Fluent human-robot collaboration requires a robot teammate to understand, learn, and adapt to the human's psycho-physiological state. Such collaborations require a physiological computing system that monitors human biological 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). We proposed a human comfort index estimation system (CIES) that uses biological signals and subjective metrics. Subjective metrics (surprise, anxiety, boredom, calmness, and comfortability) and physiological signals were collected during a human-robot collaboration experiment that varied the robot's behavior. The emotion circumplex model is adapted to calculate the CI from the participant's quantitative data as well as physiological data. This thesis developed a physiological computing system that estimates human comfort levels from physiological by using the circumplex model approach. The data was collected from multiple experiments and machine learning models trained, and their performance was evaluated. As a result, a subject-independent model was tested to determine the robot behavior based on human comfort level. The results from multiple experiments indicate that the proposed CIES model improves human comfort by providing feedback to the robot. In conclusion, physiological signals can be used for personalized robots, and it has the potential to improve safety for humans and increase the fluency of collaboration.
Electrical and Computer Engineering (Ph.D)
Department, Program, or Center
Department of Electrical and Microelectronic Engineering (KGCOE)
Savur, Celal, "A Physiological Computing System to Improve Human-Robot Collaboration by Using Human Comfort Index" (2022). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus