Presenters:

Alexander Artikis, Periklis Mantenoglou

Abstract:

Activity recognition systems detect temporal combinations of 'low-level' or 'short-term' activities on sensor data streams. Such streams exhibit various types of uncertainty, often leading to erroneous recognition. We will present an extension of an interval-based activity recognition system which operates on top of a probabilistic Event Calculus implementation. Our proposed system performs online recognition, as opposed to batch processing, thus supporting streaming applications. Our empirical analysis demonstrates the efficacy of our system, comparing it to interval-based batch recognition and point-based recognition.