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Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care

Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine...

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Detalles Bibliográficos
Autores principales: Bayo-Monton, Jose-Luis, Martinez-Millana, Antonio, Han, Weisi, Fernandez-Llatas, Carlos, Sun, Yan, Traver, Vicente
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022128/
https://www.ncbi.nlm.nih.gov/pubmed/29882790
http://dx.doi.org/10.3390/s18061851
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author Bayo-Monton, Jose-Luis
Martinez-Millana, Antonio
Han, Weisi
Fernandez-Llatas, Carlos
Sun, Yan
Traver, Vicente
author_facet Bayo-Monton, Jose-Luis
Martinez-Millana, Antonio
Han, Weisi
Fernandez-Llatas, Carlos
Sun, Yan
Traver, Vicente
author_sort Bayo-Monton, Jose-Luis
collection PubMed
description Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 ± 0.0035 s (max = 0.2504 s), whereas the Raspberry Pi yields a delay of 0.0175 ± 0.149 s (max = 0.294 s) for N = 300. Our analysis confirms that portable devices ([Formula: see text]) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems.
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spelling pubmed-60221282018-07-02 Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care Bayo-Monton, Jose-Luis Martinez-Millana, Antonio Han, Weisi Fernandez-Llatas, Carlos Sun, Yan Traver, Vicente Sensors (Basel) Article Health and sociological indicators alert that life expectancy is increasing, hence so are the years that patients have to live with chronic diseases and co-morbidities. With the advancement in ICT, new tools and paradigms are been explored to provide effective and efficient health care. Telemedicine and health sensors stand as indispensable tools for promoting patient engagement, self-management of diseases and assist doctors to remotely follow up patients. In this paper, we evaluate a rapid prototyping solution for information merging based on five health sensors and two low-cost ubiquitous computing components: Arduino and Raspberry Pi. Our study, which is entirely described with the purpose of reproducibility, aimed to evaluate the extent to which portable technologies are capable of integrating wearable sensors by comparing two deployment scenarios: Raspberry Pi 3 and Personal Computer. The integration is implemented using a choreography engine to transmit data from sensors to a display unit using web services and a simple communication protocol with two modes of data retrieval. Performance of the two set-ups is compared by means of the latency in the wearable data transmission and data loss. PC has a delay of 0.051 ± 0.0035 s (max = 0.2504 s), whereas the Raspberry Pi yields a delay of 0.0175 ± 0.149 s (max = 0.294 s) for N = 300. Our analysis confirms that portable devices ([Formula: see text]) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems. MDPI 2018-06-06 /pmc/articles/PMC6022128/ /pubmed/29882790 http://dx.doi.org/10.3390/s18061851 Text en © 2018 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/).
spellingShingle Article
Bayo-Monton, Jose-Luis
Martinez-Millana, Antonio
Han, Weisi
Fernandez-Llatas, Carlos
Sun, Yan
Traver, Vicente
Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care
title Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care
title_full Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care
title_fullStr Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care
title_full_unstemmed Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care
title_short Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care
title_sort wearable sensors integrated with internet of things for advancing ehealth care
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022128/
https://www.ncbi.nlm.nih.gov/pubmed/29882790
http://dx.doi.org/10.3390/s18061851
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