Application of Supervised Machine Learning for Behavioral Biomarkers of Autism Spectrum Disorder Based on Electrodermal Activity and Virtual Reality

A study by @malcaniz et al. shows that using #biomarkers from devices like the E4, it is possible to distinguish between regular and atypical symptoms in people with #Autism. This will aid diagnosis by reliably tracking its progression. Details: https://t.

@malcaniz et al combined EDA comparison models (using the E4 sensors) with #ML models, resulting in accurate outcomes. They inferred that this method can be applied for increasing knowledge in developing specific treatments for ASD. Discover more: https://

Researchers at @UV_EG, have discovered that biomarkers, obtained through devices like the E4 can be used to distinguish typical from atypical functioning in people with #Autism. This reliably tracks the progression of the illness, aiding diagnosis ➡️ https

LeDoux, Bargh, Greenwald all mixed into one simple and false model of human behavior. https://t.co/ACduJ6Z6Jr https://t.co/sahSl3wneo