CN106781283B - A kind of method for detecting fatigue driving based on soft set - Google Patents
A kind of method for detecting fatigue driving based on soft set Download PDFInfo
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- CN106781283B CN106781283B CN201611244964.6A CN201611244964A CN106781283B CN 106781283 B CN106781283 B CN 106781283B CN 201611244964 A CN201611244964 A CN 201611244964A CN 106781283 B CN106781283 B CN 106781283B
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2503/00—Evaluating a particular growth phase or type of persons or animals
- A61B2503/20—Workers
- A61B2503/22—Motor vehicles operators, e.g. drivers, pilots, captains
Abstract
The invention discloses a kind of method for detecting fatigue driving based on soft set, comprising the following steps: S1 acquires surface electromyogram signal of the default muscle sites in predetermined time of driver；S2 extracts and analyzes the muscular states feature of the surface electromyogram signal；S3 establishes the tired quantitative model based on soft set according to the electromyography signal extracted；Fatigue driving decision content is calculated according to the tired quantitative model based on soft set in S4.The present invention passes through acquisition driver's surface electromyogram signal, and after carrying out analysis pretreatment to surface electromyogram signal, analyze the abnormal data in signal, Analysis of Policy Making is carried out to the surface electromyogram signal of driver, the degree of fatigue of each period driver is obtained, so as to analyze the tired decrease speed of driver；It is realized when guaranteeing that key message retains, and is analyzed data, is simplified and decision, can substantially reduce the knowledge representation space dimensionality of object.To the selection unrestriction of parameter.
Description
Technical field
The present invention relates to a kind of method for detecting fatigue driving based on soft set, belong to intelligent transportation field.
Background technique
Fatigue is reduced as the alertness that a kind of common and complicated physiological phenomenon will result directly in driver, reaction is slow
It is slow, it is easy to cause traffic accident, very big threat is caused to the life security of driver.But tired and non-fatigue
Separation is difficult to determine, can be described as to a certain extent uncertain, varies with each individual.For this point, fatigue detecting
The problems such as identifying problem such as Face datection than other classical modes is more difficult.In addition to this, degree of fatigue on earth how
It is difficult to determine, so that fatigue state is difficult to classify and measure.
At present in the research of fatigue driving detection, be mainly all based on the variation of physiology signal under driving environment into
Capable, main purpose is measured to the physiological status such as awake, tired, sleepy of driver.Surface electromyogram signal is people
Body move when neuron-muscular activity generate bioelectrical signals, can reflect to a certain extent human body physiological activity state and
Functional status.But the difficult point of current fatigue-driving detection technology is: collected signal have it is some it is uncertain because
Element, and current theory (for example probability theory, blind number theory, fuzzy set theory, rough set theory, interval mathematical theory etc. are all
For handling probabilistic mathematical tool) in for determining that the tool of parameter is considerably less, cause quantity of parameters that can not determine, this
One problem is using these theoretical bottlenecks.
Summary of the invention
The object of the present invention is to provide a kind of method for detecting fatigue driving based on soft set, he can solve currently
The problem of technology, is realized in the case where guaranteeing that key message retains, and is analyzed data, is simplified and decision, energy
Enough substantially reduce the knowledge representation space dimensionality of object.To the selection unrestriction of parameter.
In order to solve the above technical problems, the present invention adopts the following technical scheme that: a kind of fatigue driving based on soft set
Detection method, comprising the following steps:
S1 acquires surface electromyogram signal of the default muscle sites in predetermined time of driver；
S2 extracts and analyzes the muscular states feature of the surface electromyogram signal；
S3 establishes the tired quantitative model based on soft set according to the electromyography signal extracted；
Fatigue driving decision content is calculated according to the tired quantitative model based on soft set in S4.
Preferably, in the step S1, the default muscle sites include neck Superior trapezius, shoulder deltoid muscle, back
Latissimus dorsi, lower limb rectus femoris, musculus vastus lateralis and gastrocnemius.
Preferably, in the step S1, using Wavelet noise-eliminating method and empirical mode decomposition Threshold Denoising Method to acquisition
To the surface electromyogram signal pre-processed, to remove during the acquisition of signal, pickup, conditioning because by the external world
Noise jamming and the invalid information that generates.
