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

• Published in: Gigascience 2022-12, Vol.12
• Main Authors: Kim, Hye Hyeon, Kim, Jin Yong, Jang, Bong Kyung, Lee, Joo Hyun, Kim, Jong Hyun, Lee, Dong Hoon, Yang, Hee Min, Choi, Young Jo, Sung, Myung Jun, Kang, Tae Jun, Kim, Eunah, Oh, Yang Seong, Lim, Jaehyun, Hong, Soon-Beom, Ahn, Kiok, Park, Chan Lim, Kwon, Soon Myeong, Park, Yu Rang
• Format: Article
• Language: English
• Subjects: Activity recognition; Age; Age groups; Artificial Intelligence; Behavior; Child; Child Development; Child, Preschool; Children; Children & youth; Data Note; Datasets; Developmental disabilities; Disorders; Humans; Image quality; Infant; Learning; Parental behavior
• ISSN: 2047-217X; 2047-217X
• DOI: 10.1093/gigascience/giad039

Abstract 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.