| Autism Spectrum Disorder(ASD)is a neurodevelopmental disorder characterized by impaired social communication,stereotyped behavior,and narrow interests,which can negatively impact the healthy growth of children and has a high disability rate.The incidence of ASD has been on the rise in recent years,with one out of every 44 8-yearold children confirmed to have ASD according to a report by the Centers for Disease Control and Prevention.However,the clinical diagnosis of ASD is limited by subjectivity and time-consuming procedures,which may delay timely diagnosis due to economic and medical limitations in underdeveloped areas,leading to a reported prevalence of ASD among children in certain regions that is lower than the actual prevalence.Therefore,there is an urgent need to develop a rapid,economical,and effective objective screening method to facilitate large-scale screening of ASD children.Previous studies have shown that children with ASD exhibit atypical gaze patterns,suggesting that eye-tracking technology may have great potential to aid in the diagnosis of ASD.In this study,we utilized natural emotion as a stimulus paradigm to investigate the characteristics of eye movement trajectories in children with ASD.We conducted a study on the feature extraction method of eye movement trajectories and achieved an objective quantitative representation of the salient characteristics in children with ASD.Furthermore,we explored different modeling methods to construct an eye-tracking-based automatic assisted screening model for children with ASD.The main research contents are as follows.1.We conducted a controlled experiment to explore the characteristics of eyetracking in children with ASD while perceiving natural emotions.We collected eyetracking data of children with ASD and a normal control group while they observed emotional videos of natural scenes.The areas-of-interest division of stimulus materials was established based on machine vision facial areas-of-interest division standards.We studied the feature extraction method of eye movement trajectory using the theory of visual psychology to verify the difference in eye track between ASD children and typically developing children.The results suggest that children with ASD exhibit abnormal scanning strategies that correlate with emotional content.2.We proposed two feature extraction methods for eye movement trajectories.The first method utilized saliency detection technology to automatically extract the region of interest of the stimulus material,and the attention spatial distribution of the eye track was represented by fusing the attention matrix with the video saliency region.The second method encoded the temporal visual information of eye trajectories by representing the salience regions of eye scans.We studied the method of representation learning for these two types of features based on residual network.The proposed ASD screening model based on the two features achieved 79.49% and 81.82% accuracy,respectively,which were 2.91% and 5.24% higher than the baseline model.3.We constructed an auxiliary screening model for ASD children based on eye-track feature fusion.We studied the method of integrating dynamic information features,spatial attention distribution features,and time-series visual information features of eye movement trajectories using convolutional neural network and fully connected neural network.We also explored the fusion strategy of video-level eye movement test results.The proposed ASD children’s auxiliary screening model achieved an accuracy rate of88.01%,which was 11.43% higher than the baseline model. |
