Eye-tracker algorithms to detect saccades during static and dynamic tasks: a structured review

Stuart, Samuel, Hickey, Aodhan, Vitorio, Rodrigo, Welman, Karen, Foo, Stacey, Keen, David and Godfrey, Alan (2019) Eye-tracker algorithms to detect saccades during static and dynamic tasks: a structured review. Physiological Measurement, 40 (2). 02TR01. ISSN 0967-3334

Stuart et al - Eye-tracker algorithms to detect saccades during static and dynamic tasks AAM.pdf - Accepted Version
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Official URL: https://doi.org/10.1088/1361-6579/ab02ab


Objective: Eye-tracking devices have become widely used as clinical assessment tools in a variety of applied-scientific fields to measure saccadic eye movements. With the emergence of multiple static and dynamic devices, the concurrent need for algorithm development and validation is paramount. Approach: This review assesses the prevalence of current saccade detection algorithms, their associated validation methodologies and the suitability of their application. Medline, Embase, PsychInfo, Scopus, IEEEXplore and ACM Digital Library databases were searched. Two independent reviewers and an adjudicator screened articles describing the detection of saccades from raw infrared/video-based eye-tracker data. Main results: Thirteen articles were screened and met the inclusion criteria. Overall, the majority of reviewed saccadic detection algorithms used simple velocity-based classifications with static eye-tracking systems. Studies demonstrated validity but are limited by the static nature of testing. Heterogeneity in system design, proprietary and bespoke algorithmic methods used, processing strategies, and outcome reporting is evident. Significance: This paper suggests the use of a more standardised methodology to facilitate experimental validity and improve comparison of results across studies.

Item Type: Article
Uncontrolled Keywords: Algorithm, Detection, Eye-movements, Eye-tracker, Saccades
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 29 Jan 2019 12:32
Last Modified: 31 Jul 2021 19:45
URI: http://nrl.northumbria.ac.uk/id/eprint/37784

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