  Fluent human-robot collaboration requires a robot teammate to understand,
learn, and adapt to the human's psycho-physiological state. Such collaborations
require a computing system that monitors human physiological signals during
human-robot collaboration (HRC) to quantitatively estimate a human's level of
comfort, which we have termed in this research as comfortability index (CI) and
uncomfortability index (unCI). Subjective metrics (surprise, anxiety, boredom,
calmness, and comfortability) and physiological signals were collected during a
human-robot collaboration experiment that varied robot behavior. The emotion
circumplex model is adapted to calculate the CI from the participant's
quantitative data as well as physiological data. To estimate CI/unCI from
physiological signals, time features were extracted from electrocardiogram
(ECG), galvanic skin response (GSR), and pupillometry signals. In this
research, we successfully adapt the circumplex model to find the location
(axis) of 'comfortability' and 'uncomfortability' on the circumplex model, and
its location match with the closest emotions on the circumplex model. Finally,
the study showed that the proposed approach can estimate human
comfortability/uncomfortability from physiological signals.
