Recognition of Aggressive Interactions of Children Toward Robotic Toys

Abstract

Social robots are now being considered to be a part of the therapy of children with autism. During the interactions, some aggressive behaviors could lead to harmful scenarios. The ability of a social robot to detect such behaviors and react to intervene or to notify the therapist would improve the outcomes of therapy and prevent any potential harm toward another person or to the robot. In this study, we investigate the feasibility of an artificial neural network in classifying 6 interaction behaviors between a child and a small robotic toy. The behaviors were: hit, shake, throw, pickup, drop, and no interaction or idle. Due to the ease of acquiring data from adult participants, a model was developed based on adults' data and was evaluated with children's data. The developed model was able to achieve promising results based on the accuracy (i.e. 80%), classification report (i.e. overall F1-score=80%), and confusion matrix. The findings highlight the possibility of characterizing children's negative interactions with robotic toys to improve safety.