Electromyography-based Hand Gesture Recognition System

Elhocine Boutellaa; Oussama Kerdjidj; Youcef Amine Taleb; Malika Berroudji; Oussama Azzouzi

doi:10.2991/978-94-6463-496-9_26

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Electromyography-based Hand Gesture Recognition System

Authors

Elhocine Boutellaa¹, Oussama Kerdjidj², Youcef Amine Taleb², Malika Berroudji², Oussama Azzouzi³^{, *}

¹Institute of Electrical Engineering and Electronics, University Mhamed Bougara, Boumerdes, Algeria

²Telecommunication division, Centre de développement des technologies avancées, Algiers, Algeria

³Département Informatique, Centre Universitaire El Cherif Bouchoucha d’Aflou, Laghouat, Algeria

^*Corresponding author. Email: o.azzouzi@cu-aflou.edu.dz

Corresponding Author

Oussama Azzouzi

Available Online 31 August 2024.

DOI: 10.2991/978-94-6463-496-9_26 How to use a DOI?
Keywords: EMG; Deep learning; CNN 1D and 2D; Hand gesture recognition; hand prosthesis
Abstract: Electromyography (EMG) is the bio-signal generated in muscles during their activities. EMG is used by clinicians to examine and diagnose the muscles activity, for commanding myo-prosthesis to help amputees overcome their disabilities as well as for human machine interaction applications. These fascinating applications require the classification of the EMG signals into categories depending on the targeted application. In this paper, we tackle hand gesture recognition based on EMG signal, which may be used for different tasks. We design two deep convolutional neural networks, evaluate and compare their performances on the NinaPro dataset. The proposed models show interesting results.
Copyright: © 2024 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
Series: Advances in Intelligent Systems Research
Publication Date: 31 August 2024
ISBN: 978-94-6463-496-9
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-496-9_26 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Elhocine Boutellaa
AU  - Oussama Kerdjidj
AU  - Youcef Amine Taleb
AU  - Malika Berroudji
AU  - Oussama Azzouzi
PY  - 2024
DA  - 2024/08/31
TI  - Electromyography-based Hand Gesture Recognition System
BT  - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024)
PB  - Atlantis Press
SP  - 346
EP  - 356
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-496-9_26
DO  - 10.2991/978-94-6463-496-9_26
ID  - Boutellaa2024
ER  -

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