Research on identification of flight cadets’ cognitive load based on multi-source physiological data and CGAN-DBN model
通过眼动、心电和呼吸等多源生理数据，结合条件生成对抗网络和深度置信网络模型，高精度识别飞行学员的认知负荷，有助于减少因飞行员认知负荷过高导致的飞行事故。
Modern aircraft cockpit system is highly information-intensive. Pilots often need to receive a large amount of information and make correct judgments and decisions in a short time. However, cognitive load can affect their ability to perceive, judge and make decisions accurately. Furthermore, the excessive cognitive load will induce incorrect operations and even lead to flight accidents. Accordingly, the research on cognitive load is crucial to reduce errors and even accidents caused by human factors. By using physiological acquisition systems such as eye movement, ECG, and respiration, multi-source physiological signals of flight cadets performing different flight tasks during the flight simulation experiment are obtained. Based on the characteristic indexes extracted from multi-source physiological data, the CGAN-DBN model is established by combining the conditional generative adversarial networks (CGAN) model with the deep belief network (DBN) model to identify the flight cadets' cognitive load. The research results show that the flight cadets' cognitive load identification based on the CGAN-DBN model established has high accuracy. And it can effectively identify the cognitive load of flight cadets. The research paper has important practical significance to reduce the flight accidents caused by the high cognitive load of pilots.