Looking at People: Sensing for Ubiquitous and Wearable Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensing techniques for mobile interaction
UIST '00 Proceedings of the 13th annual ACM symposium on User interface software and technology
Understanding and Using Context
Personal and Ubiquitous Computing
Advanced Interaction in Context
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Unsupervised Clustering of Symbol Strings and Context Recognition
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Indoor Navigation Using a Diverse Set of Cheap, Wearable Sensors
ISWC '99 Proceedings of the 3rd IEEE International Symposium on Wearable Computers
What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Collaborative context determination to support mobile terminal applications
IEEE Wireless Communications
Context sensitive access control
Proceedings of the tenth ACM symposium on Access control models and technologies
On the application of epidemical spreading in collaborative context-aware computing
ACM SIGMOBILE Mobile Computing and Communications Review
A service science perspective on the design of social media activities
International Journal of Web Engineering and Technology
Group behavior recognition in context-aware systems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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Handheld communication devices equipped with sensing capabilities can recognize some aspects of their context to enable novel applications. We seek to improve the reliability of context recognition through an analogy to human behavior. Where multiple devices are around, they can jointly negotiate on a suitable context and behave accordingly. We have developed a method for this collaborative context recognition for handheld devices. The method determines the need to request and collaboratively recognize the current context of a group of handheld devices. It uses both local context time history information and spatial context information of handheld devices within a certain area. The method exploits dynamic weight parameters that describe content and reliability of context information. The performance of the method is analyzed using artificial and real context data. The results suggest that the method is capable of improving the reliability of context information.