CN105726046A - Method for detecting vigilance state of driver - Google Patents

Method for detecting vigilance state of driver Download PDF

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CN105726046A
CN105726046A CN201610066988.0A CN201610066988A CN105726046A CN 105726046 A CN105726046 A CN 105726046A CN 201610066988 A CN201610066988 A CN 201610066988A CN 105726046 A CN105726046 A CN 105726046A
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driver
eeg
eeg signals
processing module
state
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CN105726046B (en
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张祖涛
张效良
罗典媛
刘昱岗
王富斌
李晏君
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Suzhou Zhenwei Town Construction Development Co., Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems

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Abstract

The invention discloses a method for detecting the vigilance state of a driver, and relates to the technical field of automobile driver active safety.The aim of giving a prompting alarm when the driver is in fatigue driving is effectively achieved.The method mainly comprises the following steps that brain electrical signal data of the driver in a sober state or fatigue state is collected through a chip, a wireless transmission technology and a peripheral circuit brain electrical signal processing module, the collected data is used for editing a redundant dictionary, and the brain electrical signals of the driver in different time frames in the driving process are collected in real time and transmitted to the brain electrical signal processing module through the wireless transmission technology; the brain electrical signals are denoised through a discrete wavelet transform (DWT) algorithm, downsampling of the brain electrical signals, feature extraction of the brain electrical signals and pattern classification of the vigilance state of the driver are carried out, and if it is judged that the driver has a fatigue driving condition, a control signal is output, and an alarming device located on a seat of the driver is controlled to remind the driver by vibration or buzzing.The method is mainly used for endurance run fatigue alarming.

