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在未经限制的视频中定位行动的时刻患有自闭症谱系障碍的婴儿

Halil Ismail Helvaci¹, Sen-Ching Samson Cheung^1,2, Chen-Nee Chuah²

¹Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, USA.

Proceedings. International Conference on Image Processing

|October 23, 2025

概括

本研究引入了一种高效的机器学习模型,用于使用婴儿视频进行自闭症谱系障碍 (ASD) 查. 人工智能模型准确地检测关键行为,为早期诊断和干预铺平了道路.

科学领域:

发育儿科发育儿科
医疗保健中的人工智能
计算机视觉用于行为分析.

背景情况:

自闭症谱系障碍 (ASD) 诊断和干预面临挑战,需要有效的查工具.
越来越多的自闭症患病率凸显了对可访问的自动诊断方法的需求.
现有的选工具往往缺乏效率和现实世界的应用性.

研究的目的:

引入一种基于自我注意的临时动作定位 (TAL) 模型,用于婴儿自动化ASD查.
开发一种高效实用的计算机视觉解决方案,用于在自然环境中识别与自闭症相关的行为.
使用TAL.建立基线绩效指标,用于在未修剪的婴儿视频中检测行为.

主要方法:

基于自我注意的时间行动定位 (TAL) 模型的开发.
应用TAL模型来分析未经修剪的婴儿视频,以发现与自闭症相关的行为.
专注于效率和简化的建模,以实现实际的,现实世界的部署.

主要成果:

塔尔模型在检测"面孔"方面取得了70%的准确性.
该模型在检测"外观对象"时显示了79%的准确性.
报告的准确率为"微笑"的72%,"发声"的65%.

结论:

本研究为ASD查中未修剪的婴儿视频提供了第一个端到端的TAL.
开发的模型显示了与ASD相关行为的高效和准确识别的前景.
这些发现支持计算机视觉在自然环境中早期发现自闭症的潜力.

关键词:

自闭症谱系障碍自闭症谱系障碍人类行为检测检测机器学习机器学习时间行动定位定位定位

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Autism Spectrum Disorder

Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.