ArticleSensors (Basel, Switzerland)2021
Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 27 citations in OpenAlex.
- Toward preventive interventions in developmental coordination disorder: in need of early behavioral markers?Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Learning and Transfer of Graphomotor Skills in Three 7- to 10-Year-Old Children with Developmental Coordination Disorder: Case Reports.Children (Basel, Switzerland) · 2025Article
- [The application of machine learning in the auxiliary diagnosis of specific learning disorder].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2025Review
- AI-driven approaches for dysgraphia diagnosis using online and offline handwriting data: A comprehensive scoping review.PloS one · 2025Article
- Tools and Methods for Diagnosing Developmental Dysgraphia in the Digital Age: A State of the Art.Children (Basel, Switzerland) · 2023Review
- Handwriting Evaluation Using Deep Learning with SensoGrip.Sensors (Basel, Switzerland) · 2023Article
- Patients' Self-Report and Handwriting Performance Features as Indicators for Suspected Mild Cognitive Impairment in Parkinson's Disease.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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