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原文传递 Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation.

题名：	Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation.
作者：	Harrivel, A. R.; Liles, C.; Stephens, C. L.; Ellis, K. K.; Prinzel, L. J.; Pope, A. T.
关键词：	Psychophysiology, Sensitivity analysis, Airline operations, Flight safety, Situational awareness, Classifications, Attention, Pilot performance, Constraints, Commercial aircraft, Flight simulation, Neural nets, Aircraft accidents, Real time operation, Machine learning, Electroencephalography, Evoked response (psychophysiology), Galvanic skin response, Electrocardiography, Respiratory physiology
摘要：	Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.
总页数：	Harrivel, A. R.; Liles, C.; Stephens, C. L.; Ellis, K. K.; Prinzel, L. J.; Pope, A. T.
报告类型：	科技报告