计算机科学 ›› 2016, Vol. 43 ›› Issue (7): 314-318.doi: 10.11896/j.issn.1002-137X.2016.07.058
蒋建春,蒋丽,唐慧,张卓鹏,吴雪刚
JIANG Jian-chun, JIANG Li, TANG Hui, ZHANG Zhuo-peng and WU Xue-gang
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。
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