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Time–frequency Analysis of the EMG Digital Signals

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the article comparison of time-frequency spectra of EMG signals obtained by the following methods: Fast Fourier Transform, predictive analysis and wavelet analysis is presented. The EMG spectra of biceps and triceps while an adult man was flexing his arm were analysed. The advantages of the predictive analysis were shown as far as averaging of the spectra and determining the main maxima are concerned. The Continuous Wavelet Transform method was applied, which allows for the proper distribution of the scales, aiming at an accurate analysis and localisation of frequency maxima as well as the identification of impulses which are characteristic of such signals (bursts) in the scale of time. The modified Morlet wavelet was suggested as the mother wavelet. The wavelet analysis allows for the examination of the changes in the frequency spectrum in particular stages of the muscle contraction. Predictive analysis may also be very useful while smoothing and averaging the EMG signal spectrum in time.
Rocznik
Strony
20--25
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
  • Institute of Computer Science, Maria Curie-Skłodowska University, pl. M. Curie-Skłodowskiej 1, 20-036 Lublin, Poland
  • Department of Biomechanics and Computer Science, External Faculty of Physical Education in Biała Podlaska, Academy of Physical Education in Warsaw
autor
  • Department of Biomechanics and Computer Science, External Faculty of Physical Education in Biała Podlaska, Academy of Physical Education in Warsaw
autor
  • Department of Biomechanics and Computer Science, External Faculty of Physical Education in Biała Podlaska Ac,ademy of Physical Education in Warsaw
autor
  • Institute of Computer Science, Maria Curie-Skłodowska University, pl. M. Curie-Skłodowskiej 1, 20-036 Lublin, Poland
Bibliografia
  • [1] Olmo G., Latenza F., Lo Presti L., Matched wavelet approach in stretching analysis of electrically evoked surface EMG signal, Signal Processing 80 (2000): 671.
  • [2] Moshou D., Hostens I., Papaioannou, Ramon H., Dynamic muscle fatigue detection using selforganizing maps, Applied Soft Computing 5 (2005): 391.
  • [3] Manal K., Buchanan T. S., A one-parameter neural activation to muscle activation model: estimation isometric joint moments from electromyograms, Journal of Biomechanics 36 (2003): 1197.
  • [4] Faina D., Falla D., Estimation of muscle conduction velocity from two-dimensional surface EMG recordings in dynamic tasks, Biomechanical Signal Processing and Control 3 (2008): 138.
  • [5] Coorevits P., Danneels L., Cambier D., Ramon H., Druyts H., Karlsson J. S., De Moor G., Vanderstraeten G., Correlations between short-time Fourier and continuous wavelet transforms in the analysis of localized back and hip muscle fatigue during isometric contractions, Journal of Electromyography and Kinesiology 18 (2008): 637.
  • [6] Fele-Žorž G., Kavšek G., Novak-Antolič Ž., Jager F., A comparison of various linear and nonlinear processing techniques to separate EMG records of term and pre-term delivery groups, Med. Biol. Eng. Comp. 46 (2008): 911.
  • [7] Flanders M., Choosing a wavelet for single-trial EMG, Journal of Neuroscience Methods, 116 (2002): 165.
  • [8] von Tscharner V., Goephert B., Nigg B.M., Changes in EMG signals for muscle tibialis anterior while running barefoot or with shoes resolved by non-lenarly scaled wavelets, Journal of Biomechanics 36 (2003): 1169.
  • [9] Nuwer M., R., Comi G., Emerson R., Fuglsang-Frederiksen, A., Guérit J. M., Hinrichs H., Ikeda A., Luccas F. J. C., IFCN standards for digital recording of clinical EEG, Electroencephalography and Clinical Neurophysiology 106 (1998): 259.
  • [10] Codello I., Kuniszyk-JóźkowiakW., Digital signals analysis with the LPC method, Annales UMCS Informatica AI5 (2006): 315.
  • [11] Codello I., Kuniszyk-Jóźkowiak W.,’Wave Blaster’ – A comprehensive tool for speech analysis and its application for vowel recognition using wavelet continuous transform with bark scales, 56th open Seminar in Acoustics (2009): 141.
  • [12] Codello I., Kuniszyk-Jóźkowiak W., Smołka E., Suszynski W., Speaker Recognition Using Continuous Wavelet Transform with Bark Scales, Polish Journal of Environmental Studies, 18(3B) (2009): 78.
  • [13] Zielinski T. P., Digital Signal Processing, WKŁ, Warszawa 2005 (in Polish).
  • [14] Rabiner L. R., Schafer., Digital Processing of Speech Signals, Prentice-Hall, Inc. New Jrsey, (1978).
  • [15] Addison P. S., The Illustrated Wavelet Transform Handbook, Taylor & Francis Group New York (2002).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c6e70fda-b8c5-4859-9b5f-00a656080598
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