Adaptive weighted imbalance learning with application to abnormal activity recognition
文献类型：期刊论文
| 作者 | Gao, Xingyu1,2; Chen, Zhenyu1; Tang, Sheng1; Zhang, Yongdong1; Li, Jintao1 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2016-01-15 |
| 卷号 | 173页码:1927-1935 |
| 关键词 | MHealth Imbalance learning Two-stage Fall detection |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2015.09.064 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000366879800143 |
| 出版者 | ELSEVIER SCIENCE BV |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/9053]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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