An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
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Título: | An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments |
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Autor/es: | Mora, Higinio | Gil, David | Muñoz Terol, Rafael | Azorin-Lopez, Jorge | Szymanski, Julian |
Grupo/s de investigación o GITE: | Informática Industrial y Redes de Computadores | Lucentia |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Internet of Things | Healthcare monitoring | Wearable sensing | Sensor network | Case studies |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 10-oct-2017 |
Editor: | MDPI |
Cita bibliográfica: | Mora H, Gil D, Terol RM, Azorín J, Szymanski J. An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments. Sensors. 2017; 17(10):2302. doi:10.3390/s17102302 |
Resumen: | The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries. |
Patrocinador/es: | This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educación, Investigación, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087. This work has also been partially funded by Vicerrectorado de Innovación, University of Alicante, Spain (Vigrob). |
URI: | http://hdl.handle.net/10045/70088 |
ISSN: | 1424-8220 |
DOI: | 10.3390/s17102302 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.3390/s17102302 |
Aparece en las colecciones: | INV - I2RC - Artículos de Revistas INV - LUCENTIA - Artículos de Revistas INV - AIA - Artículos de Revistas |
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