Luis Muñoz-Saavedra is a software engineer with an MSc in Computer Science and a Ph.D. in Industrial Systems. He is a postdoc lecturer in the Computer Architecture and Technology Department, University of Seville, Spain. His research focuses on characteristics extraction, emotion classification, and machine learning. He has a great interest in high-performance computations, virtual reality, and embedded systems.
Applied to health field, Internet of Things (IoT) systems provides continuous and ubiquitous monitoring and assistance, allowing the creation of valuable tools for diagnosis, health empowerment, and personalized treatment, among others. Advances in these systems follow different approaches, such as the integration of new protocols and standards, combination with artificial intelligence algorithms, application of big data processing methodologies, among others. These new systems and applications also should face different challenges when applying this kind of technology into health areas, such as the management of personal data sensed, integration with electronic health records, make sensing devices comfortable to wear, and achieve an accurate acquisition of the sensed data. The objective of this chapter is to present the state of the art, indicating the most current IoT trends applied to the health field, their contributions, technologies applied, and challenges faced.
Part of the book: Internet of Things
In this paper, we present a novel framework for the study and design of AI-assisted musical devices (AIMEs). Initially, we present taxonomy of these devices and illustrate it with a set of scenarios and personas. Later, we propose a generic architecture for the implementation of AIMEs and present some examples from the scenarios. We show that the proposed framework and architecture are a valid tool for the study of intelligent musical devices.
Part of the book: Internet of Things