A New Perspective on Stress Detection: An Automated Approach for Detecting Eustress and Distress.

Journal: IEEE transactions on affective computing
Published Date: Oct 16, 2023

Abstract

Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be classified according to its influence on individuals; the way people perceive a stressor determines whether the stress reaction is considered as eustress (positive stress) or distress (negative stress). Thus, this study aims to assess the potential of using an ML approach to determine stress appraisal and identify eustress and distress instances using physiological and behavioral features. The results indicate that distress leads to higher perceived stress arousal compared to eustress. An XGBoost model that combined physiological and behavioral features using a 30 second time window had 83.38% and 78.79% F-scores for predicting eustress and distress, respectively. Gender-based models resulted in an average increase of 2-4% in eustress and distress prediction accuracy. Finally, a model to predict the simultaneous assessment of eustress and distress, distinguishing between pure eustress, pure distress, eustress-distress coexistence, and the absence of stress achieved a moderate F-score of 65.12%. The results of this study lay the foundation for work management interventions to maximize eustress and minimize distress in the workplace.

Authors

  • Mohamad Awada
    Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, USA.
  • Burcin Becerik-Geber
    Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, USA.
  • Gale M Lucas
    1) Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, USA; 2) USC Institute for Creative Technologies, Los Angeles, CA, USA.
  • Shawn C Roll
    Chan Division of Occupational Science and Occupational Therapy, University of Southern California (USC), Los Angeles, CA, USA.
  • Ruying Liu
    Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA, USA.

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