Breathing and Heart Rate Estimation by FMCW Radar System Based on Adaptive Nonlinear Filtering

Accurate and reliable vital signs monitoring has the potential to detect different health conditions, driver fatigue, or even depression. Contactless approaches for heartbeat and respiration rate monitoring are more comfortable than conventional contact-based monitoring devices. In this paper, heartbeat and breathing data were estimated using Frequency-Modulated Continuous-Wave (FMCW) radar (AWR1843, Texas Instruments, USA) placed underneath the bed on which the participant was lying on the back for 5 minutes. The Wearable Wellness System (Smartex, Italy) was used to obtain the reference signal for breathing and heart rate. FMCW data from three participants were acquired during the resting state scenario. First, a slow-time phase correlation method was applied to FMCW data to increase the signal-to-noise ratio. Subsequently, an Adaptive Nonlinear Least Square (ANLS) framework was used to explore the frequency characteristics of the phase signal. Additionally, the Adaptive Notch filter (ANF) removed the second and third breathing harmonics. Finally, the Kalman filter (KF) was applied for better heart rate accuracy. The average root mean square error (RMSE) for estimation from 10-second time windows was 1.66 BPM for the breathing rate estimation and 1.61 BPM for the heart rate estimation.