International Journal of Science, Engineering and TechnologyAuthors: Kabir, Poonam, Meenakshi, Divanshi, Reenu Batra
Abstract: Next-day mental stress has important consequences for health, learning, and work performance, yet most digital phenotyping studies focus on detecting current stress rather than forecasting it. Wearables, smartphones, and in-the-moment self-reports provide continuous information about physiology, behaviour, and experience, and explainable artificial intelligence offers tools to make prediction models more transparent to users and clinicians. This narrative review synthesized recent research on multimodal, explainable next-day stress prediction, with particular attention to biophysical signals, smartphone-based behaviour, micro ecological momentary assessment (micro-EMA), and visual emotion analysis, and it outlines a conceptual framework for explainable next-day stress prediction that can guide future empirical work. The review examined studies that used physiological signals such as electrodermal activity, heart rate, and skin temperature, smartphone-derived features, EMA or micro-EMA self-reports, and facial-expression data for stress detection or short-term stress prediction. Major scientific databases were searched using combinations of terms related to stress detection, digital phenotyping, micro-EMA, next-day stress, explainable AI, and facial-expression datasets, and the identified studies were grouped thematically into four domains: biophysical stress detection, smartphone and micro-EMA-based sensing and prediction, explainable models in stress and mental-health prediction, and visual analysis of stress. Across the literature, biophysical models based on electrodermal activity, heart rate, and skin temperature provided robust stress markers, while smartphone features and micro-EMA ratings enabled forecasting of short-term and next-day perceived stress in daily life. Explainable AI methods, especially feature-attribution techniques such as SHAP, were used to highlight which behavioural or physiological variables drive model outputs, and facial-expression models trained on datasets such as RAF-DB and CK+ could separate stress-related emotions from non-stress states. However, fully integrated pipelines that combine biophysical, behavioural, self-report, and visual data for explainable next-day stress prediction remain rare. Existing work demonstrates that next-day stress can be predicted from recent physiology, behaviour, and micro-EMA, and that explainable models make these predictions more interpretable. Future research should move toward multimodal, explainable next-day stress frameworks that unify biophysical, smartphone, and visual signals, incorporate user-centred explanations, and are evaluated in ecologically valid student and worker populations.
DOI: http://doi.org/10.5281/zenodo.21772269
