Abstract

Activity recognition systems detect temporal combinations of ‘low-level’ or ‘short-term’ activities on sensor data. These systems exhibit various types of uncertainty, often leading to erroneous detection. We present an extension of an interval-based activity recognition system which operates on top of a probabilistic Event Calculus implementation. Our proposed system performs on-line recognition, as opposed to batch processing, thus supporting data streams. The empirical analysis demonstrates the efficacy of our system, comparing it to interval-based batch recognition, point-based recognition, as well as structure and weight learning models.