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Research Papers

Denoising and Chaotic Feature Extraction of Electrocardial Signals for Driver Fatigue Detection by Kolmogorov Entropy

[+] Author and Article Information
Yongxiang Jiang

Institute of Robotics and Intelligent Equipment,
Tianjin University of Technology and Education,
1310 Dagunan Road, Hexi District,
Tianjin 300222, China
e-mail: jiangyongxiang@tute.edu.cn

Shijie Guo

State Key Laboratory of Reliability and
Intelligence of Electrical Equipment,
Hebei Key Laboratory of Robot Sensing and
Human-Robot Interaction,
Hebei University of Technology,
8 Dingzigu Yihaolu, Hongqiao District,
Tianjin 300130, China
e-mail: guoshijie@hebut.edu.cn

Sanpeng Deng

Institute of Robotics and Intelligent Equipment,
Tianjin University of Technology and Education,
1310 Dagunan Road, Hexi District,
Tianjin 300222, China
e-mail: sanpeng@yeah.net

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received August 22, 2017; final manuscript received August 28, 2018; published online October 19, 2018. Assoc. Editor: Dumitru I. Caruntu.

J. Dyn. Sys., Meas., Control 141(2), 021013 (Oct 19, 2018) (7 pages) Paper No: DS-17-1419; doi: 10.1115/1.4041355 History: Received August 22, 2017; Revised August 28, 2018

This paper proposes a detection method of driver fatigue by use of electrocardial signals. First, lifting wavelet transform (LWT) was used to reduce signal noise and its effect was confirmed by applying it to the denoising of a white-noise-mixed Lorenz signal. Second, phase space reconstruction was conducted for extracting chaotic features of the measured electrocardial signals. The phase diagrams show fractal geometry features even under a strong noise background. Finally, Kolmogorov entropy, which is a factor reflecting the uncertainty in and the chaotic level of a nonlinear dynamic system, was used as an indicator of driver fatigue. The effectiveness of Kolmogorov entropy in the judgment of driver fatigue was confirmed by comparison with a semantic differential (SD) subjective evaluation experiment. It was demonstrated that Kolmogorov entropy has a strong relationship with driver fatigue. It decreases when fatigue occurs. Furthermore, the influences of delay time and sampling points on Kolmogorov entropy were investigated, since the two factors are important to the actual use of the proposed detection method. Delay time may have significant influence on fatigue determination, but sampling points are relatively inconsequential. This result indicates that real-time detection can be realized by selecting a reasonably small number of sampling points.

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Figures

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Fig. 2

High-speed electrocardial detector PC-80B

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Fig. 1

Reconstructed phase diagram of electrocardial signals: (a) phase diagram of electrocardial signals in a driver simulator test, (b) phase diagram of electrocardial signals in a real driving test, and (c) phase diagram of LWT denoised electrocardial signals in a real driving test

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Fig. 3

Locations of electrodes for collecting electrocardial signals

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Fig. 4

The car used in real driving test

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Fig. 5

Picture of driver simulator test

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Fig. 9

Simulation results of LWT denoising: (a) phase diagram of Lorenz signal, (b) phase diagram of 100% white noise mixed Lorenz signal, and (c) phase diagram of 100% white noise mixed Lorenz signal after LWT denoising

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Fig. 6

Kolmogorov entropy and Index of SD subjective evaluation in real driving test

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Fig. 7

Influence of delay time τ on Kolmogorov entropy

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Fig. 8

Influence of sampling points N on Kolmogorov entropy

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