Children with autism spectrum conditions often have trouble recognizing the emotional states of people around them — distinguishing a happy face from a fearful face, for instance. To remedy this, some therapists use a kid-friendly robot to demonstrate those emotions and to engage the children in imitating the emotions and responding to them in appropriate ways.
This type of therapy works best, however, if the robot can smoothly interpret the child’s own behavior — whether he or she is interested and excited or paying attention — during the therapy. Researchers at the MIT Media Lab have now developed a type of personalized machine learning that helps robots estimate the engagement and interest of each child during these interactions, using data that are unique to that child.
Armed with this personalized “deep learning” network, the robots’ perception of the children’s responses agreed with assessments by human experts, with a correlation score of 60 percent.
It can be challenging for human observers to reach high levels of agreement about a child’s engagement and behavior. Their correlation scores are usually between 50 and 55 percent. Rudovic and his colleagues suggest that robots that are trained on human observations, as in this study, could someday provide more consistent estimates of these behaviors.
“The long-term goal is not to create robots that will replace human therapists, but to augment them with key information that the therapists can use to personalize the therapy content and also make more engaging and naturalistic interactions between the robots and children with autism,” explains Oggi Rudovic, a postdoc at the Media Lab and first author of the study. https://www.tynmagazine.com/personalized-deep-learning-equips-robots-for-autism-therapy/