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当前位置: 首页 > 文章 > 基于心肌电的联合收获机驾驶人疲劳检测研究 农机化研究 2020 (2) 8-14,43
Position: Home > Articles > 基于心肌电的联合收获机驾驶人疲劳检测研究 Journal of Agricultural Mechanization Research 2020 (2) 8-14,43

基于心肌电的联合收获机驾驶人疲劳检测研究

作  者：
祝荣欣;王金武
关键词：
联合收获机;疲劳评价;心率变异性;表面肌电信号;支持向量机
摘  要：
为探究联合收获机驾驶人疲劳的产生和变化规律,基于心电和肌电信号建立了联合收获机驾驶人疲劳检测方法.通过驾驶疲劳监测实验采集了10名联合收获机驾驶人120min的心电和颈部、腰部表面肌电数据,提取心率变异性和表面肌电信号的非线性特征参数C0复杂度和样本熵,探究特征参数随驾驶时间的变化规律;划分疲劳状态为轻度和重度2个等级,采用主成分分析法对特征参数降维,基于支持向量机建立了联合收获机驾驶人疲劳状态识别模型.结果表明:心率变异性、颈部和腰部表面肌电信号的C0复杂度和样本熵随驾驶时间的增加均呈下降趋势,并在1时段和12时段存在显著性差异;联合收获机驾驶人疲劳状态识别模型的识别准确率平均为91.75%,识别准确率较高.基于心肌电的联合收获机驾驶疲劳检测方法,可全面地反映驾驶人疲劳时的生理特征,有效识别联合收获机驾驶人的疲劳状态.
作  者：
Zhu Rongxin;Wang Jinwu;Guangxi Aviation Logistics Research Center,Guilin University of Aerospace Technology;Engineering Institute, Northeast Agricultural University;
单  位：
Zhu Rongxin%Wang Jinwu%Guangxi Aviation Logistics Research Center,Guilin University of Aerospace Technology%Engineering Institute, Northeast Agricultural University
关键词：
combine harvester;;fatigue evaluation;;heart rate variability;;surface electromyography;;support vector machine
摘  要：
To explore the generation and change rule of combine harvester driver's fatigue,based on electrocardiogram( ECG) and surface electromyography( s EMG) signals,the paper establishes a fatigue detection method for the driver of the combine harvester. The data of ECG and s EMG of neck and waist of 10 combine harvester drivers were collected for120 min through the driving fatigue monitoring experiment. The C0 complexity and SampEn of the nonlinear characteristic parameters of heart rate variability( HRV) and s EMG were extracted. The change rule of characteristic parameters with driving time was analyzed. The fatigue state was divided into mild and severe fatigue levels. The dimensional reduction of characteristic parameters was performed by principal component analysis. Based on the support vector machine,the driver's fatigue state recognition model of combine harvester was established. The results demonstrate that the C0 complexity and SampEn of HRV and neck and waist s EMG decrease with the increase of driving time,and there were significant differences between first and 12 th period. The average recognition accuracy rate of recognition model reaches 91.75%,which shows that the model has high-precise recognition. The identification method of combine harvester driver's fatigue state based on ECG and s EMG can reflect the physiological characteristics comprehensively and from multiple-perspective,and effectively identify the combine harvester driver's fatigue status.

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