Study on flight cadets′ cognitive load based on ensemble learning model

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 are obtained through flight simulation experiments; Then, the characteristics of RESP (respiratory) and ECG(Electrocardiograph) signals are 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, combined with support vector machine, K-nearest neighbor, artificial neural network and other methods, an ensemble learning model is established to evaluate the flight cadets′ cognitive load. Furthermore, it is compared with single algorithms. The results show that the flight cadets′ cognitive load evaluation model established in this paper has a high accuracy rate and can better reflect the flight cadets′ cognitive load level.

Keywords

• physiological signal
• flight cadets
• cognitive load
• ensemble learning
• feature selection
• flight safety