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Improved gesture recognition based on sEMG signals and TCN. / Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios.

2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. IEEE, 2019. p. 1169-1173 8683239 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceedingChapter

Harvard

Tsinganos, P, Cornelis, B, Cornelis, J, Jansen, B & Skodras, A 2019, Improved gesture recognition based on sEMG signals and TCN. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8683239, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, IEEE, pp. 1169-1173. https://doi.org/10.1109/ICASSP.2019.8683239

APA

Tsinganos, P., Cornelis, B., Cornelis, J., Jansen, B., & Skodras, A. (2019). Improved gesture recognition based on sEMG signals and TCN. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 1169-1173). [8683239] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). IEEE. https://doi.org/10.1109/ICASSP.2019.8683239

Vancouver

Tsinganos P, Cornelis B, Cornelis J, Jansen B, Skodras A. Improved gesture recognition based on sEMG signals and TCN. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. IEEE. 2019. p. 1169-1173. 8683239. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2019.8683239

Author

Tsinganos, Panagiotis ; Cornelis, Bruno ; Cornelis, Jan ; Jansen, Bart ; Skodras, Athanassios. / Improved gesture recognition based on sEMG signals and TCN. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. IEEE, 2019. pp. 1169-1173 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

BibTeX

@inbook{72f8490904554e8985c16893a65af50c,
title = "Improved gesture recognition based on sEMG signals and TCN",
abstract = "In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approachelectromyography-based hand gesture recognition as a sequence classificationproblem using Temporal Convolutional Networks. The proposed network yields an improvement in gesture recognition of almost 5{\%} to the state of the art reported in the literature, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.",
keywords = "CNN, Deep Learning, Gesture Recognition, TCN, sEMG",
author = "Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras",
year = "2019",
month = "4",
day = "16",
doi = "10.1109/ICASSP.2019.8683239",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "IEEE",
pages = "1169--1173",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",

}

RIS

TY - CHAP

T1 - Improved gesture recognition based on sEMG signals and TCN

AU - Tsinganos, Panagiotis

AU - Cornelis, Bruno

AU - Cornelis, Jan

AU - Jansen, Bart

AU - Skodras, Athanassios

PY - 2019/4/16

Y1 - 2019/4/16

N2 - In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approachelectromyography-based hand gesture recognition as a sequence classificationproblem using Temporal Convolutional Networks. The proposed network yields an improvement in gesture recognition of almost 5% to the state of the art reported in the literature, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.

AB - In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approachelectromyography-based hand gesture recognition as a sequence classificationproblem using Temporal Convolutional Networks. The proposed network yields an improvement in gesture recognition of almost 5% to the state of the art reported in the literature, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.

KW - CNN

KW - Deep Learning

KW - Gesture Recognition

KW - TCN

KW - sEMG

UR - http://www.scopus.com/inward/record.url?scp=85069005469&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2019.8683239

DO - 10.1109/ICASSP.2019.8683239

M3 - Chapter

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 1169

EP - 1173

BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings

PB - IEEE

ER -

ID: 45236523