Autism diagnosis presents a major challenge due to the vast heterogeneity of the condition and the elusive nature of early detection. Atypical gait and gesture patterns are dominant behavioral characteristics of autism and can provide crucial insights for diagnosis. Furthermore, these data can be collected efficiently in a non-intrusive way, facilitating early intervention to optimize positive outcomes. Existing research mainly focuses on associating facial and eye-gaze features with autism. However, very few studies have investigated movement and gesture patterns which can reveal subtle variations and characteristics that are specific to autism. To address this gap, we present an analysis of gesture and gait activity in videos to identify children with autism and quantify the severity of their condition by regressing autism diagnostic observation schedule scores. Our proposed architecture addresses two key factors: (1) an effective feature representation to manifest irregular gesture patterns and (2) a two-stream co-learning framework to enable a comprehensive understanding of its relation to autism from diverse perspectives without explicitly using additional data modality. Experimental results demonstrate the efficacy of utilizing gesture and gait-activity videos for autism analysis.

翻译：自闭症诊断面临重大挑战，这源于该病症的高度异质性以及早期检测的难以捉摸性。非典型步态与手势模式是自闭症的核心行为特征，可为诊断提供关键见解。此外，这些数据可通过非侵入性方式高效采集，有助于早期干预以优化积极预后。现有研究主要关注面部及眼动特征与自闭症的关联，但鲜有研究探索手势与步态模式，而这些模式能够揭示自闭症特有的细微变化与特征。为填补这一空白，我们提出通过视频中的手势与步态活动分析，识别自闭症儿童并通过回归自闭症诊断观察量表评分量化其病症严重程度。我们的架构主要解决两个关键问题：(1) 设计有效特征表示以展现异常手势模式；(2) 构建双流协同学习框架，在无需显式使用额外数据模态的前提下，从多维度全面理解其与自闭症的关联。实验结果表明，利用手势与步态活动视频进行自闭症分析具有显著有效性。