EMG Signal Processing and Application Based on Empirical Mode Decomposition
Mathematics and Computer Science
Volume 4, Issue 6, November 2019, Pages: 99-103
Received: Oct. 10, 2019;
Accepted: Nov. 22, 2019;
Published: Dec. 6, 2019
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Xu Mengying, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
Yang Xiaoli, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
Xu Chenli, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
Yang Bin, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
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With the development of rehabilitation medicine and kinematics, the study of Electromyographic (EMG) signal come into people’s sight. The information obtained from the surface EMG signals can not only reflect the motion state of muscles and joints, but also judge people's motion type, which is one of the important indexes in the study of human body. Based on the EMG as the research object with the detailed analysis to understand the EMG of time domain, frequency domain and SNR, etc. The study of EMG signal denoising and feature extraction is of great value and significance in the field of medical diagnosis. Such as using sEMG signals to assess muscle status and determine postoperative recovery status. Empirical Mode Decomposition (EMD) based on hilbert-huang is a time frequency analysis method for non-linear and non-stationary signals like EMG signals, which has unique advantages and broad prospects in signal analysis and processing. In this paper, we used EMD to decompose signal which contain multiple frequency component into a series of inherent modal parameters, and then combine the method of EMD decomposition and wavelet transform to carry out denoising processing and feature extraction for EMG signals, which can effectively weaken the noise of surface EMG signals and reflect the essential characteristics of the original signal, and classify the damage of EMG signals by analyzing the characteristic values.
EMG, Wavelet Transform, EMD
To cite this article
EMG Signal Processing and Application Based on Empirical Mode Decomposition, Mathematics and Computer Science.
Vol. 4, No. 6,
2019, pp. 99-103.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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