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Sharvin R.
Universiti Teknologi Malaysia

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Analysis of surface electromyography for hand gesure classification Najla Ilyana A.M; Nor Aini Z; Sharvin R.; Norlaili M.S
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i3.pp1366-1373

Abstract

Electromyography (EMG) is the measure of electrical activity produced by skeletal muscle. It is useful in prosthetic and rehabilitation technology as well as ability to handle electronic devices and robotics. If the EMG signal from the body especially hand movement can be apprehended, better value for people all around the world can be provided. Furthermore, it can be used to control smart-phone and be integrated with wearable technology. Another interesting application of this technology is in sign language recognition which is able to assist many disabled people in their daily lives. In this paper, hand gesture signals are acquired, extracted, analysed and classified. The EMG data from hand gesture which are rock, paper and scissors managed to be extracted. We use time domain feature to classified using Principal Component Analysis and regression tree. The result was highly accurate with 72.59% and 80.85% for PCA and regression tree respectively.
Co-Authors Najla Ilyana A.M Nor Aini Z Norlaili M.S