CN108903958A - Driver attention evaluates early warning system and its implementation method - Google Patents
Driver attention evaluates early warning system and its implementation method Download PDFInfo
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- CN108903958A CN108903958A CN201810447393.9A CN201810447393A CN108903958A CN 108903958 A CN108903958 A CN 108903958A CN 201810447393 A CN201810447393 A CN 201810447393A CN 108903958 A CN108903958 A CN 108903958A
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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
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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
- A61B5/168—Evaluating attention deficit, hyperactivity
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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
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- 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]
Abstract
Present invention discloses a kind of evaluation of the driver attention of single-point acquiring and early warning solution, the driving conditions of real-time monitoring driver, reliably to remind when driver is in a state of fatigue.It mainly includes brain wave acquisition subsystem, attention Evaluation subsystem and driving early warning subsystem.EEG signal from driver's forehead is recorded using E.E.G earphone in real time, is analyzed by Bluetooth transmission to attention Evaluation subsystem；Attention Evaluation subsystem constructs respective sample space using the data for starting collected θ in 1 minute, alpha rhythm wave.It is transferred to driving early warning subsystem by the driving fatigue state classification of driver and by analysis result, the smart phone of driver show that simultaneously correspondingly early warning operates automatic implementation to analysis result.Early warning scheme is evaluated using driver attention of the invention, and device structure is simple, wearing easy to carry, early warning are timely, while reducing algorithm complexity, traffic accident rate caused by capable of reducing because of fatigue driving.
Description
Technical field
The present invention relates to a kind of driver fatigue monitor systems based on electroencephalogramsignal signal analyzing, belong to combination that is electromechanical and communicating
Field.
Background technique
Investigation display, fatigue are the key factors for influencing driver safety and driving.When tired driver, it may appear that view
Line, which obscures, aches all over, acts stiff, trick swells or have energy not concentrate, is slow in reacting, think deeply not thorough, rhembasmus,
Phenomena such as anxiety, irritability.If still driving vehicle reluctantly, traffic accident is likely resulted in.It is complete according to incompletely statistics
The etesian traffic accident in the world is up to more than 10 hundred million times, and the accident due to caused by the fatigue driving of driver accounts for about total Accident Parts
Several 20%~30%, and accident death rate caused by fatigue driving accounts for 70% or so of all traffic mortalities.Therefore in recent years
Come, tired driver drives the problem concern by the more and more researchers in countries in the world, wherein detecting for fatigue driving
Method and the more important realistic meaning of research carried out.In order to control motor vehicle driving accident, the solution of fatigue driving becomes
The most important thing.
In recent years, with brain-computer interface（BCI）The rapid development of field technology, so that only by the thinking activities of human brain
Realization is possibly realized the control of external environment.Lot of documents points out, EEG signals（English abbreviation EEG）Each rhythm and pace of moving things wave（δ wave, θ
Wave, α wave, β wave）Have with states such as vigilance locating for human brain, fatigues and closely contacts.When human body is in alertness
When, the α wave in EEG signals is significantly stronger than its all band in the activity of prefrontal area；And when human body is in a state of fatigue, α wave will
It is suppressed, and activity of the θ wave in temporal lobe region becomes the most significant.Therefore, the situation of change of EEG signals rhythm and pace of moving things wave is utilized
To judge that driver's status has become possibility.
Summary of the invention
In order to solve the above-mentioned technical problem, the purpose of the present invention is directed to a kind of driver attention evaluation of single-point acquiring
And early warning scheme, the degree of fatigue of driver is detected based on the analysis of the attention of EEG signals, solves to feel fatigue as driver
When system give automatic the problem of reminding in time.
The present invention realizes that a kind of technical solution of above-mentioned purpose is, driver attention evaluates early warning system, with driver
Smart phone it is associated, it is characterised in that system composition includes：
Brain wave acquisition subsystem：For recording the EEG signal of driver in driving condition in real time, and signal processing is commented as attention
The source data of valence；
Attention Evaluation subsystem：For the time domain waveform of required wave band to be filtered, extracted to source data and carries out algorithm point
Analysis and Classification and Identification, obtain fatigue state；
Drive early warning subsystem：It for the APP based on smart mobile phone hardware, is used for according to obtained tired driver state, and ties
The driving mode for closing setting feeds back the warning information of the corresponding weak collected state of attention by smart phone；
Three subsystems, which gradually communicate, to be connected and data interaction.
Further, the brain wave acquisition subsystem is commented towards attention Evaluation subsystem one-way communication, the attention
Valence subsystem is towards driving early warning subsystem one-way communication, and one-way communication is realized based on bluetooth equipment.
