PDF(Pubmed)
Abstract：
BACKGROUND: Accurate and timely assessment of children\'s developmental status is crucial for early diagnosis and intervention. More accurate and automated developmental assessments are essential due to the lack of trained health care providers and imprecise parental reporting. In various areas of development, gross motor development in toddlers is known to be predictive of subsequent childhood developments.
OBJECTIVE: The purpose of this study was to develop a model to assess gross motor behavior and integrate the results to determine the overall gross motor status of toddlers. This study also aimed to identify behaviors that are important in the assessment of overall gross motor skills and detect critical moments and important body parts for the assessment of each behavior.
METHODS: We used behavioral videos of toddlers aged 18-35 months. To assess gross motor development, we selected 4 behaviors (climb up the stairs, go down the stairs, throw the ball, and stand on 1 foot) that have been validated with the Korean Developmental Screening Test for Infants and Children. In the child behavior videos, we estimated each child\'s position as a bounding box and extracted human keypoints within the box. In the first stage, the videos with the extracted human keypoints of each behavior were evaluated separately using a graph convolutional networks (GCN)-based algorithm. The probability values obtained for each label in the first-stage model were used as input for the second-stage model, the extreme gradient boosting (XGBoost) algorithm, to predict the overall gross motor status. For interpretability, we used gradient-weighted class activation mapping (Grad-CAM) to identify important moments and relevant body parts during the movements. The Shapley additive explanations method was used for the assessment of variable importance, to determine the movements that contributed the most to the overall developmental assessment.
RESULTS: Behavioral videos of 4 gross motor skills were collected from 147 children, resulting in a total of 2395 videos. The stage-1 GCN model to evaluate each behavior had an area under the receiver operating characteristic curve (AUROC) of 0.79 to 0.90. Keypoint-mapping Grad-CAM visualization identified important moments in each behavior and differences in important body parts. The stage-2 XGBoost model to assess the overall gross motor status had an AUROC of 0.90. Among the 4 behaviors, \"go down the stairs\" contributed the most to the overall developmental assessment.
CONCLUSIONS: Using movement videos of toddlers aged 18-35 months, we developed objective and automated models to evaluate each behavior and assess each child\'s overall gross motor performance. We identified the important behaviors for assessing gross motor performance and developed methods to recognize important moments and body parts while evaluating gross motor performance.
OBJECTIVE: The purpose of this study was to develop a model to assess gross motor behavior and integrate the results to determine the overall gross motor status of toddlers. This study also aimed to identify behaviors that are important in the assessment of overall gross motor skills and detect critical moments and important body parts for the assessment of each behavior.
METHODS: We used behavioral videos of toddlers aged 18-35 months. To assess gross motor development, we selected 4 behaviors (climb up the stairs, go down the stairs, throw the ball, and stand on 1 foot) that have been validated with the Korean Developmental Screening Test for Infants and Children. In the child behavior videos, we estimated each child\'s position as a bounding box and extracted human keypoints within the box. In the first stage, the videos with the extracted human keypoints of each behavior were evaluated separately using a graph convolutional networks (GCN)-based algorithm. The probability values obtained for each label in the first-stage model were used as input for the second-stage model, the extreme gradient boosting (XGBoost) algorithm, to predict the overall gross motor status. For interpretability, we used gradient-weighted class activation mapping (Grad-CAM) to identify important moments and relevant body parts during the movements. The Shapley additive explanations method was used for the assessment of variable importance, to determine the movements that contributed the most to the overall developmental assessment.
RESULTS: Behavioral videos of 4 gross motor skills were collected from 147 children, resulting in a total of 2395 videos. The stage-1 GCN model to evaluate each behavior had an area under the receiver operating characteristic curve (AUROC) of 0.79 to 0.90. Keypoint-mapping Grad-CAM visualization identified important moments in each behavior and differences in important body parts. The stage-2 XGBoost model to assess the overall gross motor status had an AUROC of 0.90. Among the 4 behaviors, \"go down the stairs\" contributed the most to the overall developmental assessment.
CONCLUSIONS: Using movement videos of toddlers aged 18-35 months, we developed objective and automated models to evaluate each behavior and assess each child\'s overall gross motor performance. We identified the important behaviors for assessing gross motor performance and developed methods to recognize important moments and body parts while evaluating gross motor performance.
