Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals
By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/3/672 |
| _version_ | 1811303713326759936 |
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| author | Lin Chen Jianting Fu Yuheng Wu Haochen Li Bin Zheng |
| author_facet | Lin Chen Jianting Fu Yuheng Wu Haochen Li Bin Zheng |
| author_sort | Lin Chen |
| collection | DOAJ |
| description | By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results. |
| first_indexed | 2024-04-13T07:52:56Z |
| format | Article |
| id | doaj.art-b90043cf2805440e9131aa494ab5a303 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:52:56Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b90043cf2805440e9131aa494ab5a3032022-12-22T02:55:29ZengMDPI AGSensors1424-82202020-01-0120367210.3390/s20030672s20030672Hand Gesture Recognition Using Compact CNN via Surface Electromyography SignalsLin Chen0Jianting Fu1Yuheng Wu2Haochen Li3Bin Zheng4Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, ChinaChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, ChinaChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, ChinaChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, ChinaChongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, ChinaBy training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.https://www.mdpi.com/1424-8220/20/3/672surface electromyography (semg)convolution neural networks (cnns)hand gesture recognition |
| spellingShingle | Lin Chen Jianting Fu Yuheng Wu Haochen Li Bin Zheng Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals Sensors surface electromyography (semg) convolution neural networks (cnns) hand gesture recognition |
| title | Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals |
| title_full | Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals |
| title_fullStr | Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals |
| title_full_unstemmed | Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals |
| title_short | Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals |
| title_sort | hand gesture recognition using compact cnn via surface electromyography signals |
| topic | surface electromyography (semg) convolution neural networks (cnns) hand gesture recognition |
| url | https://www.mdpi.com/1424-8220/20/3/672 |
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