Automatic Stress Classification With Pupil Diameter Analysis

Abstract

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.

ACKNOWLEDGEMENT

We thank the anonymous reviewers for their helpful comments.

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Notes

¹One participant's data were excluded because of poor recording quality.

²One participant was excluded because of too many artifacts in the SC record.

FIG. 5. (A) Skin Conductance Response to stressful sounds. (B) Skin Conductance Response factor loadings (only loadings > 0.7 are considered for interpretation).

³One participant quit the experiment after t₂ because of simulator sickness.