Autism Spectrum Disorder diagnosis based on trajectories of eye tracking data

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Cardoso, Thiago, V ; Michelassi, Gabriel C. ; Franco, Felipe O. ; Sumiya, Fernando M. ; Portolese, Joana ; Brentani, Helena ; Machado-Lima, Ariane ; Nunes, Fatima L. S. ; Almeida, JR ; Gonzalez, AR ; Shen, L ; Kane, B ; Traina, A ; Soda, P ; Oliveira, JL

• Document type: Journal article
• Source: 2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS); v. N/A, p. 6-pg., 2021-01-01.

Abstract

The use of Eye Tracking (ET) has been investigated as an auxiliary mechanism to diagnose Autism Spectrum Disorder (ASD). One of the paradigms investigated using ET is Joint Attention (JA), which refers to moments when two individuals are focused on the same object/event so that both are aware that the focus of attention is shared. The computational tools that assist in the diagnosis of ASD have used Image Processing and Machine Learning techniques to process images, videos and ET signals. However, the JA paradigm is still little explored and presents challenges, as it requires analyzing the gaze trajectory and needs innovative approaches. The purpose of this article is to propose a model capable of extracting features from a video used as a stimulus to capture ET signals in order to verify JA and classify individuals as belonging to the ASD or Typical Development (TD) group. The main differential in relation to the approaches in the literature is the definition and implementation of the concept of floating Regions of Interest, which allows monitoring the gaze in relation to an object, considering its semantics, even if the object presents different characteristics throughout the video. A model based on ensembles of Random Forest classifiers was implemented to classify individuals as ASD or TD using the trajectory features extracted from the ET signals. The method reached 0.75 accuracy and 0.82 F1-score, indicating that the proposed approach, based on trajectory and JA, has the potential to be applied to assist in the diagnosis of ASD. (AU)

• FAPESP's process: 20/01992-0 - Computer aid system for the diagnosis of psychiatric disorders based on facial anthropometric measurements
• Grantee: Ariane Machado Lima
• Support Opportunities: Research Grants - eScience and Data Science Program - Regular Program Grants
• FAPESP's process: 14/50889-7 - National Institute of Science and Technology Medicine Assisted by Scientific Computing (INCT-MACC)
• Grantee: José Eduardo Krieger
• Support Opportunities: Research Projects - Thematic Grants