摘要
针对传统时频特征难以很好地描述脉搏这类非平稳信号与驾驶员疲劳脉搏样本相对较少的问题,提出一种基于脉搏信号本征模函数(IMF)时频特征和支持向量数据描述(SVDD)的驾驶员疲劳检测方法。该方法充分利用了IMF适合表征非平稳信号和SVDD擅长处理不平衡样本分类问题的优势。首先,将脉搏信号进行经验模态分解;然后,提取各IMF时频特征:归一化能量、最大瞬时频率和瞬时幅值平均值;最后,用SVDD分类器对驾驶员疲劳状况做出判别并给出疲劳等级。对比实验表明,该方法能有效检测出驾驶员的疲劳状况。
To address the problems of traditional time-frequency features＇ being hard to characterize the non-stationary signal （e. g. , pulse signal） and the fewer samples of driver fatigue pulses, an approach was proposed to detect driver＇s fatigue based on the time-frequency features of intrinsic mode function （IMF） of pulse signal and support vector data description （SVDD）. This approach makes full use of the advantages of the IMF＇ being suitable for characterizing non- stationary signal and SVDD＇ being good at addressing the classification with unbalanced samples. First, the pulse sig- nals are decomposed by using empirical mode decomposition method to obtain multiple IMF components. Then, the time-frequency features of IMF are extracted, which consists of the normalized energy, the maximum instantaneous fre- quency and the average of instantaneous amplitude. Finally, the SVDD classifier is used to detect the fatigue status of drivers and give corresponding fatigue level. Comparison experiments suggest that this approach can effectively detect the fatigue status of drivers.
出处
《计算机科学》
CSCD
北大核心
2016年第7期314-318,共5页
Computer Science
基金
城市交通车路协同控制仿真系统开发项目(cstc2014yykfb40001)
重庆市教委科学技术研究项目(KJ1500442)
国家自然科学基金项目(91438104)资助
关键词
疲劳驾驶
脉搏信号
本征模函数
支持向量数据描述
Fatigue driving, Pulse signal, Intrinsic mode function, Support vector data description
