CN105701973A - Fatigue detection and early warning method based on brain wave acquisition and system thereof - Google Patents
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Abstract
The invention discloses a fatigue detection and early warning method based on brain wave acquisition and a system thereof. The method comprises an indicator acquisition step and a monitoring and early warning step. In the indicator acquisition phase, correlation judgment is performed on brain wave and blink characteristic values so that robustness of latter data detection can be enhanced. According to the analysis result, slow alpha% of power percentage indicators and slow alpha/beta and theta/slow alpha of power ratio indicators act as auxiliary examination indicators in the sleepy state in the monitoring and early warning step, and (theta+slow alpha)/beta of power adding ratio indicators and slow alpha/beta of the power ratio indicators act as the auxiliary examination indicators in the sleep state in the monitoring and early warning step so that different modes of detection can be performed on the fatigue of different levels, and different modes of early warning can be performed on the fatigue of different levels.
Description
Technical field
The present invention relates to a kind of fatigue detecting based on acquiring brain waves and method for early warning and system。
Background technology
Along with developing rapidly of Modern Traffic transport service, vehicle accident has become the serious problems that Present Global is encountered, and how effectively research prevention and monitoring driver tired driving state have important realistic meaning。Fatigue driving refers to drives vehicle for a long time continuously due to driver, produces the imbalance of physiological function and psychological function, and in the phenomenon objectively occurring that driving efficiency declines。From the angle of medical science, some physiological feature, the change such as physiological signals such as nictation, yawn, heart beating, blood pressure, brain waves can reflect the degree that controller is tired。
Brain wave (Electroencephalogram, EEG) be brain when activity, the postsynaptic potential that a large amount of neurons synchronize to occur is formed after summation。Electric wave change during its record cerebral activity, is overall anti-in cerebral cortex or scalp surface of the bioelectrical activity of cranial nerve cell。Brain wave derives from the postsynaptic potential of pyramidal cell top dendron。The formation of the synchronization of brain wave rhythm and pace of moving things is also relevant with the activity of cortex thalamic nonspecific projection system。Brain wave is that some spontaneous rhythmic neuroelectricities are movable, its frequency variation scope is between 1-30 time per second, four wave bands can be divided into, namely δ (1-3Hz), θ (4-7Hz), α (8-13Hz), β (14-30Hz)。In addition, when awakening and being absorbed in a certain thing, the γ ripple that a kind of frequency of Chang Kejian is higher compared with β ripple, its frequency is 30～80Hz, and wave amplitude scope is indefinite；And it may also occur that, when sleep, the normal brain activity electric wave that other waveforms are comparatively special, such as hump ripple, σ ripple, λ ripple, κ-complex wave, μ ripple etc.。Prior art detects, but without based on brain wave, the method and apparatus whether driver is in fatigue state, or the reliability for detecting the method whether driver is in fatigue state is poor。
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, it is provided that a kind of fatigue detecting based on acquiring brain waves and method for early warning and system；By brain wave acquisition sensor acquisition EEG signals, then it is carried out Wavelet Denoising Method process, remove brain electricity artefact and high-frequency noise, finally by Treatment Analysis EEG signals, thus providing the degree of fatigue of controller。For the degree of fatigue data obtained, adopt interface circuit, drive the response of a series of ancillary equipment such as seat masseur, warning signal, warning lamp, remote notification。
It is an object of the invention to be achieved through the following technical solutions: based on fatigue detecting and the method for early warning of acquiring brain waves, including index selection step and monitoring and warning step, described index selection step includes following sub-step:
S11: by sensor measurement brain wave, carries out omnidistance real-time recording with high-definition camera to measured's face feature simultaneously, obtains measured's brain wave data under different fatigue state and data of blinking respectively；
S12: brain wave data is carried out denoising Processing, then carry out fast fourier transform the brain wave data based on time domain is converted into frequency domain data；
