2019
Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios
Improved Gesture Recognition Based on sEMG Signals and TCN Proceedings Article
In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1169–1173, IEEE, Brighton, UK, 2019, ISBN: 978-1-4799-8131-1.
Abstract | Links | BibTeX | Tags: CNN, Deep learning, Gesture Recognition, semg, TCN
@inproceedings{Tsinganos2019c,
title = {Improved Gesture Recognition Based on sEMG Signals and TCN},
author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},
url = {https://ieeexplore.ieee.org/document/8683239/},
doi = {10.1109/ICASSP.2019.8683239},
isbn = {978-1-4799-8131-1},
year = {2019},
date = {2019-05-01},
booktitle = {ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1169--1173},
publisher = {IEEE},
address = {Brighton, UK},
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 approach electromyography-based hand gesture recognition as a sequence classification problem 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, semg, TCN},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios
Deep Learning in EMG-based Gesture Recognition Proceedings Article
In: Proceedings of the 5th International Conference on Physiological Computing Systems, pp. 107–114, SCITEPRESS - Science and Technology Publications, Seville, Spain, 2018, ISBN: 978-989-758-329-2.
Abstract | Links | BibTeX | Tags: CNN, Deep learning, Gesture Recognition, semg
@inproceedings{Tsinganos2018b,
title = {Deep Learning in EMG-based Gesture Recognition},
author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},
url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006960201070114},
doi = {10.5220/0006960201070114},
isbn = {978-989-758-329-2},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 5th International Conference on Physiological Computing Systems},
pages = {107--114},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Seville, Spain},
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.},
keywords = {CNN, Deep learning, Gesture Recognition, semg},
pubstate = {published},
tppubtype = {inproceedings}
}