Deep learning in pervasive health monitoring, design goals, applications, and architectures: An overview and a brief synthesis

Abstract

The continuous growth of an aging population in some countries, and patients with chronic conditions needs the development of efficient solutions for healthcare. Pervasive Health Monitoring (PHM) is an important pervasive computing application that has the potential to provide patients with a high-quality medical service and enable quick-response alerting of critical conditions. To that end, PHM enables continuous and ubiquitous monitoring of patients' health and wellbeing using Internet of Things (IoT) technologies, such as wearables and ambient sensors. In recent years, deep learning (DL) has attracted a growing interest from the research community to improve PHM applications. In this paper, we discuss the state-of-the-art of DL-based PHM, through identifying, (1) the main PHM applications where DL is successful, (2) design goals and objectives of using DL in PHM, and (3) design notes including DL architectures and data preprocessing. Finally, main advantages, limitations and challenges of the adoption of DL in PHM are discussed.