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
Real world sensorization for observing human behavior and its application to behavior-to-speech
Proceedings of the 9th international conference on Intelligent user interfaces
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
Mining models of human activities from the web
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Ubiquitous Home: Real-Life Testbed for Home Context-Aware Service
TRIDENTCOM '05 Proceedings of the First International Conference on Testbeds and Research Infrastructures for the DEvelopment of NeTworks and COMmunities
Improvement of behavior detection by dynamic threshold
DNCOCO'07 Proceedings of the 9th WSEAS International Conference on Data Networks, Communications, Computers
Personalizing Threshold Values on Behavior Detection with Collaborative Filtering
UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
Dynamic threshold determination for stable behavior detection
WSEAS Transactions on Computers
An Ambient Intelligent Approach to Control Behaviours on a Tagged World
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Fuzzy method to disclose behaviour patterns in a Tagged World
Expert Systems with Applications: An International Journal
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A ubiquitous environment enable us to enjoy various services "anytime" "anywhere". However, "everyone" is not realized. We research an intelligent space "everyone" can enjoy services. This paper proposes a method to detect user behavior to provide services according to user context in home. We focus on scenes user's mode significantly changes, such as going out and going to bed. People often have characteristic behavior in these scenes. Our method extracts this characteristic as a behavioral pattern and detects user behavior in these scenes by matching current user behavior online with it. The method characterizes each scene with kind of objects a user touched and the order of them. The method realizes early start of providing services by creating a behavioral pattern from user behavior logs in short duration. The experiment proves the high potency of our method and discusses its weakness at the same time.