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Open AccessArticle

Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

by 1,2,†, 1,2,†, 1,3, 1,2 and 1,*
1
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400700, China
2
School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, China
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Sensors 2020, 20(3), 672; https://doi.org/10.3390/s20030672
Received: 25 December 2019 / Revised: 21 January 2020 / Accepted: 22 January 2020 / Published: 26 January 2020
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. View Full-Text
Keywords: surface electromyography (sEMG); convolution neural networks (CNNs); hand gesture recognition surface electromyography (sEMG); convolution neural networks (CNNs); hand gesture recognition
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MDPI and ACS Style

Chen, L.; Fu, J.; Wu, Y.; Li, H.; Zheng, B. Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals. Sensors 2020, 20, 672.

AMA Style

Chen L, Fu J, Wu Y, Li H, Zheng B. Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals. Sensors. 2020; 20(3):672.

Chicago/Turabian Style

Chen, Lin; Fu, Jianting; Wu, Yuheng; Li, Haochen; Zheng, Bin. 2020. "Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals" Sensors 20, no. 3: 672.

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