| dc.description.abstract | Remote monitoring of sleep and physiological signals is critical for tracking human health, managing diseases, and enabling early intervention. However, existing monitoring solutions face two major limitations: (1) they are often unsuitable for vulnerable populations—such as infants and seniors—and (2) most of them raise concerns about measurement accuracy. We propose a novel, contactless approach that addresses both challenges by combining advances in artificial intelligence (AI) and radio-frequency (RF) sensing. Our solution makes monitoring more comfortable, accessible, and affordable, while still delivering clinically meaningful insights. This thesis makes four fundamental contributions: First, we introduce a system that can extract high-fidelity breathing signals from ambient RF reflections, even in complex scenarios where multiple individuals are present, such as couples sharing a bed. Second, we develop an AI-based sleep monitoring framework that generates sleep hypnograms and detects respiratory events entirely without the need for on-body sensors. Third we develop AI models that infer critical biomarkers—such as blood oxygen saturation (SpO₂) and inflammation (C-reactive protein levels)—in a fully passive and non-intrusive manner. Finally, inspired by the success of large language models, we show that physiological signals can be represented and interpreted analogously to language. This insight enables effective translation between modalities (e.g., from respiration to EEG) and unlocks robust representation learning for downstream clinical tasks. Together, these contributions establish a new paradigm for remote sleep and physiological monitoring—one that is contactless, continuous, and passive. We validate our system on real world datasets and demonstrate its potential to fundamentally transform clinical care and home health monitoring. | |
