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General Information
    • ISSN: 2010-3719 (Online)
    • Abbreviated Title: Int. J. Inf. Electron. Eng.
    • Frequency: Quarterly
    • DOI: 10.18178/IJIEE
    • Editor-in-Chief: Prof. Chandratilak De Silva Liyanage
    • Executive Editor: Jennifer Zeng
    • Abstracting/ Indexing : Google Scholar, Electronic Journals Library, Crossref and ProQuest, Ei (INSPEC, IET), EBSCO.
    • E-mail ijiee@ejournal.net
Editor-in-chief

 
University of Brunei Darussalam, Brunei Darussalam   
" It is a great honor to serve as the editor-in-chief of IJIEE. I'll work together with the editorial team. Hopefully, The value of IJIEE will be well recognized among the readers in the related field."

IJIEE 2014 Vol.4(3): 189-194 ISSN: 2010-3719
DOI: 10.7763/IJIEE.2014.V4.433

Machine Learning Algorithms for Characterization of EMG Signals

Bekir Karlık
Abstract— In the last decades, the researchers of the human arm prosthesis are using different types of machine learning algorithms. This review article firstly gives a brief explanation about type of machine learning methods. Secondly, some recent applications of myoelectric control of human arm prosthesis by using machine learning algorithms are compared. This study presents two different comparisons based on feature extraction methods which are time series modeling and wavelet transform of EMG signal. Finally, of characterization of EMG for of human arm prosthesis have been and discussed.

Index Terms— Machine learning, characterization, EMG signal, prosthesis.

Bekir KARLIK is with the Department of Computer Engineering, Engineering Faculty, Selcuk University in Konya, Turkey (e-mail: bkarlik@selcuk.edu.tr).

[PDF]

Cite: Bekir Karlık, " Machine Learning Algorithms for Characterization of EMG Signals," International Journal of Information and Electronics Engineering vol. 4, no. 3, pp. 189-194, 2014.

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