Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Towards the detection of unusual temporal events during activities using HMMs
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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The idea of Smart Walkers is to equip customary rolling walkers with sensors in order to assist users, caregivers and clinicians. The integral part of the Smart Walkers is an autonomous agent which monitors the activity of the user, assesses his physical conditions, and detects potential risks of falls. In this paper, we study methods which enable the agent to recognize the user activity from the sensor measurements. The proposed methods use Conditional Random Fields with features based on discriminant rules. A special case are features which, in order to distinguish between two activities, compare the sensor measurements to thresholds learned by a linear classifier. Experiments with real user data show that the methods achieve a good accuracy; the best results are obtained using "smooth" thresholds based on sigmoid functions.