A probabilistic ontological framework for the recognition of multilevel human activities

Authors:
Rim Helaoui;Daniele Riboni;Heiner Stuckenschmidt
Affiliations:
Data and Web Science Research Group. University of Mannheim, Mannheim, Germany;Department of Computer Science, University of Milano, Milan, Italy;Data and Web Science Research Group. University of Mannheim, Mannheim, Germany
Venue:
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Year:
2013

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Abstract

A major challenge of ubiquitous computing resides in the acquisition and modelling of rich and heterogeneous context data, among which, ongoing human activities at different degrees of granularity. In a previous work, we advocated the use of probabilistic description logics (DLs) in a multilevel activity recognition framework. In this paper, we present an in-depth study of activity modeling and reasoning within that framework, as well as an experimental evaluation with a large real-world dataset. Our solution allows us to cope with the uncertain nature of ontological descriptions of activities, while exploiting the expressive power and inference tools of the OWL 2 language. Targeting a large dataset of real human activities, we developed a probabilistic ontology modeling nearly 150 activities and actions of daily living. Experiments with a prototype implementation of our framework confirm the viability of our solution.