There are a lot of myths and misunderstandings about autism.
Simply put, it is a physical disorder associated with brain biology and chemistry that impairs an individual’s social and communications skills. The exact cause is unknown, but it is generally believed that genetic factors play a large role.
Early diagnosis is extremely important. As there is no ‘cure,’ treatments are behavioral and run over the course of years. Starting therapy early is a huge benefit to the patient’s quality of life. That said, it is a difficult and expensive process to evaluate a toddler for autism, one that requires expert monitoring over a long period of time. But what if that could be automated?
That’s where Jordan Hashemi and his team at the University of Minnesota come in. Using video footage of at-risk toddler behavior, which families can record at home or at a clinic, they have developed a computer vision system that analyses the movements of children as young as two years old and identifies behaviors linked with autism.
By tracking position and movement of the child’s head, torso, arms and legs, the program can recognize the restricted, repetitive behaviors that are indicative of autism. For example, autistic children tend to walk on their toes, hold their limbs in asymmetrical positions while moving, and show delays in tracking objects in their field of vision.
Hashemi’s system looks for these behaviors automatically and has performed well in tests. The research team videoed 15 children who were identified as at risk for autism and showed the footage to a specialist in child autism, a child psychiatrist, two psychology students and their system. Taking the specialist’s opinion as the standard, the computer system did better than both the psychiatrist and the students in that it agreed more often with the specialist’s assessment. Pretty impressive for a first generation technology.
Of course the system requires lots of additional testing. But that shouldn’t be too hard for Hashemi and his team; they can use archival footage of autistic children whose condition is already known to calibrate the system’s assessments. Once perfected, this technology could greatly reduce the time required to diagnose autism and other developmental disorders. Of course a human expert would need to review the conclusions, but the time saved in diagnosis would lead to earlier interventions and improved quality of life for patients.
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