Separating Acute Psychological Stress from Physical Exertion in Biometric Signals

2026-05-15

AI Summary

This study addresses the challenge of disentangling the confounding effects of psychological stress and physical activity on physiological signals in everyday scenarios. Through a controlled experiment, participants performed cognitive stress-inducing tasks—including n-back, social stress, and monetary incentives—while seated, walking, or cycling, with concurrent recordings of electrodermal activity (EDA), trapezius electromyography, heart rate, heart rate variability (RMSSD), and respiration rate. Employing multilevel linear mixed models and repeated-measures ANOVA, the work systematically decouples the independent and interactive effects of stress and movement, establishing a hierarchical profile of physiological responses. Results demonstrate that EDA exhibits robust additive sensitivity to both psychological stress (r = 0.48) and physical activity (r = 0.67), outperforming all other measures and confirming its status as the most promising biomarker for stress detection in real-world settings.

Abstract

Acute psychological stress occurs in a wide range of everyday contexts, including transportation, occupational settings, and physical activity, where its reliable detection could enable adaptive system responses and support human well-being. A persistent challenge in automated stress recognition is disentangling the biometric signatures of acute psychological stress from those of concurrent physical exertion. This study examined how five physiological signals (tonic electrodermal activity, trapezius electromyography, heart rate, heart rate variability, and respiration rate) respond to cognitive stress and physical activity, independently and in combination. Nineteen participants completed a 2x3 within-subjects design in which acute psychological stress was induced via an n-back arithmetic task combined with social pressure and financial reward, across three activity conditions: idle sitting, walking, and stationary cycling. Multilevel linear mixed models and repeated-measures ANOVA were used to decompose main effects and interactions for each sensor. Tonic electrodermal activity showed a robust, additive response to both cognitive stress (r=0.48) and physical exertion (r=0.67), with no interaction, making it the most promising candidate for stress detection during physical activity. Heart rate and trapezius electromyography were driven almost exclusively by physical exertion, with no reliable sensitivity to the stress task. RMSSD was strongly suppressed by physical activity and showed only marginal sensitivity to cognitive load. Respiration rate was dominated by physical activity, with no reliable stress effect in the primary analysis. These findings provide a sensor-specific hierarchy for real-world stress detection and highlight tonic electrodermal activity as the most informative channel when cognitive stress must be identified in physically active populations.

Problem

• acute psychological stress
• physical exertion
• biometric signals
• stress recognition
• physiological responses

Innovation

• tonic electrodermal activity
• stress detection
• physical exertion
• physiological signals
• multilevel modeling