Modeling human activity semantics for improved recognition performance

Authors:
Eunju Kim;Sumi Helal
Affiliations:
Mobile and Pervasive Computing Laboratory, The Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL;Mobile and Pervasive Computing Laboratory, The Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL
Venue:
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Year:
2011

Citing 6
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Abstract

Activity recognition performance is significantly dependent on the accuracy of the underlying activity model. Therefore, it is essential to examine and develop an activity model that can capture and represent the complex nature of human activities precisely. To address this issue, we introduce a new activity modeling technique, which utilizes simple yet often ignored activity semantics. Activity semantics are highly evidential knowledge that can identify an activity more accurately in ambiguous situations. We classify semantics into three types and apply them to generic activity framework, which is a refined hierarchical composition structure of the traditional activity theory. We compare the introduced activity model with the traditional model and the hierarchical models in terms of attainable recognition certainty. The comparison study shows superior performance of our semantic model using activities of daily living scenario.