ArticleScientific reports2020
Dysgraphia detection through machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Digital Transition and Mental Health in School Settings: Does Handwriting Still Matter? A Multidisciplinary Perspective.Children (Basel, Switzerland) · 2026Review
- Quantitative analysis of handwriting kinematics in primary and lower secondary school children through a sensorized ink pen: A cross-sectional population-based study.PLOS digital health · 2026Article
- [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
- Clinical Application of Machine Learning in Biomedical Engineering for the Early Detection of Neurological Disorders.Annals of biomedical engineering · 2025Article
- AI-driven approaches for dysgraphia diagnosis using online and offline handwriting data: A comprehensive scoping review.PloS one · 2025Article
- Analyzing handwriting legibility through hand kinematics.Frontiers in artificial intelligence · 2025Article
- Analyzing Arabic Handwriting Style through Hand Kinematics.Sensors (Basel, Switzerland) · 2024Article
- Development of potential dysgraphia handwriting dataset.Data in brief · 2024Article
- Tools and Methods for Diagnosing Developmental Dysgraphia in the Digital Age: A State of the Art.Children (Basel, Switzerland) · 2023Review
- Teaching and Rehabilitation of Handwriting for Children in the Digital Age: Issues and Challenges.Children (Basel, Switzerland) · 2023Review
- Attenuated Total Reflection Fourier Transform Infrared Spectroscopy for Forensic Screening of Long-Term Alcohol Consumption from Human Nails.ACS omega · 2023Article
- A screening method for cervical myelopathy using machine learning to analyze a drawing behavior.Scientific reports · 2023Article
- Handwriting Evaluation Using Deep Learning with SensoGrip.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application.Life (Basel, Switzerland) · 2023Article
- Assessing handwriting task difficulty levels through kinematic features: a deep-learning approach.Frontiers in robotics and AI · 2023Article
- Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage.Scientific reports · 2022Article
- RALF: an adaptive reinforcement learning framework for teaching dyslexic students.Multimedia tools and applications · 2022Article
- Analysis of Graphomotor Tests with Machine Learning Algorithms for an Early and Universal Pre-Diagnosis of Dysgraphia.Sensors (Basel, Switzerland) · 2021Article
- "It Is Not the Robot Who Learns, It Is Me." Treating Severe Dysgraphia Using Child-Robot Interaction.Frontiers in psychiatry · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.