Multiview child motor development dataset for AI-driven assessment of child development

Kim, Hye Hyeon; Kim, Jin Yong; Jang, Bumjoon; Lee, JooHyun; Kim, Jong Hyun; Lee, Dong Hoon; Yang, Hee Min; Choi, Young; Sung, Myung Jun; Kang, Tae Jun; Kim, Eun-Ah; Oh, Yang Seong; Lim, Jae-Hyun; Hong, Soon-Beom; Ahn, Kwang‐Soon; Park, Chan Lim; Kwon, Soonhak; Park, Yu Rang

doi:10.1093/gigascience/giad039

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The data set used in this study is from our previous study and it was constructed while adhering to the ethical principles of the Declaration of Helsinki [ 28 ]. The construction of the data set was approved by the Institutional Review Board of Severance Hospital, Yonsei University College of Medicine (4-2021-0845), and the requirement for informed consent was waived due to the retrospective nature of the study.…”

Section: Methodsmentioning

confidence: 99%

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Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study

Chun,

Jang,

Kim

et al. 2024

JMIR Form Res

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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.

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Section: Methodsmentioning

confidence: 99%

“…We used publicly available data from our previous research [ 28 ]. These data are available on GitHub [ 46 ].…”

Section: Data Availabilitymentioning

confidence: 99%

Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study

Chun,

Jang,

Kim

et al. 2024

JMIR Form Res

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Multiview child motor development dataset for AI-driven assessment of child development

Kim

Jang

et al. 2022

GigaScience

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Background Children's motor development is a crucial tool for assessing developmental levels, identifying developmental disorders early, and taking appropriate action. Although the Korean Developmental Screening Test for Infants and Children (K-DST) can accurately assess childhood development, its dependence on parental surveys rather than reliable, professional observation limits it. This study constructed a dataset based on a skeleton of recordings of K-DST behaviors in children aged between 20 and 71 months, with and without developmental disorders. The dataset was validated using a child behavior artificial intelligence (AI) learning model to highlight its possibilities. Results The 339 participating children were divided into 3 groups by age. We collected videos of 4 behaviors by age group from 3 different angles and extracted skeletons from them. The raw data were used to annotate labels for each image, denoting whether each child performed the behavior properly. Behaviors were selected from the K-DST's gross motor section. The number of images collected differed by age group. The original dataset underwent additional processing to improve its quality. Finally, we confirmed that our dataset can be used in the AI model with 93.94%, 87.50%, and 96.31% test accuracy for the 3 age groups in an action recognition model. Additionally, the models trained with data including multiple views showed the best performance. Conclusion Ours is the first publicly available dataset that constitutes skeleton-based action recognition in young children according to the standardized criteria (K-DST). This dataset will enable the development of various models for developmental tests and screenings.

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Burn trauma in infants

Yiğit

Taş

2023

Egypt Pediatric Association Gaz

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Background A wealth of data exists concerning pediatric burn epidemiology in general, but more is explicitly needed in infants under 1 year of age, a particular group in which mobility begins to develop. Methods In this study,206 patients followed up for burns in Diyarbakır Gazi Yaşargil Training and Research Hospital Burn Center between January 2016 and September 2021 were included in the study. Results Of the patients, 120 (58.3%) were male, and 86 (41.7%) were female. The mean age was 10.0 ± 2.0 months. The total number of cases ranged from 3 to 12 months, with the incidence of burns peaking at 12 months. When we look at the etiology of burns, scalding burns were the most common, with 184 (89.3%) cases. Most of the burns occurred in the kitchen because their mothers and children spent a lot of time there. It was positive in 23.8% of the wound culture results obtained with a punch biopsy. Staphylococcus aureus was the most abundant microorganism in wound culture results, with 5.8%. The mean hospital stay of the patients was 4.99 ± 3.67 days. Our mortality rate was 1.94% (n:4). Conclusion In conclusion, As preventive measures, it is necessary to increase protective measures at home and to raise awareness of families about burn etiologies.

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Multiview child motor development dataset for AI-driven assessment of child development

Cited by 4 publications

References 29 publications

Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study

Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study

Multiview child motor development dataset for AI-driven assessment of child development

Burn trauma in infants

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