Drivers Pressures States Recognition based on Heart Rate Variability

Kongjian Qin, Hongwei Liu, Mingjun Zhang, Jinchong Zhang
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Abstract

: Drivers pressures are major causes of road accidents, and thus drivers’ pressures states recognition become an important topic in Advanced Driver Assistant System (ADAS). Physiological signals provide information about the internal functioning of human body and thereby provide accurate, reliable and robust information on the driver’s state. In this work, the several features, which are 8 heart rate variability features and 10 mathematical features, are trained using three classifiers: Support Vector Machine (SVM), K-nearest-neighbor (KNN) and Ensemble. The algorithms based pNN5 and LF/HF achieved best performance in HRV linear features evaluation, and the accuracy (AC), sensitivity (SE), specificity (SP) for Stress Recognition in Automobile Drivers data are 89.0%, 91.8% and 77.3% respectively. The mathematical features result in 98.6%,99.1% and 91.5% for accuracy (AC), sensitivity (SE), specificity, respectively.
基于心率变异性的驾驶员压力状态识别
:驾驶员压力是道路交通事故的主要原因,因此驾驶员压力状态识别成为高级驾驶辅助系统(ADAS)中的一个重要课题。生理信号提供有关人体内部功能的信息,从而为驾驶员的状态提供准确、可靠和稳健的信息。在这项工作中,使用支持向量机(SVM)、k -近邻(KNN)和集成(Ensemble)三种分类器训练了8个心率变异性特征和10个数学特征。基于pNN5和LF/HF的算法在HRV线性特征评价中表现最佳,对汽车驾驶员数据应力识别的准确率(AC)、灵敏度(SE)和特异性(SP)分别为89.0%、91.8%和77.3%。数学特征的准确度(AC)、灵敏度(SE)和特异性分别为98.6%、99.1%和91.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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