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dc.contributor.authorRodríguez Moreno, Itsaso
dc.contributor.authorMartínez Otzeta, José María
dc.contributor.authorSierra Araujo, Basilio ORCID
dc.date.accessioned2023-06-27T17:15:42Z
dc.date.available2023-06-27T17:15:42Z
dc.date.issued2023-04
dc.identifier.citationExpert Systems with Applications 215 : (2023) // Article ID 119365es_ES
dc.identifier.issn1873-6793
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/10810/61775
dc.description.abstractCommunication between people from different communities can sometimes be hampered by the lack of knowledge of each other's language. A large number of people needs to learn a language in order to ensure a fluid communication or want to do it just out of intellectual curiosity. To assist language learners' needs tutor tools have been developed. In this paper we present a tutor for learning the basic 42 hand configurations of the Spanish Sign Language, as well as more than one hundred of common words. This tutor registers the user image from an off-the-shelf webcam and challenges her to perform the hand configuration she chooses to practice. The system looks for the configuration, out of the 42 in its database, closest to the configuration performed by the user, and shows it to her, to help her to improve through knowledge of her errors in real time. The similarities between configurations are computed using Procrustes analysis. A table with the most frequent mistakes is also recorded and available to the user. The user may advance to choose a word and practice the hand configurations needed for that word. Sign languages have been historically neglected and deaf people still face important challenges in their daily activities. This research is a first step in the development of a Spanish Sign Language tutor and the tool is available as open source. A multidimensional scaling analysis of the clustering of the 42 hand configurations induced by Procrustes similarity is also presented.es_ES
dc.description.sponsorshipThis work has been partially funded by the Basque Government, Spain, under Grant number IT1427-22; the Spanish Ministry of Science (MCIU), the State Research Agency (AEI), the European Regional Development Fund (FEDER), under Grant number PID2021-122402OB-C21 (MCIU/AEI/FEDER, UE); and the Spanish Ministry of Science, Innovation and Universities, under Grant FPU18/04737. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2021-122402OB-C21es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectsign languagees_ES
dc.subjectlanguage tutores_ES
dc.subjectaction recognitiones_ES
dc.subjectprocrustes similarityes_ES
dc.subjectmultidimensional scalinges_ES
dc.titleHAKA: HierArchical Knowledge Acquisition in a sign language tutores_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0957417422023831es_ES
dc.identifier.doi10.1016/j.eswa.2022.119365
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


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© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Except where otherwise noted, this item's license is described as © 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)