Privacy and the access of information in a smart house environment
Proceedings of the 15th international conference on Multimedia
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Dynamic privacy assessment in a smart house environment using multimodal sensing
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A framework for the design of privacy preserving pervasive healthcare
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Activity recognition using semi-Markov models on real world smart home datasets
Journal of Ambient Intelligence and Smart Environments
An activity monitoring system for elderly care using generative and discriminative models
Personal and Ubiquitous Computing
Enhancing activity recognition in smart homes using feature induction
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Unsupervised recognition of ADLs
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Transferring knowledge of activity recognition across sensor networks
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Activity recognition using a spectral entropy signature
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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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.