Location and Activity Modelling in Intelligent Environments

This paper describes ULAP, a framework for scrutable modeling and prediction of people’s locations and activities, based upon a diverse collection of sensors, with varying reliability. It supports transformation and aggregation of sensor data, using this to build individual user models of location and activity. We propose an approach to indicate the certainty of predictions about users based upon unobtrusive data for location: it can be provided to applications and also serves as a form of explanation to users. We use this to report experiments involving 32 users, each with varying amounts of historic sensor data for machine activity, formal schedule and Bluetooth device detections. This is combined with group membership.