Virtual QT is a social robot designed to interact with humans in social and educational contexts. Equipped with visual and vocal interfaces, the robot can recognize and respond to various human inputs, providing emotional support and social interaction. It uses facial expressions, gestures, and movements to communicate, encouraging engagement and supporting learning through playful and educational activities. Its ergonomic design and interactive approach make it suitable for both clinical and educational settings. The aim of this study is to examine the effectiveness of the QTrobot in interventions for children with Autism Spectrum Disorder (ASD) and to assess whether children's attention and imitation are equally effective with the QTrobot compared to interacting with a human being. Furthermore, the presence of significant differences between motor and expressive imitation, when interacting with the robot as opposed to a human interlocutor, will be investigated. A key aspect of this investigation is to assess whether the presence of the QTrobot can positively influence the repetitive or stereotyped behaviors exhibited by the children, compared to interaction with a person. Additionally, to further understanding, the children's heart rate will be monitored through the use of a sensor that will allow to evaluate how heart rate affects performance outcomes during interaction with the QTrobot and with a human being. In this study, children will be recruited to take part in two separate sessions, both involving the same task. During the first session, the task will be carried out between the child and the human operator. Subsequently, in the same task, the child will interact with the QT robot. Throughout both task administrations, the child will wear a sensor to detect their heart rate. Furthermore, all task sessions will be video-recorded to conduct subsequent video analysis and make notes on imitation behaviors and stereotypical actions relevant to the study.
Age range
4 Years – 13 Years
Sex
ALL
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Video analysis of performance
Timeframe: The video analysis needs approximately 15 minutes per child