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Design and Measurements of mmWave FMCW Radar Based Non-Contact Multi-Patient Heart Rate and Breath Rate Monitoring System

Jewel Benny, Pranjal Mahajan, Srayan Sankar Chatterjee, Mohd Wajid, Abhishek Srivastava

TL;DR

This work tackles non-contact, multi-patient BR and HR monitoring using a 77–81 GHz FMCW mmWave radar with multi-receiver beamforming. It proposes a processing pipeline that localizes subjects via a Vital Activity Map, extracts phase signals, and fuses Fourier, autocorrelation, and peak-detection estimates with a least-squares linear combination to improve accuracy. Experimental results demonstrate BR accuracy around 98% and HR accuracy around 93% at distances up to about 5 meters, validating the approach for mass monitoring. The method offers a scalable, non-invasive solution suitable for clinical and crowd environments with minimal prior information about subject azimuths.

Abstract

Recent developments in mmWave radar technologies have enabled the truly non-contact heart-rate (HR) and breath-rate (BR) measurement approaches, which provides a great ease in patient monitoring. Additionally, these technologies also provide opportunities to simultaneously detect HR and BR of multiple patients, which has become increasingly important for efficient mass monitoring scenarios. In this work, a frequency modulated continuous wave (FMCW) mmWave radar based truly non-contact multiple patient HR and BR monitoring system has been presented. Furthermore, a novel approach is also proposed, which combines multiple processing methods using a least squares solution to improve measurement accuracy, generalization, and handle measurement error. The proposed system has been developed using Texas Instruments' FMCW radar and experimental results with multiple subjects are also presented, which show >97% and >93% accuracy in the measured BR and HR values, respectively.

Design and Measurements of mmWave FMCW Radar Based Non-Contact Multi-Patient Heart Rate and Breath Rate Monitoring System

TL;DR

This work tackles non-contact, multi-patient BR and HR monitoring using a 77–81 GHz FMCW mmWave radar with multi-receiver beamforming. It proposes a processing pipeline that localizes subjects via a Vital Activity Map, extracts phase signals, and fuses Fourier, autocorrelation, and peak-detection estimates with a least-squares linear combination to improve accuracy. Experimental results demonstrate BR accuracy around 98% and HR accuracy around 93% at distances up to about 5 meters, validating the approach for mass monitoring. The method offers a scalable, non-invasive solution suitable for clinical and crowd environments with minimal prior information about subject azimuths.

Abstract

Recent developments in mmWave radar technologies have enabled the truly non-contact heart-rate (HR) and breath-rate (BR) measurement approaches, which provides a great ease in patient monitoring. Additionally, these technologies also provide opportunities to simultaneously detect HR and BR of multiple patients, which has become increasingly important for efficient mass monitoring scenarios. In this work, a frequency modulated continuous wave (FMCW) mmWave radar based truly non-contact multiple patient HR and BR monitoring system has been presented. Furthermore, a novel approach is also proposed, which combines multiple processing methods using a least squares solution to improve measurement accuracy, generalization, and handle measurement error. The proposed system has been developed using Texas Instruments' FMCW radar and experimental results with multiple subjects are also presented, which show >97% and >93% accuracy in the measured BR and HR values, respectively.

Paper Structure

This paper contains 12 sections, 17 equations, 5 figures, 2 tables.

Figures (5)

  • Figure 1: (a) Heart rate and breath rate estimation using FMCW radar (b) Multi-subject HR & BR mesurement (c) F-T plot of FMCW chirps (d) Chest displacement due to breathing and heart beat
  • Figure 2: Signal processing flow to measure HR & BR
  • Figure 3: Chest displacement signals extracted from the phase signal of a range-azimuth bin with a human subject (left) and without a human subject (right).
  • Figure 4: (a) Extracted phase signal (b) Unwrapped phase signal (c) Extracted breathing signal (d) Extracted heartbeat signal
  • Figure 5: (a) The overall experiment setup (b) Single-subject HR & BR measurement (c) Multi-subject HR & BR measurement (d) Range-azimuth map (e) Vital activity map after clutter removal