by , , ,
Abstract:
Context-aware computing describes the situation where a wearable / mobile computer is aware of its user's state and surroundings and modifies its behavior based on this information. We designed, implemented and evaluated a wearable system which can determine typical user context and context transition probabilities online and without external supervision. The system relies on techniques from machine learning, statistical analysis and graph algorithms. It can be used for online classification and prediction. Our results indicate the power of our method to determine a meaningful user context model while only requiring data from a comfortable physiological sensor device.
Reference:
Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing A. Krause, D. Siewiorek, A. Smailagic, J. FarringdonIn In. Proc. 7th International Symposium on Wearable Computers (ISWC), 2003
Bibtex Entry:
@inproceedings{krause03unsupervised,
	Author = {Andreas Krause and Daniel Siewiorek and Asim Smailagic and Jonny Farringdon},
	Booktitle = {In. Proc. 7th International Symposium on Wearable Computers (ISWC)},
	Title = {Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing},
	Year = {2003}}