Electrodermal Temperature-Adjusted Electrodermal Activity (EDA) for Stress Detection in Virtual Reality.

Precise stress identification in virtual reality (VR) settings continues to be difficult because of thermoregulatory mechanisms that modify electrodermal activity (EDA) independently of emotional responses. This research presents a temperature-corrected framework that distinguishes authentic stress-induced EDA from heat-associated physiological reactions by combining two complementary thermal modeling techniques: a proportionality model and a data-driven adaptive scaling approach. Utilizing the Wearable Emotion Sensing and Detection (WESAD) dataset, temperature variations were synchronized with observed conductance patterns to adjust for thermal distortions that mask stress-specific indicators. The temperature-corrected features enhanced differentiation between stress-related and thermally influenced EDA activities, improving physiological precision and ecological authenticity. Statistical examination revealed strong distinction between affective states in both raw conductance and peripheral temperature measurements. Additionally, the adaptive scaling model produced more distinct condition-specific patterns than the proportionality method. Feature importance findings showed temperature-derived parameters as reliable contributors to classification consistency. These results emphasize temperature compensation as an essential preprocessing procedure for dependable stress identification in VR settings, allowing more accurate interpretation of EDA across different thermal circumstances.