A computer vision system that automatically tracks body movement in infants is helping to diagnose neurodevelopmental disorders like autism, say researchers.

Diagnosing autism in children is an important task. Various studies show that early intervention can significantly improve the outcome for children in later life.

The idea is to teach autistic children important social skills and behavioural patterns before other patterns become ingrained. And to be most effective, that needs to be done as early as possible, at say 2 or 3 years old.

And yet the average age for diagnosis in the USis 5.That's largely because the process of diagnosis in young children is particularly difficult and taxing. It requires a psychologist with specialist expertise in autism who analyses a child's behaviour in a one-on-one setting over a significant period of time.

Video plays an increasingly important role in this process but again it requires detailed frame-by-frame analysis by an expert. That's why this kind of diagnosisis time consuming and expensive.

That looks set to change. Today,Jordan Hashemi at theUniversity of Minnesota and a few pals say they've developed a computer vision technique to automatically identify behaviour that shows an increased risk of autism.

The system relies on video footage of the child in an ordinary setting and automatically works out the position of the child's head and how it changes during activities, as well as the position of the arms, torso and legs for analysis of body position and gait.

Autism is generally associated with impaired social interaction and impaired communication. This is most easily diagnosed in children beyond the age of 5 when the differences with their peers become most apparent.

But in recent years, child psychologists have found various other indicators they can use to spot toddlers and infants with a higher risk of autism.

See original here:
Computer Vision System Helps Diagnose Autism in Infants