02511nas a2200289   4500000000100000000000100001008004100002260001200043653002200055653002200077653002700099653001800126653003900144653002400183653002600207100003100233700003600264700004200300700003900342700002700381245011700408856010000525300001200625490000600637520156400643022001402207       2019                            d  c12/201910aAugmented Reality10aImage Recognition10aSupport Vector Machine10aDecision Tree10aConvolutional Neural Network (CNN)10aK-Nearest Neighbors10aImmersive Environment1 aPaulo Alonso Gaona-García1 aCarlos Enrique Montenegro-Marin1 ade Íñigo Sarría Martínez Mendivil1 aAndrés Ovidio Restrepo Rodríguez1 aMaddyzeth Ariza Riaño00aImage Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/10/ijimai20195_7_15_pdf_81774.pdf  a151-1580 v53 aFine motor skills allow to carry 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.  a1989-1660