@inproceedings{6f3211ea420f499ca0fd6a9ed76dbab3,
title = "Demo: A full human respiration detection system using commodity Wi-Fi devices",
abstract = "In recent years, human respiration detection based on Wi-Fi signals has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, latest studies show that respiration sensing performance varies at different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call {"}blind spots{"}. In this demo, we will demonstrate a human respiration detection system which enables full location coverage with no blind spot.",
keywords = "Channel State Information (CSI), Respiration Detection, Wi-Fi",
author = "Ruiyang Gao and Daqing Zhang and Youwei Zeng and Enze Yi and Dan Wu",
note = "Publisher Copyright: {\textcopyright} 2018 Copyright is held by the owner/author(s).; 2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018 ; Conference date: 08-10-2018 Through 12-10-2018",
year = "2018",
month = oct,
day = "8",
doi = "10.1145/3267305.3267560",
language = "English",
series = "UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers",
publisher = "Association for Computing Machinery, Inc",
pages = "480--483",
booktitle = "UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers",
}