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Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children by Nada Kojovic; Shreyasvi Natraj; Sharada Prasanna Mohanty; Thomas Maillart; Marie Schaer is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children about?

Clinical research in autism has recently witnessed promising digital phenotyping results, mainly focused on single feature extraction, such as gaze, head turn on name-calling or visual tracking of the moving object. The main drawback of these studies is the focus on relatively isolated behaviors elicited by largely controlled prompts. We recognize that while the diagnosis process understands the indexing of the specific behaviors, ASD also comes with broad impairments that often transcend single behavioral acts. For instance, the atypical nonverbal behaviors manifest through global patterns of atypical postures and movements, fewer gestures used and often decoupled from visual contact, facial affect, speech. Here, we tested the hypothesis that a deep neural network trained on the non-verbal aspects of social interaction can effectively differentiate between children with ASD and their typically developing peers. Our model achieves an accuracy of 80.9% (F1 score: 0.818; precision: 0.784; recall: 0.854) with the prediction probability positively correlated to the overall level of symptoms of autism in social affect and repetitive and restricted behaviors domain. Provided the non-inva

Who reads Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children?

Author

Nada Kojovic; Shreyasvi Natraj; Sharada Prasanna Mohanty; Thomas Maillart; Marie Schaer

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)