Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System
The continuous advancement of electronic technology has led to the gradual integration of automated intelligent devices into various aspects of human life. Motion gesture-based human–computer interaction systems offer abundant information, user-friendly functionalities, and visual cues. Surface elec...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/2/454 |
| _version_ | 1797344134189547520 |
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| author | Zhongpeng Zhang Tuanjun Han Chaojun Huang Chunjiang Shuai |
| author_facet | Zhongpeng Zhang Tuanjun Han Chaojun Huang Chunjiang Shuai |
| author_sort | Zhongpeng Zhang |
| collection | DOAJ |
| description | The continuous advancement of electronic technology has led to the gradual integration of automated intelligent devices into various aspects of human life. Motion gesture-based human–computer interaction systems offer abundant information, user-friendly functionalities, and visual cues. Surface electromyography (sEMG) signals enable the decoding of muscle movements, facilitating the realization of corresponding control functions. Considering the inherent instability and minuscule nature of sEMG signals, this thesis proposes the integration of a dynamic time regularization algorithm to enhance gesture recognition detection accuracy and real-time system performance. The application of the dynamic time warping algorithm allows the fusion of three sEMG signals, enabling for the calculation of similarity between the sample and the model. This process facilitates gesture recognition and ensures effective communication between individuals and the 3D printed prosthesis. Utilizing this algorithm, the best feature model was generated by amalgamating six types of gesture classification model. A total of 600 training and evaluation experiments were performed, with each movement recognized 100 times. The experimental tests demonstrate that the accuracy of gesture recognition and prosthetic limb control using the temporal dynamic regularization algorithm achieves an impressive 93.75%, surpassing the performance of the traditional threshold control switch. |
| first_indexed | 2024-03-08T10:57:56Z |
| format | Article |
| id | doaj.art-28fe51b2dfc24097bd4773fc3ebdfe47 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T10:57:56Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-28fe51b2dfc24097bd4773fc3ebdfe472024-01-26T16:15:14ZengMDPI AGElectronics2079-92922024-01-0113245410.3390/electronics13020454Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control SystemZhongpeng Zhang0Tuanjun Han1Chaojun Huang2Chunjiang Shuai3Trine Engineering Institute, Shaanxi University of Technology, Hanzhong 723001, ChinaSchool of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong 723001, ChinaSchool of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong 723001, ChinaSchool of Physics and Telecommunication Engineering, Shaanxi University of Technology, Hanzhong 723001, ChinaThe continuous advancement of electronic technology has led to the gradual integration of automated intelligent devices into various aspects of human life. Motion gesture-based human–computer interaction systems offer abundant information, user-friendly functionalities, and visual cues. Surface electromyography (sEMG) signals enable the decoding of muscle movements, facilitating the realization of corresponding control functions. Considering the inherent instability and minuscule nature of sEMG signals, this thesis proposes the integration of a dynamic time regularization algorithm to enhance gesture recognition detection accuracy and real-time system performance. The application of the dynamic time warping algorithm allows the fusion of three sEMG signals, enabling for the calculation of similarity between the sample and the model. This process facilitates gesture recognition and ensures effective communication between individuals and the 3D printed prosthesis. Utilizing this algorithm, the best feature model was generated by amalgamating six types of gesture classification model. A total of 600 training and evaluation experiments were performed, with each movement recognized 100 times. The experimental tests demonstrate that the accuracy of gesture recognition and prosthetic limb control using the temporal dynamic regularization algorithm achieves an impressive 93.75%, surpassing the performance of the traditional threshold control switch.https://www.mdpi.com/2079-9292/13/2/454anthropomorphic prosthetic armdynamic time warping algorithmfeature extractiongesture recognitionsurface electromyography signal |
| spellingShingle | Zhongpeng Zhang Tuanjun Han Chaojun Huang Chunjiang Shuai Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System Electronics anthropomorphic prosthetic arm dynamic time warping algorithm feature extraction gesture recognition surface electromyography signal |
| title | Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System |
| title_full | Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System |
| title_fullStr | Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System |
| title_full_unstemmed | Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System |
| title_short | Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System |
| title_sort | hardware and software design and implementation of surface emg based gesture recognition and control system |
| topic | anthropomorphic prosthetic arm dynamic time warping algorithm feature extraction gesture recognition surface electromyography signal |
| url | https://www.mdpi.com/2079-9292/13/2/454 |
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