Automated Recognition of Hand Grasps Using Electromyography Signal Based on LWT and DTCWT of Wavelet Energy

Authors

A. Haiter Lenin^{1, *,} , S. Mary Vasanthi², T. Jayasree³

¹School of Mechanical and Chemical Engineering, Kombolcha Institute of Technology, Wollo University, Dessie, Ethiopia

²Department of Electronics and Communication Engineering, St. Xavier's Catholic College of Engineering, Nagercoil, India

³Department of Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, India

^*Corresponding author. Email: drahlenin@kiot.edu.et

DOI

10.2991/ijcis.d.200724.001How to use a DOI?

Keywords

Signal processing; Electromyogram; Discrete wavelet transform; Feature extraction; Pattern recognition; Support vector machine; Deep Learning Neural Network

Abstract

This paper presents a novel framework that automatically classifies hand grasps using Electromyogram (EMG) signals based on advanced Wavelet Transform (WT). This method is motivated by the observation that there lies a unique correlation between different samples of the signal at various frequency levels obtained by Discrete WT. In the proposed approach, EMG signals captured from the subjects are subjected to denoising using symlet wavelets, followed by Principal Component Analysis (PCA) for dimensionality reduction. Further, the important attributes of the signal are extracted using Lifting Wavelet Transform (LWT) and Dual Tree Complex WT (DTCWT). Multiple classifiers such as Feed Forward Neural Networks (FFNN), Cascaded Feed Forward Neural Networks (CFNN), Support Vector Machine (SVM) and Deep Learning Neural Network (DLNN) are used for classification. The simulation results are compared with various training algorithms and it is observed that DTCWT features combined with CFNN and trained with Gradient Descent with Adaptive Back Propagation (GDABP) algorithm achieved the best performance. The advantages of the proposed method were proved by comparing with the earlier conventional methods, in terms of recognition performance. These experimental results prove that the proposed method gives a potential performance in the recognition of hand grasps using EMG signals. In addition, the proposed method supports clinicians to improve the performance of myoelectric pattern recognition.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - A. Haiter Lenin
AU  - S. Mary Vasanthi
AU  - T. Jayasree
PY  - 2020
DA  - 2020/07/31
TI  - Automated Recognition of Hand Grasps Using Electromyography Signal Based on LWT and DTCWT of Wavelet Energy
JO  - International Journal of Computational Intelligence Systems
SP  - 1027
EP  - 1035
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200724.001
DO  - 10.2991/ijcis.d.200724.001
ID  - Lenin2020
ER  -