Towards Workload-Balanced, Live Deep Learning Analytics for Confidentiality-Aware IoT Medical Platforms

Abstract

Internet of Things (IoT) applications for healthcare are one of the most studied aspects in the research landscape due to the promise of more efficient resource allocation for hospitals and as a companion tool for health professionals. Yet, the requirements in terms of low power, latency and knowledge extraction from the large amount of physiological data generated represent a challenge to be addressed by the research community. In this work, we examine the balance between power consumption, performance and latency among edge, gateway, fog and cloud layers in an IoT medical platform featuring inference by Deep Learning models. We setup an IoT architecture to acquire and classify multichannel electrocardiogram (ECG) signals into normal or abnormal states which could represent a clinically relevant condition by combining custom embedded devices with contemporary open source machine learning packages such as TensorFlow. Different hardware platforms are tested in order to find the best compromise in terms of convenience, latency, power consumption and performance. Our experiments indicate that the real time requisites are fulfilled, however there is a need to reduce energy expenditure by means of incorporating low power SoCs with integrated neuromorphic blocks.