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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.

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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 iD for Phung, D.Q. orcid.org/0000-0002-9977-8247
Bui, H.H.
Venkatesh, S.ORCID iD for 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
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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.