Fine-grained activity recognition by aggregating abstract object usage

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 proba...

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Bibliographic Details
Published inNinth IEEE International Symposium on Wearable Computers (ISWC'05) pp. 44 - 51
Main Authors Patterson, D.J., Fox, D., Kautz, H., Philipose, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Character recognition
Inference algorithms
Machine vision
Multimodal sensors
Object detection
Radiofrequency identification
Robustness
Sensor phenomena and characterization
Wearable computers
Wearable sensors
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Summary: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.
ISBN:0769524192
9780769524191
ISSN:1550-4816
2376-8541
DOI:10.1109/ISWC.2005.22