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Digital Phenotyping Autism: Investigating Child Vocalization and Movement

AJAS · 2022 Behavioral Science

Emilin Mathew

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Overview

Digital Phenotyping Autism: Investigating Child Vocalization and Movement Chaperone: Mrs. Leya Joykutty Academy: Florida Junior Academy of Science Autism Spectrum Disorder is a neurodevelopmental disorder that lacks objective diagnostics. The current gold standard, Autism Diagnostic Observation Schedule (ADOS), suffers from problems such as subjective evaluations of clinicians, inability to distinguish symptom severities, and poor early detection because it only evaluates existing conditions of children. Additionally, COVID-19 severely affects this diagnostic because it prevents the close social interaction necessary to observe children's behavior. As the pandemic disrupts autism diagnoses, new methods are needed. This research used computer vision motion tracking technology to assess mean distances of autistic children from parent and examiner during the ADOS and vocalization recorders to analyze children’s language ability. Both predictive measures (movement and vocalization) were analyzed for correlations with the total ADOS score, Restricted and Repetitive Behavior score, and Social Affect score to characterize autism severity. Using univariate correlations and stepwise multiple regressions, the researcher identified significant models to quantify autism severity. The vocalization hypothesis, which predicted the number of conversational exchanges (CE) initiated by the child to be correlated with the social affect score, was supported and produced the best fit model accounting for 16.1% variance. It predicted that a decrease in child-initiated CEs would correlate to an increase in autism social affect symptoms. Overall, this study provides a foundation for automating autism diagnosis through digital phenotyping which may be integrated into smartphone apps. This easy and portable method can provide real time data in natural environments and revolutionize autism diagnosis, especially in the crisis of COVID-19.

From the student

My best friend was diagnosed with autism in sixth grade, yet as a low-income Latina, she couldn’t receive the care she deserved. In high school, I sought solutions to my questions: Could she have been diagnosed earlier? Is there more effective screening?

Understanding the disparities vulnerable patients like my friend face in mental healthcare, I now bridge bioinformatics and machine learning to design a mobile app for screening autism. Last year, I used computer vision based motion technology and vocalization analysis to create a digital autism diagnostic. I won First place at the International Science Fair and was inspired to continue my research so I am continuing to expand my diagnostic.

Images (15)

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Awards (1)

  • AJAS Fellows Badge

Competition history

  • AJAS 2022 Behavioral Science

Resources

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Source: ProjectBoard / American Junior Academy of Science

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