Application of gabor transform in the classification of myoelectric signal
Jingwei Too, Abdul Rahim Abdullah, Norhashimah Mohd Saad, Nursabillilah Mohd Ali, Tengku Nor Shuhada Tengku Zawawi
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 feature can achivev promising performance in the classification of EMG signals.
electromyography; gabor transform; K-nearest neighbor; support vector machine