098c47d7-a3b2-4818-b162-e01df5f758c920211127070017475naun:naunmdt@crossref.orgMDT DepositInternational Journal of Mathematics and Computers in Simulation1998-015910.46300/9102http://www.naun.org/cms.action?id=2826329202132920211510.46300/9102.2021.15https://www.naun.org/cms.action?id=23312Control of Upper Limb Active Prosthesis Using Surface ElectromyographyMuhammad AsimWarisSchool of Mechanical & Manufacturing Engineering (SMME) National University of Sciences & Technology (NUST) Islamabad, PakistanMohsinJamilSchool of Mechanical & Manufacturing Engineering (SMME) National University of Sciences & Technology (NUST) Islamabad, PakistanSyed OmerGilaniSchool of Mechanical & Manufacturing Engineering (SMME) National University of Sciences & Technology (NUST) Islamabad, PakistanYasarAyazSchool of Mechanical & Manufacturing Engineering (SMME) National University of Sciences & Technology (NUST) Islamabad, PakistanElectromyographic prosthesis with higher degrees of freedom is an expanding area of research. In this paper, active prosthesis with four degrees of freedom has been investigated, which can be used to fit a limb with amputation below elbow. The system comprises of multichannel inputs which correspond to the flexion and extension as well as supination and pronation. To find maximum surface neural activity, accurate placement of electrodes has been carried out on 10 subjects aged between 22-30 years. Signals (0-500 hertz) acquired from contracting voluntary muscles with minimum cross talk and common mode noise. Clean filtered EMG signal is then amplified precisely. Finally digitization is being done to drive bionic hand. Practical demonstration on a simple DC motor proved providential using this method for the two motions of an actual human arm. EMG Signals emanating from muscles dedicated to individual fingers have been recorded. Moreover modern classifiers; KNN and NN have been investigated carefully with selected features through different time and noise levels.1127202111272021929617https://www.naun.org/main/NAUN/mcs/2021/a342002-017(2021).pdf10.46300/9102.2021.15.17https://www.naun.org/main/NAUN/mcs/2021/a342002-017(2021).pdf10.1049/ic:19960642A.G. Outten, S.J. Roberts and M.J. Stokes “Analysis of human muscle activity”, Artificial Intelligence Methods for Biomedical Data Processing, IEEE Colloquium, London, 26 April,1996. 10.1109/iembs.1997.756609M.L. Harba and G.E. Chee “Muscle Mechanomyographic and electromyographic signals compared with reference to action potential average propagation velocity”, Engineering in Medicine and Biology Society, 19th Annual International Conference of the IEEE, Vol.3, and 6th August 2002. 10.1109/ner.2011.5910608G. Matrone, C. Cipriani, M. C. Carrozza and G. 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