Discovering Activities to Recognize and Track in a Smart Environment. Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. IEEE Transactions on Knowledge and Data Engineering, 23(4):527–539, April, 2011. Conference Name: IEEE Transactions on Knowledge and Data Engineeringdoi abstract bibtex The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties 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. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.
@article{rashidi_discovering_2011,
title = {Discovering {Activities} to {Recognize} and {Track} in a {Smart} {Environment}},
volume = {23},
issn = {1558-2191},
doi = {10.1109/TKDE.2010.148},
abstract = {The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties 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. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.},
number = {4},
journal = {IEEE Transactions on Knowledge and Data Engineering},
author = {Rashidi, Parisa and Cook, Diane J. and Holder, Lawrence B. and Schmitter-Edgecombe, Maureen},
month = apr,
year = {2011},
note = {Conference Name: IEEE Transactions on Knowledge and Data Engineering},
keywords = {Activity recognition, Clustering algorithms, Data mining, Hidden Markov models, Intelligent sensors, Monitoring, Smart homes, clustering, data mining, sequence mining, smart homes.},
pages = {527--539},
}
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