Real-Time Activity Recognition and Assistance in Smart Classrooms

In this paper we present our work in a real-time, context-aware sys-tem, applied in a smart classroom domain, which aims to assist its users after rec-ognizing any occurring activity. We exploit the advantages of ontologies in order to model the context and introduce as well a method for extracting information from an ontology and using it in a machine learning dataset. This method enables realtime reasoning on high-level-activities recognition. We describe the overview of our system as well as a typical usage scenario to indicate how our system would react in this specific situation. An experimental evaluation of our system in a real, publicly available lecture is also presented.