Autism Spectrum Disorder diagnosis based on trajectories of eye tracking data

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art_MICHELASSI_Autism_Spectrum_Disorder_diagnosis_based_on_trajectories_of_2021.PDF (308.36 KB)
Citações na Scopus
4
Tipo de produção
conferenceObject
Data de publicação
2021
DOI
Scopus
Web of Science
Título da Revista
ISSN da Revista
Título do Volume
Editora
IEEE
Autores
V, Thiago Cardoso
MICHELASSI, Gabriel C.
FRANCO, Felipe O.
MACHADO-LIMA, Ariane
NUNES, Fatima L. S.
Citação
2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), p.50-55, 2021
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
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.
Palavras-chave
Autism Spectrum Disorder, Ensemble Method, Eye tracking, Image processing, Joint attention, Machine Learning, Random Forest, Trajectory, Typical Development
URI
https://observatorio.fm.usp.br/handle/OPI/50272
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Coleções
Comunicações em Eventos - FM/MPS
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