  Virtual and robotic agents capable of perceiving human empathy have the
potential to participate in engaging and meaningful human-machine interactions
that support human well-being. Prior research in computational empathy has
focused on designing empathic agents that use verbal and nonverbal behaviors to
simulate empathy and attempt to elicit empathic responses from humans. The
challenge of developing agents with the ability to automatically perceive
elicited empathy in humans remains largely unexplored. Our paper presents the
first approach to modeling user empathy elicited during interactions with a
robotic agent. We collected a new dataset from the novel interaction context of
participants listening to a robot storyteller (46 participants, 6.9 hours of
video). After each storytelling interaction, participants answered a
questionnaire that assessed their level of elicited empathy during the
interaction with the robot. We conducted experiments with 8 classical machine
learning models and 2 deep learning models (long short-term memory networks and
temporal convolutional networks) to detect empathy by leveraging patterns in
participants' visual behaviors while they were listening to the robot
storyteller. Our highest-performing approach, based on XGBoost, achieved an
accuracy of 69% and AUC of 72% when detecting empathy in videos. We contribute
insights regarding modeling approaches and visual features for automated
empathy detection. Our research informs and motivates future development of
empathy perception models that can be leveraged by virtual and robotic agents
during human-machine interactions.
