Driving fatigue monitoring method based on human body multi-class characteristics

A driving fatigue and human body technology, applied in the field of real-time detection of driver's driving fatigue state, can solve problems such as traffic accidents and driving dangers, and achieve the effects of strong applicability, easy system, and easy method.

Active Publication Date: 2021-03-09
NORTHEAST DIANLI UNIVERSITY
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Problems solved by technology

[0003] Although domestic and foreign scholars have made a lot of research results in the research of driving fatigue discrimination methods, most of the existing detection schemes use the collection of a single feature or the fusion of similar features as the monitoring index. Although a single index can be used to evaluate the driver's fatigue, The ideal effect cannot be achieved, so it may still cause danger to driving and traffic accidents

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  • Driving fatigue monitoring method based on human body multi-class characteristics
  • Driving fatigue monitoring method based on human body multi-class characteristics
  • Driving fatigue monitoring method based on human body multi-class characteristics

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Embodiment Construction

[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0039] A driving fatigue monitoring method based on the multi-class features of the human body of the present invention comprises the steps of monitoring the driver's brain electrical signal and monitoring the driver's head posture:

[0040] 1) Driver EEG monitoring:

[0041] (a) Use the EEG acquisition equipment to collect the EEG signals of the driver's leads;

[0042] (b) preprocessing the collected EEG signals to obtain preprocessed EEG signals;

[0043] (c) Decomposing the processed EEG signal by wavelet packet, and extracting the EEG signal of the corresponding band;

[0044] (d) calculating the entropy value of the EEG signal of the corresponding band, and constructing the entropy value matrix of the EEG signal corresponding to the band;

[0045] (e) dimensionality reduction analysis is performed on the entropy value matrix, and the EEG signal i...

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Abstract

The invention discloses a driving fatigue monitoring method based on human body multi-class characteristics. The driving fatigue monitoring method comprises driver electroencephalogram signal monitoring and driver head posture monitoring. The driver electroencephalogram signal monitoring comprises the steps that an entropy value is calculated through electroencephalogram signals, and electroencephalogram signal driving fatigue monitoring indexes are formed through dimensionality reduction; according to driver head posture information monitoring, a miniature infrared temperature sensor is usedfor recording driver head posture information, and head posture driving fatigue monitoring indexes are generated; and finally, the correlation between the electroencephalogram signal monitoring indexes and the head posture information monitoring indexes are analyzed by using Pearson correlation analysis, and the influence of various interferences on the monitoring indexes is lowered, thereby forming comprehensive driving fatigue monitoring of various characteristics of the driver. The method is high in recognition degree, the system is easy to establish, the method is easy to implement, the purpose of more accurately monitoring the fatigue state of the driver is achieved, and traffic hidden dangers caused by driving fatigue are reduced. The method has the advantages of being scientific, reasonable, high in applicability and good in effect.

Description

technical field [0001] The invention relates to a method for real-time detection of a driver's driving fatigue state, and is a driving fatigue monitoring method based on multiple types of human body characteristics. Background technique [0002] In recent years, automobiles have become more and more common travel tools for people. While automobiles bring convenience to people's travel, they also cause serious traffic accidents. According to statistics, among urban accident cases, road traffic accidents are the most harmful events. According to the statistics of road traffic accidents, 90% of traffic accidents are caused by human factors of drivers, followed by road environmental factors and vehicle failure factors. 57% of drivers consider fatigue driving a serious problem, more than 50% of respondents have experienced driving while fatigued, and 20% of drivers have fallen asleep or napped while driving at least once in the past year . [0003] Although scholars at home an...

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Application Information

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IPC IPC(8): A61B5/18A61B5/374
CPCA61B5/18
Inventor 王福旺路斌康小刚徐卿李吉献袁震
Owner NORTHEAST DIANLI UNIVERSITY
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