Tsinganos, P., Cornelis, B., Cornelis, J., Jansen, B., & Skodras, A. (2018). Deep Learning in EMG-based Gesture Recognition. In M. J. D. Morales, J.-M. Belda-Lois, A. Pope, H. P. da Silva, & C. Wang (Eds.), PhyCS 2018 - Proceedings of the 5th International Conference on Physiological Computing Systems (pp. 107-114)

@inproceedings{0cca0131ebe44cbab1735603ba8c5605,
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 engineer- ing 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 perfor- mance.",
keywords = "CNN, Deep learning, Gesture recognition, SEMG",
author = "Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras",
year = "2018",
month = sep,
day = "19",
language = "English",
pages = "107--114",
editor = "Morales, {Manuel Jesus Dominguez} and Juan-Manuel Belda-Lois and Alan Pope and {da Silva}, {Hugo Placido} and Chen Wang",
booktitle = "PhyCS 2018 - Proceedings of the 5th International Conference on Physiological Computing Systems",
}