Chapter 7 - Machine learning-enabled Internet of Things for medical informatics

Abstract

The exponential increase in wireless data traffic is intensifying human-machine interaction. Imperceptible diagnostics, ubiquitous monitoring, and the availability of digital assistive systems are conceptualized as essential milestones in revolutionizing the modes, by which the Internet of Things (IoT) is transforming healthcare applications. This is referred as healthcare IoT (H-IoT) systems. H-IoT is continuously evolving, driven by the advances in the underlying technologies in wireless body area network (WBAN). The machine learning (ML) is considered as a pivotal solution in fulfilling the needs of H-IoT applications and devices. This chapter serves as an introductory guideline to address the challenges and opportunities, while designing ML-enabled H-IoT networks. Section 1 provides a discussion on traditional H-IoT, challenges, and opportunities in the Network 2030 paradigm. Section 2 focuses on the applications of H-IoT. Section 3 provides a detailed comparison of types of ML approaches. Moreover, this section discusses potential ML techniques compatible with H-IoT. Finally, Section 4 points out open issues and future research directions.