Pretreatment of sEMG Using Wavelet Threshold Method

Surface electromyography (sEMG) is derived from human skeletal muscle and can be employed as an accurate signal to reflect the body's muscle state. Because of its non-invasiveness and convenience, it is commonly used in many fields such as pattern recognition-based medical rehabilitation and in...

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Bibliographic Details
Published in2018 International Conference on Machine Learning and Cybernetics (ICMLC) Vol. 2; pp. 638 - 643
Main Authors Jiang, Du, Li, Gong-Fa, Sun, Ying, Jiang, Guo-Zhang, Kong, Jian-Yi, Xu, Shuang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Electrodes
Electromyography
Gesture recognition
Muscles
Noise reduction
sEMG signal
Signal denoising
Signal to noise ratio
Thumb
Wavelet threshold method
Wavelet transforms
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Summary:Surface electromyography (sEMG) is derived from human skeletal muscle and can be employed as an accurate signal to reflect the body's muscle state. Because of its non-invasiveness and convenience, it is commonly used in many fields such as pattern recognition-based medical rehabilitation and intelligent humanoid robots. In view of the fact that sEMG signals are weak and easy to be interference by various noise, this paper proposes a method of sEMG preprocessing based on wavelet threshold method to eliminate the noise. At the same time, the RMS value, the waveform length feature, and the nonlinear feature sample entropy are extracted to form the feature vector for subsequent pattern recognition. Finally, sEMG signals of 9 common hand movements were used to establish the gesture recognition model based on SVM. The recognition rate reached over 99%, which verified the effectiveness of the proposed method.
ISSN:2160-1348
DOI:10.1109/ICMLC.2018.8527030