Daoud H.G.Ragai H.F.Department of Electrical Communications and Electronics SystemsFaculty of EngineeringMSA UniversityCairo41511Egypt; Department of Electrical Communications and ElectronicsFaculty of EngineeringAin Shams UniversityCairo11517Egypt2020-01-252020-01-25201213682156https://doi.org/10.1504/IJHTM.2012.048971https://www.inderscience.com/info/inarticle.php?artid=48972ScopusThe primary purpose of the present study is to construct behavioural modelling of the detection and analysis of the Mechanomyogram (MMG) signal for different muscles using virtual muscle model. Mechanomyography is the superficial recording of low frequency vibrations detected over contracting muscles. In this study, a MEMS based accelerometer model is used. Three decomposition techniques which are Discrete Wavelet Transform, Principle Component Analysis and empirical mode decomposition are applied on the MMG for the purpose of feature extraction which could be used for the diagnosis process. A comparison between results of the different techniques as well as hybrid techniques is studied to reach the best one. Copyright � 2012 Inderscience Enterprises Ltd.EnglishAccelerometerMechanomyogramMMGSignal decompositionaccelerometerarticlebehaviordecompositiondiagnostic procedureextractionintermethod comparisonmechanomyographymethodologymicroelectromechanical systemmodelmusclemuscle contractilitymyographyprincipal component analysissignal detectionvibrationvirtual realityArticlehttps://doi.org/10.1504/IJHTM.2012.048971