Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education
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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universidad Internacional de La Rioja (UNIR)
2019-12-01
|
| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.ijimai.org/journal/node/3534 |
| _version_ | 1818496892950020096 |
|---|---|
| author | Andrés Ovidio Restrepo Rodríguez Maddyzeth Ariza Riaño Paulo Alonso Gaona-García Carlos Enrique Montenegro-Marin Íñigo Sarría Martínez Mendivil |
| author_facet | Andrés Ovidio Restrepo Rodríguez Maddyzeth Ariza Riaño Paulo Alonso Gaona-García Carlos Enrique Montenegro-Marin Íñigo Sarría Martínez Mendivil |
| author_sort | Andrés Ovidio Restrepo Rodríguez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T18:38:19Z |
| format | Article |
| id | doaj.art-9b60b7ba05a143cbbc6a6fd066f512df |
| institution | Directory Open Access Journal |
| issn | 1989-1660 1989-1660 |
| language | English |
| last_indexed | 2024-12-10T18:38:19Z |
| publishDate | 2019-12-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-9b60b7ba05a143cbbc6a6fd066f512df2022-12-22T01:37:44ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602019-12-015715115810.9781/ijimai.2019.10.004ijimai.2019.10.004Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early EducationAndrés Ovidio Restrepo RodríguezMaddyzeth Ariza RiañoPaulo Alonso Gaona-GarcíaCarlos Enrique Montenegro-MarinÍñigo Sarría Martínez MendivilFine 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.http://www.ijimai.org/journal/node/3534augmented realityconvolution neural networkdecision treeimage recognitionimmersive environmentk-nearest neighborssupport vector machine |
| spellingShingle | Andrés Ovidio Restrepo Rodríguez Maddyzeth Ariza Riaño Paulo Alonso Gaona-García Carlos Enrique Montenegro-Marin Íñigo Sarría Martínez Mendivil Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education International Journal of Interactive Multimedia and Artificial Intelligence augmented reality convolution neural network decision tree image recognition immersive environment k-nearest neighbors support vector machine |
| title | Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education |
| title_full | Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education |
| title_fullStr | Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education |
| title_full_unstemmed | Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education |
| title_short | Image Classification Methods Applied in Immersive Environments for Fine Motor Skills Training in Early Education |
| title_sort | image classification methods applied in immersive environments for fine motor skills training in early education |
| topic | augmented reality convolution neural network decision tree image recognition immersive environment k-nearest neighbors support vector machine |
| url | http://www.ijimai.org/journal/node/3534 |
| work_keys_str_mv | AT andresovidiorestreporodriguez imageclassificationmethodsappliedinimmersiveenvironmentsforfinemotorskillstraininginearlyeducation AT maddyzetharizariano imageclassificationmethodsappliedinimmersiveenvironmentsforfinemotorskillstraininginearlyeducation AT pauloalonsogaonagarcia imageclassificationmethodsappliedinimmersiveenvironmentsforfinemotorskillstraininginearlyeducation AT carlosenriquemontenegromarin imageclassificationmethodsappliedinimmersiveenvironmentsforfinemotorskillstraininginearlyeducation AT inigosarriamartinezmendivil imageclassificationmethodsappliedinimmersiveenvironmentsforfinemotorskillstraininginearlyeducation |