Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control
The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this stu...
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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 |
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| Online Access: | https://ieeexplore.ieee.org/document/10078367/ |
| _version_ | 1797805162343956480 |
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| author | Chen Chen Yang Yu Xinjun Sheng Jianjun Meng Xiangyang Zhu |
| author_facet | Chen Chen Yang Yu Xinjun Sheng Jianjun Meng Xiangyang Zhu |
| author_sort | Chen Chen |
| collection | DOAJ |
| description | The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this study, we aimed to map the motor unit discharges, which were identified from high-density surface EMG, to the three degrees of freedom (DoFs) wrist movements. The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight non-disabled subjects. The experimental protocol included single-DoF movements and their various combinations. We decoded the motor unit discharges from the EMG signals using a segment-wise decomposition algorithm. Then the neural features were extracted from motor unit discharges and projected to wrist torques with a multiple linear regression model. We compared the performance of two neural features (twitch model and spike counting) and two training schemes (single-DoF and multi-DoF training). On average, 145 ± 33 motor units were identified from each subject, with a pulse-to-noise ratio of 30.8 ± 4.2 dB. Both neural features exhibited high estimation accuracy of 3-DoF wrist torques, with an average <inline-formula> <tex-math notation="LaTeX">$\text{R}^{{2}}$ </tex-math></inline-formula> of 0.76 ± 0.12 and normalized root mean square error of 11.4 ± 3.1%. These results demonstrated the efficiency of the proposed method in continuous estimation of 3-DoF wrist torques, which has the potential to advance dexterous myoelectric control based on neural information. |
| first_indexed | 2024-03-13T05:47:51Z |
| format | Article |
| id | doaj.art-7c064ece675b4762b856b8d2cf6ffe6f |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:47:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-7c064ece675b4762b856b8d2cf6ffe6f2023-06-13T20:07:06ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01311807181510.1109/TNSRE.2023.326020910078367Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric ControlChen Chen0https://orcid.org/0000-0002-3007-4364Yang Yu1https://orcid.org/0000-0001-5582-4558Xinjun Sheng2https://orcid.org/0000-0001-6124-8665Jianjun Meng3https://orcid.org/0000-0003-0813-652XXiangyang Zhu4https://orcid.org/0000-0003-4914-6636State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Mechanical System and Vibration and the Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Mechanical System and Vibration and the Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai, ChinaThe surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this study, we aimed to map the motor unit discharges, which were identified from high-density surface EMG, to the three degrees of freedom (DoFs) wrist movements. The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight non-disabled subjects. The experimental protocol included single-DoF movements and their various combinations. We decoded the motor unit discharges from the EMG signals using a segment-wise decomposition algorithm. Then the neural features were extracted from motor unit discharges and projected to wrist torques with a multiple linear regression model. We compared the performance of two neural features (twitch model and spike counting) and two training schemes (single-DoF and multi-DoF training). On average, 145 ± 33 motor units were identified from each subject, with a pulse-to-noise ratio of 30.8 ± 4.2 dB. Both neural features exhibited high estimation accuracy of 3-DoF wrist torques, with an average <inline-formula> <tex-math notation="LaTeX">$\text{R}^{{2}}$ </tex-math></inline-formula> of 0.76 ± 0.12 and normalized root mean square error of 11.4 ± 3.1%. These results demonstrated the efficiency of the proposed method in continuous estimation of 3-DoF wrist torques, which has the potential to advance dexterous myoelectric control based on neural information.https://ieeexplore.ieee.org/document/10078367/Continuous myoelectric controlelectromyography decompositionmotor unit dischargelinear regressionwrist torque |
| spellingShingle | Chen Chen Yang Yu Xinjun Sheng Jianjun Meng Xiangyang Zhu Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control IEEE Transactions on Neural Systems and Rehabilitation Engineering Continuous myoelectric control electromyography decomposition motor unit discharge linear regression wrist torque |
| title | Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control |
| title_full | Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control |
| title_fullStr | Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control |
| title_full_unstemmed | Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control |
| title_short | Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control |
| title_sort | mapping individual motor unit activity to continuous three dof wrist torques perspectives for myoelectric control |
| topic | Continuous myoelectric control electromyography decomposition motor unit discharge linear regression wrist torque |
| url | https://ieeexplore.ieee.org/document/10078367/ |
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