Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction

Abstract

Existing work on estimating user trust in robotic systems has primarily utilised datasets that monitored variations in physiological behaviours (PBs), evolving from one context of interaction. Consequently,in this paper, we created two datasets from two different human-robot interaction (HRI) contexts, namely competitive and collaborative, to explore trust dynamics comprehensively. The datasets consisted of participants’ electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) across multiple sessions of collaborative HRI during trust and distrust states. We investigated the differences in PBs between trustand distrust states and explored the potential of incremental transfer learning methods in predicting trust levels during HRI using the two datasets. The findings showed significant differences in HR between trust and distrust groups. It further showed that the Decision Tree classifier achieved the best accuracy of 89% in classifying trust, outperforming the previous work, while HR, BVP, and BR were the important features. Overall, the findings indicate the potential for applying incremental transfer learning to real-time datasets collected from different HRI contexts to estimate trust during HRI