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Open AccessArticle

Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care

Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Unidad Mixta de Reingeniería de Procesos Sociosanitarios (eRPSS), Instituto de Investigación Sanitaria del Hospital Universitario y Politecnico La Fe, Bulevar Sur S/N, Valencia 46026, Spain
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1851;
Received: 15 May 2018 / Revised: 1 June 2018 / Accepted: 4 June 2018 / Published: 6 June 2018
(This article belongs to the Special Issue Data Analytics and Applications of the Wearable Sensors in Healthcare)
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 ( p < < 0 . 01 ) are suitable to support the transmission and analysis of biometric signals into scalable telemedicine systems. View Full-Text
Keywords: eHealth; wearable; monitoring; services; integration; IoT; Telemedicine eHealth; wearable; monitoring; services; integration; IoT; Telemedicine
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MDPI and ACS Style

Bayo-Monton, J.-L.; Martinez-Millana, A.; Han, W.; Fernandez-Llatas, C.; Sun, Y.; Traver, V. Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care. Sensors 2018, 18, 1851.

AMA Style

Bayo-Monton J-L, Martinez-Millana A, Han W, Fernandez-Llatas C, Sun Y, Traver V. Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care. Sensors. 2018; 18(6):1851.

Chicago/Turabian Style

Bayo-Monton, Jose-Luis; Martinez-Millana, Antonio; Han, Weisi; Fernandez-Llatas, Carlos; Sun, Yan; Traver, Vicente. 2018. "Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care" Sensors 18, no. 6: 1851.

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