CN114847951A - Fatigue state wireless monitoring method, system, computer and readable storage medium - Google Patents

Fatigue state wireless monitoring method, system, computer and readable storage medium Download PDF

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CN114847951A
CN114847951A CN202210391316.2A CN202210391316A CN114847951A CN 114847951 A CN114847951 A CN 114847951A CN 202210391316 A CN202210391316 A CN 202210391316A CN 114847951 A CN114847951 A CN 114847951A
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brain wave
current driver
compressed
fatigue state
electroencephalogram
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辛增念
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Jiangxi University of Technology
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Jiangxi University of Technology
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0006—ECG or EEG signals
    • 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]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • H04W28/065—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information using assembly or disassembly of packets
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention provides a wireless monitoring method, a wireless monitoring system, a computer and a readable storage medium for fatigue states, wherein the method comprises the following steps: compressing and sampling the original human body brain wave signal of the current driver at preset time intervals by an EEG data acquisition instrument to generate corresponding compressed EEG data; receiving compressed electroencephalogram data wirelessly transmitted by an EEG data acquisition instrument, and reconstructing the compressed electroencephalogram data through a preset first algorithm to recover human brain wave signals in the compressed electroencephalogram data; and judging whether the current driver is in a fatigue state according to the proportion of the theta wave, the alpha wave and the beta wave in the recovered human brain wave signals. By the method, the original human brain wave signals collected by the EEG data collector can be compressed, so that the burden of sampling and data transmission for detection equipment can be reduced, and redundant detection data are avoided.

Description

Fatigue state wireless monitoring method, system, computer and readable storage medium
Technical Field
The invention relates to the technical field of data processing, in particular to a fatigue state wireless monitoring method, a fatigue state wireless monitoring system, a computer and a readable storage medium.
Background
With the progress of science and technology and the rapid development of productivity, automobiles are popularized in daily life of people, become one of indispensable transportation tools for people to go out, and bring great convenience to the life and work of people.
Among them, fatigue driving is one of the important factors causing traffic accidents, next to speeding, and fatigue driving is particularly prevalent among the professional driver population, particularly among long-distance passenger transport and logistics drivers. In the process of long-distance driving, due to the fact that the sitting posture and the action of a driver are fixed and repeated for a long time, the physiological function and the psychological state of the driver can slowly change, the driver is distracted, dozed, narrow in visual field, overlooked in information and slow in response judgment, misoperation of the driver occurs or the driving ability is completely lost, and therefore traffic accidents occur.
The fatigue state wireless monitoring method provided by the prior art can generate a large amount of redundant detection data in the actual detection process, so that the load of detection equipment can be greatly increased, the system breakdown phenomenon is easy to occur, and certain hidden dangers exist.
Disclosure of Invention
Based on this, the present invention provides a method, a system, a computer and a readable storage medium for wireless monitoring of fatigue state, so as to solve the problem that the load of the detection device is greatly increased due to a large amount of redundant detection data generated in the actual detection process in the prior art.
The first aspect of the embodiments of the present invention provides a method for wirelessly monitoring a fatigue state, where the method includes:
compressing and sampling original human brain wave signals of a current driver at preset time intervals by an EEG data acquisition instrument to generate corresponding compressed EEG data, wherein the original human brain wave signals comprise theta waves, alpha waves and beta waves;
receiving compressed electroencephalogram data wirelessly transmitted by the EEG data acquisition instrument, and reconstructing the compressed electroencephalogram data through a preset first algorithm to recover human brain wave signals in the compressed electroencephalogram data;
and judging whether the current driver is in a fatigue state according to the proportion of theta waves, alpha waves and beta waves in the recovered human brain wave signals.
