文献类型：期刊文献

中文题名：基于单模态生理信号无监督特征学习的驾驶压力识别

英文题名：Driving Stress Detection Based on Unsupervised Feature Learning of Single Module Physiological Signal

作者：江润强[1];陈兰岚[1];谌鈫[1]

机构：[1]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237

年份：2021

卷号：47

期号：4

起止页码：475

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家自然科学基金(61976091);中央高校基本科研业务费专项资金。

语种：中文

中文关键词：驾驶压力识别;单模态生理信号;卷积自编码器;抽象特征;集成学习;

外文关键词：driving stress detection;single module physiological signal;convolutional auto-encoder;abstract feature;ensemble learning;

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%。
The traffic accidents are mainly related to unfavorable driving status,e.g.fatigue,stress,distraction and sleepiness,which may result in a considerable amount of vehicle collisions and casualties every year.Generally,stress may be taken as a normal response of human body to dangerous or difficult events.With the development of wearable sensor and wireless technique,researchers are paying more attention to the physiological measures that are highly correlated to driver’s mental states.In-vehicle intelligent systems,the physiological measures can be utilized automatically in various manners to help drivers better manage their negative driving status.However,the driving stress detection based on multimodal physiological signals might affect the driving comfort of drivers,and traditional physiological feature extraction techniques largely rely on the prior knowledge.Aiming at the above shortcoming,this paper proposes a new driving stress detection method by means of single module physiological signal,i.e.,GSR signal on the foot(FGSR).Firstly,the abstract features are generated by unsupervised feature learning using 1D-convolutional auto-encoder(CAE),and are further sent to four different base classifiers,i.e.,k-nearest neighbor(KNN),gradient boosting decision tree(GBDT),support vector machine(SVM)and random forest(RF).And then,the outputs of different base classifiers are integrated via the voting method to improve the stability and accuracy of driving stress detection.Finally,the proposed model is validated via the MIT-drivedb data set,from which the features extracted from GSR using convolutional auto-encoder have good representational ability for the driving stress.Moreover,the ensemble of different base classifiers can effectively improve the accuracy of final recognition results。