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Sensor selection to support practical use of health‐monitoring smart environments
Author(s) -
Cook Diane J.,
Holder Lawrence B.
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.20
Subject(s) - computer science , home automation , order (exchange) , selection (genetic algorithm) , smart environment , data science , activity recognition , assisted living , human–computer interaction , internet privacy , risk analysis (engineering) , internet of things , computer security , machine learning , business , telecommunications , medicine , gerontology , finance
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 This article is categorized under: Application Areas > Science and Technology