孤独症谱系障碍儿童情感面部表情的非典型化量化。

Angeliki Metallinou, Ruth B Grossman, Shrikanth Narayanan
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引用次数: 29

摘要

本研究主要对高功能自闭症儿童情感面部表情的非典型特征进行分析、量化和可视化。我们使用各种统计方法,包括功能数据分析,研究了典型发育(TD)儿童和HFA儿童的面部动作捕捉数据,以量化非典型表达特征并揭示两种人群的表达进化模式。结果表明,HFA患儿面部区域间运动的非同步性更高,面部和头部运动更粗糙,面部区域运动的范围更大。总的来说,患有HFA的受试者在他们所使用的面部表情手势上始终表现出更大的可变性。我们的分析证明了计算方法在理解行为数据方面的实用性,并为自闭症领域带来了新的见解,即通常与HFA受试者面部表情相关的非典型性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
QUANTIFYING ATYPICALITY IN AFFECTIVE FACIAL EXPRESSIONS OF CHILDREN WITH AUTISM SPECTRUM DISORDERS.

We focus on the analysis, quantification and visualization of atypicality in affective facial expressions of children with High Functioning Autism (HFA). We examine facial Motion Capture data from typically developing (TD) children and children with HFA, using various statistical methods, including Functional Data Analysis, in order to quantify atypical expression characteristics and uncover patterns of expression evolution in the two populations. Our results show that children with HFA display higher asynchrony of motion between facial regions, more rough facial and head motion, and a larger range of facial region motion. Overall, subjects with HFA consistently display a wider variability in the expressive facial gestures that they employ. Our analysis demonstrates the utility of computational approaches for understanding behavioral data and brings new insights into the autism domain regarding the atypicality that is often associated with facial expressions of subjects with HFA.

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