Abstract：
The increasing prevalence of behavioral disorders in children is of growing concern within the medical community. Recognising the significance of early identification and intervention for atypical behaviors, there is a consensus on their pivotal role in improving outcomes. Due to inadequate facilities and a shortage of medical professionals with specialized expertise, traditional diagnostic methods have been unable to effectively address the rising incidence of behavioral disorders. Hence, there is a need to develop automated approaches for the diagnosis of behavioral disorders in children, to overcome the challenges with traditional methods. The purpose of this study is to develop an automated model capable of analyzing videos to differentiate between typical and atypical repetitive head movements in. To address problems resulting from the limited availability of child datasets, various learning methods are employed to mitigate these issues. In this work, we present a fusion of transformer networks, and Non-deterministic Finite Automata (NFA) techniques, which classify repetitive head movements of a child as typical or atypical based on an analysis of gender, age, and type of repetitive head movement, along with count, duration, and frequency of each repetitive head movement. Experimentation was carried out with different transfer learning methods to enhance the performance of the model. The experimental results on five datasets: NIR face dataset, Bosphorus 3D face dataset, ASD dataset, SSBD dataset, and the Head Movements in the Wild dataset, indicate that our proposed model has outperformed many state-of-the-art frameworks when distinguishing typical and atypical repetitive head movements in children.
摘要：
儿童行为障碍的患病率越来越高，这在医学界引起了越来越多的关注。认识到早期识别和干预非典型行为的重要性，他们在改善成果方面的关键作用已经达成共识。由于设施不足和缺乏具有专业知识的医疗专业人员，传统的诊断方法已无法有效解决行为障碍发病率上升的问题。因此，有必要开发自动诊断儿童行为障碍的方法，克服传统方法的挑战。这项研究的目的是开发一种自动模型，该模型能够分析视频以区分典型和非典型的重复头部运动。为了解决由于子数据集的可用性有限而导致的问题，采用各种学习方法来缓解这些问题。在这项工作中,我们提出了变压器网络的融合，和非确定性有限自动机(NFA)技术，根据对性别的分析，将儿童的重复头部运动分类为典型或非典型，年龄,和重复头部运动的类型，还有伯爵,持续时间,和每个重复的头部运动的频率。使用不同的迁移学习方法进行了实验，以增强模型的性能。在五个数据集上的实验结果：NIR人脸数据集，博斯普鲁斯海峡3D人脸数据集，ASD数据集，SSBD数据集，和野生数据集中的头部运动，表明我们提出的模型在区分儿童的典型和非典型重复性头部运动时优于许多最先进的框架。
