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Activity recognition using temporal evidence theory

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dc.contributor.author McKeever, Susan
dc.contributor.author Ye, Juan
dc.contributor.author Coyle, Lorcan
dc.contributor.author Bleakley, Chris
dc.contributor.author Dobson, Simon
dc.date.accessioned 2011-01-14T17:51:15Z
dc.date.available 2011-01-14T17:51:15Z
dc.date.issued 2010
dc.identifier.uri http://hdl.handle.net/10344/641
dc.description non-peer-reviewed en_US
dc.description.abstract The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason about activities. Peoples‟ activities in the home often have an identifiable routine; activities take place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved when time patterns and activity durations are included in activity recognition. We also compare our approach with Naïve Bayes classifier and J48 Decision Tree, with temporal evidence theory achieving higher accuracies than both classifiers. en_US
dc.language.iso eng en_US
dc.relation.ispartofseries Journal of Ambient Intelligence and Smart Environments;Pre-print; 2(3) pp 253-269
dc.relation.uri http://portal.acm.org/citation.cfm?id=1834671
dc.subject context reasoning en_US
dc.subject activity recognition en_US
dc.subject evidence theory en_US
dc.subject dempster-shafer theory en_US
dc.subject temporal en_US
dc.subject smart home dataset en_US
dc.subject time en_US
dc.title Activity recognition using temporal evidence theory en_US
dc.type Article en_US
dc.type.supercollection all_ul_research en_US
dc.type.restriction none en
dc.contributor.sponsor HEA
dc.contributor.sponsor SFI
dc.relation.projectid 03/CE2/I303_1
dc.relation.projectid R10891
dc.internal.authorcontactother lorcan.coyle@ul.ie


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