Investigating Electrodermal Activity for Trust Assessment in Industrial Human-Robot Collaboration

In the Industry 5.0 framework, due to the close collaboration between humans and robots, providing a safe environment and balance workload becomes an essential requirement. In this context, evaluating the trustworthiness of robots from a human-centric perspective is essential as trust impacts the interaction in human-robot collaborations. Numerous researchers in the literature have delved into physiological responses as indicators of user trust in robots. In this research endeavor, multiple machine learning models were employed, leveraging skin conductance response (SCR) to classify the trust level of the human operator. A chemical industry scenario was developed, where a collaborative robot supported a human operator by handing over a beaker used for the pouring of chemicals. The machine learning models achieved a moderate accuracy rate of 68.99% and AUC of 0.73 for the handover task. Nonetheless, this study underscores the importance of sensor fusion techniques to improve the accuracy of trust assessment within the context of human-robot collaborations.