%0 Journal Article
%T Electromyography Analysis for Person Identification
%A Suresh.M
%A Krishnamohan.P.G & Mallikarjun S Holi
%J International Journal of Biometric and Bioinformatics
%D 2011
%I Computer Science Journals
%X Physiological descriptions of the electromyography signal and other literature say that when wemake a motion, the motor neurons of respective muscle get activated and all the innervatedmotor units in that zone produce motor unit action potential. These motor unit action potentialstravel through the muscle fibers with conduction velocity and superimposed signal gets recordedat the electrode site. Here we have taken an analogy from the speech production system modelas the excitation signal travels through vocal tract to produce speech; similarly, an impulse trainof firing rate frequency goes through the system with impulse response of motor unit actionpotentials and travels along the muscle fiber of that person. As the vocal tract contains thespeaker information, we can also separate the muscle fiber pattern part and motor unit dischargepattern through proper selection of features and its classification to identify the respective person.Cepstral and non uniform filter bank features models the variation in the spectrum of the signals.Vector quantization and Gaussian mixture model are the two techniques of pattern matching havebeen applied.
%K Biometrics
%K Electromyogram
%K Gaussian mixture model (GMM)
%K Identification
%K Vector Quantization.
%U http://cscjournals.org/csc/manuscript/Journals/IJBB/volume5/Issue3/IJBB-121.pdf