Mental fatigue is a leading cause of motor vehicle accidents, medical errors, loss of workplace productivity, and student disengagements in e-learning environment. Development of sensors and systems that can reliably track mental fatigue can prevent accidents, reduce errors, and help increase workplace productivity. This review provides a critical summary of theoretical models of mental fatigue, a description of key enabling sensor technologies, and a systematic review of recent studies using biosensor-based systems for tracking mental fatigue in humans. We conducted a systematic search and review of recent literature which focused on detection and tracking of mental fatigue in humans. The search yielded 57 studies (N=1082), majority of which used electroencephalography (EEG) based sensors for tracking mental fatigue. We found that EEG-based sensors can provide a moderate to good sensitivity for fatigue detection. Notably, we found no incremental benefit of using high-density EEG sensors for application in mental fatigue detection. Given the findings, we provide a critical discussion on the integration of wearable EEG and ambient sensors in the context of achieving real-world monitoring. Future work required to advance and adapt the technologies toward widespread deployment of wearable sensors and systems for fatigue monitoring in semi-autonomous and autonomous industries is examined.

翻译：精神疲劳是导致机动车事故、医疗差错、工作场所生产力下降以及在线学习环境中学生参与度降低的主要原因。开发能够可靠追踪精神疲劳的传感器与系统，有助于预防事故、减少差错并提升工作场所生产力。本综述对精神疲劳的理论模型进行了批判性总结，描述了关键使能传感器技术，并对近期利用生物传感器系统追踪人类精神疲劳的研究进行了系统性回顾。我们针对人类精神疲劳的检测与追踪开展了系统化文献检索与综述，共筛选出57项研究（总样本量N=1082），其中大多数采用基于脑电图（EEG）的传感器进行精神疲劳追踪。研究发现，基于EEG的传感器对疲劳检测具有中等至良好的灵敏度。值得注意的是，在高密度EEG传感器应用于精神疲劳检测时，未发现其带来增量效益。基于上述发现，我们围绕可穿戴EEG与环境传感器在实现真实场景监测中的集成进行了批判性讨论。本文还探讨了未来需推进的工作，以促进可穿戴传感器与疲劳监测系统在半自主及自主工业领域的广泛部署。