Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model
The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2021-01-01
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| Series: | Cyborg and Bionic Systems |
| Online Access: | http://dx.doi.org/10.34133/2021/9794610 |
| _version_ | 1818407205705089024 |
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| author | Dianchun Bai Tie Liu Xinghua Han Hongyu Yi |
| author_facet | Dianchun Bai Tie Liu Xinghua Han Hongyu Yi |
| author_sort | Dianchun Bai |
| collection | DOAJ |
| description | The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit. The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition. The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1 MB. The model can still control the artificial hand accurately when the model is small and the precision is high. |
| first_indexed | 2024-12-14T09:24:08Z |
| format | Article |
| id | doaj.art-801366f59fad4fb3b10248f293a51173 |
| institution | Directory Open Access Journal |
| issn | 2692-7632 |
| language | English |
| last_indexed | 2024-12-14T09:24:08Z |
| publishDate | 2021-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Cyborg and Bionic Systems |
| spelling | doaj.art-801366f59fad4fb3b10248f293a511732022-12-21T23:08:14ZengAmerican Association for the Advancement of Science (AAAS)Cyborg and Bionic Systems2692-76322021-01-01202110.34133/2021/9794610Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM ModelDianchun Bai0Tie Liu1Xinghua Han2Hongyu Yi3School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China; Department of Mechanical Engineering and Intelligent Systems, University of Electro-Communications, Tokyo 182-8585, JapanSchool of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, ChinaThe deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit. The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition. The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1 MB. The model can still control the artificial hand accurately when the model is small and the precision is high.http://dx.doi.org/10.34133/2021/9794610 |
| spellingShingle | Dianchun Bai Tie Liu Xinghua Han Hongyu Yi Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model Cyborg and Bionic Systems |
| title | Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model |
| title_full | Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model |
| title_fullStr | Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model |
| title_full_unstemmed | Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model |
| title_short | Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model |
| title_sort | application research on optimization algorithm of semg gesture recognition based on light cnn lstm model |
| url | http://dx.doi.org/10.34133/2021/9794610 |
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