Dysgraphia detection through machine learning

Drotár, Peter; Dobeš, Marek

doi:10.1038/s41598-020-78611-9

Cited by 62 publications

(43 citation statements)

References 35 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: 43%

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: 43%

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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“…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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“…Using principal component analysis, Asselborn et al (21) defined three independent dimensions and four computerized scores related to kinematics, pressure, pen tilt, and static features to characterize dysgraphia. Several authors developed machine learning methods to diagnose children with dysgraphia based on handwriting on tablets (22)(23)(24). To our knowledge, the use of ICTs in a treatment perspective is very limited.…”

Section: Introductionmentioning

confidence: 99%

“It Is Not the Robot Who Learns, It Is Me.” Treating Severe Dysgraphia Using Child–Robot Interaction

et al. 2021

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Writing disorders are frequent and impairing. However, social robots may help to improve children's motivation and to propose enjoyable and tailored activities. Here, we have used the Co-writer scenario in which a child is asked to teach a robot how to write via demonstration on a tablet, combined with a series of games we developed to train specifically pressure, tilt, speed, and letter liaison controls. This setup was proposed to a 10-year-old boy with a complex neurodevelopmental disorder combining phonological disorder, attention deficit/hyperactivity disorder, dyslexia, and developmental coordination disorder with severe dysgraphia. Writing impairments were severe and limited his participation in classroom activities despite 2 years of specific support in school and professional speech and motor remediation. We implemented the setup during his occupational therapy for 20 consecutive weekly sessions. We found that his motivation was restored; avoidance behaviors disappeared both during sessions and at school; handwriting quality and posture improved dramatically. In conclusion, treating dysgraphia using child–robot interaction is feasible and improves writing. Larger clinical studies are required to confirm that children with dysgraphia could benefit from this setup.

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Dysgraphia detection through machine learning

Cited by 62 publications

References 35 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

“It Is Not the Robot Who Learns, It Is Me.” Treating Severe Dysgraphia Using Child–Robot Interaction

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