Modelling Spatial And Temporal Context To Support Activity Recognition

With the dawn of mobile computing and the Internet of Things, context-aware computing has gained increasing attention. The idea is to not only consider explicit inputs when computing an output, but also to consider contextual factors such as location, time, and environmental data. Context-aware computing is not restricted to mobile computing and the Internet of Things, but is also beneficial in other areas. The area we focus on in this chapter is activity recognition in smart environments.In the last ten years or so, a number of approaches have been developed that aim to recognise activities in smart environments such as smart homes or smart offices, ranging from logic-based approaches to probabilistic machine learning approaches. These approaches still have deficits and only work under certain assumptions. We argue in this chapter that using context information can lead to an improvement in activity recognition. To prove our point, we focus on spatial and temporal context information and introduce a number of methods to reason about this type of information.