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On using temporal features to create more accurate human-activity classifiers

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dc.contributor.author Ye, Juan
dc.contributor.author Clear, Adrian K.
dc.contributor.author Coyle, Lorcan
dc.contributor.author Dobson, Simon
dc.date.accessioned 2011-07-22T11:42:16Z
dc.date.available 2011-07-22T11:42:16Z
dc.date.issued 2009
dc.identifier.uri http://hdl.handle.net/10344/1177
dc.description peer-reviewed en_US
dc.description.abstract Through advances in sensing technology, a huge amount of data is available to context-aware applications. A major challenge is extracting features of this data that correlate to high-level human activities. Time, while being semantically rich and an essentially free source of information, has not received sufficient attention for this task. In this paper, we examine the potential for taking temporal features—inherent in human activities—into account when classifying them. Preliminary experiments using the PlaceLab dataset show that absolute time and temporal relationships between activities can improve the accuracy of activity classifiers. en_US
dc.language.iso eng en_US
dc.publisher Springer-Verlag en_US
dc.relation.ispartofseries 20th Conference on Artificial Intelligence and Cognitive Science; pp 247-283
dc.subject sensing technology en_US
dc.subject context-aware applications en_US
dc.title On using temporal features to create more accurate human-activity classifiers 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 07/CE/I1147
dc.relation.projectid 03/CE2/I303-1


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