基于MEEMD和DFA的现场工作表面肌电信号降噪研究

2022 IEEE 3rd China International Youth Conference on Electrical Engineering (CIYCEE) Pub Date : 2022-11-03 DOI:10.1109/CIYCEE55749.2022.9959026

Xiaoqian Tang, Jian Ying, Qiang Zhu, Hangjun Chen, Guoyin Yang, Like Jiang, Hao Cai, Mengting Huang

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引用次数: 0

摘要

为了消除工作状态下表面肌电信号中的噪声，提出了一种基于MEEMD和DFA的表面肌电信号去噪方法。采集典型工况下佩戴绝缘手套后的右臂二头肌表面肌电信号，采用趋势波动分析(dettrend Fluctuation Analysis, DFA)作为滤波指标，增强修正集合经验模态分解(Modified Ensemble Empirical Mode Decomposition, MEEMD)对表面肌电信号中有效信息的识别能力，提高二次降噪效果。结果表明，对于典型的强噪声表面肌电信号，DFA-MEEMD二次去噪方法的MAE、MSE和信噪比分别为4.40×10-3、5.80x10−5和25.4dB，为从表面肌电信号中提取有用信息提供了一种方法。

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Research of noise reduction about sEMG signal for live working based on MEEMD and DFA

In order to eliminate the noise in Surface Electromyography(sEMG) signals for live working, a sEMG signal denoising method based on MEEMD and DFA was proposed. The sEMG signals of right arm biceps after wearing insulating gloves were collected under typical working conditions, and the Detrend Fluctuation Analysis (DFA) was used as a filtering index to enhance the recognition ability of Modified Ensemble Empirical Mode Decomposition (MEEMD) on the effective information in sEMG signals, so as to improve the effect of secondary noise reduction. The results show that the MAE, MSE and SNR of DFA-MEEMD secondary denoising method are 4.40×10-3, 5.80x10−5 and 25.4dB respectively for typical sEMG signals with strong noise, which can provide a method for e1xtracting useful information from sEMG signals.

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2022 IEEE 3rd China International Youth Conference on Electrical Engineering (CIYCEE)

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