S13: extract brain wave data segment under sample different fatigue state successively, and corresponding Video Document is arranged, extract eigenvalue nictation including number of winks and closed-eye time；
S14: analyze the variation characteristic of different-waveband under different fatigue state, add and ratio including performance number, power percentage, power ratio, power；Desired value each under different fatigue state is carried out ANOVA Variability Analysis, extracts diversity and reach the index of significance level, and analyze the dependency of these indexs and nictation；
S15: the F assay of brain wave index and showing with the correlation analysis result of nictation: the slow α % in power percentage index and the slow α/β in power ratio index are high with the significance of difference level of θ/slow α these three index, with the correlation coefficient maximum absolute value of nictation under doze state；Simultaneously power add with (θ+slow α)/β in ratio index and the slow α/β the two index in power ratio index under hypnagogic state the most obvious with the dependency of nictation；Therefore using the slow α % in power percentage index, the slow α/β in power ratio index and θ/slow α these three index as adjunct test index during doze state in monitoring and warning step, power adds with (θ+slow the α)/β in ratio index and the slow α/β in power ratio index as adjunct test index during hypnagogic state in monitoring and warning step, and obtains concrete threshold value according to analyzing result；
Described monitoring and warning step includes following sub-step:
S21: gather EEG signals by brain wave sensor；
S22: the EEG signals collected is carried out Wavelet Denoising Method process, removes brain electricity artefact and high-frequency noise；
S23: EEG signals after denoising is analyzed:
(1) threshold value obtained by analyzing the slow α % in the power percentage index obtained, the slow α/β in power ratio index and θ/slow α and step S15 is compared, judge whether user is in doze state, if being in doze state, then carry out first order feedback；
(2) add compare passing through to analyze the power that obtains with the step S15 threshold value obtained with (θ+slow α)/β in ratio index and the slow α/β in power ratio index, judge whether user is in hypnagogic state, if being in hypnagogic state, then carry out second level feedback。
Described first order feedback include driving seat masseur, warning signal, warning lamp, remote notification one or more；Described second level feedback include driving seat masseur, warning signal, warning lamp, remote notification one or more。
Based on fatigue detecting and the early warning system of acquiring brain waves, described system includes:
Brain wave sensor: for the brain wave data under each fatigue state of index selection phase acquisition and in the real-time brain wave data of monitoring and warning phase acquisition；
High-definition camera: obtain data nictation under different fatigue state for measured's face feature being carried out omnidistance real-time recording in the index selection stage；
Data preprocessing module: for brain wave data being carried out denoising Processing in the index selection stage, carry out again fast fourier transform the brain wave data based on time domain is converted into frequency domain data and the monitoring and warning stage obtain brain wave data carry out Wavelet Denoising Method process, remove brain electricity artefact and high-frequency noise；
Data extraction module: for extracting brain wave data segment under sample different fatigue state successively in the index selection stage, and corresponding Video Document is arranged, extracts eigenvalue nictation including number of winks and closed-eye time；
Data analysis module: for the variation characteristic of different-waveband under index selection phase analysis different fatigue state, add and ratio including performance number, power percentage, power ratio, power, desired value each under different fatigue state is carried out ANOVA Variability Analysis simultaneously, extract diversity and reach the index of significance level, and analyze the dependency of these indexs and nictation, and obtain threshold value；Also after the monitoring and warning stage is to denoising, EEG signals is analyzed, it is judged that whether user is in doze state or hypnagogic state；
Data outputting module: for sending control signal in the monitoring and warning stage to feedback module；
Feedback module: for carrying out feedback processing when user is in doze state or hypnagogic state in the monitoring and warning stage when judging。
Described brain wave sensor adopts brain wave detecting chip to realize, and described brain wave is detected chip, denoising module, data extraction module, data analysis module and data outputting module and is integrated on a mainboard。
Described feedback module includes seat massage device, warning signal, warning lamp, intelligent terminal。
