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HABITS: A Bayesian filter approach to indoor tracking and location

Furey, E, Curran, K and McKevitt, P (2011) HABITS: A Bayesian filter approach to indoor tracking and location. In: Proc. of the 22nd Irish Conference on Artificial Intelligence and Cognitive Science (AICS-2011), University of Ulster, Magee, Derry/Londonderry, Northern Ireland. University of Ulster, Northern Ireland. 15 pp. [Conference contribution]

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URL: http://isrc.ulster.ac.uk/aics2011/images/files/AICS_2011_Proceedings.pdf


Knowledge of the location of people and things has always been a valuable commodity. The explosion of new devices and techniques has brought people and item tracking out of the experimental stage and into the wider world.Using Wi-Fi signals is an attractive and reasonably affordable option to deal with the currently unsolved problem of widespread tracking in an indoor environment. HABITS (History Aware Based Indoor Tracking System) models human movement patterns by applying a discrete Bayesian filter to predict the areas that will, or will not, be visited in the future. We outline here the operation of the HABITS Real-Time Location System (RTLS) and discuss the implementation in relation to indoor Wi-Fi tracking with a large wireless network. Testing of HABITS shows that it gives comparable levels of accuracy to those achieved by doubling the number of access points. These probabilistic predictions may be used as an additional input into building automation systems for intelligent control of heating and lighting. It is twice as accurate as existing systems in dealing with signal black spots and it can predict the final destination of a person within the test environment almost 80% of the time.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Faculty of Arts
Faculty of Arts > School of Creative Arts and Technologies
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:21226
Deposited By: Professor Paul McKevitt
Deposited On:06 Mar 2012 10:49
Last Modified:09 Dec 2015 11:02

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