine interfaces machine learning for robot control rehabilitation robotics sEMG muscle fatigue detection |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1161187/full |
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