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Author Patterson, Donald J. ♦ Fox, Dieter ♦ Kautz, Henry
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Activity Recognition ♦ Rfid Glove ♦ Powerful Probabilistic Graphical Model ♦ Context-aware Computing Application ♦ Unique Object Instance ♦ Fine-grained Activity Recognition ♦ Aggregating Abstract Object Usage ♦ Morning Household Routine ♦ Additional Complexity ♦ Present Result ♦ Object Instance
Description 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. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2005-01-01
Publisher Institution Ninth IEEE International Symposium on Wearable Computers