@inproceedings{303dc8f0ac634b709db7d3f482768c55,
title = "Cardiac and Respiratory Sensing from a Hovering UAV Radar Platform",
abstract = "In this paper, we present a novel unmanned aerial vehicle (UAV) radar platform to measure people's heart rate and breathing rate from a distance. The vital drone operates as follows: fly towards the potential human targets, hover and hold its position, and then start sensing the subtle physiological motion through the onboard ultra-wideband (UWB) radar sensor. It is known that the platform motion causes the conventional radar based vital sign detection methods to fail. We propose to use an efficient data-driven method to cancel the unwanted platform fidgeting via signal-of-opportunity and to further refine the motion residual using the denoising algorithm variational model decomposition (VMD) for vital sign detection. For validation, we compare the vital sign detection performance of the UAV radar based approach against a red, green and blue color (RGB) camera based approach which is an onboard camera sensor with three-axis gimbal system for stabilization. The standard real-time pulse oximeter (PPG sensor) is used to provide the ground truth.",
keywords = "Drone, Heart Rate Estimation, Motion Cancellation, Remote Sensing, UWB, Vital Signs",
author = "Yu Rong and Andrew Herschfelt and Jacob Holtom and Bliss, {Daniel W.}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 21st IEEE Statistical Signal Processing Workshop, SSP 2021 ; Conference date: 11-07-2021 Through 14-07-2021",
year = "2021",
month = jul,
day = "11",
doi = "10.1109/SSP49050.2021.9513771",
language = "English (US)",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
publisher = "IEEE Computer Society",
pages = "541--545",
booktitle = "2021 IEEE Statistical Signal Processing Workshop, SSP 2021",
}