The invention has the beneficial effects that: acquiring an original human body brain wave signal of a current driver at preset time intervals by an EEG data acquisition instrument, and further compressing the acquired original human body brain wave signal by the EEG data acquisition instrument to generate corresponding compressed brain wave data, wherein the original human body brain wave signal comprises theta waves, alpha waves and beta waves; then, compressed electroencephalogram data wirelessly transmitted by an EEG data acquisition instrument is received in real time, and the compressed electroencephalogram data is reconstructed through a preset first algorithm so as to recover human brain wave signals in the compressed electroencephalogram data; and finally, judging whether the current driver is in a fatigue state according to the proportion of theta waves, alpha waves and beta waves in the recovered human brain wave signals. By the method, original human brain wave signals collected by the EEG data acquisition instrument can be effectively compressed, so that sampling and data transmission can be effectively reduced, burden caused by detection equipment is reduced, redundant detection data can be effectively avoided, detection efficiency is correspondingly improved, and the EEG data acquisition instrument is suitable for large-scale popularization and use.
Preferably, the step of compressing and sampling the original human body brain wave signal of the current driver at preset time intervals by the EEG data acquisition instrument to generate the corresponding compressed brain wave data comprises:
when the driving time of the current driver is greater than a first threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a first preset time;
when the driving time of the current driver is greater than a second threshold value, acquiring the original human body brain wave signal of the current driver at intervals of second preset time;
when the driving time of the current driver is greater than a third threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a third preset time;
wherein the third threshold is greater than the second threshold, which is greater than the first threshold.
Preferably, the step of compressing and sampling the original human body brain wave signal of the current driver by the EEG data acquisition instrument every preset time to generate the corresponding compressed brain wave data further comprises:
identifying M-dimensional electroencephalogram signals x in the original human brain wave signals through the EEG data acquisition instrument M×1 And the M-dimensional electroencephalogram signal x is converted into the M-dimensional electroencephalogram signal x M×1 Performing sparse processing on the discrete cosine basis to generate a corresponding M-dimensional sparse vector alpha M×1 ；
By presetting a Hadamard matrix H N×M The M-dimensional sparse vector alpha is processed M×1 Dimension reduction into an N-dimensional vector y N×1 And completing the compression sampling of the original human body brain wave signals, wherein N is less than M.
Preferably, the step of receiving the compressed electroencephalogram data wirelessly transmitted by the EEG data acquisition instrument and reconstructing the compressed electroencephalogram data by a preset first algorithm to recover the human brain wave signals in the compressed electroencephalogram data comprises:
establishing a Bluetooth communication connection with the EEG data acquisition instrument so as to receive compressed EEG data wirelessly transmitted by the EEG data acquisition instrument;
reconstructing the compressed electroencephalogram data based on a preset basis tracking reconstruction algorithm to obtain a corresponding coefficient vector through calculation;
restoring the human brain wave signals in the compressed electroencephalogram data according to a preset first formula and the coefficient vector;
and sequentially filtering, interference removing and drifting the restored human brain wave signals.
Preferably, after the step of determining whether the current driver is in a fatigue state according to a ratio of θ waves, α waves, and β waves in the restored human brain wave signals, the method further includes:
and if the current driver is judged to be in the fatigue state, controlling a loudspeaker to emit a sound warning, and displaying a picture warning on an instrument panel.
A second aspect of the embodiments of the present invention provides a fatigue state detection system, including:
the compression sampling module is used for compressing and sampling original human brain wave signals of a current driver at preset time intervals through an EEG data acquisition instrument so as to generate corresponding compressed brain wave data, wherein the original human brain wave signals comprise theta waves, alpha waves and beta waves;
the recovery module is used for receiving compressed electroencephalogram data wirelessly transmitted by the EEG data acquisition instrument and reconstructing the compressed electroencephalogram data through a preset first algorithm so as to recover human brain wave signals in the compressed electroencephalogram data;
and the judging module is used for judging whether the current driver is in a fatigue state according to the proportion of theta waves, alpha waves and beta waves in the restored human brain wave signals.
In the above fatigue state detection system, the compression sampling module is specifically configured to:
when the driving time of the current driver is greater than a first threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a first preset time;
when the driving time of the current driver is greater than a second threshold value, acquiring the original human body brain wave signal of the current driver at intervals of second preset time;
when the driving time of the current driver is greater than a third threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a third preset time;
wherein the third threshold is greater than the second threshold, which is greater than the first threshold.
