Tools and Methods for Diagnosing Developmental Dysgraphia in the Digital Age: A State of the Art

Danna, Jérémy; Puyjarinet, Frédéric; Jolly, Caroline

doi:10.3390/children10121925

Cited by 2 publications

(1 citation statement)

References 83 publications

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“…To facilitate the identification procedure, Optical Character Recognition (OCR) is incorporated, particularly in languages with a limited set of letters like English [26]. It is worth noting that dysgraphia identification research [27] has focused predominantly on studying the kinematic and static data of in-process handwriting, both in alphabetic languages and Chinese [28].…”

Section: Predictions and Identification Of Dyslexia Using Handwriting...mentioning

confidence: 99%

DysDiTect: Dyslexia Identification Using CNN-Positional-LSTM-Attention Modeling with Chinese Dictation Task

Liu,

Wang,

Tong

2024

Brain Sciences

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Handwriting difficulty is a defining feature of Chinese developmental dyslexia (DD) due to the complex structure and dense information contained within compound characters. Despite previous attempts to use deep neural network models to extract handwriting features, the temporal property of writing characters in sequential order during dictation tasks has been neglected. By combining transfer learning of convolutional neural network (CNN) and positional encoding with the temporal-sequential encoding of long short-term memory (LSTM) and attention mechanism, we trained and tested the model with handwriting images of 100,000 Chinese characters from 1064 children in Grades 2–6 (DD = 483; Typically Developing [TD] = 581). Using handwriting features only, the best model reached 83.2% accuracy, 79.2% sensitivity, 86.4% specificity, and 91.2% AUC. With grade information, the best model achieved 85.0% classification accuracy, 83.3% sensitivity, 86.4% specificity, and 89.7% AUC. These findings suggest the potential of utilizing machine learning technology to identify children at risk for dyslexia at an early age.

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Section: Predictions and Identification Of Dyslexia Using Handwriting...mentioning

confidence: 99%

DysDiTect: Dyslexia Identification Using CNN-Positional-LSTM-Attention Modeling with Chinese Dictation Task

Liu,

Wang,

Tong

2024

Brain Sciences

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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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Tools and Methods for Diagnosing Developmental Dysgraphia in the Digital Age: A State of the Art

Cited by 2 publications

References 83 publications

DysDiTect: Dyslexia Identification Using CNN-Positional-LSTM-Attention Modeling with Chinese Dictation Task

DysDiTect: Dyslexia Identification Using CNN-Positional-LSTM-Attention Modeling with Chinese Dictation Task

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

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