sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle
Continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurat...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4503271 |
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| author | Xugang Xi Chen Yang Seyed M. Miran Yun-Bo Zhao Shuliang Lin Zhizeng Luo |
| author_facet | Xugang Xi Chen Yang Seyed M. Miran Yun-Bo Zhao Shuliang Lin Zhizeng Luo |
| author_sort | Xugang Xi |
| collection | DOAJ |
| description | Continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurately. The model combines forward dynamics with a Hill-based muscle model that estimates joint torque only in a nonfeedback form, making the extended model capable of predicting the multijoint motion directly. The sEMG and MMG features, including the Wilson amplitude and permutation entropy, are then extracted to construct a measurement equation to reduce system error and external disturbances. Using the proposed model, a closed-loop prediction-correction approach, unscented particle filtering, is used to estimate the joint angle from sEMG and MMG signals. Comprehensive experiments are conducted on the human elbow and shoulder joint, and remarkable improvements are demonstrated compared with conventional methods. |
| format | Article |
| id | doaj-art-7ae4f01d2ab34fbab91469cffcd5516f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7ae4f01d2ab34fbab91469cffcd5516f2025-02-03T01:20:47ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/45032714503271sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint AngleXugang Xi0Chen Yang1Seyed M. Miran2Yun-Bo Zhao3Shuliang Lin4Zhizeng Luo5School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, ChinaBiomedical Informatics Center, George Washington University, Washington, DC 20052, USADepartment of Automation, Zhejiang University of Technology, Hangzhou 310023, ChinaJinhua Municipal Central Hospital, Jinhua 321000, ChinaSchool of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, ChinaContinuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurately. The model combines forward dynamics with a Hill-based muscle model that estimates joint torque only in a nonfeedback form, making the extended model capable of predicting the multijoint motion directly. The sEMG and MMG features, including the Wilson amplitude and permutation entropy, are then extracted to construct a measurement equation to reduce system error and external disturbances. Using the proposed model, a closed-loop prediction-correction approach, unscented particle filtering, is used to estimate the joint angle from sEMG and MMG signals. Comprehensive experiments are conducted on the human elbow and shoulder joint, and remarkable improvements are demonstrated compared with conventional methods.http://dx.doi.org/10.1155/2020/4503271 |
| spellingShingle | Xugang Xi Chen Yang Seyed M. Miran Yun-Bo Zhao Shuliang Lin Zhizeng Luo sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle Complexity |
| title | sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle |
| title_full | sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle |
| title_fullStr | sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle |
| title_full_unstemmed | sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle |
| title_short | sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle |
| title_sort | semg mmg state space model for the continuous estimation of multijoint angle |
| url | http://dx.doi.org/10.1155/2020/4503271 |
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