典型文献
Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal
文献摘要：
To explore the influence of the fusion of different features on recognition, this paper took the electromyography (EMG) signals of rectus femoris under different motions (walk, step, ramp, squat, and sitting) as samples, linear features (time-domain features (variance (VAR) and root mean square (RMS)), frequency-domain features (mean frequency (MF) and mean power frequency (MPF)), and nonlinear features (empirical mode decomposition (EMD)) of the samples were extracted. Two feature fusion algorithms, the series splicing method and complex vector method, were designed, which were verified by a double hidden layer (BP) error back propagation neural network. Results show that with the increase of the types and complexity of feature fusions, the recognition rate of the EMG signal to actions is gradually improved. When the EMG signal is used in the series splicing method, the recognition rate of time-domain + frequency-domain + empirical mode decomposition (TD + FD + EMD) splicing is the highest, and the average recognition rate is 92.32％. And this rate is raised to 96.1％ by using the complex vector method, and the variance of the BP system is also reduced.
文献关键词：
中图分类号：
作者姓名：
Jun-Yao Wang;Yue-Hong Dai;Xia-Xi Si
作者机构：
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731;Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731
文献出处：
引用格式：
[1]Jun-Yao Wang;Yue-Hong Dai;Xia-Xi Si-.Feature Layer Fusion of Linear Features and Empirical Mode Decomposition of Human EMG Signal)[J].电子科技学刊,2022(03):257-269
A类：
B类：
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AB值：
0.566301
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