CN204833608U - Tired monitoring devices of driver based on brain machine interface - Google Patents
Tired monitoring devices of driver based on brain machine interface Download PDFInfo
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- CN204833608U CN204833608U CN201520588369.9U CN201520588369U CN204833608U CN 204833608 U CN204833608 U CN 204833608U CN 201520588369 U CN201520588369 U CN 201520588369U CN 204833608 U CN204833608 U CN 204833608U
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Abstract
The utility model discloses a tired monitoring devices of driver based on brain machine interface, include to driver's brain wave signals gather and the EEG signal acquisition device of preliminary treatment with lay the EEG signal monitoring devices in driver institute steering vehicle, communicate with wireless communication between EEG signal acquisition device and the EEG signal monitoring devices, EEG signal monitoring devices includes main control chip and the 2nd wireless communication module, the 3rd wireless communication module and voice prompt unit, just it includes EEG signal extraction element and EEG signal preprocessing device to EEG signal acquisition device for the TGAM module, and main control chip communicates by letter with the host computer through the 3rd wireless communication module, main control chip is the arduino controller, the 3rd wireless communication module is GPRS wireless communication module. The utility model discloses simple structure, small, portable and use easy and simple to handle, excellent in use effect can portably monitor driver's driver fatigue state.
Description
Technical field
The utility model relates to a kind of monitoring device, especially relates to a kind of driver fatigue monitoring device based on brain-computer interface.
Background technology
In recent years, along with the increase of automobile pollution and the expansion of highway construction scale, the problems such as traffic hazard become increasingly conspicuous.China is the country that world population is maximum, and road fatalities is also the country that the whole world is the highest, ranks first in the world for several years running always.Driver takes a risk fatigue driving, can bring hidden danger undoubtedly to the safety of oneself and passenger.The research of driving fatigue is divided into subjectivity and objectivity two kinds of methods, and subjective research method has subjective survey table, driver oneself record, sleep habit questionnaire, Stamford sleep yardstick table four kind.Objective research method has the measuring method such as temperature and cardiogram when electroencephalogram, electroculogram, electromyogram, respiratory air flow, effect of breathing, arterial blood oxygen saturation.Although the driving fatigue result of determination of said method is more accurately, but because said method is generally measured before driving or after driving, because of but advanced or delayed, and non real-time, moreover in the limited space of pilothouse, settle complicated detecting instrument to be also very difficult; And the state of mind that driver departs from pilothouse or do not enter pilothouse is different, then the measurement result of accurate instrument also can be greatly affected.
Brain wave control technology is in biomedicine, and the fields such as computing machine become one of hot research in recent years.Traditional subcutaneous E.E.G acquisition method, both complicated, inconvenient again, be therefore difficult to be generalized to other field.At present, brain-computer interface technology is in the development starting stage at home, and relevant research is also fewer.TGAM (ThinkGearAM) module is the brain-wave sensor ASIC module of U.S. NeuroSky (god reads science and technology) company designed by general marketplace application, also claims TGAM brain electricity module.This TGAM (ThinkGearAM) module can process and export the eSense parameter of frequency of brain wave spectrum, EEG signals quality, original brain wave and three Neurosky: focus, allowance and detecting nictation.The interface of TGAM (ThinkGearAM) module and human body only needs a simple stem grafting contact, so can apply in toy, video-game and healthy equipment easily, again because energy consumption is little, be suitable for use in in the application of battery powered portable consumer devices.Therefore, need develop that a kind of structure is simple, volume is little, be easy to carry and use easy and simple to handle, that result of use the is good driver fatigue monitoring device based on brain-computer interface, can monitor by the easy fatigue driving state to driver.
Utility model content
Technical problem to be solved in the utility model is for above-mentioned deficiency of the prior art, a kind of driver fatigue monitoring device based on brain-computer interface is provided, its structure is simple, volume is little, be easy to carry and use easy and simple to handle, result of use good, can the easy fatigue driving state to driver monitor.
