Time-frequency Features for sEMG Signals Classification

Somar Karheily, Ali Moukadem, Jean-Baptiste Courbot, Djaffar Ould Abdeslam

2020

Abstract

This paper proposes a new approach for the identification of hand movements in order to control prosthetic hand. sEMG signals were used to identify movements by using two time frequency transforms: Short Time Fourier Transform and Stockwell transform. Then, we apply Singular Value Decomposition (SVD) to decrease the features dimension and to form the final features’ vector. These extracted features were used by two kinds of classifiers: K nearest neighbours and linear discriminant analysis. Finally, we numerically study these methods on a database of 10 subjects and 17 hand gestures.

Paper Citation

in Harvard Style

Karheily S., Moukadem A., Courbot J. and Abdeslam D. (2020). Time-frequency Features for sEMG Signals Classification. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS; ISBN 978-989-758-398-8, SciTePress, pages 244-249. DOI: 10.5220/0008971902440249

in Bibtex Style

@conference{biosignals20,
author={Somar Karheily and Ali Moukadem and Jean-Baptiste Courbot and Djaffar Ould Abdeslam},
title={Time-frequency Features for sEMG Signals Classification},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS},
year={2020},
pages={244-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008971902440249},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS
TI - Time-frequency Features for sEMG Signals Classification
SN - 978-989-758-398-8
AU - Karheily S.
AU - Moukadem A.
AU - Courbot J.
AU - Abdeslam D.
PY - 2020
SP - 244
EP - 249
DO - 10.5220/0008971902440249
PB - SciTePress