计算机科学 ›› 2019, Vol. 46 ›› Issue (11): 304-308.doi: 10.11896/jsjkx.190600143

• 交叉与前沿 • 上一篇    下一篇

基于Wi-Fi信号的免训练呼吸检测

于怡然, 常俊, 吴柳繁, 张永鸿   

  1. (云南大学信息学院 昆明650500)
  • 收稿日期:2019-06-26 出版日期:2019-11-15 发布日期:2019-11-14
  • 通讯作者: 常俊(1970-),男,硕士,副教授,CCF会员,主要研究方向为无线通信与网络,E-mail:changjun@ynu.edu.cn
  • 作者简介:于怡然(1995-),男,硕士生,CCF会员,主要研究方向为无线感知,E-mail:yyr.yyr@foxmail.com;吴柳繁(1997-),主要研究方向为无线传输网络;张永鸿(1997-),主要研究方向为通信与信息系统。
  • 基金资助:
    本文受云南省省教育厅科研基金(2019J0007)资助。

Training-free Human Respiration Sensing Based on Wi-Fi Signal

YU Yi-ran, CHANG Jun, WU Liu-fan, ZHANG Yong-hong   

  1. (School of Information Science & Engineering,Yunnan University,Kunming 650500,China)
  • Received:2019-06-26 Online:2019-11-15 Published:2019-11-14

摘要: 随着无线通信技术的飞速发展,Wi-Fi已被广泛应用于公共和私人领域。基于无线技术的非入侵式呼吸检测技术在智能家居领域有着广阔的应用前景。针对现有的解决方案难以解释不同场景下存在的巨大性能差异,文中在自由空间中引入菲涅耳区刃形绕射模型,设计了一种基于Wi-Fi信号的免训练呼吸检测方案。首先,通过菲涅耳区刃形绕射模型,在室内环境中验证了Wi-Fi信号的衍射传播特性;其次,研究了人体呼吸对接收端Wi-Fi信号的影响,并量化了衍射增益与人体呼吸时微小胸腔位移之间的关系,不仅解释了可以使用Wi-Fi设备检测到人体呼吸的原理,还论证了在哪些位置更容易检测到呼吸;最后,通过快速傅里叶变换(FFT)从接收信号强度(RSS)中估计呼吸速率。利用所提算法,可以清楚地知道呼吸检测的好位置和坏位置的分布,并且对于好的位置来说,平均呼吸估计的准确率可达93.8%。实验结果证明了仅使用一对收发器便可使厘米尺度的呼吸感知成为可能,并有望通过普及的Wi-Fi基础设施提供一种无处不在的呼吸检测方案。

关键词: WI-FI信号, 菲涅耳区, 呼吸检测, 免训练, 刃形绕射模型

Abstract: With the rapid development of wireless communication technology,Wi-Fi has been widely used in public and private fields.Non-invasive breath detection technology based on wireless technology has a broad application prospect in the field of smart home.Considering that the existing solutions are difficult to explain the huge performance differences in different scenarios,this paper introduced the Fresnel edge diffraction model in the free space and designd a training free breathing detection sensing based on Wi-Fi signals.Firstly,we introduced the Fresnel Zone knife-edge diffraction model in free space,then verified the diffraction propagation characteristics of Wi-Fi signals in indoor environment.Se-condly,we accurately quantified the relationship between diffraction gain and micro thoracic displacement in human respiration,which Not only explains why Wi-Fi devices can be used to detect human breathing,but also demonstrates where is easier to detect.Finally,respiratory rate is estimated from RSS by fast Fourier transform (FFT).The algorithm in this paper can clearly know the distribution of good and bad positions of breath detection,and for good positions,the accuracy of breath estimation can reach 93.8%.Experiment results show that using a pair of transceivers makes centimeter-scale breathing perception possible and it is expected to provide a ubiquitous respiratory detection solution through a popular Wi-Fi infrastructure.

Key words: Fresnel zone, Human respiration sensing, Knife-edge diffraction model, Training-free, Wi-Fi signals

中图分类号: 

  • TN99
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