Preferably, the step S2 includes: using mean square root method, median frequency method or fuzzy approximation entropy algorithm to described
Surface electromyogram signal is analyzed, and the muscular states feature of surface electromyogram signal is obtained.
Preferably, the step S3 includes: the fatigue state for setting the first soft set (F, E) characterization driver, wherein opinion
Domain U is set of the driver in the muscular states feature of different moments, i.e. U={ h1,h2,h3,h4...h25, E is parameter set, ginseng
Number is the fuzzy approximation entropy of the surface electromyogram signal, and entropy algorithm statistical value is stablized, strong antijamming capability, to mixed signal
It is adaptable, and the variation of result curve and body state variation have preferable consistency.
It is furthermore preferred that the step S3 further include: each ginseng is respectively set according to the degree of correlation of parameter each in E and body state
Several weights obtains the second soft set (T, E) according to the weight of each parameter and first soft set.
It is furthermore preferred that calculating the degree of correlation of each parameter and body state in E using line fitting method.
It is furthermore preferred that the step S3 further include: determine the abnormal data in second soft set (T, E), and to institute
Abnormal data is stated to be handled.
It is furthermore preferred that determining the formula of the abnormal data in second soft set (T, E) are as follows:And enable the abnormal data hij=* obtains third soft set { T, E*, to avoid
Using some abnormal datas beyond normal range (NR), lead to the consequences such as incorrect decision or application error.
It is furthermore preferred that by third soft set { T, the E*In abnormal data use front and back two groups of data average value into
Row replacement, obtains the 4th soft set { G, E }, using the 4th soft set { G, E }, final decision content is obtained, thus using less
Data obtain desired replacement values, simplify algorithm.
It is furthermore preferred that in the step S4: the circular of the decision content are as follows: di=e1′+e2′+e3′+e4′+
e5′+e6', giving fatigue pre-warning threshold value is arranged according to the decision content, when decision content reaches the giving fatigue pre-warning threshold value, output alarm
Information, so as to the speed to analyze the fatigue decline of body, it is proposed that driver rests in time, can effectively avoid traffic thing
Therefore generation.
Compared with prior art, the present invention passes through acquisition six kinds of surface electromyogram signals of driver, and to surface electromyogram signal
After carrying out analysis pretreatment, using the abnormal data in soft set method analysis signal, to the surface electromyogram signal of driver into
Row Analysis of Policy Making obtains the degree of fatigue of each period driver, so as to analyze the tired decrease speed of driver.It is real
Show in the case where guaranteeing that key message retains, data are analyzed, are simplified and decision, knowing for object can be substantially reduced
Know expression of space dimension；To the selection unrestriction of parameter, i.e., the parameter that we detect fatigue driving can be arbitrary, and be embodied
It is not limited to a certain feature of object in form in the selection of parameter, such as six parameters that we select in the present invention, parameter value
For the value of a normalized of surface electromyogram signal, thus the means of detection can appropriate adjustment according to the actual situation, more
Multiplicity is practical.
Detailed description of the invention
Fig. 1 is flesh schematic diagram in surface measured by the embodiment of the present invention；
Fig. 2 is the straight line fitting figure of latissimus dorsi provided in an embodiment of the present invention, rectus femoris and the Superior trapezius degree of correlation,
Middle round expression latissimus dorsi, five-pointed star indicate that Superior trapezius, triangle indicate latissimus dorsi；
Fig. 3 is the flow diagram of the embodiment of the present invention；
Fig. 4 is human muscle's stress diagram in driving condition；
Fig. 5 is human muscle's stress analysis schematic diagram in driving condition.
The present invention is further illustrated with reference to the accompanying drawings and detailed description.
