Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic Hand
The natural interaction between the prosthetic hand and the upper limb amputation patient is important and directly affects the rehabilitation effect and operation ability. Most previous studies only focused on the interaction of gestures but ignored the force levels. This paper proposes a simultane...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9861657/ |
| _version_ | 1827926984569651200 |
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| author | Bin Fang Chengyin Wang Fuchun Sun Ziming Chen Jianhua Shan Huaping Liu Wenlong Ding Wenyuan Liang |
| author_facet | Bin Fang Chengyin Wang Fuchun Sun Ziming Chen Jianhua Shan Huaping Liu Wenlong Ding Wenyuan Liang |
| author_sort | Bin Fang |
| collection | DOAJ |
| description | The natural interaction between the prosthetic hand and the upper limb amputation patient is important and directly affects the rehabilitation effect and operation ability. Most previous studies only focused on the interaction of gestures but ignored the force levels. This paper proposes a simultaneous recognition method of gestures and forces for interaction with a prosthetic hand. The multitask classification algorithm based on a convolutional neural network (CNN) is designed to improve recognition efficiency and ensure recognition accuracy. The offline experimental results show that the algorithm proposed in this study outperforms other methods in both training speed and accuracy. To prove the effectiveness of the proposed method, a myoelectric prosthetic hand integrated with tactile sensors is developed, and surface electromyography (sEMG) datasets of healthy persons and amputees are built. The online experimental results show that the amputee can control the prosthetic hand to continuously make gestures under different force levels, and the effect of hand coordination on the hand perception of amputees is explored. The results show that gesture classification operation tasks with different force levels based on sEMG signals can be accurately recognized and comfortably interact with prosthetic hands in real time. It improves the amputees’ operation ability and relieves their muscle fatigue. |
| first_indexed | 2024-03-13T05:46:46Z |
| format | Article |
| id | doaj.art-2f7668ab1fba419a8fde81f4bb6504d7 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:46Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-2f7668ab1fba419a8fde81f4bb6504d72023-06-13T20:09:15ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-01302426243610.1109/TNSRE.2022.31998099861657Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic HandBin Fang0https://orcid.org/0000-0002-9149-7336Chengyin Wang1Fuchun Sun2https://orcid.org/0000-0003-3546-6305Ziming Chen3Jianhua Shan4https://orcid.org/0000-0002-7710-1729Huaping Liu5https://orcid.org/0000-0002-4042-6044Wenlong Ding6https://orcid.org/0000-0003-0271-9425Wenyuan Liang7Department of Computer Science and Technology, Tsinghua University, Beijing, ChinaCollege of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, ChinaDepartment of Computer Science and Technology, Tsinghua University, Beijing, ChinaLaboratory for Embedded System and Intelligent Robot, Wuhan University of Science and Technology, Wuhan, ChinaDepartment of Mechanical Engineering, Anhui University of Technology, Anhui, Maanshan, ChinaDepartment of Computer Science and Technology, Tsinghua University, Beijing, ChinaDepartment of Mechanical Engineering, Anhui University of Technology, Anhui, Maanshan, ChinaNational Research Center for Rehabilitation Technical Aids, Beijing, ChinaThe natural interaction between the prosthetic hand and the upper limb amputation patient is important and directly affects the rehabilitation effect and operation ability. Most previous studies only focused on the interaction of gestures but ignored the force levels. This paper proposes a simultaneous recognition method of gestures and forces for interaction with a prosthetic hand. The multitask classification algorithm based on a convolutional neural network (CNN) is designed to improve recognition efficiency and ensure recognition accuracy. The offline experimental results show that the algorithm proposed in this study outperforms other methods in both training speed and accuracy. To prove the effectiveness of the proposed method, a myoelectric prosthetic hand integrated with tactile sensors is developed, and surface electromyography (sEMG) datasets of healthy persons and amputees are built. The online experimental results show that the amputee can control the prosthetic hand to continuously make gestures under different force levels, and the effect of hand coordination on the hand perception of amputees is explored. The results show that gesture classification operation tasks with different force levels based on sEMG signals can be accurately recognized and comfortably interact with prosthetic hands in real time. It improves the amputees’ operation ability and relieves their muscle fatigue.https://ieeexplore.ieee.org/document/9861657/sEMGCNNmultitask classification algorithmgestureforce levelamputees |
| spellingShingle | Bin Fang Chengyin Wang Fuchun Sun Ziming Chen Jianhua Shan Huaping Liu Wenlong Ding Wenyuan Liang Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic Hand IEEE Transactions on Neural Systems and Rehabilitation Engineering sEMG CNN multitask classification algorithm gesture force level amputees |
| title | Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic Hand |
| title_full | Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic Hand |
| title_fullStr | Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic Hand |
| title_full_unstemmed | Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic Hand |
| title_short | Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic Hand |
| title_sort | simultaneous semg recognition of gestures and force levels for interaction with prosthetic hand |
| topic | sEMG CNN multitask classification algorithm gesture force level amputees |
| url | https://ieeexplore.ieee.org/document/9861657/ |
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