Deep Learning in EMG-based Gesture Recognition

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen, A. Skodras

2018

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.

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Paper Citation


in Harvard Style

Tsinganos P., Cornelis B., Cornelis J., Jansen B. and Skodras A. (2018). Deep Learning in EMG-based Gesture Recognition.In Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-329-2, pages 107-114. DOI: 10.5220/0006960201070114


in Bibtex Style

@conference{phycs18,
author={P. Tsinganos and B. Cornelis and J. Cornelis and B. Jansen and A. Skodras},
title={Deep Learning in EMG-based Gesture Recognition},
booktitle={Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2018},
pages={107-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006960201070114},
isbn={978-989-758-329-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Deep Learning in EMG-based Gesture Recognition
SN - 978-989-758-329-2
AU - Tsinganos P.
AU - Cornelis B.
AU - Cornelis J.
AU - Jansen B.
AU - Skodras A.
PY - 2018
SP - 107
EP - 114
DO - 10.5220/0006960201070114