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In this paper, we consider the detection of a decrease of engagement by usersspontaneously interacting with a socially assistive robot in a public space. Wefirst describe the UE-HRI dataset that collects spontaneous Human-RobotInteractions following the guidelines provided by the Affective Computingresearch community to collect data "in-the-wild". We then analyze the users'behaviors, focusing on proxemics, gaze, head motion, facial expressions andspeech during interactions with the robot. Finally, we investigate the use ofdeep learning techniques (Recurrent and Deep Neural Networks) to detect userengagement decrease in realtime. The results of this work highlight, inparticular, the relevance of taking into account the temporal dynamics of auser's behavior. Allowing 1 to 2 seconds as buffer delay improves theperformance of taking a decision on user engagement.

On-the-fly Detection of User Engagement Decrease in Spontaneous Human-Robot Interaction, International Journal of Social Robotics, 2019