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Two stage EMG onset detection method

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Detection of the moment when a muscle begins to activate on the basis of EMG signal is important task for a number of biomechanical studies. In order to provide high accuracy of EMG onset detection, we developed novel method, that give results similar to that obtained by an expert. By means of this method, EMG is processed in two stages. The first stage gives rough estimation of EMG onset, whereas the second stage performs local, precise searching. The method was applied to support signal processing in biomechanical study concerning effect of body position on EMG activity and peak muscle torque stabilizing spinal column under static conditions.
Rocznik
Strony
427--440
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
autor
Bibliografia
  • [1] M. Basseville and I. V. Nikiforov: Detection of abrupt changes: Theory and application, Englewood Cliffs, NJ: PTR Prentice Hall, 1993.
  • [2] C. M. Bishop: Pattern recognition and machine learning, Springer-Verlag New York, Inc. Secaucus, NJ, USA, 2001.
  • [3] P. Bonato, T. D’Alessio and M. Knaflitz: A statistical method for the measurement of muscle activation intervals from surface myoelectric signal during gait. IEEE Trans. on Biomedical Engineering, 45(3), (1998), 287-299.
  • [4] P. R. Cavanagh And P. V. Komi: Electromechanical delay in human skeletal muscle under concentric and eccentric contractions. European J. of Applied Physiologyand Occupational Physiology, 42(3), (1979), 159-163.
  • [5] S. Conforto, P. Mathieu, M. Schmid, D. Bibbo, J. R. Florestal and T. D. Alessio: How much can we trust the electromechanical delay estimated by using electromyography?. Proc. of the 28th IEEE EMBS Annual Int. Conf., New York City, USA, (2006), 1256-1259.
  • [6] W. El Falou, M. Khalil and J. Duchene: Adaptive approach for change detection in emg recordings. Proc. of the 23rd Annual Int. Conf. of the IEEE OnEngineering in Medicine and Biology Society, (2001), 1875-1878.
  • [7] C. Frigo, M. Ferrarin, W. Frasson, E. Pavan and R. Thorsen: Emg signals detection and processing for on-line control of functional electrical stimulation. J. of Electromyography and Kinesiology, 10(5), (2000), 351-360.
  • [8] F. Gustafsson: Adaptive filtering and change detection, John Wiley & Sons, 2000.
  • [9] M. Khalil and J. Duchene: Detection and classification of multiple events in piecewise stationary signals: Comparison between autoregressive and multiscale approaches. Signal Processing, 75 (1999), 239-251.
  • [10] J. K. Leader, J. R. Boston and C. A. Moore: A data dependent computer algorithm for the detection of muscle activity. Electroencephalography and ClinicalNeurophysiology/Electromyography and Motor Control, 109(2), (1998), 119-123.
  • [11] X. Li, P. Zhou and A. S. Aruin: Teager - kaiser energy operation of surface emg improves muscle activity onset detection. Annals of Biomedical Engineering, 35(9), (2007), 1532-1538.
  • [12] A. Merlo, D. Farina and R. Merletti: A fast and reliable technique for muscle activity detection from surface emg signals. IEEE Trans. on Biomedical Engineering, 50(3), (2003), 316-323.
  • [13] S. Micera, A. M. Sabatini and P. Dario: An algorithm for detecting the onset of muscle contraction by emg signal processing. Medical Engineering & Physics, 20(3), (1998), 211-215.
  • [14] S. Micera, G. Vannozzi, A. M. Sabatini and P. Dario: Improving detection of muscle activation intervals. IEEE Engineering in Medicine and Biology Magazine, 20(6), (2001), 38-46.
  • [15] V. Moskvina and A. A. Zhigljavsky: Improving detection of muscle activation intervals, submitted. J. of Time Series Analysis, (2001), 1-32.
  • [16] M. B. I. Reaz, M. S. Hussain and F. Mohd-Yasin: Techniques of emg signal analysis: detection, processing, classification and applications. Biological ProceduresOnline, 8(1), (2006), 11-35.
  • [17] S. Solnik, P. Rider, K. Steinweg, P. Devita and T. Hortobágyi: Teager - kaiser energy operator signal conditioning improves emg onset detection. Eur J.of Applied Physiology, 110 (2010), 489-498.
  • [18] G. Staude, C. Flachenecker, M. Daumer and W. Wolf: Onset detection in surface electromyographic signals: A systematic comparison of methods. EURASIP J. of Applied Signal Processing, 2 (2001), 67-81.
  • [19] G. Staude and W. Wolf: Objective motor response onset detection in surface myoelectric signals. Medical Engineering & Physics, 21 (1999), 449-467.
  • [20] A. Szpala, A. Rutkowska-Kucharska, J. Drapała and K. Brzostowski: Choosing the right body position for assessing trunk flexors and extensors torque output. Human Movement, 12(1), (2011), 57-64.
  • [21] A. Szpala, A. Rutkowska-Kucharska, J. Drapała, K. Brzostowski and J. Zawadzki: Assymetry of electromechanical delay (emd) and torque in the muscles stabilizing spinal column. Acta of Bioengineering and Biomechanics, 12(4), (2010), 11-18.
  • [22] A. J. Thexton: A randomisation method for discriminating between signal and noise in recordings of rhythmic electromyographic activity. J. of neurosciencemethods, 66(2), (1996), 93-98.
  • [23] L. Vaisman, J. Zariffa and M. R. Popovic: Application of singular spectrumbased change-point analysis to emg-onset detection. J. of Electromyography andKinesiology, 20(4), (2010), 750-760.
  • [24] L. Xu and A. Adler: An improved method for muscle activation detection during gait. Canadian Conf. on Electrical and Computer Engineering, (2004), 357-360.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0103-0011
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