Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation
Functional electrical stimulation (FES) can be used to stimulate the lower-limb muscles to provide walking assistance to stroke patients. However, the existing surface electromyography (sEMG)-based FES control methods mostly only consider a single muscle with a fixed stimulation intensity and freque...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10246446/ |
| _version_ | 1797682056419868672 |
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| author | Chunfu Lu Ruite Ge Zhichuan Tang Xiaoyun Fu Lekai Zhang Keshuai Yang Xuan Xu |
| author_facet | Chunfu Lu Ruite Ge Zhichuan Tang Xiaoyun Fu Lekai Zhang Keshuai Yang Xuan Xu |
| author_sort | Chunfu Lu |
| collection | DOAJ |
| description | Functional electrical stimulation (FES) can be used to stimulate the lower-limb muscles to provide walking assistance to stroke patients. However, the existing surface electromyography (sEMG)-based FES control methods mostly only consider a single muscle with a fixed stimulation intensity and frequency. This study proposes a multi-channel FES gait rehabilitation assistance system based on adaptive myoelectric modulation. The proposed system collects sEMG of the vastus lateralis muscle on the non-affected side to predict the sEMG values of four targeted lower-limb muscles on the affected side using a bidirectional long short-term memory (BILSTM) model. Next, the proposed system modulates the real-time FES output frequency for four targeted muscles based on the predicted sEMG values to provide muscle force compensation. Fifteen healthy subjects were recruited to participate in an offline model-building experiment conducted to evaluate the feasibility of the proposed BILSTM model in predicting the sEMG values. The experimental results showed that the <inline-formula> <tex-math notation="LaTeX">$\text{R}^{{2}}$ </tex-math></inline-formula> value of the best-obtained prediction result reached 0.85 using the BILSTM model, which was significantly higher than that using traditional prediction methods. Moreover, two patients after stroke were recruited in the online assisted-walking experiment to verify the effectiveness of the proposed walking-assistance system. The experimental results showed that the activation of the target muscles of the patients was higher after FES, and the gait movement data were significantly different before and after FES. The proposed system can be effectively applied to walking assistance for stroke patients, and the experimental results can provide new ideas and methods for sEMG-controlled FES rehabilitation applications. |
| first_indexed | 2024-03-11T23:53:59Z |
| format | Article |
| id | doaj.art-50d3ea3369224f1aa6bd59c7c8ae50df |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-11T23:53:59Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-50d3ea3369224f1aa6bd59c7c8ae50df2023-09-18T23:00:10ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01313652366310.1109/TNSRE.2023.331361710246446Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG ModulationChunfu Lu0https://orcid.org/0009-0004-2217-7031Ruite Ge1https://orcid.org/0009-0000-9700-4072Zhichuan Tang2https://orcid.org/0000-0002-1730-1120Xiaoyun Fu3https://orcid.org/0009-0002-0538-6144Lekai Zhang4https://orcid.org/0000-0002-8136-5882Keshuai Yang5https://orcid.org/0009-0004-6048-3064Xuan Xu6https://orcid.org/0009-0002-7665-8880Industrial Design Institute, Zhejiang University of Technology, Hangzhou, ChinaIndustrial Design Institute, Zhejiang University of Technology, Hangzhou, ChinaIndustrial Design Institute, Zhejiang University of Technology, Hangzhou, ChinaIndustrial Design Institute, Zhejiang University of Technology, Hangzhou, ChinaIndustrial Design Institute, Zhejiang University of Technology, Hangzhou, ChinaIndustrial Design Institute, Zhejiang University of Technology, Hangzhou, ChinaIndustrial Design Institute, Zhejiang University of Technology, Hangzhou, ChinaFunctional electrical stimulation (FES) can be used to stimulate the lower-limb muscles to provide walking assistance to stroke patients. However, the existing surface electromyography (sEMG)-based FES control methods mostly only consider a single muscle with a fixed stimulation intensity and frequency. This study proposes a multi-channel FES gait rehabilitation assistance system based on adaptive myoelectric modulation. The proposed system collects sEMG of the vastus lateralis muscle on the non-affected side to predict the sEMG values of four targeted lower-limb muscles on the affected side using a bidirectional long short-term memory (BILSTM) model. Next, the proposed system modulates the real-time FES output frequency for four targeted muscles based on the predicted sEMG values to provide muscle force compensation. Fifteen healthy subjects were recruited to participate in an offline model-building experiment conducted to evaluate the feasibility of the proposed BILSTM model in predicting the sEMG values. The experimental results showed that the <inline-formula> <tex-math notation="LaTeX">$\text{R}^{{2}}$ </tex-math></inline-formula> value of the best-obtained prediction result reached 0.85 using the BILSTM model, which was significantly higher than that using traditional prediction methods. Moreover, two patients after stroke were recruited in the online assisted-walking experiment to verify the effectiveness of the proposed walking-assistance system. The experimental results showed that the activation of the target muscles of the patients was higher after FES, and the gait movement data were significantly different before and after FES. The proposed system can be effectively applied to walking assistance for stroke patients, and the experimental results can provide new ideas and methods for sEMG-controlled FES rehabilitation applications.https://ieeexplore.ieee.org/document/10246446/BILSTMEMG predictionfunctional electrical stimulationgait rehabilitation |
| spellingShingle | Chunfu Lu Ruite Ge Zhichuan Tang Xiaoyun Fu Lekai Zhang Keshuai Yang Xuan Xu Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation IEEE Transactions on Neural Systems and Rehabilitation Engineering BILSTM EMG prediction functional electrical stimulation gait rehabilitation |
| title | Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation |
| title_full | Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation |
| title_fullStr | Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation |
| title_full_unstemmed | Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation |
| title_short | Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation |
| title_sort | multi channel fes gait rehabilitation assistance system based on adaptive semg modulation |
| topic | BILSTM EMG prediction functional electrical stimulation gait rehabilitation |
| url | https://ieeexplore.ieee.org/document/10246446/ |
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