Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning
Joint Authors
Luo, Jian
Tang, Jin
Xiao, Xiaoming
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-29
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
A cloud based health care system is proposed in this paper for the elderly by providing abnormal gait behavior detection, classification, online diagnosis, and remote aid service.
Intelligent mobile terminals with triaxial acceleration sensor embedded are used to capture the movement and ambulation information of elderly.
The collected signals are first enhanced by a Kalman filter.
And the magnitude of signal vector features is then extracted and decomposed into a linear combination of enhanced Gabor atoms.
The Wigner-Ville analysis method is introduced and the problem is studied by joint time-frequency analysis.
In order to solve the large-scale abnormal behavior data lacking problem in training process, a cloud based incremental SVM (CI-SVM) learning method is proposed.
The original abnormal behavior data are first used to get the initial SVM classifier.
And the larger abnormal behavior data of elderly collected by mobile devices are then gathered in cloud platform to conduct incremental training and get the new SVM classifier.
By the CI-SVM learning method, the knowledge of SVM classifier could be accumulated due to the dynamic incremental learning.
Experimental results demonstrate that the proposed method is feasible and can be applied to aged care, emergency aid, and related fields.
American Psychological Association (APA)
Luo, Jian& Tang, Jin& Xiao, Xiaoming. 2016. Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning. Journal of Sensors،Vol. 2016, no. 2016, pp.1-18.
https://search.emarefa.net/detail/BIM-1110526
Modern Language Association (MLA)
Luo, Jian…[et al.]. Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning. Journal of Sensors No. 2016 (2016), pp.1-18.
https://search.emarefa.net/detail/BIM-1110526
American Medical Association (AMA)
Luo, Jian& Tang, Jin& Xiao, Xiaoming. Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-18.
https://search.emarefa.net/detail/BIM-1110526
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110526