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Generative models for automatic recognition of human daily activities from a single triaxial accelerometer
conference contribution
posted on 2012-01-01, 00:00 authored by Jin Wang, Ronghua Chen, Xiangping Sun, Fenghua She, Lingxue KongLingxue KongIn 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.
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Event
International Joint Conference on Neural Networks (2012 : Brisbane, Qld.)Publisher
IEEELocation
Brisbane, Qld.Place of publication
Piscataway, N. J.Start date
2012-06-10End date
2012-06-15ISBN-13
9781467314909ISBN-10
1467314900Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural NetworksUsage metrics
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