A survey of visual and procedural handwriting analysis for neuropsychological assessment

Moetesum, Momina; Diaz, Moises; Masroor, Uzma; Siddiqi, Imran; Vessio, Gennaro

doi:10.1007/s00521-022-07185-6

Cited by 13 publications

(5 citation statements)

References 131 publications

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“…However, to the best of our knowledge, there has been no such investigation for identification of DLB or differentiation between AD and DLB, which we addressed in this study. In addition, several recent studies have investigated local temporal/spatial pattens within a drawing task, by using either hand-crafted, task-specific features (e.g., pauses before drawing numbers or hands in the CDT) [ 54, 55, 57 ], or machine-learned features extracted automatically through neural networks [ 61 ]. Those approaches differ from ours that used global features to capture the overall profile of the drawing process for each task.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Drawing Process Differentiate Alzheimer’s Disease and Dementia with Lewy Bodies

Yamada

Kobayashi

Shinkawa

et al. 2022

JAD

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Background: Early differential diagnosis of Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) is important for treatment and disease management, but it remains challenging. Although computer-based drawing analysis may help differentiate AD and DLB, it has not been extensively studied. Objective: We aimed to identify the differences in features characterizing the drawing process between AD, DLB, and cognitively normal (CN) individuals, and to evaluate the validity of using these features to identify and differentiate AD and DLB. Methods: We collected drawing data with a digitizing tablet and pen from 123 community-dwelling older adults in three clinical diagnostic groups of mild cognitive impairment or dementia due to AD (n = 47) or Lewy body disease (LBD; n = 27), and CN (n = 49), matched for their age, sex, and years of education. We then investigated drawing features in terms of the drawing speed, pressure, and pauses. Results: Reduced speed and reduced smoothness in speed and pressure were observed particularly in the LBD group, while increased pauses and total durations were observed in both the AD and LBD groups. Machine-learning models using these features achieved an area under the receiver operating characteristic curve (AUC) of 0.80 for AD versus CN, 0.88 for LBD versus CN, and 0.77 for AD versus LBD. Conclusion: Our results indicate how different types of drawing features were particularly discriminative between the diagnostic groups, and how the combination of these features can facilitate the identification and differentiation of AD and DLB.

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Section: Discussionmentioning

confidence: 99%

Characteristics of Drawing Process Differentiate Alzheimer’s Disease and Dementia with Lewy Bodies

Yamada

Kobayashi

Shinkawa

et al. 2022

JAD

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“…An input character would be compared to these templates, with the closest match assigned as the recognized character. However, this approach proved ineffective for children's handwriting due to its inherent lack of conformity [19,20]. However, there are a number of limitations identified in the template matching approaches, particularly for characters with significant variations in form.…”

Section: Children's Handwriting Classificationmentioning

confidence: 99%

Dhad—A Children’s Handwritten Arabic Characters Dataset for Automated Recognition

AlMuhaideb,

Altwaijry,

AlGhamdy

et al. 2024

Applied Sciences

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This study delves into the intricate realm of recognizing handwritten Arabic characters, specifically targeting children’s script. Given the inherent complexities of the Arabic script, encompassing semi-cursive styles, distinct character forms based on position, and the inclusion of diacritical marks, the domain demands specialized attention. While prior research has largely concentrated on adult handwriting, the spotlight here is on children’s handwritten Arabic characters, an area marked by its distinct challenges, such as variations in writing quality and increased distortions. To this end, we introduce a novel dataset, “Dhad”, refined for enhanced quality and quantity. Our investigation employs a tri-fold experimental approach, encompassing the exploration of pre-trained deep learning models (i.e., MobileNet, ResNet50, and DenseNet121), custom-designed Convolutional Neural Network (CNN) architecture, and traditional classifiers (i.e., Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP)), leveraging deep visual features. The results illuminate the efficacy of fine-tuned pre-existing models, the potential of custom CNN designs, and the intricacies associated with disjointed classification paradigms. The pre-trained model MobileNet achieved the best test accuracy of 93.59% on the Dhad dataset. Additionally, as a conceptual proposal, we introduce the idea of a computer application designed specifically for children aged 7–12, aimed at improving Arabic handwriting skills. Our concluding reflections emphasize the need for nuanced dataset curation, advanced model architectures, and cohesive training strategies to navigate the multifaceted challenges of Arabic character recognition.

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“…Several types of variables can be analyzed, depending on the tools used for the evaluation: posture, finger and arm movements, pen grip and finger pressure on the pen, in-air and on-paper durations, pen velocity, pen pressure, etc. The increasing number of publications on the analysis of the handwriting process over the past years attests to the growing interest of researchers in this field (e.g., [39][40][41][42][43]).…”

Section: Handwriting Deficitsmentioning

confidence: 99%

“…Since dysgraphia is a very heterogenous disorder encompassing a large variety of difficulties, it would be interesting to think about developing a reliable, comprehensive, and universal diagnostic tool for dysgraphia, combining computer and paper-and-pen tools. The main diagnostic features assessed with the paper-and-pen tools can be complemented with the computerized tools, which would provide precise information about the specific handwriting difficulties of each child (for a review, see [43]).…”

Section: Perspectives: Toward a Universal Standardized Test Of Dysgra...mentioning

confidence: 99%

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

Danna,

Puyjarinet,

Jolly

2023

Children

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Handwriting is a complex perceptual motor task that requires years of training and practice before complete mastery. Its acquisition is crucial, since handwriting is the basis, together with reading, of the acquisition of higher-level skills such as spelling, grammar, syntax, and text composition. Despite the correct learning and practice of handwriting, some children never master this skill to a sufficient level. These handwriting deficits, referred to as developmental dysgraphia, can seriously impact the acquisition of other skills and thus the academic success of the child if they are not diagnosed and handled early. In this review, we present a non-exhaustive listing of the tools that are the most reported in the literature for the analysis of handwriting and the diagnosis of dysgraphia. A variety of tools focusing on either the final handwriting product or the handwriting process are described here. On one hand, paper-and-pen tools are widely used throughout the world to assess handwriting quality and/or speed, but no universal gold-standard diagnostic test exists. On the other hand, several very promising computerized tools for the diagnosis of dysgraphia have been developed in the last decade, but some improvements are required before they can be available to clinicians. Based on these observations, we will discuss the pros and cons of the existing tools and the perspectives related to the development of a universal, standardized test of dysgraphia combining both paper-and-pen and computerized approaches and including different graphomotor and writing tasks.

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A survey of visual and procedural handwriting analysis for neuropsychological assessment

Cited by 13 publications

References 131 publications

Characteristics of Drawing Process Differentiate Alzheimer’s Disease and Dementia with Lewy Bodies

Characteristics of Drawing Process Differentiate Alzheimer’s Disease and Dementia with Lewy Bodies

Dhad—A Children’s Handwritten Arabic Characters Dataset for Automated Recognition

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

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