Advancing Clinical Generalization in Remote Photoplethysmography via Age-Specific Physiological Features

Camera-based remote photoplethysmography (rPPG) enables contactless monitoring of important physiological signals such as heart rate (HR) and respiratory rate (RR), with transformative clinical potential. Despite recent progress under laboratory conditions, most deep-learning approaches overlook age-specific physiological variations and therefore perform poorly in complex clinical scenarios. In this paper, we propose a novel rPPG framework that integrates both explicit and implicit age-specific physiological feature adaptation mechanisms. First, a dynamic channel weighting (DCW) module that enriches the rPPG channel features according to age-related skin optical properties. Second, we propose a prompt-embedded hyper-convolutional (P-HC) layer dynamically adjusts the age-aware convolution kernels using explicit domain priors. Third, a factorized spatio-temporal attention (FAST) mechanism implicitly decomposes spatio-temporal features into age-invariant physiological bases and age-dependent modulation coefficients, thereby enhancing cross-age generalizability. Extensive experiments on five public datasets and clinical validation on 25 preterm infants in neonatal intensive care units demonstrate that the proposed method outperforms state-of-the-art approaches. These findings validate the method's clinical potential for demoaraphically inclusive monitoring.