Enhancing Classification Accuracy of Wrist Movement by Denoising sEMG signals

被引：0

作者：

Abbas, Baqar ^{[1
]}

Farooq, Omar ^{[1
]}

Uzzaman, Yusuf ^{[1
]}

Khan, Abid Ali ^{[2
]}

Vyas, A. L. ^{[3
]}

机构：

[1] Aligarh Muslim Univ, Dept Elect Engn, Aligarh, Uttar Pradesh, India

[2] Aligarh Muslim Univ, Dept Engn Mech, Aligarh, Uttar Pradesh, India

[3] IIT, Instrument Design Dev Ctr, New Delhi, India

来源：

2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2013年

关键词：

SURFACE EMG;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

This paper presents identification of 4 different wrist movements by analyzing fore-arm surface Electromyogram (sEMG) signals. In order to reduce noise picked up during the recording, wavelet based denoising is applied using Daubechies mother wavelet. Spectral features along with Wilson's amplitude were extracted and given to a linear classifier. The experimental result shows better recognition performance using the given features when denoising is applied. The maximum accuracy for identification of four wrist movement was 97.5% which is quite significant as compared to the previous researches.

引用

页码：5762 / 5764

页数：3

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