For solving the problems of the technologies described above, the technical solution adopted in the utility model is: a kind of driver fatigue monitoring device based on brain-computer interface, it is characterized in that: comprise and to gather the eeg signal of driver and pretreated EEG signals acquisition device and the EEG signals monitoring device that is laid in driver institute steering vehicle, described EEG signals acquisition device communicates with communication with between EEG signals monitoring device, described EEG signals monitoring device comprises shell and is laid in the electronic circuit board in described shell, described electronic circuit board is provided with main control chip and the second wireless communication module connected with main control chip respectively, the 3rd wireless communication module and power supply unit, described shell is provided with the voice alerting unit of driver being carried out to alarm, described voice alerting unit is undertaken controlling by main control chip and it connects with main control chip, described EEG signals acquisition device is TGAM module, described TGAM module comprises the EEG signals extraction element that extracts the eeg signal of driver and samples and pretreated EEG signals pretreatment unit to EEG signals signal that extraction element extracts, described EEG signals pretreatment unit connects with EEG signals extraction element, described EEG signals extraction element comprises and carries out the first electrode for encephalograms of real-time sampling to the current potential in driver's frontal lobe district and carry out the second electrode for encephalograms of real-time sampling and tritencepehalon electricity electrode to ear's current potential of driver, described first electrode for encephalograms, second electrode for encephalograms and tritencepehalon electricity electrode all connect with EEG signals pretreatment unit, described EEG signals pretreatment unit connects with the first wireless communication module, described EEG signals pretreatment unit is communicated with main control chip with the second wireless communication module by the first wireless communication module, and described main control chip is communicated with host computer by the 3rd wireless communication module, described main control chip is Arduino controller, described 3rd wireless communication module is GPRS wireless communication module.
The above-mentioned driver fatigue monitoring device based on brain-computer interface, is characterized in that: described EEG signals pretreatment unit is the TGAM chip of NeuroSky company of U.S. research and development; The EEG pin of the output termination TGAM chip of described first electrode for encephalograms, the REF pin of the output termination TGAM chip of the second electrode for encephalograms, the EEG_GND pin of the output termination TGAM chip of tritencepehalon electricity electrode.
The above-mentioned driver fatigue monitoring device based on brain-computer interface, is characterized in that: described first wireless communication module and the second wireless communication module are Bluetooth wireless communication module.
The above-mentioned driver fatigue monitoring device based on brain-computer interface, is characterized in that: described Bluetooth wireless communication module is HL-MD08R-C2A module.
The above-mentioned driver fatigue monitoring device based on brain-computer interface, is characterized in that: described voice alerting unit is loudspeaker LS, and one end of described loudspeaker LS connects and its other end ground connection with the 8th pin of described Arduino controller.
The above-mentioned driver fatigue monitoring device based on brain-computer interface, it is characterized in that: described first electrode for encephalograms is placed on according to 10-on the left antinion of the driver that 20 system electrode placement methods are determined, described second electrode for encephalograms and tritencepehalon electricity electrode are all placed on according to 10-in the left temporo of the driver that 20 system electrode placement methods are determined.
The above-mentioned driver fatigue monitoring device based on brain-computer interface, is characterized in that: described GPRS wireless communication module is GTM-900C wireless communication module.
The utility model compared with prior art has the following advantages:
1, simple, reasonable in design, the easy-to-connect of circuit and use easy and simple to handle, input cost is lower.
2, structure is simple, volume is little, is easy to carry and wiring is easy, install lay easy.
3, result of use is good and practical value is high, Real-Time Monitoring can be carried out by the easy fatigue driving state to driver, in driving conditions, carry out gathering to the eeg signal of driver in real time by EEG signals acquisition device and the focus D of institute's test driver under automatically exporting current state, and focus D is sent to main control chip, realize the Real-Time Monitoring to driver state, and when main control chip receive focus D that EEG signals acquisition device transmits be less than the threshold value preset time, control voice alerting unit and carry out audio alert, driver is allowed to be in clear state in real time, the generation cutd down traffic accidents, thus there is real-time, monitoring effect is good.
