北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (11): 2292-2298.doi: 10.13700/j.bh.1001-5965.2018.0122

• 论文 • 上一篇    下一篇

HRV在管制员疲劳检测中的适用性

靳慧斌1, 张静2, 吕川3   

  1. 1. 中国民航大学 通用航空学院, 天津 300300;
    2. 中国民航大学 国家空管运行安全技术重点实验室, 天津 300300;
    3. 中国民航大学 中欧航空工程师学院, 天津 300300
  • 收稿日期:2018-03-09 修回日期:2018-04-20 出版日期:2018-11-20 发布日期:2018-11-27
  • 通讯作者: 靳慧斌,E-mail:airhf207@163.com E-mail:airhf207@163.com
  • 作者简介:靳慧斌,男,博士,副研究员。主要研究方向:航空人为因素与安全人机交互;张静,女,硕士研究生。主要研究方向:航空人为因素;吕川,男,硕士研究生。主要研究方向:航空人为因素。
  • 基金资助:
    中国民用航空局安全能力建设资金(TMSA20177246-1/2);中央高校基本科研业务费专项资金(3122016F003)

Application of HRV in air traffic controllers' fatigue detection

JIN Huibin1, ZHANG Jing2, LYU Chuan3   

  1. 1. General Aviation College, Civil Aviation University of China, Tianjin 300300, China;
    2. National Key Laboratory of Air Traffic Operation Safety Technology, Civil Aviation University of China, Tianjin 300300, China;
    3. Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China
  • Received:2018-03-09 Revised:2018-04-20 Online:2018-11-20 Published:2018-11-27
  • Supported by:
    CAAC's Safety Capability Construction Fund (TMSA2017246-1/2); the Fundamental Research Funds for the Central Universities (3122016F003)

摘要: 为研究心率变异性(HRV)指标在管制员(ATC)疲劳检测中的适用性,搭建模拟管制实验平台,利用生理记录仪实时记录20名被试正常和疲劳状态下的心电(ECG)信号,并采集其主观疲劳度(卡罗林斯卡嗜睡量表)和操作绩效。利用偏相关分析选取与被试疲劳等级相关性高的心率变异性指标,并用于管制员疲劳检测的多元线性回归建模。分析结果表明:SDNN与被试的疲劳状态无相关性;LFnorm和HFnorm与疲劳程度呈弱相关;RR间期均值、LF/HF均与被试的疲劳度存在较强的相关性,二者结合建立的多元线性回归模型,拟合优度大于0.5,RR间期均值和LF/HF可作为检测管制员疲劳的有效指标。本文研究成果可为未来的管制员疲劳实时检测提供科学依据和实验支撑。

关键词: 疲劳检测, 偏相关分析, 适用性, 心率变异性(HRV), 多元线性回归

Abstract: In order to study the application of heart rate variability (HRV) indexes in the fatigue detection of the air traffic controllers (ATC), the simulation control experiment platform was set up, the real-time physiological recorder was used to record the electrocardiogram (ECG) signals of 20 subjects in real time under normal and fatigue conditions, and their subjective fatigue (Karolinsaka sleepingness scale) and operational performance were collected. The HRV index with high correlation with fatigue grade was selected by partial correlation analysis and used to model the multivariate linear regression model for fatigue detection. The analysis results show that there is no correlation between the SDNN and the fatigue status of the subjects; LFnorm and HFnorm are weakly correlated with the fatigue; RR interval and LF/HF have a strong correlation with the fatigue degree of the controlled subjects; The multivariate linear regression model, the goodness of fit is greater than 0.5, RR interval and LF/HF can be used as valid indicators of controller fatigue detection. This study can provide scientific evidence and experimental support for the future real-time detection of controller fatigue.

Key words: fatigue detection, partial correlation analysis, application, heart rate variability (HRV), multivariate linear regression

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