@inproceedings{e57f10a049ba4154804c0bdecbd4c1d9,
title = "A Robust Method for Estimating Respiration Rate Using Wi-Fi in Noisy Environments",
abstract = "Respiration rate is a critical parameter for assessing the physiological well-being of patients. Traditional methods typically rely on contact-based and visual devices, which pose challenges for long-term monitoring due to issues of comfort and privacy. This paper proposes a non-contact method for acquiring respiration rates using Wi-Fi Radio Frequency (RF) electromagnetic waves. We address the challenge of accurately estimating respiration rates by presenting a robust approach that leverages receiver antenna diversity to enhance the signal-to-noise ratio (SNR), thereby significantly improving estimation accuracy. Our method employs the maximal ratio combining technique to integrate the received signals effectively. Experimental results demonstrate that the proposed method achieves the overall Mean Absolute Error (MAE) of 0.7 bpm across all trials, compared to ground truth wearable respiration belts, indicating reliable respiration estimation using Wi-Fi system.",
keywords = "RF sensing, Remote sensing, Wi-Fi sensing, respiration rate estimation, respiration sensing, vital signs",
author = "Prisila Ishabakaki and William Taylor and Muhammad Farooq and Hira Hameed and Michael Mollel and Hasan Abbas and Muhammad Imran and Qammer Abbasi",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2nd International Conference on Microwave, Antennas and Circuits, ICMAC 2025 ; Conference date: 17-04-2025 Through 18-04-2025",
year = "2025",
doi = "10.1109/ICMAC64768.2025.11003231",
language = "English",
series = "2025 2nd International Conference on Microwave, Antennas and Circuits, ICMAC 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 2nd International Conference on Microwave, Antennas and Circuits, ICMAC 2025",
address = "United States",
}