Research on the Identification of Pilots’ Fatigue Status Based on Functional Near-Infrared Spectroscopy

Fatigue can lead to sluggish responses, misjudgments, flight illusions and other problems for pilots, which could easily bring about serious flight accidents. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) device was used to record the changes of hemoglobin concentration of...

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Bibliographic Details
Published in:Aerospace 2022-03, Vol.9 (3), p.173
Main Authors: Pan, Ting, Wang, Haibo, Si, Haiqing, Liu, Haibo, Xu, Mengyue
Format: Article
Language:English
Subjects:
Accidents
Aircraft accidents & safety
Aviation
Brain research
Coefficient of variation
Datasets
Discriminant analysis
Electrocardiography
Experiments
Fatigue
flight safety
functional near-infrared spectroscopy
Hemoglobin
Illusions
Infrared spectra
Infrared spectroscopy
Kurtosis
Medical imaging
Modelling
Near infrared radiation
Near infrared spectroscopy
Oxyhemoglobin
Physiology
Pilot fatigue
Pilots
Research methodology
Sleep
Spectrum analysis
stacked noise reduction autoencoder
Support vector machines
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Summary:Fatigue can lead to sluggish responses, misjudgments, flight illusions and other problems for pilots, which could easily bring about serious flight accidents. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) device was used to record the changes of hemoglobin concentration of pilots during flight missions. The data was pre-processed, and 1080 valid samples were determined. Then, mean value, variance, standard deviation, kurtosis, skewness, coefficient of variation, peak value, and range of oxyhemoglobin (HbO2) in each channel were extracted. These indexes were regarded as the input of a stacked denoising autoencoder (SDAE) and were used to train the identification model of pilots’ fatigue state. The identification model of pilots’ fatigue status was established. The identification accuracy of the SDAE model was 91.32%, which was 23.26% and 15.97% higher than that of linear discriminant analysis (LDA) models and support vector machines (SVM) models, respectively. Results show that the SDAE model established in our study has high identification accuracy, which can accurately identify different fatigue states of pilots. Identification of pilots’ fatigue status based on fNIRS has important practical significance for reducing flight accidents caused by pilot fatigue.
ISSN:2226-4310
2226-4310
DOI:10.3390/aerospace9030173