Paper Info
Title
Measuring changes in activity patterns during a norovirus epidemic at a retirement community.
Abstract
Ubiquitous and unobtrusive in-home monitoring has the potential to detect physical and mental decline earlier and with higher precision than current clinical methods. However, given that this field is in its infancy, the specific metrics through which these changes are detected are not well defined. The work presented here offers room-transitions, the act of physically moving from one area of a home to another, as a quantifiable measure for total daily activity that can be inferred from a network of passive infrared sensors. We describe a method to calculate this value from raw sensor data and validate this method on an acute health event: an 18-day quarantine at a retirement community that was initiated in the midst of a norovirus outbreak. The results from this case study show that room-transition values increased significantly as subjects remained in their homes during the quarantine, demonstrating a mean increase of 12 transitions per day. Furthermore, a time-adjusted measure of room-transitions is examined that did not significantly change across the group. Finally, the healthy subjects and those that fell ill were analyzed separately, and significant differences were found between them for both the raw and time-adjusted metrics. As detection algorithms improve, these types of measures may be useful in the early detection of a change in health status.
Year
DOI
Venue
2011
10.1109/IEMBS.2011.6091675
EMBC
Keywords
Field
DocType
retirement community,norovirus epidemic,healthy subjects,diseases,telemedicine,biomedical measurement,norovirus outbreak,microorganisms,patient monitoring,activity patterns,passive infrared sensors,rna,time 18 day,molecular biophysics,room-transition values,infrared detectors,acute health event,total daily activity,algorithms,activities of daily living,movement,motion,infrared,aging,cohort studies
Assisted Living Facility,Early detection,Retirement community,Remote patient monitoring,Artificial intelligence,Environmental health,Cohort study,Medicine,Norovirus,Computer vision,Activities of daily living,Simulation,Quarantine
Conference
Volume
ISSN
ISBN
2011
1557-170X
978-1-4244-4122-8
Citations 
PageRank 
References 
1
0.39
2
Authors
7
Name
Order
Citations
PageRank
Campbell Ian H110.39
Daniel Austin29117.29
Tamara L Hayes3333.52
Misha Pavel476099.04
Riley Thomas581.13
Mattek Nora621.24
Jeffrey Kaye713113.09