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Automatic evaluation of eye gestural reactions to sound in video sequences
dc.contributor.author | Fernández Arias, Alba | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Penedo, Manuel | |
dc.date.accessioned | 2023-12-12T17:40:44Z | |
dc.date.available | 2023-12-12T17:40:44Z | |
dc.date.issued | 2019-10 | |
dc.identifier.citation | Fernández, A., Ortega, M., de Moura, J., Novo, J., & Penedo, M. G. (2019). Automatic evaluation of eye gestural reactions to sound in video sequences. Engineering Applications of Artificial Intelligence, 85, 164–174. doi:10.1016/j.engappai.2019.06.009 | es_ES |
dc.identifier.issn | 0952-1976 | |
dc.identifier.uri | http://hdl.handle.net/2183/34467 | |
dc.description | © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Fernández, A., Ortega, M., de Moura, J., Novo, J., & Penedo, M. G. (2019). “Automatic evaluation of eye gestural reactions to sound in video sequences”, has been accepted for publication in Engineering Applications of Artificial Intelligence, 85, 164–174. The Version of Record is available online at: https://doi.org/10.1016/j.engappai.2019.06.009. | es_ES |
dc.description.abstract | [Abstract]: Hearing loss is a common disorder that often intensifies with age. In some cases, especially in the elderly population, the hearing loss may decrease in physical, mental and social well-being capacities. In particular, patients with signs of cognitive impairment typically present specific clinical–pathological conditions, which complicates the analysis and diagnosis of the type and severity of hearing loss by clinical specialists. In these patients, unconscious changes in gaze direction may indicate a certain perception of sound through their auditory system. In this context, this work presents a new system that supports clinical experts in the identification and classification of eye gestures that are associated with reactions to auditory stimuli by patients with different levels of cognitive impairment. The proposed system was validated using the public Video Audiometry Sequence Test (VAST) dataset, providing a global accuracy of 97.12% for the classification of eye gestures and a 100% for gestural reactions to auditory stimuli. The proposed system offers a complete analysis of audiometric video sequences, being applicable in daily clinical practice, improving the well-being and quality of life of the patients. | es_ES |
dc.description.sponsorship | This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union, Spain through the DTS18/00136 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund — ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019 , Ref. ED431G/01; and Grupos de Referencia Competitiva , Ref. ED431C 2016-047. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2016-047 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier & International Federation of Automatic Control (IFAC) | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2015-69948-R/ES/IDENTIFICACION Y CARACTERIZACION DEL EDEMA MACULAR DIABETICO MEDIANTE ANALISIS AUTOMATICO DE TOMOGRAFIAS DE COHERENCIA OPTICA Y TECNICAS DE APRENDIZAJE MAQUINA | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DTS18%2F00136/ES/Plataforma online para prevención y detección precoz de enfermedad vascular mediante análisis automatizado de información e imagen clínica | es_ES |
dc.relation.isversionof | https://doi.org/10.1016/j.engappai.2019.06.009 | |
dc.relation.uri | https://doi.org/10.1016/j.engappai.2019.06.009 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC-BY-NC-ND 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Sound responses | es_ES |
dc.subject | Gestural reactions | es_ES |
dc.subject | Hearing screening | es_ES |
dc.subject | Movement detection | es_ES |
dc.subject | Screening methods | es_ES |
dc.subject | Video sequences | es_ES |
dc.title | Automatic evaluation of eye gestural reactions to sound in video sequences | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Engineering Applications of Artificial Intelligence | es_ES |
UDC.volume | 85 | es_ES |
UDC.startPage | 164 | es_ES |
UDC.endPage | 174 | es_ES |
dc.identifier.doi | 10.1016/j.engappai.2019.06.009 |
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