Context-Aware Activity Recognition through a Combination of Ontological and Statistical Reasoning

In the last years, techniques for activity recognition have attracted increasing attention. Among many applications, a special interest is in the pervasive e-Health domain where automatic activity recognition is used in rehabilitation systems, chronic disease management, monitoring of the elderly, as well as in personal well being applications. Research in this field has mainly adopted techniques based on supervised learning algorithms to recognize activities based on contextual conditions (e.g., location, surrounding environment, used objects) and data retrieved from body-worn sensors. Since these systems rely on a sufficiently large amount of training data which is hard to collect, scalability with respect to the number of considered activities and contextual data is a major issue. In this paper, we propose the use of ontologies and ontological reasoning combined with statistical inferencing to address this problem. Our technique relies on the use of semantic relationships that express the feasibility of performing a given activity in a given context. The proposed technique neither increases the obtrusiveness of the statistical activity recognition system, nor introduces significant computational overhead to real-time activity recognition. The results of extensive experiments with data collected from sensors worn by a group of volunteers performing activities both indoor and outdoor show the superiority of the combined technique with respect to a solely statistical approach. To the best of our knowledge, this is the first work that systematically investigates the integration of statistical and ontological reasoning for activity recognition.