Specific embodiment
The embodiment of the present invention: a kind of method for detecting fatigue driving based on soft set, as shown in figure 3, including following step
It is rapid:
S1 acquires surface electromyogram signal of the default muscle sites in predetermined time of driver；Including neck Superior trapezius,
Shoulder deltoid muscle, back latissimus dorsi, lower limb rectus femoris, musculus vastus lateralis and gastrocnemius, and utilize Wavelet noise-eliminating method and Empirical Mode
State decomposition threshold denoising method pre-processes the collected surface electromyogram signal.In driving procedure, the head of people,
The weight of arm, neck and upper torso is born by back protuberance, ischium position and its muscle of attachment, such as Fig. 4
With shown in Fig. 5.Buttocks muscles and femoral region are located at below ischium, when driving, buttocks and thigh Surrounding muscles due to by
Static load pressure and deform, the nutrition input of the circulatory system and the exclusion of metabolin are affected, with the increasing of driving time
Add lactic acid to gather, lead to muscular fatigue and aches.In driving procedure, the movement of neck is the most frequent, the motility of neck and steady
It qualitatively maintains to complete mainly by muscle and articular ligament, neck joint mainly plays a supportive role, therefore carries out musculi colli
Detection is extremely necessary.Deltoid muscle is located at the junction of shoulder and upper limb, can make shoulder abduction, and front muscle fibre is received
Contracting can make shoulder joint anteflexion and omit medial rotation；Rear portion muscle fibers contract can be such that shoulder joint extendes back and slightly revolve outer.When driving, human body both arms
Outreach controls steering wheel, and the deltoid muscle moment is in running order, and the degree of fatigue of deltoid muscle has larger shadow to the state of driver
It rings, so deltoid muscle is one of the target site of sEMG acquisition.
The muscle that the present embodiment chooses six strong correlation positions altogether is used to collection surface electromyography signal, respectively on neck tiltedly
Quadratus, shoulder deltoid muscle, back latissimus dorsi, lower limb rectus femoris, musculus vastus lateralis and gastrocnemius, muscle distribution situation are as shown in Figure 1.
By the method for fuse information, an integrality of human body is assessed, the conclusion obtained in this way is more accurate reliable, it is not easy to by
External environment influences, strong robustness.Due to surface electromyogram signal be it is a kind of it is non-linear it is unstable, there is lot of complexity and nature
The signal of chaos.Denoising method based on wavelet decomposition can preferably keep the energy feature of original signal, can by threshold value setting
To eliminate certain High-frequency Interference and low-frequency disturbance, but still there are many burrs, illustrate that high-frequency noise has certain residual.Experience
Fixed noise is eliminated after mode decomposition filtering, signal is more obvious to high-frequency noise denoising effect, but EMD (Empirical
Mode Decomposition empirical mode decomposition) there are certain mode mixing phenomenons.Two methods use simultaneously, carry out pre-
Processing, while if use, there is filter effect to high and low frequency noise.It is exactly a kind of tradeoff, it is more preferable that effect is used in combination.
S2 extracts and analyzes the muscular states feature of the surface electromyogram signal, comprising: using mean square root method, middle position frequency
Rate method or fuzzy approximation entropy algorithm analyze the surface electromyogram signal, and the muscular states for obtaining surface electromyogram signal are special
Sign.
S3 establishes the tired quantitative model based on soft set according to the electromyography signal extracted, i.e. the first soft collection of setting
Closing the fatigue state of (F, E) characterization driver, wherein domain U is set of the driver in the muscular states feature of different moments,
That is U={ h1,h2,h3,h4...h25, h1-h25It is to represent different moments, E is parameter set, and parameter is the surface electromyogram signal
Fuzzy approximation entropy calculates the degree of correlation of each parameter and body state in E, each parameter and human body in E using line fitting method
The weight of each parameter is respectively set in the degree of correlation of state, and obtains according to the weight of each parameter and first soft set
Two soft sets (T, E), and determine the abnormal data in second soft set (T, E), determine second soft set (T, E)
In abnormal data formula are as follows:And enable the abnormal data hij=*, obtains third
Soft set { T, E*, by third soft set { T, the E*In abnormal data use front and back two groups of data average value replaced
It changes, i.e. *ij=(hi-1,j+hi+1,j)/2 obtain the 4th soft set { G, E }, finally soft according to the 4th using soft set decision-making technique
Gather { G, E }, obtains decision content.Abnormal data is a certain moment, and certain muscle signals have exceeded expected or acquisition data
Directly there is exception, is judged by certain method, handled as abnormal data.Determine that abnormal data is than heavier
The step of wanting, it can allow result more reliable accurate, be replaced when replacing abnormal data using the average value of the two groups of data in front and back
It changes, need to only less data be used to obtain desired replacement values, simplify algorithm.
