首个面向3岁以下幼儿大动作识别的数据集，助力发育迟缓早期发现
ToddlerAct: A Toddler Action Recognition Dataset for Gross Motor Development Assessment
- 构建专用于幼儿大动作识别的视频数据集，覆盖常见运动行为
- 在自建数据集上验证多类模型性能，凸显领域专用数据重要性
- 适合儿童发育研究、计算机视觉与医疗健康交叉方向学者使用
评估幼儿大动作发育对理解其身体发展及识别潜在发育迟缓或障碍至关重要。然而，现有动作识别数据集主要面向成人，缺乏适用于幼儿的多样性与特异性。本文提出ToddlerAct，一个针对三岁以下幼儿大动作识别的数据集，旨在推动早期儿童发育研究。数据集包含多种典型大动作行为的视频记录，涵盖不同年龄阶段的婴幼儿。我们详细描述了数据采集流程、标注方法及数据特性，并在该数据集上对多种先进图像与骨架基动作识别方法进行了基准测试。结果表明，领域专用数据集对准确评估幼儿大动作发育具有关键作用，为该领域未来研究奠定基础。数据集将公开于 https://github.com/ipl-uw/ToddlerAct。
原文摘要 · Abstract (English)
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.
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