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FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder
Traditional training methods such as card teaching, assistive technologies (e.g., augmented reality/virtual reality games and smartphone apps), DVDs, human-computer interactions, and human-robot interactions are widely applied in autistic rehabilitation training in recent years. In this article, we...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9068315/ https://www.ncbi.nlm.nih.gov/pubmed/35528364 http://dx.doi.org/10.1155/2022/9213526 |
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author | Wan, Guobin Deng, Fuhao Jiang, Zijian Song, Sifan Hu, Di Chen, Lifu Wang, Haibo Li, Miaochun Chen, Gong Yan, Ting Su, Jionglong Zhang, Jiaming |
author_facet | Wan, Guobin Deng, Fuhao Jiang, Zijian Song, Sifan Hu, Di Chen, Lifu Wang, Haibo Li, Miaochun Chen, Gong Yan, Ting Su, Jionglong Zhang, Jiaming |
author_sort | Wan, Guobin |
collection | PubMed |
description | Traditional training methods such as card teaching, assistive technologies (e.g., augmented reality/virtual reality games and smartphone apps), DVDs, human-computer interactions, and human-robot interactions are widely applied in autistic rehabilitation training in recent years. In this article, we propose a novel framework for human-computer/robot interaction and introduce a preliminary intervention study for improving the emotion recognition of Chinese children with an autism spectrum disorder. The core of the framework is the Facial Emotion Cognition and Training System (FECTS, including six tasks to train children with ASD to match, infer, and imitate the facial expressions of happiness, sadness, fear, and anger) based on Simon Baron-Cohen's E-S (empathizing-systemizing) theory. Our system may be implemented on PCs, smartphones, mobile devices such as PADs, and robots. The training record (e.g., a tracked record of emotion imitation) of the Chinese autistic children interacting with the device implemented using our FECTS will be uploaded and stored in the database of a cloud-based evaluation system. Therapists and parents can access the analysis of the emotion learning progress of these autistic children using the cloud-based evaluation system. Deep-learning algorithms of facial expressions recognition and attention analysis will be deployed in the back end (e.g., devices such as a PC, a robotic system, or a cloud system) implementing our FECTS, which can perform real-time tracking of the imitation quality and attention of the autistic children during the expression imitation phase. In this preliminary clinical study, a total of 10 Chinese autistic children aged 3–8 are recruited, and each of them received a single 20-minute training session every day for four consecutive days. Our preliminary results validated the feasibility of the developed FECTS and the effectiveness of our algorithms based on Chinese children with an autism spectrum disorder. To verify that our FECTS can be further adapted to children from other countries, children with different cultural/sociological/linguistic contexts should be recruited in future studies. |
format | Online Article Text |
id | pubmed-9068315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-90683152022-05-05 FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder Wan, Guobin Deng, Fuhao Jiang, Zijian Song, Sifan Hu, Di Chen, Lifu Wang, Haibo Li, Miaochun Chen, Gong Yan, Ting Su, Jionglong Zhang, Jiaming Comput Intell Neurosci Research Article Traditional training methods such as card teaching, assistive technologies (e.g., augmented reality/virtual reality games and smartphone apps), DVDs, human-computer interactions, and human-robot interactions are widely applied in autistic rehabilitation training in recent years. In this article, we propose a novel framework for human-computer/robot interaction and introduce a preliminary intervention study for improving the emotion recognition of Chinese children with an autism spectrum disorder. The core of the framework is the Facial Emotion Cognition and Training System (FECTS, including six tasks to train children with ASD to match, infer, and imitate the facial expressions of happiness, sadness, fear, and anger) based on Simon Baron-Cohen's E-S (empathizing-systemizing) theory. Our system may be implemented on PCs, smartphones, mobile devices such as PADs, and robots. The training record (e.g., a tracked record of emotion imitation) of the Chinese autistic children interacting with the device implemented using our FECTS will be uploaded and stored in the database of a cloud-based evaluation system. Therapists and parents can access the analysis of the emotion learning progress of these autistic children using the cloud-based evaluation system. Deep-learning algorithms of facial expressions recognition and attention analysis will be deployed in the back end (e.g., devices such as a PC, a robotic system, or a cloud system) implementing our FECTS, which can perform real-time tracking of the imitation quality and attention of the autistic children during the expression imitation phase. In this preliminary clinical study, a total of 10 Chinese autistic children aged 3–8 are recruited, and each of them received a single 20-minute training session every day for four consecutive days. Our preliminary results validated the feasibility of the developed FECTS and the effectiveness of our algorithms based on Chinese children with an autism spectrum disorder. To verify that our FECTS can be further adapted to children from other countries, children with different cultural/sociological/linguistic contexts should be recruited in future studies. Hindawi 2022-04-27 /pmc/articles/PMC9068315/ /pubmed/35528364 http://dx.doi.org/10.1155/2022/9213526 Text en Copyright © 2022 Guobin Wan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wan, Guobin Deng, Fuhao Jiang, Zijian Song, Sifan Hu, Di Chen, Lifu Wang, Haibo Li, Miaochun Chen, Gong Yan, Ting Su, Jionglong Zhang, Jiaming FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder |
title | FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder |
title_full | FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder |
title_fullStr | FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder |
title_full_unstemmed | FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder |
title_short | FECTS: A Facial Emotion Cognition and Training System for Chinese Children with Autism Spectrum Disorder |
title_sort | fects: a facial emotion cognition and training system for chinese children with autism spectrum disorder |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9068315/ https://www.ncbi.nlm.nih.gov/pubmed/35528364 http://dx.doi.org/10.1155/2022/9213526 |
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