ArticleNPJ digital medicine2018
Automated human-level diagnosis of dysgraphia using a consumer tablet.
Article in NPJ digital medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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The trial behind it
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Who cites it
34 citing papers in PubMed, 151 citations in OpenAlex.
- 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
- Major clinical improvement in a boy with developmental disabilities and a PTPN4 mutation with intensive re-education and an enriched environment in a day care hospital: a case report.Journal of medical case reports · 2026Article
- Immunotherapy-associated dysgraphia as an early neurocognitive manifestation of immune effector cell-associated neurotoxicity syndrome: clinical characteristics, mechanistic insights, and assessment challenges.Frontiers in immunology · 2026Review
- Comprehensive search for assessment indicators that influence the level of handwriting difficulties among children in educational settings.Scientific reports · 2025Article
- Quantitative analysis of the correlation between body posture quality assessment scores and handwriting quality measures.Scientific reports · 2025Article
- Handwriting in primary school: comparing standardized tests and evaluating impact of grapho-motor parameters.Reading and writing · 2025Article
- AI-driven approaches for dysgraphia diagnosis using online and offline handwriting data: A comprehensive scoping review.PloS one · 2025Article
- Assessing handwriting skills in a web browser: Development and validation of an automated online test in Japanese Kanji.Behavior research methods · 2024Article
- Tools and Methods for Diagnosing Developmental Dysgraphia in the Digital Age: A State of the Art.Children (Basel, Switzerland) · 2023Review
- Enhancing Handwriting Performance of Children with Developmental Coordination Disorder (DCD) Using Computerized Visual Feedback.Children (Basel, Switzerland) · 2023Article
- Teaching and Rehabilitation of Handwriting for Children in the Digital Age: Issues and Challenges.Children (Basel, Switzerland) · 2023Review
- 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
- Investigating Visual Perception Impairments through Serious Games and Eye Tracking to Anticipate Handwriting Difficulties.Sensors (Basel, Switzerland) · 2023Article
- Reliability, validity, and measurement invariance of a Chinese handwriting legibility scale among primary students in central China.Frontiers in psychology · 2023Article
- Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions.Frontiers in rehabilitation sciences · 2023Review
- 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
- Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets.JMIR formative research · 2022Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The academic and behavioral progress of children is associated with the timely development of reading and writing skills. Dysgraphia, characterized as a handwriting learning disability, is usually associated with dyslexia, developmental coordination disorder (dyspraxia), or attention deficit disorder, which are all neuro-developmental disorders. Dysgraphia can seriously impair children in their everyday life and require therapeutic care. Early detection of handwriting difficulties is, therefore, of great importance in pediatrics. Since the beginning of the 20th century, numerous handwriting scales have been developed to assess the quality of handwriting. However, these tests usually involve an expert investigating visually sentences written by a subject on paper, and, therefore, they are subjective, expensive, and scale poorly. Moreover, they ignore potentially important characteristics of motor control such as writing dynamics, pen pressure, or pen tilt. However, with the increasing availability of digital tablets, features to measure these ignored characteristics are now potentially available at scale and very low cost. In this work, we developed a diagnostic tool requiring only a commodity tablet. To this end, we modeled data of 298 children, including 56 with dysgraphia. Children performed the BHK test on a digital tablet covered with a sheet of paper. We extracted 53 handwriting features describing various aspects of handwriting, and used the Random Forest classifier to diagnose dysgraphia. Our method achieved 96.6% sensibility and 99.2% specificity. Given the intra-rater and inter-rater levels of agreement in the BHK test, our technique has comparable accuracy for experts and can be deployed directly as a diagnostics tool.
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Registered trials
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.