In the above fatigue state detection system, the compression sampling module is further specifically configured to:
identifying M-dimensional electroencephalogram signals x in the original human brain wave signals through the EEG data acquisition instrument M×1 And the M-dimensional electroencephalogram signal x is converted into the M-dimensional electroencephalogram signal x M×1 Performing sparse processing on the discrete cosine basis to generate a corresponding M-dimensional sparse vector alpha M×1 ；
By presetting a Hadamard matrix H N×M The M-dimensional sparse vector alpha is processed M×1 Dimension reduction into an N-dimensional vector y N×1 To perform compressive sampling of the original human brain wave signal, whichIn the formula, N is less than M.
In the above fatigue state detection system, the recovery module is specifically configured to:
establishing a Bluetooth communication connection with the EEG data acquisition instrument so as to receive compressed EEG data wirelessly transmitted by the EEG data acquisition instrument;
reconstructing the compressed electroencephalogram data based on a preset basis tracking reconstruction algorithm to obtain a corresponding coefficient vector through calculation;
restoring the human brain wave signals in the compressed electroencephalogram data according to a preset first formula and the coefficient vector;
and sequentially filtering, interference removing and drifting the restored human brain wave signals.
In the above fatigue state detection system, the fatigue state detection system further includes an alarm module, and the alarm module is specifically configured to:
and if the current driver is judged to be in the fatigue state, controlling a loudspeaker to emit a sound warning, and displaying a picture warning on an instrument panel.
A third aspect of the embodiments of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the wireless fatigue state monitoring method as described above.
A fourth aspect of the embodiments of the present invention provides a readable storage medium, on which a computer program is stored, which when executed by a processor, implements the wireless fatigue state monitoring method as described above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a flowchart of a fatigue state wireless monitoring method according to a first embodiment of the present invention;
fig. 2 is a block diagram of a fatigue state detection system according to a third embodiment of the present invention.
The following detailed description will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Several embodiments of the invention are presented in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The fatigue state wireless monitoring method provided by the prior art can generate a large amount of redundant detection data in the actual detection process, so that the load of detection equipment can be greatly increased, the system breakdown phenomenon is easy to occur, and certain hidden dangers exist.
Referring to fig. 1, a wireless fatigue state monitoring method according to a first embodiment of the present invention is shown, and the wireless fatigue state monitoring method according to the first embodiment of the present invention can effectively compress original human brain wave signals collected by an EEG data collector, so as to effectively reduce the burden of sampling and data transmission to a detection device, effectively avoid redundant detection data, correspondingly improve detection efficiency, and is suitable for wide popularization and use.
Specifically, the fatigue state wireless monitoring method provided in this embodiment specifically includes the following steps:
step S10, compressing and sampling the original human brain wave signal of the current driver by an EEG data acquisition instrument at preset time intervals to generate corresponding compressed brain wave data, wherein the original human brain wave signal comprises theta wave, alpha wave and beta wave;
specifically, in this embodiment, it should be noted that the fatigue state wireless monitoring method provided in this embodiment is specifically applied between an EEG data collector and a vehicle-mounted terminal, where the vehicle-mounted terminal is essentially a vehicle-mounted controller, and the vehicle-mounted controller can control software and hardware of a vehicle body. In the actual monitoring process, the EEG data acquisition instrument is worn on the head of a driver and is used for acquiring the EEG signals of the scalp surface of the driver in real time.
Specifically, in this step, the EEG data acquisition instrument is worn on the head of the driver, and original human brain wave signals generated by the driver in real time are compressed and sampled every preset time by the EEG data acquisition instrument, specifically, the original human brain wave signals include delta waves, theta waves, alpha waves, beta waves and gamma waves, wherein the frequency range of the delta waves is below 3Hz, and the amplitude is 20-200 uA; the theta wave frequency range is 4-7 Hz, and the wave amplitude is 20-40 uV; the frequency range of the alpha wave is 8-13 Hz, and the wave amplitude is 25-75 uV; the beta wave frequency range is 14-30 Hz, and the wave amplitude is 20-100 uV; the gamma wave frequency range is more than 30 Hz.
