Bayesian approach to sensor-based context awareness
Personal and Ubiquitous Computing
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
MMM2: mobile media metadata for media sharing
Proceedings of the 13th annual ACM international conference on Multimedia
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Activity-based serendipitous recommendations with the Magitti mobile leisure guide
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
Landmark detection from mobile life log using a modular Bayesian network model
Expert Systems with Applications: An International Journal
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Providing user context for mobile and social networking applications
Pervasive and Mobile Computing
Intelligent location-based mobile news service system with automatic news summarization
Expert Systems with Applications: An International Journal
Using activity theory to model context awareness
MRC'05 Proceedings of the Second international conference on Modeling and Retrieval of Context
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Since smart phones with diverse functionalities become the general trend, many context-aware services have been studied and launched. The services exploit a variety of contextual information in the mobile environment. Even though it has attempted to infer activities using a mobile device, it is difficult to infer human activities from uncertain, incomplete and insufficient mobile contextual information. We present a method to infer a person's activities from mobile contexts using hierarchically structured Bayesian networks. Mobile contextual information collected for one month is used to evaluate the method. The results show the usefulness of the proposed method.