Probabilistic Ontology Reasoning in Ambient Assistance: Predicting Human Actions

Abstract

Providing reminders to elderly people in their home environment, while they perform their daily activities, is considered as a user support activity, and thus a relevant topic in Active and Assisted Living (AAL) research and development. Determining such reminders implies decision-making, since the actions' flow (behavior) usually involves probabilistic branches. An automated system needs to decide which of the next actions is the best one for the user in a given situation. Problems of this nature involve uncertainty levels that have to be dealt with. Many approaches to this problem exploit statistical data only, thus ignoring important semantic data as, for instance, are provided by Ontologies. However, ontologies do not support reasoning over uncertainty natively. In this paper, we present a probabilistic semantic model that enables reasoning over uncertainty without losing semantic information. This model will be exemplified by an extension of the Human Behavior Monitoring and Support [HBMS] approach that provides a conceptual model for representing the user's behavior and its context in her/his living environment. The performance of this approach was evaluated using real data collected from a smart home prototype equipped with sensors. The experiments provided promising results which we will discuss regarding limits and challenges to overcome.