DL-Aided NOMP: A Deep Learning-Based Vital Sign Estimating Scheme Using FMCW Radar

Hsin Yuan Chang, Chia Hung Lin, Yu Chien Lin, Wei Ho Chung, Ta Sung Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Recently, non-contact vital sign estimating devices, which are used for health monitoring, have gradually gained interest among researchers. However, most of these devices have the disadvantages of high power consumption and high cost, which limit their practicality. Therefore, a less-expensive radar-based system is suggested for long-term health monitoring. Existing radar-based vital sign estimating schemes introduce unacceptable estimating errors. In order to improve the precision and stability, we employ Newtonized Orthogonal Matching Pursuit (NOMP) algorithm. NOMP provides better estimating results compared to existing schemes in vital sign estimation tasks. However, the performance of NOMP deteriorates severely under conditions of low signal-to-noise ratio, which causes poor power efficiency. In this study, we propose deep learning (DL)-aided NOMP schemes to tackle the aforementioned issue. Our simulation results and over the air measurements suggest that DL-aided NOMP schemes are superior to existing schemes.

Original languageEnglish
Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728152073
StatePublished - May 2020
Event91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
Duration: 25 May 202028 May 2020

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020


  • deep learning (DL)
  • mmWave radar
  • neural networks (NN)
  • NOMP
  • OTA measurements
  • vital sign detection

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