The described fatigue detecting based on acquiring brain waves and early warning system also include a memory module, for storing the data of the threshold value of data analysis module, the data of brain wave sensor and high-definition camera。
The invention has the beneficial effects as follows:
(1) present invention is in the index selection stage, and with eigenvalue nictation, brain wave is carried out dependency judgement, improves the robustness of later data detection。
(1) present invention is according to analyzing result, using the slow α % in power percentage index, the slow α/β in power ratio index and θ/slow α these three index as adjunct test index during doze state in monitoring and warning step, power adds with (θ+slow the α)/β in ratio index and the slow α/β in power ratio index as adjunct test index during hypnagogic state in monitoring and warning step, the different grades of fatigue of user is carried out the detection of different modes, simultaneously can early warning in different ways to different grades of fatigue。
(3) brain wave data of user and data of blinking are analyzed and judgement by the present invention in advance, form the concrete threshold value of this user, and solution eyes of user is little and adopts current techique to cause data to process inaccurate problem, it is adaptable to personalized fatigue detecting；Monitoring and warning step after index selection need not adopt high-definition camera to detect again simultaneously, saves resource。
Accompanying drawing explanation
Fig. 1 is the inventive method flow chart；
Fig. 2 is present configuration block diagram。
Detailed description of the invention
Below in conjunction with accompanying drawing, technical scheme is described in further detail: as it is shown in figure 1, based on the fatigue detecting of acquiring brain waves and method for early warning, including index selection step and monitoring and warning step, described index selection step includes following sub-step:
S11: by sensor measurement brain wave, carries out omnidistance real-time recording with high-definition camera to measured's face feature simultaneously, obtains measured's brain wave data under different fatigue state and data of blinking respectively；
S12: brain wave data is carried out denoising Processing, then carry out fast fourier transform the brain wave data based on time domain is converted into frequency domain data；
S13: extract brain wave data segment under sample different fatigue state successively, and corresponding Video Document is arranged, extract eigenvalue nictation including number of winks and closed-eye time；
S14: analyze the variation characteristic of different-waveband under different fatigue state, add and ratio including performance number, power percentage, power ratio, power；Desired value each under different fatigue state is carried out ANOVA Variability Analysis, extracts diversity and reach the index of significance level, and analyze the dependency of these indexs and nictation；
S15: the F assay of brain wave index and showing with the correlation analysis result of nictation: the slow α % in power percentage index and the slow α/β in power ratio index are high with the significance of difference level of θ/slow α these three index, with the correlation coefficient maximum absolute value of nictation under doze state；Simultaneously power add with (θ+slow α)/β in ratio index and the slow α/β the two index in power ratio index under hypnagogic state the most obvious with the dependency of nictation；Therefore using the slow α % in power percentage index, the slow α/β in power ratio index and θ/slow α these three index as adjunct test index during doze state in monitoring and warning step, power adds with (θ+slow the α)/β in ratio index and the slow α/β in power ratio index as adjunct test index during hypnagogic state in monitoring and warning step, and obtains concrete threshold value according to analyzing result；
Described monitoring and warning step includes following sub-step:
S21: gather EEG signals by brain wave sensor；
S22: the EEG signals collected is carried out Wavelet Denoising Method process, removes brain electricity artefact and high-frequency noise；
S23: EEG signals after denoising is analyzed:
(1) threshold value obtained by analyzing the slow α % in the power percentage index obtained, the slow α/β in power ratio index and θ/slow α and step S15 is compared, judge whether user is in doze state, if being in doze state, then carry out first order feedback；
(2) add compare passing through to analyze the power that obtains with the step S15 threshold value obtained with (θ+slow α)/β in ratio index and the slow α/β in power ratio index, judge whether user is in hypnagogic state, if being in hypnagogic state, then carry out second level feedback。
Described first order feedback include driving seat masseur, warning signal, warning lamp, remote notification one or more；Described second level feedback include driving seat masseur, warning signal, warning lamp, remote notification one or more。