Further, the brain wave acquisition subsystem is the fluctuation tendency and individual for EEG signal in the normal range
The single channel collector of difference dynamic compensation, collection point is set as the lateral forehead of driver and reference point is set as driver one picks up the ears
It hangs down.
Further, the composition of the attention Evaluation subsystem includes：
Characteristic signal module is extracted, for extracting the feature rhythm and pace of moving things wave θ and α from EEG signal using wavelet transformation；
Feature samples module, for utilizing initial setting period collected feature rhythm and pace of moving things wave θ and α construction feature sample space；
Distance calculates weighting block, for calculating live signal simultaneously weighted sum at a distance from feature samples space；
Pattern Matching Module, for comparing the relationship of resulting distance with preset threshold, matching obtains focusing on note from attention
The fatigue state that meaning power laxes two or more degree determines result.
Further, the driving early warning subsystem is based on smart phone and is equipped with driving mode input interface and for exporting
Acoustooptic cell, vibration unit and the self-navigation unit of pre-warning signal.
The present invention realizes that another technical solution of above-mentioned purpose is, driver attention evaluates method for early warning, with department
The smart phone of machine, which is associated with, to be realized, it is characterised in that including step：
Brain wave acquisition：Record the EEG signal of driver in driving condition, and signal processing conduct in real time using brain wave acquisition subsystem
The source data of attention evaluation；
Attention evaluation：The time domain waveform of wave band needed for being filtered, being extracted to source data using attention Evaluation subsystem is simultaneously
Algorithm analysis and Classification and Identification are carried out, fatigue state is obtained；
Drive early warning：Using APP of the early warning subsystem based on smart mobile phone hardware is driven, according to obtained tired driver shape
State, and the driving mode of setting is combined to feed back the warning information for corresponding to the weak collected state of attention by smart phone；
Three subsystems, which gradually communicate, to be connected and data interaction.
Further, the brain wave acquisition subsystem is commented towards attention Evaluation subsystem one-way communication, the attention
Valence subsystem is towards driving early warning subsystem one-way communication, and one-way communication is realized by the way of bluetooth.
Further, the brain wave acquisition is for EEG signal fluctuation tendency in the normal range and individual difference dynamic
Compensate and take headgear mode that collection point is set as the lateral forehead of driver, reference point is set as the side ear-lobe of driver, carry out single
Channel acquires EEG signal.
Further, it is characterised in that the attention evaluation includes step：
Characteristic signal is extracted, extracts the feature rhythm and pace of moving things wave θ and α from EEG signal using wavelet transformation；
Feature samples are established, initial setting period collected feature rhythm and pace of moving things wave θ and α construction feature sample space is utilized；
Distance calculates weighting, calculates live signal simultaneously weighted sum at a distance from feature samples space；
Pattern match, compares the relationship of resulting distance with preset threshold, and matching, which obtains focusing on attention from attention, laxes
The fatigue state of two or more degree determines result.
Further, it drives in early warning based on smart phone setting driving mode input interface and acoustooptic cell, vibration list
Member and self-navigation unit, driver is manually entered or smart phone intelligent recognition driving mode, and smart phone is according to tired driver
Judgement result response output voice prompting, photochromic prompting, vibratory stimulation or the self-navigation guidance driver of state drive into nearest
Service area rest.
Using driver attention of the present invention evaluation and early warning scheme, have substantive distinguishing features outstanding and significant progress
Property：Present invention employs portable brain electric acquisition and analytical equipments, and brain wave acquisition equipment uses single channel brain wave acquisition side
Formula keeps structure simpler, easy to carry, reduces the complexity of algorithm, improves the comfort level of driver.Based on being collected
EEG signals be filtered, signal processing, algorithm are analyzed and obtain tired driver state and early warning, realize and prevent fatigue and drive
The function of sailing, and judge that precision is high, early warning is timely, easily operated, it can effectively solve the problems, such as fatigue driving on expressway,
Practical value with higher.
Detailed description of the invention
Fig. 1 is the framework and communication scheme that driver attention of the present invention evaluates early warning system.
Fig. 2 is the scheme of installation of brain wave acquisition equipment.
Fig. 3 is the scheme of installation at another visual angle Fig. 2.
Fig. 4 is the algorithm flow chart that driver attention of the present invention evaluates method for early warning.
Fig. 5 is that smart phone APP initially uses interface simplified schematic diagram.
Fig. 6 is that smart phone APP general modfel uses interface simplified schematic diagram.
