  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.
