부산대학교 도서관

Remote Photoplethysmography (rPPG) leverages standard RGB cameras for contactless heart rate monitoring, overcoming the limitations of traditional PPG technology in telemedicine and offering a highly scalable, cost-effective health monitoring solution. Despite the advancements of current deep learning methods, which utilize spatiotemporal convolutional networks to capture subtle rPPG signals, these approaches often fail to fully exploit local similarities and global quasi-periodicity in both spatial and temporal dimensions. Additionally, non-physiological noise remains prevalent in the representation space, impeding the accurate estimation of physiological parameters across diverse representation domains. To address these measurement challenges, we propose Spore, a novel training strategy that integrates a Spatio-Temporal Cooperative Perception Network (STCPNet) and a Separable Network (SpNet). Spore effectively disentangles noise and extracts physiological signals through differential orthogonal disentanglement and parallel approximation techniques, ensuring precise measurement of heart rate. STCPNet meticulously aggregates semantic context across spatial and temporal dimensions, enhancing global-level and trend cross-correlations in a fine-grained manner. Meanwhile, the resource-efficient SpNet identifies and constructs target representation spaces by realigning the distribution of the source latent space, thereby adaptively capturing disentangled physiological signal patterns from the computationally intensive STCPNet. For validation, extensive experiments were conducted not only on multiple benchmark datasets but also through deployment testing in real-world scenarios. The results demonstrate that our proposed training strategy achieves state-of-the-art performance in heart rate measurement while maintaining resource efficiency. The code will be released at https://github.com/zacheryzhang/spore.