Specific embodiment
Just attached drawing in conjunction with the embodiments below, the embodiment of the present invention is described in further detail, so that of the invention
Technical solution is more readily understood, grasps, and relatively sharp defining and supporting to make to protection scope of the present invention.
The present invention is a kind of driver fatigue monitor system based on brain electricity analytical, as shown in Figure 1, this system mainly includes three
A part：Brain wave acquisition subsystem, attention Evaluation subsystem and driving early warning subsystem.Subsystems are independently of each other again
It complements each other, can carry out data transmission, carry out data interaction according to specific transport protocol.Brain wave acquisition subsystem and attention
Evaluation subsystem is connected by bluetooth equipment, for acquiring the EEG signals of driver in driving condition in real time；Attention evaluation
The data that subsystem is obtained according to brain wave acquisition subsystem carry out algorithm analysis, and analysis result are passed through Bluetooth transmission equipment
It is sent to and drives early warning subsystem.The APP software that early warning subsystem is independent development in mobile phone is driven, early warning subsystem is driven and exists
After the data for receiving attention Evaluation subsystem, judge whether driver is in absent minded shape according to the data received
State.When detecting that driver is in absent minded state, mobile phone vibration, and sound an alarm and remind driver.For convenient for
Understand implementation, details are as follows from the implementation detail of each subsystem.
One, brain wave acquisition subsystem, using " adaptive at a slow speed " algorithm.For different users' eeg signal normal
Fluctuation tendency and individual difference in range carry out dynamic compensation, to carry out the calibration of signal.It can be suitable for different crowds
With different surrounding enviroment.There can be extraordinary accuracy and reliability under different application scenarios.Using singly leading
Dry electrode technology, eeg signal acquisition become easy to use, safe wearing, comfortable.ThinkGear is separated from noisy environment
Eeg signal out generates clearly eeg signal by enhanced processing.And single channel acquisition mode is used, structure is made
It is simpler, it is easy to carry, the complexity of algorithm is reduced, the comfort level of driver is improved.
ThinkGear asic chip technical characteristic：1, using singly leading dry electrode measurement technology, 2, integrated physiological signal
Filtering and enlarging function, 3, integrated eSense algorithm（User is without carrying out additional signal processing）, 4, using industry
The serial UART input/output interface of standard, 5, the original brain wave patterns data of output, data output frequencies are up to 512Hz power consumption
Low, power consumption is small.
As shown in Figures 2 and 3, which acquires the EEG signals of four leads, and collection point is located at left front
Volume.Reference point is ear-lobe.Acquisition headgear is taken, headgear will be adjacent to corresponding pole.When certain actual implementation, corresponding left and right directions
It can flexible choice according to the actual situation.
Two, attention Evaluation subsystem, with several parts such as EEG Processing, classification and identification algorithm, Forewarn evaluation
It forming, the signal processing algorithm in system is high-efficient, and it is small in size, it is easy to operate, because brain wave acquisition subsystem uses single-pass
The mode of road acquisition, so intelligent classification algorithm has many advantages, such as that recognition time is short, required learning data amount is smaller.
The pretreated effect of EEG signals be data in order to obtain required EEG signals and inhibit Hz noise and
Noise.Since EEG signals intensity is that microvolt rank is extremely faint, and is highly prone to the industrial frequency noise and 0.5Hz of extraneous 50Hz
The influence of polarization signal below.In order to ensure the accuracy of result, EEG signals are pre-processed first, using FIR number
Word filter extracts the 4-30Hz frequency range of EEG signals, and check baseline drifts about.
Analyzing subsystem also has intelligent classification recognizer, and EEG signals conversion is carried out processing analysis, analysis is tied
Attention in fruit is divided into different brackets.By machine learning, constantly improve recognizer, will improve identification stability and
Accuracy.It will finally analyze as a result, i.e. normal condition degree passes through Bluetooth transmission equipment real-time transmission to driving early warning subsystem.
The algorithm is broadly divided into the following steps realization：1) characteristic signal, is extracted.It is extracted from original EEG signals using wavelet transformation
Feature rhythm and pace of moving things wave.2) feature samples space, is established.The collected feature rhythm and pace of moving things wave θ and α building is extracted respectively using 1min is started
Feature samples space, be used for subsequent calculating.3) real-time observed data, is calculated at a distance from feature samples space, and is carried out
Weighted sum.4), pattern match is determined whether by the relationship of sample above space length and threshold value in fatigue, if matching
Success, then be determined as fatigue, and carry out alarm, otherwise continue to monitor.
Concrete operation step includes：Data receiver, algorithm identification are transmitted with data.