In sum, the utility model structure is simple, volume is little, be easy to carry and use easy and simple to handle, result of use good, can the easy fatigue driving state to driver monitor.
Below by drawings and Examples, the technical solution of the utility model is described in further detail.
Accompanying drawing explanation
Fig. 1 is schematic block circuit diagram of the present utility model.
Fig. 2 is the circuit theory diagrams of the utility model EEG signals acquisition device and the first wireless communication module.
Fig. 3 is the circuit theory diagrams of the utility model EEG signals monitoring device.
Description of reference numerals:
1-EEG signals acquisition device; 1-1-EEG signals extraction element;
1-11-the first electrode for encephalograms; 1-12-the second electrode for encephalograms; 1-13-tritencepehalon electricity electrode;
1-2-EEG signals pretreatment unit; 2-EEG signals monitoring device;
2-1-main control chip; 2-2-the second wireless communication module;
2-3-the 3rd wireless communication module; 2-4-power supply unit;
2-5-voice alerting unit; 2-6-parameter input unit; 2-7-display;
3-the first wireless communication module; 4-host computer.
Embodiment
As shown in Figure 1 and Figure 2, the utility model comprises and to gather the eeg signal of driver and pretreated EEG signals acquisition device 1 and the EEG signals monitoring device 2 that is laid in driver institute steering vehicle, and described EEG signals acquisition device 1 communicates with communication with between EEG signals monitoring device 2.Described EEG signals monitoring device comprises shell and is laid in the electronic circuit board in described shell, described electronic circuit board is provided with main control chip 2-1 and the second wireless communication module 2-2 connected with main control chip 2-1 respectively, the 3rd wireless communication module 2-3 and power supply unit 2-4, described shell is provided with voice alerting unit 2-5 driver being carried out to alarm, described voice alerting unit 2-5 is undertaken controlling by main control chip 2-1 and it connects with main control chip 2-1.Described EEG signals acquisition device 1 is TGAM module, described TGAM module comprises the EEG signals extraction element 1-1 that extracts the eeg signal of driver and samples and pretreated EEG signals pretreatment unit 1-2 to EEG signals extraction element signal that 1-1 extracts, described EEG signals pretreatment unit 1-2 connects with EEG signals extraction element 1-1, described EEG signals extraction element 1-1 comprises and carries out the first electrode for encephalograms 1-11 of real-time sampling to the current potential in driver's frontal lobe district and carry out the second electrode for encephalograms 1-12 of real-time sampling and tritencepehalon electricity electrode 1-13 to ear's current potential of driver, described first electrode for encephalograms 1-11, second electrode for encephalograms 1-12 and tritencepehalon electricity electrode 1-13 all connects with EEG signals pretreatment unit 1-2.Described EEG signals pretreatment unit 1-2 connects with the first wireless communication module 3, described EEG signals pretreatment unit 1-2 is communicated with main control chip 2-1 with the second wireless communication module 2-2 by the first wireless communication module 3, and described main control chip 2-1 is communicated with host computer 4 by the 3rd wireless communication module 2-3; Described main control chip 2-1 is Arduino controller.Described 3rd wireless communication module 2-3 is GPRS wireless communication module.
In the present embodiment, as shown in Figure 2, described EEG signals pretreatment unit 1-2 is the TGAM chip of NeuroSky company of U.S. research and development.The EEG pin of the output termination TGAM chip of described first electrode for encephalograms 1-11, the REF pin of the output termination TGAM chip of the second electrode for encephalograms 1-12, the EEG_GND pin of the output termination TGAM chip of tritencepehalon electricity electrode 1-13.During actual use, described second electrode for encephalograms 1-12 is reference electrode.
