多视图儿童运动发育数据集用于人工智能驱动的儿童发育评估
Hye Hyeon Kim1, Jin Yong Kim1, Bong Kyung Jang1
1Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
GigaScience
|May 27, 2023
概括
这项研究使用韩国婴儿和儿童发育查测试 (K-DST) 的骨架数据创建了一个新的AI-ready儿童运动技能数据集. 这一数据集有助于开发人工智能模型,用于早期检测发育障碍.
科学领域:
- 儿科发展评估儿童的发展评估.
- 医疗保健中的人工智能
- 儿童运动发育 儿童运动发育
背景情况:
- 儿童运动发育是早期识别发育障碍的关键.
- 韩国婴儿和儿童发育查测试 (K-DST) 是有价值的,但依赖于主观的父母报告.
- 需要客观,数据驱动的工具来进行发育查.
研究的目的:
- 构建一个新的,公开可用的儿童运动行为数据集.
- 为了使人工智能模型的开发能够用于客观的发育查.
- 提高发展评估的准确性和可靠性.
主要方法:
- 收集了339名儿童 (20-71个月) 从多个角度执行K-DST总运动行为的视频.
- 从视频录制中提取了骨数据.
- 为正确的行为执行注释数据,并使用AI动作识别模型进行验证.
主要成果:
- 在使用人工智能动作识别模型的三个年龄组中,实现了高测试准确率 (93.94%, 87.50%, 96.31%).
- 证明了数据集对人工智能学习的有效性.
- 发现使用多视图数据训练的模型表现最好.
结论:
- 这是根据K-DST标准在儿童身上基于骨架的动作识别的第一个公共数据集.
- 该数据集有助于创建用于发育测试和查的多种AI模型.
- 它为更客观和可扩展的儿童发展评估提供了基础.
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