A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG
The electromyography(EMG) signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand. Increasing the number of myoelectric-signal channels can yield richer information of motion in...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2024-01-01
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| Series: | Cyborg and Bionic Systems |
| Online Access: | https://spj.science.org/doi/10.34133/cbsystems.0066 |
| _version_ | 1797340321687797760 |
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| author | Yuxuan Wang Ye Tian Jinying Zhu Haotian She Yinlai Jiang Zhihong Jiang Hiroshi Yokoi |
| author_facet | Yuxuan Wang Ye Tian Jinying Zhu Haotian She Yinlai Jiang Zhihong Jiang Hiroshi Yokoi |
| author_sort | Yuxuan Wang |
| collection | DOAJ |
| description | The electromyography(EMG) signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand. Increasing the number of myoelectric-signal channels can yield richer information of motion intention and improve the accuracy of gesture recognition. However, as the number of acquisition channels increases, its effect on the improvement of the accuracy of gesture recognition gradually diminishes, resulting in the improvement of the control effect reaching a plateau. To address these problems, this paper presents a proposed method to improve gesture recognition accuracy by virtually increasing the number of EMG signal channels. This method is able to improve the recognition accuracy of various gestures by virtually increasing the number of EMG signal channels and enriching the motion intention information extracted from data collected from a certain number of physical channels, ultimately providing a solution to the issue of the recognition accuracy plateau caused by saturation of information from physical recordings. Meanwhile, based on the idea of the filtered feature selection method, a quantitative measure of sample sets (separability of feature vectors [SFV]) derived from the divergence and correlation of the extracted features is introduced. The SFV value can predict the classification effect before performing the classification, and the effectiveness of the virtual-dimension increase strategy is verified from the perspective of feature set differentiability change. Compared to the statistical motion intention recognition success rate, SFV is a more representative and faster measure of classification effectiveness and is also suitable for small sample sets. |
| first_indexed | 2024-03-08T10:00:23Z |
| format | Article |
| id | doaj.art-f7d73e73558544e7b84085fe60cbfa2c |
| institution | Directory Open Access Journal |
| issn | 2692-7632 |
| language | English |
| last_indexed | 2024-03-08T10:00:23Z |
| publishDate | 2024-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Cyborg and Bionic Systems |
| spelling | doaj.art-f7d73e73558544e7b84085fe60cbfa2c2024-01-29T12:59:15ZengAmerican Association for the Advancement of Science (AAAS)Cyborg and Bionic Systems2692-76322024-01-01510.34133/cbsystems.0066A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMGYuxuan Wang0Ye Tian1Jinying Zhu2Haotian She3Yinlai Jiang4Zhihong Jiang5Hiroshi Yokoi6School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.Faculty of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan.School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.Faculty of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan.The electromyography(EMG) signal is the biocurrent associated with muscle contraction and can be used as the input signal to a myoelectric intelligent bionic hand to control different gestures of the hand. Increasing the number of myoelectric-signal channels can yield richer information of motion intention and improve the accuracy of gesture recognition. However, as the number of acquisition channels increases, its effect on the improvement of the accuracy of gesture recognition gradually diminishes, resulting in the improvement of the control effect reaching a plateau. To address these problems, this paper presents a proposed method to improve gesture recognition accuracy by virtually increasing the number of EMG signal channels. This method is able to improve the recognition accuracy of various gestures by virtually increasing the number of EMG signal channels and enriching the motion intention information extracted from data collected from a certain number of physical channels, ultimately providing a solution to the issue of the recognition accuracy plateau caused by saturation of information from physical recordings. Meanwhile, based on the idea of the filtered feature selection method, a quantitative measure of sample sets (separability of feature vectors [SFV]) derived from the divergence and correlation of the extracted features is introduced. The SFV value can predict the classification effect before performing the classification, and the effectiveness of the virtual-dimension increase strategy is verified from the perspective of feature set differentiability change. Compared to the statistical motion intention recognition success rate, SFV is a more representative and faster measure of classification effectiveness and is also suitable for small sample sets.https://spj.science.org/doi/10.34133/cbsystems.0066 |
| spellingShingle | Yuxuan Wang Ye Tian Jinying Zhu Haotian She Yinlai Jiang Zhihong Jiang Hiroshi Yokoi A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG Cyborg and Bionic Systems |
| title | A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG |
| title_full | A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG |
| title_fullStr | A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG |
| title_full_unstemmed | A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG |
| title_short | A Hand Gesture Recognition Strategy Based on Virtual-Dimension Increase of EMG |
| title_sort | hand gesture recognition strategy based on virtual dimension increase of emg |
| url | https://spj.science.org/doi/10.34133/cbsystems.0066 |
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