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DOI: 10.14569/IJACSA.2023.0140405
PDF

Classification of Hand Movements Based on EMG Signals using Topological Features

Author 1: Jianyang Li
Author 2: Lei Yang
Author 3: Yunan He
Author 4: Osamu Fukuda

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 4, 2023.

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Abstract: Hand movement classification based on Electromyo-graphy (EMG) signals has been extensively investigated in the past decades as a promising approach used for controlling upper prosthetics or robotics. Topological data analysis is a relatively new and increasingly popular tool in data science that uses mathematical techniques from topology to analyze and understand complex data sets. This paper proposes a method for classifying hand movements based on EMG signals using topological features crafted with the tools of TDA. The main findings of this work on hand movement EMG classification are as follows: (1) topological features are effective in classifying EMG signals and outperform other time domain features tested in the experiments; (2) the 0-th Betti numbers are more effective than the 1-st Betti numbers; (3) Betti amplitude is a more stable and powerful feature than other topological features discussed in this paper. Additionally, Betti curves were used to visualize topological patterns for hand movement EMG.

Keywords: EMG classification; persistent homology; topological features; betti curve

Jianyang Li, Lei Yang, Yunan He and Osamu Fukuda, “Classification of Hand Movements Based on EMG Signals using Topological Features” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140405

@article{Li2023,
title = {Classification of Hand Movements Based on EMG Signals using Topological Features},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140405},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140405},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Jianyang Li and Lei Yang and Yunan He and Osamu Fukuda}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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