Abstract
Autism Spectrum Disorder (ASD) presents significant challenges in early diagnosis and intervention, impacting children and their families. With prevalence rates rising, there is a critical need for accessible and efficient screening tools. Leveraging machine learning (ML) techniques, in particular Temporal Action Localization (TAL), holds promise for automating ASD screening. This paper introduces a self-attention based TAL model designed to identify ASD-related behaviors in infant videos. Unlike existing methods, our approach simplifies complex modeling and emphasizes efficiency, which is essential for practical deployment in real-world scenarios. Importantly, this work underscores the importance of developing computer vision methods capable of operating in naturilistic environments with little equipment control, addressing key challenges in ASD screening. This study is the first to conduct end-to-end temporal action localization in untrimmed videos of infants with ASD, offering promising avenues for early intervention and support. We report baseline results of behavior detection using our TAL model. We achieve 70% accuracy for look face, 79% accuracy for look object, 72% for smile and 65% for vocalization.
Abstract (translated)
autism spectrum disorder（ASD）在早期诊断和干预方面带来了显著的挑战，影响了儿童及其家庭。随着发病率的上升，对于可访问和高效的筛查工具的需求越来越迫切。利用机器学习（ML）技术，特别是Temporal Action Localization（TAL）技术，ASD筛查有望实现自动化。本文介绍了一种基于自注意力的TAL模型，用于识别婴儿视频中的ASD相关行为。与现有方法不同，我们的方法简化了复杂建模，突出了效率，这对于在现实场景中进行实际部署至关重要。重要的是，这项工作强调了开发能够在自然环境中运行的计算机视觉方法的重要性，该方法具有少量的设备控制，这是ASD筛查中的关键挑战。本研究是第一个对带有ASD的婴儿视频进行端到端TAL的，为早期干预和支持提供了有前途的途径。我们报告了使用我们的TAL模型的行为检测结果。我们获得了70%的看脸准确率，79%的看物准确率，72%的微笑准确率和65%的语音准确率。
URL
https://arxiv.org/abs/2404.05849
