A Data-Driven Approach Utilizing Body Motion Data for Trust Evaluation in Industrial Human-Robot Collaboration

Industry 5.0 signifies a transformative era where humans and robots collaborate closely, leading to advancements in manufacturing efficiency and personalization. In light of this, it becomes essential to assess the robot’s trustworthiness to ensure a secure environment and equitable workload distribution. The majority of trust assessments hinge on post-hoc questionnaires for the extent of trust experienced during the interaction. A data-driven approach is required to promptly assess trust levels in real-time, allowing for the adjustment of robot behavior to align with human needs. The paper proposes a chemical industry scenario where a robot assisted a human in the process of mixing chemicals. Several machine learning models, including deep learning, were developed using body motion data to categorize the level of trust exhibited by the human operator. The models achieve an accuracy exceeding 90%. The results clearly show the feasibility of data-driven trust assessment.