Abnormal Gait Behavior Detection for Elderly Based on Enhanced Wigner-Ville Analysis and Cloud Incremental SVM Learning
المؤلفون المشاركون
Luo, Jian
Tang, Jin
Xiao, Xiaoming
المصدر
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-02-29
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1110526
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر