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The Use of Deep Learning-Based Gesture Interactive Robot in the Treatment of Autistic Children Under Music Perception Education
The purpose of this study was to apply deep learning to music perception education. Music perception therapy for autistic children using gesture interactive robots based on the concept of educational psychology and deep learning technology is proposed. First, the experimental problems are defined an...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866172/ https://www.ncbi.nlm.nih.gov/pubmed/35222179 http://dx.doi.org/10.3389/fpsyg.2022.762701 |
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author | Zhang, Yiyao Zhang, Chao Cheng, Lei Qi, Mingwei |
author_facet | Zhang, Yiyao Zhang, Chao Cheng, Lei Qi, Mingwei |
author_sort | Zhang, Yiyao |
collection | PubMed |
description | The purpose of this study was to apply deep learning to music perception education. Music perception therapy for autistic children using gesture interactive robots based on the concept of educational psychology and deep learning technology is proposed. First, the experimental problems are defined and explained based on the relevant theories of pedagogy. Next, gesture interactive robots and music perception education classrooms are studied based on recurrent neural networks (RNNs). Then, autistic children are treated by music perception, and an electroencephalogram (EEG) is used to collect the music perception effect and disease diagnosis results of children. Due to significant advantages of signal feature extraction and classification, RNN is used to analyze the EEG of autistic children receiving different music perception treatments to improve classification accuracy. The experimental results are as follows. The analysis of EEG signals proves that different people have different perceptions of music, but this difference fluctuates in a certain range. The classification accuracy of the designed model is about 72–94%, and the average classification accuracy is about 85%. The average accuracy of the model for EEG classification of autistic children is 85%, and that of healthy children is 84%. The test results with similar models also prove the excellent performance of the design model. This exploration provides a reference for applying the artificial intelligence (AI) technology in music perception education to diagnose and treat autistic children. |
format | Online Article Text |
id | pubmed-8866172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88661722022-02-25 The Use of Deep Learning-Based Gesture Interactive Robot in the Treatment of Autistic Children Under Music Perception Education Zhang, Yiyao Zhang, Chao Cheng, Lei Qi, Mingwei Front Psychol Psychology The purpose of this study was to apply deep learning to music perception education. Music perception therapy for autistic children using gesture interactive robots based on the concept of educational psychology and deep learning technology is proposed. First, the experimental problems are defined and explained based on the relevant theories of pedagogy. Next, gesture interactive robots and music perception education classrooms are studied based on recurrent neural networks (RNNs). Then, autistic children are treated by music perception, and an electroencephalogram (EEG) is used to collect the music perception effect and disease diagnosis results of children. Due to significant advantages of signal feature extraction and classification, RNN is used to analyze the EEG of autistic children receiving different music perception treatments to improve classification accuracy. The experimental results are as follows. The analysis of EEG signals proves that different people have different perceptions of music, but this difference fluctuates in a certain range. The classification accuracy of the designed model is about 72–94%, and the average classification accuracy is about 85%. The average accuracy of the model for EEG classification of autistic children is 85%, and that of healthy children is 84%. The test results with similar models also prove the excellent performance of the design model. This exploration provides a reference for applying the artificial intelligence (AI) technology in music perception education to diagnose and treat autistic children. Frontiers Media S.A. 2022-02-10 /pmc/articles/PMC8866172/ /pubmed/35222179 http://dx.doi.org/10.3389/fpsyg.2022.762701 Text en Copyright © 2022 Zhang, Zhang, Cheng and Qi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Zhang, Yiyao Zhang, Chao Cheng, Lei Qi, Mingwei The Use of Deep Learning-Based Gesture Interactive Robot in the Treatment of Autistic Children Under Music Perception Education |
title | The Use of Deep Learning-Based Gesture Interactive Robot in the Treatment of Autistic Children Under Music Perception Education |
title_full | The Use of Deep Learning-Based Gesture Interactive Robot in the Treatment of Autistic Children Under Music Perception Education |
title_fullStr | The Use of Deep Learning-Based Gesture Interactive Robot in the Treatment of Autistic Children Under Music Perception Education |
title_full_unstemmed | The Use of Deep Learning-Based Gesture Interactive Robot in the Treatment of Autistic Children Under Music Perception Education |
title_short | The Use of Deep Learning-Based Gesture Interactive Robot in the Treatment of Autistic Children Under Music Perception Education |
title_sort | use of deep learning-based gesture interactive robot in the treatment of autistic children under music perception education |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866172/ https://www.ncbi.nlm.nih.gov/pubmed/35222179 http://dx.doi.org/10.3389/fpsyg.2022.762701 |
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