CN205230274U - Driver fatigue monitoring system - Google Patents
Driver fatigue monitoring system Download PDFInfo
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- CN205230274U CN205230274U CN201521081184.5U CN201521081184U CN205230274U CN 205230274 U CN205230274 U CN 205230274U CN 201521081184 U CN201521081184 U CN 201521081184U CN 205230274 U CN205230274 U CN 205230274U
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Abstract
The utility model discloses a driver fatigue monitoring system, include: the communication device that the earphone detected the sensor and sent the brain wave signals that the brain wave detected the sensor collection including the brain wave of gathering brain wave signals, facial feature extraction device is including taking the 2nd communication device and the early warning device that facial characteristic pattern sent like the camera device in order to extract facial characteristic points, with facial characteristic image information, the controller includes the 3rd communication device, the brain wave database and the facial characteristic points database that prestores prestore, the 3rd communication device is connected with a communication device, the 2nd communication device and early warning device electricity respectively, power supply module is connected to facial feature extraction device and controller respectively. The utility model discloses realize the contrast of brain wave and facial vision characteristic, completion driver fatigue's monitoring and early warning, monitoring system is simple, the operation is convenient, monitoring accuracy height.
Description
Technical field
The utility model belongs to safe driving field, is specifically related to a kind of real-time driving fatigue monitoring system.
Background technology
Along with development that is scientific and technological and social level, automobile has progressed in the life of increasing people with its distinctive superiority, brings convenience, comfort level to the life of people.But, people are while enjoying the superiority brought of automobile, also painful cost has been paid, such as, due to the fast pace of the modern life, the fatigue driving causing people to be in fatigue state going steering vehicle or long drives vehicle etc. to cause, for vehicle drive brings insecurity, fatigue driving is in the great majority in traffic accident reason, causes huge loss to the lives and properties of the country and people masses.Therefore, the monitoring system whether a kind of effective and monitoring driver that accuracy is high be in fatigue driving is studied significant.
Utility model content
Utility model object: for above-mentioned prior art Problems existing and deficiency, the purpose of this utility model is to provide a kind of real-time driving fatigue monitoring system, realize brain wave and the contrast of facial visual signature, complete monitoring and the early warning of fatigue driving, monitoring system is simple, operation is convenient, monitoring degree of accuracy is high.
Technical scheme: the utility model discloses a kind of real-time driving fatigue monitoring system, comprising:
Earphone, it comprises the brain wave detecting sensor gathering eeg signal and the first communication device sent by the eeg signal of described brain wave detecting sensor collection;
Facial feature extraction device, it comprise shooting facial feature image with extract face feature point camera head, by the secondary communication device of described facial feature image delivering and prior-warning device;
Controller, it is electrically connected with described brain wave detecting sensor and described camera head respectively; Described controller comprises third communication device, the brain wave data that prestores storehouse and the facial characteristics point data base that prestores; Described third communication device is electrically connected with described first communication device, described secondary communication device and described prior-warning device respectively; And,
Power supply module, is connected respectively to described facial feature extraction device and described controller;
Wherein, described brain wave detecting sensor is brain electrode sheet, and described brain electrode sheet is detachably attached near the side of brain when described earphone is worn, and described brain electrode sheet laminating brain surface;
Described facial characteristics comprises eye feature and face feature.
Preferably, power supply module comprises solar energy photovoltaic panel, solar electrical energy generation charge controller, battery pack, inverter; The output terminal of described solar energy photovoltaic panel connects the charging circuit of battery pack by solar electrical energy generation charge controller; The discharge circuit of described battery pack is connected respectively to described facial feature extraction device and described controller by described inverter.
Preferably, described power supply module also comprises the angle demodulator being arranged on roof, is connected to roof below described solar energy photovoltaic panel by angle demodulator.
Preferably, described first communication device and described secondary communication device are identical with the communication pattern of described third communication device respectively, and the communication pattern of described third communication device is radio communication.
Preferably, the communication pattern of described third communication device is bluetooth or WIFI.
Preferably, described prior-warning device is sound prior-warning device, and it comprises the audio unit producing early warning sound.
Preferably, described camera head comprises CCD camera.
Preferably, described controller is single-chip microcomputer or FPGA.
