An SVM-based identification of acute stress in pilots using objective indicators
通过模拟飞行中发动机故障诱发37名学员急性应激，采集生理、生化及行为数据，用支持向量机模型以86.49%准确率区分应激与非应激状态，为客观监测飞行员应激水平提供方法。
Sudden incidents during flight trigger acute stress in pilots, compromising safety. An approach task was designed, with an engine failure during the landing phase to induce acute stress in 37 flight cadets using a C172-G1000 simulator. State anxiety scores, heart rates, heart rate variability, salivary cortisol concentrations, flight altitude, and heading were collected. Results revealed significant differences in physiological, biochemical, and behavioural data between stress and non-stress states. A support vector machine model was trained through feature selection, normalisation, and hyperparameter tuning. The model achieved an accuracy of 86.49% in distinguishing stress from non-stress states. This study provides a methodology for objective monitoring of pilot stress levels.