Sparse Processing for Driver Respiration Monitoring using In-Vehicle mmWave Radar

Yu Rong, Kumar Vijay Mishra, Daniel W. Bliss

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

Abstract

We investigate non-contact driver's breath-rate monitoring using a compact millimeter-wave (mmWave) in-vehicle radar. The small form-factor mmWave sensor allows deploying multiple antennas in space-constrained automotive applications while also improving the spatial resolution of the radar. We formulate the breathing rate extraction as a sparse and low-rank recovery problem. Our sparse spectral estimation approach then accurately extracts driver respiration rate from highly cluttered and noisy radar measurements. We validate our proposed approach through real-data collected from a 77 GHz TI AWR6843 radar sensor deployed in a driving simulator.

Original languageEnglish (US)
Title of host publication2022 IEEE/MTT-S International Microwave Symposium, IMS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages440-443
Number of pages4
ISBN (Electronic)9781665496131
DOIs
StatePublished - 2022
Event2022 IEEE/MTT-S International Microwave Symposium, IMS 2022 - Denver, United States
Duration: Jun 19 2022Jun 24 2022

Publication series

NameIEEE MTT-S International Microwave Symposium Digest
Volume2022-June
ISSN (Print)0149-645X

Conference

Conference2022 IEEE/MTT-S International Microwave Symposium, IMS 2022
Country/TerritoryUnited States
CityDenver
Period6/19/226/24/22

Keywords

  • Automotive sensing
  • low-rank denoising
  • mmWave radar
  • sparse reconstruction
  • vital sign monitoring

ASJC Scopus subject areas

  • Radiation
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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