@inproceedings{819f62801c4d42abad2aa7c66e75cb59,
title = "Robust Respiration Sensing Based on Wi-Fi Beamforming",
abstract = "Currently, the robustness of most Wi-Fi sensing systems is very limited due to that the target{\textquoteright}s reflection signal is quite weak and can be easily submerged by the ambient noise. To address this issue, we take advantage of the fact that Wi-Fi devices are commonly equipped with multiple antennas and introduce the beamforming technology to enhance the reflected signal as well as reduce the time-varying noise. We adopt the dynamic signal energy ratio for sub-carrier selection to solve the location dependency problem, based on which a robust respiration sensing system is designed and implemented. Experimental results show that when the distance between the target and the transceiver is 7 m, the mean absolute error of the respiration sensing system is less than 0.729 bpm and the corresponding accuracy reaches 94.79\%, which outperforms the baseline methods.",
keywords = "Beamforming, Respiration Sensing, Robustness, Wi-Fi",
author = "Wenchao Song and Zhu Wang and Zhuo Sun and Hualei Zhang and Bin Guo and Zhiwen Yu and Ho, \{Chih Chun\} and Liming Chen",
note = "Publisher Copyright: {\textcopyright} 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PH 2022 ; Conference date: 12-12-2022 Through 14-12-2022",
year = "2023",
doi = "10.1007/978-3-031-34586-9\_1",
language = "英语",
isbn = "9783031345852",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--17",
editor = "Athanasios Tsanas and Andreas Triantafyllidis",
booktitle = "Pervasive Computing Technologies for Healthcare - 16th EAI International Conference, PervasiveHealth 2022, Proceedings",
}