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

Context-aware computing describes the situationwhere a wearable / mobile computer is aware of itsuser's state and surroundings and modifies its behaviorbased on this information. We designed, implemented andevaluated a wearable system which can determine typicaluser context and context transition probabilities onlineand without external supervision. The system relies ontechniques from machine learning, statistical analysisand graph algorithms. It can be used for onlineclassification and prediction. Our results indicate thepower of our method to determine a meaningful usercontext model while only requiring data from acomfortable physiological sensor device.