In actual use procedure, the EEG signals that the EEG of described TGAM chip holds input first electrode for encephalograms 1-11 to sample, the effect of EEG_shiled end be shielding the first electrode for encephalograms 1-11 institute sample EEG signals input TGAM chip before interference during this period of time; The EEG signals that REF holds input second electrode for encephalograms 1-2 to sample, ear's EEG signals of being sampled by the second electrode for encephalograms 1-12, can effective filtering self start type brain wave as with reference to current potential; REF_shiled end mainly shield the second electrode for encephalograms 1-12 institute sample EEG signals input TGAM chip before interference during this period of time; E.E.G ground wire is also connected to the ear of human body, the i.e. tritencepehalon electricity electrode 1-13 EEG signals of sampling, main effect is the impact in order to shield the following electric wave of human body head, such as electrocardio ripple is exactly a kind of stronger interference wave, and the connection of E.E.G ground wire can effective filtering electrocardio ripple.That is, tritencepehalon electricity electrode 1-13 is the electrode gathering brain wave ground signalling.
In the present embodiment, described first wireless communication module 3 and the second wireless communication module 2-2 are Bluetooth wireless communication module.
Further, described Bluetooth wireless communication module is HL-MD08R-C2A module.
During actual use, described first wireless communication module 3 and the second wireless communication module 2-2 also can adopt the wireless communication module of other type.
In the present embodiment, described first electrode for encephalograms 1-11 is placed on according to 10-and on the left antinion of the driver that 20 system electrode placement methods are determined, described second electrode for encephalograms 1-12 and tritencepehalon electricity electrode 1-13 is all placed on according to 10-in the left temporo of the driver that 20 system electrode placement methods are determined.Wherein, 10-20 system electrode placement methods, i.e. international electroencephalogram association specified standard electrode placement methods.
Thus, what EEG signals extraction element 1-1 mainly gathered is prefrontal area, the current potential specifically in left antinion (Fp1) this electrode site.Described second electrode for encephalograms 1-12 and tritencepehalon electricity electrode 1-13 to be all placed in left temporo in (T3) this electrode site.
According to 10-20 system electrode placement methods, electrode title goes name according to the position of brain anatomy, as pillow, top, temporo, volume etc. (usually adopting capitalization O, P, T, F etc. of the initial of each position English name to represent).The each region of anatomy electrode in related brain areas should represent and embody the function in each Cerebral cortex district:
1. sagittal line before and after: draw a line to external occipital protuberance from the nasion, on this line, mark 5 points from front to back successively, and called after: antinion mid point (Fpz), metopion (Fz), central point (Cz), summit (Pz), pillow point (Oz).Because the nasion account for 10% of wire length to the distance of antinion mid point and external occipital protuberance separately to the distance of resting the head on a little, other point is also spaced from each other with 20% of wire length.10-20 systematic names get thus.
2. transverse presentation: begin to pass central point from left preauricular point and arrive right preauricular point, and draw a line, from the left and right sides of line, symmetry marks (T4) in (T3) in left temporo, right temporo, left centre (C3), right median (C4).T3 point and T4 account for 10% of this line length to the distance of preauricular point, and other each point (comprising Cz point) is also spaced from each other with 20% of this wire length.
3. position, side: arrive pillow point by T3 and T4 point backward from antinion mid point after, get the line of left and right sides respectively, symmetrically from front to back on line mark left antinion (Fp1), right antinion (Fp2), left front temporo (F7), right front temporo (F8), left back temporo (T5), right back temporo (T6), left pillow (O1), right pillow (O2) each point.Fp1 and Fp2 point all account for 10% of this wire length to the distance of antinion mid point (Fpz) and O1 and O2 point to the distance of Oz point, and other each point (comprising T3, T4) is also spaced from each other with 20% of this line total length.
4. left volume (F3) is positioned at the centre of Fp1 point and Fp2 point; Right volume (F4) point respectively with the centre of C3 point and C4 point; Left top (P3) is positioned at the centre of C3 point and C4 point; Right top (P4) point respectively with the centre of O1 point O2 point.
