######AutiBot is a health hack capable of analyzing brain activity to detect young children's risk for developing autism.
####Inspiration Autism spectrum disorder (ASD) refers to a range of neurological development disorders including Asperger’s Syndrome and autism that affect approximately 1 in 50 American children. Although the onset of these disorders typically occurs before children are three years old, current methods to diagnose ASD at very early ages are often ineffective, depending on the observation of certain distinctive mannerisms (such as inability to make eye contact) that may not be detected or associated with ASD. Recent research studies, however, have indicated that EEGs can offer a better, more scientific diagnosis of ASD. When children are faced with the task of integrating several different visual and audial stimuli, autistic children experience significantly reduced brain activity because of their inability to quickly process these stimuli.
We were inspired by the massive potential of this new technology to make the cheapest possible EEG-based ASD-detection hack. Our goal was to create a multi-sensory game integrating many different types of stimuli, such as color, shape, and sound, to determine children’s chances of developing ASD.
####How it works Our hack, AutiBot, is a simple, accessible game that tests the response of young children to several different visual and auditory stimuli at once by asking them to tap certain dots on the screen. While teaching children about color, shape, number, and more, it also records the time taken by the user to respond to the stimuli and values for the EEG brain activity of the user using the Muse headband technology. Then, by running an Anomaly Prediction machine learning model based on pre-existing datasets of non-autistic , the app calculates the probability of the user developing a mental disease like Autism. The app also has features to store and display the user’s results over time in the form of graphs and recommend actions to the child’s family, like contacting a local doctor, based on the results of the autism-prediction machine learning model.