As in figure 2 it is shown, based on the fatigue detecting of acquiring brain waves and early warning system, described system includes:
Brain wave sensor: for the brain wave data under each fatigue state of index selection phase acquisition and in the real-time brain wave data of monitoring and warning phase acquisition；
High-definition camera: obtain data nictation under different fatigue state for measured's face feature being carried out omnidistance real-time recording in the index selection stage；
Data preprocessing module: for brain wave data being carried out denoising Processing in the index selection stage, carry out again fast fourier transform the brain wave data based on time domain is converted into frequency domain data and the monitoring and warning stage obtain brain wave data carry out Wavelet Denoising Method process, remove brain electricity artefact and high-frequency noise；
Data extraction module: for extracting brain wave data segment under sample different fatigue state successively in the index selection stage, and corresponding Video Document is arranged, extracts eigenvalue nictation including number of winks and closed-eye time；
Data analysis module: for the variation characteristic of different-waveband under index selection phase analysis different fatigue state, add and ratio including performance number, power percentage, power ratio, power, desired value each under different fatigue state is carried out ANOVA Variability Analysis simultaneously, extract diversity and reach the index of significance level, and analyze the dependency of these indexs and nictation, and obtain threshold value；Also after the monitoring and warning stage is to denoising, EEG signals is analyzed, it is judged that whether user is in doze state or hypnagogic state；
Data outputting module: for sending control signal in the monitoring and warning stage to feedback module；
Feedback module: for carrying out feedback processing when user is in doze state or hypnagogic state in the monitoring and warning stage when judging。
Described brain wave sensor adopts brain wave detecting chip to realize, and described brain wave is detected chip, denoising module, data extraction module, data analysis module and data outputting module and is integrated on a mainboard。
Described feedback module includes seat massage device, warning signal, warning lamp, intelligent terminal。
The described fatigue detecting based on acquiring brain waves and early warning system also include a memory module, for storing the data of the threshold value of data analysis module, the data of brain wave sensor and high-definition camera。
Claims (6)
1. based on the fatigue detecting of acquiring brain waves and method for early warning, it is characterised in that: including index selection step and monitoring and warning step, described index selection step includes following sub-step:
S11: by sensor measurement brain wave, carries out omnidistance real-time recording with high-definition camera to measured's face feature simultaneously, obtains measured's brain wave data under different fatigue state and data of blinking respectively；
S12: brain wave data is carried out denoising Processing, then carry out fast fourier transform the brain wave data based on time domain is converted into frequency domain data；
S13: extract brain wave data segment under sample different fatigue state successively, and corresponding Video Document is arranged, extract eigenvalue nictation including number of winks and closed-eye time；
S14: analyze the variation characteristic of different-waveband under different fatigue state, add and ratio including performance number, power percentage, power ratio, power；Desired value each under different fatigue state is carried out ANOVA Variability Analysis, extracts diversity and reach the index of significance level, and analyze the dependency of these indexs and nictation；
S15: the F assay of brain wave index and showing with the correlation analysis result of nictation: the slow α % in power percentage index and the slow α/β in power ratio index are high with the significance of difference level of θ/slow α these three index, with the correlation coefficient maximum absolute value of nictation under doze state；Simultaneously power add with (θ+slow α)/β in ratio index and the slow α/β the two index in power ratio index under hypnagogic state the most obvious with the dependency of nictation；Therefore using the slow α % in power percentage index, the slow α/β in power ratio index and θ/slow α these three index as adjunct test index during doze state in monitoring and warning step, power adds with (θ+slow the α)/β in ratio index and the slow α/β in power ratio index as adjunct test index during hypnagogic state in monitoring and warning step, and obtains concrete threshold value according to analyzing result；
Described monitoring and warning step includes following sub-step:
S21: gather EEG signals by brain wave sensor；
S22: the EEG signals collected is carried out Wavelet Denoising Method process, removes brain electricity artefact and high-frequency noise；
S23: EEG signals after denoising is analyzed:
(1) threshold value obtained by analyzing the slow α % in the power percentage index obtained, the slow α/β in power ratio index and θ/slow α and step S15 is compared, judge whether user is in doze state, if being in doze state, then carry out first order feedback；
(2) add compare passing through to analyze the power that obtains with the step S15 threshold value obtained with (θ+slow α)/β in ratio index and the slow α/β in power ratio index, judge whether user is in hypnagogic state, if being in hypnagogic state, then carry out second level feedback。
2. the fatigue detecting based on acquiring brain waves according to claim 1 and method for early warning, it is characterised in that: described first order feedback include driving seat masseur, warning signal, warning lamp, remote notification one or more；Described second level feedback include driving seat masseur, warning signal, warning lamp, remote notification one or more。
3. based on the fatigue detecting of acquiring brain waves and early warning system, it is characterised in that: described system includes:
Brain wave sensor: for the brain wave data under each fatigue state of index selection phase acquisition and in the real-time brain wave data of monitoring and warning phase acquisition；
High-definition camera: obtain data nictation under different fatigue state for measured's face feature being carried out omnidistance real-time recording in the index selection stage；
Data preprocessing module: for brain wave data being carried out denoising Processing in the index selection stage, carry out again fast fourier transform the brain wave data based on time domain is converted into frequency domain data and the monitoring and warning stage obtain brain wave data carry out Wavelet Denoising Method process, remove brain electricity artefact and high-frequency noise；
Data extraction module: for extracting brain wave data segment under sample different fatigue state successively in the index selection stage, and corresponding Video Document is arranged, extracts eigenvalue nictation including number of winks and closed-eye time；
Data analysis module: for the variation characteristic of different-waveband under index selection phase analysis different fatigue state, add and ratio including performance number, power percentage, power ratio, power, desired value each under different fatigue state is carried out ANOVA Variability Analysis simultaneously, extract diversity and reach the index of significance level, and analyze the dependency of these indexs and nictation, and obtain threshold value；Also after the monitoring and warning stage is to denoising, EEG signals is analyzed, it is judged that whether user is in doze state or hypnagogic state；
Data outputting module: for sending control signal in the monitoring and warning stage to feedback module；
Feedback module: for carrying out feedback processing when user is in doze state or hypnagogic state in the monitoring and warning stage when judging。
4. the fatigue detecting based on acquiring brain waves according to claim 3 and early warning system, it is characterized in that: described brain wave sensor adopts brain wave detecting chip to realize, and described brain wave is detected chip, denoising module, data extraction module, data analysis module and data outputting module and is integrated on a mainboard。
5. the fatigue detecting based on acquiring brain waves according to claim 3 and early warning system, it is characterised in that: described feedback module includes seat massage device, warning signal, warning lamp, intelligent terminal。
6. the fatigue detecting based on acquiring brain waves according to any one in claim 3 ~ 5 and early warning system, it is characterized in that: also include a memory module, for storing the data of the threshold value of data analysis module, the data of brain wave sensor and high-definition camera。
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| CN106580350A (en) * | 2016-12-07 | 2017-04-26 | 中国民用航空总局第二研究所 | Fatigue condition monitoring method and device |
| CN106580351A (en) * | 2016-12-07 | 2017-04-26 | 中国民用航空总局第二研究所 | Fatigue condition monitoring method and apparatus |
| CN106691443A (en) * | 2017-01-11 | 2017-05-24 | 中国科学技术大学 | Electroencephalogram-based wearable anti-fatigue intelligent monitoring and pre-warning system for driver |
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| CN109147278A (en) * | 2018-09-29 | 2019-01-04 | 浙江大学 | A kind of tired driver driving monitoring device based on brain-computer interface |
| CN109697831A (en) * | 2019-02-25 | 2019-04-30 | 湖北亿咖通科技有限公司 | Fatigue driving monitoring method, device and computer readable storage medium |
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