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dc.contributor.authorRestrepo Rodríguez, Andrés Ovidio
dc.contributor.authorAriza Riano, Maddyzeth
dc.contributor.authorGaona-Garcia, Paulo A.
dc.contributor.authorMontenegro-Marin, Carlos Enrique
dc.contributor.authorSarría, Íñigo (1)
dc.date2019-12
dc.date.accessioned2020-01-24T08:23:28Z
dc.date.available2020-01-24T08:23:28Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/9749
dc.description.abstractFine motor skills allow to early out the execution of crucial tasks in people's daily lives, increasing their independence and self-esteem. Among the alternatives for working these skills. immersive environments are found providing a set of elements arranged to have a haptic experience through gestural control devices. However, generally, these environments do not have a mechanism for evaluation and feedback of the exercise performed, which does not easily identify the objective's fulfillment. For this reason, this study aims to carry out a comparison of image recognition methods such as Convolutional Neural Network (CNN), K-Nearest Neighbor (K-NN). Support Vector Machine (SVM) and Decision Tree (DT), for the purpose of performing an evaluation and feedback of exercises. The assessment of the techniques is carried out using images captured from an immersive environment, calculating metrics such as confusion matrix, cross validation and classification report. As a result of this process, it was obtained that the CNN model has a better supported performance in 82.5% accuracy, showing an increase of 23.5% compared to SVM, 30% compared to K-NN and 25% compared to DT. Finally, it is concluded that in order to implement a method of evaluation and feedback in an immersive environment for academic training in the first school years. a low margin of error must be taken in the percentage of successes of the image recognition technique implemented. to ensure the proper development of these skills considering their great importance in childhood.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligencees_ES
dc.relation.ispartofseries;vol. 5, nº 7
dc.relation.urihttps://www.ijimai.org/journal/node/3534es_ES
dc.rightsopenAccesses_ES
dc.subjectaugmented realityes_ES
dc.subjectconvolution neural networkes_ES
dc.subjectdecision treees_ES
dc.subjectimage recognitiones_ES
dc.subjectimmersive environmentes_ES
dc.subjectK-nearest neighborses_ES
dc.subjectsupport vector machineies_ES
dc.subjectJCRes_ES
dc.titleImage Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Educationes_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttp://dx.doi.org/10.9781/ijimai.2019.10.004


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