Fatigue driving decision content, the decision content is calculated according to the tired quantitative model based on soft set in S4
Circular are as follows: di=e1′+e2′+e3′+e4′+e5′+e6', giving fatigue pre-warning threshold value is arranged according to the decision content, when
When decision content reaches the giving fatigue pre-warning threshold value, warning message is exported to driver.It realizes and is protecting by means of the present invention
It demonstrate,proves in the case that key message retains, data is analyzed, are simplified and decision, the knowledge representation that can substantially reduce object is empty
Between dimension；To the selection unrestriction of parameter, i.e., the parameter that we detect fatigue driving can be arbitrary, and be embodied in parameter
Selection is not limited to a certain feature of object in form, and such as six parameters that we select in the present invention, parameter value is surface flesh
The value of one normalized of electric signal, thus the means of detection can appropriate adjustment according to the actual situation, it is more various practical.
The present inventor has also done following work, demonstrates the present invention by another embodiment, specific as follows:
Measure six kinds of deltoid muscle, latissimus dorsi, Superior trapezius, rectus femoris, musculus vastus lateralis and gastrocnemius surface fleshes of human body
Electric signal, using the surface electromyogram signal information of soft set method analysis different moments, to analyze the degree of fatigue of driver.
The acquisition and processing of A1 surface electromyogram signal
(1) surface electromyogram signal acquisition: the physiological signal collection platform of science and technology production is sent out using saliva, 16 can be acquired simultaneously and lead
Physiological signal wirelessly sends and receivees signal.The muscle that six positions are chosen in this experiment altogether is used to collection surface
Electromyography signal, respectively neck Superior trapezius, shoulder deltoid muscle, back latissimus dorsi, lower limb rectus femoris, musculus vastus lateralis and sura
Flesh, muscle distribution situation are as shown in Figure 1.Since the quantity of human skeletal muscle is very more and is completely embedded, if electromyographic electrode
Placement location selection it is improper, it will so that collected data is lost accuracy.Therefore, have by experimental measurement result
Pointedly determine electrode placement position to obtain the signal of reliable in quality.When measuring electrode is placed on phase along muscle fibre direction
At the belly of muscle for closing muscle, reference electrode is placed on SEMG (surface electromyogram, surface electromyography) signal and compares
When at faint tendon, the SEMG signal of reliable in quality can be collected.
Surface electromyogram signal is being believed by being attached to the collected faint bioelectrical signals of the electrode slice of skin surface
Number acquisition, pickup, be frequently subjected to extraneous noise jamming during conditioning so that collected surface electromyogram signal includes
Some garbages pre-process signal using two methods of Wavelet noise-eliminating method and empirical mode decomposition threshold denoising.
(2) feature extraction: time-domain analysis is carried out to surface electromyogram signal using mean square root method, is found with muscular fatigue journey
Degree increases, the decline of root mean square amplitude, carries out frequency-domain analysis to surface electromyogram signal using median frequency method.Using fuzzy close
Linear Characteristics are carried out to surface electromyogram signal like entropy algorithm, this method statistical value is stablized, strong antijamming capability, to mixing
Signal it is adaptable be entropy algorithm advantage, the variation of result curve and body state variation have preferable consistency.
(3) application of the soft set theory in fatigue-driving detection technology
The bioelectrical signals of the human body surface myoelectric signal faint complexity that each meat fiber generates when being neuron-muscular activity,
It is comprehensive superposition of the electric field of many muscle fibres in room and time distribution, state feature also has randomness, complexity
The characteristics of with vulnerable to interference.It is acquired and has been handled by the surface electromyogram signal to six positions of driver, then used
After the methods of time-domain analysis, frequency-domain analysis are analyzed and extracted to the muscular states feature at each position, the present invention establishes base
The multiple information of human body are merged in the tired quantitative model of soft set, the exception information of appearance is handled, then into
Row Comprehensive Evaluation.
