Automatic Assessment of Motor Impairments in Autism Spectrum Disorders: A Systematic Review

Gargot, Thomas; Archambault, Dominique; Chétouani, Mohamed; Cohen, David; Johal, Wafa; Anzalone, Salvatore Maria

doi:10.1007/s12559-021-09940-8

Cited by 10 publications

(4 citation statements)

References 197 publications

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“…A revisão incluiu 12 estudos que investigaram os efeitos de diferentes tipos de intervenc ¸ões, como jogos, animac ¸ões, vídeos e outras atividades interativas. Além disso, [Gargot et al 2022] realizou uma revisão que avaliou a eficácia de diferentes métodos, como análise de movimento, reconhecimento de padrões e técnicas baseadas em sensores, para avaliar deficiências motoras em indivíduos com TEA.…”

Section: Trabalhos Relacionadosunclassified

Jogos Sérios como Subsídio Tecnológico na Educação de Crianças com Transtorno do Espectro Autista

Pires,

Silva,

Fernandes

et al. 2023

Anais Do XXXIV Simpósio Brasileiro De Informática Na Educação (SBIE 2023)

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Os recentes levantamentos escolares revelam um aumento significativo no número de matrículas de alunos com Transtorno do Espectro Autista (TEA) na educação infantil. Diante disso, é necessário investigar meios de intervenção pedagógica por meio da tecnologia, visando uma educação mais inclusiva. Este artigo apresenta uma Revisão Sistemática da Literatura para examinar o uso de jogos sérios como instrumento pedagógico para crianças com TEA. No total, foram selecionados 31 artigos que destacaram os principais requisitos de usabilidade, estratégias pedagógicas e tecnológicas, além das habilidades cognitivas comumente observadas por meio dos jogos sérios.

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Section: Trabalhos Relacionadosunclassified

Jogos Sérios como Subsídio Tecnológico na Educação de Crianças com Transtorno do Espectro Autista

Pires,

Silva,

Fernandes

et al. 2023

Anais Do XXXIV Simpósio Brasileiro De Informática Na Educação (SBIE 2023)

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“…Reinforcement learning is a subfield of AI (Artificial Intelligence) that guides intelligent entities' behavior based on a reward-based environment [101]. [66] in multiple stimuli, single Child-Robot Interaction (CRI) session measured head postures, joint-attention, and eye-gaze data [102] using RGBD sensors and cameras. They used CNN (Convolutional Neural Network), CVA, and CLNF (Constrained Local Neural Field), differentiating TD and ASD children.…”

Section: ) Postural and Head Movement Datamentioning

confidence: 99%

The Role of Intelligent Technologies in Early Detection of Autism Spectrum Disorder (ASD): A Scoping Review

2022

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Background: Two-year delay is reported between the first developmental concern raised by the parents and the diagnosis of ASD (Autism Spectrum Disorder), delaying the start of early intervention programs most beneficial within the first three years. Aim: Evaluate the role of technology in ASD detection by answering four research questions analyzing 1) evolution of technology, 2) use of various bio-behavioral data sources, 3) demographic categories, databases, controls, comparators, and assessment instruments, and 4) data collection, processing, and outcomes of the technology-based methods in ASD detection. Methods: Scoping review included behavioral-based ASD screening and diagnostic studies, published between 1st January 2011 to 31st December 2021 in PUBMED, SCOPUS, and IEEE Xplore databases for children under six years. The studies were assessed using the Critical Appraisal Skills Programm (CASP) and the PRISMA scoping review checklist (PRISMA-ScR). Results: The shortlisted 35 studies were categorized into seven bio-behavioral categories. The review suggested extensive use of machine learning (ML) and Deep Learning (DL) technologies with multimodal structured and unstructured data to detect infants at risk of ASD and Other developmental delays (ODD) as early as 9 to 12 months. However, the review reported various internal and external validity threats. Conclusion: Technology can significantly improve the current ASD detection process. The validation and adoption of technology can be fast-tracked by 1) designing robust study protocols, 2) executing multicultural field trials, 3) standardizing datasets, data quality and feature engineering methods, 4) recruiting statistically significant participants from ASD, typically developing (TD) and other developmental disorders (ODD) groups to ensure technological generalization, validation, and adoption outside laboratory settings.

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“…But not addressed about dysgraphia. Furthermore, the focus was just on the machine learning algorithms not on data collection or features Gargot et al [21] This study systematically reviewed the literature up to 2018 to identify computational methods for automating the assessment of motor impairments in children with ASD. It explored a wide range of motor behaviors in ASD, utilizing advanced technologies for more comprehensive and ecologically valid assessments, potentially aiding in distinguishing ASD from other motor disorders and monitoring children's progress.…”

Section: Introductionmentioning

confidence: 99%

Automated systems for diagnosis of dysgraphia in children: a survey and novel framework

Kunhoth,

Al-Maadeed,

Kunhoth

et al. 2024

IJDAR

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Learning disabilities, which primarily interfere with basic learning skills such as reading, writing, and math, are known to affect around 10% of children in the world. The poor motor skills and motor coordination as part of the neurodevelopmental disorder can become a causative factor for the difficulty in learning to write (dysgraphia), hindering the academic track of an individual. The signs and symptoms of dysgraphia include but are not limited to irregular handwriting, improper handling of writing medium, slow or labored writing, unusual hand position, etc. The widely accepted assessment criterion for all types of learning disabilities including dysgraphia has traditionally relied on examinations conducted by medical expert. However, in recent years, artificial intelligence has been employed to develop diagnostic systems for learning disabilities, utilizing diverse modalities of data, including handwriting analysis. This work presents a review of the existing automated dysgraphia diagnosis systems for children in the literature. The main focus of the work is to review artificial intelligence-based systems for dysgraphia diagnosis in children. This work discusses the data collection method, important handwriting features, and machine learning algorithms employed in the literature for the diagnosis of dysgraphia. Apart from that, this article discusses some of the non-artificial intelligence-based automated systems. Furthermore, this article discusses the drawbacks of existing systems and proposes a novel framework for dysgraphia diagnosis and assistance evaluation.

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Automatic Assessment of Motor Impairments in Autism Spectrum Disorders: A Systematic Review

Cited by 10 publications

References 197 publications

Jogos Sérios como Subsídio Tecnológico na Educação de Crianças com Transtorno do Espectro Autista

Jogos Sérios como Subsídio Tecnológico na Educação de Crianças com Transtorno do Espectro Autista

The Role of Intelligent Technologies in Early Detection of Autism Spectrum Disorder (ASD): A Scoping Review

Automated systems for diagnosis of dysgraphia in children: a survey and novel framework

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