Multimodal group action clustering in meetings
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Deterministic and Probabilistic Implementation of Context
PERCOMW '06 Proceedings of the 4th annual IEEE international conference on Pervasive Computing and Communications Workshops
Automatic Acquisition of Context Models and its Application to Video Surveillance
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Extracting activities from multimodal observation
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Context-Aware Activity Recognition through a Combination of Ontological and Statistical Reasoning
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
A survey of context modelling and reasoning techniques
Pervasive and Mobile Computing
Automatic feature selection for context recognition in mobile devices
Pervasive and Mobile Computing
COSAR: hybrid reasoning for context-aware activity recognition
Personal and Ubiquitous Computing
OWL 2 modeling and reasoning with complex human activities
Pervasive and Mobile Computing
Context Inference Engine (CiE): Inferring Context
International Journal of Advanced Pervasive and Ubiquitous Computing
Complex activity recognition using context-driven activity theory and activity signatures
ACM Transactions on Computer-Human Interaction (TOCHI)
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In order to provide information and communication services without disrupting human activity, information services must implicitly conform to the current context of human activity. However, the variability of human environments and human preferences make it impossible to preprogram the appropriate behaviors for a context aware service. One approach to overcoming this obstacle is to have services adapt behavior to individual preferences though feedback from users. This article describes a method for learning situation models to drive context-aware services. With this approach an initial simplified situation model is adapted to accommodate user preferences by a supervised learning algorithm using feedback from users. To bootstrap this process, the initial situation model is acquired by applying an automatic segmentation process to sample observation of human activities. This model is subsequently adapted to different operating environments and human preferences through interaction with users, using a supervised learning algorithm.