Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Trade-Offs in FMCW Radar-Based Respiration and Heart Rate Variability

Silvia Mura, Davide Scazzoli, Lorenzo Fineschi, Maurizio Magarini

TL;DR

Findings define a fundamental trade-off: the radar ensures robust estimation of average RR and HR but exhibits restricted precision in high-resolution beat-to-beat and breath-to-breath monitoring.

Abstract

This study presents a comprehensive experimental assessment of a low-cost frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar for non-contact vital sign monitoring, focusing on respiratory rate (RR) and heart rate (HR) estimation. The influence of sensing distance and number of transmitted chirps on measurement accuracy is systematically quantified. Results exhibit a U-shaped error profile with optimal performance near $70~cm$, achieving mean absolute errors of $0.8~bpm$ for RR and $3.2~bpm$ for HR. Accuracy deteriorates at short ($<60~cm$) and long ($>100~cm$) distances due to multipath, near-field, and signal-to-noise effects. Increasing chirp count enhances performance: RR errors converge asymptotically for $\geq96$ chirps, while HR requires at least 96 chirps for stable detection. Variability metrics, including heart and respiratory rate variability, remain less accurate ($>15$--$30\%$ error), indicating limited capability in capturing instantaneous fluctuations. These findings define a fundamental trade-off: the radar ensures robust estimation of average RR and HR but exhibits restricted precision in high-resolution beat-to-beat and breath-to-breath monitoring.

Trade-Offs in FMCW Radar-Based Respiration and Heart Rate Variability

TL;DR

Findings define a fundamental trade-off: the radar ensures robust estimation of average RR and HR but exhibits restricted precision in high-resolution beat-to-beat and breath-to-breath monitoring.

Abstract

This study presents a comprehensive experimental assessment of a low-cost frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar for non-contact vital sign monitoring, focusing on respiratory rate (RR) and heart rate (HR) estimation. The influence of sensing distance and number of transmitted chirps on measurement accuracy is systematically quantified. Results exhibit a U-shaped error profile with optimal performance near , achieving mean absolute errors of for RR and for HR. Accuracy deteriorates at short () and long () distances due to multipath, near-field, and signal-to-noise effects. Increasing chirp count enhances performance: RR errors converge asymptotically for chirps, while HR requires at least 96 chirps for stable detection. Variability metrics, including heart and respiratory rate variability, remain less accurate (-- error), indicating limited capability in capturing instantaneous fluctuations. These findings define a fundamental trade-off: the radar ensures robust estimation of average RR and HR but exhibits restricted precision in high-resolution beat-to-beat and breath-to-breath monitoring.
Paper Structure (14 sections, 6 equations, 6 figures)

This paper contains 14 sections, 6 equations, 6 figures.

Table of Contents

  1. Introduction
  2. System model
  3. Radar Model
  4. Cardiovascular and Respiratory Physiology
  5. Radar Signal Processing
  6. Cardiovascular Metrics
  7. Heart Rate Variability (HRV) Metrics
  8. Breath Rate Variability (BRV) Metrics
  9. Experimental Campaign
  10. Experimental Setup
  11. Radar Setup:
  12. Ground truth sensors:
  13. Numerical Results
  14. Conclusion

Figures (6)

  • Figure 1: Experimental setup showing the sensors used. An Arduino Nano is tasked with collecting the sensors outputs for ground truth. A python script synchronizes the captures from both radar and the Arduino.
  • Figure 2: Ground truth respiration and heart rate readings obtained from the Arduino (left), 2D FFT plot extracted from a single frame of the capture with the person placed at $50\:cm$ from the radar (right).
  • Figure 3: RR estimation error versus distance of subject from the radar.
  • Figure 4: HR estimation error versus distance of subject from the radar.
  • Figure 5: RR estimation error versus number of chirps used for subject at a distance of 70cm.
  • ...and 1 more figures