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Issue No.01 - January/February (2012 vol.27)
pp: 32-38
Diane J. Cook , Washington State University
<p>Smart home activity recognition systems can learn generalized models for common activities that span multiple environment settings and resident types.</p>
machine learning, ubiquitous computing, pervasive computing, activity recognition
Diane J. Cook, "Learning Setting-Generalized Activity Models for Smart Spaces", IEEE Intelligent Systems, vol.27, no. 1, pp. 32-38, January/February 2012, doi:10.1109/MIS.2010.112
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3. V. Wadley et al., "Mild Cognitive Impairment and Everyday Function: Evidence of Reduced Speed in Performing Instrumental Activities of Daily Living," Am. J. Geriatric Psychiatry, vol. 16, no. 5, 2008, pp. 416–424.
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7. D. Cook and M. Schmitter-Edgecombe, "Assessing the Quality of Activities in a Smart Environment," Methods Information in Medicine, vol. 48, no. 5, 2009, pp. 480–485.
8. L. Liao, D. Fox, and H. Kautz, "Location-Based Activity Recognition Using Relational Markov Networks," Proc. Int'l Joint Conf. Artificial Intelligence, ICJAI, 2005, pp. 773–778.
9. A. Viterbi, "Error Bounds for Convolutional Codes and an Asymptotically-Optimum Decoding Algorithm," IEEE Trans. Information Theory, vol. 13, no. 2, 1967, pp. 260–269.
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11. C.-X. Zhang and J.-S. Zhang, "A Local Boosting Algorithm for Solving Classification Problems," Computational Statistics and Data Analysis, vol. 52, no. 4, 2008, pp. 1928–1941.
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