Context-Awareness in Wearable and Ubiquitous Computing
ISWC '97 Proceedings of the 1st IEEE International Symposium on Wearable Computers
What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mobile Context Provider for Social Networking
OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
Providing user context for mobile and social networking applications
Pervasive and Mobile Computing
Complex activity recognition using context-driven activity theory and activity signatures
ACM Transactions on Computer-Human Interaction (TOCHI)
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Many devices, like mobile phones, use contextual profiles like "in the car" or "in a meeting" to quickly switch between behaviors. Achieving automatic context detection, usually by analysis of small hardware sensors, is a fundamental problem in human-computer interaction. However, mapping the sensor data to a context is a difficult problem involving near real-time classification and training of patterns out of noisy sensor signals. This paper proposes an adaptive approach that uses a Kohonen Self-Organizing Map, augmented with on-line k-means clustering for classification of the incoming sensor data. Overwriting of prototypes on the map, especially during the untangling phase of the Self-Organizing Map, is avoided by a refined k-means clustering of labeled input vectors.