Step 1：Attention Evaluation subsystem is opened, the bluetooth equipment in attention Evaluation subsystem is connected automatically to brain
Electric acquisition subsystem, and then carry out data receiver.
Step 2：The state of measured is broadly divided into two class events in experiment, and one kind is waking state event, another kind of to be
Fatigue state event.What the present invention mainly studied is fatigue state, wherein the θ and β section when entering fatigue state by waking state
Rule wave will appear biggish variation, therefore use θ and the respective sample space of beta response wave component, wherein by brain fax
Sensor initially sets the period（Embodiment preferably 1 minute）Acquire θ the and β feature rhythm and pace of moving things wave component obtained、Sample space,
Wherein the mathematic expectaion of θ and β sample value is respectively、, covariance matrix is, then real-time observed data x is to totally
SampleMahalanobis distance be：d(x,)=。
Pass through the mahalanobis distance of the available real-time characteristic signal of above formula to sample space,, and due to θ and alpha rhythm
The mahalanobis distance of wave significant changes can occur with the variation of fatigue state, therefore consider the mahalanobis distance of θ and alpha rhythm wave
Foundation of the weighting as degree of fatigue homeostasis.Assuming that Mda be theta rhythm wave mahalanobis distance, Mdb be alpha rhythm wave geneva away from
From then Weighted distance=λa+(1-λ)B, wherein 0≤λ≤1, and the threshold value that fatigue determinesThen pass through experimental result
Analysis is obtained when satisfaction according to ROC curve≥When, then it can determine that its is in a state of fatigue, otherwise not tired, algorithm stream
Journey figure is as shown in Figure 4.
Step 3：Wireless transmission, is transferred to source of early warning for attention grade.Transmitting terminal is bluetooth host, and use is one-to-many
Sending mode, so that intelligent early-warning bracelet and onboard system obtain attention grade simultaneously.Bluetooth communication modules use CC2540
For master chip, Bluetooth4.0 agreement is embedded, can directly be communicated with smart phone.
Three, early warning subsystem is driven, independently to write cell phone application.APP initial interface refers to attached drawing 5, can choose
APP is general modfel or highway auxiliary mode.Under general modfel, with reference to attached drawing 6, it is shown that current driver's is normal
Driving condition, screen bottom side are bluetooth connection refresh button, if when bluetooth occurs that this button weight can be pressed when connection is abnormal
New connection.Its main function is that the driver status for generating attention Evaluation subsystem is reported by Bluetooth transmission to smart phone
Or driver is fed back in vehicle intelligent system, and driver is reminded to notice that Don't Drive When Tired.When normal condition, mobile phone is depressed
It moves, not jingle bell.When driver is slightly tired, mobile phone intermittent control shaking, the bright orange light in interface, display scatter light state and intermittently sound
Bell reminds driver's focal attention；When driver is in major fatigue state, mobile phone sustained vibration, interface azarin light, display
Major fatigue simultaneously continues jingle bell and reminds driver that should stop driving a vehicle at once, and makees appropriate rest.
When APP is under highway auxiliary mode, have the function of under general modfel, and there is navigation feature, APP
The service area nearest apart from driver can be intelligently found, and is navigated to driver, driver is made to go to service area, can be obtained
To abundant rest, the generation to avoid traffic accident.
Beneficial effects of the present invention are：Present invention employs portable brain electric acquisition and analytical equipment, and brain wave acquisitions
Equipment uses single channel brain wave acquisition mode, keeps structure simpler, easy to carry, reduces the complexity of algorithm, improves
The comfort level of driver.Attention Evaluation subsystem, which is sent, by the collected EEG signals of brain wave acquisition equipment carries out feature extraction
Carry out intelligent algorithm analysis, driving condition locating for driver is divided into normal condition, slight fatigue and tight by application class algorithm
Classification results, are transferred in the smart phone or onboard system of driver, and adapt to by weight fatigue three grades eventually by bluetooth
Property early warning movement.The present invention is truly realized a practical fatigue driving early warning subsystem, and it judges precision height, early warning and
When, it is easy to carry, it is easily operated, and can effectively solve the problems, such as fatigue driving on expressway, it is with higher practical
Value.
The preferred embodiment of the present invention has been described above in detail, and still, the invention is not limited to above-mentioned particular implementations
Mode, those skilled in the art can modify within the scope of the claims or equivalents, should be included in this hair
Within bright protection scope.
