Abstract
Ubiquitous healthcare aims to transcend all boundaries, providing healthcare services to anyone, anytime, anywhere. The implementation of such services not only increases the coverage and quality of healthcare facilities, but their adoption in disaster medical delivery could save time, money, and lives. The term Ubiquitous Computing implies the presence of interconnected hardware and software systems, whose presence is not identified. The technology is pervasive, yet invisible to enable people to go about their daily life activities and their interactions with the environment, without being distracted by the technology surrounding them. To achieve this, multiple wearable physiological monitoring devices, Internet of things-based architectures, and participatory sensing systems have been developed. However, with such constant monitoring, the healthcare data get increasingly big and more difficult to process. Moreover, since daily life activities are composite in nature, they could be part of more complex routines and behaviors, and precise, instance-wise labels are difficult to create. Instance-wise labeling for multiple activities, along with the sequence of the activities for every individual window, is required for proper predictions. This chapter explores the applications and challenges in U-healthcare systems while analyzing secure architectures to resolve certain specific issues in this area of research. Further, advanced data processing techniques are studied for accurate labeling and prediction of possible health issues based on previous data accumulated through various resources, such as prior health records and incoming data through sensor networks.
Published Version
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