Abstract
The recent advancement of the Internet of Things (IoT) paves the way for the ubiquitous connection of things, which enables these things to collect and exchange data, and thus made a huge difference in the way we live. Healthcare can be considered as one such major domain, which reaps the benefits of this, where the latest movement in IoT is to bridge disparate technologies to enable new insights, by connecting physical objects, to help smart and intelligent decision-making. As the amount of IoT sensing devices in healthcare increase, it also makes doubt of proper analysis of this large volume of data, where recent advancements in Machine Learning (ML) and other associated Artificial Intelligence (AI) algorithms have made it possible to collect, analyze, and interpret an unprecedented amount of loT sensory information for effective health and wellbeing monitoring. Accumulating raw data in an efficient way and processing that data for better insights will enable a new era in healthcare including the prediction of chronic diseases in early-stage, and personalized and physiological health monitoring systems. In this regard, this paper aims to synthesize the literature mainly on AI-enabled IoT systems for health and wellbeing monitoring, highlighting current research and applications in practice.
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