KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals | |
Chen, Lin1,2 ; Fu, Jianting1,2; Wu, Yuheng1,3; Li, Haochen1,2; Zheng, Bin1
| |
| 2020-02-01 | |
| 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. |
| 关键词 | surface electromyography (sEMG) convolution neural networks (CNNs) hand gesture recognition |
| DOI | 10.3390/s20030672 |
| 发表期刊 | SENSORS
 |
| 卷号 | 20期号:3页码:15 |
| 通讯作者 | Zheng, Bin(zhengbin@cigit.ac.cn) |
| 收录类别 | SCI |
| WOS记录号 | WOS:000517786200096 |
| 语种 | 英语 |
