| Abstract | ocial engagement refers the expressions of existing interpersonal relationships during the
interaction which represents the actual interesting of human in the interaction. However, social
engagement measurement is a significant concern in social human-robot interaction (HRI)
because of its role in understanding the interaction’s trend and adapt robot’s behavior
accordingly. Hence, we achieved the two main objectives of this study. Firstly, enrichment the
theoretical literature and related concepts. Secondly, proposed a robust neural network model
which is multi layer perceptron (MLP) classifier to measure social engagement state during
interaction. PInSoRo dataset was used for training and testing purpose. In particular, the
parameters of MLP model were meticulously crafted to recognize the social engagement
accurately. We evaluated the model’s performance by several metrics and the result showed an
interesting accuracy reached 94.85%. Given that, it supports the robot to has adaptive and
responsive behavior in real time applications which is improving HRI eventually.
Keywords: Human-Robot Interaction, Neural Network, Social Engagement, Social Robotics
User Engagement
|
|---|
