Multi-Modal Subjective Context Modelling and Recognition

Applications like personal assistants need to be aware of the user’s context, e.g., where they are, what they are doing, and with whom. Context information is usually inferred from sensor data, like GPS sensors and accelerometers on the user’ smartphone. This prediction task is known as context recognition. A well-defined context model is fundamental for successful recognition. Existing models, however, have two major limitations. First, they focus on few aspects, like location or activity, meaning that recognition methods based on them can only compute and leverage few inter-aspect correlations. Second, existing models typically assume that context is objective, whereas in most applications context is best viewed from the user’s perspective. Neglecting these factors limits the usefulness of the context model and hinders recognition. We present a novel ontological context model that captures four dimensions, namely time, location, activity, and social relations. Moreover, our model defines three levels of description (objective context, machine context, and subjective context) that naturally support subjective annotations and reasoning. An initial context recognition experiment on real-world data hints at the promise of our model.