BEHAVIOR RECOGNITION BASED ON WI-FI CSI: PART 2

被引：6

作者：

Guo, Bin ^{[1
]}

Chen, Yingying ^{[2
,3
,4
,5
]}

Lane, Nic ^{[6
,7
,8
]}

Liu, Yunxin ^{[9
]}

Yu, Zhiwen ^{[1
,10
]}

机构：

[1] Northwestern Polytech Univ, Xian, Shaanxi, Peoples R China

[2] Stevens Inst Technol, Hoboken, NJ 07030 USA

[3] Data Anal & Informat Secur DAISY Lab, Hoboken, NJ USA

[4] IDE, Hoboken, NJ USA

[5] NIS, Hoboken, NJ USA

[6] UCL, London, England

[7] Nokia Bell Labs, Murray Hill, NJ USA

[8] Microsoft Res, Beijing, Peoples R China

[9] Microsoft Res Asia, Syst Res Grp, Beijing, Peoples R China

[10] Mannheim Univ, Mannheim, Germany

来源：

IEEE COMMUNICATIONS MAGAZINE | 2018年 / 56卷 / 05期

关键词：

Data mining - Behavioral research - Channel state information;

D O I：

10.1109/MCOM.2018.8360859

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The four articles in this special section focus on behavior recognition-based Wi-Fi channel state estimation(CSI). Wi-Fi CSI-based human behavior recognition has become a promising research area in recent years. The rationale is that different human behaviors introduce different multi-path distortions in Wi-Fi CSI, which presents several unique advantages: unaffected by external light, with better coverage (even supporting through-wall sensing), and user privacy preservation. There are also numerous research challenges of CSI-based human behavior recognition, including behavior recognition model/theory using Wi-Fi CSI, Wi-Fi CSI data mining, quality-enhanced and adaptive sensing models with Wi-Fi CSI, behavior recognition with individual differences, the flexibility of such CSI-based behavior recognition systems, and so on. © 1979-2012 IEEE.

引用

页码：108 / 108

页数：1