Physiological responses to elevated carbon dioxide concentration and mental workload during performing MATB tasks

Background: Spatial disorientation is one of the most frequent causes of aircraft accidents, and is thus a major problem affecting air safety. Although a number of studies have examined spatial disorientation, the precise physiological changes occurring as a direct result of spatial disorientation and motion sickness remain unclear. The present study sought to investigate electrodermal activity (EDA) and subjective autonomic symptoms during spatial disorientation training, and to develop an indicator of physiological changes for pilot candidates. Methods: In the current study, we investigated changes in EDA measured using a wrist-worn device, and subjective autonomic nervous system symptoms during spatial disorientation training for pilot candidates. We then used the Graybiel diagnostic criteria to develop a novel physiological biomarker. Results: We found that maximum EDA change and peak amplitude were significantly increased in participants with a Graybiel score of ≥3 points compared with those who scored < 2 points. Furthermore, for symptoms of cold sweating and saliva secretion (from the seven Graybiel diagnostic criteria), the maximum EDA change in participants with scores ≥1 point was significantly higher than that of participants scoring 0 points. Conclusion: Our results indicate that EDA data measured with a wrist-worn device could provide a useful method for objective evaluation of the severity of spatial disorientation and motion sickness.

Wrist-Worn Electrodermal Activity as a Novel Neurophysiological Biomarker of Autonomic Symptoms in Spatial Disorientation.

Machine learning-based approach for identifying mental workload of pilots

Estimating Cognitive Load and Cybersickness of Pilots in VR Simulations via Unobtrusive Physiological Sensors

Predicting aviation training performance with multimodal affective inferences

BACKGROUND The prediction and evaluation of pilot workload is a key problem in human factor airworthiness of cockpit. OBJECTIVE A pilot traffic pattern task was designed in a flight simulation environment in order to carry out the pilot workload prediction and improve the evaluation method. METHODS The prediction of typical flight subtasks and dynamic workloads (cruise, approach, and landing) were built up based on multiple resource theory, and a favorable validity was achieved by the correlation analysis verification between sensitive physiological data and the predicted value. RESULTS Statistical analysis indicated that eye movement indices (fixation frequency, mean fixation time, saccade frequency, mean saccade time, and mean pupil diameter), Electrocardiogram indices (mean normal-to-normal interval and the ratio between low frequency and sum of low frequency and high frequency), and Electrodermal Activity indices (mean tonic and mean phasic) were all sensitive to typical workloads of subjects. CONCLUSION A multinominal logistic regression model based on combination of physiological indices (fixation frequency, mean normal-to-normal interval, the ratio between low frequency and sum of low frequency and high frequency, and mean tonic) was constructed, and the discriminate accuracy was comparatively ideal with a rate of 84.85%.

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Kun Yang

Papers

A comprehensive prediction and evaluation method of pilot workload.