The Aware Home: A Living Laboratory for Ubiquitous Computing Research
CoBuild '99 Proceedings of the Second International Workshop on Cooperative Buildings, Integrating Information, Organization, and Architecture
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
Mining models of human activities from the web
Proceedings of the 13th international conference on World Wide Web
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
Hierarchical recognition of daily human actions based on continuous hidden Markov models
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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We aim to provide ubiquitous services proactively in a ubiquitous environment according to user intention. User behavior must be precisely recognized for providing appropriate services for individual user. This paper proposes a user behavior detection method with a personalized behavioral pattern in an intelligent space which identify objects a user touches. They are adapted to individual user. The proposed method focuses on some special scenes in which user's mode significantly changes, such as a scene of going out. In such scenes, user can be provided services most effectively. The method can detect user behavior precisely with a behavioral pattern created by focusing on discrete order of objects a user touches. It separates the order check from a probabilistic model. Because a behavioral pattern can be adapted to individual user in a short time, the method can start providing services to user early. Experiments have proved that our method detects more than 90% of user behavior correctly with a behavioral pattern which is created in a practical short time with less than 10 sample behavior logs.