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Scalable Multisubject Vital Sign Monitoring With mmWave FMCW Radar and FPGA Prototyping

Jewel Benny, Narahari N. Moudhgalya, Mujeev Khan, Hemant Kumar Meena, Mohd Wajid, Abhishek Srivastava

TL;DR

The paper tackles non-contact vital sign monitoring for multiple individuals using mmWave FMCW radar. It introduces a phase-based processing pipeline with beamforming, Range FFT, phase unwrapping, and DI-variance localization, augmented by Variational Mode Decomposition and a regression model to estimate HR/BR from multiple subjects. An FPGA-based prototype demonstrates substantial speedups and LUT reductions over software, validating real-time, portable deployment. Results show accurate BR (MAE ~0.39) and competitive HR (MAE ~3.64) for up to five subjects within 6 m, highlighting the method's potential for clinical and homecare settings.

Abstract

In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in calibration, and the risk of infection transmission through contact measurement devices. To address these issues, this research is motivated by the need for versatile, non-contact vital monitoring solutions applicable in various critical scenarios. This work also explores the challenges of extending this capability to an arbitrary number of subjects, including hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the paper illustrates the system's potential to redefine vital sign monitoring. An FPGA-based implementation is also presented as proof of concept for a hardware-based and portable solution, improving upon previous works by offering 2.7x faster execution and 18.4% less Look-Up Table (LUT) utilization, as well as providing over 7400x acceleration compared to its software counterpart.

Scalable Multisubject Vital Sign Monitoring With mmWave FMCW Radar and FPGA Prototyping

TL;DR

The paper tackles non-contact vital sign monitoring for multiple individuals using mmWave FMCW radar. It introduces a phase-based processing pipeline with beamforming, Range FFT, phase unwrapping, and DI-variance localization, augmented by Variational Mode Decomposition and a regression model to estimate HR/BR from multiple subjects. An FPGA-based prototype demonstrates substantial speedups and LUT reductions over software, validating real-time, portable deployment. Results show accurate BR (MAE ~0.39) and competitive HR (MAE ~3.64) for up to five subjects within 6 m, highlighting the method's potential for clinical and homecare settings.

Abstract

In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in calibration, and the risk of infection transmission through contact measurement devices. To address these issues, this research is motivated by the need for versatile, non-contact vital monitoring solutions applicable in various critical scenarios. This work also explores the challenges of extending this capability to an arbitrary number of subjects, including hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the paper illustrates the system's potential to redefine vital sign monitoring. An FPGA-based implementation is also presented as proof of concept for a hardware-based and portable solution, improving upon previous works by offering 2.7x faster execution and 18.4% less Look-Up Table (LUT) utilization, as well as providing over 7400x acceleration compared to its software counterpart.

Paper Structure

This paper contains 34 sections, 18 equations, 18 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Fundamentals of FMCW Radar and Proposed Algorithm for HR and BR Extraction
  3. IF Signal Sampling for Different Azimuths Using Beamforming
  4. Range FFT to Determine IF Signal Frequency Components
  5. Phase Signal Extraction From Temporal Range FFT Information
  6. Range-Azimuth Bin Selection for Localizing Subjects
  7. HR/BR Features Extraction and Measurement
  8. Welch Spectrum
  9. Auto-correlation
  10. Peak Detection
  11. Regression Model Training for HR/BR Estimation
  12. System Configuration and Constraints
  13. System Setup and Configuration
  14. Hardware Limitations on Subject Detection
  15. Maximum Detectable Range
  16. ...and 19 more sections

Figures (18)

  • Figure 1: Basic operation principle of FMCW radars for HR/BR measurement
  • Figure 2: Proposed algorithm integrating VMD, Comb Filter, and Regression Model for improved MAE in vital measurements.
  • Figure 3: DI Spread vs. Azimuth - The plots (a)-(d) show the distribution of DI amplitude across azimuth bins, and plot (e) shows variance vs. azimuth.
  • Figure 4: DI Spread vs. Range - The plots (a)-(d) show the distribution of DI amplitude across range bins and plot (e) shows variance vs. range.
  • Figure 5: Range-Azimuth map showing DI variance - Two distinct peaks (subjects) are visible along with noise peaks with low DI variance.
  • ...and 13 more figures