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

Abstract. 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...

• Type: Conference
• Year: 2009
• Where: UIC
• Authors: Daniele Riboni, Claudio Bettini
• Keywords: Activity Recognition, Applied Computing, Automatic Activity Recognition, Statistical Activity Recognition, UIC 2009