实时基于熵复杂度的驾驶员疲劳自动识别方法

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本文主要探讨了"基于熵和复杂度的驾驶员疲劳自动检测"这一主题，发表在2014年2月的IEEE Transactions on Intelligent Transportation Systems第15卷第1期上。研究由Chi Zhang、Hong Wang和Rongrong Fu三位作者共同完成。他们提出了一种实时方法，该方法利用电极脑电图(EEG)、肌电图(emg)和眼电图(eog)信号中的熵和复杂度测量来自动检测和识别驾驶者的疲劳状态。首先，文章关注的是复杂度特征在区分新手与熟练司机之间的应用，通过计算EEG信号的Lempel-Ziv复杂性（LZC）近似熵（ApEn）。这种方法允许为不同类型的驾驶员设置不同的阈值，提高了区分能力。接下来，研究引入了基于熵的特征，包括小波熵(WE)，峰峰值ApEn(PP-ApEn)以及峰峰值样本熵(PP-SampEn)，这些特征从收集的信号中提取，用于评估驾驶者的疲劳程度。为了实现实时分析，作者们进一步提出了滑动窗口下的小波熵(WES)、滑动窗口下的ApEn(PP-ApEnS)和样本熵(PP-SampEnS)计算方法。滑动窗口策略使得系统能够在连续信号中动态捕捉疲劳变化，而不是仅仅依赖于单个时间点的数据。这不仅增加了检测的敏感性，还能够实时反应驾驶者状态的变化，对于行车安全具有重要意义。通过这种方式，研究人员能够准确估计驾驶者疲劳的程度，并可能在疲劳达到危险水平时及时发出警告，从而降低交通事故的风险。这篇研究论文提供了一个实用且有效的驾驶员疲劳监控系统，结合了复杂度理论和信息熵分析，对于提升道路运输系统的安全性有着积极的贡献。它展示了如何利用先进的信号处理技术在车辆智能化和驾驶行为分析领域中发挥作用。

168 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 15, NO. 1, FEBRUARY 2014

Automated Detection of Driver Fatigue Based

on Entropy and Complexity Measures

Chi Zhang, Hong Wang, and Rongrong Fu

Abstract—This paper presents a real-time method based on

various entropy and complexity measures for detection and iden-

tiﬁcation of driving fatigue from recorded electroencephalogram

(EEG), electromyogram, and electrooculogram signals. The com-

plexity features were used to distinguish whether the subjects are

experienced drivers by calculating the Lempel–Ziv complexity of

EEG approximate entropy (ApEn). Different threshold values can

be set for the two kinds of drivers individually. The entropy-based

features, namely, the wavelet entropy (WE), the peak-to-peak

value of ApEn (PP-ApEn), and the peak-to-peak value of sample

entropy (PP-SampEn), were extracted from the collected signals

to estimate the driving fatigue stages. We proposed WE in a

sliding window (WES), PP-ApEn in a sliding window (PP-ApEnS),

and PP-SampEn in a sliding window (PP-SampEnS) for real-time

analysis of driver fatigue. The real-time features obtained by WE,

PP-ApEn, and PP-SampEn with sliding window were applied to

artiﬁcial neural network for training and testing the system, which

gives four situations for the fatigue level of the subjects, namely,

normal state, mild fatigue, mood swing, and excessive fatigue.

Then, the driver fatigue level can be determined in real time. The

accuracy of estimation is about 96.5%–99.5%. Receiver operating

characteristic (ROC) curve was used to present the performance

of the neural network classiﬁer. The area under the ROC curve

is 0.9931. The results show that the developed method is valuable

for the application of avoiding some trafﬁc accidents caused by

driver’s fatigue.

Index Terms—Driver fatigue, electroencephalogram (EEG),

electromyogram (EMG), electrooculogram (EOG), entropy, neu-

ral network.

