Research Article
Robust Respiration Sensing Based on Wi-Fi Beamforming
@INPROCEEDINGS{10.1007/978-3-031-34586-9_1, author={Wenchao Song and Zhu Wang and Zhuo Sun and Hualei Zhang and Bin Guo and Zhiwen Yu and Chih-Chun Ho and Liming Chen}, title={Robust Respiration Sensing Based on Wi-Fi Beamforming}, proceedings={Pervasive Computing Technologies for Healthcare. 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2023}, month={6}, keywords={Beamforming Respiration Sensing Robustness Wi-Fi}, doi={10.1007/978-3-031-34586-9_1} }- Wenchao Song
Zhu Wang
Zhuo Sun
Hualei Zhang
Bin Guo
Zhiwen Yu
Chih-Chun Ho
Liming Chen
Year: 2023
Robust Respiration Sensing Based on Wi-Fi Beamforming
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-031-34586-9_1
Abstract
Currently, the robustness of most Wi-Fi sensing systems is very limited due to that the target’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.
