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

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.

@article{Too2019,
  title     = {A Comparative Analysis of Wavelet Families for the Classification of Finger Motions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {4},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Jingwei Too and Abdul Rahim Abdullah and Norhashimah Mohd Saad},
  doi       = {10.14569/IJACSA.2019.0100426},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100426}
}