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Using ontologies in case-based activity recognition

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dc.contributor.author Knox, Stephen
dc.contributor.author Coyle, Lorcan
dc.contributor.author Dobson, Simon
dc.date.accessioned 2011-01-26T17:20:54Z
dc.date.available 2011-01-26T17:20:54Z
dc.date.issued 2010
dc.identifier.uri http://hdl.handle.net/10344/672
dc.description peer-reviewed en_US
dc.description.abstract Pervasive computing requires the ability to detect user activity in order to provide situation-specific services. Case-based reasoning can be used for activity recognition by using sensor data obtained from the environment. Pervasive computing systems can grow to be very large, containing many users, sensors, objects and situations, thus raising the issue of scalability. This paper presents a case-based reasoning approach to activity recognition in a smart home setting. An analysis is performed on scalability with respect to case storage, and an ontology-based approach is proposed for case base maintenance. We succeeded in reducing the casebase size by a factor of one thousand, while increasing the accuracy in recognising some activities. en_US
dc.language.iso eng en_US
dc.publisher Association for the Advancement of Artificial Intelligence
dc.relation.ispartofseries FLAIRS-23, Proceedings of the 23rd International Conference of the Florida Artificial Intelligence Research Society, Daytona Beach, FL. May 2010;pp. 336-341
dc.relation.uri http://www.aaai.org/ocs/index.php/FLAIRS/2010/paper/view/1253
dc.subject pervasive computing en_US
dc.title Using ontologies in case-based activity recognition en_US
dc.type Conference item en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.type.restriction none en
dc.contributor.sponsor SFI
dc.relation.projectid 03/CE2/I303_1
dc.relation.projectid 07/CE/I1147
dc.relation.projectid 05/RFP/CMS0062


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