Affective computing with eye-tracking data in the study of the visual perception of architectural spaces

Abstract

In the presented study the usefulness of eye-tracking data for classification of architectural spaces as stressful or relaxing was examined. The eye movements and pupillary response data were collected using the eye-tracker from 202 adult volunteers in the laboratory experiment in a well-controlled environment. Twenty features were extracted from the eye-tracking data and after the selection process the features were used in automated binary classification with a variety of machine learning classifiers including neural networks. The results of the classification using eye-tracking data features yielded 68% accuracy score, which can be considered satisfactory. Moreover, statistical analysis showed statistically significant differences in eye activity patterns between visualisations labelled as stressful or relaxing.