Millimeter-wave radar, with advantages such as non-contact penetration detection and privacy protection, has become a promising solution for unobtrusive monitoring in the field of smart elderly care. To solve the problem of whether there are human body in the elderly care scene, this study proposed a method for judging the presence of a human body based on adaptive dual thresholds to reduce invalid vital sign detection in an empty environment. This method used a low-frequency energy ratio as the core judgment basis. It combined adaptive thresholds to accurately judge the presence of human targets, effectively reducing false detections caused by background interference. In addition, given the defect that variational mode extraction (VME) needs to rely on manual parameter adjustment based on empirical values, the crown porcupine optimization (CPO) algorithm is introduced to optimize the VME parameters adaptively, and the optimized VME is used to reconstruct the heartbeat signal to improve the signal purity. Then, the multiple signal classification (MUSIC) algorithm was used for spectrum analysis to improve the accuracy of heart rate estimation. The results show that in the experimental judgment of personnel, the miss rate in the case of personnel presence is 2.2%, and the false alarm rate in the case of no personnel is only 3.2%; the root mean square error and mean absolute error of the proposed heart rate (HR) estimation method are reduced by 4.4 beat per minute and 3.05 beat per minute respectively compared with the traditional VME, verifying its excellence.

Heart rate variability (HRV), as a key indicator for evaluating autonomic nervous system function, has significant value in areas such as cardiovascular disease screening and emotion monitoring. Although traditional contact-based measurement methods offer high precision, they suffer from issues such as poor comfort and low user compliance. This paper proposes a non-contact HRV monitoring method using frequency modulated continuous wave (FMCW) radar, highlighting adaptive cycle segmentation and peak extraction as core innovations. Key advantages of this method include: 1) effective suppression of motion artifacts and respiratory harmonics by leveraging cardiac energy concentration; 2) precise heartbeat cycle identification across physiological states via adaptive segmentation, addressing time-varying differences; 3) adaptive threshold adjustment using discrete energy signals and a support vector machine (SVM) model based on morphological-temporal-spectral characteristics, reducing complexity while maintaining precision. Previous approaches predominantly process radar signals holistically through algorithms to uniformly extract inter-beat intervals (IBIs), which may result in high computational complexity and inadequate dynamic adaptability. In contrast, our method achieved higher precision than conventional holistic processing approaches, while maintaining comparable precision with lower computational complexity than previous optimization algorithms. Experimental results demonstrate that the system achieves an average IBI error of 8.28 ms (RMSE of 15.3 ms), which is reduced by about 66% compared with the traditional holistically peak seeking method. The average errors of SDNN and RMSSD are 2.65 ms and 4.33 ms, respectively. More than 92% of the IBI errors are controlled within 20 ms. The distance adaptability test showed that although the accuracy of long-distance measurement decreased slightly (<6 ms), the overall detection performance remained robust at different distances. This study provided a novel estimation algorithm for non-contact HRV detection, offering new perspectives for future health monitoring.