An automatic social engagement measurement during human-robot interaction

Abstract

Social 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 multilayer 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.