We explored how robot-assisted therapy based on smile analysis may facilitate the prosocial behaviors of children with autism spectrum disorder. Prosocial behaviors, which are actions for the benefit of others, are required to belong to society and increase the quality of life. As smiling is a candidate for predicting prosocial behaviors in robot-assisted therapy, we measured smiles by annotating behaviors that were recorded with video cameras and by classifying facial muscle activities recorded with a wearable device. While interacting with a robot, the participants experienced two situations where participants' prosocial behaviors are expected, which were supporting the robot to walk and helping the robot from falling. We first explored the overall smiles at specific timings and prosocial behaviors. Then, we explored the smiles triggered by a robot and behavior changes before engaging in prosocial behaviors. The results show that the specific timing of smiles and prosocial behaviors increased in the second session of children with autism spectrum disorder. Additionally, a smile was followed by a series of behaviors before prosocial behavior. With a proposed Bayesian model, smiling, or heading predicted prosocial behaviors with higher accuracy compared to other variables. Particularly, voluntary prosocial behaviors were observed after smiling. The findings of this exploratory study imply that smiles might be a signal of prosocial behaviors. We also suggest a probabilistic model for predicting prosocial behaviors based on smile analysis, which could be applied to personalized robot-assisted therapy by controlling a robot's movements to arouse smiles and increase the probability that a child with autism spectrum disorder will engage in prosocial behaviors.

No abstract

We examined the influence of a parent on robot-assisted activities for a child with Autism Spectrum Disorder. We observed the interactions between a robot and the child wearing a wearable device during free play sessions. The child participated in four sessions with the parent and interacted willingly with the robot, therapist, and parent. The parent intervened when the child did not interact with the robot, considered “disengagement with the robot”. The number and method of intervention were decided solely by the parent. This study adopted video recording for behavioral observations and specifically observed the situations before the disengagement with the robot, the child’s behaviors during disengagement, and the parent’s intervention. The results showed that mostly the child abruptly discontinued the interactions with the robot without being stimulated by the surrounding environment. The second most common reason was being distracted by various devices in the play sessions, such as the wearable device, a video camera, and a laptop. Once he was disengaged with the robot, he primarily exhibited inappropriate and repetitive behaviors accentuating the symptoms of autism spectrum disorder. The child could re-initiate the interaction with the robot with an 85% chance through the parent’s intervention. This suggests that engagement with a robot may differ depending on the parent’s participation. Moreover, we must consider types of parental feedback to re-initiate engagement with a robot to benefit from the therapy adequately. In addition, environmental distractions must be considered, especially when using multiple devices for therapy.

No abstract

No abstract