Standard

Deep Learning in EMG-based Gesture Recognition. / Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios.

Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS. Scitepress, 2018. p. 107-114.

Research output: Chapter in Book/Report/Conference proceedingChapter

Harvard

Tsinganos, P, Cornelis, B, Cornelis, J, Jansen, B & Skodras, A 2018, Deep Learning in EMG-based Gesture Recognition. in Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS. Scitepress, pp. 107-114. https://doi.org/10.5220/0006960201070114

APA

Tsinganos, P., Cornelis, B., Cornelis, J., Jansen, B., & Skodras, A. (2018). Deep Learning in EMG-based Gesture Recognition. In Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS (pp. 107-114). Scitepress. https://doi.org/10.5220/0006960201070114

Vancouver

Tsinganos P, Cornelis B, Cornelis J, Jansen B, Skodras A. Deep Learning in EMG-based Gesture Recognition. In Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS. Scitepress. 2018. p. 107-114 https://doi.org/10.5220/0006960201070114

Author

Tsinganos, Panagiotis ; Cornelis, Bruno ; Cornelis, Jan ; Jansen, Bart ; Skodras, Athanassios. / Deep Learning in EMG-based Gesture Recognition. Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS. Scitepress, 2018. pp. 107-114

BibTeX

@inbook{8d9a6cd44a7548f88e21c73aa5aca745,
title = "Deep Learning in EMG-based Gesture Recognition",
abstract = "In recent years, Deep Learning methods have been successfully applied to a wide range of image and speech recognition problems highly impacting other research fields. As a result, new works in biomedical engineering are directed towards the application of these methods to electromyography-based gesture recognition. In this paper, we present a brief overview of Deep Learning methods for electromyography-based hand gesture recognition along with an analysis of a modified simple model based on Convolutional Neural Networks. The proposed network yields a 3{\%} improvement on the classification accuracy of the basic model, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve the performance.",
author = "Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras",
year = "2018",
month = "9",
day = "27",
doi = "10.5220/0006960201070114",
language = "English",
isbn = "978-989-758-329-2",
pages = "107--114",
booktitle = "Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS",
publisher = "Scitepress",

}

RIS

TY - CHAP

T1 - Deep Learning in EMG-based Gesture Recognition

AU - Tsinganos, Panagiotis

AU - Cornelis, Bruno

AU - Cornelis, Jan

AU - Jansen, Bart

AU - Skodras, Athanassios

PY - 2018/9/27

Y1 - 2018/9/27

N2 - In recent years, Deep Learning methods have been successfully applied to a wide range of image and speech recognition problems highly impacting other research fields. As a result, new works in biomedical engineering are directed towards the application of these methods to electromyography-based gesture recognition. In this paper, we present a brief overview of Deep Learning methods for electromyography-based hand gesture recognition along with an analysis of a modified simple model based on Convolutional Neural Networks. The proposed network yields a 3% improvement on the classification accuracy of the basic model, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve the performance.

AB - In recent years, Deep Learning methods have been successfully applied to a wide range of image and speech recognition problems highly impacting other research fields. As a result, new works in biomedical engineering are directed towards the application of these methods to electromyography-based gesture recognition. In this paper, we present a brief overview of Deep Learning methods for electromyography-based hand gesture recognition along with an analysis of a modified simple model based on Convolutional Neural Networks. The proposed network yields a 3% improvement on the classification accuracy of the basic model, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve the performance.

U2 - 10.5220/0006960201070114

DO - 10.5220/0006960201070114

M3 - Chapter

SN - 978-989-758-329-2

SP - 107

EP - 114

BT - Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS

PB - Scitepress

ER -

ID: 44819480