ABSTRACT
IoT and artificial intelligence are currently two of the most relevant and trending pieces for innovation and predictive analysis in healthcare. The benefit of using IoT and artificial intelligence for community and population health is better health outcomes for the population and communities. Recently, wearable devices are rapidly emerging and forming a new segment––“wearable IoT (WIoT)”—due to their capability of sensing, computing and communication. Future generations of WIoT promise to transform the healthcare sector, wherein individuals are seamlessly tracked by wearable sensors for personalized health and wellness information––body vital parameters, physical activity, behaviors, and other critical parameters impacting quality of daily life. End devices, such as smartphones and Internet-of-Things sensors, are generating data that need to be analyzed in real time using deep learning or used to train deep learning models. However, deep learning inference and training require substantial computation resources to run quickly. Edge computing, where a fine mesh of compute nodes are placed close to end devices, is a viable way to meet the high computation and low-latency requirements of deep learning on edge devices and also provides additional benefits in terms of privacy, bandwidth efficiency, and scalability. This chapter aims to provide a comprehensive review of the current state of the art at the intersection of deep learning and edge computing. The new generation of machine-learning algorithms can use large, standardized datasets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. In this chapter we have described technologies; wearable devices eHealth is most significant due to health monitoring of chronic illnesses, lifesaving in emergency situations.
