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Classification of Hand Gestures Based on Multi-channel EMG by Scale Average Wavelet Transform and Convolutional Neural Network

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  • Intelligent Control and Applications
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Abstract

Predicting and accurately classifying intentions for human hand gestures can be used not only for active prosthetic hands, rehabilitation robots and entertainment robots but also for artificial intelligence robots in general. In this paper, first of all, source data of three hand gestures of grasping and three hand gestures of sign language are acquired by using the armband combined with eight sEMG (surface Electromyography) sensors. To classify these hand gestures, basically simple CNN (convolutional neural network) models with raw data, short-time Fourier transform (STFT), wavelet transform (WT), and scale average wavelet transform (SAWT) are applied, and their performances are compared. Finally, it is shown that by using a CNN with SAWT images, the accuracy can be improved up to 94.6% for selected hand gestures with higher accuracy and lower computational burden than conventional multi-channel STFT or WT.

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Authors and Affiliations

  1. School of Biomedical Engineering, Konyang University, 158 Gwanjeodong-ro, Seo-gu, Daejeon, 35365, Korea

    Do-Chang Oh & Yong-Un Jo

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  1. Do-Chang Oh
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  2. Yong-Un Jo
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Correspondence to Do-Chang Oh.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Jong-Han Kim under the direction of Editor Doo Yong Lee. This paper was supported by the Konyang University Research Fund in 2019.

Do-Chang Oh received his B.S., M.S., and Ph.D. degrees in electronics from Kyungpook National University in 1991, 1993, and 1997, respectively. He was with the University of Florida as a Courtesy Associate Professor for one year from July 2007. He is currently a professor at the School of Biomedical Engineering, Konyang University. His research interests include robust control, model reduction, rehabilitation robot, and biomedical applications of deep learning.

Yong-Un Jo received his B.S. degree in biomedical engineering from Konyang University in 2019. He is currently studying for a master’s degree at Konyang University. His research interests include ANN(artificial neural network), rehabilitation robot, and biomedical applications of deep learning.

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Oh, DC., Jo, YU. Classification of Hand Gestures Based on Multi-channel EMG by Scale Average Wavelet Transform and Convolutional Neural Network. Int. J. Control Autom. Syst. 19, 1443–1450 (2021). https://doi.org/10.1007/s12555-019-0802-1

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  • DOI: https://doi.org/10.1007/s12555-019-0802-1

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