PPG-IDR: Leveraging User Identity for Robust Cross-user PPG Sensing via Disentangled Representations

Addresses cross-user generalization failure of population-trained PPG models (HR, BP, etc.). Hypothesis: identity/biometric traits encoded in PPG are a primary confound. PPG-IDR dual-branch disentanglement: partition feature space with adversarial + orthogonality constraints to suppress identity leakage, plus self-supervised objective to refine medical representations.

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Photoplethysmography (PPG) is widely used in non-invasive health monitoring applications such as heart rate and blood pressure estimation. Despite deep learning substantially advancing PPG sensing accuracy, models trained on a population often struggle with cross-user generalization , exhibiting significant performance degradation when applied to unseen individuals. Building on the evidence that PPG signals encode biometric traits for user authentication, we hypothesize that these identity-specific features are a primary confounding noise of poor cross-user generalization. To validate this hypothesis and address the issue, we propose PPG-IDR (Identity Disentangled Representations), a framework designed to disentangle medical physiological features from identity-specific information. PPG-IDR utilizes a dual-branch design to partition the feature space, employing adversarial and orthogonality constraints to suppress identity leakage, alongside a self-supervised objective to refine medical representations. We evaluated PPG-IDR using multiple datasets across six downstream tasks, and the results demonstrate that PPG-IDR consistently outperforms strong baselines in unseen-user scenarios. These findings highlight the importance of identity disentanglement for scalable and robust cross-user PPG sensing.

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