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
Text categorization by boosting automatically extracted concepts
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Boosting support vector machines for text classification through parameter-free threshold relaxation
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Common sense based joint training of human activity recognizers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Personalizing Threshold Values on Behavior Detection with Collaborative Filtering
UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
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We are developing a context-aware application for use in homes, which detects high-level user behavior, such as "leaving the home" and "going to bed", and provides services according to the behavior proactively. To detect user behavior, a behavioral pattern is created by extracting frequent characteristics from the user's behavior logs acquired from sensors online, using an extraction threshold based on the criterion of frequency. Most context-aware applications need to determine such a threshold. A conventional model determines a fixed common threshold value for all users. However, the common value is improper for some users because proper values vary among users. This paper proposes a detection method of high-level behavior with a model for determining the threshold value dynamically according to individual behavioral pattern.