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Time-Frequency Analysis of Myoelectric Signals During Dynamic Contractions: A Comparative Study
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Science and Technology, Mathematical statistics.
Thayer School of Engineering, Dartmouth College,.
2000 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 47, no 2, 228-238 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we introduce the nonstationary signal analysis methods to analyze the myoelectric (ME) signals during dynamic contractions by estimating the time-dependent spectral moments. The time-frequency analysis methods including the short-time Fourier transform, the Wigner–Ville distribution, the Choi–Williams distribution, and the continuous wavelet transform were compared for estimation accuracy and precision on synthesized and real ME signals. It is found that the estimates providedby the continuous wavelet transform have better accuracy and precision than those obtained with the other time-frequency analysis methods on simulated data sets. In addition, ME signals from four subjects during three different tests (maximum static voluntary contraction, ramp contraction, and repeated isokinetic contractions) were also examined.

Place, publisher, year, edition, pages
2000. Vol. 47, no 2, 228-238 p.
Keyword [en]
Choi–Williams distribution (CWD), continuous wavelet transform (CWT), myoelectric (ME) signal, pseudo Wigner–Ville distribution (PWVD), running windowed exponential distribution (RWED), short-time Fourier transform (STFT), spectral change indicator, time-frequency analysis, Wigner–Ville distribution (WVD).
National Category
Medical Engineering
Research subject
Signal Processing
URN: urn:nbn:se:umu:diva-63698DOI: 10.1109/10.821766ISI: 000085188200011OAI: diva2:582390
Available from: 2013-01-04 Created: 2013-01-04 Last updated: 2013-10-08Bibliographically approved

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