Rapid identification and accurate documentation of interfering and high-risk behaviors in ASD, such as aggression, self-injury, disruption, and restricted repetitive behaviors, are important in daily classroom environments for tracking intervention effectiveness and allocating appropriate resources to manage care needs. However, having a staff dedicated solely to observing is costly and uncommon in most educational settings. Recently, multiple research studies have explored developing automated, continuous, and objective tools using machine learning models to quantify behaviors in ASD. However, the majority of the work was conducted under a controlled environment and has not been validated for real-world conditions. In this work, we demonstrate that the latest advances in video-based group activity recognition techniques can quantify behaviors in ASD in real-world activities in classroom environments while preserving privacy. Our explainable model could detect the episode of problem behaviors with a 77% F1-score and capture distinctive behavior features in different types of behaviors in ASD. To the best of our knowledge, this is the first work that shows the promise of objectively quantifying behaviors in ASD in a real-world environment, which is an important step toward the development of a practical tool that can ease the burden of data collection for classroom staff.

翻译：在自闭症谱系障碍（ASD）的日常课堂环境中，快速识别并准确记录干扰性与高风险行为（如攻击、自伤、破坏性行为及受限重复行为）对于追踪干预效果及分配适当资源以满足照护需求至关重要。然而，在大多数教育环境中，配备专职观察人员成本高昂且并不常见。近期，多项研究探索利用机器学习模型开发自动化、连续且客观的工具来量化ASD行为。然而，现有工作大多在受控环境下进行，尚未在真实场景中得到验证。本研究证明，基于视频的群体活动识别技术的最新进展能够在保护隐私的前提下，量化课堂真实活动中ASD个体的行为。我们的可解释模型能以77%的F1分数检测问题行为事件，并能捕捉不同类型ASD行为的特征性行为模式。据我们所知，这是首个在真实环境中客观量化ASD行为的研究，为开发减轻课堂工作人员数据收集负担的实用工具迈出了重要一步。