A method for enhancing eye movements data from eye-tracking devices

Autores
Dimieri, Leonardo Daniel; Castro, Liliana Raquel; Agamennoni, Osvaldo Enrique
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Eye movements play an important role in actual neuroscience and in the last twenty years, many eye-tracking devices have emerged with different methods and performance features. Generally, the highest quality ones with best performance in terms of accuracy and high framerates, are the most expensive apparatus and very often complicated to assembly. Also, they tend to work in fixed setups and it is hard to perform outdoor experiments, like driving a real car or walking long distances. The comfortable and cheaper ones are usually those having the poorest measuring characteristics, reaching maximum framerates below 250fps, yet with great advantages. These modern remote eye-tracking systems allow, in general, small head movements and the subject has not wear any kind of hardware. This feature is especially important when working with children or people with some kind of physical impairment. They are independent, small and one-piece hardware ready to plug into a mobile computer or laptop, making easy to set a large variety of experiments. In this work, we propose to use wavelet methods to improve real eye movements data, allowing the reconstruction of the signal at a higher resolution than the original one. Transformed data was upsampled and the new coefficients were obtained by interpolation using different techniques and looking for a minimum percentage error between the original and recovered signals. Then, treating the eyetracker data with low samplerate as a complete signal with periodic missing parts or information and inspired in a method for restoring very damaged images, we present an approach to adapt one of the the algorithms for images to 1D signals.
Fil: Dimieri, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Castro, Liliana Raquel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Agamennoni, Osvaldo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Materia
EYE MOVEMENT
EYE-TRACKING
WAVELETS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/64644
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/64644
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling A method for enhancing eye movements data from eye-tracking devicesDimieri, Leonardo DanielCastro, Liliana RaquelAgamennoni, Osvaldo EnriqueEYE MOVEMENTEYE-TRACKINGWAVELETShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Eye movements play an important role in actual neuroscience and in the last twenty years, many eye-tracking devices have emerged with different methods and performance features. Generally, the highest quality ones with best performance in terms of accuracy and high framerates, are the most expensive apparatus and very often complicated to assembly. Also, they tend to work in fixed setups and it is hard to perform outdoor experiments, like driving a real car or walking long distances. The comfortable and cheaper ones are usually those having the poorest measuring characteristics, reaching maximum framerates below 250fps, yet with great advantages. These modern remote eye-tracking systems allow, in general, small head movements and the subject has not wear any kind of hardware. This feature is especially important when working with children or people with some kind of physical impairment. They are independent, small and one-piece hardware ready to plug into a mobile computer or laptop, making easy to set a large variety of experiments. In this work, we propose to use wavelet methods to improve real eye movements data, allowing the reconstruction of the signal at a higher resolution than the original one. Transformed data was upsampled and the new coefficients were obtained by interpolation using different techniques and looking for a minimum percentage error between the original and recovered signals. Then, treating the eyetracker data with low samplerate as a complete signal with periodic missing parts or information and inspired in a method for restoring very damaged images, we present an approach to adapt one of the the algorithms for images to 1D signals.Fil: Dimieri, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Castro, Liliana Raquel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Agamennoni, Osvaldo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaAsociación Argentina de Mecánica Computacional2017-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/64644Dimieri, Leonardo Daniel; Castro, Liliana Raquel; Agamennoni, Osvaldo Enrique; A method for enhancing eye movements data from eye-tracking devices; Asociación Argentina de Mecánica Computacional; Mecánica Computacional; XXXV; 43; 11-2017; 2521-25322591-3522CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://cimec.org.ar/ojs/index.php/mc/article/view/5466info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:49:51Zoai:ri.conicet.gov.ar:11336/64644instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:49:51.444CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A method for enhancing eye movements data from eye-tracking devices
title A method for enhancing eye movements data from eye-tracking devices
spellingShingle A method for enhancing eye movements data from eye-tracking devices
Dimieri, Leonardo Daniel
EYE MOVEMENT
EYE-TRACKING
WAVELETS
title_short A method for enhancing eye movements data from eye-tracking devices
