Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG
sEMG is a non-invasive biomedical engineering technique that can detect and record electrical signals generated by muscles, reflecting both motor intentions and the degree of muscle contraction. This study aims to classify and recognize nine types of upper limb motor intentions based on surface elec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/4/1057 |
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| author | Tao Song Kunpeng Zhang Zhe Yan Yuwen Li Shuai Guo Xianhua Li |
| author_facet | Tao Song Kunpeng Zhang Zhe Yan Yuwen Li Shuai Guo Xianhua Li |
| author_sort | Tao Song |
| collection | DOAJ |
| description | sEMG is a non-invasive biomedical engineering technique that can detect and record electrical signals generated by muscles, reflecting both motor intentions and the degree of muscle contraction. This study aims to classify and recognize nine types of upper limb motor intentions based on surface electromyography (sEMG) and apply them to the interactive control of an end-effector rehabilitation robot. The research begins with selecting muscles and data preprocessing, incorporating the generation mechanism of sEMG along with the anatomical and kinesiological principles of upper limb muscles. Next, a musculoskeletal model of the upper limb is established and validated through simulations in OpenSim. To avoid the drawbacks of modeling methods, traditional machine learning and deep learning methods are employed to perform a nine-class classification task on the sEMG data, comparing the classification accuracy of different approaches. Finally, the motor intentions extracted using a multi-stream convolutional neural network (MLCNN) are utilized to control the iReMo<sup>®</sup> end-effector rehabilitation robot, with the system’s motion smoothness and accuracy evaluated through tests involving different trajectories. |
| format | Article |
| id | doaj-art-0659a329173d40c982bcddeb7abaf6f6 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0659a329173d40c982bcddeb7abaf6f62025-08-20T03:12:23ZengMDPI AGSensors1424-82202025-02-01254105710.3390/s25041057Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMGTao Song0Kunpeng Zhang1Zhe Yan2Yuwen Li3Shuai Guo4Xianhua Li5Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaSchool of Mechatronics Engineering, Anhui University of Science and Technology, Huainan 232001, ChinasEMG is a non-invasive biomedical engineering technique that can detect and record electrical signals generated by muscles, reflecting both motor intentions and the degree of muscle contraction. This study aims to classify and recognize nine types of upper limb motor intentions based on surface electromyography (sEMG) and apply them to the interactive control of an end-effector rehabilitation robot. The research begins with selecting muscles and data preprocessing, incorporating the generation mechanism of sEMG along with the anatomical and kinesiological principles of upper limb muscles. Next, a musculoskeletal model of the upper limb is established and validated through simulations in OpenSim. To avoid the drawbacks of modeling methods, traditional machine learning and deep learning methods are employed to perform a nine-class classification task on the sEMG data, comparing the classification accuracy of different approaches. Finally, the motor intentions extracted using a multi-stream convolutional neural network (MLCNN) are utilized to control the iReMo<sup>®</sup> end-effector rehabilitation robot, with the system’s motion smoothness and accuracy evaluated through tests involving different trajectories.https://www.mdpi.com/1424-8220/25/4/1057strokesurface myoelectricityupper limb rehabilitation robotinteractive control |
| spellingShingle | Tao Song Kunpeng Zhang Zhe Yan Yuwen Li Shuai Guo Xianhua Li Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG Sensors stroke surface myoelectricity upper limb rehabilitation robot interactive control |
| title | Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG |
| title_full | Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG |
| title_fullStr | Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG |
| title_full_unstemmed | Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG |
| title_short | Research on Upper Limb Motion Intention Classification and Rehabilitation Robot Control Based on sEMG |
| title_sort | research on upper limb motion intention classification and rehabilitation robot control based on semg |
| topic | stroke surface myoelectricity upper limb rehabilitation robot interactive control |
| url | https://www.mdpi.com/1424-8220/25/4/1057 |
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