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.
BACKGROUND Digital technologies such as mobile technology, the Internet of Things (IoT), artificial intelligence (AI), and virtual reality have revolutionized healthcare, and the COVID-19 pandemic has further highlighted the importance of implementing digital health in healthcare settings. However, the key indicators of digital health dissemination and application are yet to be identified, making it challenging to adopt digital health innovations in healthcare. Therefore, to promote digital health adoption, it is critical to identify the key indicators and applications of digital health-related technologies. OBJECTIVE This study aimed to identify the key indicators of digital health dissemination and application in healthcare settings by surveying digital health experts, workers in medical institutions, and industry experts regarding the priorities and demand for digital health adoption, and to determine if demand gaps exist. METHODS We surveyed 254 participants in South Korea between January 2022 and May 2022 to identify the key indicators, priorities, and demands related to digital health dissemination and application in healthcare settings. An Analytical Hierarchy Process (AHP) method was applied to derive the weight of the key indicators of digital health dissemination and application, and an online survey was conducted to obtain related information from workers in medical institutions and digital health-related fields. RESULTS Three surveys were conducted among 68 digital health experts and 186 medical workers to identify the key indicators of digital health dissemination and application. The results indicated that the standardization of healthcare information is essential for digital health adoption (AHP-weighted mean score: 78.11), with healthcare providers prioritizing digital health use for mental illness and chronic diseases. In addition, the findings revealed inconsistencies in the demand for digital health technology among digital health experts, workers in medical institutions, and workers in digital health-related industries. While digital health experts prioritized digital health systems, medical workers expressed a high demand for mobile healthcare and telemedicine. CONCLUSIONS Identifying the key indicators of digital health dissemination and application is crucial to facilitate the adoption of digital health. Our study found that the standardization of healthcare information is essential for digital health, with healthcare providers prioritizing its use in mental illness and chronic disease management. Prioritizing the dissemination and application of digital health at the point of care based on key indicators and the needs of healthcare providers is necessary for the successful implementation of digital health in the healthcare sector.
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