This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2872) Developmental Problems (66) Artificial Intelligence (1786) Machine Learning (1780)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51996, first published .
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

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

Authors of this article:

Sulim Chun1 Author Orcid Image ;   Sooyoung Jang1 Author Orcid Image ;   Jin Yong Kim1 Author Orcid Image ;   Chanyoung Ko1 Author Orcid Image ;   JooHyun Lee1 Author Orcid Image ;   JaeSeong Hong1 Author Orcid Image ;   Yu Rang Park1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Kvintova J, Veselsky P, Novotny J, Plevova I, Liu H, Vachova L, Petrova J, Dockalova J, Pugnerova M. Adaptation to the School Environment Moderates the Effect of Skill‐Specific Performance on Pupils' School Achievement in the First Grade of Elementary School. Psychology in the Schools 2025 View

Conference Proceedings

  1. Ye R, He Y, Gao Q, Gao Y, Shao G, Li T. 2024 International Conference on Intelligent Computing and Robotics (ICICR). Research on Tea Disease Model Based on Improved ResNet34 and Transfer Learning View