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DOI: 10.14569/IJACSA.2022.0131001
PDF

Hand Motion Estimation using Super-Resolution of Multipoint Surface Electromyogram by Deep Learning

Author 1: Keigo FUKUSHIMA
Author 2: Yoshiaki YASUMURA

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

  • Abstract and Keywords
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Abstract: This paper proposes a method for hand motion estimation using super-resolution of multipoint surface electromyogram for prosthetic hands. In general, obtaining more EMGs (ElectroMyoGraphy) improves the accuracy of hand motion estimation, but it is costly and hard to use. Therefore, this method improves the accuracy of hand motion estimation by estimating a large number of EMG signals from a small number of EMG signals using super-resolution. This super-resolution is achieved by learning the relationship between few and many myoelectric signals using a deep neural network. Then, hand motions are estimated from the high-resolution signal using a deep neural network. Experiments using actual EMG signals show that the proposed method improves the accuracy of hand motion estimation.

Keywords: Hand motion estimation; super-resolution; deep neural network; prosthetic hand; electromyography

Keigo FUKUSHIMA and Yoshiaki YASUMURA, “Hand Motion Estimation using Super-Resolution of Multipoint Surface Electromyogram by Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131001

@article{FUKUSHIMA2022,
title = {Hand Motion Estimation using Super-Resolution of Multipoint Surface Electromyogram by Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131001},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131001},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Keigo FUKUSHIMA and Yoshiaki YASUMURA}
}



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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