In addition, the EEG data acquisition apparatus provided in this embodiment performs compression processing to reduce the amount of data processing while acquiring the original human brain wave signals.
Step S20, compressed electroencephalogram data wirelessly transmitted by the EEG data acquisition instrument are received, and the compressed electroencephalogram data are subjected to reconstruction processing through a preset first algorithm so as to recover human brain wave signals in the compressed electroencephalogram data;
specifically, in this step, it should be noted that the EEG data collector provided in this embodiment can establish a wireless communication connection with the vehicle-mounted terminal, and preferably, in this embodiment, a bluetooth wireless communication connection between the EEG data collector and the vehicle-mounted terminal is established.
On the basis, the vehicle-mounted terminal provided by this embodiment receives the compressed electroencephalogram data transmitted by the EEG data acquisition instrument in real time, and immediately reconstructs the compressed electroencephalogram data through a basis tracking reconstruction algorithm preset in the vehicle-mounted terminal, so as to recover the human brain wave signals in the compressed electroencephalogram data, specifically, to recover the θ wave, the α wave, and the β wave signals in the human brain wave signals.
And step S30, determining whether the current driver is in a fatigue state according to the ratio of the θ wave, the α wave and the β wave in the restored human brain wave signal.
Finally, in this step, it should be noted that, based on the common general knowledge in the art, when a person enters a fatigue state, the occupancy of the low-frequency wave significantly increases, and correspondingly, the occupancy of the high-frequency wave significantly decreases. Therefore, in this step, the in-vehicle terminal determines whether the current driver is in a fatigue state in real time from the ratios of the θ wave, the α wave, and the β wave in the restored human brain wave signal. That is, when the vehicle-mounted terminal detects that the occupation ratio of theta waves and alpha waves in the restored human brain wave signals is remarkably improved, and the occupation ratio of beta waves is remarkably reduced, the current driver can be accurately judged to be in a fatigue state.
When the driver electroencephalograph is used, an EEG data acquisition instrument acquires original human body electroencephalogram signals of a current driver at preset time intervals, and further, the EEG data acquisition instrument compresses the acquired original human body electroencephalogram signals to generate corresponding compressed electroencephalogram data, wherein the original human body electroencephalogram signals comprise theta waves, alpha waves and beta waves; then, compressed electroencephalogram data wirelessly transmitted by an EEG data acquisition instrument is received in real time, and the compressed electroencephalogram data is reconstructed through a preset first algorithm so as to recover human brain wave signals in the compressed electroencephalogram data; and finally, judging whether the current driver is in a fatigue state according to the proportion of theta waves, alpha waves and beta waves in the recovered human brain wave signals. By the method, original human brain wave signals collected by the EEG data acquisition instrument can be effectively compressed, so that sampling and data transmission can be effectively reduced, burden caused by detection equipment is reduced, redundant detection data can be effectively avoided, detection efficiency is correspondingly improved, and the EEG data acquisition instrument is suitable for large-scale popularization and use.
It should be noted that the above implementation process is only for illustrating the applicability of the present application, but this does not represent that the fatigue status wireless monitoring method of the present application has only the above implementation procedure, and on the contrary, the fatigue status wireless monitoring method of the present application can be incorporated into the feasible embodiments of the present application as long as the method can be implemented.
In summary, the wireless monitoring method for the fatigue state provided by the embodiments of the present invention can effectively compress the original human brain wave signals collected by the EEG data collector, so as to effectively reduce the burden of sampling and data transmission to the detection device, effectively avoid generating redundant detection data, correspondingly improve the detection efficiency, and is suitable for wide popularization and use.
The second embodiment of the present invention also provides a method for wirelessly monitoring a fatigue state, where the method for adjusting the sound effect of a broadcast speaker provided in this embodiment further includes:
in the same way, it should be noted that the fatigue state wireless monitoring method provided in this embodiment is specifically applied between an EEG data acquisition instrument and a vehicle-mounted terminal, where the essence of the vehicle-mounted terminal is a vehicle-mounted controller, and the vehicle-mounted controller can control software and hardware of a vehicle body. In the actual monitoring process, the EEG data acquisition instrument is worn on the head of a driver and is used for acquiring the EEG signals of the scalp surface of the driver in real time.
