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

Details

• Personal Author:
  Awada M ; Becerik-Geber B ; Liu R ; Lucas GM ; Roll SC
• Description:
  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 1 -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 1 -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. [Description provided by NIOSH]
• Subjects:
  Behavior Mental Health Nervous System Occupational Health Safety Stress, Psychological
• Keywords:
  Algorithms Artificial Intelligence Author Keywords: Psychological Stress Behavioral Data Behavior Patterns Job Stress Machine Learning Mental Health Office Workers Physiological Data Physiological Response

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• ISSN:
  1949-3045
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  15
• Issue:
  3
• NIOSHTIC Number:
  nn:20070343
• Citation:
  IEEE Trans Affect Comput 2024 Jul-Sep; 13(2):1153-1165
• Contact Point Address:
  Mohamad Awada, Civil and Environmental Engineering, University of Southern California, Los Angeles, CA 90007 USA
• Email:
  awadam@usc.edu
• Federal Fiscal Year:
  2024
• Performing Organization:
  University of California Los Angeles
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  IEEE Transactions on Affective Computing
• End Date:
  20270630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:fa4b3a98157e31638b66df411e943f47d3cd44c926cf80072519f93053d23b9c5768da62751233eefba2b65947c223b75d177fbef8b64e354715a8aa0575610c
• Download URL:
  https://stacks.cdc.gov/view/cdc/208576/cdc_208576_DS1.pdf
• File Type:
  [PDF - 1.98 MB ]