Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model
Recently, the musculoskeletal model (MM) has been widely studied for decoding movement intent from electromyography (EMG) signals. However, the decoding performance of the MM is impaired for the coordinated movements of multiple degrees of freedom (DoFs) due to the crosstalk between signals of multi...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10896736/ |
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| author | Lizhi Pan Diyi Liu Ruyi Wang Jinhua Li |
| author_facet | Lizhi Pan Diyi Liu Ruyi Wang Jinhua Li |
| author_sort | Lizhi Pan |
| collection | DOAJ |
| description | Recently, the musculoskeletal model (MM) has been widely studied for decoding movement intent from electromyography (EMG) signals. However, the decoding performance of the MM is impaired for the coordinated movements of multiple degrees of freedom (DoFs) due to the crosstalk between signals of multiple muscles. To address this problem, this study proposed an enhanced MM for 3-DoF motion prediction by taking the “divide and conquer” (DC) strategy and integrating the non-negative matrix factorization (NMF) algorithm, which is named as DC-NMF-MM. The control signals of wrist flexion/extension and MCP flexion/extension were obtained from four independent muscles, and the control signals of wrist pronation/supination were obtained from eight-channel surface EMG signals. Eight non-disabled subjects were recruited for offline and online experiment. For offline experiment, another two MMs were established and taken as the control groups for validation of the proposed DC-NMF-MM, including the MM totally taking the NMF algorithm (T-NMF-MM) and that partly taking the NMF algorithm (P-NMF-MM) for predicting the wrist pronation/supination only. The Pearson’s correlation coefficient and the normalized root mean square error were employed to compare the prediction performance of three models. The results showed that the proposed method performs better than the other two models. Moreover, artificial neural network and linear regression model were established to compare with the proposed model and the results showed that DC-NMF-MM is more accurate in predicting joint Angle. For online experiment, a general 3-DOF musculoskeletal model based on DC-NMF-MM was established and the completion time, the number of overshoots, and the path efficiency were taken as evaluation indexes. The results further demonstrated the feasibility of the proposed method to achieve 3-DoF motion control. The proposed enhanced MM provides a prerequisite for the realization of clinical hand myoelectric control. |
| format | Article |
| id | doaj-art-0a507b5a9b5a42cdbacb94d75ed0d09c |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-0a507b5a9b5a42cdbacb94d75ed0d09c2025-08-20T03:07:37ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-013384785710.1109/TNSRE.2025.354391210896736Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal ModelLizhi Pan0https://orcid.org/0000-0001-7217-6935Diyi Liu1https://orcid.org/0009-0003-3690-6840Ruyi Wang2Jinhua Li3https://orcid.org/0000-0002-6497-8185Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, ChinaKey Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, ChinaRecently, the musculoskeletal model (MM) has been widely studied for decoding movement intent from electromyography (EMG) signals. However, the decoding performance of the MM is impaired for the coordinated movements of multiple degrees of freedom (DoFs) due to the crosstalk between signals of multiple muscles. To address this problem, this study proposed an enhanced MM for 3-DoF motion prediction by taking the “divide and conquer” (DC) strategy and integrating the non-negative matrix factorization (NMF) algorithm, which is named as DC-NMF-MM. The control signals of wrist flexion/extension and MCP flexion/extension were obtained from four independent muscles, and the control signals of wrist pronation/supination were obtained from eight-channel surface EMG signals. Eight non-disabled subjects were recruited for offline and online experiment. For offline experiment, another two MMs were established and taken as the control groups for validation of the proposed DC-NMF-MM, including the MM totally taking the NMF algorithm (T-NMF-MM) and that partly taking the NMF algorithm (P-NMF-MM) for predicting the wrist pronation/supination only. The Pearson’s correlation coefficient and the normalized root mean square error were employed to compare the prediction performance of three models. The results showed that the proposed method performs better than the other two models. Moreover, artificial neural network and linear regression model were established to compare with the proposed model and the results showed that DC-NMF-MM is more accurate in predicting joint Angle. For online experiment, a general 3-DOF musculoskeletal model based on DC-NMF-MM was established and the completion time, the number of overshoots, and the path efficiency were taken as evaluation indexes. The results further demonstrated the feasibility of the proposed method to achieve 3-DoF motion control. The proposed enhanced MM provides a prerequisite for the realization of clinical hand myoelectric control.https://ieeexplore.ieee.org/document/10896736/Electromyographyhuman-machine interfacemusculoskeletal modelnon-negative matrix factorization |
| spellingShingle | Lizhi Pan Diyi Liu Ruyi Wang Jinhua Li Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model IEEE Transactions on Neural Systems and Rehabilitation Engineering Electromyography human-machine interface musculoskeletal model non-negative matrix factorization |
| title | Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model |
| title_full | Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model |
| title_fullStr | Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model |
| title_full_unstemmed | Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model |
| title_short | Simultaneous and Proportional Control Based on an Enhanced Musculoskeletal Model |
| title_sort | simultaneous and proportional control based on an enhanced musculoskeletal model |
| topic | Electromyography human-machine interface musculoskeletal model non-negative matrix factorization |
| url | https://ieeexplore.ieee.org/document/10896736/ |
| work_keys_str_mv | AT lizhipan simultaneousandproportionalcontrolbasedonanenhancedmusculoskeletalmodel AT diyiliu simultaneousandproportionalcontrolbasedonanenhancedmusculoskeletalmodel AT ruyiwang simultaneousandproportionalcontrolbasedonanenhancedmusculoskeletalmodel AT jinhuali simultaneousandproportionalcontrolbasedonanenhancedmusculoskeletalmodel |