ROBOT-AIDED SYSTEM AND METHOD FOR DIAGNOSIS OF AUTISM SPECTRUM DISORDER

摘要

The disclosed system uses facial expressions and upper body movement patterns to detect autism spectrum disorder. Emotionally expressive robots participate in sensory experiences by reacting to stimuli designed to resemble typical everyday experiences, such as uncontrolled sounds and light or tactile contact with different textures. The robot-child interactions elicit social engagement from the children, which is captured by a camera. A convolutional neural network, which has been trained to evaluate multimodal behavioral data collected during those robot-child interactions, identifies children that are at risk for autism spectrum disorder. Because the robot-assisted framework effectively engages the participants and models behaviors in ways that are easily interpreted by the participants, the disclosed system may also be used to teach children with autism spectrum disorder to communicate their feelings about discomforting sensory stimulation (as modeled by the robots) instead of allowing uncomfortable experiences to escalate into extreme negative reactions (e.g., tantrums or meltdowns).