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Cited by 162Non-Invasive Vital Sign MonitoringRadar Systems and Signal ProcessingWireless Body Area Networks

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Signal-to-Noise Ratio in Doppler Radar System for Heart and Respiratory Rate Measurements

Droitcour, A.D., Boric-Lubecke, O., Kovacs, G.T.A.·2009·IEEE Transactions on Microwave Theory and Techniques
DOI 10.1109/tmtt.2009.2029668
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Abstract

A CMOS Doppler radar sensor has been developed and used to measure motion due to heart and respiration. The quadrature direct-conversion radar transceiver has been fully integrated in 0.25-mum CMOS, the baseband analog signal conditioning has been developed on a printed circuit board, and digital signal processing has been performed inMatlab. The theoretical signal-to-noise ratio (SNR) is derived based on the radar equation, the direct-conversion receiver's properties, oscillator phase noise, range correlation, and receiver noise. Heart and respiration signatures and rates have been measured at ranges from 0.5 to 2.0 m on 22 human subjects wearing normal T-shirts. The theoretical SNR expression was validated with this study. The heart rates found with the radar sensor were compared with a three-lead electrocardiogram, and they were within 5 beats/min with 95% confidence for 16 of 22 subjects at a 0.5-m range and 11 of 22 subjects at a 1.0-m range. The respiration rates found with the radar sensor were compared with those found using a piezoelectric respiratory effort belt, and the respiration rates were within five respirations per minute for 18 of 22 subjects at a 0.5-m range, 17 of 22 subjects at a 1.0-m range, and 19 of 22 subjects at a 1.5-m range.

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Crossref · 10.1109/tmtt.2009.2029668

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Through signal processing, it is possible to extract biological information, such as breathing patterns, heart rate and full-body motion, from radio signals.

    “Through signal processing, it is possible to extract biological information, such as breathing patterns, heart rate and full-body motion from these findings71,73–75.”

    From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [75]

  • High frequency and sub-millimetre wavelength radio technologies have demonstrated the ability to capture physiological signals without body contact.

    “In the past decade, high frequency and sub-millimetre wavelength radio technologies have demonstrated the ability to capture physiological signals without body contact.”

    From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [75]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 26

  1. 10.1063/1.2798937 · DOI
  2. droitcour, 2002, 0.25 <formula formulatype="inline"><tex notation="tex">$\mu{\hbox{m}}$</tex></formula> cmos and bicmos single-chip direct-conversion doppler radars for remote sensing of vital signs, Int Solid-State Circuits Conf Dig, 1, 348
  3. 10.1109/TMTT.2004.823552 · DOI
  4. droitcour, 2004, chest motion sensing with modified silicon base station chips, IEICE Trans Electron, e87 c, 1524
  5. 10.1109/LMWC.2008.925113 · DOI
  6. 10.1016/S0190-9622(99)70126-X · DOI
  7. 10.1016/j.burns.2003.10.003 · DOI
  8. 2000, Code of federal regulations title 47 Telecommunications part 15 Radio frequency devices
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  10. skolnik, 1990, Radar Handbook, 1 1
  11. host-madsen, 2008, Signal Processing Techniques for Knowledge Extraction and Information Fusion
  12. 10.1109/IEMBS.2009.5333206 · DOI
  13. 10.1109/TMTT.2007.895653 · DOI
  14. li, 2008, complex signal demodulation and random body movement cancellation techniques for non-contact vital sign detection, IEEE MTT-S Int Microw Symp Dig, 567
  15. 10.1063/1.2798937 · DOI
  16. oum, 2008, two frequency radar sensor for non-contact vital sign monitor, IEEE MTT-S Int Microw Symp Dig, 919
  17. 10.1109/MWSYM.2009.5165949 · DOI
  18. 10.1109/62.978359 · DOI
  19. 10.1109/MWSYM.2001.966866 · DOI
  20. 10.1002/bem.2250130610 · DOI
  21. 10.1183/09031936.97.10081865 · DOI
  22. 10.1007/BF02441473 · DOI
  23. degroote, 1997, chest wall motion during tidal breathing, J Appl Physiol, 83, 1531, 10.1152/jappl.1997.83.5.1531 · DOI
  24. 10.1109/10.362922 · DOI
  25. jollife, 2002, Principal Component Analysis
  26. 10.1109/MWSYM.2008.4633266 · DOI

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Metadata

Authors
Droitcour, A. D., Boric-Lubecke, O., & Kovacs, G. T. A.
Affiliations
Stanford University (US)
ORCID
Venue
IEEE Transactions on Microwave Theory and Techniques
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2009-10
Volume
57
Issue
10
Pages
2498-2507
ISSN
  • 0018-9480 (ISSN-L)
  • 1557-9670
DOI
10.1109/tmtt.2009.2029668
OpenAlex
W2098649495
Cited by
162
References
1
Citations by year
2026: 4 · 2025: 5 · 2024: 10 · 2023: 13 · 2022: 7 · 2021: 19 · 2020: 15 · 2019: 8 · 2018: 16 · 2017: 17 · 2016: 16 · 2015: 5 · 2014: 10 · 2013: 6 · 2012: 3
FWCI
2.20
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
IEEE Transactions on Microwave Theory and Techniques (publishedVersion)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
  • Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
Keywords
Doppler radar, signal-to-noise ratio, heart rate measurement, respiratory rate measurement, direct conversion receiver, oscillator phase noise, radar equation, non-contact vital sign monitoring

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