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Chmielewska, Magdalena; Dzieńkowski, Mariusz; Bogucki, Jacek; Kocki, Wojciech; Kwiatkowski, Bartłomiej; Pełka, Jarosław; Tuszyńska-Bogucka, Wioletta
MATEC Web of Conferences, 2019, Letnik: 252Journal Article, Conference Proceeding
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.
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Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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