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Uncorrelated fuzzy neighborhood preserving analysis based feature projection for driver drowsiness recognition

机译：基于不相关模糊邻域保存分析的特征投影技术用于驾驶员困倦识别

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摘要

Driver drowsiness is reported as one of the main causal factors in many traffic accidents as it progressively impairs the driver's awareness about external events. Drowsiness detection can be approached through monitoring physiological signals while driving to correlate drowsiness with the change in the corresponding patterns of the Electroencephalogram (EEG). Electrooculogram (EOG), and Electrocardiogram (ECG) signals. The main challenge in such an approach is to extract a set of features that can highly discriminate between the different drowsiness levels. This paper proposes a new Fuzzy Neighborhood Preserving Analysis (FNPA) feature projection method that is used to extract the discriminant information relevant to the loss of attention caused by drowsiness. Unlike existing methods, FNPA considers the fuzzy memberships of the input measurements into the different classes while constructing the graph Laplacian. Thus, it is able to identify both the discriminant and the geometrical structure of the input data while accounting for the overlapping nature of the drowsiness patterns. Furthermore, in order to address the singularity problem that occurs in many real world problems, the singular value decomposition (SVD). and later the QR-Decomposition, are utilized to extract a set of statistically uncorrelated features presenting the Uncorrelated FNPA (UFNPA). In the current preliminary study with datasets collected from 31 subjects only, while performing a driving simulation task, the proposed method is capable of accurately classifying the drowsiness levels using a small number of features with an average accuracy of ≈ 93%. On the other hand, the possibility of developing a subject-independent drowsiness recognition system is also investigated when the problem is converted into a binary classification task, as imposed by the number of drowsiness levels exhibited by the drivers, with accuracies ranging from 82%-to-84%.
机译：据报告，驾驶员的困倦是许多交通事故的主要因果关系之一，因为它逐渐损害了驾驶员对外部事件的意识。嗜睡检测可以通过在驾驶时监测生理信号来实现，以使嗜睡与脑电图（EEG）相应模式的变化相关。心电图（EOG）和心电图（ECG）信号。这种方法的主要挑战是提取可以高度地区分不同睡意程度的一组功能。本文提出了一种新的模糊邻域保存分析（FNPA）特征投影方法，该方法用于提取与睡意引起的注意力丧失有关的判别信息。与现有方法不同，FNPA在构造图拉普拉斯算子时会考虑将输入测量值的模糊成员划分为不同的类。因此，在考虑嗜睡模式的重叠性质的同时，能够识别输入数据的判别式和几何结构。此外，为了解决在许多实际问题中出现的奇异性问题，请使用奇异值分解（SVD）。然后使用QR分解提取一组表示不相关FNPA（UFNPA）的统计上不相关的特征。在当前的初步研究中，仅从31个受试者中收集了数据集，同时执行了驾驶模拟任务，该方法能够使用少量特征（平均准确度约为93％）对睡意程度进行准确分类。另一方面，当问题转化为由驾驶员表现出的睡意程度而引起的二元分类任务时，也研究了开发与主体无关的睡意识别系统的可能性，其准确性范围为82％-至-84％。

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  • 来源
    《Fuzzy sets and systems》 |2013年第16期|90-111|共22页
  • 作者

    Rami N. Khushaba; Sarath Kodagoda; Sara Lal; Gamini Dissanayake;

  • 作者单位

    Centre for Intelligent Mechatronic Systems (CIMS), Faculty of Engineering and Information Technology. University of Technology, Sydney. Broadway. NSW 2007. Australia;

    Centre for Intelligent Mechatronic Systems (CIMS), Faculty of Engineering and Information Technology. University of Technology, Sydney. Broadway. NSW 2007. Australia;

    Department of Medical and Molecular Biosciences, Faculty of Science. University of Technology. Sydney. Broadway. NSW 2007. Australia;

    Centre for Intelligent Mechatronic Systems (CIMS), Faculty of Engineering and Information Technology. University of Technology, Sydney. Broadway. NSW 2007. Australia;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    driver drowsiness; feature extraction; fuzzy discriminant analysis; signal processing;

    机译：驾驶员困倦特征提取;模糊判别分析;信号处理;

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