基于自然情绪感知下异常眼动轨迹的孤独症预测模型

孤独症谱系障碍(autism spectrum disorder，ASD)是一类以社会交流、刻板行为和狭隘兴趣为主要特征的神经发育障碍性疾病，致残率较高，严重影响着儿童的健康成长。ASD 主观临床诊断存在耗时长、主观性强等问题。因此，迫切需要一种快速、经济、有效的客观筛查方法。研究发现，ASD 儿童具有非典型的情绪视觉感知模式，有望将眼动追踪技术用于 ASD 的辅助诊断。该文提出一个在自然场景下，ASD 非典型情绪视觉感知模式结合机器学习的自动筛查 ASD 患者的模型。该模型可提取自然场景下感知情绪的眼动轨迹特征，通过机器学习模型进行建模，以实现根据眼动轨迹自动识别 ASD 患儿。实验结果表明，该方法的准确率为 79.71%，有望成为一种 ASD 儿童早期筛查的辅助工具。

Autism Spectrum Disorder Prediction Model Based on Gaze Trajectory of Natural Emotional Perception

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social communication, repetitive behaviors, and restricted interests. Signs of autism usually appear by age 3, but the cinical diagnosis is time-consuming and subjective. Therefore, a rapid and cost-effective assessment method is urgently needed. Children with ASD have atypical gaze patterns when they precept emotional stimuli, which suggests a great potential use of eye-tracking technology as an assessment method for ASD detection. This paper proposes a model for automatically assessing children with ASD based on atypical gaze patterns. The model extracts the eye movement trajectory features of perceived emotions in natural scenes, and uses the machine learning model to learn to automatically identify ASD according to the eye movement trajectory features. Results show that the accuracy reaches 79.71%, which has potentially become an early ASD children screening approach.