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dc.contributor.authorBelmonte-Fernández, Óscar
dc.contributor.authorPuertas-Cabedo, Adrian
dc.contributor.authorTorres-Sospedra, Joaquín
dc.contributor.authorMontoliu Colás, Raul
dc.contributor.authorTrilles, Sergio
dc.date.accessioned2017-01-17T18:40:26Z
dc.date.available2017-01-17T18:40:26Z
dc.date.issued2016
dc.identifier.citationBELMONTE-FERNÁNDEZ, Óscar, et al. An Indoor Positioning System Based on Wearables for Ambient-Assisted Living. Sensors, 2016, vol. 17, núm. 1, p. 36ca_CA
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10234/165378
dc.description.abstractThe urban population is growing at such a rate that by 2050 it is estimated that 84% of the world’s population will live in cities, with flats being the most common living place. Moreover, WiFi technology is present in most developed country urban areas, with a quick growth in developing countries. New Ambient-Assisted Living applications will be developed in the near future having user positioning as ground technology: elderly tele-care, energy consumption, security and the like are strongly based on indoor positioning information. We present an indoor positioning system for wearable devices based on WiFi fingerprinting. Smart-watch wearable devices are used to acquire the WiFi strength signals of the surrounding Wireless Access Points used to build an ensemble of Machine Learning classification algorithms. Once built, the ensemble algorithm is used to locate a user based on the WiFi strength signals provided by the wearable device. Experimental results for five different urban flats are reported, showing that the system is robust and reliable enough for locating a user at room level into his/her home. Another interesting characteristic of the presented system is that it does not require deployment of any infrastructure, and it is unobtrusive, the only device required for it to work is a smart-watch.ca_CA
dc.description.sponsorShipThis work has been partially funded by the Spanish Ministry of Economy and Competitiveness through the “Proyectos I + D Excelencia” programme (TIN2015-70202-P) and the “Redes de Excelencia” programme (TEC2015-71426-REDT), and from the Regional Government of Valencia (‘Proyectos de I + D para Grupos de Investigación Emergentes’ GV/2016/159). Special thanks to Víctor, Maricarmen, Inma and Daniel who lent their houses for performing the experiments.ca_CA
dc.format.extent22 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherMDPIca_CA
dc.relation.isPartOfSensors, 2016, vol. 17, núm. 1ca_CA
dc.rights© 2016 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/)ca_CA
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectAmbient-Assisted Living (AAL)ca_CA
dc.subjectIndoor positioningca_CA
dc.subjectMachine Learningca_CA
dc.subjectMessage Queuing Telemetry Transport (MQTT) connectivity protocolca_CA
dc.titleAn Indoor Positioning System Based on Wearables for Ambient-Assisted Livingca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.3390/s17010036
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://www.mdpi.com/1424-8220/17/1/36/htmca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersion


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© 2016 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/)
Excepto si se señala otra cosa, la licencia del ítem se describe como: © 2016 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/)