AnxietySense: A Multimodal Smartwatch Framework for Predicting State Anxiety via Weakly Supervised Meta-Learning

Social anxiety, characterized by fear of social interactions and negative evaluation, is both pervasive and impairing. Passive sensing offers opportunities for timely detection and intervention, yet most prior work has emphasized trait-level anxiety, with limited success in predicting intra-day fluctuations in state anxiety. To address this gap, we studied 72 socially anxious students who used our smartwatch–smartphone system for an average of nine days. The smartwatch collected multimodal physiological and behavioral signals (e.g., heart rate, movement), while the smartphone delivered seven randomly timed ecological momentary assessments per day to capture state anxiety. These multimodal data formed the basis for anxiety modeling. We present a weakly supervised multimodal framework that trains a base model on a public dataset, then transfers learning and personalizes predictions via meta-learning. Our model outperformed baselines, achieving 72.1% balanced accuracy on our dataset and 66.9% on an external dataset, demonstrating potential to support just-in-time-adaptive interventions.