BRPDNet: A BioRegion Prompt Distillation Network for Physiological Monitoring

Physiological signal extraction from video data is challenging in dynamic and occluded environments, requiring both accuracy and real-time performance. Existing methods struggle to balance accuracy with model efficiency, particularly under partial facial occlusion or redundant signals. We propose BRPDNet, a novel framework for efficient physiological signal extraction which includes a BioRegion Prompt module for adaptive convolution and a Hyper Distillation module to reduce signal redundancy, ensuring high accuracy and robustness, especially in dynamic and occluded environments. Additionally, the teacher-student network structure enhances the model's adaptability to occlusions and reduces computational complexity without relying on explicit segmentation. Experimental results show that BRPDNet outperforms stateof-the-art models in accuracy, robustness, and efficiency across multiple datasets. For instance, BRPDNet achieves an Mean Absolute Error (MAE) of 1.55 beats per minute (bpm) and a Pearson Correlation Coefficient (PCC) of 0.76 on PURE and UBFC-rPPG datasets with fewer parameters than existing models, ensuring efficient real-time performance.