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Detecting Human Movement by Differential Air Pressure Sensing in HVAC System Ductwork: An Exploration in Infrastructure Mediated Sensing
| Book Series | Lecture Notes in Computer Science |
| Publisher | Springer Berlin / Heidelberg |
| ISSN | 0302-9743 (Print) 1611-3349 (Online) |
| Volume | Volume 5013/2008 |
| Book | Pervasive Computing |
| DOI | 10.1007/978-3-540-79576-6 |
| Copyright | 2008 |
| ISBN | 978-3-540-79575-9 |
| DOI | 10.1007/978-3-540-79576-6_1 |
| Pages | 1-18 |
| Subject Collection | Computer Science |
| SpringerLink Date | Friday, May 16, 2008 |
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Detecting Human Movement by Differential Air Pressure Sensing in HVAC System Ductwork: An Exploration in Infrastructure Mediated
Sensing
Shwetak N. Patel1 , Matthew S. Reynolds2 and Gregory D. Abowd1 
| (1) |
College of Computing, School of Interactive Computing, & GVU Center, Georgia Institute of Technology, 85 5th Street NW, Atlanta, GA 30332-0280, USA |
| (2) |
Department of Electrical and Computer Engineering, Duke University, Box 90291, Durham, NC 27708, USA |
Abstract
We have developed an approach for whole-house gross movement and room transition detection through sensing at only one point
in the home. We consider this system to be one member of an important new class of human activity monitoring approaches based
on what we call infrastructure mediated sensing, or "home bus snooping." Our solution leverages the existing ductwork infrastructure
of central heating, ventilation, and air conditioning (HVAC) systems found in many homes. Disruptions in airflow, caused by
human inter-room movement, result in static pressure changes in the HVAC air handler unit. This is particularly apparent for
room-to-room transitions and door open/close events involving full or partial blockage of doorways and thresholds. We detect
and record this pressure variation from sensors mounted on the air filter and classify where certain movement events are occurring
in the house, such as an adult walking through a particular doorway or the opening and closing of a particular door. In contrast
to more complex distributed sensing approaches for motion detection in the home, our method requires the installation of only
a single sensing unit (i.e., an instrumented air filter) connected to an embedded or personal computer that performs the classification function. Preliminary
results show we can classify unique transition events with up to 75-80% accuracy.
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