SHIELDNet: Multi-Region Fusion and Denoising for Enhanced rPPG Signal Extraction in Healthcare Monitoring

Accurate extraction of Remote Photoplethysmography (rPPG) signals from video data is critical for medical applications such as remote patient monitoring. However, the process is hindered by significant challenges, including noise interference, occlusions, and multi bio-region signal processing. To address these, we propose SHIELDNet, an efficient and robust framework for real-time extraction of rPPG signals from multiple anatomical regions, incorporating advanced noise reduction mechanisms. SHIELDNet integrates a novel Differential Attention (DA) module, which adaptively focuses on multiple anatomical regions, enabling the model to effectively handle dynamic real-world conditions. Additionally, the network leverages an advanced Efficient Space Attention Module (ESAM) to enhance spatial feature extraction and multi bio-region signal fusion. BioRegion Prompt Module (BRPM) is further introduced to prioritize region-specific features, reducing the model's dependence on facial features alone. Futhermore, we introduce M-rPPG dataset, a comprehensive multi bio-region reference for BioRegion-based studies with full-body details at higher resolution than existing datasets. Extensive evaluations on multiple public datasets demonstrate significant improvements in Mean Absolute Error (MAE =5.28 bpm) and Pearson Correlation Coefficient (PCC = 0.80), outperforming current state-of-the-art models. SHIELDNet provides an effective solution for noncontact, multi bio-region rPPG monitoring. We will release our code upon acceptance.