Open Journal of Biophysics, 2013, 3, 178-190 http://dx.doi.org/10.4236/ojbiphy.2013.33021 Published Online July 2013 (http://www.scirp.org/journal/ojbiphy) The Influence of Window Length Analysis on the Time and Frequency Domain of Mechanomyographic and Electromyographic Signals of Submaximal Fatiguing Contractions Guilherme Nogueira-Neto 1,2 , Eduardo Scheeren 2,3 , Eddy Krueger 3 , Percy Nohama 1,2,3 , Vera Lúcia S. N. Button 1 1 Departamento de Engenharia Biomédica/CEB, Universidade Estadual de Campinas-UNICAMP, Campinas, Brazil 2 Laboratório de Engenharia de Reabilitação, Pontifícia Universidade Católica do Paraná-PUCPR, Curitiba, Brazil 3 CPGEI, Universidade Tecnológica Federal do Paraná-UTFPR, Curitiba, Brazil Email: nogueira.g@pucpr.br, escheeren@gmail.com, kruegereddy@gmail.com, percy.nohama@gmail.com, vera@ceb.unicamp.br Received May 1, 2013; revised June 7, 2013; accepted June 15, 2013 Copyright © 2013 Guilherme Nogueira-Neto et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 physio- logical events could be compromised due to imprecision in the determination of parameters. Therefore, this study inves- tigated the influence of WLA variation on different MMG and EMG parameters during submaximal isometric contrac- tions 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. Keywords: Mechanomyography; Electromyography; Window Length Analysis; Local Muscle Fatigue 1. Introduction During fatiguing muscle contractions, a reduction in maximum voluntary contraction (MVC) occurs due to the inability of myofibrils to produce more force [1] and to the reduction in muscle contraction velocity as well [2]. Electromyography (EMG) has been a useful tool for studying muscle overall electrical activity, function and fatigue [3]. Alternatively, the acquisition of muscle os- cillatory response can be useful in monitoring muscle condition [4]. Different types of transducers, from piezo- electric to laser-based distance sensors, can record oscil- lations non-invasively. Such waves have been measured using accelerometers [5] and the technique is defined as mechanomyography (MMG). MMG is a non-invasive monitoring technique that can assist in investigating mechanical properties of muscle voluntary contraction and muscle fatigue [6]. Previous studies demonstrated that MMG can provide different information from that obtained with EMG recordings [7], especially muscle fatigue [8], and both could provide Copyright © 2013 SciRes. OJBiphy