Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction

Abstract

Existing work on the measurements of trust during Human-Robot Interaction (HRI) indicates that psychophysiological behaviours (PBs) have the potential to measure trust. However, we see limited work on the use of multiple PBs in combination to calibrate human’s trust in robots in real-time during HRI. Therefore, this study aims to estimate human trust in robots by examining the differences in PBs between trust and distrust states. It further investigates the changes in PBs across repeated HRI and also explores the potential of machine learning classifiers in predicting trust levels during HRI. We collected participants’ electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) during repeated HRI. The results showed significant differences in HR and SKT between trust and distrust groups and no significant interaction effect of session and decision for all PBs. Random Forest classifier achieved the best accuracy of 68.6% to classify trust, while SKT, HR, BR, and BD were the important features. These findings highlight the value of PBs in measuring trust in real-time during HRI and encourage further investigation of trust measures with PBs in various HRI settings.