Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach

Accurate stress monitoring in everyday settings requires informative labels without excessive user burden. Ecological Momentary Assessments (EMAs) provide self-reported labels, but poorly timed or frequent prompts can reduce their utility. This paper presents a context-aware active reinforcement learning algorithm for stress detection using photoplethysmography (PPG) from smartwatches and contextual data from smartphones. The algorithm uses the user's current context and response history to decide when to trigger an EMA. We first evaluate context-aware label selection in an offline study and then deploy the approach for real-time EMA triggering in a separate online cohort. Under the reported evaluation, Random Forest F1 increased from 0.21 for the offline cohort to 0.32 for the online cohort; adding contextual features increased it to 0.36. Personalization increased AUC by up to 0.10 across Random Forest, XGBoost, and SVM classifiers. These results support context-aware EMA scheduling and personalized modeling for real-world stress monitoring, while differences between the study cohorts limit causal attribution of the observed performance gains.