摘要
Vital signs are fundamental in diagnosing diseases and determining human health conditions. Considering the importance of developing a noncontact solution for vital sign estimation during an epidemic outbreak, extensive research based on millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radar has been proposed. Current well-known deep learning-based methods perform well, but have been challenged when the number of training samples is limited, leading to risks of overfitting and poor generalization. In this study, a Bayesian-based weighted scheme (BWS) is proposed as a nonparametric data-driven solution to provide satisfactory performance under small data conditions. The proposed scheme enhances adaptability to diverse propagation environments with limited data. This is achieved by leveraging nonparametric statistical calculations to reduce data dependency and by employing a data-driven weighting mechanism, which dynamically assigns greater importance to more accurate estimations. The experimental results first offer foundational insights into the proposed scheme in a single-person scenario, followed by extensive evaluation using a publicly available dataset of multiperson scenarios at long detection distances. The proposed BWS achieves convincing performance in terms of accuracy and heart rate variability (HRV) monitoring while demonstrating superior data efficiency.