Why the study?
Current contactless radio signal heartbeat sensing methods overlook signal interaction at non-normal incidence angles to the torso surface, leading to unreliable performance in practical settings.
The OSense framework enables direction-robust contactless heartbeat sensing using radio signals by canceling non-stationary interference caused by physiologically-driven spatial variation of the body surface.
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Non-normal incidence modeling may improve contactless radio heartbeat sensing reliability; leaves open clinical validation before practice adoption.
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.
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Gong et al. (2025) studied this question.
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