Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Rd, Malabe, 10115, Colombo, Sri Lanka.
Information Systems and Networking Institute (ISIN), University of Applied Sciences and Arts of Southern Switzerland, Via Pobiette, Manno, 6928, Switzerland.
Phys Eng Sci Med. 2023 Dec;46(4):1427-1445. doi: 10.1007/s13246-023-01309-5. Epub 2023 Oct 9.
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.
儿童行为障碍的患病率不断上升，引起了医学界的日益关注。鉴于早期识别和干预异常行为的重要性，人们普遍认为这对于改善预后具有关键作用。由于设施不足以及缺乏具有专业知识的医疗专业人员，传统的诊断方法无法有效应对行为障碍发病率的上升。因此，需要开发用于儿童行为障碍诊断的自动化方法，以克服传统方法的挑战。本研究旨在开发一种能够分析视频以区分儿童正常和异常重复性头部运动的自动化模型。为了解决儿童数据集有限的问题，采用了各种学习方法来减轻这些问题。在这项工作中，我们提出了一种融合转换器网络和非确定有限自动机（NFA）技术的方法，该方法根据性别、年龄和重复性头部运动的类型，以及每个重复性头部运动的计数、持续时间和频率，对儿童的重复性头部运动进行分类，判断其是正常还是异常。我们还进行了不同的迁移学习方法的实验，以提高模型的性能。在五个数据集（NIR 人脸数据集、博斯普鲁斯 3D 人脸数据集、ASD 数据集、SSBD 数据集和 Head Movements in the Wild 数据集）上的实验结果表明，与许多最先进的框架相比，我们提出的模型在区分儿童正常和异常重复性头部运动方面表现更优。
