Research Area:  Machine Learning

Abstract:

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.

Keywords:  
surface electromyography (sEMG)
convolution neural networks (CNNs)
hand gesture recognition

Author(s) Name:  Lin Chen,Jianting Fu,Yuheng Wu ,Haochen Li and Bin Zheng

Publisher name:  MDPI

DOI:  10.3390/s20030672

Volume Information:  Volume 20, Issue 3

Paper Link:   https://www.mdpi.com/1424-8220/20/3/672