Autism Spectrum Disorder (ASD) is a diverse collection of neurobiological conditions marked by challenges in social communication and reciprocal interactions, as well as repetitive and stereotypical behaviors. Atypical behavior patterns in a long, untrimmed video can serve as biomarkers for children with ASD. In this paper, we propose a video-based weakly-supervised method that takes spatio-temporal features of long videos to learn typical and atypical behaviors for autism detection. On top of that, we propose a shallow TCN-MLP network, which is designed to further categorize the severity score. We evaluate our method on actual evaluation videos of children with autism collected and annotated (for severity score) by clinical professionals. Experimental results demonstrate the effectiveness of behavioral biomarkers that could help clinicians in autism spectrum analysis.

翻译：自闭症谱系障碍（ASD）是一组多样化的神经生物学状况，其标志性特征包括社交沟通和互动困难，以及重复性和刻板行为。在未经剪辑的长视频中，非典型行为模式可作为儿童自闭症的生物标志物。本文提出一种基于视频的弱监督方法，利用长视频的时空特征来学习典型与非典型行为，以进行自闭症检测。在此基础上，我们提出一个浅层TCN-MLP网络，旨在进一步对严重程度评分进行分类。我们在由临床专业人员收集并标注（针对严重程度评分）的真实自闭症儿童评估视频上评估了我们的方法。实验结果证明了行为生物标志物的有效性，这些标志物可辅助临床医生进行自闭症谱系分析。