Automated vs Human Recognition of Emotional Facial Expressions of High-Functioning Children with Autism in a Diagnostic-Technological Context: Explorations via a Bottom-Up Approach

Gyori, Miklos; Borsos, Zsófia; Stefanik, Krisztina; Jakab, Zoltán; Varga, Fanni; Csákvári, Judit

doi:10.1007/978-3-319-94277-3_72

Cited by 5 publications

(10 citation statements)

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“…Studies demonstrated high levels of heterogeneity across tasks used to assess diagnostic discriminative ability, the type of technology used to implement them, primary metrics evaluated and developmental domains assessed. Tasks were presented on portable technologies, such as laptops ( H. Li & Leung, 2020 ; Lu et al, 2019 ), tablet computers ( Anzulewicz et al, 2016 ; Bovery et al, 2021 ; Campbell et al, 2019 ; Carlsson et al, 2018 ; Carpenter et al, 2021 ; Chen et al, 2019 ; Chetcuti et al, 2019 ; Dawson et al, 2018 ; Fleury et al, 2013 ; Gale et al, 2019 ; Jones et al, 2018 ; Mahmoudi-Nejad et al, 2017 ; Ruta et al, 2017 ), smartphones ( Mahmoudi-Nejad et al, 2017 ; Rafique et al, 2019 ; Zhao & Lu, 2020 ), intelligent toys ( Moradi et al, 2017 ) and digital audio recorders ( Nakai et al, 2014 ; Wijesinghe et al, 2019 ), and non-portable technologies, such as desktop computers ( Aresti-Bartolome et al, 2015 ; Borsos & Gyori, 2017 ; Chaminade et al, 2015 ; Crippa et al, 2013 ; Deschamps et al, 2014 ; Dowd et al, 2012 ; Gardiner et al, 2017 ; Gyori et al, 2018 ; Hetzroni et al, 2019 ; J. Li et al, 2020 ; P. Li et al, 2016 ; Lin et al, 2013 ; Martin et al, 2018 ; Veenstra et al, 2012 ) and VR platforms of varying sophistication ( Jung et al, 2006 ; Jyoti & Lahiri, 2020 ; Alcañiz Raya et al, 2020 ;…”

Section: Resultsmentioning

confidence: 99%

“…In total, 21 studies (55.3%) used gamified tasks ( Anzulewicz et al, 2016 ; Aresti-Bartolome et al, 2015 ; Carlsson et al, 2018 ; Chaminade et al, 2015 ; Chen et al, 2019 ; Chetcuti et al, 2019 ; Crippa et al, 2013 ; Deschamps et al, 2014 ; Dowd et al, 2012 ; Fleury et al, 2013 ; Gale et al, 2019 ; Gardiner et al, 2017 ; Hetzroni et al, 2019 ; Jones et al, 2018 ; P. Li et al, 2016 ; H. Li & Leung, 2020 ; Lu et al, 2019 ; Mahmoudi-Nejad et al, 2017 ; Rafique et al, 2019 ; Ruta et al, 2017 ; Veenstra et al, 2012 ), making these the most common type of performance-based tasks to detect autism risk in early childhood. Other types of assessments included video recording of children’s behaviours while they viewed or interacted with stimuli presented on a screen ( n = 9; 23.7%) ( Borsos & Gyori, 2017 ; Bovery et al, 2021 ; Campbell et al, 2019 ; Carpenter et al, 2021 ; Dawson et al, 2018 ; Gyori et al, 2018 ; J. Li et al, 2020 ; Martin et al, 2018 ; Zhao & Lu, 2020 ), tasks using VR platforms ( n = 4; 10.5%) ( Jung et al, 2006 ; Jyoti & Lahiri, 2020 ; Alcañiz Raya et al, 2020 ; Shahab et al, 2017 ) and audio recording of children’s speech ( n = 2; 5.2%) ( Nakai et al, 2014 ; Wijesinghe et al, 2019 ). One study used a toy car with an embedded accelerometer to record the child’s movement characteristics while they played with the toy ( Moradi et al, 2017 ).…”

Section: Resultsmentioning

confidence: 99%

“…The two studies assessing speech and language ( Lin et al, 2013 ; Nakai et al, 2014 ) used very different metrics to assess group differences (pitch characteristics vs accuracy), so no comparison was possible. Similarly, the results from facial expression analysis using the Noldus FaceReader ( Borsos & Gyori, 2017 ; Gyori et al, 2018 ) were too preliminary to determine their utility for use as autism screening measures.…”

Section: Resultsmentioning

confidence: 99%

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Digital tools for direct assessment of autism risk during early childhood: A systematic review

