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Detecting and exploring deviating behaviour of smart home residents
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. (SA3L - Situation Awareness for Ambient Assisted Living)ORCID iD: 0000-0001-8804-5884
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. (SA3L)
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. (SA3L)ORCID iD: 0000-0003-2185-8973
2016 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 55, 429-440 p.Article in journal (Refereed) Published
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Text
Abstract [en]

A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.

Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.

The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2016. Vol. 55, 429-440 p.
Keyword [en]
Ambient assisted living, Random forests, Stochastic neighbour embedding, Markov chain, Intelligent environments
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-30594DOI: 10.1016/j.eswa.2016.02.030Scopus ID: 2-s2.0-84960082873OAI: oai:DiVA.org:hh-30594DiVA: diva2:915471
Projects
CAISR / SA3L
Funder
Knowledge Foundation, 2010/0271
Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2016-03-31Bibliographically approved

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