In one corner of the classroom, rocking back and forth, sits Julio, 16. Julio was not always aggressive. He was diagnosed with autism at age 3 and was docile until about a year ago, when he became increasingly moody and began lashing out, says his mother, Desirae Brown.
At first, he hit only himself. But in April, he chased his mother into the house with a knife.
When she found out about the study to predict aggression, she immediately signed Julio up. The project is still in an early phase; the researchers have analyzed data from only 20 children so far. But in June , they reported that an algorithm trained on the data predicts aggression one minute in advance with 71 percent accuracy.
[Mathew] Siegel and his colleagues are part of a growing cadre of scientists turning to wearable sensors, microphones, cameras and other devices to track autism traits. This approach, called ‘digital phenotyping,’ has gained widespread popularity over the past five years. It might have particular value for autism research: These objective measurements lie in stark contrast to the subjective observations of clinicians or parents that are typically used for diagnosis.
Read full, original post: Autism, through the eyes of a computer