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Detecting and exploring deviating behaviour of people in their own homes
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.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.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0003-2185-8973
(English)Manuscript (preprint) (Other academic)
Abstract [en]

A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. It is believed that such systems will be very important in ambient assisted living services. Three types of deviations are considered in this work: deviation in activity intensity, deviation in time and deviation in space. Detection of deviations in activity intensity is formulated as the on-line quickest detection of a parameter shift in a sequence of independent Poisson random variables. Random forests trained in an unsupervised fashion are used to learn the spatial and temporal structure of data representing normal behaviour and are thereafter utilised to find deviations.The experimental investigations have shown that the Page and Shiryaev change-point detection methods are preferable in terms of expected delay of motivated alarm. Interestingly only a little is lost when the methods are specified with estimated intensity parameters rather than the true intensity values which are not available in a real situation. As to the spatial and temporal deviations, they can be revealed through analysis of a 2D map of high dimensional data. It was demonstrated that such a map is stable in terms of the number of clusters formed. We have shown that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree.

National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-25317OAI: oai:DiVA.org:hh-25317DiVA: diva2:716356
Projects
SA3L - Situation Awareness for Ambient Assisted Living
Funder
Knowledge Foundation
Note

Som manuskript i avhandling. As manuscript in dissertation.

Available from: 2014-05-09 Created: 2014-05-09 Last updated: 2016-04-08Bibliographically approved
In thesis
1. Situation Awareness in Colour Printing and Beyond
Open this publication in new window or tab >>Situation Awareness in Colour Printing and Beyond
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Machine learning methods are increasingly being used to solve real-world problems in the society. Often, the complexity of the methods are well hidden for users. However, integrating machine learning methods in real-world applications is not a straightforward process and requires knowledge both about the methods and domain knowledge of the problem. Two such domains are colour print quality assessment and anomaly detection in smart homes, which are currently driven by manual monitoring of complex situations. The goal of the presented work is to develop methods, algorithms and tools to facilitate monitoring and understanding of the complex situations which arise in colour print quality assessment and anomaly detection for smart homes. The proposed approach builds on the use and adaption of supervised and unsupervised machine learning methods.

Novel algorithms for computing objective measures of print quality in production are proposed in this work. Objective measures are also modelled to study how paper and press parameters influence print quality. Moreover, a study on how print quality is perceived by humans is presented and experiments aiming to understand how subjective assessments of print quality relate to objective measurements are explained. The obtained results show that the objective measures reflect important aspects of print quality, these measures are also modelled with reasonable accuracy using paper and press parameters. The models of objective  measures are shown to reveal relationships consistent to known print quality phenomena.

In the second part of this thesis the application area of anomaly detection in smart homes is explored. A method for modelling human behaviour patterns is proposed. The model is used in order to detect deviating behaviour patterns using contextual information from both time and space. The proposed behaviour pattern model is tested using simulated data and is shown to be suitable given four types of scenarios.

The thesis shows that parts of offset lithographic printing, which traditionally is a human-centered process, can be automated by the introduction of image processing and machine learning methods. Moreover, it is concluded that in order to facilitate robust and accurate anomaly detection in smart homes, a holistic approach which makes use of several contextual aspects is required.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2014. 51 p.
Series
Halmstad University Dissertations, 6
Keyword
Machine learning, Data mining, Colour printing, Smart homes
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-25318 (URN)978-91-87045-12-7 (ISBN)978-91-87045-11-0 (ISBN)
Public defence
2014-06-13, Wigforssalen, Visionen, Kristian IV:s väg 3, Halmstad, 13:15 (English)
Opponent
Supervisors
Projects
PPQSA3L
Funder
Knowledge Foundation
Available from: 2014-05-09 Created: 2014-05-09 Last updated: 2015-09-11Bibliographically approved

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