Health-Radar: Noncontact Multitarget Heart Rate Variability Detection Using FMCW Radar

Heart rate variability (HRV), indicating the variation in intervals between consecutive heartbeats, is a crucial physiological indicator of human health. However, detecting HRV using frequency-modulated continuous-wave (FMCW) radar is highly susceptible to interference from respiration, minor body movements, and environmental noise, especially in multitarget scenarios. To address these challenges, we propose the Health-Radar system, which comprises three functional modules. In the target detection module, the system accurately identifies the number and locations of targets. In the phase extraction module, the signal undergoes dc offset calibration to extract the chest displacement signals. In the heartbeat signal extraction module, we introduce Health-VMD, an adaptive parameter variational mode decomposition (VMD) method. This method optimizes the VMD parameters using an improved grasshopper optimization algorithm (GOA) and accurately extracts vital sign signals from chest displacement signals to estimate HRV. In addition, we propose a novel objective function, composed of permutation entropy, mutual information, and energy loss rate (PME), specifically designed for vital sign extraction. Experiments with multiple participants in various scenarios demonstrated that the designed system can accurately identify different targets and detect HRV with high precision. The root-mean-square error (RMSE) of the detected interbeat intervals (IBIs) is 29.72 ms, the RMSE of the standard deviation of NN intervals (SDNN) is 4.1 ms, and the RMSE of the root mean square of successive differences (RMSSD) is 18.61 ms, outperforming existing methods.