A formal autism diagnosis can be an inefficient and lengthy process. Families may wait months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies which detect the presence of behaviors related to autism can scale access to pediatric diagnoses. This work aims to demonstrate the feasibility of deep learning technologies for detecting hand flapping from unstructured home videos as a first step towards validating whether models and digital technologies can be leveraged to aid with autism diagnoses. We used the Self-Stimulatory Behavior Dataset (SSBD), which contains 75 videos of hand flapping, head banging, and spinning exhibited by children. From all the hand flapping videos, we extracted 100 positive and control videos of hand flapping, each between 2 to 5 seconds in duration. Utilizing both landmark-driven-approaches and MobileNet V2's pretrained convolutional layers, our highest performing model achieved a testing F1 score of 84% (90% precision and 80% recall) when evaluating with 5-fold cross validation 100 times. This work provides the first step towards developing precise deep learning methods for activity detection of autism-related behaviors.
翻译:正规自闭症诊断可能是一个效率低且漫长的过程。 家庭可能等待数月或更长时间后才得到对孩子的诊断,尽管有证据表明早期干预可以导致更好的治疗结果。 检测自闭症相关行为的数字技术可以扩大儿科诊断的准入。 这项工作旨在展示深学习技术从无结构的家庭视频中探测手掌掌掌掌的可行性,作为验证模型和数字技术能否用于自闭诊断的第一步。 我们使用自闭疗法数据集(SSSBD),其中包含75个手拍、头拍和儿童旋转的视频。 从所有手拍视频中,我们提取了100个积极和控制手拍的视频,每两至五秒钟。 利用里程碑式自动手动和移动网络V2的预培训的古代文化,我们最先进的模型在用5倍交叉校验100次时,获得了84%的测试(90%的精确度和80%的回顾)。 这项工作为制定精确的深度学习行为方法,用于检测自闭症相关活动提供了第一步。