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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-6022128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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