A revised pilot oxygen mask design was developed for better fit to the Korean Air Force pilots’ faces. The present study compared an existing pilot oxygen mask and a prototype of the revised mask design with 88 Korean Air Force pilots in terms of subjective discomfort, facial contact pressure, and slip distance on the face in high gravity. The average discomfort levels, facial contact pressures, and slip distance of the revised mask were reduced by 33%–56%, 11%–33%, and 24%, respectively, compared to those of the existing oxygen mask. The mask evaluation method employed in the study can be applied to ergonomic evaluation of full- or half-face mask designs.

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An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male
participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG
measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%).@en