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dc.contributor.authorMoncrieff, Simon
dc.contributor.authorVenkatesh, Svetha
dc.contributor.authorWest, Geoffrey
dc.contributor.authorGreenhill, Steward
dc.identifier.citationMoncrieff, Simon and Venkatesh, Svetha and West, Geoffrey and Greenhill, Stewart. 2007. Multi-modal emotive computing in a smart house environment. Pervasive and Mobile Computing. 3 (2): pp. 74-94.

We determine hazards within a smart house environment using an emotive computing framework. Representing a hazardous situation as an abnormal activity, we model normality using the concept of anxiety, using an agent based probabilistic approach. Interactions between a user and the environment are determined using multi-modal sensor data. The anxiety framework is a scalable, real-time approach that is able to incorporate data from a number of sources, or agents, and able to accommodate interleaving event sequences. In addition to using simple sensors, we introduce a method for using audio as a pervasive sensor indicating the presence of an activity. The audio data enabled the detection of activity when interactions between a user and a monitored device didn’t occur, successfully preventing false hazardous situations from being detected. We present results for a number of activity sequences, both normal and abnormal.

dc.publisherElsevier Science Inc
dc.titleMulti-modal emotive computing in a smart house environment
dc.typeJournal Article
dcterms.source.titlePervasive and Mobile Computing

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Copyright © 2007 Elsevier Ltd. All rights reserved

curtin.accessStatusFulltext not available
curtin.facultySchool of Electrical Engineering and Computing
curtin.facultyDepartment of Computing
curtin.facultyFaculty of Science and Engineering

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