影响因子：2.4
DOI码：10.1080/00140139.2023.2223784
发表刊物：人因工程
刊物所在地：英国
关键字：用户体验：监控室：投入度评估：姿态评估：人工智能
摘要：In safety-critical automatic systems, safety can be compromised if operators lack engagement. Effective detection of undesirable engagement states can inform the design of interventions for enhancing engagement. However, the existing engagement measurement methods suffer from several limitations which damage their effectiveness in the work environment. A novel engage- ment evaluation methodology, which adopts Artificial Intelligence (AI) technologies, has been proposed. It was developed using motorway control room operators as subjects. Openpose and Open Source Computer Vision Library (OpenCV) were used to estimate the body postures of operators, then a Support Vector Machine (SVM) was utilised to build the engagement evalu- ation model based on discrete states of operator engagement. The average accuracy of the evaluation results reached 0.89 and the weighted average precision, recall, and F1-score were all above 0.84. This study emphasises the importance of specific data labelling when measuring typical engagement states, forming the basis for potential control room improvements.
第一作者：金林轶
论文类型：SCI
通讯作者：金林轶
学科门类：工学
是否译文：否
发表时间：2023-06-25
收录刊物：SCI
发布期刊链接：https://www.tandfonline.com/doi/full/10.1080/00140139.2023.2223784