疲劳检测的新进展：fNIRS 与非自愿注意脑功能实验的整合
Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments.
机构信息
Institute of Biomedical Engineering, Chinese Academy Medical Sciences & Peking Union Medical College, Tianjin 300192, China.
Institute of Integrated Circuit Science and Engineering, University of Electronical Science and Technology of China, Chengdu 611731, China.
出版信息
Sensors (Basel). 2024 May 16;24(10):3175. doi: 10.3390/s24103175.
BACKGROUND
Driving fatigue is a significant concern in contemporary society, contributing to a considerable number of traffic accidents annually. This study explores novel methods for fatigue detection, aiming to enhance driving safety.
METHODS
This study utilizes electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to monitor driver fatigue during simulated driving experiments lasting up to 7 h.
RESULTS
Analysis reveals a significant correlation between behavioral data and hemodynamic changes in the prefrontal lobe, particularly around the 4 h mark, indicating a critical period for driver performance decline. Despite a small participant cohort, the study's outcomes align closely with established fatigue standards for drivers.
CONCLUSIONS
By integrating fNIRS into non-voluntary attention brain function experiments, this research demonstrates promising efficacy in accurately detecting driving fatigue. These findings offer insights into fatigue dynamics and have implications for shaping effective safety measures and policies in various industrial settings.
背景
驾驶疲劳是当代社会的一个重大关注点，每年都会导致大量的交通事故。本研究探索了新颖的疲劳检测方法，旨在提高驾驶安全性。
方法
本研究使用脑电图（EEG）和功能近红外光谱（fNIRS）来监测模拟驾驶实验中长达 7 小时的驾驶员疲劳。
结果
分析表明，行为数据与前额叶的血液动力学变化之间存在显著相关性，特别是在 4 小时左右，这表明驾驶员表现下降的关键时期。尽管参与者人数较少，但该研究的结果与驾驶员的疲劳标准非常吻合。
结论
通过将 fNIRS 整合到非自愿注意力脑功能实验中，本研究证明了在准确检测驾驶疲劳方面具有有前途的效果。这些发现深入了解了疲劳动态，对制定各种工业环境中的有效安全措施和政策具有重要意义。
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