Unsupervised, dynamic identification of physiological and activity context in wearable computing

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.