The rapidly-advancing technology of deep learning (DL) into the world of the Internet of Things (IoT) has not fully entered in the fields of m-Health yet. Among the main reasons are the high computational demands of DL algorithms and the inherent resource-limitation of wearable devices. In this paper, we present initial results for two deep learning architectures used to diagnose and analyze sleep patterns, and we compare them with a previously presented hand-crafted algorithm. The algorithms are designed to be reliable for consumer healthcare applications and to be integrated into low-power wearables with limited computational resources.
@article{arxiv.1906.09905,
title = {Embedded Deep Learning for Sleep Staging},
author = {Engin Türetken and Jérôme Van Zaen and Ricard Delgado-Gonzalo},
journal= {arXiv preprint arXiv:1906.09905},
year = {2020}
}