Computerized Tool to Diagnose, Treat Autism

Researchers from Georgia Institute of Technology are developing a computerized early warning system that can be used to diagnose children with autism.

Researchers from Georgia Institute of Technology are developing a computerized early warning system that can be used to diagnose children with autism.

Georgia Tech is part of a consortium of universities that was granted a $10 million “Expeditions in Computing” award from the National Science Foundation (NSF) to develop digital tools for measuring and analyzing behavior that can be distributed widely. These technologies will be used to enable new approaches for identifying children at risk for autism and other developmental delays that may potentially improve the delivery and evaluation of treatment.

The award, one of only 10 given out by the NSF since 2008, provides up to $2 million in funding each year for five years and is designed to push boundaries in computer science by deploying behavioral science, which draws equally from computer science and psychology to transform the study of human behavior.

Autism affects one of every 110 children in the US, and the long-term outcomes for a child who is at risk for the condition can be significantly improved with early treatment. As a result, it is widely accepted that all children should be screened for developmental delays as early in life as possible.

"Direct observation of a child by highly trained specialists is an important step in assessing risk for developmental disorders, but such an approach cannot be easily scaled to the large number of individuals needing evaluation and treatment," said the project’s lead principal investigator James Rehg, in a press report (

For this project, the researchers will design vision, speech and wearable sensor technologies to analyze child behavior. Data will be collected from interactions between caregivers and children, children playing and socializing in a daycare environment, and clinicians interacting with children during individual therapy sessions. Multiple sensing technologies are necessary to obtain a comprehensive and integrated portrait of expressed behavior.

"People use eye gaze, hand gestures, facial expressions, and tone of voice to convey engagement and regulate social interactions," said co-principal investigator Gregory Abowd, a professor in the School of Interactive Computing at Georgia Tech. "In addition, physiological responses, such as increased heart rate, can impact the expression of these behaviors."

Cameras and microphones will provide an inexpensive and noninvasive way to measure eye gaze and facial and body expressions, along with speech and non-speech utterances. Wearable sensors will measure physiological variables such as heart rate and skin conductivity, which contain important clues about levels of internal stress and arousal that are linked to behavior.

The research team will also develop capabilities for synchronizing the signals from the microphones, cameras and on-body sensors. By developing and using models of social interactions, the researchers will analyze the sensor data to quantify engagement.

As part of this award, the researchers will use a behavioral screening instrument called Rapid-ABC, which is currently under development. The researchers intend to utilize the information gathered from the microphones, cameras and on-body sensors to automate some of the scoring for the Rapid-ABC test.

"We hope that by incorporating this screening protocol into well-child doctor visits for children less than two years old, we can reduce the average age of autism diagnosis, which is currently about four years old," said Arriaga.

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  • Mother’s Sensitivity May Help Language Growth in Children with Autism Spectrum Disorder
  • Autism Spectrum Disorder CME