Kurtosis and negentropy investigation of myo electric signals during different MVCs

Naik, GR; Kumar, DK; Arjunan, SP

Kurtosis and negentropy investigation of myo electric signals during different MVCs

Naik, GR

Kumar, DK Arjunan, SP

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Publication Type:: Conference Proceeding
Citation:: 2011 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living, BRC 2011, 2011, pp. 40 - 43
Issue Date:: 2011-05-19

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Kumar, DK	en_US
dc.contributor.author	Arjunan, SP	en_US
dc.date.issued	2011-05-19	en_US
dc.identifier.citation	2011 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living, BRC 2011, 2011, pp. 40 - 43	en_US
dc.identifier.isbn	9781424482122	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26716
dc.description.abstract	This research deals with computing the non-Gaussianity in Surface Electromyogram signal (sEMG) using Negative entropy and Kurtosis values. The signal was acquired from three different finger and wrist actions at four different levels of Maximum Voluntary Contractions (MVCs). Our experimental analysis shows that at constant force and for non-fatiguing contractions, probability density function (pdf) of sEMG signals were non-Gaussian. For lesser MVCs (below 30% of MVC) pdf measures tends to be Gaussian process. The above measures were verified by computing the kurtosis values for different MVCs. © 2011 IEEE.	en_US
dc.relation.ispartof	2011 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living, BRC 2011	en_US
dc.relation.isbasedon	10.1109/BRC.2011.5740669	en_US
dc.title	Kurtosis and negentropy investigation of myo electric signals during different MVCs	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Vitoria	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This research deals with computing the non-Gaussianity in Surface Electromyogram signal (sEMG) using Negative entropy and Kurtosis values. The signal was acquired from three different finger and wrist actions at four different levels of Maximum Voluntary Contractions (MVCs). Our experimental analysis shows that at constant force and for non-fatiguing contractions, probability density function (pdf) of sEMG signals were non-Gaussian. For lesser MVCs (below 30% of MVC) pdf measures tends to be Gaussian process. The above measures were verified by computing the kurtosis values for different MVCs. © 2011 IEEE.

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http://hdl.handle.net/10453/26716