Step S11, when the driving time of the current driver is greater than a first threshold value, collecting the original human body brain wave signal of the current driver at intervals of a first preset time;
when the driving time of the current driver is greater than a second threshold value, acquiring the original human body brain wave signal of the current driver at intervals of second preset time;
when the driving time of the current driver is greater than a third threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a third preset time;
wherein the third threshold is greater than the second threshold, which is greater than the first threshold.
Specifically, in this embodiment, it should be noted that, when a driver starts a vehicle and starts driving, the probability that the driver is in a fatigue state at present is very low, and therefore, in order to effectively reduce the energy consumption of the EEG data acquisition instrument and reduce the workload of the EEG data acquisition instrument, in this embodiment, a plurality of time thresholds and corresponding interval acquisition times are set in advance inside the EEG data acquisition instrument.
For example, when a driver has just started driving a vehicle, the current EEG data collector samples every 10 minutes; when the driving time of a driver is longer than 30 minutes, the current EEG data acquisition instrument performs sampling once every 5 minutes; when the driving time of a driver is longer than 60 minutes, the current EEG data acquisition instrument performs sampling every 1 minute; when the driver's driving time is longer than 90 minutes, the current EEG data collector samples every 30 seconds.
Step S21, recognizing M-dimensional electroencephalogram signals x in the original human brain wave signals through the EEG data acquisition instrument M×1 And the M-dimensional electroencephalogram signal x is converted into the M-dimensional electroencephalogram signal x M×1 Performing sparse processing on the discrete cosine basis to generate a corresponding M-dimensional sparse vector alpha M×1 (ii) a By presetting a Hadamard matrix H N×M The M-dimensional sparse vector alpha is processed M×1 Dimension reduction into an N-dimensional vector y N×1 And completing the compression sampling of the original human body brain wave signals, wherein N is less than M.
Specifically, in this step, the EEG data acquisition instrument can acquire M data at a time, that is, M-dimensional electroencephalogram signal x in the current original human brain wave signal M×1 Further, the current M dimension is setElectroencephalogram signal x M×1 Performing sparse processing on a discrete cosine basis, wherein the discrete cosine basis comprises a matrix D M×M 。
In particular, matrix D M×M Comprises the following steps:
Figure BDA0003597034680000091
further, the M-dimensional electroencephalogram signal x M×1 At the D M×M Carrying out sparse processing: x is the number of M×1 ＝D M×M ·α M×1 According to
Figure BDA0003597034680000092
Obtaining M-dimensional sparse vector alpha M×1 。
Further, by presetting the Hadamard matrix H N×M The M-dimensional sparse vector alpha is processed M×1 Dimension reduction into an N-dimensional vector y N×1 To complete the compression process of the original human brain wave signal, wherein, the Hadamard matrix H N×M Comprises the following steps:
Figure 1
wherein, the vector y is in N dimension N×1 ＝H N×M ·α M×1 Thereby enabling to apply M-dimensional sparse vectors alpha M×1 Dimension reduction into an N-dimensional vector y N×1 And completing the process of compressing the M-dimensional vector x into the N-dimensional vector y.
Step S31, establishing a Bluetooth communication connection with the EEG data acquisition instrument to receive compressed EEG data wirelessly transmitted by the EEG data acquisition instrument; reconstructing the compressed electroencephalogram data based on a preset basis tracking reconstruction algorithm to obtain a corresponding coefficient vector through calculation; restoring the human brain wave signals in the compressed electroencephalogram data according to a preset first formula and the coefficient vector; and sequentially filtering, interference removing and drifting the restored human brain wave signals.
