Cardiac and Respiratory Sensing from a Hovering UAV Radar Platform

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2021 IEEE Statistical Signal Processing Workshop, SSP 2021
PublisherIEEE Computer Society
Pages541-545
Number of pages5
ISBN (Electronic)9781728157672
DOIs
StatePublished - Jul 11 2021
Event21st IEEE Statistical Signal Processing Workshop, SSP 2021 - Virtual, Rio de Janeiro, Brazil
Duration: Jul 11 2021Jul 14 2021

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2021-July

Conference

Conference21st IEEE Statistical Signal Processing Workshop, SSP 2021
Country/TerritoryBrazil
CityVirtual, Rio de Janeiro
Period7/11/217/14/21

Keywords

  • Drone
  • Heart Rate Estimation
  • Motion Cancellation
  • Remote Sensing
  • UWB
  • Vital Signs

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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