• DocumentCode
    2821438
  • Title

    Kurtosis and negentropy investigation of myo electric signals during different MVCs

  • Author

    Naik, Ganesh R. ; Kumar, Dinesh K. ; Arjunan, Sridhar P.

  • Author_Institution
    Sch. of Electr. & Comput. Eng., RMIT Univ., Melbourne, VIC, Australia
  • fYear
    2011
  • fDate
    6-8 Jan. 2011
  • Firstpage
    1
  • Lastpage
    4
  • 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.
  • Keywords
    electromyography; entropy; medical signal processing; Maximum Voluntary Contraction; kurtosis; negative entropy; negentropy; nonGaussianity; probability density function; surface electromyogram signal; Electromyography; Fingers; Force; Force measurement; Force sensors; Independent component analysis; Muscles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biosignals and Biorobotics Conference (BRC), 2011 ISSNIP
  • Conference_Location
    Vitoria
  • Print_ISBN
    978-1-4244-8212-2
  • Type

    conf

  • DOI
    10.1109/BRC.2011.5740669
  • Filename
    5740669