Computational Measurement of Motor Imitation and Imitative Learning Differences in Autism Spectrum Disorder: Computational Motor Imitation Measurement in ASD.
Computational Measurement of Motor Imitation and Imitative Learning Differences in Autism Spectrum Disorder: Computational Motor Imitation Measurement in ASD.
复制标题
自闭症谱系障碍中运动模仿和模仿学习差异的计算测量：自闭症谱系障碍中的计算运动模仿测量。
DOI：
10.1145/3461615.3485426
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Tunç,Birkan
中科院分区：
文献类型：
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作者：
Zampella,CaseyJ;Sariyanidi,Evangelos;Hutchinson,AnneG;Bartley,GKeith;Schultz,RobertT;Tunç,Birkan
运动模仿是一个关键的发展技能领域，与自闭症谱系障碍 (ASD) 密切相关。然而，不同研究之间的方法差异阻碍了对自闭症谱系障碍模仿差异的程度和影响的清晰理解，这凸显了对更自动化、更精细的测量方法的需求，以提供更高的精度和一致性。在本文中，我们研究了一种新颖的运动模仿测量方法的实用性，以准确地区分患有 ASD 的青少年和典型发育 (TD) 的青少年。研究结果表明，在重复执行一项简短的简单任务后，患有自闭症谱系障碍 (ASD) 的青少年模仿身体动作的方式与 TD 青少年显着不同，并且基于从该任务得出的身体协调特征的分类器可以区分自闭症青少年和 TD 青少年，准确度为 82%。我们的方法表明，群体差异不仅是由模仿视频刺激的人际协调驱动的，而且是由人际协调驱动的。 2D 和 3D 跟踪的比较表明，两种方法都实现了 82% 的相同分类精度，这对于较大样本和一系列非实验室设置的可扩展性而言非常有前景。这项工作丰富了快速增长的文献，强调了计算行为分析在检测和表征 ASD 运动差异以及识别潜在运动生物标志物方面的前景。
Motor imitation is a critical developmental skill area that has been strongly and specifically linked to autism spectrum disorder (ASD). However, methodological variability across studies has precluded a clear understanding of the extent and impact of imitation differences in ASD, underscoring a need for more automated, granular measurement approaches that offer greater precision and consistency. In this paper, we investigate the utility of a novel motor imitation measurement approach for accurately differentiating between youth with ASD and typically developing (TD) youth. Findings indicate that youth with ASD imitate body movements significantly differently from TD youth upon repeated administration of a brief, simple task, and that a classifier based on body coordination features derived from this task can differentiate between autistic and TD youth with 82% accuracy. Our method illustrates that group differences are driven not only by interpersonal coordination with the imitated video stimulus, but also by intrapersonal coordination. Comparison of 2D and 3D tracking shows that both approaches achieve the same classification accuracy of 82%, which is highly promising with regard to scalability for larger samples and a range of non-laboratory settings. This work adds to a rapidly growing literature highlighting the promise of computational behavior analysis for detecting and characterizing motor differences in ASD and identifying potential motor biomarkers.