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	Fall detection with the optimal feature vectors based on support vector machine
	Zhang Jing; Wang Yongfeng; Ma Yingnan; Gao Xing; Li Huiqi; Zhao Guoru
	2014
会议名称	3rd International Conference on Health Information Science, HIS 2014
会议地点	Shenzhen, China
英文摘要	Falls have caused extensive interest of the researchers for it becomes the second largest accidental injury to death in the world. And there are lots of approaches to fall detection at present. However, on account for the complexity of this problem, a preferable effective method for fall detection hasn’t been present so far. This paper adopts a relatively high-predicted and stable SVM classifier to predict falls. 10 healthy young subjects participated in this study based on the Xsens MVN Biomech system. With the extraction of feature vectors, as well as the exploration of the best position, it found that the waist would be the best to measure body’s motion, and the simple accelerometer can offer the preferable features for the classifier to determinate the falls well. Meanwhile it can get a high accuracy up to 96% by setting an optimal C and g with five-fold cross-validation testing.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/5915]
专题	深圳先进技术研究院_医工所
作者单位	2014
推荐引用方式 GB/T 7714	Zhang Jing,Wang Yongfeng,Ma Yingnan,et al. Fall detection with the optimal feature vectors based on support vector machine[C]. 见:3rd International Conference on Health Information Science, HIS 2014. Shenzhen, China.

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