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.

本文综述了心理疲劳的理论模型、关键的技术手段和最新的人类疲劳生物传感技术，发现 EEG 传感器能够提供对疲劳检测的中等到良好的灵敏度，并讨论了可穿戴 EEG 以及环境传感器相互融合在实时监测方面的关键性作用和未来的发展方向。

用于监测精神疲劳的传感器和系统：系统综述

Sensors and Systems for Monitoring Mental Fatigue: A systematic review

The effectiveness of computerized cognitive training in slowing cognitive
decline and brain aging in dementia is often limited by the engagement of
participants in the training. Monitoring older users' real-time engagement in
domains of attention, motivation, and affect is crucial to understanding the
overall effectiveness of such training. In this paper, we propose to predict
engagement, quantified via an established mental fatigue measure assessing
users' perceived attention, motivation, and affect throughout computerized
cognitive training sessions, in older adults with mild cognitive impairment
(MCI), by monitoring their real-time video-recorded facial gestures in training
sessions. To achieve the goal, we used computer vision, analyzing video frames
every 5 seconds to optimize the balance between information retention and data
size, and developed a novel Recurrent Video Transformer (RVT). Our RVT model,
which combines a clip-wise transformer encoder module and a session-wise
Recurrent Neural Network (RNN) classifier, achieved the highest balanced
accuracy, F1 score, and precision compared to other state-of-the-art models for
both detecting mental fatigue/disengagement cases (binary classification) and
rating the level of mental fatigue (multi-class classification). By leveraging
dynamic temporal information, the RVT model demonstrates the potential to
accurately predict engagement among computerized cognitive training users,
which lays the foundation for future work to modulate the level of engagement
in computerized cognitive training interventions. The code will be released.

本论文利用面部表情分析技术，运用回归视频转换（RVT）模型对老年轻度认知障碍患者的实时动态监测，实现对认知训练过程中注意力、动机和情感等方面的可持续参与度的评估，该模型根据视频帧分析进行计算，能够精确预测认知训练中的参与度，对于之后的计算机认知训练干预工作奠定了基础。