Further, in this step, it should be noted that,after the EEG data acquisition instrument acquires the compressed EEG data, the EEG data acquisition instrument establishes Bluetooth communication connection with the vehicle-mounted terminal through a Bluetooth module in the EEG data acquisition instrument and transmits the compressed EEG data to the current vehicle-mounted terminal. Meanwhile, the current vehicle-mounted terminal reconstructs the compressed electroencephalogram data received in real time based on a basis tracking reconstruction algorithm preset in the current vehicle-mounted terminal, and concretely, the step can be realized through a formula: min y ||Hα-y|| 2 +λ||α|| 1 Reducing to obtain M-dimensional sparse vector alpha M×1 And further by the formula: x is the number of M×1 ＝D M×M ·α M×1 Restoring M-dimensional electroencephalogram signal x M×1 So as to restore the brain wave signal of the human body.
On the basis, the step can also carry out filtering, interference removing and drifting processing on the recovered human brain wave signals in sequence.
Step S41, judging whether the current driver is in a fatigue state according to the proportion of theta wave, alpha wave and beta wave in the restored human brain wave signal;
in addition, in this step, it is to be noted that, based on the conventional common knowledge, when a person enters a fatigue state, the occupancy of the low-frequency wave significantly increases, and correspondingly, the occupancy of the high-frequency wave significantly decreases. Therefore, in this step, the in-vehicle terminal determines whether the current driver is in a fatigue state in real time from the ratios of the θ wave, the α wave, and the β wave in the restored human brain wave signal. That is, when the vehicle-mounted terminal detects that the occupation ratio of theta waves and alpha waves in the restored human brain wave signals is remarkably improved, and the occupation ratio of beta waves is remarkably reduced, the current driver can be accurately judged to be in a fatigue state.
For example: when a driver just starts to drive the vehicle, the proportions of the theta wave, the alpha wave and the beta wave in the human body brain wave signal of the current driver are respectively 20%, 30% and 50%, after a period of driving time, for example, after driving for 90 minutes, the proportions of the theta wave, the alpha wave and the beta wave in the human body brain wave signal of the current driver are respectively 60%, 30% and 10%, so that the proportions of the theta wave and the alpha wave are obviously improved, the proportion of the beta wave is obviously reduced, and the driver in the current period can be accurately judged to be in a fatigue driving state.
In addition, in this embodiment, after the step of determining whether the current driver is in a fatigue state according to the proportion of the θ wave, the α wave, and the β wave in the restored human brain wave signal, the method further includes:
and step S51, if the current driver is in a fatigue state, controlling a loudspeaker to emit a sound warning, and displaying a picture warning on an instrument panel.
Finally, in this step, it should be noted that, when the vehicle-mounted terminal finally determines that the current driver is in a fatigue driving state, the current vehicle-mounted terminal immediately controls a speaker in the vehicle to emit a sound warning, and at the same time, displays a picture warning on an instrument panel to remind the current driver to find a rest place for rest.
It should be noted that the method provided by the second embodiment of the present invention, which implements the same principle and produces some technical effects as the first embodiment, can be referred to the first embodiment for providing corresponding contents for the sake of brief description, where this embodiment is not mentioned.
In summary, the wireless fatigue state monitoring method provided by the above embodiment of the present invention can effectively compress the original human brain wave signals collected by the EEG data collector, so as to effectively reduce the burden of sampling and data transmission to the detection device, effectively avoid generating redundant detection data, correspondingly improve the detection efficiency, and is suitable for wide popularization and use.
Referring to fig. 2, a fatigue state detecting system according to a third embodiment of the present invention is shown, the system includes:
the compression sampling module 12 is configured to compressively sample an original human brain wave signal of a current driver at preset time intervals by an EEG data acquisition instrument to generate corresponding compressed brain wave data, where the original human brain wave signal includes a θ wave, an α wave, and a β wave;
the recovery module 22 is configured to receive compressed electroencephalogram data wirelessly transmitted by the EEG data acquisition instrument, and perform reconstruction processing on the compressed electroencephalogram data through a preset first algorithm to recover a human brain wave signal in the compressed electroencephalogram data;
and the judging module 32 is configured to judge whether the current driver is in a fatigue state according to the proportion of the θ wave, the α wave and the β wave in the restored human brain wave signal.
