The Robotics Institute

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In this thesis, we explore automatic health monitoring on several levels. First, we conduct a two-phased formative study to examine the work practices of professionals who currently perform in-home monitoring for elderly clients. With these findings in mind, we introduce the simultaneous tracking and activity recognition (STAR) problem, whose solution provides vital information for automatic in-home health monitoring. We describe and evaluate a particle filter approach that uses data from simple sensors commonly found in home security systems to provide room-level tracking and activity recognition. Next, we introduce the "context-aware recognition survey," a novel data collection method that helps users label anonymous episodes of activity for use as training examples in a supervised learner. Finally, we derive the k-Edits Viterbi algorithm, which works within a Bayesian framework to automatically rate routine activities and detect irregular patterns of behavior.

This thesis contributes to the field of automatic health monitoring through a combination of intensive background study, efficient approaches for location and activity inference, a novel unsupervised data collection technique, and a practical activity rating application.