ReDef: Context-aware Recognition of Interleaved Activities using OWL 2 and Defeasible Reasoning.
Γεώργιος Μεδίτσκος, Efstratios Kontopoulos, Ioannis Yiannis Kompatsiaris · 2015
Understanding human activities in pervasive environments is a key challenge that involves fusion and correlation of multimodal sensor information. Many research efforts have been recently focused on knowledge-driven solutions to human activity recognition, using ontologies for defining activity models and for capturing contextual information. In most cases, however, the unrealistic assumption is made that activities are performed in a sequential, non-interrupted manner, hampering their applicability in real-world scenarios. In this paper, we present a framework for detecting interleaved activities of daily living (ADL) using (a) OWL 2 for implementing the underlying model semantics capturing contextual dependencies among activities, and (b) defeasible reasoning for introducing a flexible conflict resolution mechanism. The proposed framework has been integrated in an existing context-aware ADL recognition framework, which is being used for supporting the diagnosis of the Alzheimer’s disease in a controlled environment.