Claims (10)
1. driver attention evaluates early warning system, associated with the smart phone of driver, it is characterised in that system, which is constituted, includes：
Brain wave acquisition subsystem：For recording the EEG signal of driver in driving condition in real time, and signal processing is commented as attention
The source data of valence；
Attention Evaluation subsystem：For the time domain waveform of required wave band to be filtered, extracted to source data and carries out algorithm point
Analysis and Classification and Identification, obtain fatigue state；
Drive early warning subsystem：It for the APP based on smart mobile phone hardware, is used for according to obtained tired driver state, and ties
The driving mode for closing setting feeds back the warning information of the corresponding weak collected state of attention by smart phone；
Three subsystems, which gradually communicate, to be connected and data interaction.
2. driver attention evaluates early warning system according to claim 1, it is characterised in that：Brain wave acquisition subsystem face
To attention Evaluation subsystem one-way communication, the attention Evaluation subsystem towards drive early warning subsystem one-way communication, and
One-way communication is realized based on bluetooth equipment.
3. driver attention evaluates early warning system according to claim 1, it is characterised in that：The brain wave acquisition subsystem is
For the single channel collector of EEG signal fluctuation tendency in the normal range and individual difference dynamic compensation, collection point is set as
The lateral forehead of driver and reference point are set as the side ear-lobe of driver.
4. driver attention evaluates early warning system according to claim 1, it is characterised in that the attention Evaluation subsystem
Composition include：
Characteristic signal module is extracted, for extracting the feature rhythm and pace of moving things wave θ and α from EEG signal using wavelet transformation；
Feature samples module, for utilizing initial setting period collected feature rhythm and pace of moving things wave θ and α construction feature sample space；
Distance calculates weighting block, for calculating live signal simultaneously weighted sum at a distance from feature samples space；
Pattern Matching Module, for comparing the relationship of resulting distance with preset threshold, matching obtains focusing on note from attention
The fatigue state that meaning power laxes two or more degree determines result.
5. driver attention evaluates early warning system according to claim 1, it is characterised in that：The driving early warning subsystem base
Driving mode input interface is equipped in smart phone and acoustooptic cell, vibration unit and self-navigation for exporting pre-warning signal
Unit.
6. driver attention evaluates method for early warning, realization is associated with the smart phone of driver, it is characterised in that including step：
Brain wave acquisition：Record the EEG signal of driver in driving condition, and signal processing conduct in real time using brain wave acquisition subsystem
The source data of attention evaluation；
Attention evaluation：The time domain waveform of wave band needed for being filtered, being extracted to source data using attention Evaluation subsystem is simultaneously
Algorithm analysis and Classification and Identification are carried out, fatigue state is obtained；
Drive early warning：Using APP of the early warning subsystem based on smart mobile phone hardware is driven, according to obtained tired driver shape
State, and the driving mode of setting is combined to feed back the warning information for corresponding to the weak collected state of attention by smart phone；
Three subsystems, which gradually communicate, to be connected and data interaction.
7. driver attention evaluates method for early warning according to claim 1, it is characterised in that：Brain wave acquisition subsystem face
To attention Evaluation subsystem one-way communication, the attention Evaluation subsystem towards drive early warning subsystem one-way communication, and
One-way communication is realized by the way of bluetooth.
8. driver attention evaluates method for early warning according to claim 1, it is characterised in that：The brain wave acquisition is directed to EEG
Signal fluctuation tendency in the normal range and individual difference dynamic compensate and take headgear mode that collection point is set as driver's
Lateral forehead, reference point are set as the side ear-lobe of driver, carry out single channel and acquire EEG signal.
9. driver attention evaluates method for early warning according to claim 1, it is characterised in that the attention evaluation includes step
Suddenly：
Characteristic signal is extracted, extracts the feature rhythm and pace of moving things wave θ and α from EEG signal using wavelet transformation；
Feature samples are established, initial setting period collected feature rhythm and pace of moving things wave θ and α construction feature sample space is utilized；
Distance calculates weighting, calculates live signal simultaneously weighted sum at a distance from feature samples space；
Pattern match, compares the relationship of resulting distance with preset threshold, and matching, which obtains focusing on attention from attention, laxes
The fatigue state of two or more degree determines result.
10. driver attention evaluates method for early warning according to claim 1, it is characterised in that：It drives in early warning based on intelligence
Driving mode input interface and acoustooptic cell, vibration unit and self-navigation unit is arranged in mobile phone, and driver is manually entered or intelligence
Cell phone intelligent identifies that driving mode, smart phone respond output voice prompting, photochromic according to the judgement result of tired driver state
It reminds, vibratory stimulation or self-navigation guidance driver drive into nearest service area rest.
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| CN109394248A (en) * | 2018-12-22 | 2019-03-01 | 江西科技学院 | Driving fatigue detection method and system |
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