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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
N. Mohd Saad
Universiti Teknikal Malaysia Melaka
Computer Science & IT

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Application of gabor transform in the classification of myoelectric signal Jingwei Too; A. R. Abdullah; N. Mohd Saad; N. Mohd Ali; T. N. S. Tengku Zawawi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.9257

Abstract

In recent day, Electromyography (EMG) signal are widely applied in myoelectric control. Unfortunately, most of studies focused on the classification of EMG signals based on healthy subjects. Due to the lack of study in amputee subject, this paper aims to investigate the performance of healthy and amputee subjects for the classification of multiple hand movement types. In this work, Gabor transform (GT) is used to transform the EMG signal into time-frequency representation. Five time-frequency features are extracted from GT coefficient. Feature extraction is an effective way to reduce the dimensionality, as well as keeping the valuable information. Two popular classifiers namely k-nearest neighbor (KNN) and support vector machine (SVM) are employed for performance evaluation. The developed system is evaluated using the EMG data acquired from the publicy available NinaPro Database. The results revealed that the extracting GT features can achieve promising performance in the classification of EMG signals.
Co-Authors A. R. Abdullah Jingwei Too N. Mohd Ali T. N. S. Tengku Zawawi