Description

A kind of driver's Alertness condition detection method
Technical field
The present invention relates to driver's active safety technologies field.
Technical background
Traffic safety problem has had become as global problem.Being recognized by By consulting literatures data, the whole world is annual because the number of vehicle accident death is about 500,000.Being on the increase due to the automobile in city in recent years, the vehicle accident caused due to driver tired driving is also significantly increased therewith.
Driver is in judgement decline, bradykinesia and operational error during fatigue driving to be increased.When driver is in slight tired, it may appear that gear shift not in time, inaccurate；When driver is in moderate fatigue, operational motion is dull, sometimes even can forget operation；When driver is in severe fatigue, often subconsciousness operates or short time sleep phenomenon occurs, can lose the control ability to vehicle time serious.During driver fatigue, it may appear that blurred vision, ache all over, action is stiff, trick is swollen or have energy not concentrate, bradykinesia, the phenomenon such as thinking not thorough, rhembasmus, anxiety, irritability.If still driving vehicle reluctantly, then may result in the generation of vehicle accident.Detect currently for the Alertness of driver and be concentrated mainly on three aspects: one, by detecting the bio signal of human body, arrange such as EEG signals, electromyographic signal, heart rate etc., judge the Alertness state of human body with this；Two, by detecting the action of human body, for instance the motion that head moves judges the Alertness state of human body；Three, the Alertness state of human body is detected by detecting the frequency of wink of human body.
And the human body Alertness detection based on EEG signals is " golden standard " in Alertness detection, for other physiological signals, can more directly react activity of brain itself, and there is higher temporal resolution.China Patent Publication No. is that CN102622600A discloses " the high-speed train driving person's Alertness detection method based on image surface Yu eye movement analysis ".It mainly uses the image surface of driver and the Alertness state of eye movement analysis driver, and the present invention is based on the Alertness analysis of driver's EEG signals, has accuracy more preferably.Publication number is the Chinese patent EEG signals Alertness detection method based on wavelet transformation of CN102058413B.It adopts wavelet function to obtain the eigenvalue of wavelet coefficient of EEG signals sequence as feature set, use sample training support vector machine after feature set being ranked up simplification by random forest method again, and adopt the support vector machine that training obtains that EEG signals is carried out Alertness detection.The Chinese patent application of application number 201520878190.7 discloses a kind of wearable driver's eeg signal acquisition headband.Its structure being primarily directed to electroencephalogramsignal signal collection equipment has done corresponding description, and process and algorithm to EEG signals are not described in detail, and the purpose of the application needs to utilize and should " headband " realize.
Summary of the invention
It is an object of the invention to provide a kind of driver's Alertness condition detection method, it can efficiently solve driver and be in prompting warning problem during fatigue driving.
The present invention realizes its goal of the invention and be the technical scheme is that a kind of driver's Alertness condition detection method, comprises the following steps:
1, a kind of driver's Alertness condition detection method, comprises the following steps:
The first step, constructing function module
Utilize the development board including there is the dsp chip TMS320F28335 of wireless transmission function, build EEG Processing module；
Second step, driver's EEG signals collection
First, one eeg signal acquisition headband of the head-mount of driver, driver is in clear-headed or fatigue state EEG signals data and is acquired, the data edition redundant dictionary that will collect, and be stored in EEG Processing module；Secondly, the EEG signals of different periods in driver drives vehicle way being carried out Real-time Collection, the driver's EEG signals collected is transferred to EEG Processing module by Radio Transmission Technology；
3rd step, EEG signals denoising
The EEG signals of the EEG Processing module driver to collecting uses wavelet transform (DWT) algorithm that original EEG signals carries out 6 layers of decomposition of a 5dB small echo, obtains subband wavelet details coefficient (Di, i=1,2,3,4,5,6) and approximation coefficient (Ai, i=1,2,3,4,5,6)；By rebuilding decomposition coefficient D3, D4, D5, D6Remove the low frequency in primary signal and High-frequency Interference thus obtaining useful EEG signals；
4th step, EEG signals down-sampling
The EEG signals of the driver after denoising is carried out down-sampling process by EEG Processing module, and sample frequency is 128Hz；
5th step, EEG signals feature extraction
EEG signals after processing through down-sampling is carried out the feature extraction based on fast Fourier transform (FFT) algorithm by EEG Processing module, and fft algorithm is 128 points；
6th step, driver's Alertness state pattern classification
The EEG signals of the driver through feature extraction is carried out the pattern classification of the sparse classification expression algorithm based on K-SVD by EEG Processing module, obtains Alertness state class (y) of driver；
Class (y)=argmini||y-Dα′i||2
Wherein y is data D=[D to be sorteda,Dd] for the redundant dictionary of driver's Alertness state, DaThe redundant dictionary of waking state, D it is in for driverdBe in the redundant dictionary of fatigue state for driver, α is data to be sorted, the sparse coefficient of y；
7th step, warning reminding
Driver's Alertness state that EEG Processing module transmits, if judging is that driver exists fatigue driving situation, then exports control signal, and driver is shaken or buzzing prompting by the alarming device controlling to be positioned on pilot set.
Compared with prior art, the invention has the beneficial effects as follows:
One, the fft algorithm of N=128 is have employed in feature extraction phases, compare than traditional discrete Fourier transform (DFT) algorithm, greatly reduce amount of calculation so that the feature extraction time is only 1/37 that DFT algorithm is about, thus reducing the time of whole process.
Two, adopt the rarefaction representation based on K-SVD to classify in the pattern classification stage to calculate.The purpose of sparse signal representation is exactly represent signal with the least possible atom in given super complete dictionary, the representation that signal is more succinct can be obtained, so that we more easily obtain the information contained in signal, K-SVD is the dictionary training algorithm of a kind of classics, according to error minimum principle, error term being carried out SVD decomposition, selecting what make error minimum to decompose item as the dictionary atom updated and corresponding atomic, through continuous iteration thus obtaining the solution optimized.
Accompanying drawing explanation
Fig. 1 is the Alertness detection algorithm flowchart based on EEG signals of the present invention.
Fig. 2 is EEG signals of the present invention at the forward and backward time domain comparison diagram of denoising.
Fig. 3 is EEG signals of the present invention at the forward and backward frequency domain comparison diagram of denoising.
Fig. 4 is waking state and fatigue state time domain comparison diagram after EEG signals denoising of the present invention.
Fig. 5 is waking state and fatigue state frequency domain comparison diagram after EEG signals denoising of the present invention.
Specific implementation method
Embodiment one, this experimental subject is a tired driver driven through more than three hours, is designated as driver 1.
Its specific implementation method is, a kind of driver's Alertness condition detection method, comprises the following steps:
A, driver's EEG signals collection
One eeg signal acquisition cap of the head-mount of driver, the EEG signals of driver is acquired by driver's eeg signal acquisition cap when driving a car, and obtains the EEG signals f of driver 11, and the driver EEG signals f gathered1It is transferred to EEG Processing module by Radio Transmission Technology；
B, driver's EEG signals denoising
The EEG Processing module EEG signals f to the driver that eeg signal acquisition cap collects1Use DWT algorithm to carry out denoising, obtain the driver EEG signals g after denoising1；
C, driver's EEG signals down-sampling
The EEG Processing module EEG signals g to the driver after denoising1Carry out down-sampling process, obtain the driver EEG signals k after down-sampling1, to reduce the data volume of sample；
D, driver's EEG signals feature extraction
EEG Processing module is to the driver EEG signals k after processing through down-sampling1Carry out the feature extraction based on fast Fourier transform (FFT) algorithm, obtain data Y to be sorted1；
E, driver's Alertness state pattern classification
The EEG signals of the driver through feature extraction is carried out the pattern classification of the sparse classification expression algorithm based on K-SVD by EEG Processing module, obtains Alertness state class (y) of driver；
Class (y)=argmini||Y1-Dα′i||2
Wherein Y1For data to be sorted, D=[Da,Dd] for the redundant dictionary of driver status, DaThe redundant dictionary of waking state, D it is in for driverdBe in the redundant dictionary of fatigue state for driver, α is the sparse coefficient of data y to be sorted；
The result obtained is in fatigue state for driver, and the confidence level of test is 0.9565.
Embodiment two/tri-/tetra-repeats the operation of above A～E step.
Driver's Alertness is detected test result and sees following table by the inventive method.
Driver numbers 1 2 3 4
Experimental result Tired Tired Tired Clear-headed
Confidence level 0.9565 0.9504 0.9647 0.9387
Visible, the present invention has higher confidence level on driver's Alertness detects.

