Deficits in Handwriting of Individuals with Autism: a Review on Identification and Intervention Approaches

Verma, Pragya; Lahiri, Uttama

doi:10.1007/s40489-020-00234-7

Cited by 7 publications

(4 citation statements)

References 76 publications

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“…Moreover, CAI provides predictable set of actions which motivates the target users to perform the tasks (Ramdoss, Mulloy, Lang et al, 2011b). CAI has been used in the following skills development process of individuals with ASD: vocabulary, reading, writing, numeracy, laundry, setting up a table, washing dishes and social communication (Alresheed et al, 2018; Cullen & Morgan, 2015; Chia et al, 2018; Verma & Lahiri, 2021).…”

Section: Proliferation Of Technology In the Learnability Intervention...mentioning

confidence: 99%

Determinants of Technological Interventions for Children With Autism - A Systematic Review

Hasan,

Nene

2023

Journal of Educational Computing Research

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Children with Autism face several significant challenges, including deficits in both verbal and nonverbal communication, difficulties with concentration, limited interest in their surroundings, non-responsiveness, and struggles with adapting to new situations. It is imperative to consider and address these challenges when implementing technological interventions aimed at enhancing the skills of children with Autism. This study aims to identify the research emphasis on the following four determinants of technological interventions for children with Autism - (i) involvement of caregivers, (ii) design considerations, (iii) persuasive technology, (iv) psychological and physiological effects of technologies. These determinants address their deficiencies in communication, coping with new situations, responsiveness and effects of using technologies respectively. A total of seventy six review articles from 2010 to 2022 have been selected through PRISMA guidelines for this study. Most of the selected review articles have not evaluated these four determinants in their studies. This study discusses this research scope and presents future research directions to bridge this gap to develop effective technological solutions to upskill the children with Autism.

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Section: Proliferation Of Technology In the Learnability Intervention...mentioning

confidence: 99%

Determinants of Technological Interventions for Children With Autism - A Systematic Review

Hasan,

Nene

2023

Journal of Educational Computing Research

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“…Writing evaluation has shown that patients with ASD have lower handwriting scores. In addition, the handwriting quality of ASD participants is impaired with bigger size of letters and lower measures of legibility (for a review see Finnegan [62] and Verma [197]). We failed to find in the literature any automatic assessment of writing in children with ASD although recent research is available on the subject for control children [15,68].…”

Section: Writing (N = 1)mentioning

confidence: 99%

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

et al. 2022

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“…However, several literature studies demonstrated the prevalence of motor abnormalities in individuals with ASD even before the onset of language and social interaction difficulties [e.g., [6][7][8]. Motor disorders manifest heterogeneously, and researchers have investigated various aspects of motor skills affected by autism, such as walking [9][10][11][12], hand-eye coordination [13][14][15] and the reach-to-grasp movement [16,17]. Despite the relevance and presence of motor abnormalities in the clinical autistic profile currently there are no adequate tools available to accurately assess motor function in children with autism [18].…”

Section: Introductionmentioning

confidence: 99%

Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification

Luongo,

Simeoli,

Marocco

et al. 2024

PLoS ONE

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In recent years, research has been demonstrating that movement analysis, utilizing machine learning methods, can be a promising aid for clinicians in supporting autism diagnostic process. Within this field of research, we aim to explore new models and delve into the detailed observation of certain features that previous literature has identified as prominent in the classification process. Our study employs a game-based tablet application to collect motor data. We use artificial neural networks to analyze raw trajectories in a "drag and drop" task. We compare a two-features model (utilizing only raw coordinates) with a four-features model (including velocities and accelerations). The aim is to assess the effectiveness of raw data analysis and determine the impact of acceleration on autism classification. Our results revealed that both models demonstrate promising accuracy in classifying motor trajectories. The four-features model consistently outperforms the two-features model, as evidenced by accuracy values (0.90 vs. 0.76). However, our findings support the potential of raw data analysis in objectively assessing motor behaviors related to autism. While the four-features model excels, the two-features model still achieves reasonable accuracy. Addressing limitations related to sample size and noise is essential for future research. Our study emphasizes the importance of integrating intelligent solutions to enhance and assist autism traditional diagnostic process and intervention, paving the way for more effective tools in assessing motor skills.

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Deficits in Handwriting of Individuals with Autism: a Review on Identification and Intervention Approaches

Cited by 7 publications

References 76 publications

Determinants of Technological Interventions for Children With Autism - A Systematic Review

Determinants of Technological Interventions for Children With Autism - A Systematic Review

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

Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification

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