Beneficial effect: the utility model compared with prior art, has the following advantages:
1) eeg signal of brain electrode sheet collection is set near brain side when being worn by earphone, arrange camera head shooting send to controller with the facial feature image extracting face feature point, realize eeg signal data and facial characteristics point data contrasts respectively and comparison information is sent to prior-warning device, complete monitoring and the early warning of fatigue driving, monitoring system simply, operation is convenient, monitoring degree of accuracy is high;
2) generated electricity by solar energy photovoltaic panel and solar electrical energy generation charge controller, generated energy is stored in battery pack, then powered to facial feature extraction device and controller by inverter, save the energy, cleanliness without any pollution;
3) first communication device and secondary communication device are identical with the communication pattern of third communication device respectively, and the communication pattern of third communication device is radio communication, improves the communication facilitation of real-time driving fatigue monitoring system, dirigibility and compatibility; Third communication device is bluetooth or WIFI (WirelessFidelity, WLAN (wireless local area network)), bluetooth or WIFI communication are the conventional and communication modes easily of communicating out of doors, further increase communication facilitation and the dirigibility of real-time driving fatigue monitoring system.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of real-time driving fatigue monitoring system of the present utility model.
Embodiment
As shown in Figure 1, the present embodiment provides a kind of monitoring system of fatigue driving, comprising:
Earphone 10, it comprises the brain wave detecting sensor 11 gathering eeg signal and the first communication device 12 sent by the eeg signal that brain wave detecting sensor 11 gathers;
Facial feature extraction device 20, it comprise shooting facial feature image with extract face feature point camera head 21, by the secondary communication device 22 of facial feature image delivering and prior-warning device 23;
Controller 30, it is electrically connected with brain wave detecting sensor 11 and camera head 21 respectively; Controller 30 comprises third communication device 31, the brain wave data that prestores storehouse 32 and the facial characteristics point data base 33 that prestores; Third communication device 31 is electrically connected with first communication device 12, secondary communication device 22 and prior-warning device 23 respectively; And,
Power supply module 40, it comprises solar energy photovoltaic panel 41, solar electrical energy generation charge controller 42, battery pack 43, inverter 44; The output terminal of solar energy photovoltaic panel 41 connects the charging circuit of battery pack 43 by solar electrical energy generation charge controller 42; The discharge circuit of battery pack 43 is connected respectively to facial feature extraction device 20 and controller 30 by inverter 44.
Wherein, brain wave detecting sensor 11 is brain electrode sheets, brain electrode sheet is detachably attached to when earphone 10 is worn near the side of brain, and brain electrode sheet laminating brain surface, without the need to carrying out damage process to brain, can realize gathering eeg signal by contact, improve security and the convenience of eeg signal collection.Each earphone 10 is provided with at least one brain wave detecting sensor 11, the data that at least one brain wave detecting sensor 11 can provide at least one eeg signal to gather, and is conducive to the accuracy improving eeg signal collection.
Earphone 10 is hung on one of single ear or be hung on two of two ears respectively.Earphone 10 is one that is hung on single ear, wears quick, easy to use; Earphone 10 is two that are hung on two ears respectively, the earphone 10 of two ears is hung on respectively by two, brain both sides and every close eeg signal located of ear can be gathered, add place and the number of times of collection, add the accuracy that eeg signal gathers.
Controller 30 is received eeg signal that first communication device 12 sends by third communication device 31 and is carried out data processing and draws real-time brain wave data.Specifically, eeg signal mixes much noise in gatherer process, and the data processing first step of controller 30 pairs of eeg signals must first filtering.Non-linear and non-stationary for eeg signal, the wave filter that controller 30 adopts effective Time-Frequency Analysis Method wavelet transformation to come design eeg signal, what the utility model adopted is wavelet transform fast.It should be noted that, controller 30 can be arranged separately, also can be integrated in facial feature extraction device 20.