In the above fatigue state detection system, the compression sampling module 12 is specifically configured to:
when the driving time of the current driver is greater than a first threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a first preset time;
when the driving time of the current driver is greater than a second threshold value, acquiring the original human body brain wave signal of the current driver at intervals of second preset time;
when the driving time of the current driver is greater than a third threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a third preset time;
wherein the third threshold is greater than the second threshold, which is greater than the first threshold.
In the above fatigue state detection system, the compression sampling module 12 is further specifically configured to:
identifying M-dimensional electroencephalogram signals x in the original human brain wave signals through the EEG data acquisition instrument M×1 And the M-dimensional electroencephalogram signal x is converted into the M-dimensional electroencephalogram signal x M×1 Performing sparse processing on the discrete cosine basis to generate a corresponding M-dimensional sparse vector alpha M×1 ；
By presetting a Hadamard matrix H N×M The M-dimensional sparse vector alpha is processed M×1 Dimension reduction into an N-dimensional vector y N×1 And completing the compression sampling of the original human body brain wave signals, wherein N is less than M.
In the above fatigue state detection system, the recovery module 22 is specifically configured to:
establishing a Bluetooth communication connection with the EEG data acquisition instrument so as to receive compressed EEG data wirelessly transmitted by the EEG data acquisition instrument;
reconstructing the compressed electroencephalogram data based on a preset basis tracking reconstruction algorithm to obtain a corresponding coefficient vector through calculation;
restoring the human brain wave signals in the compressed electroencephalogram data according to a preset first formula and the coefficient vector;
and sequentially filtering, interference removing and drifting the restored human brain wave signals.
In the above fatigue state detection system, the fatigue state detection system further includes an alarm module 42, where the alarm module 42 is specifically configured to:
and if the current driver is judged to be in the fatigue state, controlling a loudspeaker to emit a sound warning, and displaying a picture warning on an instrument panel.
A fourth embodiment of the present invention provides a computer, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the fatigue state wireless monitoring method provided in the first embodiment or the second embodiment.
A fifth embodiment of the present invention provides a readable storage medium, on which a computer program is stored, which when executed by a processor, implements the wireless fatigue state monitoring method provided in the first or second embodiment described above.
In summary, the wireless monitoring method, the wireless monitoring system, the wireless monitoring computer and the readable storage medium for the fatigue state provided by the embodiments of the present invention can effectively compress the original human brain wave signals collected by the EEG data collector, so as to effectively reduce the burden of sampling and data transmission to the detection device, effectively avoid redundant detection data, correspondingly improve the detection efficiency, and are suitable for wide popularization and use.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for wireless monitoring of a fatigue state, the method comprising:
compressing and sampling original human brain wave signals of a current driver at preset time intervals by an EEG data acquisition instrument to generate corresponding compressed EEG data, wherein the original human brain wave signals comprise theta waves, alpha waves and beta waves;
receiving compressed electroencephalogram data wirelessly transmitted by the EEG data acquisition instrument, and reconstructing the compressed electroencephalogram data through a preset first algorithm to recover human brain wave signals in the compressed electroencephalogram data;
and judging whether the current driver is in a fatigue state or not according to the proportion of theta waves, alpha waves and beta waves in the restored human brain wave signals.
2. The wireless fatigue state monitoring method of claim 1, wherein: the step of compressing and sampling the original human body brain wave signal of the current driver by an EEG data acquisition instrument at preset time intervals to generate corresponding compressed EEG data comprises the following steps:
when the driving time of the current driver is greater than a first threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a first preset time;
when the driving time of the current driver is greater than a second threshold value, acquiring the original human body brain wave signal of the current driver at intervals of second preset time;
when the driving time of the current driver is greater than a third threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a third preset time;
wherein the third threshold is greater than the second threshold, which is greater than the first threshold.
