Wearable-Based Stress Detection for Real-World Data: Perspective on Challenges and Recommendations (Preprint)

BACKGROUND Machine learning methods succeed in stress detection under controlled laboratory conditions. However, transferring these models to real-world environments remains challenging. This performance gap is often considered as signal noise, overlooking fundamental issues in evaluation methodology and context-aware modeling. OBJECTIVE This work discusses the obstacles preventing the transition to real-world deployment and provides recommendations towards robust real-world stress detection methods. METHODS We synthesize current literature to map six critical challenges: high inter-subject physiological variability, motion/environmental artifacts, temporal signal misalignment, lack of contextual differentiation, biased ground truth labels, and inherent class imbalance in ambulatory data. RESULTS This perspective provides methodological recommendations for designing, evaluating, and reporting wearable stress detection studies, and strategies to avoid common experimental pitfalls, to ensure robust, trustworthy stress monitoring in real-world settings. CONCLUSIONS : Reliable mHealth stress monitoring requires a shift from laboratory-based models to context-aware, subject-independent frameworks. By adopting the recommended evaluation and preprocessing standards, researchers can ensure that reported performance metrics reflect actual deployment reliability, improving the utility of wearable-based mental health interventions.