JMIR Publications

ABSTRACT

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.