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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 learn context-dependent personal preferences by identifying individual user states and observing how the user interacts with the system in these states. This learning occurs online and does not require external supervision. The system relies on techniques from machine learning and statistical analysis. A case study integrates the approach in a context-aware mobile phone. The results indicate that the method is able to create a meaningful user context model while only requiring data from comfortable wearable sensor devices.
Reference:
Context-Aware Mobile Computing: Learning Context-Dependent Personal Preferences from a Wearable Sensor Array A. Krause, A. Smailagic, D. SiewiorekIn IEEE Transactions on Mobile Computing, volume 5, 2006Winner of the NRW Undergraduate Science Award
Bibtex Entry:
@article{krause06context,
	Author = {Andreas Krause and Asim Smailagic and Daniel Siewiorek},
	Doi = {10.1109/TMC.2006.18},
	Journal = {IEEE Transactions on Mobile Computing},
	Number = {2},
	Pages = {113-127},
	Title = {Context-Aware Mobile Computing: Learning Context-Dependent Personal Preferences from a Wearable Sensor Array},
	Volume = {5},
	Year = {2006}}