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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于单模态生理信号无监督特征学习的驾驶压力识别

江润强 陈兰岚 谌鈫

江润强, 陈兰岚, 谌鈫. 基于单模态生理信号无监督特征学习的驾驶压力识别[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20200728001
引用本文: 江润强, 陈兰岚, 谌鈫. 基于单模态生理信号无监督特征学习的驾驶压力识别[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20200728001
JIANG Runqiang, CHEN Lanlan, CHEN Qin. Driving Stress Detection Based on Unsupervised Feature Learning of Single Module Physiological Signal[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20200728001
Citation: JIANG Runqiang, CHEN Lanlan, CHEN Qin. Driving Stress Detection Based on Unsupervised Feature Learning of Single Module Physiological Signal[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20200728001

基于单模态生理信号无监督特征学习的驾驶压力识别

doi: 10.14135/j.cnki.1006-3080.20200728001
基金项目: 国家自然科学基金(61976091);中央高校基本科研业务费专项资金
详细信息
    作者简介:

    江润强(1994—),男,山东淄博人,硕士生,主要研究方向为机器学习及其应用。E-mail:rqjiang_ecust@163.com

    通讯作者:

    陈兰岚,E-mail:llchen@ecust.edu.cn

  • 中图分类号: TP391

Driving Stress Detection Based on Unsupervised Feature Learning of Single Module Physiological Signal

  • 摘要: 针对基于多模态生理信号分析的驾驶压力识别会影响驾驶员的行车舒适性,且传统的生理特征的提取需要依赖先验知识的问题,构建了基于单模态生理信号无监督特征学习的驾驶压力识别模型。首先采用单模态生理信号,通过构造卷积自编码器进行无监督的特征学习来提取抽象特征;然后将特征依次送入支持向量机、随机森林、K最近邻、梯度提升决策树4种不同的基分类器进行建模;最后引入集成学习的思想对不同基分类器的输出进行投票集成来提高驾驶压力识别的稳定性与准确性。实验结果表明,该模型在MIT-drivedb数据集的驾驶压力三分类任务中的准确率可达92.8%。

     

  • 图  1  驾驶压力识别模型框架

    Figure  1.  Framework of driving stress estimation model

    图  2  一维卷积自编码器示意图

    Figure  2.  Schematic diagram of one dimensional convolution auto-encoder

    图  3  数据集实验流程

    Figure  3.  Experiment flow of data set

    图  4  一维卷积自编码器的训练

    Figure  4.  Training of one dimensional convolutional auto-encoder

    图  5  抽象特征的可视化

    Figure  5.  Visualization of abstract features

    图  6  不同路况下的ROC曲线以及AUC

    Figure  6.  ROC curves and AUC under different road conditions

    图  7  不同维度下的特征学习算法比较

    Figure  7.  Comparison of feature learning algorithms in different dimensions

    表  1  卷积自编码器的超参数

    Table  1.   Hyperparameters of the convolutional auto-encoder

    LayerTypeHyperparametersFeature map
    1Input(3 100,1)
    2Convolution5×1×16(3 100,16)
    3Pooling2×1(1 550,16)
    4Convolution5×1×32(1 550,32)
    5Pooling2×1(775,32)
    6Reshape(24 800,1)
    7Dense(dim,1)
    8Dense(775×dim,1)
    9Reshape(775,dim)
    10Upsampling2×1(1 550,dim)
    11Deconvolution5×1×32(1 550,32)
    12Upsampling2×1(3 100,32)
    13Deconvolution5×1×16(3 100,16)
    14Deconvolution5×1×1(3 100,1)
    下载: 导出CSV

    表  2  基分类器及参数设置

    Table  2.   Base classifier and parameter setting

    ClassifierParameter
    KNNn_neighbors:1,2,3,4,5
    RFmax_feature:1,2,3,4,5
    GBDTmax_feature:1,2,3,4,5
    SVMC、gamma:[0.000 1,0.001,…,100]
    下载: 导出CSV

    表  3  不同基分类器不同维度下的识别结果

    Table  3.   Recognition results under different dimensions of different base classifiers

    DimensionAccuracy/%
    KNNGBDTRFSVM
    286.72986.57984.13586.917
    490.60289.24890.03890.150
    892.51989.92590.78992.030
    1692.33188.34689.69992.105
    3287.78285.82787.63288.684
    6488.57186.61788.19588.008
    下载: 导出CSV

    表  4  基分类器的投票集成

    Table  4.   Voting integration of base classifiers

    MethodPrecision/%Recall/%F1/%Accuracy/%
    Mean(base classifiers)91.29191.24490.72191.316
    Voting(soft)92.80592.77192.34092.820
    Voting(hard)92.38492.56991.86492.444
    下载: 导出CSV

    表  5  同类研究的对比

    Table  5.   Comparison of similar studies

    MethodPhysiological signalAccuracy/%
    Reference[19]ECG,EMG,GSR,RESP97.4
    Reference[24]ECG83
    Reference[25]ECG98.6
    Reference[6]FGSR88.91
    Reference[26]FGSR81.82
    This paperFGSR92.82
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-28
  • 网络出版日期:  2020-12-16

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