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Feature Extraction and Classification of Surface EMG Signals for Robotic Hand Simulation

Sakshi Sharma, Hemu Farooq, Nidhi Chahal. Published in Signal Processing.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Sakshi Sharma, Hemu Farooq, Nidhi Chahal
10.5120/cae2016652042

Sakshi Sharma, Hemu Farooq and Nidhi Chahal. Article: Feature Extraction and Classification of Surface EMG Signals for Robotic Hand Simulation. Communications on Applied Electronics 4(2):27-31, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Sakshi Sharma and Hemu Farooq and Nidhi Chahal},
	title = {Article: Feature Extraction and Classification of Surface EMG Signals for Robotic Hand Simulation},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {2},
	pages = {27-31},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Feature Extraction and Classification of Surface Electromyography (EMG) signals provide an access for the development of Robotic Hand. EMG signals stands for electromyography signals. These are called the bio signals. Bio signal means a collective electrical signal acquired from any organ that represents a physical variable of interest. The EMG signal being a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities. [1]. The nervous system always controls the muscle activity (contraction/relaxation). Hence, the EMG signal is a complicated signal, which is controlled by the nervous system and is dependent on the anatomical and physiological properties of muscles.

In this paper, we have discussed different steps in analyzing the EMG signals. The first step is to analyze the surface EMG signal from the subject’s forearm using Discrete Wavelet Transform and extract features using the singular value decomposition.

The second step is to call the different feature values into linguistic terms by using Fuzzy Logic Classifiers in order to recognize different degrees of freedom like open to close, close to open etc.

This paper will give in depth insight in the field of EMG signal and has provided more efficient work when compared to conventional works and efficiency is 99%.

References

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Keywords

EMG signal, DWT, fuzzy classifier, feature extraction