Spatial activity recognition in a smart home environment using a chemotactic model Riedel, Daniel E., Venkatesh, Svetha and Liu, Wanquan 2005, Spatial activity recognition in a smart home environment using a chemotactic model, in Proceedings of the 2005 intelligent sensors, sensor networks and information processing conference, IEEE, Piscataway, N.J., pp. 301-306, doi: 10.1109/ISSNIP.2005.1595596. Attached Files Name Description MIMEType Size Downloads venkatesh-spatialactivity-2005.pdf Published version application/pdf 1.30MB 70 Title Spatial activity recognition in a smart home environment using a chemotactic model Author(s) Riedel, Daniel E. Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Liu, Wanquan Conference name Intelligent sensors, sensor networks and information processing conference (2nd : 2005 : Melbourne, Vic.) Conference location Melbourne, Vic. Conference dates 5- 8 Dec. 2005 Title of proceedings Proceedings of the 2005 intelligent sensors, sensor networks and information processing conference Editor(s) Palaniswami, M. Publication date 2005 Conference series Intelligent Sensors, Sensor Networks and Information Conference Start page 301 End page 306 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) biological system modeling biology computing chemical processes chemical technology hidden Markov models microorganisms organisms signal processing smart homes working environment noise Summary Spatial activity recognition is challenging due to the amount of noise incorporated during video tracking in everyday environments. We address the spatial recognition problem with a biologically-inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to change motile behaviour in relation to environmental gradients. Through adoption of chemotactic principles, we propose the chemotactic model and evaluate its performance in a smart house environment. The model exhibits greater than 99% recognition performance with a diverse six class dataset and outperforms the Hidden Markov Model (HMM). The approach also maintains high accuracy (90-99%) with small training sets of one to five sequences. Importantly, unlike other low-level spatial activity recognition models, we show that the chemotactic model is capable of recognising simple interwoven activities. Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ISBN 9780780393998 0780393996 Language eng DOI 10.1109/ISSNIP.2005.1595596 Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed Copyright notice ©2005, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30044626 Document type: Conference Paper Collections: School of Information Technology Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 20 Apr 2012, 11:36:10 EST Fri, 27 Apr 2012, 11:56:59 EST Fri, 27 Apr 2012, 11:58:19 EST Fri, 27 Apr 2012, 11:59:31 EST Thu, 03 May 2012, 12:32:25 EST Tue, 06 Sep 2016, 18:46:47 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 7 times in Scopus Search Google Scholar Access Statistics: 233 Abstract Views, 70 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:36:10 EST

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