Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression
In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human–robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4934 |
| _version_ | 1797598321688182784 |
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| author | Hui-Bin Li Xiao-Rong Guan Zhong Li Kai-Fan Zou Long He |
| author_facet | Hui-Bin Li Xiao-Rong Guan Zhong Li Kai-Fan Zou Long He |
| author_sort | Hui-Bin Li |
| collection | DOAJ |
| description | In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human–robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimation model for knee joint angle based on the novel method of multiple kernel relevance vector regression (MKRVR) through offline learning. The root mean square error, mean absolute error, and R2_score are used as performance indicators. By comparing the estimation model of MKRVR and least squares support vector regression (LSSVR), the MKRVR performs better on the estimation of the knee joint angle. The results showed that the MKRVR can estimate the knee joint angle with a continuous global MAE of 3.27° ± 1.2°, RMSE of 4.81° ± 1.37°, and R<sup>2</sup> of 0.8946 ± 0.07. Therefore, we concluded that the MKRVR for the estimation of the knee joint angle from sEMG is viable and could be used for motion analysis and the application of recognition of the wearer’s motion intentions in human–robot collaboration control. |
| first_indexed | 2024-03-11T03:20:37Z |
| format | Article |
| id | doaj.art-9fb65363fe5448569c347dcfed46ae07 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:37Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9fb65363fe5448569c347dcfed46ae072023-11-18T03:14:48ZengMDPI AGSensors1424-82202023-05-012310493410.3390/s23104934Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector RegressionHui-Bin Li0Xiao-Rong Guan1Zhong Li2Kai-Fan Zou3Long He4School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaIn wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human–robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimation model for knee joint angle based on the novel method of multiple kernel relevance vector regression (MKRVR) through offline learning. The root mean square error, mean absolute error, and R2_score are used as performance indicators. By comparing the estimation model of MKRVR and least squares support vector regression (LSSVR), the MKRVR performs better on the estimation of the knee joint angle. The results showed that the MKRVR can estimate the knee joint angle with a continuous global MAE of 3.27° ± 1.2°, RMSE of 4.81° ± 1.37°, and R<sup>2</sup> of 0.8946 ± 0.07. Therefore, we concluded that the MKRVR for the estimation of the knee joint angle from sEMG is viable and could be used for motion analysis and the application of recognition of the wearer’s motion intentions in human–robot collaboration control.https://www.mdpi.com/1424-8220/23/10/4934motion intentionsurface electromyography (sEMG)joint angle estimation |
| spellingShingle | Hui-Bin Li Xiao-Rong Guan Zhong Li Kai-Fan Zou Long He Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression Sensors motion intention surface electromyography (sEMG) joint angle estimation |
| title | Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression |
| title_full | Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression |
| title_fullStr | Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression |
| title_full_unstemmed | Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression |
| title_short | Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression |
| title_sort | estimation of knee joint angle from surface emg using multiple kernels relevance vector regression |
| topic | motion intention surface electromyography (sEMG) joint angle estimation |
| url | https://www.mdpi.com/1424-8220/23/10/4934 |
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