TY - JOUR
KW - Augmented Reality
KW - Image Recognition
KW - Support Vector Machine
KW - Decision Tree
KW - Convolutional Neural Network (CNN)
KW - K-Nearest Neighbors
KW - Immersive Environment
AU - Paulo Alonso Gaona-García
AU - Carlos Enrique Montenegro-Marín
AU - de Íñigo Sarría Martínez Mendivil
AU - Andrés Ovidio Restrepo Rodríguez
AU - Maddyzeth Ariza Riaño
AB - Fine 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.
IS - Regular Issue
M1 - 7
N2 - Fine 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.
PY - 2019
SP - 151
EP - 158
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education
UR - https://www.ijimai.org/journal/sites/default/files/files/2019/10/ijimai20195_7_15_pdf_81774.pdf
VL - 5
SN - 1989-1660
ER -