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R&P: An Low-Cost Device-Free Activity Recognition for E-Health
Li, Liyao1; Bai, Rui1; Xie, Binbin1; Peng, Yao1; Wang, Anwen1; Wang, Wei1; Jiang, Bo1; Liang, Jian2; Chen, Xiaojiang1
作者部门信息光子学研究室
2018
发表期刊IEEE ACCESS
ISSN2169-3536
卷号6页码:81-90
产权排序2
摘要

Activity recognition is important for taking care of patients and old men especially in e-Health. The activity recognition system without carrying any wearable devices is widely used in our daily life. Current methods employing uneconomical equipment or even dedicated devices lead to cost-inefficiency for large-scale deployments. This paper introduces R&P, a device-free activity recognition system only using cheap radio frequency identification devices (RFID) tags. Based on the analysis of RFID signals, we extract received signal strength fingerprints and phase fingerprints for each activity and synthesize these two kinds of fingerprints to accurately recognize activities. Moreover, we also modify the dynamic time warping (DTW) algorithm and propose T-DTW method to improve the recognition efficiency. We use commercial passive RFID hardware and verify R&Pin three different environments with different targets and six activities. The results demonstrate that our solution can recognize activities with an average accuracy of 87.9%.

 

关键词Rfid Device-free Rss Phase T-dtw
DOI10.1109/ACCESS.2017.2749323
收录类别SCI ; EI
语种英语
WOS记录号WOS:000424078700005
EI入藏号20180604767049
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30779
专题信息光子学研究室
作者单位1.Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Shaanxi, Peoples R China;
2.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714
Li, Liyao,Bai, Rui,Xie, Binbin,et al. R&P: An Low-Cost Device-Free Activity Recognition for E-Health[J]. IEEE ACCESS,2018,6:81-90.
APA Li, Liyao.,Bai, Rui.,Xie, Binbin.,Peng, Yao.,Wang, Anwen.,...&Chen, Xiaojiang.(2018).R&P: An Low-Cost Device-Free Activity Recognition for E-Health.IEEE ACCESS,6,81-90.
MLA Li, Liyao,et al."R&P: An Low-Cost Device-Free Activity Recognition for E-Health".IEEE ACCESS 6(2018):81-90.
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