ToddlerAct: A Toddler Action Recognition Dataset for Gross Motor Development Assessment
<|reference_start|>ToddlerAct: A Toddler Action Recognition Dataset for Gross Motor Development Assessment: Assessing gross motor development in toddlers is crucial for understanding their physical development and identifying potential developmental delays or disorders. However, existing datasets for action recognition primarily focus on adults, lacking the diversity and specificity required for accurate assessment in toddlers. In this paper, we present ToddlerAct, a toddler gross motor action recognition dataset, aiming to facilitate research in early childhood development. The dataset consists of video recordings capturing a variety of gross motor activities commonly observed in toddlers aged under three years old. We describe the data collection process, annotation methodology, and dataset characteristics. Furthermore, we benchmarked multiple state-of-the-art methods including image-based and skeleton-based action recognition methods on our datasets. Our findings highlight the importance of domain-specific datasets for accurate assessment of gross motor development in toddlers and lay the foundation for future research in this critical area. Our dataset will be available at https://github.com/ipl-uw/ToddlerAct.<|reference_end|>
@article{huang2024toddleract:,
    title={ToddlerAct: A Toddler Action Recognition Dataset for Gross Motor
  Development Assessment},
    author={Hsiang-Wei Huang, Jiacheng Sun, Cheng-Yen Yang, Zhongyu Jiang, Li-Yu
  Huang, Jenq-Neng Hwang, Yu-Ching Yeh},
    journal={arXiv preprint arXiv:2409.00349},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00349},
    primaryClass={cs.CV}
}