Evaluation of Digitalized Handwriting for Dysgraphia Detection Using Random Forest Classification Method

Dankovicova, Zuzana; Hurtuk, Ján; Fecil'ak, Peter

doi:10.1109/sisy47553.2019.9111567

Cited by 15 publications

(7 citation statements)

References 16 publications

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“…Thus, irregularities in writing should not be filtered, but instead interpreted as they are relevant for detecting children with dysgraphia. The classification performances presented are a little bit below the ones given in some recent papers [3,34], but close or better than some others [16,[35][36][37], and they are presented in TABLE 6. The results in these papers are good and very interesting but each of these articles has a limitation that seems to make it unfit to be extended for a large-scale dysgraphia pre-diagnosis.…”

Section: Comments On the Feature Selected For The Final Modelsupporting

confidence: 44%

“…On the contrary, we have included children with dysgraphia from schools, improving generalizability of the model if used on populations not from medical centers. On the other hand, [16,35,36] only focus on a small number of children (91 and 78), which can be an issue especially when the data have high dimensions. Moreover [16] and [35] use writing on a tablet device (Android), which is known to affect movement control and execution [10][11][12].…”

Section: Comments On the Feature Selected For The Final Modelmentioning

confidence: 99%

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Development of a Pre-Diagnosis Tool Based on Machine Learning Algorithms on the BHK Test to Improve the Diagnosis of Dysgraphia

Deschamps¹,

Devillaine²,

Gaffet³

et al. 2021

AAIML

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Dysgraphia is a writing disorder that affects a significant part of the population, especially school aged children and particularly boys. Nowadays, dysgraphia is insufficiently diagnosed, partly because of the cumbersomeness of the existing tests. This study aims at developing an automated pre-diagnosis tool for dysgraphia allowing a wide screening among children. Indeed, a wider screening of the population would allow a better care for children with handwriting deficits. This study is based on the world's largest known database of handwriting samples and uses supervised learning algorithms (Support Vector Machine). Four graphic tablets and two acquisition software solutions were used, in order to ensure that the tool is not tablet dependent and can be used widely. A total of 580 children from 2nd to 5th grade, among which 122 with dysgraphia, were asked to perform the French version of the BHK test on a graphic tablet. Almost a hundred features were developed from these written tracks. The hyperparameters of the SVM and the most discriminating features between children with and without dysgraphia were selected on the training dataset comprised of 80% of the database (461 children). With these hyperparameters and features, the performances on the test dataset (119 children) were a sensitivity of 91% and a specificity of 81% for the detection of children with dysgraphia. Thus, our tool has an accuracy level similar to a human examiner. Moreover, it is widely usable, because of its independence to the tablet, to the acquisition software and to the age of the children thanks to a careful calibration and the use of a moving z-score calculation.

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Section: Comments On the Feature Selected For The Final Modelsupporting

confidence: 44%

Section: Comments On the Feature Selected For The Final Modelmentioning

confidence: 99%

Development of a Pre-Diagnosis Tool Based on Machine Learning Algorithms on the BHK Test to Improve the Diagnosis of Dysgraphia

Deschamps¹,

Devillaine²,

Gaffet³

et al. 2021

AAIML

View full text Add to dashboard Cite

show abstract

“…DYS/TD Repartition Model Used Score [11] 27/27 Random Forest 96.43% [12] 56/242 Random Forest 97.9% [13] 48/43 SVM (RBF Kernel) 82.51% [16] 24/971 RNN 90% 1 [13] 42/36 Random Forest 67% [14] 42/36 SVM 66% [14] 42/36 AdaBoost 64% [15] 57/63 Random Forest 77.6% 2 [15] 57/63 SVM 78.8% 2 [15] 57/63 AdaBoost 79.5% 2 [20] 122/458 SVM (RBF Kernel) 83%…”

Section: Referencementioning

confidence: 99%

“…Because we have a labeled database, we worked with supervised machine learning algorithms for our study. Several classes of algorithms have been used in the literature [11][12][13][14][15][16]40], therefore 9 models were tested, with different sets of hyperparameters, to select the best ones for each model.…”

Section: Preprocessing Stepsmentioning

confidence: 99%

“…The aim of this study is to design a pre-diagnosis tool that would help the examiners and give a first insight into the writing difficulties children can have. Several attempts to create such a tool have already been published [11][12][13][14][15][16][17]. They generally involve graphic tablets that are able to record the path of the pen as a function of time, and usually a classification model that will do a prediction task, with the notable exception of [16].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia

Devillaine

Lambert

Boutet

et al. 2021

Sensors

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Five to ten percent of school-aged children display dysgraphia, a neuro-motor disorder that causes difficulties in handwriting, which becomes a handicap in the daily life of these children. Yet, the diagnosis of dysgraphia remains tedious, subjective and dependent to the language besides stepping in late in the schooling. We propose a pre-diagnosis tool for dysgraphia using drawings called graphomotor tests. These tests are recorded using graphical tablets. We evaluate several machine-learning models and compare them to build this tool. A database comprising 305 children from the region of Grenoble, including 43 children with dysgraphia, has been established and diagnosed by specialists using the BHK test, which is the gold standard for the diagnosis of dysgraphia in France. We performed tests of classification by extracting, correcting and selecting features from the raw data collected with the tablets and achieved a maximum accuracy of 73% with cross-validation for three models. These promising results highlight the relevance of graphomotor tests to diagnose dysgraphia earlier and more broadly.

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Comparison of Data Collection Models in an Intelligent Tutoring System for the Inclusive Education of the Learning-Disabled

Dutt

Ahuja

2023

Lecture Notes in Networks and Systems

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Evaluation of Digitalized Handwriting for Dysgraphia Detection Using Random Forest Classification Method

Cited by 15 publications

References 16 publications

Development of a Pre-Diagnosis Tool Based on Machine Learning Algorithms on the BHK Test to Improve the Diagnosis of Dysgraphia

Development of a Pre-Diagnosis Tool Based on Machine Learning Algorithms on the BHK Test to Improve the Diagnosis of Dysgraphia

Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia

Comparison of Data Collection Models in an Intelligent Tutoring System for the Inclusive Education of the Learning-Disabled

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