I. INTRODUCTION

ATIGUE is a feeling of extreme physical or mental tired-

ness. Almost everyone becomes fatigued at some time, but

driver’s fatigue is a serious problem that leads to thousands of

automobile crashes each year [1]–[3].

Fatigue process is often a change from the alertness and

vigor state to the tiredness and weakness state. It is not only

accompanied by drowsiness but also has a negative impact

on mood. There have been studies to detect and quantify

fatigue from the measurement of physiology variables such

as electroencephalogram (EEG), electrooculogram (EOG), and

electromyogram (EMG) [4]–[6]. However, simultaneous usage

Manuscript received December 31, 2012; revised April 8, 2013 and June 23,

2013; accepted July 18, 2013. Date of publication September 5, 2013; date

of current version January 31, 2014. This work was supported in part by the

National Science Foundation of China under Grant 61071057 and in part by

the Innovation Groups of the Chinese Ministry of Education. The Associate

Editor for this paper was R. I. Hammoud. (Corresponding author: H. Wang.)

The authors are with the Department of Mechanical Engineering and

Automation, Northeastern University, Shenyang 110819, China (e-mail: zhch_

angi@163.com; hongwang@mail.neu.edu.cn; fu@ece.neu.edu).

Digital Object Identiﬁer 10.1109/TITS.2013.2275192

and real-time analysis of EEG, EMG, and EOG signals were

not given. This is the novelty that this paper brings.

The EEG signals do a pretty good job of state discrimination.

All the physical and mental activities associated with driving

are reﬂected in EEG signals [4]. The EMG signals are inﬂu-

enced by muscle activities; a person gets lower tonus of EMG

when his fatigue process gets further [7]. The EOG signals can

be very useful to detect drowsiness. It has been observed that

eye movement decreases while blink rate increases as a person

enters into the state of fatigue [8]. Obviously, the simultaneous

usage of EEG, EMG, and EOG signals can increase the accu-

racy of identiﬁcation and classiﬁcation results.

The recorded physiological signals are nonlinear, time-

varying, space-varying, and nonstationary in nature. Nonlinear

dynamical analysis can provide complementary information

about the dynamics under physiological or psychological states

compared with classical linear time series analysis methods

such as Fourier or spectral analysis [9], [10]. Nonlinear dynam-

ical analysis techniques derived from the theory of nonlinear

dynamical systems such as the correlation integral, Lyapunov

exponents, and correlation dimension have been recently used

in a number of ﬁelds of application. Assessment of driver’s

fatigue is one of the special application areas [4], [11], [12].

One approach to the nonlinear estimation of dynamical

EEG, EOG, and EMG activity is complexity analysis. Among

complexity analysis approaches, entropy-based algorithms have

been useful and robust estimators for evaluating regularity or

predictability [13]. Shannon entropy (SE) is a disorder quan-

tiﬁer and is a measure of the ﬂatness of energy spectrum in

the wavelet domain [4], [14]. In this paper, we use the energy

sequence distribution to replace the distribution of the prob-

ability distribution of SE to calculate wavelet entropy (WE).

Approximate entropy (ApEn) and its reﬁned version, i.e., sam-

ple entropy (SampEn), were developed as practically tractable

physiological measures in view of their robustness to noise and

ﬁnitude of data sets and applicability to stochastic, nonlinear

deterministic, and composite processes [15], [16]. Lempel–Ziv

complexity (LZC) is an approach to the problem that links the

complexity of a speciﬁc sequence to the gradual buildup of new

patterns along the given sequence [17]. It can show the approx-

imation degree between ﬁnite sequence and random sequence.

The greater the LZC of the sequence is, the closer to random

sequence it will be. EEG LZC, which reﬂects the amount of the

EEG information, can reveal brain activity regularity [18].

This paper presents a methodology for automatic detection

of normal and fatigue states from recorded EEG, EOG, and

EMG signals. The complexity features were used to distinguish

whether the subjects are experienced drivers by calculating the

IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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