Hand Motion Pattern Recognition Analysis Of Forearm Muscle Using MMG Signals

Mohamad Ismail, M. R. and Lam, Chee Kiang and Sundaraj, Kenneth and Fazalul Rahiman, Mohd Hafiz (2019) Hand Motion Pattern Recognition Analysis Of Forearm Muscle Using MMG Signals. Bulletin Of Electrical Engineering And Informatics, 8 (2). 533 - 540. ISSN 2302-9285

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Abstract

Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier

Item Type:	Article
Uncontrolled Keywords:	Classification, Forearm Muscle, Linear Discriminant Analysis, Mechanomyography, Hand Motion Pattern Recognition, Forearm Muscle, MMG Signals
Divisions:	Faculty of Electronics and Computer Engineering > Department of Industrial Electronics
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	28 Oct 2020 13:05
Last Modified:	28 Oct 2020 13:05
URI:	http://eprints.utem.edu.my/id/eprint/24362
Statistic Details:	View Download Statistic

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