Robust HRV Monitoring via Massive Radio Sensing

Heart Rate Variability (HRV) is a crucial biomarker in health monitoring and disease management. Radio sensing has emerged as a promising contactless alternative, addressing the limitations of conventional contact-based techniques. However, a major challenge of existing approaches is their poor generalization performance in real-world deployments. This arises from the inherent sensitivity of radio signals to environmental variations, causing intrinsic shifts in signal distribution and representation. As a result, current methods struggle to adapt to different deployment conditions, leading to performance degradation in real-world applications where complex environments are unavoidable. In this paper, we systematically analyze the generalization challenge posed by environmental variations from the perspective of statistical signal modeling and formulate it as an estimation problem under a global environmental distribution. Inspired by the law of large numbers, we assume that assembling a sufficiently large number of environment-variant samples allows the empirical risk to approximate the true risk, thereby yielding a robust estimator. Accordingly, we propose a novel Massive Radio Sensing framework that leverages massive environment-variant signal sampling and a structured deep learning optimization strategy to statistically derive a robust HRV estimator. We evaluate our method across a broad spectrum of real-world deployment scenarios. Specifically, testing on 30 participants across 32 application-oriented environments—including home and workplace settings—demonstrates a 29.7% improvement in performance compared to the current state-of-the-art method. To further assess clinical generalizability, we evaluate our method on 130 inpatients across 8 distinct hospital environments, achieving a 41.7% performance improvement. These results highlight the effectiveness of our approach and its strong potential for real-world deployment in radio-based HRV monitoring.