Refining Visual Activity Recognition with Semantic Reasoning

As elderly care is getting more and more important, monitoring of activity of daily living (ADL) has become an active research topic. Both robotic and pervasive computing domains, through smart homes, are creating opportunities to move forward in ADL field. Multiple techniques were proposed to identify activities, each with their features, advantages and limits. However, it is a very challenging issue and none of the existing methods provides robust results, in particular in real daily living scenarios. This is particularly true for vision based approaches used by robots. In this paper, we propose to refine a robot's visual activity recognition process by relying on smart home sensors. We assert that the consideration of further sensors and the knowledge about the target user together with the semantic by means of an ontology and a reasoning layer in the recognition process, has improved the existing works results. We experimented through multiple activity recognition scenarios with and without refinement to assess the relevance of such a combination. Although our tests reveal positive results, they also point out limits and challenges that we discuss in this paper.