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Technologies 2018, 6(1), 16; doi:10.3390/technologies6010016

Behavior Drift Detection Based on Anomalies Identification in Home Living Quantitative Indicators

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
This paper is an extended version of our paper in Proceedings of AAATE2017 Congress, Sheffield, UK, 13–14 September 2017.
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Received: 15 December 2017 / Revised: 9 January 2018 / Accepted: 24 January 2018 / Published: 25 January 2018
(This article belongs to the Special Issue Selected Papers from AAATE2017 Congress)
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Abstract

Home Automation and Smart Homes diffusion are providing an interesting opportunity to implement elderly monitoring. This is a new valid technological support to allow in-place aging of seniors by means of a detection system to notify potential anomalies. Monitoring has been implemented by means of Complex Event Processing on live streams of home automation data: this allows the analysis of the behavior of the house inhabitant through quantitative indicators. Different kinds of quantitative indicators for monitoring and behavior drift detection have been identified and implemented using the Esper complex event processing engine. The chosen solution permits us not only to exploit the queries when run “online”, but enables also “offline” (re-)execution for testing and a posteriori analysis. Indicators were developed on both real world data and on realistic simulations. Tests were made on a dataset of 180 days: the obtained results prove that it is possible to evidence behavior changes for an evaluation of a person’s condition. View Full-Text
Keywords: ambient intelligence; ubiquitous computing; home automation; smart homes; ageing; behavior; monitoring ambient intelligence; ubiquitous computing; home automation; smart homes; ageing; behavior; monitoring
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Veronese, F.; Masciadri, A.; Comai, S.; Matteucci, M.; Salice, F. Behavior Drift Detection Based on Anomalies Identification in Home Living Quantitative Indicators. Technologies 2018, 6, 16.

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