Huang Chien-Yu, Yu Yen-Ting, Chen Kuan-Lin, Lien Jenn-Jier, Lin Gong-Hong, Hsieh Ching-Lin
School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan.
Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan.
JMIR Form Res. 2025 Jan 10;9:e58421. doi: 10.2196/58421.
Origami 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.
This 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.
A 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 z scores and VMI z scores using the training group. The trained models were then tested using the testing group, and the accuracy of the predicted VMI developmental status was also calculated.
The R2 of the age and the VMI trained models ranged from 0.50 to 0.73 and from 0.50 to 0.66, respectively. The AI models that obtained an R2>0.70 for the age model and an R2>0.60 for the VMI model were selected for model testing. Those models were further examined for the accuracy of the VMI developmental status, the correlations, and the mean absolute error (MAE) of both the age and the VMI models. The accuracy of the VMI developmental status was about 71%-76%. The correlations between the final predicted age z score and the real age z score ranged from 0.84 to 0.85, and the correlations of the final predicted VMI z scores to the real z scores ranged from 0.77 to 0.81. The MAE of the age models ranged from 0.42 to 0.46 and those of the VMI models ranged from 0.43 to 0.48.
Our 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.
折纸是学龄前儿童中一项受欢迎的活动，治疗师可将其用作评估工具，在临床环境中评估儿童的发育情况。它易于实施，对儿童有吸引力，且省时高效，仅需简单材料——纸张。此外，折纸作品可能反映儿童的年龄及其视觉运动整合（VMI）发育情况。然而，治疗师通常主要基于个人背景知识和临床经验来评估儿童的折纸作品，导致反馈主观且具描述性。因此，利用折纸作品确定儿童年龄和VMI发育情况的有效性缺乏实证支持。
本研究有两个主要目标。首先，我们试图将人工智能（AI）技术应用于折纸作品，以预测儿童的年龄和VMI发育情况，包括VMI水平（标准化分数）和VMI发育状态（典型、临界或延迟）。其次，我们使用从不同角度拍摄的照片的所有组合来探索AI模型的性能。
共招募了515名2至6岁的儿童，并以4:1的比例分为训练组和测试组。儿童制作折纸狗，并从8个不同角度拍摄。使用第6版贝利-布克滕尼卡视觉运动整合发育测试来评估儿童的VMI水平和发育状态。依次组合三个AI模型——ResNet-50、XGBoost和多层感知器，使用训练组来预测年龄z分数和VMI z分数。然后使用测试组对训练好的模型进行测试，并计算预测的VMI发育状态的准确性。
年龄和VMI训练模型的R2分别在0.50至0.73和0.50至0.66之间。选择年龄模型R2>0.70且VMI模型R2>0.60的AI模型进行模型测试。进一步检查这些模型的VMI发育状态准确性、相关性以及年龄和VMI模型的平均绝对误差（MAE）。VMI发育状态的准确性约为71%-76%。最终预测的年龄z分数与实际年龄z分数之间的相关性在0.84至0.85之间，最终预测的VMI z分数与实际z分数之间的相关性在0.77至0.81之间。年龄模型的MAE在0.42至0.46之间，VMI模型的MAE在0.43至0.48之间。
我们的研究结果表明，AI技术在预测儿童发育方面具有巨大潜力。AI提供的见解可能有助于治疗师更好地解读儿童在活动中的表现。
