基于阿凡达的图片交换交流系统加强自闭症儿童的联合注意力训练

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yongjun Ren, Runze Liu, Huinan Sang, Xiaofeng Yu
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引用次数: 0

摘要

患有自闭症谱系障碍(ASD)的儿童通常在社交沟通方面很吃力,在互动环境中会感到焦虑。图片交流沟通系统(PECS)通常用于提高自闭症儿童的基本沟通技能,但它在减少治疗师互动过程中的社交焦虑和保持儿童参与方面存在不足。本文建议在进行 PECS 训练的同时使用虚拟人物技术来解决这些问题。通过整合虚拟化身,可以逐步提高儿童的沟通技能和表达需求的能力。这种方法还能减少焦虑,增强培训的互动性和吸引力。经过 T 检验发现,虚拟化身辅助的 PECS 能显著提高儿童对活动的专注度,并增强他们的行为反应能力。针对无约束环境下注视估计准确性差的问题,本研究进一步开发了一种基于视觉特征的注视估计算法--三通道注视网络(TCG-Net)。它利用双目图像细化注视方向,并从面部图像推断主要焦点。TCG-Net 在 MPIIGaze 数据集上的角度误差为 4.0,在 EyeDiap 数据集上的角度误差为 5.0,在 RT-Gene 数据集上的角度误差为 6.8,这证实了我们的方法在提高注视准确性和社交互动质量方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Avatar-Based Picture Exchange Communication System Enhancing Joint Attention Training for Children With Autism.

Children with Autism Spectrum Disorder (ASD) often struggle with social communication and feel anxious in interactive situations. The Picture Exchange Communication System (PECS) is commonly used to enhance basic communication skills in children with ASD, but it falls short in reducing social anxiety during therapist interactions and in keeping children engaged. This paper proposes the use of virtual character technology alongside PECS training to address these issues. By integrating a virtual avatar, children's communication skills and ability to express needs can be gradually improved. This approach also reduces anxiety and enhances the interactivity and attractiveness of the training. After conducting a T-test, it was found that PECS assisted by a virtual avatar significantly improves children's focus on activities and enhances their behavioral responsiveness. To address the problem of poor accuracy of gaze estimation in unconstrained environments, this study further developed a visual feature-based gaze estimation algorithm, the three-channel gaze network (TCG-Net). It utilizes binocular images to refine the gaze direction and infer the primary focus from facial images. Our focus was on enhancing gaze tracking accuracy in natural environments, crucial for evaluating and improving Joint Attention (JA) in children during interactive processes.TCG-Net achieved an angular error of 4.0 on the MPIIGaze dataset, 5.0 on the EyeDiap dataset, and 6.8 on the RT-Gene dataset, confirming the effectiveness of our approach in improving gaze accuracy and the quality of social interactions.

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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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