Researchers have developed two new high-tech lated instruments to automatically measure behaviors of children.
Accurate assessment of relevant behaviors in children improves the understanding of behavioral disorders such as autism.
Georgia Tech’s scientists say that one of the tools provides an innovative method to automatically track when a child makes eye contact. The new system uses special gaze-tracking glasses coupled with facial-analysis software to identify when a child makes eye contact with the glasses-wearer.
Another tool is a wearable system that uses accelerometers to monitor and categorize problem behaviors in children with behavioral disorders.
Both technologies are designed to help practitioners and diagnosticians apply computational methods to screening, measurement and understanding of autism and other behavioral disorders.
Health care professionals know that children at risk for autism often display distinct behaviors beginning at a very young age. The new automated technology will allow earlier and better detection of the behavior markers.
The diagnostic tools would significantly improve autism screening, allowing assessment of much larger populations than are currently reached.
The eye-contact tracking system begins with a commercially available pair of glasses that can record the focal point of their wearer’s gaze.
Researchers took video of a child captured by a front-facing camera on the glasses, worn by an adult who was interacting with the child. The researchers processed the video using facial recognition software available from a second manufacturer.
The new technology uses the glasses’ hard-wired ability to detect wearer gaze with the facial-recognition software’s ability to detect the child’s gaze direction. The system can detect eye contact in a test interaction with a 22-month-old with 80 percent accuracy.
“Eye gaze has been a tricky thing to measure in laboratory settings, and typically it’s very labor-intensive, involving hours and hours of looking at frames of video to pinpoint moments of eye contact,” said researcher James Rehg, Ph.D.
“The exciting thing about our method is that it can produce these measures automatically and could be used in the future to measure eye contact outside the laboratory setting. We call these results preliminary because they were obtained from a single subject, but all humans’ eyes work pretty much the same way, so we’re confident the successful results will be replicated with future subjects.”
The other new system is a package of sensors, worn via straps on the wrists and ankles, that uses accelerometers to detect movement by the wearer. Algorithms developed by the team analyze the sensor data to automatically detect episodes of problem behavior and classify them as aggressive, self-injurious or disruptive (e.g., throwing objects).
In a study, the new technology detected an autistic child’s problem-behavior episodes with 81 percent accuracy and classified them with 70 percent accuracy.
“These results are very promising in leading the way toward more accurate and reliable measurement of problem behavior, which is important in determining whether treatments targeting these behaviors are working,” said Child Study Lab Director Agata Rozga, Ph.D.
“Our ultimate goal with this wearable sensing system is to be able to gather data on the child’s behavior beyond the clinic, in settings where the child spends most of their time, such as their home or school.
In this way, parents, teachers and others who care for the child can be potentially alerted to times and situations when problem behaviors occur so that they can address them immediately.”
Source: Georgia Tech