A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near-Infrared Spectroscopy (fNIRS)
A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near-Infrared Spectroscopy (fNIRS)
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DOI：
10.1016/j.aei.2020.101153
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发表时间：
2020-10
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影响因子：
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通讯作者：
Yangming Shi;Yibo Zhu;Ranjana K. Mehta;E. Du
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文献类型：
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作者：
Yangming Shi;Yibo Zhu;Ranjana K. Mehta;E. Du
停机维护，即短时间关闭设施以进行更新或更换操作，是一项压力很大的任务。由于时间有限、操作流程复杂，人为压力是主要风险。尤其是停机检修工，往往需要经过过多、压力较大的现场培训，才能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很难预测，因为大多数培训仅在停工维护操作完成后进行评估。需要主动评估或干预来评估培训期间工人的压力状态和任务绩效，以便进行早期预警和干预。这项研究提出了一种神经生理学方法来评估不同虚拟培训场景下工人的压力状态和任务表现。开发了集成眼动追踪功能的虚拟现实（VR）系统，以模拟正常和压力情况下更换换热器的电厂停机维护操作。同时，还利用便携式神经成像设备——功能性近红外光谱仪（fNIRS）通过测量与神经元行为相关的血流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），以评估参与者的神经活动模式和与其压力状态和最终任务表现相关的生理指标（注视运动）。每个参与者都需要在短时间内复习管道维护任务的操作说明，然后根据他们的记忆在正常和有压力的情况下执行任务。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，这表明训练期间压力源的使用是一个重要且有用的控制因素。我们进一步发现复习阶段的注视运动模式与最终任务表现之间以及神经特征与最终任务表现之间存在显着相关性。总之，我们提出了各种监督机器学习分类模型，这些模型在审查会话中使用 fNIRS 数据来估计个人的任务表现。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取得了最佳的平均分类精度（80.38％）。我们研究的贡献是帮助建立工业操作培训期间基于神经生理学测量的最终任务绩效预警和评估系统的知识和方法基础。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，启发为产业工人设计认知驱动的个性化培训系统。
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.