Discriminating Stress From Cognitive Load Using a Wearable EDA Device

The inferred cost of work-related stress call for prevention strategies that aim at detecting early warning signs at the workplace. This paper goes one step towards the goal of developing a personal health system for detecting stress. We analyze the discriminative power of electrodermal activity (EDA) in distinguishing stress from cognitive load in an office environment. A collective of 33 subjects underwent a laboratory intervention that included mild cognitive load and two stress factors, which are relevant at the workplace: mental stress induced by solving arithmetic problems under time pressure and psychosocial stress induced by social-evaluative threat. During the experiments, a wearable device was used to monitor the EDA as a measure of the individual stress reaction. Analysis of the data showed that the distributions of the EDA peak height and the instantaneous peak rate carry information about the stress level of a person. Six classifiers were investigated regarding their ability to discriminate cognitive load from stress. A maximum accuracy of 82.8% was achieved for discriminating stress from cognitive load. This would allow keeping track of stressful phases during a working day by using a wearable EDA device.

[1]  Zhiwei Zhu,et al.  A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[2]  K. Dedovic,et al.  The Montreal Imaging Stress Task: using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain. , 2005, Journal of psychiatry & neuroscience : JPN.

[3]  Gerhard Tröster,et al.  Effect of Movements on the Electrodermal Response after a Startle Event , 2008, Methods of Information in Medicine.

[4]  Mi-hee Lee,et al.  Development stress monitoring system based on personal digital assistant (PDA) , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  J. Lagopoulos Electrodermal activity , 2007, Acta Neuropsychiatrica.

[6]  R. Lazarus Psychological stress and the coping process , 1970 .

[7]  C. Darrow,et al.  THE RATIONALE FOR TREATING THE CHANGE IN GALVANIC SKIN RESPONSE AS A CHANGE IN CONDUCTANCE. , 1964, Psychophysiology.

[8]  P. Stone,et al.  Use of skin conductance changes during mental stress testing as an index of autonomic arousal in cardiovascular research. , 1994, American heart journal.

[9]  H. Selye A Syndrome produced by Diverse Nocuous Agents , 1936, Nature.

[10]  J. Cacioppo,et al.  Handbook Of Psychophysiology , 2019 .

[11]  D. Fotiadis,et al.  An integrated telemedicine platform for the assessment of affective physiological states , 2006, Diagnostic pathology.

[12]  R. Lazarus,et al.  Surprise versus suspense in the production of stress reaction. , 1968, Journal of personality and social psychology.

[13]  Wolfram Boucsein,et al.  Psychophysiological investigation of stress induced by temporal factors in human-computer interaction , 1988 .

[14]  Rosalind W. Picard The Galvactivator: A glove that senses and communicates skin conductivity , 2001 .

[15]  W Kuhmann,et al.  Experimental investigation of psychophysiological stress-reactions induced by different system response times in human-computer interaction. , 1987, Ergonomics.

[16]  Richard S. Lazarus,et al.  CHAPTER 10 – The Study of Psychological Stress: A Summary of Theoretical Formulations and Experimental Findings , 1966 .

[17]  A. Barreto,et al.  Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  이수정 해외산업간호정보 - 미국 산업안전보건연구원(National Institute for Occupational Safety and Health) 소개 , 2009 .

[19]  M. Bachlin,et al.  Effect of Movements on the Electrodermal Response after a Startle Event , 2008 .

[20]  E. Poutsma,et al.  EUROPEAN FOUNDATION for the Improvement of Living and Working Conditions , 1999 .

[21]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[22]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.

[23]  W. Marsden I and J , 2012 .

[24]  Neil Genzlinger A. and Q , 2006 .