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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..

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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
Kong, Lingxue
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 9781467314886
9781467314893
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
Collection: Institute for Frontier Materials
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