Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by repetitive behaviors, narrow interests, and deficits in social interaction and communication ability. An increasing emphasis is being placed on the development of innovative digital and mobile systems for their potential in therapeutic applications outside of clinical environments. Due to recent advances in the field of computer vision, various emotion classifiers have been developed, which have potential to play a significant role in mobile screening and therapy for developmental delays that impair emotion recognition and expression. However, these classifiers are trained on datasets of predominantly neurotypical adults and can sometimes fail to generalize to children with autism. The need to improve existing classifiers and develop new systems that overcome these limitations necessitates novel methods to crowdsource labeled emotion data from children. In this paper, we present a mobile charades-style game, Guess What?, from which we derive egocentric video with a high density of varied emotion from a 90-second game session. We then present a framework for semi-automatic labeled frame extraction from these videos using meta information from the game session coupled with classification confidence scores. Results show that 94%, 81%, 92%, and 56% of frames were automatically labeled correctly for categories disgust, neutral, surprise, and scared respectively, though performance for angry and happy did not improve significantly from the baseline.

自闭症谱系障碍（ASD）是一种神经发育障碍，其特征是重复行为、兴趣狭隘以及社交互动和沟通能力缺陷。人们越来越重视创新数字和移动系统的开发，因为它们在临床环境之外的治疗应用中具有潜力。由于计算机视觉领域的最新进展，各种情绪分类器已经被开发出来，它们有可能在移动筛查和治疗损害情绪识别和表达的发育迟缓方面发挥重要作用。然而，这些分类器是在主要是神经正常成年人的数据集上进行训练的，有时无法推广到自闭症儿童。改进现有分类器并开发克服这些限制的新系统的需要需要新的方法来众包来自儿童的标记情感数据。在本文中，我们展示了一款手机字谜游戏，猜猜怎么着？，我们从 90 秒的游戏会话中得出以自我为中心的视频，该视频具有高密度的各种情感。然后，我们提出了一个框架，用于使用游戏会话中的元信息以及分类置信度分数从这些视频中半自动标记帧提取。结果显示，94%、81%、92% 和 56% 的帧分别被自动正确标记为厌恶、中性、惊讶和害怕类别，尽管愤怒和快乐的表现与基线相比没有显着改善。