Daniel Wilson
Robotics Institute, Carnegie Mellon University
As people grow older, they depend more heavily upon outside support for health assessment and medical care. The current healthcare infrastructure in America is widely considered to be inadequate to meet the needs of an increasingly older population. One solution, called aging in place, is to ensure that the elderly can live safely and independently in their own homes for as long as possible. Automatic health monitoring is a technological approach which helps people age in place by continuously providing key information to caregivers.
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 introduce 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.
A copy of the thesis oral document can be found at http://www.cs.cmu.edu/~dwilson/papers/thesis.pdf.
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