Real-Time Trust Measurement in Human-Robot Interaction: Insights from Physiological Behaviours

摘要

Existing work has shown that physiological behaviours (PBs) can effectively measure trust. However, there is a limited exploration of using multiple PBs concurrently to calibrate human trust in robots during real-time HRI. Additionally, most datasets are based on one-off interactions or a single context. This project addresses this gap by examining differences in PBs between trust and distrust states and investigating how these PBs change over repeated interactions in different contexts. We conducted two experiments to collect data on electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) from participants across multiple HRI sessions. The results showed significant differences in HR and SKT between trust and distrust states in Study 1, and significant differences in HR in Study 2. Furthermore, the Decision Tree classifier achieved the highest accuracy of 79% in classifying trust when using the incremental transfer learning algorithm for collective datasets. These results highlight the potential of using PBs for real-time trust measurement in HRI and suggest further exploration of incremental transfer learning methods to enhance trust prediction across different interaction contexts.