Autism Screening Using an Intelligent Toy Car

Moradi, Hadi; Amiri, Sorour E.; Ghanavi, Rozhina; Aarabi, Babak Nadjar; Pouretemad, Hamid-Reaza

doi:10.1007/978-3-319-67585-5_79

Cited by 8 publications

(17 citation statements)

References 19 publications

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“…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%

“…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%

“…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%

“…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.…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

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%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Parents, clinicians, and caregivers will benefit from this technology since the process changes decision trees and improves their correctness. When it comes to early detection, Moradi et al [25] used a smart toy automobile. In the toy car, the SVM algorithm was used to tell between healthy children and autistic youngsters.…”

Section: Asd Approaches In the Iot Fieldsmentioning

confidence: 99%

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

et al. 2023

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The behaviors of children with autism spectrum disorder (ASD) are often erratic and difficult to predict. Most of the time, they are unable to communicate effectively in their own language. Instead, they communicate using hand gestures and pointing phrases. Because of this, it can be difficult for caregivers to grasp their patients’ requirements, although early detection of the condition can make this much simpler. Assistive technology and the Internet of Things (IoT) can alleviate the absence of verbal and nonverbal communication in the community. The IoT-based solutions use machine Learning (ML) and deep learning (DL) algorithms to diagnose and enhance the lives of patients. A thorough review of ASD techniques in the setting of IoT devices is presented in this research. Identifying important trends in IoT-based health care research is the primary objective of this review. There is also a technical taxonomy for organizing the current articles on ASD algorithms and methodologies based on different factors such as AI, SS network, ML, and IoT. On the basis of criteria such as accuracy and sensitivity, the statistical and operational analyses of the examined ASD techniques are presented.

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A Survey on the Application of the Internet of Things in the Diagnosis of Autism Spectrum Disorder

Arbaoui

Hari

Saidi

2022

Advanced Technologies for Humanity

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Autism Screening Using an Intelligent Toy Car

Cited by 8 publications

References 19 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

Evaluation of Autism Spectrum Disorder Based on the Healthcare by Using Artificial Intelligence Strategies

A Survey on the Application of the Internet of Things in the Diagnosis of Autism Spectrum Disorder

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