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Objective To determine viability of drowsiness detection, researchers study the feasibility of photoplethysmogram (PPG) data collection from the geography of the aviation headset, correlating to electrocardiogram (ECG) reference. Background Fatigue has been a probable cause, contributing factor, or a finding in 20% of transportation incidents and accidents studied between January 2001 and December 2012. This operational hazard is particularly troublesome within aviation and airline operations. Method PPG and ECG data were collected synchronously from Federal Aviation Administration (FAA) commercially rated pilots during flight simulation in the window of circadian low (WOCL). Valid PPG and ECG data from 14 participants were analyzed, which yielded approximately 2 hr of data per participant for fatigue-related analysis. Results The results of the study demonstrate clear trends toward decreased heart rate for both ECG and PPG and suggest progression of drowsiness between four separate periods (T1, T2, T3, and T4) selected during the study; however, the mean heart rate change from T1 to T4 was statistically significant. Conclusion The results suggest that ECG and PPG data can be an important tool to observe conditions where drowsiness or fatigue may add risk to the operation. In addition, the data show high correlation between ECG and PPG data, further suggesting that a simpler PPG sensor, mounted within the geography of the aviation headset, may streamline the operationalization of important physiological data. Application Incorporation of PPG sensors and associated signal processing methods into facilitating equipment, such as the aviation headset, may add a layer to operational safety.

Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.

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