To recognize boredom in users interacting with machines is
valuable to improve user experiences in human-machine long term inter-
actions, especially for intelligent tutoring systems, health-care systems,
and social assistants. This paper proposes a two-staged framework and
feature design for boredom recognition in multiparty human-robot in-
teractions. At the rst stage the proposed framework detects boredom-
indicating user behaviors based on skeletal data obtained by motion cap-
ture, and then it recognizes boredom in combination with detection re-
sults and two types of multiparty information, i.e., gaze direction to other
participants and incoming-and-outgoing of participants. We experimen-
tally conrmed the effectiveness of both the proposed framework and the
multiparty information. In comparison with a simple baseline method,
the proposed framework gained 35 percentage points in the F1 score.
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