Automatic Stress Classification With Pupil Diameter Analysis

This article proposes a method based on wavelet transform and neural networks for relating pupillary behavior to psychological stress. The proposed method was tested by recording pupil diameter and electrodermal activity during a simulated driving task. Self-report measures were also collected. Part...

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Bibliographic Details
Published inInternational journal of human-computer interaction Vol. 30; no. 3; pp. 220 - 236
Main Authors Pedrotti, Marco, Mirzaei, Mohammad Ali, Tedesco, Adrien, Chardonnet, Jean-Rémy, Mérienne, Frédéric, Benedetto, Simone, Baccino, Thierry
Format Journal Article
LanguageEnglish
Published Norwood Taylor & Francis 04.03.2014
Lawrence Erlbaum Associates, Inc
Subjects
Classification
Computer Science
Computer simulation
Correlation analysis
Eyes & eyesight
Human
Human-Computer Interaction
Modeling and Simulation
Neural networks
Pupils
Recording
Signal transduction
Stress
Stresses
Tasks
Wavelet transforms
Stress analysis
Wavelet transform
Neural network
Pupilometry
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Summary:This article proposes a method based on wavelet transform and neural networks for relating pupillary behavior to psychological stress. The proposed method was tested by recording pupil diameter and electrodermal activity during a simulated driving task. Self-report measures were also collected. Participants performed a baseline run with the driving task only, followed by three stress runs where they were required to perform the driving task along with sound alerts, the presence of two human evaluators, and both. Self-reports and pupil diameter successfully indexed stress manipulation, and significant correlations were found between these measures. However, electrodermal activity did not vary accordingly. After training, the four-way parallel neural network classifier could guess whether a given unknown pupil diameter signal came from one of the four experimental trials with 79.2% precision. The present study shows that pupil diameter signal has good discriminating power for stress detection.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1044-7318
1532-7590
1044-7318
DOI:10.1080/10447318.2013.848320