Online context recognition in multisensor systems using Dynamic Time Warping Ko, Ming Hsiao, West, Geoff, Venkatesh, Svetha and Kumar, Mohan 2005, Online context recognition in multisensor systems using Dynamic Time Warping, in Proceedings of the 2005 intelligent sensors, sensor networks and information processing conference, IEEE, Piscataway, N.J., pp. 283-288, doi: 10.1109/ISSNIP.2005.1595593. Attached Files Name Description MIMEType Size Downloads venkatesh-onlinecontext-2005.pdf Published version application/pdf 2.54MB 284 Title Online context recognition in multisensor systems using Dynamic Time Warping Author(s) Ko, Ming Hsiao West, Geoff Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Kumar, Mohan 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 283 End page 288 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) accelerometers computer science embedded computing hidden Markov models multimodal sensors multisensor systems performance evaluation pervasive computing sensor systems speech recognition Summary In this paper, we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA's benchmark dataset for context recognition. The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy. 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.2005.1595593 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:30044625 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, 11:36:07 EST Fri, 27 Apr 2012, 11:46:20 EST Fri, 27 Apr 2012, 11:47:58 EST Thu, 03 May 2012, 12:24:41 EST Tue, 06 Sep 2016, 18:46:42 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 30 times in Scopus Search Google Scholar Access Statistics: 260 Abstract Views, 284 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:36:07 EST

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