Human Comfortability Index Estimation in Industrial Human-Robot Collaboration Task
This work addresses the need for robots to adapt to human psycho-physiological states in industrial collaboration, though it appears incremental as it adapts existing models to a new application.
The researchers tackled the problem of estimating human comfort and discomfort levels during human-robot collaboration by adapting an emotion circumplex model to analyze physiological signals like ECG, GSR, and pupillometry, successfully demonstrating that their approach can estimate these indices from the data.
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.