Paper Info
Title
Evaluation Of Higher Order Statistics Parameters For Multi Channel Semg Using Different Force Levels
Abstract
The electromyograpy (EMG) signal provides information about the performance of muscles and nerves. The shape of the muscle signal and motor unit action potential (MUAP) varies due to the movement of the position of the electrode or due to changes in contraction level. This research deals with evaluating the non-Gaussianity in Surface Electromyogram signal (sEMG) using higher order statistics (HOS) parameters. To achieve this, experiments were conducted for four different finger and wrist actions at different levels of Maximum Voluntary Contractions (MVCs). Our experimental analysis shows that at constant force and for non-fatiguing contractions, probability density functions (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.
Year
DOI
Venue
2011
10.1109/IEMBS.2011.6090961
2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
probability density function,signal processing,biomechanics,electrodes,force,probability,estimation,gaussian process,experimental analysis
Signal processing,Computer science,Multi channel,Artificial intelligence,Gaussian process,Biomechanics,Kurtosis,Computer vision,Pattern recognition,Higher-order statistics,Electromyography,Speech recognition,Probability density function
Conference
Volume
ISSN
Citations 
2011
1557-170X
2
PageRank 
References 
Authors
0.44
6
2
Name
Order
Citations
PageRank
Ganesh R Naik130.84
Dinesh K. Kumar2839.17