Convolutional neural network human gesture recognition algorithm based on phase portrait of surface electromyography energy kernel.
10.7507/1001-5515.202010080
- Author:
Liukai XU
1
;
Keqin ZHANG
2
;
Zhaohong XU
2
;
Genke YANG
1
Author Information
1. Ningbo Artificial Intelligence Institute of Shanghai Jiao Tong University, Ningbo, Zhejiang 315000, P.R.China.
2. Ningbo Industrial Internet Institute, Ningbo, Zhejiang 315000, P.R.China.
- Publication Type:Journal Article
- Keywords:
convolutional neural network;
energy kernel;
gesture recognition;
surface electromyography
- MeSH:
Algorithms;
Electromyography;
Gestures;
Humans;
Neural Networks, Computer;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2021;38(4):621-629
- CountryChina
- Language:Chinese
-
Abstract:
Surface electromyography (sEMG) is a weak signal which is non-stationary and non-periodic. The sEMG classification methods based on time domain and frequency domain features have low recognition rate and poor stability. Based on the modeling and analysis of sEMG energy kernel, this paper proposes a new method to recognize human gestures utilizing convolutional neural network (CNN) and phase portrait of sEMG energy kernel. Firstly, the matrix counting method is used to process the sEMG energy kernel phase portrait into a grayscale image. Secondly, the grayscale image is preprocessed by moving average method. Finally, CNN is used to recognize sEMG of gestures. Experiments on gesture sEMG signal data set show that the effectiveness of the recognition framework and the recognition method of CNN combined with the energy kernel phase portrait have obvious advantages in recognition accuracy and computational efficiency over the area extraction methods. The algorithm in this paper provides a new feasible method for sEMG signal modeling analysis and real-time identification.
