在自闭症谱系障碍中探索面部动态的复杂性
Pradeep Raj Krishnappa Babu1, J Matias Di Martino1, Zhuoqing Chang1
1Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
IEEE transactions on affective computing
|June 2, 2023
概括
研究人员使用计算机视觉分析幼儿的面部运动,发现患有自闭症谱系障碍 (ASD) 的儿童表现出不太可预测的面部动态,为早期检测提供了潜在的新生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 发展心理学 发展心理学
- 计算机视觉 计算机视觉
背景情况:
- 非典型的面部表情,标志着不规则和不协调的运动,是自闭症谱系障碍 (ASD) 的早期指标.
- 对这些面部行为的自动分析可以为ASD查,诊断和监测提供有价值的生物标志物.
- 目前的诊断方法可能无法完全捕捉ASD早期行为症状的细微差别.
研究的目的:
- 研究计算机视觉算法的潜力,以自动量化幼儿面部里程碑动态.
- 确定面部运动的复杂性分析是否可以区分ASD的幼儿与典型的发育同龄人.
- 通过面部运动分析,探索用于早期ASD检测的新生物标志物.
主要方法:
- 40名患有自闭症的幼儿和396名典型发育的幼儿在儿科访问期间在平板电脑上观看有吸引力的电影.
- 一个平板电脑的前置摄像头记录了幼儿的面部镜头.
- 计算机视觉算法计算了面部里程碑动态,多尺度估计了眉毛和口腔区域的复杂性.
主要成果:
- 与典型发育的幼儿相比,患有自闭症的幼儿在面部里程碑动态上表现出明显更高的复杂性 (更少的可预测性).
- 计算的面部复杂性提供了超越传统面部影响分析的信息.
- 面部动态的这种复杂性是早期自闭症相关行为差异的潜在指标.
结论:
- 对面部里程碑运动的计算机视觉分析显示,它是一个有前途的工具,用于检测和量化ASD早期行为症状.
- 量化面部动态为理解与自闭症相关的发育差异提供了一种新的客观方法.
- 这种方法可以增强自闭症谱系障碍的早期识别和干预策略.
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