Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education

Abstract

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.

A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education

“…Among these, the CNN model outperformed the others, achieving an accuracy of 82%. This study is a testament to the potential of ML in evaluating students' abilities in various educational contexts [78].…”

“…One significant challenge is the removal of multi-media noise from the immersive AR environment, a process that can be time consuming [78]. Additionally, researchers face the need to conduct experiments with kindergarten students to test the AR devices they have developed.…”

A Comprehensive Survey on Investigation of Machine Learning-Powered Augmented Reality Applications in Education

“…Among these, the CNN model outperformed the others, achieving an accuracy of 82%. This study is a testament to the potential of ML in evaluating students' abilities in various educational contexts [75].…”

“…One significant challenge is the removal of multi-media noise from the immersive AR environment, a process that can be time-consuming [75]. Additionally, researchers face the need to conduct experiments with kindergarten students to test the AR devices they've developed.…”

Editor’s Note

“…This regular issue ends with a study by Rodríguez et al [15] that aims to carry out a comparison of image recognition methods for the purpose of evaluating exercises performed in an immersive environment for motor skills training. The compared methods are convolutional neural network, K-nearest neighbor, support vector machine and decision tree.…”