Assessing Visual Attention Using Eye Tracking Sensors in Intelligent Cognitive Therapies Based on Serious Games

Frutos-Pascual, Maite and Garcia-Zapirain, Begonya (2015) Assessing Visual Attention Using Eye Tracking Sensors in Intelligent Cognitive Therapies Based on Serious Games. Sensors, 15 (5). pp. 11092-11117. ISSN 1424-8220

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Abstract

This study examines the use of eye tracking sensors as a means to identify children's behavior in attention-enhancement therapies. For this purpose, a set of data collected from 32 children with different attention skills is analyzed during their interaction with a set of puzzle games. The authors of this study hypothesize that participants with better performance may have quantifiably different eye-movement patterns from users with poorer results. The use of eye trackers outside the research community may help to extend their potential with available intelligent therapies, bringing state-of-the-art technologies to users. The use of gaze data constitutes a new information source in intelligent therapies that may help to build new approaches that are fully-customized to final users' needs. This may be achieved by implementing machine learning algorithms for classification. The initial study of the dataset has proven a 0.88 (±0.11) classification accuracy with a random forest classifier, using cross-validation and hierarchical tree-based feature selection. Further approaches need to be examined in order to establish more detailed attention behaviors and patterns among children with and without attention problems.

Item Type: Article
Identification Number: https://doi.org/10.3390/s150511092
Date: 12 May 2015
Uncontrolled Keywords: eye tracker, attention, intelligent therapies, serious games, children
Subjects: G400 Computer Science
Divisions: REF UoA Output Collections > REF2021 UoA11: Computer Science and Informatics
Depositing User: Maite Frutos Pascual
Date Deposited: 26 Jul 2019 08:16
Last Modified: 15 May 2020 05:30
URI: http://www.open-access.bcu.ac.uk/id/eprint/7742

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