DuBCG: Unlocking In-Home, Clinically Comparable mmWave Cardiac Monitoring for Co-Sleepers via Physics-Informed Spatial Sensing

Continuous, fine-grained cardiac monitoring during sleep is vital for cardiovascular health. Emerging as a promising alternative to cumbersome wearables, mmWave radar offers a contactless, privacy-preserving modality capable of capturing minute skin vibrations. However, transitioning mmWave radar from labs to real-world bedrooms remains challenging. Our rigorous empirical evaluation of state-of-the-art systems reveals a severe reliability gap: valid Inter-Beat Intervals (IBI) are captured for only 24%-42% of the night within an error threshold of 50 ms. Since clinical HRV analysis requires millisecond-level precision (e.g., RMSSD changes), such coarse and intermittent tracking renders existing methods medically insufficient. We attribute this failure to two fundamental hurdles: (i) instability driven by overwhelming respiratory interference and postural diversity, and (ii) signal entanglement in co-sleeping scenarios where angular separation defies hardware resolution. To bridge this gap, we present DuBCG. First, to tackle instability, we introduce a Head-Facing configuration to capture the Radar Ballistocardiogram (R-BCG). By leveraging the body's longitudinal recoil, this approach achieves orthogonal respiratory suppression and posture-invariant robustness. Second, to resolve entanglement, we propose the Multi-point Scattering Spatial Selectivity Model, theoretically proving that signal separability is achievable beyond physical resolution limits. Extensive real-world benchmarks demonstrate that DuBCG achieves a median IBI error of 8.4 ms—an 8× improvement over existing methods. This precision bridges the gap from existing intermittent monitoring to long-term, clinically comparable continuous HRV assessment.