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.
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Acknowledgment
This work is partially supported by the National Natural Science Foundation of China (No. 61960206008, 62072375, 62102322), and the Fundamental Research Funds for the Central Universities (No. D5000210786).
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Song, W. et al. (2023). Robust Respiration Sensing Based on Wi-Fi Beamforming. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_1
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DOI: https://doi.org/10.1007/978-3-031-34586-9_1
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