Tool Support for Activity Recognition with Computational Causal Behaviour Models

Context-aware activity recognition plays an important role in different types of assistive systems and the approaches with which the context information is represented is a topic of various current projects. Here we present a tool support for activity recognition using computational causal behaviour models that allow the combination of symbolic causal model representation and probabilistic inference. The aim of the tool is to provide a flexible way of generating probabilistic inference engines from prior knowledge which reduces the need for collecting expensive training data.