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.

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