Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals

被引：136

作者：

Chen, Lin ^{[1
,2
]}

Fu, Jianting ^{[1
,2
]}

Wu, Yuheng ^{[1
,3
]}

Li, Haochen ^{[1
,2
]}

Zheng, Bin ^{[1
]}

机构：

[1] Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400700, Peoples R China

[2] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China

[3] Changchun Univ Sci & Technol, Sch Mechatron Engn, Changchun 130022, Peoples R China

来源：

SENSORS | 2020年 / 20卷 / 03期

关键词：

surface electromyography (sEMG); convolution neural networks (CNNs); hand gesture recognition; EMG;

D O I：

10.3390/s20030672

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：15

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