Behavior Drift Detection Based on Anomalies Identification in Home Living Quantitative Indicators†
AbstractHome 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
Share & Cite This Article
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.
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(1):16.Chicago/Turabian Style
Veronese, Fabio; Masciadri, Andrea; Comai, Sara; Matteucci, Matteo; Salice, Fabio. 2018. "Behavior Drift Detection Based on Anomalies Identification in Home Living Quantitative Indicators." Technologies 6, no. 1: 16.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.