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Published on: May 24, 2020
Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments
Ting Li1, Peishuai Liu1, Yuan Gao2
1Institute of Biomedical Engineering, Chinese Academy Medical Sciences & Peking Union Medical College, Tianjin 300192, China.
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.