Claims (1)

1. driver's Alertness condition detection method, comprises the following steps:
The first step, constructing function module
Utilize the development board including there is the dsp chip TMS320F28335 of wireless transmission function, build EEG Processing module；
Second step, driver's EEG signals collection
First, one eeg signal acquisition headband of the head-mount of driver, driver is in clear-headed or fatigue state EEG signals data and is acquired, the data edition redundant dictionary that will collect, and exist in storage EEG Processing module；Secondly, the EEG signals of different periods in driver drives vehicle way being carried out Real-time Collection, the driver's EEG signals collected is transferred to EEG Processing module by Radio Transmission Technology；
3rd step, EEG signals denoising
The EEG signals of the EEG Processing module driver to collecting uses wavelet transform (DWT) algorithm that original EEG signals carries out 6 layers of decomposition of a 5dB small echo, obtains subband wavelet details coefficient (Di, i=1,2,3,4,5,6) and approximation coefficient (Ai, i=1,2,3,4,5,6)；By rebuilding decomposition coefficient D3, D4, D5, D6Remove the low frequency in primary signal and High-frequency Interference thus obtaining useful EEG signals；
4th step, EEG signals down-sampling
The EEG signals of the driver after denoising is carried out down-sampling process by EEG Processing module, and sample frequency is 128Hz；
5th step, EEG signals feature extraction
EEG signals after processing through down-sampling is carried out the feature extraction based on fast Fourier transform (FFT) algorithm by EEG Processing module, and fft algorithm is 128 points；
6th step, driver's Alertness state pattern classification
The EEG signals of the driver through feature extraction is carried out the pattern classification of the sparse classification expression algorithm based on K-SVD by EEG Processing module, obtains Alertness state class (y) of driver；
Class (y)=argmini||y-Dα'i||2
Wherein y is data D=[D to be sorteda,Dd] for the redundant dictionary of driver's Alertness state, DaThe redundant dictionary of waking state, D it is in for driverdBe in the redundant dictionary of fatigue state for driver, α is data to be sorted, the sparse coefficient of y；
7th step, warning reminding
Driver's Alertness state that EEG Processing module transmits, if judging is that driver exists fatigue driving situation, then exports control signal, and driver is shaken or buzzing prompting by the alarming device controlling to be positioned on pilot set.
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Cited By (10)

