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Automatic assessment of fine motor development in children through hand-drawn shape images

Nai-Hsuan Hwang1, Sheng-Shan Chen1, Tun-Wen Pai2

  • 1Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan.

Pediatrics and Neonatology
|May 16, 2025
PubMed

Insights

This study introduces an automated system to assess children's fine motor skills by analyzing geometric shape drawings. The AI-powered tool accurately identifies developmental delays, aiding early intervention.

Area of Science:

  • Pediatric Neurology
  • Developmental Psychology
  • Artificial Intelligence in Healthcare

Background:

  • Fine motor skills are key indicators of neurological maturity in children.
  • Current clinical assessments are resource-intensive.
  • An automated, efficient system for fine motor skill evaluation is needed.

Purpose of the Study:

  • To develop and validate an automated system for assessing fine motor skill development in children.
  • To provide a resource-efficient alternative to traditional clinical assessments.

Main Methods:

  • An AI-based system evaluated 82 children (36-72 months) drawing five geometric shapes.
  • The system analyzed 23 features and assigned a maturity score.
  • Performance metrics included macro F1-score (0.9236) and accuracy (0.9268).

Main Results:

  • A strong positive correlation was found between fine motor skill maturity and chronological age.
  • The system successfully identified children with delayed fine motor development.
  • High accuracy in classifying geometric shapes indicates system reliability.

Conclusions:

  • The automated system effectively assesses children's fine motor maturity using structured drawing tasks.
  • The scoring system differentiates developmental stages, aiding clinical reference.
  • This online tool reduces healthcare professional burden and supports early intervention.
Abstract

Keywords:
Automated scoring systemChildren's drawingsFine motor skills

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