A SSA-based de-noising technique for surface electromyography signals

DOI

10.2991/jimec-16.2016.12How to use a DOI?

Keywords

Singular spectrum analysis; Surface electromyography; De-noising; Bioinformatics

Abstract

The surface electromyography(sEMG) signal emanates when people contract their muscles. The sEMG signal contains plenty of information about muscle activity. Therefore,it can be used in activity recognition, which makes great contribution to medical devices, e.g., protheses or orthoses control systems. Here,a de-noising technique is presented which applies singular spectrum analysis(SSA) to de-noise sEMG signals. The principle of SSA is to decompose the original time series into a set of additive time series in which noise can be easily distinguished from the useful signal. Unlike transform-based algorithms, such as discrete wavelet transform, SSA is a time-series analysis algorithm which is completely driven by signal itself. This data-driven nature makes SSA very useful for sEMG signal de-noising.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xuanliang Deng
AU  - Kang Wang
PY  - 2016/10
DA  - 2016/10
TI  - A SSA-based de-noising technique for surface electromyography signals
BT  - Proceedings of the 2016 Joint International Information Technology, Mechanical and Electronic Engineering
PB  - Atlantis Press
SP  - 62
EP  - 67
SN  - 2352-5401
UR  - https://doi.org/10.2991/jimec-16.2016.12
DO  - 10.2991/jimec-16.2016.12
ID  - Deng2016/10
ER  -