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CN106562787A (en) * 2016-10-28 2017-04-19 许昌学院 High-altitude outward bound training monitoring system based on surface electromyogram signals
CN106919956A (en) * 2017-03-09 2017-07-04 温州大学 Brain wave age forecasting system based on random forest
CN107334481A (en) * 2017-05-15 2017-11-10 清华大学 One kind drives divert one's attention detection method and system
CN108498092A (en) * 2017-02-28 2018-09-07 中国航天员科研训练中心 Wrong method for early warning and system based on brain electrical feature
CN108742603A (en) * 2018-04-03 2018-11-06 山东大学 It is a kind of using kernel function and dictionary to the brain electric detection method and device of learning model
CN110464371A (en) * 2019-08-29 2019-11-19 苏州中科先进技术研究院有限公司 Method for detecting fatigue driving and system based on machine learning
CN110811573A (en) * 2019-10-29 2020-02-21 依脉人工智能医疗科技(天津)有限公司 Device and method for regulating and controlling brain alertness based on photoelectric pulse feedback
CN111227851A (en) * 2018-11-29 2020-06-05 天津职业技术师范大学 Driver alertness detection mechanism based on electroencephalogram signals, detection method and application
CN111242065A (en) * 2020-01-17 2020-06-05 江苏润杨汽车零部件制造有限公司 Portable vehicle-mounted intelligent driving system
CN112041910A (en) * 2018-03-30 2020-12-04 索尼半导体解决方案公司 Information processing apparatus, mobile device, method, and program

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US20110121976A1 (en) * 2008-07-18 2011-05-26 Optalert Pty Ltd Alertness sensing device
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CN102274032A (en) * 2011-05-10 2011-12-14 北京师范大学 Driver fatigue detection system based on electroencephalographic (EEG) signals
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Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106562787A (en) * 2016-10-28 2017-04-19 许昌学院 High-altitude outward bound training monitoring system based on surface electromyogram signals
CN108498092A (en) * 2017-02-28 2018-09-07 中国航天员科研训练中心 Wrong method for early warning and system based on brain electrical feature
CN106919956A (en) * 2017-03-09 2017-07-04 温州大学 Brain wave age forecasting system based on random forest
CN107334481A (en) * 2017-05-15 2017-11-10 清华大学 One kind drives divert one's attention detection method and system
CN107334481B (en) * 2017-05-15 2020-04-28 清华大学 Driving distraction detection method and system
CN112041910A (en) * 2018-03-30 2020-12-04 索尼半导体解决方案公司 Information processing apparatus, mobile device, method, and program
CN112041910B (en) * 2018-03-30 2023-08-18 索尼半导体解决方案公司 Information processing apparatus, mobile device, method, and program
CN108742603A (en) * 2018-04-03 2018-11-06 山东大学 It is a kind of using kernel function and dictionary to the brain electric detection method and device of learning model
CN111227851A (en) * 2018-11-29 2020-06-05 天津职业技术师范大学 Driver alertness detection mechanism based on electroencephalogram signals, detection method and application
CN110464371A (en) * 2019-08-29 2019-11-19 苏州中科先进技术研究院有限公司 Method for detecting fatigue driving and system based on machine learning
CN110811573A (en) * 2019-10-29 2020-02-21 依脉人工智能医疗科技(天津)有限公司 Device and method for regulating and controlling brain alertness based on photoelectric pulse feedback
CN111242065B (en) * 2020-01-17 2020-10-13 江苏润杨汽车零部件制造有限公司 Portable vehicle-mounted intelligent driving system
CN111242065A (en) * 2020-01-17 2020-06-05 江苏润杨汽车零部件制造有限公司 Portable vehicle-mounted intelligent driving system

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