Cargando…

DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy

In Mexico, according to data from the General Directorate of Health Information (2018), there is an annual incidence of 689 newborns with Trisomy 21, well-known as Down Syndrome. Worldwide, this incidence is estimated between 1 in every 1000 newborns, approximately. That is why this work focuses on...

Descripción completa

Detalles Bibliográficos
Autores principales: Moreno Escobar, Jesús Jaime, Morales Matamoros, Oswaldo, Aguilar del Villar, Erika Yolanda, Quintana Espinosa, Hugo, Chanona Hernández, Liliana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10454875/
https://www.ncbi.nlm.nih.gov/pubmed/37628493
http://dx.doi.org/10.3390/healthcare11162295
_version_ 1785096307029835776
author Moreno Escobar, Jesús Jaime
Morales Matamoros, Oswaldo
Aguilar del Villar, Erika Yolanda
Quintana Espinosa, Hugo
Chanona Hernández, Liliana
author_facet Moreno Escobar, Jesús Jaime
Morales Matamoros, Oswaldo
Aguilar del Villar, Erika Yolanda
Quintana Espinosa, Hugo
Chanona Hernández, Liliana
author_sort Moreno Escobar, Jesús Jaime
collection PubMed
description In Mexico, according to data from the General Directorate of Health Information (2018), there is an annual incidence of 689 newborns with Trisomy 21, well-known as Down Syndrome. Worldwide, this incidence is estimated between 1 in every 1000 newborns, approximately. That is why this work focuses on the detection and analysis of facial emotions in children with Down Syndrome in order to predict their emotions throughout a dolphin-assisted therapy. In this work, two databases are used: Exploratory Data Analysis, with a total of 20,214 images, and the Down’s Syndrome Dataset database, with 1445 images for training, validation, and testing of the neural network models. The construction of two architectures based on a Deep Convolutional Neural Network manages an efficiency of 79%, when these architectures are tested with a large reference image database. Then, the architecture that achieves better results is trained, validated, and tested in a small-image database with the facial emotions of children with Down Syndrome, obtaining an efficiency of 72%. However, this increases by 9% when the brain activity of the child is included in the training, resulting in an average precision of 81%. Using electroencephalogram (EEG) signals in a Convolutional Neural Network (CNN) along with the Down’s Syndrome Dataset (DSDS) has promising advantages in the field of brain–computer interfaces. EEG provides direct access to the electrical activity of the brain, allowing for real-time monitoring and analysis of cognitive states. Integrating EEG signals into a CNN architecture can enhance learning and decision-making capabilities. It is important to note that this work has the primary objective of addressing a doubly vulnerable population, as these children also have a disability.
format Online
Article
Text
id pubmed-10454875
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104548752023-08-26 DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy Moreno Escobar, Jesús Jaime Morales Matamoros, Oswaldo Aguilar del Villar, Erika Yolanda Quintana Espinosa, Hugo Chanona Hernández, Liliana Healthcare (Basel) Article In Mexico, according to data from the General Directorate of Health Information (2018), there is an annual incidence of 689 newborns with Trisomy 21, well-known as Down Syndrome. Worldwide, this incidence is estimated between 1 in every 1000 newborns, approximately. That is why this work focuses on the detection and analysis of facial emotions in children with Down Syndrome in order to predict their emotions throughout a dolphin-assisted therapy. In this work, two databases are used: Exploratory Data Analysis, with a total of 20,214 images, and the Down’s Syndrome Dataset database, with 1445 images for training, validation, and testing of the neural network models. The construction of two architectures based on a Deep Convolutional Neural Network manages an efficiency of 79%, when these architectures are tested with a large reference image database. Then, the architecture that achieves better results is trained, validated, and tested in a small-image database with the facial emotions of children with Down Syndrome, obtaining an efficiency of 72%. However, this increases by 9% when the brain activity of the child is included in the training, resulting in an average precision of 81%. Using electroencephalogram (EEG) signals in a Convolutional Neural Network (CNN) along with the Down’s Syndrome Dataset (DSDS) has promising advantages in the field of brain–computer interfaces. EEG provides direct access to the electrical activity of the brain, allowing for real-time monitoring and analysis of cognitive states. Integrating EEG signals into a CNN architecture can enhance learning and decision-making capabilities. It is important to note that this work has the primary objective of addressing a doubly vulnerable population, as these children also have a disability. MDPI 2023-08-14 /pmc/articles/PMC10454875/ /pubmed/37628493 http://dx.doi.org/10.3390/healthcare11162295 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Moreno Escobar, Jesús Jaime
Morales Matamoros, Oswaldo
Aguilar del Villar, Erika Yolanda
Quintana Espinosa, Hugo
Chanona Hernández, Liliana
DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy
title DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy
title_full DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy
title_fullStr DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy
title_full_unstemmed DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy
title_short DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy
title_sort ds-cnn: deep convolutional neural networks for facial emotion detection in children with down syndrome during dolphin-assisted therapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10454875/
https://www.ncbi.nlm.nih.gov/pubmed/37628493
http://dx.doi.org/10.3390/healthcare11162295
work_keys_str_mv AT morenoescobarjesusjaime dscnndeepconvolutionalneuralnetworksforfacialemotiondetectioninchildrenwithdownsyndromeduringdolphinassistedtherapy
AT moralesmatamorososwaldo dscnndeepconvolutionalneuralnetworksforfacialemotiondetectioninchildrenwithdownsyndromeduringdolphinassistedtherapy
AT aguilardelvillarerikayolanda dscnndeepconvolutionalneuralnetworksforfacialemotiondetectioninchildrenwithdownsyndromeduringdolphinassistedtherapy
AT quintanaespinosahugo dscnndeepconvolutionalneuralnetworksforfacialemotiondetectioninchildrenwithdownsyndromeduringdolphinassistedtherapy
AT chanonahernandezliliana dscnndeepconvolutionalneuralnetworksforfacialemotiondetectioninchildrenwithdownsyndromeduringdolphinassistedtherapy