Smartwatch-Based Unobtrusive Continuous Anxiety Tracker for Evaluating Post-Stroke Patients' Quality of Life.

Abstract

Post-stroke Anxiety (PSA) affects 20-30% of stroke survivors and significantly impacts their quality of life (QoL) and rehabilitation outcomes. Traditional emotion assessment methods rely on subjective self-reports, which have a limited ability to capture real-time fluctuations in emotional states. This study proposes a self-supervised learning (SSL) framework combined with a transformer-based emotion classification model to enable continuous anxiety tracking in patients with stroke using smartwatch-derived photoplethysmography (PPG) signals. The SSL model was pretrained on the VitalDB dataset using R-peak-to-peak intervals (RRIs) extracted from electrocardiography (ECG) signals. The pretrained encoder is then integrated into a transformer-based classifier trained on the Psycho-physiology of Positive and Negative Emotions POPANE dataset containing labeled emotional responses. Finally, the trained model was applied to smartwatch-derived pulse peak intervals (PPI) from patients with stroke, and the predictions were applied to Generalized Anxiety Disorder-7 (GAD-7) scores and we evaluated it against GAD-7 scores using both group-level and 90-day longitudinal analyses, alongside on-device feasibility on a Galaxy Watch6. Over the 30 days preceding the second survey, between-group differences were significant by Welch's t-test (p = 0.0086), and discrimination reached AUC 0.859 with a 95% confidence interval of 0.664-0.992. In 90-day monitoring, generalized estimating equations (GEE) showed persistent divergence when groups were defined by the second survey, with significant differences across multiple weeks preceding the survey, consistent with the retrospective GAD-7 window. These findings indicate that ECG-pretrained cardiac representations can be translated to smartwatch PPG to support unobtrusive, continuous anxiety tracking in stroke. Results constitute feasibility-level evidence and motivate multi-site external validation, larger cohorts with clinician-rated assessments, and prospective studies toward clinical deployment.