Predicting Age and Visual-Motor Integration Using Origami Photographs: Deep Learning Study
Abstract BackgroundOrigami is a popular activity among preschool children and can be used by therapists as an evaluation tool to assess children’s development in clinical settings. It is easy to implement, appealing to children, and time-efficient, requiring only simple materi...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-01-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2025/1/e58421 |
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| Summary: | Abstract
BackgroundOrigami is a popular activity among preschool children and can be used by therapists as an evaluation tool to assess children’s development in clinical settings. It is easy to implement, appealing to children, and time-efficient, requiring only simple materials—pieces of paper. Furthermore, the products of origami may reflect children’s ages and their visual-motor integration (VMI) development. However, therapists typically evaluate children’s origami creations based primarily on their personal background knowledge and clinical experience, leading to subjective and descriptive feedback. Consequently, the effectiveness of using origami products to determine children’s age and VMI development lacks empirical support.
ObjectiveThis study had two main aims. First, we sought to apply artificial intelligence (AI) techniques to origami products to predict children’s ages and VMI development, including VMI level (standardized scores) and VMI developmental status (typical, borderline, or delayed). Second, we explored the performance of the AI models using all combinations of photographs taken from different angles.
MethodsA total of 515 children aged 2-6 years were recruited and divided into training and testing groups at a 4:1 ratio. Children created origami dogs, which were photographed from 8 different angles. The Beery–Buktenica Developmental Test of Visual-Motor Integration, 6th Edition, was used to assess the children’s VMI levels and developmental status. Three AI models—ResNet-50, XGBoost, and a multilayer perceptron—were combined sequentially to predict age zz
ResultsThe R2R2R2zzzz
ConclusionOur findings indicate that AI techniques have a significant potential for predicting children’s development. The insights provided by AI may assist therapists in better interpreting children’s performance in activities. |
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| ISSN: | 2561-326X |