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Article citations


K. Sogaard, A. K. Blangsted, L. V. Jorgensen, P. Madeleine and G. Sjogaard, “Evidence of Long Term Muscle Fatigue Following Prolonged Intermittent Contractions Based on Mechano-and Electromyograms,” Journal of Electromyography and Kinesiology, Vol. 13, No. 5, 2003, pp. 441-450. doi:10.1016/S1050-6411(03)00075-0??

has been cited by the following article:

  • TITLE: The Influence of Window Length Analysis on the Time and Frequency Domain of Mechanomyographic and Electromyographic Signals of Submaximal Fatiguing Contractions

    AUTHORS: Guilherme Nogueira-Neto, Eduardo Scheeren, Eddy Krueger, Percy Nohama, Vera Lúcia S. N. Button

    KEYWORDS: Mechanomyography; Electromyography; Window Length Analysis; Local Muscle Fatigue

    JOURNAL NAME: Open Journal of Biophysics, Vol.3 No.3, July 12, 2013

    ABSTRACT: Mechanomyography (MMG) acquires the oscillatory waves of contracting muscles. Electromyography (EMG) is a tool for monitoring muscle overall electrical activity. During muscle contractions, both techniques can investigate the changes that occur in the muscle properties. EMG and MMG parameters have been used for detecting muscle fatigue with diverse test protocols, sensors and filtering. Depending on the analysis window length (WLA), monitoring physiological events could be compromised due to imprecision in the determination of parameters. Therefore, this study investigated the influence of WLA variation on different MMG and EMG parameters during submaximal isometric contractions monitoring MMG and EMG parameters. Ten male volunteers performed isometric contractions of elbow joint. Triaxial accelerometer-based MMG sensor and EMG electrodes were positioned on the biceps brachii muscle belly. Torque was monitored with a load cell. Volunteers remained seated with hip and elbow joint at angles of 110° and 90°, respectively. The protocol consisted in maintaining torque at 70% of maximum voluntary contraction as long as they could. Parameter data of EMG and the modulus of MMG were determined for four segments of the signal. Statistical analysis consisted of analyses of variance and Fisher’s least square differences post-hoc test. Also, Pearson’s correlation was calculated to determine whether parameters that monitor similar physiological events would have strong correlation. The modulus of MMG mean power frequency (MPF) and the number of crossings in the baseline could detect changes between fresh and fatigued muscle with 1.0 s WLA. MPF and the skewness of the spectrum (μ3), parameters related to the compression of the spectrum, behaved differently when monitored with a triaxial MMG sensor. The EMG results show that for the 1.0 s and 2.0 s WLAs have normalized RMS difference with fatigued muscle and that there was strong correlation between parameters of different domains.