Efficient Coxian duration modelling for activity recognition in smart environment with the hidden semi-Markov model Duong, T.V., Phung, D.Q., Bui, H.H. and Venkatesh, S. 2005, Efficient Coxian duration modelling for activity recognition in smart environment with the hidden semi-Markov model, in Proceedings of the 2005 intelligent sensors, sensor networks and information processing conference, IEEE, Piscataway, N.J., pp. 277-282, doi: 10.1109/ISSNIP.2005.1595592. Attached Files Name Description MIMEType Size Downloads venkatesh-efficientcoxian-2005.pdf Published version application/pdf 2.96MB 52 Title Efficient Coxian duration modelling for activity recognition in smart environment with the hidden semi-Markov model Author(s) Duong, T.V. Phung, D.Q. orcid.org/0000-0002-9977-8247 Bui, H.H. Venkatesh, S. orcid.org/0000-0001-8675-6631 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 processing conference Start page 277 End page 282 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) aging character recognition closed-form solution computational efficiency computerized monitoring degradation distributed computing hidden Markov models senior citizens stochastic processes Summary In this paper, we exploit the discrete Coxian distribution and propose a novel form of stochastic model, termed as the Coxian hidden semi-Makov model (Cox-HSMM), and apply it to the task of recognising activities of daily living (ADLs) in a smart house environment. The use of the Coxian has several advantages over traditional parameterization (e.g. multinomial or continuous distributions) including the low number of free parameters needed, its computational efficiency, and the existing of closed-form solution. To further enrich the model in real-world applications, we also address the problem of handling missing observation for the proposed Cox-HSMM. In the domain of ADLs, we emphasize the importance of the duration information and model it via the Cox-HSMM. Our experimental results have shown the superiority of the Cox-HSMM in all cases when compared with the standard HMM. Our results have further shown that outstanding recognition accuracy can be achieved with relatively low number of phases required in the Coxian, thus making the Cox-HSMM particularly suitable in recognizing ADLs whose movement trajectories are typically very long in nature. 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.2005.1595592 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044916 Document type: Conference Paper Collections: School of Information Technology Open Access Collection Related Links Link Description Connect to published version 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 Tue, 01 May 2012, 11:06:22 EST Wed, 09 May 2012, 16:04:14 EST Wed, 09 May 2012, 16:05:28 EST Tue, 16 Sep 2014, 14:59:17 EST Tue, 06 Sep 2016, 18:54:00 EST Sat, 21 Oct 2017, 02:04:10 EST Tue, 05 Dec 2017, 12:32:13 EST Filtered Full Citation counts:   Cited 2 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 301 Abstract Views, 52 File Downloads  -  Detailed Statistics Created: Tue, 01 May 2012, 11:06:21 EST

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.