The description of A2 problem
Domain U is the set of driver's different moments muscular states, i.e. U={ h1,h2,h3,h4...h25, E is parameter set,
The respectively fuzzy approximation entropy of deltoid muscle, latissimus dorsi, Superior trapezius, rectus femoris, musculus vastus lateralis and gastrocnemius, i.e. E=
{e1,e2,e3,e4,e5,e6, the fatigue state that Fuzzy Soft Sets close (F, E) characterization driver is set now, and formula (1) can be obtained,
In, the value of formula (1) is by collected surface electromyogram signal, and after above-mentioned various preprocess method processing, each position is fuzzy close
Like the result after entropy normalization.
{ F, E }={ (e1,{h1/0.8772,h2/1,h3/0.9908,h4/0.9931...h25/0.2687}),
(e2,{h1/1,h2/0.9230,h3/0.9136,h4/0.8144...h25/0.3031}),
(e3,{h1/0.8902,h2/1,h3/8498,h4/0.8197...h25/0.2917}),
(e4,{h1/0.9678,h2/0.9465,h3/0.9379,h4/1...h25/0.5071})
(e5,{h1/0.9634,h2/0.9241,h3/0.9501,h4/0.9526...h25/0.5283}),
(e6,{h1/0.9930,h2/1,h3/0.8998,h4/0.8244...h25/0.5171})} (1)
Soft set is indicated with form, as shown in table 1.
The form of 1 soft set of table (F, E)
A3. the determination of parameters weighting
From table 1 it follows that the fuzzy approximation entropy of leg muscle is easy fluctuation, and as time increases, entropy
The speed for being worth decline is slower, and deltoid muscle and the very fast amplitude of latissimus dorsi decrease speed are larger.Due to this difference physiologically, I
Need to be adjusted different parameters, according to parameter and the degree of correlation of body state setting parameter weight, thus as far as possible
The difference eliminated between parameter is interfered caused by the result of decision.
The calculating of the degree of correlation uses line fitting method, using the absolute value of fitting a straight line slope as standard, with latissimus dorsi, stock
As shown in Fig. 2, wherein round indicate that latissimus dorsi, five-pointed star indicate that Superior trapezius, triangle indicate back for rectus and Superior trapezius
Platysma.Represent the straight slope absolute value difference of deltoid muscle, musculus vastus lateralis, gastrocnemius, latissimus dorsi, rectus femoris and Superior trapezius
Are as follows: 0.0325,0.0205,0.0198,0.0283,0.0237,0.0317.If slope is k, each parameters weighting is w, then weight is pressed
Formula (2) calculates.
The weight obtained is respectively wi=[1.000,0.871,0.975,0.729,0.631,0.609].After weight setting
The form of soft set is as shown in table 2.
The form of soft set (T, E) after the setting of 2 weight of table
A4. the processing of abnormal data
In experiment collection process, often by various interference, the result after leading to data processing is beyond normal value
Range, if directly removing analysis and decision using these abnormal datas, may obtain with virtual condition be not consistent as a result, leading
Cause the consequences such as incorrect decision or application error.
Firstly, we are it needs to be determined that whether a data are abnormal data, since the acquisition of physiologic information is in time
Continuously, therefore, data have certain rule with time change.If the gap mistake of a data and its former and later two data
Greatly, we are just seen as abnormal data, and since preceding 30 minutes human bodies are in excitatory state, data fluctuations are larger to be belonged to normally
Physiological phenomenon, therefore first three groups signal is not considered.Determination basis formula (3) of abnormal data
It can regard the data in italics in table 2 as abnormal data by calculating, enable abnormal data hij=*, has obtained not
Soft set { T, the E of complete information*, form is as shown in table 3.
We use the average value of the two groups of data in front and back for the filling of abnormal data, because, two groups of front and back data time interval
It is identical, and data are in the trend of monotonic decreasing, therefore it may only be necessary to which data set can preferably be shown by calculating average value
Essential characteristic and implicit rule.This method equally uses the thought of simplified algorithm, i.e., is obtained as far as possible using less data
Desired replacement values out, rather than all data are considered every possible angle.