Facial characteristics comprises eye feature and face feature, and eye feature comprises eyes normal characteristics point and eye fatigue unique point, and face feature comprises face normal characteristics point and face fatigue characteristic point.Controller 30 is provided with the facial characteristics point data base 33 that prestores, and it specifically comprises: prestore eye feature point set and the face feature point set that prestores.Wherein, the eye feature point set that prestores is prestore eyes normal characteristics point set and the eye fatigue feature point set that prestores demarcating the unique point around a face by the height that calculating eyes normally open and tiring moments opens and formed; The face feature point set that prestores is prestore face normal characteristics point set and the face fatigue characteristic point set that prestores demarcating the unique point around face by the amplitude that calculating face normally opens and tiring moments opens and formed.Facial characteristics is as the extraction of eye feature and face feature, what the utility model adopted is based on ASM (ActiveShapeModel, active shape model) characteristic point positioning method of model, namely first manual unique point described point is carried out to each feature of eyes and face, then identify by the described point of ASM model to manual unique point and locate.But ASM is a kind of algorithm of sensitivity, and sensitive prime is in original state.Original state OK whether directly affect final Search Results, in order to give optimum original state, be used alone ASM and can not obtain very accurate result, therefore, adopt Optimal-threshold segmentation method, namely by the method that ASM combines with Bayes's optimal threshold, Bayesian optimal threshold is utilized to provide a rational original state to ASM, to improve the accuracy of eye feature and face feature extraction.
Power supply module 40 is generated electricity by solar energy photovoltaic panel 41 and solar electrical energy generation charge controller 42, generated energy is stored in battery pack 43, then powers to facial feature extraction device 20 and controller 30 by inverter 44, saves the energy, cleanliness without any pollution.
The real-time driving fatigue monitoring system that the present embodiment provides, on the one hand, controller 30 is received eeg signal that first communication device 12 sends by third communication device 31 and is carried out data processing and draws real-time brain wave data, and real-time brain wave data are made comparisons with the brain wave data storehouse that prestores and drawn brain wave data comparison information by controller 30; On the other hand, controller 30 is received facial feature image that secondary communication device 22 sends by third communication device 31 and is carried out data processing and draws real-time facial feature point data and make comparisons with the facial characteristics point data base 33 that prestores, concrete, real-time facial feature point data and eyes normal characteristics point set, the eye fatigue that prestores feature point set, face normal characteristics point set and the face fatigue characteristic point set that prestores are made comparisons by controller 30, draw face feature point comparison information; Finally, brain wave data comparison information and face feature point comparison information send to prior-warning device 23 to realize giving fatigue pre-warning to drive prior-warning device by controller 30 respectively.The present embodiment realizes brain wave data comparison information and combines with face feature point comparison information and monitor fatigue driving state and early warning, and monitoring system is simple, operation is convenient, monitoring degree of accuracy is high.
In technique scheme, power supply module 40 also comprises the angle demodulator being arranged on roof, is connected to roof below solar energy photovoltaic panel 41 by angle demodulator.By angle demodulator, solar energy photovoltaic panel 41 is installed to roof and angle adjustable, achieves the convenience of solar energy photovoltaic panel 41 daylighting and lighting efficiency is high.
For improving the communication facilitation of this system, dirigibility and compatibility further, the first communication device 12 of the present embodiment is identical with the communication pattern of third communication device 31 respectively with secondary communication device 22, and the communication pattern of third communication device 31 is radio communications.
The third communication device 31 of the present embodiment adopts bluetooth or WIFI.Bluetooth or WIFI are the conventional and communication modes easily of communicating out of doors, and third communication device 31 is bluetooth or WIFI, further increases communication facilitation and the dirigibility of real-time driving fatigue monitoring system.
The prior-warning device 23 of the present embodiment is sound prior-warning device 23, and it comprises the audio unit producing early warning sound.For avoiding light on the impact of driver security, what the prior-warning device 23 of the present embodiment adopted is the sound prior-warning device being provided with the audio unit producing early warning sound, when brain wave data comparison information is combined with facial vision data comparison information the situation meeting fatigue driving, then the brain wave data comparison information and facial vision data comparison information that meet fatigue driving send to prior-warning device 23 to produce early warning sound to drive prior-warning device 23 by controller 30, remind driver, adjustment spirit or rest.
The camera head 21 of the present embodiment comprises CCD (Charge-coupledDevice, charge coupled cell) camera, the color of pictures taken and good imaging quality, size is little, sharpness is high, show that real-time brain wave data provide clear high-quality picture for controller 30 carries out data processing, improve the accuracy of real-time driving fatigue monitoring system.
The controller of the present embodiment is single-chip microcomputer or FPGA (Field-ProgrammableGateArray, field programmable gate array).Single chip application is general, and FPGA calculation processing power is powerful, calculation process speed fast, and the controller of single-chip microcomputer or FPGA is all applicable to real-time driving fatigue monitoring system of the present utility model.The single-chip microcomputer of the present embodiment is preferably STM32F103RBT6, and FPGA is preferably EP2C20F484C6.
