From fresnel diffraction model to fine-grained human respiration sensing with commodity Wi-Fi devices

被引：138

作者：

Zhang, Fusang ^{[1
,2
]}

Zhang, Daqing ^{[1
]}

Xiong, Jie ^{[3
]}

Wang, Hao ^{[1
]}

Niu, Kai ^{[1
]}

Jin, Beihong ^{[2
]}

Wang, Yuxiang ^{[1
]}

机构：

[1] Key Laboratory of High Confidence Software Technologies, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing, China

[2] State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, Beijing, China

[3] College of Information and Computer Sciences, University of Massachusetts, Amherst, United States

来源：

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies | 2018年 / 2卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Wireless local area networks (WLAN) - Diffraction - Machinery - Cost effectiveness - Antennas - Computation theory;

D O I：

10.1145/3191785

中图分类号：

学科分类号：

摘要：

Non-intrusive respiration sensing without any device attached to the target plays a particular important role in our everyday lives. However, existing solutions either require dedicated hardware or employ special-purpose signals which are not cost-effective, significantly limiting their real-life applications. Also very few work concerns about the theory behind and can explain the large performance variations in different scenarios. In this paper, we employ the cheap commodity Wi-Fi hardware already ubiquitously deployed around us for respiration sensing. For the first time, we utilize the Fresnel diffraction model to accurately quantify the relationship between the diffraction gain and human target s subtle chest displacement and thus successfully turn the previously considered destructive obstruction diffraction in the First Fresnel Zone (FFZ) into beneficial sensing capability. By not just considering the chest displacement at the frontside as the existing solutions, but also the subtle displacement at the backside, we achieve surprisingly matching results with respect to the theoretical plots and become the first to clearly explain the theory behind the performance distinction between lying and sitting for respiration sensing. With two cheap commodity Wi-Fi cards each equipped with just one antenna, we are able to achieve higher than 98% accuracy of respiration rate monitoring at more than 60% of the locations in the FFZ. Furthermore, we are able to present the detail heatmap of the sensing capability at each location inside the FFZ to guide the respiration sensing so users clearly know where are the good positions for respiration monitoring and if located at a bad position, how to move just slightly to reach a good position. 2018 Association for Computing Machinery.

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