Industry 5.0 focuses on human-centric collaboration between humans and robots, prioritizing safety, comfort, and trust. This study introduces a data-driven framework to assess trust using behavioral indicators. The framework employs a Preference-Based Optimization algorithm to generate trust-enhancing trajectories based on operator feedback. This feedback serves as ground truth for training machine learning models to predict trust levels from behavioral indicators. The framework was tested in a chemical industry scenario where a robot assisted a human operator in mixing chemicals. Machine learning models classified trust with over 80\% accuracy, with the Voting Classifier achieving 84.07\% accuracy and an AUC-ROC score of 0.90. These findings underscore the effectiveness of data-driven methods in assessing trust within human-robot collaboration, emphasizing the valuable role behavioral indicators play in predicting the dynamics of human trust.

翻译：工业5.0聚焦于以人为中心的人机协作，将安全性、舒适性与信任度置于优先地位。本研究提出一种数据驱动的框架，通过行为指标评估信任度。该框架采用基于偏好的优化算法，根据操作员反馈生成增强信任度的轨迹。该反馈作为训练机器学习模型的真实标签，以从行为指标预测信任水平。该框架在化工行业场景中进行了测试，其中机器人协助人类操作员进行化学品混合。机器学习模型对信任度的分类准确率超过80%，其中投票分类器达到84.07%的准确率，AUC-ROC得分为0.90。这些发现凸显了数据驱动方法在评估人机协作信任度方面的有效性，并强调了行为指标在预测人类信任动态中所发挥的重要作用。