Wireless estimation of canine pose for search and rescue

Ribeiro, Cristina, Ferworn, Alexander, Denko, Mieso, Tran, James and Mawson, Chris 2008, Wireless estimation of canine pose for search and rescue, in ICSOS 2008 : IEEE International Conference on System of Systems Engineering, IEEE, Piscataway, N.J., pp. 1-6.

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Title Wireless estimation of canine pose for search and rescue
Author(s) Ribeiro, Cristina
Ferworn, Alexander
Denko, Mieso
Tran, James
Mawson, Chris
Conference name IEEE International Conference on System of Systems Engineering (2008 : Monterey Bay, Calif.)
Conference location Monterey Bay, Calif.
Conference dates 2-5 June 2008
Title of proceedings ICSOS 2008 : IEEE International Conference on System of Systems Engineering
Editor(s) Jamshidi, M.
Publication date 2008
Conference series International Conference on System of Systems Engineering
Start page 1
End page 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) canine augmentation technology
urban search and rescue
accelerometers
Bluetooth
WiFi
Summary In this paper we discuss the use of accelerometers and Bluetooth to monitor canine pose in the context of common poses observed in urban search and rescue dogs. We discuss the use of the canine pose system in a disaster environment, and propose techniques for determining canine pose. In addition we discuss the challenges with this approach in such environments. The paper presents the experimental results obtained from the heavy urban search and rescue disaster simulation, where experiments were conducted using multiple canines, which show that angles can be derived from acceleration readings. Our experiments show that similar angles were measured for each of the poses, even when measured on multiple USAR canines of varying size. We also found measurable and consistent differences between each of the poses, making them clearly distinguishable from one another, again even when comparing with different USAR canines.
ISBN 9781424421732
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018341

Document type: Conference Paper
Collection: School of Engineering and Information Technology
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