In the present embodiment, the model of described TGAM chip is TGAM1, and described first wireless communication module 3 and the second wireless communication module 2-2 are BlueTooth chip.During physical cabling, the TXD pin of described TGAM chip connects with the RX pin of the first wireless communication module 3.The power end of described TGAM chip and the VCC pin of TGAM chip all connect+3.3V power end.
As shown in Figure 3, in the present embodiment, described voice alerting unit 2-5 is loudspeaker LS, and one end of described loudspeaker LS connects and its other end ground connection with the 8th pin of described Arduino controller.
In the present embodiment, described GPRS wireless communication module is GTM-900C wireless communication module.
During physical cabling, the RX pin of described Arduino controller connects with the RX pin of the second wireless communication module 2-2 and its TX pin connects with the RX pin of GTM-900C wireless communication module.
Meanwhile, described EEG signals monitoring device 2 also comprises parameter input unit 2-6 and display 2-7, and described parameter input unit 2-6 and display 2-7 is all laid on described shell.
In actual use procedure, undertaken gathering by the eeg signal of EEG signals acquisition device 1 couple of driver and the focus D of institute's test driver under automatically exporting current state, intuitively shown by display 2-7, and focus D is sent to main control chip 2-1 with communication; After described main control chip 2-1 receives the focus D of EEG signals acquisition device 1 transmission, carry out threshold value to the attention rate under current state to compare, and output digit signals 0 or 1 couple of voice alerting unit 2-5 control: when the focus D that main control chip 2-1 receives EEG signals acquisition device 1 transmission is less than the threshold value preset by parameter input unit 2-6, main control chip 2-1 output digit signals 1, control voice alerting unit 2-5 and carry out audio alert, driver is allowed to be in clear state in real time, the generation cutd down traffic accidents; When the focus D that main control chip 2-1 receives EEG signals acquisition device 1 transmission is not less than the threshold value preset, main control chip 2-1 output digit signals 0, voice alerting unit 2-5 stop alarm.Actual when using, described main control chip 2-1 is a comparison controller, thus any there is numerical value comparing function comparer or control chip all can substitute main control chip 2-1., adopt the opening of described Arduino controller good herein, use simple, quick.
The above; it is only preferred embodiment of the present utility model; not the utility model is imposed any restrictions; every above embodiment is done according to the utility model technical spirit any simple modification, change and equivalent structure change, all still belong in the protection domain of technical solutions of the utility model.
Claims (7)
1. the driver fatigue monitoring device based on brain-computer interface, it is characterized in that: comprise and to gather the eeg signal of driver and pretreated EEG signals acquisition device (1) and the EEG signals monitoring device (2) that is laid in driver institute steering vehicle, described EEG signals acquisition device (1) communicates with communication with between EEG signals monitoring device (2), described EEG signals monitoring device comprises shell and is laid in the electronic circuit board in described shell, described electronic circuit board is provided with main control chip (2-1) and the second wireless communication module (2-2) connected with main control chip (2-1) respectively, the 3rd wireless communication module (2-3) and power supply unit (2-4), described shell is provided with the voice alerting unit (2-5) of driver being carried out to alarm, described voice alerting unit (2-5) is undertaken controlling by main control chip (2-1) and it connects with main control chip (2-1), described EEG signals acquisition device (1) is TGAM module, described TGAM module comprise EEG signals extraction element (1-1) that the eeg signal of driver is extracted and to EEG signals extraction element (1-1) extract signal and sample and pretreated EEG signals pretreatment unit (1-2), described EEG signals pretreatment unit (1-2) connects with EEG signals extraction element (1-1), described EEG signals extraction element (1-1) comprises carries out first electrode for encephalograms (1-11) of real-time sampling to the current potential in driver's frontal lobe district and carries out second electrode for encephalograms (1-12) of real-time sampling and tritencepehalon electricity electrode (1-13) to ear's current potential of driver, described first electrode for encephalograms (1-11), second electrode for encephalograms (1-12) and tritencepehalon electricity electrode (1-13) all connect with EEG signals pretreatment unit (1-2), described EEG signals pretreatment unit (1-2) connects with the first wireless communication module (3), described EEG signals pretreatment unit (1-2) is communicated with main control chip (2-1) with the second wireless communication module (2-2) by the first wireless communication module (3), and described main control chip (2-1) is communicated with host computer (4) by the 3rd wireless communication module (2-3), described main control chip (2-1) is Arduino controller, described 3rd wireless communication module (2-3) is GPRS wireless communication module.
