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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 anddoi:10.1109/iswc.2005.22 dblp:conf/iswc/PattersonFKP05 fatcat:z5tah4feovhsrorilkgieb36pu