Measuring Human Comfort in Human–Robot Collaboration via Wearable Sensing

The development of collaborative robots has enabled a safer and more efficient human–robot collaboration (HRC) manufacturing environment. Tremendous research efforts have been conducted to improve user safety and robot working efficiency after the debut of collaborative robots. However, human comfort in HRC scenarios has not been thoroughly discussed but is critically important to the user acceptance of collaborative robots. Previous studies mostly utilize the subjective rating method to evaluate how human comfort varies as one robot factor changes, yet such method is limited in evaluating comfort online. Some other studies leverage wearable sensors to collect physiological signals to detect human emotions, but few of them implement this for a human comfort model in HRC scenarios. In this study, we designed an online comfort model for HRC using wearable sensing data. The model uses physiological signals acquired from wearable sensing and calculates the in-situ human comfort levels based on our developed algorithms. We have conducted experiments in realistic HRC tasks, and the prediction results demonstrated the effectiveness of the proposed approach in identifying human comfort levels in HRC.