Human Comfortability Index Estimation in Industrial Human-Robot Collaboration Task

TL;DR

This research develops a method to quantitatively estimate human comfort levels during human-robot collaboration by analyzing physiological signals and adapting the emotion circumplex model.

Contribution

It introduces a novel approach to measure comfortability index using physiological data and the emotion circumplex model in HRC settings.

Findings

Successfully adapted the circumplex model to locate comfortability on the emotion spectrum.

Estimated human comfort/uncomfortability from ECG, GSR, and pupillometry signals.

The approach aligns physiological data with subjective comfort metrics.

Abstract

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.