title_full A method for enhancing eye movements data from eye-tracking devices
title_fullStr A method for enhancing eye movements data from eye-tracking devices
title_full_unstemmed A method for enhancing eye movements data from eye-tracking devices
title_sort A method for enhancing eye movements data from eye-tracking devices
dc.creator.none.fl_str_mv Dimieri, Leonardo Daniel
Castro, Liliana Raquel
Agamennoni, Osvaldo Enrique
author Dimieri, Leonardo Daniel
author_facet Dimieri, Leonardo Daniel
Castro, Liliana Raquel
Agamennoni, Osvaldo Enrique
author_role author
author2 Castro, Liliana Raquel
Agamennoni, Osvaldo Enrique
author2_role author
author
dc.subject.none.fl_str_mv EYE MOVEMENT
EYE-TRACKING
WAVELETS
topic EYE MOVEMENT
EYE-TRACKING
WAVELETS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Eye movements play an important role in actual neuroscience and in the last twenty years, many eye-tracking devices have emerged with different methods and performance features. Generally, the highest quality ones with best performance in terms of accuracy and high framerates, are the most expensive apparatus and very often complicated to assembly. Also, they tend to work in fixed setups and it is hard to perform outdoor experiments, like driving a real car or walking long distances. The comfortable and cheaper ones are usually those having the poorest measuring characteristics, reaching maximum framerates below 250fps, yet with great advantages. These modern remote eye-tracking systems allow, in general, small head movements and the subject has not wear any kind of hardware. This feature is especially important when working with children or people with some kind of physical impairment. They are independent, small and one-piece hardware ready to plug into a mobile computer or laptop, making easy to set a large variety of experiments. In this work, we propose to use wavelet methods to improve real eye movements data, allowing the reconstruction of the signal at a higher resolution than the original one. Transformed data was upsampled and the new coefficients were obtained by interpolation using different techniques and looking for a minimum percentage error between the original and recovered signals. Then, treating the eyetracker data with low samplerate as a complete signal with periodic missing parts or information and inspired in a method for restoring very damaged images, we present an approach to adapt one of the the algorithms for images to 1D signals.
Fil: Dimieri, Leonardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Castro, Liliana Raquel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
Fil: Agamennoni, Osvaldo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; Argentina
description Eye movements play an important role in actual neuroscience and in the last twenty years, many eye-tracking devices have emerged with different methods and performance features. Generally, the highest quality ones with best performance in terms of accuracy and high framerates, are the most expensive apparatus and very often complicated to assembly. Also, they tend to work in fixed setups and it is hard to perform outdoor experiments, like driving a real car or walking long distances. The comfortable and cheaper ones are usually those having the poorest measuring characteristics, reaching maximum framerates below 250fps, yet with great advantages. These modern remote eye-tracking systems allow, in general, small head movements and the subject has not wear any kind of hardware. This feature is especially important when working with children or people with some kind of physical impairment. They are independent, small and one-piece hardware ready to plug into a mobile computer or laptop, making easy to set a large variety of experiments. In this work, we propose to use wavelet methods to improve real eye movements data, allowing the reconstruction of the signal at a higher resolution than the original one. Transformed data was upsampled and the new coefficients were obtained by interpolation using different techniques and looking for a minimum percentage error between the original and recovered signals. Then, treating the eyetracker data with low samplerate as a complete signal with periodic missing parts or information and inspired in a method for restoring very damaged images, we present an approach to adapt one of the the algorithms for images to 1D signals.
publishDate 2017
dc.date.none.fl_str_mv 2017-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/64644
Dimieri, Leonardo Daniel; Castro, Liliana Raquel; Agamennoni, Osvaldo Enrique; A method for enhancing eye movements data from eye-tracking devices; Asociación Argentina de Mecánica Computacional; Mecánica Computacional; XXXV; 43; 11-2017; 2521-2532
2591-3522
CONICET Digital
CONICET
url http://hdl.handle.net/11336/64644
identifier_str_mv Dimieri, Leonardo Daniel; Castro, Liliana Raquel; Agamennoni, Osvaldo Enrique; A method for enhancing eye movements data from eye-tracking devices; Asociación Argentina de Mecánica Computacional; Mecánica Computacional; XXXV; 43; 11-2017; 2521-2532
2591-3522
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://cimec.org.ar/ojs/index.php/mc/article/view/5466
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Asociación Argentina de Mecánica Computacional
publisher.none.fl_str_mv Asociación Argentina de Mecánica Computacional
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842268998816759808
score 13.13397

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