Fatigue driving monitoring method based on graph regularization extreme learning machine

A technology of extreme learning machine and fatigue driving, which is applied in the field of fatigue driving monitoring based on graph regularization extreme learning machine, can solve problems such as single form and inability to fully express driving fatigue, and achieve the objective effect of the monitoring method

Pending Publication Date: 2022-08-05
NORTHEAST DIANLI UNIVERSITY
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] However, only relying on the EOG signal to realize the monitoring of driving fatigue, th

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Fatigue driving monitoring method based on graph regularization extreme learning machine
  • Fatigue driving monitoring method based on graph regularization extreme learning machine
  • Fatigue driving monitoring method based on graph regularization extreme learning machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] In order to make the technical means, creative features, achievement goals and effects realized by the present invention easy to understand, the present invention will be further described below with reference to the specific embodiments.

[0054] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front end", "rear end", "two ends", "one end" and "the other end" The orientation or positional relationship indicated by etc. is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation, a specific orientation, and a specific orientation. The orientation configuration and operation are therefore not to be construed as limiting the invention. Furthermore, the terms "first" and "...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a fatigue driving monitoring method based on a graph regularization extreme learning machine. The fatigue driving monitoring method comprises the steps that electro-oculogram signals and face image information of a driver in the driving process are collected through electro-oculogram signal collecting equipment and a camera; respectively carrying out corresponding preprocessing on the electro-oculogram signal and the face image information; extracting eye movement features of the preprocessed facial image information, classifying three eye movement signals of the preprocessed eye movement signals, and extracting related features; when the eye closing duration in the eye movement characteristics exceeds a preset threshold value, determining that the driver is in fatigue driving and giving an alarm; when the eye closing duration in the eye movement features does not exceed a preset threshold value, the average value of the same features extracted from the electro-oculogram signals and the face image information is calculated; and inputting the average value and the extracted different features into a trained graph regularization extreme learning machine classification model, and discriminating the fatigue state through a GELM classifier. The monitoring method is objective and accurate.

Description

technical field [0001] The invention belongs to the field of driving vehicle fatigue monitoring and the technical field of data processing, and particularly relates to a fatigue driving monitoring method based on a graph regularization extreme learning machine. Background technique [0002] According to the 2015 Global State of Road Safety Report by the World Health Organization (WHO), 1.25 million people are killed in traffic accidents every year worldwide, and millions more are seriously injured in traffic accidents. Road traffic injuries are estimated to be the leading cause of death among young people, especially those aged 15-29. During the driving process, the fatigued mental state of the driver seriously affects the driving behavior, which is a major hidden danger for traffic safety driving. In Europe, a study of 19 countries found that 17% of drivers felt drowsy while driving, and 7% of those who fell asleep were involved in an accident. [0003] Although researche...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06V20/59G06V40/16G06V10/764G06V10/82G06K9/00G06N3/04G06N20/00G08B21/06
CPCG06V20/597G06V40/168G06V10/764G06V10/82G06N20/00G08B21/06G06N3/048G06F2218/04G06F2218/12
Inventor 田原嫄崔凯曹景雨
Owner NORTHEAST DIANLI UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap