A methodology for evaluating human operator's fitness for duty in nuclear power plants

Choi, Moon Kyoung; Seong, Poong Hyun

doi:10.1016/j.net.2019.10.024

Cited by 13 publications

(2 citation statements)

References 17 publications

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“…Therefore, studying brain function alterations during visual involuntary attention could unveil new indicators for assessing fatigue during prolonged driving. Many teams have investigated the fatigue status of industrial operators [21], but, at present, questionnaires are mostly used for investigation, which lack correlation with physiological signals. However, fNIRS combined with involuntary attention brain function experiments, proposed by this team, could be a better way for fatigue Monitoring.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments

Li,

Liu,

Gao

et al. 2024

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments

Li,

Liu,

Gao

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Estudos na literatura apresentam métodos para inferir diferentes particularidades do estado cognitivo de pessoas a partir de dados de eye-tracking [4,8,9]. No entanto, eles têm grande foco em métodos estatísticos baseados em grandes volumes de dados, o que pode não ser uma solução adequada para situações com quantidades limitadas.…”

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Uso De Dados De Eye-Tracking Para Modelagem De Comportamento Visual Em Sistemas Especialistas

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Evaluating the Potential of Machine Learning in Predicting Nuclear Fusion Ignition: A Comprehensive Analysis of Magnetic Fields, Instabilities, and Energy Inputs

Gupta,

Rustagi,

Handa

et al. 2024

Lecture Notes in Networks and Systems

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A methodology for evaluating human operator's fitness for duty in nuclear power plants

Cited by 13 publications

References 17 publications

Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments

Advancements in Fatigue Detection: Integrating fNIRS and Non-Voluntary Attention Brain Function Experiments

Uso De Dados De Eye-Tracking Para Modelagem De Comportamento Visual Em Sistemas Especialistas

Evaluating the Potential of Machine Learning in Predicting Nuclear Fusion Ignition: A Comprehensive Analysis of Magnetic Fields, Instabilities, and Energy Inputs

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