论文标题
自发的人类机器人互动中用户参与度下降，国际社会机器人杂志，2019年
On-the-fly Detection of User Engagement Decrease in Spontaneous Human-Robot Interaction, International Journal of Social Robotics, 2019
论文作者
论文摘要
在本文中，我们考虑了用户自发与公共空间中社会辅助机器人自发互动的参与度减少的检测。我们首先描述了UE-HRI数据集，该数据集按照情感计算研究界提供的指南收集“野外”数据的指南，该数据集收集了自发的人类机器人相互作用。然后，我们分析用户的行为，专注于与机器人互动期间的近端，凝视，头部运动，面部表情和语音。最后，我们研究了深度学习技术（经常性和深神经网络）的使用来检测实时的用户参与度减少。这项工作的结果特别突出了，考虑到用户行为的时间动态的相关性。允许1到2秒的缓冲延迟提高了对用户参与的决定的绩效。
In this paper, we consider the detection of a decrease of engagement by users spontaneously interacting with a socially assistive robot in a public space. We first describe the UE-HRI dataset that collects spontaneous Human-Robot Interactions following the guidelines provided by the Affective Computing research community to collect data "in-the-wild". We then analyze the users' behaviors, focusing on proxemics, gaze, head motion, facial expressions and speech during interactions with the robot. Finally, we investigate the use of deep learning techniques (Recurrent and Deep Neural Networks) to detect user engagement decrease in realtime. The results of this work highlight, in particular, the relevance of taking into account the temporal dynamics of a user's behavior. Allowing 1 to 2 seconds as buffer delay improves the performance of taking a decision on user engagement.
