文献类型：期刊文献

中文题名：基于禁忌搜索的混合算法在驾驶压力识别中的应用

英文题名：Hybrid Algorithm Based on Tabu Search for Drive Stress Recognition

作者：叶朋飞[1];陈兰岚[1];张傲[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2018

卷号：44

期号：5

起止页码：730

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2017】；CSCD:【CSCD_E2017_2018】；

基金：国家自然科学基金(61201124);中央高校基本科研业务费重点科研基地创新基金(222201717006)

语种：中文

中文关键词：生理信号;过滤算法;禁忌搜索;特征选择;驾驶压力识别

外文关键词：physiological signal;filter algorithm;tabu search;feature selection;drive stress recognition

摘要：驾驶员在压力状态下行车会对驾驶安全产生很大影响,严重时甚至会造成交通事故。为准确检测驾驶员的压力状态,提取了驾驶员生理信号的多模态特征并提出了一种基于多种过滤式算法(Multi-filter,MF)与禁忌搜索算法(Tabu Search,TS)相结合的混合算法来选择有效特征向量。该算法首先采用多种过滤式算法的综合评分对原始特征集进行排序和过滤,有效降低特征维度;然后利用禁忌搜索算法进一步选出最优特征组合;最后采用支持向量机对3种不同驾驶压力水平进行分类。实验结果表明,本文提出的混合算法不仅有效地消除了高维特征向量中的冗余信息,还提升了分类准确率。
Driving under stress status has a great impact on drive safety and even causes traffic accidents in severe cases.It is important to develop appropriate approaches for early detecting the driving stress and then improving the awareness and performance of drivers.Due to its strong objectivity,robustness and real-time property,the physiological signal analysis has been validated as an effective approach for reflecting physiological conditions of human operators.In order to accurately detect the stress status of drivers,multi-modal features are extracted from time,spectral,wavelet,and nonlinear domains based on physiological signals,i.e.,electrocardiogram,galvanic skin response and respiration of drivers.And then,a hybrid algorithm based on the multi-filters(MF)and tabu search(TS)is proposed in this work to select effective features,since there are many redundant features and invalid features in the feature set.Firstly,in order to effectively reduce the feature dimension,the original features are sorted and filtered according to the comprehensive score calculated by multiple filtering algorithms.Three different filter methods including Chi test,Wilcoxon test and manual information are employed to generate a new feature subset,which had smaller feature dimensional.And then,the tabu search algorithm is adopted to further select the optimal feature combination from the optimal feature subset.Finally,three different drive stress levels are classified by means of support vector machine(SVM).Some widely-used feature selection methods,including genetic algorithm(GA)and random tabu search algorithm,are also implemented for comparisons.The proposed hybrid algorithm not only eliminates redundant information in high dimensional eigenvectors effectively,but also improves the classification accuracy.