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Bayesian-Based Weighted Scheme for Wireless Vital Sign Estimation in Small Data Conditions

Year: 2025
Type: article
Abstract: Vital signs are fundamental in diagnosing diseases and determining human health conditions. Considering the importance of developing a non-contact 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 is proposed as a non-parametric 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 non-parametric 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 multi-person scenarios at long detection distances. The proposed Bayesian-based weighted scheme achieves convincing performance in terms of accuracy and heart rate variability (HRV) monitoring while demonstrating superior data efficiency.
DOI: 10.1109/jsen.2025.3556750
Language: English
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W4409223227
FWCI: 1.686
Cites: 32
Cited by: 7
Related to: 10