摘要：
背景：准确、及时地评估儿童的发育状况对于早期诊断和干预至关重要。由于缺乏训练有素的医疗保健提供者和不精确的父母报告，更准确和自动化的发展评估至关重要。在发展的各个领域，众所周知，幼儿的粗大运动发育可以预测随后的童年发展。
目的：这项研究的目的是开发一种模型来评估粗大运动行为，并将结果整合以确定幼儿的整体粗大运动状态。这项研究还旨在确定在评估总体总体运动技能方面很重要的行为，并检测关键时刻和重要的身体部位，以评估每种行为。
方法：我们使用了18-35个月幼儿的行为视频。为了评估电机总体发展，我们选择了4种行为(爬楼梯，走下楼梯,扔球,并站在1英尺上），已通过韩国婴儿和儿童发育筛查测试进行了验证。在儿童行为视频中，我们将每个孩子的位置估计为边界框，并在框内提取人类关键点。在第一阶段,使用基于图形卷积网络（GCN）的算法分别评估具有每种行为的提取的人类关键点的视频。在第一阶段模型中获得的每个标签的概率值用作第二阶段模型的输入，极端梯度提升(XGBoost)算法，预测总体运动状态。为了可解释性，我们使用梯度加权类激活映射（Grad-CAM）来识别运动过程中的重要时刻和相关身体部位。Shapley加性解释方法用于评估变量重要性，以确定对整体发展评估贡献最大的运动。
结果：从147名儿童中收集了4种粗大运动技能的行为视频，共产生2395个视频。评估每种行为的阶段1GCN模型的接受者工作特征曲线下面积（AUROC）为0.79至0.90。关键点映射Grad-CAM可视化识别了每个行为中的重要时刻以及重要身体部位的差异。评估总体粗大运动状态的阶段2XGBoost模型的AUROC为0.90。在这四种行为中，“下楼梯”对整体发展评估的贡献最大。
结论：使用18-35个月幼儿的运动视频，我们开发了客观和自动化的模型来评估每个行为和评估每个孩子的整体粗大运动表现。我们确定了评估总体电机性能的重要行为，并开发了在评估总体电机性能时识别重要力矩和身体部位的方法。
目的：这项研究的目的是开发一种模型来评估粗大运动行为，并将结果整合以确定幼儿的整体粗大运动状态。这项研究还旨在确定在评估总体总体运动技能方面很重要的行为，并检测关键时刻和重要的身体部位，以评估每种行为。
方法：我们使用了18-35个月幼儿的行为视频。为了评估电机总体发展，我们选择了4种行为(爬楼梯，走下楼梯,扔球,并站在1英尺上），已通过韩国婴儿和儿童发育筛查测试进行了验证。在儿童行为视频中，我们将每个孩子的位置估计为边界框，并在框内提取人类关键点。在第一阶段,使用基于图形卷积网络（GCN）的算法分别评估具有每种行为的提取的人类关键点的视频。在第一阶段模型中获得的每个标签的概率值用作第二阶段模型的输入，极端梯度提升(XGBoost)算法，预测总体运动状态。为了可解释性，我们使用梯度加权类激活映射（Grad-CAM）来识别运动过程中的重要时刻和相关身体部位。Shapley加性解释方法用于评估变量重要性，以确定对整体发展评估贡献最大的运动。
结果：从147名儿童中收集了4种粗大运动技能的行为视频，共产生2395个视频。评估每种行为的阶段1GCN模型的接受者工作特征曲线下面积（AUROC）为0.79至0.90。关键点映射Grad-CAM可视化识别了每个行为中的重要时刻以及重要身体部位的差异。评估总体粗大运动状态的阶段2XGBoost模型的AUROC为0.90。在这四种行为中，“下楼梯”对整体发展评估的贡献最大。
结论：使用18-35个月幼儿的运动视频，我们开发了客观和自动化的模型来评估每个行为和评估每个孩子的整体粗大运动表现。我们确定了评估总体电机性能的重要行为，并开发了在评估总体电机性能时识别重要力矩和身体部位的方法。
