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dc.contributor.authorStuart, Samuel
dc.contributor.authorHickey, Aodhan
dc.contributor.authorVitorio, Rodrigo [UNESP]
dc.contributor.authorWelman, Karen
dc.contributor.authorFoo, Stacy
dc.contributor.authorKeen, David
dc.contributor.authorGodfrey, Alan
dc.date.accessioned2019-10-04T11:57:23Z
dc.date.available2019-10-04T11:57:23Z
dc.date.issued2019-02-01
dc.identifierhttp://dx.doi.org/10.1088/1361-6579/ab02ab
dc.identifier.citationPhysiological Measurement. Bristol: Iop Publishing Ltd, v. 40, n. 2, 16 p., 2019.
dc.identifier.issn0967-3334
dc.identifier.urihttp://hdl.handle.net/11449/184380
dc.description.abstractObjective: 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.en
dc.format.extent16
dc.language.isoeng
dc.publisherIop Publishing Ltd
dc.relation.ispartofPhysiological Measurement
dc.sourceWeb of Science
dc.subjectalgorithm
dc.subjectdetection
dc.subjecteye-movements
dc.subjecteye-tracker
dc.subjectsaccades
dc.titleEye-tracker algorithms to detect saccades during static and dynamic tasks: a structured reviewen
dc.typeResenha
dcterms.licensehttp://iopscience.iop.org/page/copyright
dcterms.rightsHolderIop Publishing Ltd
dc.contributor.institutionOregon Hlth & Sci Univ
dc.contributor.institutionHSC Publ Hlth Agcy
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionStellenbosch Univ
dc.contributor.institutionUniv Western Australia
dc.contributor.institutionNanyang Technol Univ
dc.contributor.institutionUniv Warwick
dc.contributor.institutionNorthumbria Univ
dc.description.affiliationOregon Hlth & Sci Univ, Dept Neurol, Portland, OR 97201 USA
dc.description.affiliationHSC Publ Hlth Agcy, Dept Hlth Intelligence, Belfast, Antrim, North Ireland
dc.description.affiliationSao Paulo State Univ, Dept Phys Educ, Sao Paulo, Brazil
dc.description.affiliationStellenbosch Univ, Movement Lab, Dept Sport Sci, Stellenbosch, South Africa
dc.description.affiliationUniv Western Australia, Sch Human Sci Exercise & Sport Sci, Perth, WA, Australia
dc.description.affiliationNanyang Technol Univ, Natl Inst Educ, Phys Educ & Sports Sci, Singapore, Singapore
dc.description.affiliationUniv Warwick, Sch Life Sci, Warwick, England
dc.description.affiliationNorthumbria Univ, Dept Comp & Informat Sci, Newcastle Upon Tyne, Tyne & Wear, England
dc.description.affiliationUnespSao Paulo State Univ, Dept Phys Educ, Sao Paulo, Brazil
dc.identifier.doi10.1088/1361-6579/ab02ab
dc.identifier.wosWOS:000459884700001
dc.rights.accessRightsAcesso aberto
unesp.author.orcid0000-0001-6846-9372[1]
unesp.author.orcid0000-0001-7128-9452[3]
unesp.author.orcid0000-0003-4049-9291[7]
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