Arrhythmias are prevalent cardiac disorders affecting millions worldwide. By analyzing cardiac motion modulated in mmWave reflections, mmWave sensing is emerging as a promising contactless revolution in arrhythmia detection compared to conventional contact-based methods. However, the fundamental bottleneck of existing mmWave sensing methods is their restriction to controlled laboratory settings with small-scale cohorts, limiting generalization to real-world populations. This limitation arises because mmWave signals undergo complex signal transformations during propagation, resulting in an explosion of signal diversity across large populations in real-world scenarios. Such diversity significantly complicates the direct recognition of arrhythmia. In this paper, we theoretically analyze the mechanism and impact of mmWave cardiac signal diversity. Leveraging the inherent transformation properties of mmWave signals, we propose a Diversity-Invariant Contrastive mmWave Sensing framework, which learns invariant features robust to complex signal transformations encountered in real-world scenarios. We evaluate our method in a practical, clinically-oriented scenario involving a large-scale population of 7,338 subjects, achieving an average F1-score of 0.8241 across four common arrhythmias. These results demonstrate that our method effectively bridges the diversity gap, representing a significant step toward practical clinical deployment of contactless arrhythmia detection via mmWave sensing.

By analyzing cardiac motion modulated in body reflections, radio signals offer a novel contactless pathway for heartbeat sensing, attracting growing research attention. However, current studies overlooked the unique signal interaction when radio signals are incident at non-normal directions to the torso surface. This missing component significantly limits the effectiveness and results in unreliable performance in practical usage, where normal sensing direction cannot always be guaranteed. In this paper, we aim to answer the questions of what causes this performance degradation and how to solve it. Specifically, we analyze the signal interaction using a fine-grained thoracic motion model and reveal that non-stationary interference, caused by physiologically-driven spatial variation of the body surface, is the key to the problem. Correspondingly, we propose OSense, a framework based on a time-domain optimization method to cancel the non-stationary interference. This framework can be seamlessly integrated into various heartbeat sensing tasks. We validate OSense using a commercial Frequency Modulated Continuous Wave radar across multiple downstream tasks. The results demonstrate that our method effectively eliminates interference and enables direction-robust heartbeat sensing, highlighting its potential for practical cardiac monitoring using radio signals.

Contactless vital signs estimation using mmWave radar has gained significant attention. However, existing studies are built upon the radar being directed facing the thorax to capture fine-grained vital signs, ignoring the angle variation between the radar and thorax in practical deployment. In this paper, we propose a spatial-temporal optimization model to estimate the human body orientations between the radar and thorax through extracting the multi-domain features of reflected signal. By aligning the signal variation captured from different angles, we can realize orientation-free vital sign sensing. The system achieves an average angle estimation error of 13.1°, and a 14.8% discrepancy reduction in terms of the mean absolute error of the signal captured at different angles.