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Sensor selection to support practical use of health-monitoring smart environments

Authors

  • Diane J. Cook,

    Corresponding author
    1. School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, USA
    • School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, USA
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  • Lawrence B. Holder

    1. School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, USA
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Abstract

The data mining and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties in living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. One question that frequently arises, however, is how many smart home sensors are needed and where should they be placed in order to accurately recognize activities? We employ data mining techniques to look at the problem of sensor selection for activity recognition in smart homes. We analyze the results based on six datasets collected in five distinct smart home environments. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 339–351 DOI: 10.1002/widm.20

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