Standard

A Hilbert curve based representation of sEMG signals for gesture recognition. / Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios.

Proceedings of the 26th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019. p. 201-206.

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

Harvard

Tsinganos, P, Cornelis, B, Cornelis, J, Jansen, B & Skodras, A 2019, A Hilbert curve based representation of sEMG signals for gesture recognition. in Proceedings of the 26th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, pp. 201-206. https://doi.org/10.1109/IWSSIP.2019.8787290

APA

Tsinganos, P., Cornelis, B., Cornelis, J., Jansen, B., & Skodras, A. (2019). A Hilbert curve based representation of sEMG signals for gesture recognition. In Proceedings of the 26th International Conference on Systems, Signals and Image Processing (IWSSIP) (pp. 201-206). IEEE. https://doi.org/10.1109/IWSSIP.2019.8787290

Vancouver

Tsinganos P, Cornelis B, Cornelis J, Jansen B, Skodras A. A Hilbert curve based representation of sEMG signals for gesture recognition. In Proceedings of the 26th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE. 2019. p. 201-206 https://doi.org/10.1109/IWSSIP.2019.8787290

Author

Tsinganos, Panagiotis ; Cornelis, Bruno ; Cornelis, Jan ; Jansen, Bart ; Skodras, Athanassios. / A Hilbert curve based representation of sEMG signals for gesture recognition. Proceedings of the 26th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019. pp. 201-206

BibTeX

@inbook{1b65777eafb745bbafcb57beef95a758,
title = "A Hilbert curve based representation of sEMG signals for gesture recognition",
abstract = "Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed towards surface electromyography (sEMG) based gesture recognition, often addressed as an image classification problem using Convolutional Neural Networks (CNN). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals that are then classified by a CNN. The proposed method is evaluated on different network architectures and yields a classification improvement of more than 3{\%}.",
keywords = "Hilbert curve, hand gesture recognition, sEMG, electromyography, classification, CNN, Deep Learning",
author = "Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras",
year = "2019",
month = "8",
day = "5",
doi = "10.1109/IWSSIP.2019.8787290",
language = "English",
isbn = "978-1-7281-3253-2",
pages = "201--206",
booktitle = "Proceedings of the 26th International Conference on Systems, Signals and Image Processing (IWSSIP)",
publisher = "IEEE",

}

RIS

TY - CHAP

T1 - A Hilbert curve based representation of sEMG signals for gesture recognition

AU - Tsinganos, Panagiotis

AU - Cornelis, Bruno

AU - Cornelis, Jan

AU - Jansen, Bart

AU - Skodras, Athanassios

PY - 2019/8/5

Y1 - 2019/8/5

N2 - Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed towards surface electromyography (sEMG) based gesture recognition, often addressed as an image classification problem using Convolutional Neural Networks (CNN). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals that are then classified by a CNN. The proposed method is evaluated on different network architectures and yields a classification improvement of more than 3%.

AB - Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed towards surface electromyography (sEMG) based gesture recognition, often addressed as an image classification problem using Convolutional Neural Networks (CNN). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals that are then classified by a CNN. The proposed method is evaluated on different network architectures and yields a classification improvement of more than 3%.

KW - Hilbert curve

KW - hand gesture recognition

KW - sEMG

KW - electromyography

KW - classification

KW - CNN

KW - Deep Learning

U2 - 10.1109/IWSSIP.2019.8787290

DO - 10.1109/IWSSIP.2019.8787290

M3 - Chapter

SN - 978-1-7281-3253-2

SP - 201

EP - 206

BT - Proceedings of the 26th International Conference on Systems, Signals and Image Processing (IWSSIP)

PB - IEEE

ER -

ID: 45236559