The Identification of Individualized Eye Tracking Metrics in VR Using Data Driven Iterative- Adaptive Algorithm

Eye tracking metrics provide information about cognitive function and basic oculomotor characteristics. There have been many studies analyzing eye tracking signals using different algorithms. However, these algorithms generally are based on the initial setting parameter. This might cause the subject...

Full description

Saved in:
Bibliographic Details
Published inAcademic Journal of Information Technology Vol. 14; no. 52; pp. 8 - 21
Main Authors ARSLAN, Dilek Betül, SÜKUTİ, Murat, DURU, Adil Deniz
Format Journal Article
LanguageEnglish
Published Istanbul AJIT - e: Online Academic Journal of Information Technology 01.03.2023
Akademik Bilişim Araştırmaları Derneği
Subjects
Online AccessGet full text
ISSN1309-1581
1309-1581
DOI10.5824/ajite.2023.01.001.x

Cover

More Information
Summary:Eye tracking metrics provide information about cognitive function and basic oculomotor characteristics. There have been many studies analyzing eye tracking signals using different algorithms. However, these algorithms generally are based on the initial setting parameter. This might cause the subjective interpretation of eye tracking analysis. The main aim of this study was to develop a data-driven algorithm to detect fixations and saccades without any subjective settings. Three subjects were included in this study. Eye tracking signal was acquired with the VIVE Pro Eye in virtual reality (VR) environment while subjects were reading a paragraph. The algorithms based on the calculation of threshold were employed to calculate eye metrics including total fixation duration, total fixation number, total saccades number and average pupil diameter. The proposed algorithm, which is based on calculating the initial threshold, based on mean, and standard deviation of eye tracking signal within experiment duration, gave the same results obtained adaptive filtering reported in literature (average fixation duration for three subjects= 11515 ms ± 6951.2, average fixation count for three subjects= 17.33 ± 4.16). On the other hand, our proposed algorithm didn’t use any certain objective parameter as like adaptive filtering. As a conclusion, VIVE Pro Eye may be utilized as an eye movement assessment device, and, the suggested approach might be utilized to analyze objective eye tracking metrics.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1309-1581
1309-1581
DOI:10.5824/ajite.2023.01.001.x