Evaluating Generalization and Long-Term Stability of Smartwatch-Based Cuffless Blood Pressure Monitoring

Abstract

Smartwatch-based cuffless blood pressure (BP) monitoring is a promising low-burden solution for long-term cardiovascular management, but practical deployment remains constrained by model complexity, uncertain cross-dataset generalization, and insufficient evidence on long-term tracking stability. This study presents an efficient Signal-Context Gated Fusion Network (SCGBPNet) for cuffless BP estimation from photoplethysmography (PPG) signals. SCGBPNet integrates signal-level representations through self-attention and further incorporates subject-specific contextual information via a compact gated fusion module. On Aurora-BP, SCGBPNet achieved SBP/DBP mean absolute errors (MAEs) of 7.08/4.90 mmHg, compared with 6.44/4.89 mmHg under within-dataset validation on the private smartwatch dataset, indicating a modest within-dataset discrepancy. Cross-dataset transfer from Aurora-BP to the smartwatch dataset increased the MAEs to 8.98/6.47 mmHg, demonstrating substantial cross-dataset degradation. For long-term stability, the proposed method showed low week-to-week performance variation and a low daily decay rate, indicating good stability over a follow-up period of more than one month. Nevertheless, estimation accuracy deteriorated when BP deviated substantially from an individual's baseline. These findings indicate that favorable aggregate MAE alone should not be interpreted as evidence of deployment-ready performance. More systematic and stratified evaluation across dataset settings, BP ranges, and baseline-deviation levels is needed to assess the practical reliability of smartwatch-based cuffless BP monitoring.