arXiv:2606.15284v1 Announce Type: cross Abstract: Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. Yet existing approaches to universal PPG representation learning largely focus on signal-level objectives and often overlook patient-level health context, which limits generalization to complex clinical tasks and heterogeneous cohorts. To address this gap, we construct a large-scale paired PPG-EHR multimodal dataset by distilling fragmented medical histories and clinical records into cohesive, patient-level electronic health records (EHR
The proliferation of wearables and the increasing computational power for AI models make advanced PPG analysis with patient-level context a timely area for development.
This development allows for more accurate and generalized health monitoring, moving beyond signal-level analysis to integrate complex patient histories for better clinical insights.
The ability to learn universal PPG representations with patient-level supervision will improve the reliability and applicability of wearable health devices for diverse populations and clinical tasks.
- · Wearable device manufacturers
- · Healthcare AI platforms
- · Clinical diagnostics
- · Preventative medicine
- · Traditional, one-size-fits-all diagnostic approaches
Improved early detection and personalized health interventions for a wide range of conditions.
Reduced healthcare costs through preventative care and more efficient clinical decision-making.
Enhanced longevity and quality of life for individuals, potentially reshaping public health strategies and insurance models.
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Read at arXiv cs.AI