2. according to the driver fatigue monitoring device based on brain-computer interface according to claim 1, it is characterized in that: the TGAM chip that described EEG signals pretreatment unit (1-2) is researched and developed for NeuroSky company of the U.S.; The EEG pin of the output termination TGAM chip of described first electrode for encephalograms (1-11), the REF pin of the output termination TGAM chip of the second electrode for encephalograms (1-12), the EEG_GND pin of the output termination TGAM chip of tritencepehalon electricity electrode (1-13).
3. according to the driver fatigue monitoring device based on brain-computer interface described in claim 1 or 2, it is characterized in that: described first wireless communication module (3) and the second wireless communication module (2-2) are Bluetooth wireless communication module.
4. according to the driver fatigue monitoring device based on brain-computer interface according to claim 3, it is characterized in that: described Bluetooth wireless communication module is HL-MD08R-C2A module.
5. according to the driver fatigue monitoring device based on brain-computer interface described in claim 1 or 2, it is characterized in that: described voice alerting unit (2-5) is loudspeaker LS, one end of described loudspeaker LS connects and its other end ground connection with the 8th pin of described Arduino controller.
6. according to the driver fatigue monitoring device based on brain-computer interface described in claim 1 or 2, it is characterized in that: described first electrode for encephalograms (1-11) is placed on according to 10-on the left antinion of the driver that 20 system electrode placement methods are determined, described second electrode for encephalograms (1-12) and tritencepehalon electricity electrode (1-13) are all placed on according to 10-in the left temporo of the driver that 20 system electrode placement methods are determined.
7. according to the driver fatigue monitoring device based on brain-computer interface described in claim 1 or 2, it is characterized in that: described GPRS wireless communication module is GTM-900C wireless communication module.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105336106A (en) * | 2015-12-04 | 2016-02-17 | 无锡市麦泰克新材料科技有限公司 | Fatigue driving alarming and prompting system arranged during automobile refitting |
| CN105678959A (en) * | 2016-02-25 | 2016-06-15 | 重庆邮电大学 | Monitoring and early-warning method and system for fatigue driving |
| CN107049306A (en) * | 2017-03-31 | 2017-08-18 | 王晓路 | A kind of detection treatment system based on meditation degree |
| CN109907755A (en) * | 2019-04-26 | 2019-06-21 | 上海理工大学 | A kind of fatigue driving monitoring and interfering system based on BCI |
-
2015
- 2015-08-06 CN CN201520588369.9U patent/CN204833608U/en not_active Expired - Fee Related
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105336106A (en) * | 2015-12-04 | 2016-02-17 | 无锡市麦泰克新材料科技有限公司 | Fatigue driving alarming and prompting system arranged during automobile refitting |
| CN105678959A (en) * | 2016-02-25 | 2016-06-15 | 重庆邮电大学 | Monitoring and early-warning method and system for fatigue driving |
| CN105678959B (en) * | 2016-02-25 | 2018-06-15 | 重庆邮电大学 | A kind of fatigue driving monitoring method for early warning and system |
| CN107049306A (en) * | 2017-03-31 | 2017-08-18 | 王晓路 | A kind of detection treatment system based on meditation degree |
| CN109907755A (en) * | 2019-04-26 | 2019-06-21 | 上海理工大学 | A kind of fatigue driving monitoring and interfering system based on BCI |
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