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Open AccessArticle

An Indoor Positioning System Based on Wearables for Ambient-Assisted Living

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Institute of New Imaging Technologies (INIT), Jaume I University, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain
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Soluciones Cuatroochenta S.L., Av. Vicente Sos Baynat s/n, Espaitec2 Building, 12071 Castelló de la Plana, Spain
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Author to whom correspondence should be addressed.
Sensors 2017, 17(1), 36; https://doi.org/10.3390/s17010036
Received: 18 November 2016 / Revised: 21 December 2016 / Accepted: 21 December 2016 / Published: 25 December 2016
The 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. View Full-Text
Keywords: Ambient-Assisted Living (AAL); indoor positioning; machine learning; Message Queuing Telemetry Transport (MQTT) connectivity protocol Ambient-Assisted Living (AAL); indoor positioning; machine learning; Message Queuing Telemetry Transport (MQTT) connectivity protocol
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Belmonte-Fernández, Ó.; Puertas-Cabedo, A.; Torres-Sospedra, J.; Montoliu-Colás, R.; Trilles-Oliver, S. An Indoor Positioning System Based on Wearables for Ambient-Assisted Living. Sensors 2017, 17, 36.

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