MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||4|
|Section||Automation and Nontraditional Manufacturing|
|Published online||19 June 2018|
A Study of Integrated Driver fatigue Judging Algorithm Based on Principal Component Analysis
College of Automotive Engineering, Jilin University, Changchun, 130022, China
2 College of Mechanical Science and Engineering, Jilin University, Changchun, 130022, China
* Corresponding author: author:firstname.lastname@example.org
As the number of vehicles increases, the probability of fatigue driving rises accordingly, resulting in a boost in the accident rate. Based on principal component analysis, the paper presents a fatigue monitoring algorithm that integrates with facial, EEG, EMG and ECG. First of all, based on the edge detection method, the eyes are positioned and then it has an assessment through the Perclos fatigue. EEG, EMG, ECG indicators evaluate the fatigue with the corresponding fatigue characteristics. Subsequently, the related values of the above four indexes are normalized, and the principal component analysis method (PCA) is utilized to reduce the dimension and merge to get the comprehensive fatigue eigenvalue, and then we get the fatigue level referring to the fatigue level standard. Analyzing the measured data, the algorithm accuracy is 76%.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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