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.

[1]  Shyamanta M. Hazarika,et al.  Exploring a family of wavelet transforms for EMG-based grasp recognition , 2015, Signal Image Video Process..

[2]  Sazali Yaacob,et al.  A comparative study of wavelet families for classification of wrist motions , 2012, Comput. Electr. Eng..

[3]  A. M. Hager,et al.  Effect of clinical parameters on the control of myoelectric robotic prosthetic hands. , 2016, Journal of rehabilitation research and development.

[4]  Geethanjali Purushothaman,et al.  Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals , 2018, Australasian Physical & Engineering Sciences in Medicine.

[5]  Abdulhamit Subasi,et al.  Comparison of decision tree algorithms for EMG signal classification using DWT , 2015, Biomed. Signal Process. Control..

[6]  Wei-Ping Zhu,et al.  Wavelet Domain Feature Extraction Scheme Based on Dominant Motor Unit Action Potential of EMG Signal for Neuromuscular Disease Classification , 2014, IEEE Transactions on Biomedical Circuits and Systems.

[7]  Nurhazimah Nazmi,et al.  A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions , 2016, Sensors.

[8]  Chusak Limsakul,et al.  Feature Extraction and Reduction of Wavelet Transform Coefficients for EMG Pattern Classification , 2012 .

[9]  Congli Mei,et al.  Pattern Recognition of Eight Hand Motions Using Feature Extraction of Forearm EMG Signal , 2014 .

[10]  Abdulhamit Subasi,et al.  Classification of EMG signals using combined features and soft computing techniques , 2012, Appl. Soft Comput..

[11]  J. Rafiee,et al.  Wavelet basis functions in biomedical signal processing , 2011, Expert Syst. Appl..

[12]  Abdul Rahim Abdullah,et al.  EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization , 2019, Comput..

[13]  Pornchai Phukpattaranont,et al.  Fractal analysis features for weak and single-channel upper-limb EMG signals , 2012, Expert Syst. Appl..

[14]  Carlo Menon,et al.  Exploration of Force Myography and surface Electromyography in hand gesture classification. , 2017, Medical engineering & physics.

[15]  Krishnan Chemmangat,et al.  A novel pre-processing procedure for enhanced feature extraction and characterization of electromyogram signals , 2018, Biomed. Signal Process. Control..

[16]  Zairi Ismael Rizman,et al.  EMG Signals Analysis of BF and RF Muscles In Autism Spectrum Disorder (ASD) During Walking , 2016 .

[17]  Kianoush Nazarpour,et al.  Combined influence of forearm orientation and muscular contraction on EMG pattern recognition , 2016, Expert Syst. Appl..

[18]  A. Phinyomark,et al.  Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification , 2011 .

[19]  Abdulhamit Subasi,et al.  Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders , 2013, Comput. Biol. Medicine.

[20]  Gamini Dissanayake,et al.  Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm , 2011, IEEE Transactions on Biomedical Engineering.

[21]  Manfredo Atzori,et al.  Comparison of six electromyography acquisition setups on hand movement classification tasks , 2017, PloS one.

[22]  Chi-Woong Mun,et al.  Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions , 2011 .

[23]  Abdul Rahim Abdullah,et al.  A Detail Study of Wavelet Families for EMG Pattern Recognition , 2018, International Journal of Electrical and Computer Engineering (IJECE).

[24]  P. A. Karthick,et al.  Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms , 2018, Comput. Methods Programs Biomed..

[25]  Mamun Bin Ibne Reaz,et al.  Surface Electromyography Signal Processing and Classification Techniques , 2013, Sensors.