Recognition Framework for Inferring Activities of Daily Living Based on Pattern Mining

Ambient assisted living applications are very much dependent on robust activity recognition frameworks, which allow these applications to provide services based on the contextual information that has been discovered. Existing frameworks have generally focused on the application of traditional classifiers and semantics reasoning to recognize activities. Nevertheless, being able to recognize unexpected actions remains a challenge. The work in this paper presents an approach that is able to recognize activities that have been conducted in an unordered manner. The recognition framework extends an existing approach that recognizes activities by exploiting the different levels of abstraction within an activity. A frequent pattern mining algorithm has been applied to the recognition framework in order to find patterns within the stream of captured events, which in turn increases the adaptive learning ability of the proposed recognition framework. This paper also presents experimental results that validate the recognition ability of the recognition framework. The motivation of this work is to be able to detect the functional decline among elderly people suffering from Alzheimer's disease by recognizing their daily activities.