{"id":470,"date":"2005-10-23T10:04:01","date_gmt":"2005-10-23T10:04:01","guid":{"rendered":"https:\/\/www.docbug.com\/blog\/archives\/470"},"modified":"2005-10-23T10:04:01","modified_gmt":"2005-10-23T10:04:01","slug":"iswc-best-paper-winner","status":"publish","type":"post","link":"https:\/\/www.docbug.com\/blog\/archives\/470","title":{"rendered":"ISWC Best Paper winner"},"content":{"rendered":"

\"activity-recognition-rfid-glove-iswc05.jpg\" <\/p>\n

The winner of this year’s best paper award at ISWC (the first ISWC to have such an award) was a paper by Don Patterson<\/a> from the University of Washington called Fine-Grained Activity Recognition by Aggregating Abstract Object Usage<\/a><\/i>. All the authors got certificates and Don took home a new video iPod as the prize.<\/p>\n

This was one of several papers presented that used an RFID reader in a glove, in this case to classify what kind of activity a person is conducting based on the sequence of objects she has touched. This would be useful, for example, for alerting a care worker if a resident of an assistive-living home had stopped eating.<\/p>\n

From the abstract:<\/p>\n

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In this paper we present results related to achieving fine-grained 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>\n<\/blockquote>\n

Here are all six nominees for best paper from ISWC’05, which were the top 10% of full papers based on reviewer-rating:<\/p>\n