RAGCBPNet: An Efficient Feature Fusion Framework for Wearable Cuffless Blood Pressure Monitoring and Long-term Validation in Real-world Settings

Wearable and cuffless blood pressure (BP) monitoring hold great promise for preventive hypertension management, yet few studies have been validated under real-world and long-term conditions. In this study, we propose RACGBPNet, an efficient yet effective feature fusion framework for cuffless BP estimation and hypertension detection. The model leverages a two-stage fusion strategy: first, handcrafted and deep PPG representations are integrated via a self-attention mechanism to capture dynamic signal interactions; second, a simple gating module adaptively fuses signal features with demographic information. To mitigate severe class imbalance in hypertension detection, we further introduce a supervised contrastive learning scheme with a pretrained regression encoder. RACGBPNet was evaluated on two large-scale datasets, including a long-term smartwatch dataset spanning 30+ days in free-living conditions. On the public Aurora-BP dataset, RACGBPNet achieved state-of-the-art performance with mean absolute errors (MAEs) of 7.08 and 4.90 mmHg for systolic and diastolic BP, and Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8627 and 0.8301 for systolic and diastolic hypertension detection under highly imbalanced data. Long-term validation confirmed that baseline calibration was sufficient to maintain compliance with AAMI standards for SBP up to 14 days and for DBP across the entire tracking period, striking a practical balance between accuracy and usability. However, the model exhibited significantly reduced variability compared with reference BP, indicating challenges in capturing abrupt BP changes. Overall, this work demonstrates the feasibility of long-term smartwatch-based BP monitoring and offers new insights into real-world deployment settings, particularly regarding calibration frequency and cross-dataset generalization. Hypertension is one of the most important risk factors for cardiovascular diseases. Wearable devices based on photoplethysmography (PPG) have been increasingly adopted by the general population for daily health monitoring, offering great platform for early detection, prevention and management of hypertension. However, very few studies have examined whether these devices can accurately monitor blood pressure (BP) and detect hypertension in real-life settings for extended periods. We developed a novel framework, RACGBPNet, which efficiently fuses PPG features with personal information for accurate BP estimation, and addresses data imbalance to enhance hypertension detection. Validation on a new smartwatch dataset including 136 subjects, 24,910 recordings collected over more than 30 days in real-life scenarios showed high accuracy over weeks with simple baseline calibration. Our findings also provide valuable insights into calibration frequency, paving the way for reliable, real-world deployment of cuffless BP monitoring techniques.