标题:A fall detection study based on neural network algorithm using AHRS
作者:Zhang, Qingbin ;Tian, Guohui ;Ding, Nana ;Zhang, Yanru
作者机构:[Zhang, Qingbin ;Tian, Guohui ;Ding, Nana ;Zhang, Yanru ] School of Control Science and Engineering, Shandong University, Jinan, China
会议名称:2013 IEEE International Conference on Information and Automation, ICIA 2013
会议日期:26 August 2013 through 28 August 2013
来源:2013 IEEE International Conference on Information and Automation, ICIA 2013
出版年:2013
页码:773-779
DOI:10.1109/ICInfA.2013.6720398
关键词:AHRS module; Fall detection; fusion acceleration; neural network; sliding window
摘要:Human fall detection devices with high recognition rate have an important significance for the elderly and patient to detect their falls which may lead to dangerous or even death. In this paper, attitude angle and tri-axial acceleration of the Attitude and Heading Reference System (AHRS) module on the waist was used for the fall detection system. A fall detection method based on neural network was presented which could accurately distinguish falls from activities of daily living (ADL) including walking, jumping, sitting, bending, squatting, lying down, etc. The experiment was carried out with different groups of objects. The experimental results demonstrated that the proposed method was efficient, reliable as well as practical. © 2013 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:2
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894216948&doi=10.1109%2fICInfA.2013.6720398&partnerID=40&md5=bbd70b7652bfaf59d242068d885eeaa9
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