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Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care

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Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care

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dc.contributor.author Bayo-Monton, Jose Luis es_ES
dc.contributor.author Martinez-Millana, Antonio es_ES
dc.contributor.author Han, Weisi es_ES
dc.contributor.author Fernández Llatas, Carlos es_ES
dc.contributor.author Sun, Yan es_ES
dc.contributor.author Traver Salcedo, Vicente es_ES
dc.date.accessioned 2019-05-29T20:43:08Z
dc.date.available 2019-05-29T20:43:08Z
dc.date.issued 2018 es_ES
dc.identifier.uri http://hdl.handle.net/10251/121273
dc.description.abstract [EN] 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. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject EHealth es_ES
dc.subject Wearable es_ES
dc.subject Monitoring es_ES
dc.subject Services es_ES
dc.subject Integration es_ES
dc.subject IoT es_ES
dc.subject Telemedicine es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s18061851 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/692023/EU/Linking excellence in biomedical knowledge and computational intelligence research for personalized management of CVD within PHC/
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Bayo-Monton, JL.; Martinez-Millana, A.; Han, W.; Fernández Llatas, C.; Sun, Y.; Traver Salcedo, V. (2018). Wearable Sensors Integrated with Internet of Things for Advancing eHealth Care. Sensors. 18(6). https://doi.org/10.3390/s18061851 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.3390/s18061851 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 18 es_ES
dc.description.issue 6 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 29882790
dc.identifier.pmcid PMC6022128
dc.relation.pasarela S\364904 es_ES


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