Using association rule mining to discover temporal relations of daily activities

Authors:
Ehsan Nazerfard;Parisa Rashidi;Diane J. Cook
Affiliations:
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA;School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA
Venue:
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
Year:
2011

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Abstract

The increasing aging population has inspired many machine learning researchers to find innovative solutions for assisted living. A problem often encountered in assisted living settings is activity recognition. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity usually have been ignored by researchers. Temporal features can provide useful insights for building predictive activity models and for recognizing activities. In this paper, we explore the use of temporal features for activity recognition in assisted living settings. We discover temporal relations such as order of activities, as well as their corresponding start time and duration features. To validate our method, we used four months of real data collected from a smart home.