基于集成学习模型的飞行学员认知负荷研究

Study on Flight Cadets’ Cognitive Load Based on Ensemble Learning Model

摘要:

在飞行过程中，飞行员需要在短时间内接收大量信息，并做出正确的判断与决策，而过高的认知负荷会影响其感知、判断、决策等认知过程，进而影响飞行安全。首先通过飞行模拟实验获取飞行学员在执行不同飞行任务时的生理数据；然后通过时域、频域分析等方法提取呼吸和心电信号的特征，并通过统计学方法筛选出能够反映认知负荷水平的指标；最后结合支持向量机、K 最邻近、人工神经网络等方法建立集成学习模型，对飞行学员的认知负荷进行评估，并与单一算法进行对比。结果表明：本文建立的集成学习模型具有较高的准确率，能够更好地反映飞行学员认知负荷水平。

Abstract:

During flight, pilots need to receive a large amount of information in a short time and make correct judgments and decisions. The cognitive processes such as perception, judgment and decision-making will be affected by excessive cognitive load and affect flight safety. Firstly, the physiological data of flight cadets during different flight missions were obtained through flight simulation experiments; Then, the characteristics of RESP and ECG signals were extracted by time-domain and frequency-domain analysis, and the indexes that can reflect the level of cognitive load are selected by statistical methods. Finally, in the light of ensemble learning algorithm, combined with support vector machine, k-nearest neighbor, artistic neural network and other methods, a cognitive load evaluation model is established based on multiple physiological signals. Furthermore, it is compared with single models. The results show that the pilot cognitive load evaluation model established in this paper has a high accuracy rate and can better reflect the pilot’s cognitive load level.