3. The wireless fatigue state monitoring method of claim 1, wherein: the step of compressing and sampling the original human body brain wave signal of the current driver by the EEG data acquisition instrument at preset time intervals to generate corresponding compressed EEG data further comprises:
identifying M-dimensional electroencephalogram signals x in the original human brain wave signals through the EEG data acquisition instrument M×1 And the M-dimensional electroencephalogram signal x is converted into the M-dimensional electroencephalogram signal x M×1 Performing sparse processing on the discrete cosine basis to generate a corresponding M-dimensional sparse vector alpha M×1 ；
By presetting a Hadamard matrix H N×M The M-dimensional sparse vector alpha is processed M×1 Dimension reduction into an N-dimensional vector y N×1 And completing the compression sampling of the original human body brain wave signals, wherein N is less than M.
4. The wireless fatigue state monitoring method of claim 1, wherein: the step of receiving compressed electroencephalogram data wirelessly transmitted by the EEG data acquisition instrument and reconstructing the compressed electroencephalogram data through a preset first algorithm to recover human brain wave signals in the compressed electroencephalogram data comprises the following steps:
establishing a Bluetooth communication connection with the EEG data acquisition instrument so as to receive compressed EEG data wirelessly transmitted by the EEG data acquisition instrument;
reconstructing the compressed electroencephalogram data based on a preset basis tracking reconstruction algorithm to obtain a corresponding coefficient vector through calculation;
restoring the human brain wave signals in the compressed electroencephalogram data according to a preset first formula and the coefficient vector;
and sequentially filtering, interference removing and drifting the restored human brain wave signals.
5. The wireless fatigue state monitoring method of claim 1, wherein: after the step of determining whether the current driver is in a fatigue state according to the proportion of the theta wave, the alpha wave and the beta wave in the restored human brain wave signal, the method further includes:
and if the current driver is judged to be in the fatigue state, controlling a loudspeaker to emit a sound warning, and displaying a picture warning on an instrument panel.
6. A fatigue state detection system, the system comprising:
the compression sampling module is used for compressing and sampling original human brain wave signals of a current driver at preset time intervals through an EEG data acquisition instrument so as to generate corresponding compressed brain wave data, wherein the original human brain wave signals comprise theta waves, alpha waves and beta waves;
the recovery module is used for receiving compressed electroencephalogram data wirelessly transmitted by the EEG data acquisition instrument and reconstructing the compressed electroencephalogram data through a preset first algorithm so as to recover human brain wave signals in the compressed electroencephalogram data;
and the judging module is used for judging whether the current driver is in a fatigue state according to the proportion of theta waves, alpha waves and beta waves in the restored human brain wave signals.
7. The fatigue state detection system of claim 6, wherein: the compression sampling module is specifically configured to:
when the driving time of the current driver is greater than a first threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a first preset time;
when the driving duration of the current driver is greater than a second threshold value, acquiring original human body brain wave signals of the current driver at intervals of a second preset time;
when the driving time of the current driver is greater than a third threshold value, acquiring the original human body brain wave signal of the current driver at intervals of a third preset time;
wherein the third threshold is greater than the second threshold, which is greater than the first threshold.
8. The fatigue state detection system of claim 6, wherein: the compressive sampling module is further specifically configured to:
identifying M-dimensional electroencephalogram signals x in the original human brain wave signals through the EEG data acquisition instrument N×1 And the M-dimensional electroencephalogram signal x is converted into the M-dimensional electroencephalogram signal M×1 Performing sparse processing on the discrete cosine basis to generate a corresponding M-dimensional sparse vector alpha M×1 ；
By presetting a Hadamard matrix H N×M The M-dimensional sparse vector alpha is processed M×1 Dimension reduction into an N-dimensional vector alpha N×1 And completing the compression sampling of the original human body brain wave signals, wherein N is less than M.
9. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the wireless fatigue state monitoring method according to any one of claims 1 to 5 when executing the computer program.
10. A readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the wireless fatigue state monitoring method according to any one of claims 1 to 5.
CN202210391316.2A 2022-04-14 2022-04-14 Fatigue state wireless monitoring method, system, computer and readable storage medium Pending CN114847951A (en)

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