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Ninth IEEE International Symposium on Wearable Computers (ISWC'05) (2005)
Osaka, Japan
Oct. 18, 2005 to Oct. 21, 2005
ISBN: 0-7695-2419-2
pp: 44-51
Donald J. Patterson , University of Washington Department of Computer Science and Engineering Seattle, Washington, USA
Dieter Fox , University of Washington Department of Computer Science and Engineering Seattle, Washington, USA
Henry Kautz , University of Washington Department of Computer Science and Engineering Seattle, Washington, USA
Matthai Philipose , Intel Research Seattle, Seattle, Washington, USA
ABSTRACT
<p>In this paper we present results related to achieving finegrained activity recognition for context-aware computing applications. We examine the advantages and challenges of reasoning with globally unique object instances detected by an RFID glove. We present a sequence of increasingly powerful probabilistic graphical models for activity recognition. We show the advantages of adding additional complexity and conclude with a model that can reason tractably about aggregated object instances and gracefully generalizes from object instances to their classes by using abstraction smoothing. We apply these models to data collected from a morning household routine.</p>
INDEX TERMS
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CITATION

D. Fox, D. J. Patterson, M. Philipose and H. Kautz, "Fine-Grained Activity Recognition by Aggregating Abstract Object Usage," Ninth IEEE International Symposium on Wearable Computers (ISWC'05)(ISWC), Osaka, Japan, 2005, pp. 44-51.
doi:10.1109/ISWC.2005.22
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