Adaptive weighted imbalance learning with application to abnormal activity recognition

doi:10.1016/j.neucom.2015.09.064

	Adaptive weighted imbalance learning with application to abnormal activity recognition
	Gao, Xingyu 1,2; Chen, Zhenyu 1; Tang, Sheng 1; Zhang, Yongdong 1; Li, Jintao 1
	2016-01-15
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	173 页码:1927-1935
摘要	Abnormal activity recognition has been paid much attention in the field of healthcare and related applications, especially for the elderly people's physical and mental health, the high risk of the fall accident and its caused injures have gradually attracted more and more concerns. At present, wearable devices based fall detection technology can effectively and timely monitor the occurrence of fall accidents and help the injured person receive the first aid. However, the built classifiers of traditional approaches for fall detecting and monitoring suffer from a high false-alarm rate though they can reach a relatively high detection accuracy, further they have to face with the imbalance problem because sensor data of abnormal activities are usually rare in the realistic application. To address this challenge, we propose two-stage adaptive weighted extreme learning machine (AWELM) method for eyeglass and watch wearables based fall detecting and monitoring. Experimental results validate and illustrate significant efficiency and effectiveness of the proposed method and show that, our approach firstly achieves a good balance between high detection accuracy and low false-alarm rate based on our two-stage recognition scheme; secondly enables our imbalance learning approach for scarce abnormal activity data by two-stage adaptive weighted method; thirdly provides a light-weight classifier solution to resource constrained wearable devices using extreme learning machine with the fast training speed and good generalization capability, which enables large-scale mHealth applications and especially helps the elderly people to greatly reduce the risk of fall accidents finally. (C) 2015 Elsevier B.V. All rights reserved.
关键词	MHealth Imbalance learning Two-stage Fall detection
DOI	10.1016/j.neucom.2015.09.064
收录类别	SCI
语种	英语
资助项目	National High Technology Research and Development Program of China[2014AA015202] ; National Nature Science Foundation of China[61428207]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000366879800143
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：74[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/9054
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Zhenyu
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Gao, Xingyu,Chen, Zhenyu,Tang, Sheng,et al. Adaptive weighted imbalance learning with application to abnormal activity recognition[J]. NEUROCOMPUTING,2016,173:1927-1935.
APA	Gao, Xingyu,Chen, Zhenyu,Tang, Sheng,Zhang, Yongdong,&Li, Jintao.(2016).Adaptive weighted imbalance learning with application to abnormal activity recognition.NEUROCOMPUTING,173,1927-1935.
MLA	Gao, Xingyu,et al."Adaptive weighted imbalance learning with application to abnormal activity recognition".NEUROCOMPUTING 173(2016):1927-1935.