Shutdown maintenance, i.e., turning off a facility for a short period for renewal or replacement operations is a highly stressful task. With the limited time and complex operation procedures, human stress is a leading risk. Especially shutdown maintenance workers often need to go through excessive and stressful on-site trainings to digest complex operation information in limited time. The challenge is that workers’ stress status and task performance are hard to predict, as most trainings are only assessed after the shutdown maintenance operation is finished. A proactive assessment or intervention is needed to evaluate workers’ stress status and task performance during the training to enable early warning and interventions. This study proposes a neurophysiological approach to assess workers’ stress status and task performance under different virtual training scenarios. A Virtual Reality (VR) system integrated with the eye-tracking function was developed to simulate the power plant shutdown maintenance operations of replacing a heat exchanger in both normal and stressful scenarios. Meanwhile, a portable neuroimaging device – Functional Near-Infrared Spectroscopy (fNIRS) was also utilized to collect user’s brain activities by measuring hemodynamic responses associated with neuron behavior. A human–subject experiment (n = 16) was conducted to evaluate participants’ neural activity patterns and physiological metrics (gaze movement) related to their stress status and final task performance. Each participant was required to review the operational instructions for a pipe maintenance task for a short period and then perform the task based on their memory in both normal and stressful scenarios. Our experiment results indicated that stressful training had a strong impact on participants’ neural connectivity patterns and final performance, suggesting the use of stressors during training to be an important and useful control factors. We further found significant correlations between gaze movement patterns in review phase and final task performance, and between the neural features and final task performance. In summary, we proposed a variety of supervised machine learning classification models that use the fNIRS data in the review session to estimate individual’s task performance. The classification models were validated with the k-fold (k = 10) cross-validation method. The Random Forest classification model achieved the best average classification accuracy (80.38%) in classifying participants’ task performance compared to other classification models. The contribution of our study is to help establish the knowledge and methodological basis for an early warning and estimating system of the final task performance based on the neurophysiological measures during the training for industrial operations. These findings are expected to provide more evidence about an early performance warning and prediction system based on a hybrid neurophysiological measure method, inspiring the design of a cognition-driven personalized training system for industrial workers.

停机维护，即在短时间内关闭设备以进行更新或替换操作是一项非常艰巨的任务。由于时间有限且操作程序复杂，因此人为压力是主要风险。特别是停机维护人员经常需要进行过多且压力很大的现场培训，以在有限的时间内消化复杂的操作信息。挑战在于，工人的压力状态和任务绩效难以预测，因为大多数培训仅在停机维护操作完成后才能进行评估。在培训期间，需要进行积极的评估或干预以评估工人的压力状态和任务绩效，以实现预警和干预。这项研究提出了一种神经生理学方法来评估工人在不同虚拟培训情况下的压力状态和任务表现。开发了一种具有眼动追踪功能的虚拟现实（VR）系统，以模拟在正常和压力情况下更换热交换器的电厂停机维护操作。同时，便携式神经成像设备-功能近红外光谱（fNIRS）也用于通过测量与神经元行为相关的血液动力学反应来收集用户的大脑活动。进行了一项人类受试者实验（n = 16）来评估参与者的神经活动模式和与他们的压力状态和最终任务表现相关的生理指标（凝视运动）。要求每个参与者在短期内查看管道维护任务的操作说明，然后根据他们在正常和压力较大的情况下的记忆来执行任务。我们的实验结果表明，压力训练对参与者的神经连接模式和最终表现有很大影响，这表明在训练过程中使用压力源是重要且有用的控制因素。我们进一步发现在注视阶段注视运动模式与最终任务绩效之间的显着相关性，以及神经特征与最终任务绩效之间的显着相关性。总而言之，我们提出了多种有监督的机器学习分类模型，这些模型在复习会话中使用fNIRS数据来估计个人的任务绩效。使用k倍（k = 10）交叉验证方法对分类模型进行验证。与其他分类模型相比，随机森林分类模型在对参与者的任务表现进行分类时获得了最佳的平均分类精度（80.38％）。我们的研究的目的是帮助在工业操作培训期间基于神经生理学措施，为最终任务绩效的预警和评估系统建立知识和方法论基础。这些发现有望为基于混合神经生理学测量方法的早期绩效预警和预测系统提供更多证据，从而激发工业工人认知驱动的个性化培训系统的设计。与其他分类模型相比，随机森林分类模型在对参与者的任务表现进行分类时获得了最佳的平均分类精度（80.38％）。我们的研究的目的是帮助在工业操作培训期间基于神经生理学措施，为最终任务绩效的预警和评估系统建立知识和方法论基础。这些发现有望为基于混合神经生理学测量方法的早期绩效预警和预测系统提供更多证据，从而激发工业工人认知驱动的个性化培训系统的设计。与其他分类模型相比，随机森林分类模型在对参与者的任务表现进行分类时获得了最佳的平均分类精度（80.38％）。我们的研究的目的是帮助在工业操作培训期间基于神经生理学措施，为最终任务绩效的预警和评估系统建立知识和方法论基础。这些发现有望为基于混合神经生理学测量方法的早期绩效预警和预测系统提供更多证据，从而激发工业工人认知驱动的个性化培训系统的设计。我们的研究的目的是帮助在工业操作培训期间基于神经生理学措施，为最终任务绩效的预警和评估系统建立知识和方法论基础。这些发现有望为基于混合神经生理学测量方法的早期绩效预警和预测系统提供更多证据，从而激发工业工人认知驱动的个性化培训系统的设计。我们的研究的目的是帮助在工业操作培训期间基于神经生理学措施，为最终任务绩效的预警和评估系统建立知识和方法论基础。这些发现有望为基于混合神经生理学测量方法的早期绩效预警和预测系统提供更多证据，从而激发工业工人认知驱动的个性化培训系统的设计。