Traffic accidents, causing millions of deaths and billions of dollars in
economic losses each year globally, have become a significant issue. One of the
main causes of these accidents is drivers being sleepy or fatigued. Recently,
various studies have focused on detecting drivers' sleep/wake states using
camera-based solutions that do not require physical contact with the driver,
thereby enhancing ease of use. In this study, besides the eye blink frequency,
a driver adaptive eye blink behavior feature set have been evaluated to detect
the fatigue status. It is observed from the results that behavior of eye blink
carries useful information on fatigue detection. The developed image-based
system provides a solution that can work adaptively to the physical
characteristics of the drivers and their positions in the vehicle

交通事故致使全球每年数百万人死亡和数十亿美元的经济损失，成为一个重大问题。本研究关注于检测驾驶员的睡眠 / 清醒状态，使用基于摄像头的解决方案来减少事故发生，避免与驾驶员身体接触，通过评估外眼睑频率以及驾驶员自适应的眼睑行为，发现眼睑行为能够提供有用的疲劳检测信息。该研究开发了一种基于图像的系统，可以适应驾驶员的生理特征和在车辆中的位置。

利用眨眼行为检测驾驶员疲劳

Detecting Driver Fatigue With Eye Blink Behavior

Fatigue detection is of paramount importance in enhancing safety,
productivity, and well-being across diverse domains, including transportation,
healthcare, and industry. This scientific paper presents a comprehensive
investigation into the application of machine learning algorithms for the
detection of physiological fatigue using Electroencephalogram (EEG) signals.
The primary objective of this study was to assess the efficacy of various
algorithms in predicting an individual's level of fatigue based on EEG data.

应用机器学习算法检测生理疲劳的全面研究，旨在评估不同算法在基于脑电图数据预测个体疲劳水平方面的有效性。

利用脑电图预测疲劳算法综述

Review of algorithms for predicting fatigue using EEG

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。