On-the-fly Detection of User Engagement Decrease in Spontaneous Human-Robot Interaction, International Journal of Social Robotics, 2019
This work addresses the challenge of real-time engagement monitoring for socially assistive robots in public settings, representing an incremental improvement in human-robot interaction systems.
The paper tackles the problem of detecting user engagement decrease in spontaneous human-robot interactions in public spaces, using deep learning techniques like Recurrent and Deep Neural Networks on the UE-HRI dataset, and finds that incorporating a 1 to 2-second buffer delay improves decision-making performance.
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.