HRV在管制员疲劳检测中的适用性

doi:10.13700/j.bh.1001-5965.2018.0122

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

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. 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. 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/Abstract

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。

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

中图分类号:

靳慧斌, 张静, 吕川. HRV在管制员疲劳检测中的适用性[J]. 北京航空航天大学学报, 2018, 44(11): 2292-2298.

JIN Huibin, ZHANG Jing, LYU Chuan. Application of HRV in air traffic controllers' fatigue detection[J]. JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2018, 44(11): 2292-2298.

参考文献

[1] WARDS T,GABETS C,MERCER J,et al.Task demand variation in air traffic control:Implications for workload,fatigue,and performance[M]//STANTON N A.Advances in human aspects of transportation.Berlin:Springer,2017:91-102.
[2] MITLER M M,CARSKADON M A,CZEISLER C A,et al.Catastrophes,sleep,and public policy:Consensus report[J].Sleep,1988,11(1):100-109.
[3] WILLIAMSON A,LOMBARDI D A,FOLKARD S,et al.The link between fatigue and safety[J].Accident Analysis & Prevention,2011,43(2):498-515.
[4] MELTON C E,MCKENZIE J M,SMITH R C,et al.Physiological,biochemical,and psychological responses in air traffic control personnel:Comparison of the 5-day and 2-2-1 shift rotation patterns[R].Washington,D.C.:Federal Aviation Administration,1973.
[5] ESCALONA E,DE UROSA E,GONZÁLEZ R,et al.Fatigalaboralencontroladores de tránsitoaéreo[J].Saludtrab.(Maracay),1996,4(2):99-108.
[6] International Civil Aviation Organization.Air traffic services:Annex 11 to international convention on civil aviation,13th Edition[S].Montréal:International Civil Aviation Organization,2001.
[7] YUAN L P,MA G F,SUN R S.An analysis of fatigue and its characteristics:A survey on chinese air traffic controller[M]//HARRIS D.Engineering psychology and cognitive ergonomics.Berlin:Springer,2016:38-47.
[8] DIMITRAKOPOULOS G N,KAKKOS I,DAI Z,et al.Functional connectivity analysis of mental fatigue reveals different network topological alterations between driving and vigilance tasks[J].IEEE Transactions on Neural Systems & Rehabilitation Engineering,2018,26(4):740-749.
[9] DASARI D,SHOU G,DING L.Investigation of independent components based EEG metrics for mental fatigue in simulated ATC task[C]//6th International IEEE/EMBS Conference on Neural Engineering.Piscataway,NJ:IEEE Press,2013:1331-1334.
[10] ARICO P,BORGHINI G,DI FLUMERI G,et al.Human factors and neurophysiological metrics in air traffic control:Acritical review[J].IEEE Reviews in Biomedical Engineering,2017,10:250-263.
[11] 陈征.疲劳驾驶下心电信号的采集与分析系统研究[D].沈阳:东北大学,2013:1-5.CHEN Z.Research of ECG signal acquisition and analysis based on fatigue-driving[D].Shenyang:Northeastern University,2013:1-5(in Chinese).
[12] ARICÒ P,BORGHINI G,GRAZIANI I,et al.Air-traffic-controllers (ATCO):Neurophysiological analysis of training and workload[J].Italian Journal of Aerospace Medicine,2015:35-54.
[13] LEE D H,PARK K S.Multivariate analysis of mental and phy-sical load components in sinus arrhythmia scores[J].Ergonomics,1990,33(1):35-47.
[14] WU Q,ZHAO Y,BI X.Driving fatigue classified analysis based on ECG signal[C]//International Symposium on Computational Intelligence & Design.Pisctaway,NJ:IEEE Press,2013:544-547.
[15] NADAI S D,D'INCÀ M,PARODI F,et al.Enhancing safety of transport by road by on-line monitoring of driver emotions[C]//System of Systems Engineering Conference.Pisctaway,NJ:IEEE Press,2016:1-4.
[16] 董占勋,孙守迁,吴群,等.心率变异性与驾驶疲劳相关性研究[J].浙江大学学报(工学版),2010,44(1):46-50.DONG Z X,SUN S Q,WU Q, et al.Study of correlation between heart rate variability and driving fatigue[J].Journal of Zhejiang University(Engineering Science),2010,44(1):46-50(in Chinese).
[17] 刘灵.心率变异性在汽车司机驾驶疲劳监测中应用的研究[D].重庆:重庆大学,2007.LIU L.Study on application of heart rate variability in vehicle driver fatigue detecting[D].Chongqing:Chongqing University,2007(in Chinese).
[18] 马艳丽.驾驶员驾驶特性与道路交通安全对策研究[D].哈尔滨:哈尔滨工业大学,2007:30-33.MA Y L.Study on characteristics of driving and its countermeasures to road safety[D].Harbin:Harbin Institute of Technology,2007:30-33(in Chinese).
[19] WilcoPublishing.TowerSimulator[EB/OL].(2018-04-26).http://www.towersimulator.com/features.html.
[20] 房瑞雪.基于生理信号的驾驶疲劳预警对策有效性的实验研究[D].北京:北京工业大学,2010:31-34.FANG R X.Experimental research on effectiveness of pre-warning countermeasure of driving fatigue based on physiological signals[D].Beijing:Beijing University of Technology,2010:31-34(in Chinese).
[21] 祝荣欣.基于生理信号的联合收获机驾驶疲劳检测与评价[D].哈尔滨:东北农业大学,2016:46-48.ZHU R X.Detection and evaluation for driver fatigue of combine harvester based on physiological signals[D].Harbin:Northeast Agricultural University,2016:46-48(in Chinese).
[22] 付川云.疲劳状态下驾驶人生理及眼动特征研究[D].哈尔滨:哈尔滨工业大学,2011:57-72.FU C Y.Research on physiological and eye movement characteristics of driver under fatigue condition[D].Harbin:Harbin Institute of Technology,2011:57-72(in Chinese).
[23] 杨华.基于心电脉搏信号的VDT精神疲劳识别方法研究[D].兰州:兰州理工大学,2011:22-24.YANG H.The research of VDT mental fatigue estimated method based on ECG and pulse signal[D].Lanzhou:Lanzhou University of Technology,2011:22-24(in Chinese).

HRV在管制员疲劳检测中的适用性

Application of HRV in air traffic controllers' fatigue detection

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	梁坤, 左洪福, 孙见忠, 王容辉. 多元线性回归在引气系统故障诊断中的应用[J]. 北京航空航天大学学报, 2015, 41(9): 1651-1658.
[2]	潘利文, 郑立静, 张虎, 高文理. Niyama判据对铸件缩孔缩松预测的适用性[J]. 北京航空航天大学学报, 2011, 37(12): 1534-1540.
[3]	王惠文, 陈梅玲, Gilbert Saporta. Gram-Schmidt回归及在刀具磨损预报中的应用[J]. 北京航空航天大学学报, 2008, 34(06): 729-733.