Mukherjee

Bhavnani

Estrin

et al. 2022

Autism

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Current challenges in early identification of autism spectrum disorder lead to significant delays in starting interventions, thereby compromising outcomes. Digital tools can potentially address this barrier as they are accessible, can measure autism-relevant phenotypes and can be administered in children’s natural environments by non-specialists. The purpose of this systematic review is to identify and characterise potentially scalable digital tools for direct assessment of autism spectrum disorder risk in early childhood. In total, 51,953 titles, 6884 abstracts and 567 full-text articles from four databases were screened using predefined criteria. Of these, 38 met inclusion criteria. Tasks are presented on both portable and non-portable technologies, typically by researchers in laboratory or clinic settings. Gamified tasks, virtual-reality platforms and automated analysis of video or audio recordings of children’s behaviours and speech are used to assess autism spectrum disorder risk. Tasks tapping social communication/interaction and motor domains most reliably discriminate between autism spectrum disorder and typically developing groups. Digital tools employing objective data collection and analysis methods hold immense potential for early identification of autism spectrum disorder risk. Next steps should be to further validate these tools, evaluate their generalisability outside laboratory or clinic settings, and standardise derived measures across tasks. Furthermore, stakeholders from underserved communities should be involved in the research and development process. Lay abstract The challenge of finding autistic children, and finding them early enough to make a difference for them and their families, becomes all the greater in parts of the world where human and material resources are in short supply. Poverty of resources delays interventions, translating into a poverty of outcomes. Digital tools carry potential to lessen this delay because they can be administered by non-specialists in children’s homes, schools or other everyday environments, they can measure a wide range of autistic behaviours objectively and they can automate analysis without requiring an expert in computers or statistics. This literature review aimed to identify and describe digital tools for screening children who may be at risk for autism. These tools are predominantly at the ‘proof-of-concept’ stage. Both portable (laptops, mobile phones, smart toys) and fixed (desktop computers, virtual-reality platforms) technologies are used to present computerised games, or to record children’s behaviours or speech. Computerised analysis of children’s interactions with these technologies differentiates children with and without autism, with promising results. Tasks assessing social responses and hand and body movements are the most reliable in distinguishing autistic from typically developing children. Such digital tools hold immense potential for early identification of autism spectrum disorder risk at a large scale. Next steps should be to further validate these tools and to evaluate their applicability in a variety of settings. Crucially, stakeholders from underserved communities globally must be involved in this research, lest it fail to capture the issues that these stakeholders are facing.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Digital tools for direct assessment of autism risk during early childhood: A systematic review

Mukherjee

Bhavnani

Estrin

et al. 2022

Autism

View full text Add to dashboard Cite

show abstract

“…Applied Intelligence [Marchi et al 2019, Dapogny et al 2018, Piana et al 2021, Silva et al 2021, Garcia-Garcia et al 2019, Yaneva et al 2018, Reyes et al 2020, Gomez et al 2018, Gyori et al 2018, Silva et al 2019, Ghanouni et al 2021, Anzalone et al 2019, Kirst et al 2022 Existem diversas abordagens do uso da IA, como Aprendizado de Máquina (AM), Reconhecimento de Padrões (RP), Sistema Multiagente (SM) e Visão Computacional (VC). Inclusive, também foi identificado o uso de Ontologia de Domínio para classificar e definir construtos para jogos sérios, que podem ser aplicados em combinac ¸ão com AM e SM.…”

Section: Et01unclassified

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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“…The present study is part of a research-and-development project with the long-term aim of developing and validating the concept and prototype of a social serious-gamebased digital system for the screening of high-functioning cases of ASD among children at kindergarten age [58][59][60][61]. A partial prototype of the screening system is under validation and further development; the data analyzed in the present study were collected with this prototype.…”

Section: Introductionmentioning

confidence: 99%

Test–Retest Reliability in Automated Emotional Facial Expression Analysis: Exploring FaceReader 8.0 on Data from Typically Developing Children and Children with Autism

et al. 2022

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Automated emotional facial expression analysis (AEFEA) is used widely in applied research, including the development of screening/diagnostic systems for atypical human neurodevelopmental conditions. The validity of AEFEA systems has been systematically studied, but their test–retest reliability has not been researched thus far. We explored the test–retest reliability of a specific AEFEA software, Noldus FaceReader 8.0 (FR8; by Noldus Information Technology). We collected intensity estimates for 8 repeated emotions through FR8 from facial video recordings of 60 children: 31 typically developing children and 29 children with autism spectrum disorder. Test–retest reliability was imperfect in 20% of cases, affecting a substantial proportion of data points; however, the test–retest differences were small. This shows that the test–retest reliability of FR8 is high but not perfect. A proportion of cases which initially failed to show perfect test–retest reliability reached it in a subsequent analysis by FR8. This suggests that repeated analyses by FR8 can, in some cases, lead to the “stabilization” of emotion intensity datasets. Under ANOVA, the test–retest differences did not influence the pattern of cross-emotion and cross-group effects and interactions. Our study does not question the validity of previous results gained by AEFEA technology, but it shows that further exploration of the test–retest reliability of AEFEA systems is desirable.

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Automated vs Human Recognition of Emotional Facial Expressions of High-Functioning Children with Autism in a Diagnostic-Technological Context: Explorations via a Bottom-Up Approach

Cited by 5 publications

References 10 publications

Digital tools for direct assessment of autism risk during early childhood: A systematic review

Digital tools for direct assessment of autism risk during early childhood: A systematic review

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

Test–Retest Reliability in Automated Emotional Facial Expression Analysis: Exploring FaceReader 8.0 on Data from Typically Developing Children and Children with Autism

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