Short-Term Detection of Dynamic Stress Levels in Exergaming with Wearables.

This study evaluates the feasibility of using a lightweight, off-the-shelf sensing system for short-term stress detection during exergaming. Most existing studies in stress detection compare rest and task conditions, providing limited insight into continuous stress dynamics, and there is no agreement on optimal sensor configurations. To address these limitations, we investigated dynamic stress responses induced by a cognitive-motor task designed to simulate rehabilitation-like scenarios. Twenty-three participants completed the experiment, providing electrodermal activity (EDA), blood volume pulse (BVP), self-report, and in-game data. Features extracted from physiological signals were analyzed statistically, and shallow machine learning classifiers were applied to discriminate among stress levels. EDA-based features reliably differentiated stress conditions, while BVP features showed less consistent behavior. The classification achieved an overall accuracy of 0.70 across four stress levels, with most errors between adjacent levels. Correlations between EDA dynamics and perceived stress scores suggested individual variability possibly linked to chronic stress. These results demonstrate the feasibility of low-cost, unobtrusive stress monitoring in interactive environments, supporting future applications of dynamic stress detection in rehabilitation and personalized health technologies.