基于心率和心率变异性的K-Means聚类算法提升学员模拟飞行实践效果研究

    Application of K-Means clustering algorithm based on heart rate and heart rate variability in course of simulated flight practice

      摘要
    • 摘要:
      目的 基于心率和心率变异性特征参数，采用K-Means聚类算法客观评估航空航天医学专业学员在模拟飞行考核中的应激状态，并制定针对性教学方案，以探索改善模拟飞行实践课程教学效果的新型教学策略。
      方法 选取48名航空航天医学专业本科学员作为研究对象，使用初教六型模拟飞行器进行模拟飞行实践。采用百分制量化评估学员五边飞行任务的成绩；使用“飞行生理参数记录仪”实时记录学员在飞行前静息状态和着陆期间的心电信号，并提取学员中期考核中心率和心率变异性的RMSSD、LFnu和LF/HF作为特征参数，输入至K-Means聚类算法中将学员分为高应激组和低应激组，对两组学员实施针对性教学方案。通过对比学员中期和终期两次模拟飞行考核成绩和心率/心率变异性指标，评估教学效果。
      结果 ①学员在飞行着陆期间的心率和心率变异性均与静息状态存在明显差异，具体表现为心率加快（F=15.49, P<0.01），RMSSD降低（F=16.81, P<0.01），LFnu（F=61.20, P<0.01）和LF/HF（F=34.16, P<0.01）减小，表明飞行着陆任务负荷大，会引起学员产生应激反应；②在接受针对性教学方案后的终期考核中，高、低应激状态两组学员的心率和心率变异性无明显差异，模拟飞行考核成绩较中期考核均有显著提升（低应激组：F=16.80, P <0.01；高应激组：F=67.32, P <0.01），但是两组学员之间的成绩无显著差异（F=0.09, P=0.70），表明两组学员的飞行实践水平达到相似水平。
      结论 基于心率和心率变异性的K-Means聚类算法能够较为准备地判断学员的应激状态，据此制定的针对性教学方案有效地提升高应激状态学员的模拟飞行成绩，为优化模拟飞行实践课程设置提供了科学指导。

       

      Abstract:
      AIM To based on the characteristic parameters of heart rate and heart rate variability, the K-Means clustering algorithm is used to objectively evaluate the stress state of aerospace medical students in simulated flight assessment, and a targeted teaching plan is developed to explore new teaching strategies for improving the teaching effectiveness of simulated flight practice courses.
      METHODS A total of 48 aerospace medical students participated in this study. According to the K-Means clustering algorithm, they were categorized into high-stress and low-stress groups based on their HR and HRV metrics, which were recorded during two phases: resting state and landing phase of a mid-term simulated flight examination. Their performances were evaluated according to the simulated flight scores of circuit-flying and HR/HRV indexes between final and mid-term simulated flight examination to assess the teaching effectiveness.
      RESULTS The HR and HRV indexes (RMSSD, LFnu, and LF/HF) during the landing phase of simulated flight were significantly different from those in the resting state. Specifically, there was an increase in heart rate (F=15.49, P<0.01), a decrease in RMSSD (F=16.81, P<0.01), and reductions in LFnu (F=61.20, P<0.01) and LF/HF (F=34.16, P<0.01), indicating that simulated flight imposed high cognitive load and stress. The simulated flight scores of circuit-flying in the final stimulated flight examination were significantly higher than that in mid-term simulated flight examination (the group of low-stress: F=16.80, P<0.01, the group of high-stress: F=67.32, P<0.01). However, the simulated flight scores of circuit-flying and HR/HRV indexes were not significant different in the final stimulated flight examination between the two groups of high-stress and low-stress (F=0.09, P=0.70), indicating that it had a similar level of simulated flight practice between the two groups.
      CONCLUSION The teaching strategies based on stress states of aviation medical students can effectively improve the simulated flight performance of students in the course of simulated flight practice.

       

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