Embedded Deep Learning for Sleep Staging
This work addresses the problem of computational demands for sleep analysis in m-Health, but it appears incremental as it builds on existing methods for wearable integration.
The paper tackles the challenge of applying deep learning for sleep staging on resource-limited wearable devices by presenting two deep learning architectures designed for low-power integration, comparing them with a hand-crafted algorithm to enable reliable consumer healthcare applications.
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.