Monitoring and modeling simple everyday activities of the elderly at home

Authors:
George Papamatthaiakis;George C. Polyzos;George Xylomenos
Affiliations:
Mobile Multimedia Laboratory, Department of Informatics, Athens University of Economics and Business, Athens, Greece;Mobile Multimedia Laboratory, Department of Informatics, Athens University of Economics and Business, Athens, Greece;Mobile Multimedia Laboratory, Department of Informatics, Athens University of Economics and Business, Athens, Greece
Venue:
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Year:
2010

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Abstract

We present our work on a sensor-based smart system automatically trained to recognize the activities of individuals in their home. In this paper we present and analyze a method for recognizing the indoor everyday activities of a monitored individual. This method is based on the data mining technique of association rules and Allen's temporal relations. Our experimental results show that for many (but not all) activities, this method produces a recognition accuracy of nearly 100%, in contrast to other methods based on data mining classifiers. The proposed method is accurate, very flexible and adaptable to a dynamic environment such as the "Smart Home" and we believe that it deserves further attention.