Feature layer fusion of linear features and empirical mode decomposition of human EMG signal

Q1 Engineering

Journal of Electronic Science and Technology Pub Date : 2022-09-01 DOI:10.1016/j.jnlest.2022.100169

Jun-Yao Wang , Yue-Hong Dai , Xia-Xi Si

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引用次数: 2

Abstract

To explore the influence of the fusion of different features on recognition, this paper took the electromyogram (EMG) signals of rectus femoris under different motions (walk, step, ramp, squat, and sitting) as signals, 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 (EMD) of the signals were extracted. Two feature fusion algorithms, the series splicing method and complex vector method, were designed, which were verified by a double hidden layer error back propagation (BP) 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 value is raised to 96.1% by using the complex vector method, and the variance of the BP system is also reduced.

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基于线性特征与经验模态分解的肌电信号特征层融合

为了探讨不同特征融合对识别的影响，本文以不同运动(步行、踏步、坡道、蹲坐)下的股直肌肌电图(EMG)信号为信号，提取信号的线性特征(时域特征(方差(VAR)和均方根(RMS))、频域特征(平均频率(MF)和平均工频(MPF))和非线性特征(EMD)。设计了序列拼接法和复向量法两种特征融合算法，并用双隐层误差反向传播(BP)神经网络进行了验证。结果表明，随着特征融合类型和复杂度的增加，肌电信号对动作的识别率逐渐提高。采用序列拼接方法对肌电信号进行拼接时，时域+频域+经验模态分解(TD + FD + EMD)拼接的识别率最高，平均识别率为92.32%。采用复向量法将该值提高到96.1%，同时也减小了BP系统的方差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Electronic Science and Technology Engineering-Electrical and Electronic Engineering

CiteScore

4.30

自引率

0.00%

发文量

1362

审稿时长

99 days

期刊介绍： JEST (International) covers the state-of-the-art achievements in electronic science and technology, including the most highlight areas: ¨ Communication Technology ¨ Computer Science and Information Technology ¨ Information and Network Security ¨ Bioelectronics and Biomedicine ¨ Neural Networks and Intelligent Systems ¨ Electronic Systems and Array Processing ¨ Optoelectronic and Photonic Technologies ¨ Electronic Materials and Devices ¨ Sensing and Measurement ¨ Signal Processing and Image Processing JEST (International) is dedicated to building an open, high-level academic journal supported by researchers, professionals, and academicians. The Journal has been fully indexed by Ei INSPEC and has published, with great honor, the contributions from more than 20 countries and regions in the world.