A Comparative Analysis of Wavelet Families for the Classification of Finger Motions

Jingwei Too,Norhashimah Mohd,Abdul Rahim

doi:10.14569/ijacsa.2019.0100426

Jingwei Too, Norhashimah Mohd + Show 1 more

Open Access

https://doi.org/10.14569/ijacsa.2019.0100426

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Abstract

Wavelet transform (WT) has been widely used in biomedical, rehabilitation and engineering applications. Due to the natural characteristic of WT, its performance is mostly depending on the selection of mother wavelet function. A proper mother wavelet ensures the optimum performance; however, the selection of mother wavelet is mostly empirical and varies according to dataset. Hence, this paper aims to investigate the best mother wavelet of discrete wavelet transform (DWT) and wavelet packet transform (WPT) in the classification of different finger motions. In this study, twelve mother wavelets are evaluated for both DWT and WPT. The electromyography (EMG) data of 12 finger motions are acquired from online database. Four useful features are extracted from each recorded EMG signal via DWT and WPT transformation. Afterward, support vector machine (SVM) and linear discriminate analysis (LDA) are employed for performance evaluation. Our experimental results demonstrate Bior3.3 to be the most suitable mother wavelet in DWT. On the other hand, WPT with Bior2.2 overtakes other mother wavelets in the classification of finger motions. The results obtained suggest that Biorthogonal families are more suitable for accurate EMG signals classification.

Highlights

Electromyography (EMG) has becoming one of the major interest in the rehabilitation areas due to its usefulness in clinical and human machine interface (HMI) applications [1], [2] Advance in HMI raises the efficiency of control system in myoelectric prosthetic control [3]
By using the surface EMG signals recorded from the skin surface, the myoelectric interface based on EMG pattern recognition allows the amputee and patient to gain control on the artificial hand
One of the methods that have been widely applied in biomedical signal processing is wavelet transform (WT)

Summary

Introduction

Electromyography (EMG) has becoming one of the major interest in the rehabilitation areas due to its usefulness in clinical and human machine interface (HMI) applications [1], [2] Advance in HMI raises the efficiency of control system in myoelectric prosthetic control [3]. By using the surface EMG signals recorded from the skin surface, the myoelectric interface based on EMG pattern recognition allows the amputee and patient to gain control on the artificial hand. The techniques such as signal processing, feature extraction and classification are usually involving in the EMG pattern recognition. One of the methods that have been widely applied in biomedical signal processing is wavelet transform (WT). Omari et al [7] classified eight hand motions using discrete wavelet transform (DWT). Phinyomark et al [8]

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