Dataset Information

Assessment of the mental workload of trainee pilots of remotely operated aircraft using functional near-infrared spectroscopy.

ABSTRACT:

Background

Operating an aircraft is associated with a large mental workload; however, knowledge of the mental workload of ROV operators is limited. The purpose of this study was to establish a digital system for assessing the mental workload of remotely operated vehicle (ROV) operators using hemodynamic parameters, and compare results of different groups with different experience levels.

Method

Forty-one trainee pilots performed flight tasks once daily for 5 consecutive days in a flight simulation. Forty-five pilots experienced pilots and 68 experienced drivers were also included. Hemodynamic responses were measured by functional near-infrared spectroscopy (fNIRS).

Results

The median duration of peak oxyhemoglobin was 147.13 s (interquartile range [IQR] 21.97, 401.70 s) in the left brain and 180.74 s (IQR 34.37, 432.01 s) in the right brain in the experienced pilot group, and 184.42 s (IQR 3.41, 451.81 s) on day 5 in the left brain and 160.30 s (IQR 2.62, 528.20 s) in the right brain in the trainee group.

Conclusion

Navigation training reduces peak oxyhemoglobin duration, and may potentially be used as a surrogate marker for mental workload of ROV operators. Peak oxyhemoglobin concentration during s task may allow development of a simplified scheme for optimizing flight performance based on the mental workload of a pilot.

SUBMITTER: Tang L

PROVIDER: S-EPMC9055770 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Assessment of the mental workload of trainee pilots of remotely operated aircraft using functional near-infrared spectroscopy.

Tang Liya L Si Juanning J Sun Lei L Mao Gengsheng G Yu Shengyuan S

BMC neurology 20220430 1

<h4>Background</h4>Operating an aircraft is associated with a large mental workload; however, knowledge of the mental workload of ROV operators is limited. The purpose of this study was to establish a digital system for assessing the mental workload of remotely operated vehicle (ROV) operators using hemodynamic parameters, and compare results of different groups with different experience levels.<h4>Method</h4>Forty-one trainee pilots performed flight tasks once daily for 5 consecutive days in a ...[more]

PMID: 35490209

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Project description:Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.

Dataset Information

Assessment of the mental workload of trainee pilots of remotely operated aircraft using functional near-infrared spectroscopy.

Background

Method

Results

Conclusion

Publications

Assessment of the mental workload of trainee pilots of remotely operated aircraft using functional near-infrared spectroscopy.

Similar Datasets

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