Toward an Automated Measure of Social Engagement for Children With Autism Spectrum Disorder-A Personalized Computational Modeling Approach.
Toward an Automated Measure of Social Engagement for Children With Autism Spectrum Disorder-A Personalized Computational Modeling Approach.
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DOI：
10.3389/frobt.2020.00043
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发表时间：
2020
影响因子：
3.4
通讯作者：
Park CH
中科院分区：
文献类型：
--
作者：
Javed H;Lee W;Park CH
社会参与是一个人的社会情绪和认知状态的关键指标。对于自闭症谱系障碍（ASD）儿童，这是评估互动和干预质量的重要因素。到目前为止，社会参与的定性测量方法已被广泛应用于研究和实践中，但一个可靠的，客观的，定量的测量方法还没有被广泛接受和使用。在本文中，我们介绍了我们的工作，在ASD儿童的社会参与的自动化测量，可以在现实世界中使用的长期临床监测儿童的社会行为，以及正在使用的干预方法的评估框架的发展。我们提出了一种计算建模方法，以获得基于用户研究的4至12岁的儿童的社会参与度指标。这项研究是在儿童-机器人互动环境中进行的，目标是儿童的感官处理技能。我们收集了来自受试者的视频、音频和运动跟踪数据，并通过训练多通道和多层卷积神经网络来生成个性化的社交参与模型。然后，我们通过将其与传统分类器进行比较来评估该网络的性能，并评估其局限性，然后讨论下一步如何为ASD的社会参与找到一个全面而准确的指标。
Social engagement is a key indicator of an individual's socio-emotional and cognitive states. For a child with Autism Spectrum Disorder (ASD), this serves as an important factor in assessing the quality of the interactions and interventions. So far, qualitative measures of social engagement have been used extensively in research and in practice, but a reliable, objective, and quantitative measure is yet to be widely accepted and utilized. In this paper, we present our work on the development of a framework for the automated measurement of social engagement in children with ASD that can be utilized in real-world settings for the long-term clinical monitoring of a child's social behaviors as well as for the evaluation of the intervention methods being used. We present a computational modeling approach to derive the social engagement metric based on a user study with children between the ages of 4 and 12 years. The study was conducted within a child-robot interaction setting that targets sensory processing skills in children. We collected video, audio and motion-tracking data from the subjects and used them to generate personalized models of social engagement by training a multi-channel and multi-layer convolutional neural network. We then evaluated the performance of this network by comparing it with traditional classifiers and assessed its limitations, followed by discussions on the next steps toward finding a comprehensive and accurate metric for social engagement in ASD.