The active badge location system
ACM Transactions on Information Systems (TOIS)
C4.5: programs for machine learning
C4.5: programs for machine learning
Mastering regular expressions
The anatomy of a context-aware application
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Machine Learning
The Representation and Recognition of Human Movement Using Temporal Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
W4: A Real Time System for Detecting and Tracking People
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Audio-Visual Speaker Detection Using Dynamic Bayesian Networks
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Probabilistic Motion Parameter Models for Human Activity Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
VAMBAM: view and motion -based aspect models for distributed omnidirectional vision systems
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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There has been much research on location-based context-aware applications. However, any description of a person's activities must include a temporal aspect as well as a location aspect. Therefore, it is important when creating enhanced user activity support systems to consider the user's context in terms of spatio-temporal constraints. In this paper, we propose a user activity support system that employs a state sequence description scheme to describe the user's context. In this scheme, each state is described as a spatio-temporal relationship between the user and objects. Typical sequences of states are stored as models of activities performed by a user. Each segment of user activities measured by the sensors and the Radio Frequency Identification tags (RFID tags) is classified into a state by using a decision tree constructed by the machine learning algorithm called C4.5. The user's context is then obtained by matching the detected state series to a stored task model. To validate this system, we have developed an experimental house containing various embedded sensors and RFID-tagged objects. Having evaluated the performance of the proposed system, we conclude that our system is an effective way of acquiring the user's spatio-temporal context.