Activity recognition in multiple contexts for smart-house data

Predicting human behaviour from smart-house data is an active and challenging area of research. Applications include improving quality of life and personal healthcare, e.g. smart thermostats and detecting falls. Smart-house technology is becoming increasingly popular and in many cases making a house smarter is as simple as installing off-the-shelf devices. In this paper we focus on predicting the future location of a person in a simulated smart-house environment. We consider three different occupant types who exhibit various working patterns. We develop a versatile model capable of adjusting to significant changes in a person's behaviour without the need of model retraining. To this end, we build a simple event calculus framework based on the Aleph Inductive Logic Programming system. Event calculus helps to handle time and persisting sensor states. Background knowledge encodes important information about the smart-house that is otherwise difficult to learn; it also facilitates transferability of the model to different house layouts. Moreover, rule models are white-box, hence human-readable. Finally, we show that a versatile model performs significantly better than other models that do not explicitly account for the context.