Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study
BackgroundAccurate 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....
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2024-02-01
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| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2024/1/e51996 |
| _version_ | 1827345543059210240 |
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| author | Sulim Chun Sooyoung Jang Jin Yong Kim Chanyoung Ko JooHyun Lee JaeSeong Hong Yu Rang Park |
| author_facet | Sulim Chun Sooyoung Jang Jin Yong Kim Chanyoung Ko JooHyun Lee JaeSeong Hong Yu Rang Park |
| author_sort | Sulim Chun |
| collection | DOAJ |
| description |
BackgroundAccurate 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.
ObjectiveThe 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.
MethodsWe 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.
ResultsBehavioral 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.
ConclusionsUsing 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. |
| first_indexed | 2024-03-07T23:09:31Z |
| format | Article |
| id | doaj.art-ddbcc2d0e47940e88d3380227b703a56 |
| institution | Directory Open Access Journal |
| issn | 2561-326X |
| language | English |
| last_indexed | 2024-03-07T23:09:31Z |
| publishDate | 2024-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj.art-ddbcc2d0e47940e88d3380227b703a562024-02-21T16:00:34ZengJMIR PublicationsJMIR Formative Research2561-326X2024-02-018e5199610.2196/51996Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation StudySulim Chunhttps://orcid.org/0000-0003-2005-1764Sooyoung Janghttps://orcid.org/0000-0003-0746-0635Jin Yong Kimhttps://orcid.org/0000-0002-2746-2710Chanyoung Kohttps://orcid.org/0000-0002-0710-457XJooHyun Leehttps://orcid.org/0000-0002-3901-5846JaeSeong Honghttps://orcid.org/0000-0003-3228-6843Yu Rang Parkhttps://orcid.org/0000-0002-4210-2094 BackgroundAccurate 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. ObjectiveThe 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. MethodsWe 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. ResultsBehavioral 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. ConclusionsUsing 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.https://formative.jmir.org/2024/1/e51996 |
| spellingShingle | Sulim Chun Sooyoung Jang Jin Yong Kim Chanyoung Ko JooHyun Lee JaeSeong Hong Yu Rang Park Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study JMIR Formative Research |
| title | Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study |
| title_full | Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study |
| title_fullStr | Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study |
| title_full_unstemmed | Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study |
| title_short | Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study |
| title_sort | comprehensive assessment and early prediction of gross motor performance in toddlers with graph convolutional networks based deep learning development and validation study |
| url | https://formative.jmir.org/2024/1/e51996 |
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