Generative models for automatic recognition of human daily activities from a single triaxial accelerometer Wang, Jin, Chen, Ronghua, Sun, Xiangping, She, Mary and Kong, Lingxue 2012, Generative models for automatic recognition of human daily activities from a single triaxial accelerometer, in IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural Networks, IEEE, Piscataway, N. J.. Attached Files Name Description MIMEType Size Downloads Title Generative models for automatic recognition of human daily activities from a single triaxial accelerometer Author(s) Wang, Jin Chen, Ronghua Sun, Xiangping She, Mary orcid.org/0000-0001-8191-0820 Kong, Lingxue orcid.org/0000-0001-6219-3897 Conference name International Joint Conference on Neural Networks (2012 : Brisbane, Qld.) Conference location Brisbane, Qld. Conference dates 10-15 Jun. 2012 Title of proceedings IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural Networks Editor(s) [Unknown] Publication date 2012 Conference series International Joint Conference on Neural Networks Total pages 6 Publisher IEEE Place of publication Piscataway, N. J. Keyword(s) GMM HMM acceleration signal ambulatory environment pattern recognition Summary In this work, we compare two generative models including Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) with Support Vector Machine (SVM) classifier for the recognition of six human daily activity (i.e., standing, walking, running, jumping, falling, sitting-down) from a single waist-worn tri-axial accelerometer signals through 4-fold cross-validation and testing on a total of thirteen subjects, achieving an average recognition accuracy of 96.43% and 98.21% in the first experiment and 95.51% and 98.72% in the second, respectively. The results demonstrate that both HMM and GMM are not only able to learn but also capable of generalization while the former outperformed the latter in the recognition of daily activities from a single waist worn tri-axial accelerometer. In addition, these two generative models enable the assessment of human activities based on acceleration signals with varying lengths. ISBN 1467314900 9781467314909 Language eng Field of Research 109999 Technology not elsewhere classified Socio Economic Objective 970110 Expanding Knowledge in Technology HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30049558 Document type: Conference Paper Collections: Institute for Frontier Materials 2018 ERA Submission 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. Versions Version Filter Type Thu, 29 Nov 2012, 07:58:50 EST Mon, 03 Dec 2012, 12:42:14 EST Mon, 03 Dec 2012, 12:43:44 EST Fri, 15 Feb 2013, 14:05:44 EST Fri, 15 Feb 2013, 14:24:44 EST Thu, 19 Dec 2013, 09:49:48 EST Fri, 08 Aug 2014, 10:22:19 EST Wed, 27 Aug 2014, 23:13:06 EST Wed, 21 Oct 2015, 17:54:25 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 254 Abstract Views, 5 File Downloads  -  Detailed Statistics Created: Thu, 29 Nov 2012, 07:58:50 EST

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