Journal
SENSORS
Volume 20, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/s20030672
Keywords
surface electromyography (sEMG); convolution neural networks (CNNs); hand gesture recognition
Funding
- Priority research & design program of Chongqing technology innovation and application demonstration [cstc2017zdcy-zdyfX0036]
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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.
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