Claims (8)
1. a real-time driving fatigue monitoring system, is characterized in that, comprising:
Earphone, it comprises the brain wave detecting sensor gathering eeg signal and the first communication device sent by the eeg signal of described brain wave detecting sensor collection;
Facial feature extraction device, it comprise shooting facial feature image with extract face feature point camera head, by the secondary communication device of described facial feature image delivering and prior-warning device;
Controller, it is electrically connected with described brain wave detecting sensor and described camera head respectively; Described controller comprises third communication device, the brain wave data that prestores storehouse and the facial characteristics point data base that prestores; Described third communication device is electrically connected with described first communication device, described secondary communication device and described prior-warning device respectively; And,
Power supply module, is connected respectively to described facial feature extraction device and described controller;
Wherein, described brain wave detecting sensor is brain electrode sheet, and described brain electrode sheet is detachably attached near the side of brain when described earphone is worn, and described brain electrode sheet laminating brain surface;
Described facial characteristics comprises eye feature and face feature.
2. a kind of real-time driving fatigue monitoring system according to claim 1, is characterized in that: described power supply module comprises solar energy photovoltaic panel, solar electrical energy generation charge controller, battery pack, inverter; The output terminal of described solar energy photovoltaic panel connects the charging circuit of battery pack by solar electrical energy generation charge controller; The discharge circuit of described battery pack is connected respectively to described facial feature extraction device and described controller by described inverter.
3. a kind of real-time driving fatigue monitoring system according to claim 2, is characterized in that: described power supply module also comprises the angle demodulator being arranged on roof, is connected to roof below described solar energy photovoltaic panel by angle demodulator.
4. a kind of real-time driving fatigue monitoring system according to any one of claim 1-3, it is characterized in that: described first communication device and described secondary communication device are identical with the communication pattern of described third communication device respectively, and the communication pattern of described third communication device is radio communication.
5. a kind of real-time driving fatigue monitoring system according to claim 4, is characterized in that: the communication pattern of described third communication device is bluetooth or WIFI.
6. a kind of real-time driving fatigue monitoring system according to any one of claim 1-3, is characterized in that: described prior-warning device is sound prior-warning device, and it comprises the audio unit producing early warning sound.
7. a kind of real-time driving fatigue monitoring system according to any one of claim 1-3, is characterized in that: described camera head comprises CCD camera.
8. a kind of real-time driving fatigue monitoring system according to any one of claim 1-3, is characterized in that: described controller is single-chip microcomputer or FPGA.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201521081184.5U CN205230274U (en) | 2015-12-21 | 2015-12-21 | Driver fatigue monitoring system |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201521081184.5U CN205230274U (en) | 2015-12-21 | 2015-12-21 | Driver fatigue monitoring system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106056850A (en) * | 2016-06-30 | 2016-10-26 | 邓春生 | Fatigue driving monitoring system |
| CN108133573A (en) * | 2017-12-26 | 2018-06-08 | 中国神华能源股份有限公司 | Drowsy driving warning system |
| CN109917916A (en) * | 2019-03-05 | 2019-06-21 | 浙江强脑科技有限公司 | E-book control method, electronic equipment and computer readable storage medium |
| CN112754498A (en) * | 2021-01-11 | 2021-05-07 | 一汽解放汽车有限公司 | Driver fatigue detection method, device, equipment and storage medium |
-
2015
- 2015-12-21 CN CN201521081184.5U patent/CN205230274U/en not_active Expired - Fee Related
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106056850A (en) * | 2016-06-30 | 2016-10-26 | 邓春生 | Fatigue driving monitoring system |
| CN108133573A (en) * | 2017-12-26 | 2018-06-08 | 中国神华能源股份有限公司 | Drowsy driving warning system |
| CN109917916A (en) * | 2019-03-05 | 2019-06-21 | 浙江强脑科技有限公司 | E-book control method, electronic equipment and computer readable storage medium |
| CN112754498A (en) * | 2021-01-11 | 2021-05-07 | 一汽解放汽车有限公司 | Driver fatigue detection method, device, equipment and storage medium |
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| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160511 Termination date: 20201221 |