基于多生理信号的飞行警戒疲劳检测

Flight alert fatigue detection based on multi-physiological signals

为预防事故发生,保障飞行安全,提出一种基于多生理信号和支持向量机(SVM)的飞行警戒疲劳检测方法,识别飞行员飞行警戒中的疲劳状态。首先,研究疲劳评价与检测方法,并基于自主开发的飞行警戒测试系统与多导生物反馈仪和眼动仪搭建试验平台;然后,采集飞行警戒作业中的心电、眼动、呼吸等多生理信号和主观疲劳自评值;再次,通过配对样本的非参数检验,提取敏感生理指标,并以此作为特征向量,通过机器学习训练,构建基于多生理信号和SVM的疲劳检测模型;最后,依据受试者工作特征(ROC)曲线和模型准确率,对比分析各疲劳检测模型的效果。结果表明:在飞行警戒疲劳状态下,作业者的多项生理指标均有显著变化;心电、眼动和呼吸等多生理信号融合较单一信号的疲劳检测效果好,其ROC曲线下面积为0.802。基于高斯径向基核函数(RBF)构建的疲劳检测模型训练及预测准确率可达93%和87.50%。基于多生理信号和SVM方法可以实现对飞行警戒疲劳状态的检测。

In order to identify the fatigue state of flight alert,a fatigue detection method of flight alert was proposed based on multi-physiological signals and SVM.Firstly,the evaluation and detection methods of fatigue were studied,and the experimental platform was built by combining the self-developed flight alert test system with multichannel biofeedback instrument and eye movement instrument.Physiological signals such as electrocardiogram,respiration,eye movement and subjective fatigue evaluation values were collected.The sensitive physiological indexes were extracted by nonparametric test of paired samples as feature vectors.With these feature vectors,fatigue detection models based on multi-physiological signals and SVM were constructed through machine learning training.Lastly,the effects of models were compared and analyzed based on ROC curve and model accuracy.The results show that many physiological indexes of the operators change significantly in the state of flight alert fatigue.Multi-physiological signal fusion has better detection effect than single signal.Its ROC curve area is 0.802.The training and prediction accuracy of fatigue detection model based on Gaussian radial basis function(RBF)can reach 93%and 87.50%.The state detection of flight alert fatigue can be realized based on multi-physiological signals and SVM.