Soft set { T, the E of the incomplete information of table 3*Form
By soft set { T, E*Abnormal data be replaced, obtain the 4th soft set { G, E }, form such as table 4
It is shown.The form of 4 soft set of table (G, E)
Using table 4, judge value is obtained, wherein judge value di=e1′+e2′+e3′+e4′+e5′+e6', from table 4, it can be seen that
As time increases, judge value is in monotonous decreasing trend, shows the variation of driver's physical fatigue state, more tired, is determined
It is worth smaller.So as to analyze driver fatigue decline speed, it is (such as in general continuous by one threshold value of setting
It drives not above 4 hours, it is just more tired more than 4 hours, it is easy to happen danger, the flesh at multiple groups four hours can be acquired
Electric signal obtains average fatigue data, using this as threshold value of warning), judge the physical fatigue degree of driver, suggests in time
Driver's rest, can effectively avoid traffic accident.
Claims (8)
1. a kind of method for detecting fatigue driving based on soft set, which comprises the following steps:
S1 acquires surface electromyogram signal of the default muscle sites in predetermined time of driver；
S2 extracts and analyzes the muscular states feature of the surface electromyogram signal；
S3 establishes the tired quantitative model based on soft set according to the electromyography signal extracted；
Fatigue driving decision content is calculated according to the tired quantitative model based on soft set in S4；Wherein, the step S3
It include: the fatigue state for setting the first soft set (F, E) characterization driver, wherein domain U is flesh of the driver in different moments
The set of meat-like state feature, i.e. U={ h1,h2,h3,h4...h25, wherein h1~h25The muscular states for representing different moments are special
Sign, E are parameter sets, and parameter is the fuzzy approximation entropy of the surface electromyogram signal；The step S3 further include: according to each in E
The weight of each parameter is respectively set in parameter and the degree of correlation of body state, according to the weight of each parameter and the first soft collection
Conjunction obtains the second soft set (T, E).
2. a kind of method for detecting fatigue driving based on soft set according to claim 1, which is characterized in that the step
In S1, the default muscle sites include neck Superior trapezius, shoulder deltoid muscle, back latissimus dorsi, lower limb rectus femoris, thigh lateral
Flesh and gastrocnemius.
3. a kind of method for detecting fatigue driving based on soft set according to claim 1 or 2, which is characterized in that described
In step S1, using Wavelet noise-eliminating method and empirical mode decomposition Threshold Denoising Method to the collected surface electromyogram signal
It is pre-processed.
4. a kind of method for detecting fatigue driving based on soft set according to claim 3, which is characterized in that the step
S2 includes: to be analyzed using mean square root method, median frequency method or fuzzy approximation entropy algorithm the surface electromyogram signal, is obtained
To the muscular states feature of surface electromyogram signal.
5. a kind of method for detecting fatigue driving based on soft set according to claim 1, which is characterized in that use straight line
Approximating method calculates the degree of correlation of each parameter and body state in E.
6. a kind of method for detecting fatigue driving based on soft set according to claim 1 or 5, which is characterized in that described
Step S3 further include: determine the abnormal data in second soft set (T, E), determine formula specifically:And the abnormal data is handled, enable the abnormal data hij=* obtains
Three soft set { T, E*}。
7. a kind of method for detecting fatigue driving based on soft set according to claim 6, which is characterized in that by described
Three soft set { T, E*In abnormal data use front and back two groups of data average value be replaced, obtain the 4th soft set G,
E }, calculation method *ij=(hi-1,j+hi+1,j)/2, and obtained using soft set decision-making technique according to the 4th soft set { G, E }
To final decision content.
8. a kind of method for detecting fatigue driving based on soft set according to claim 7, which is characterized in that the step
In S4, the circular of decision content are as follows: di=e1′+e2′+e3′+e4′+e5′+e6', fatigue is arranged according to the decision content
Threshold value of warning exports warning message when decision content reaches the giving fatigue pre-warning threshold value.
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| CN109993180A (en) * | 2017-12-29 | 2019-07-09 | 新华网股份有限公司 | Human biological electricity data processing method and device, storage medium and processor |
| CN109431526B (en) * | 2018-12-25 | 2020-01-21 | 成都中昂科技有限公司 | WIFI-based driving state identification method and system |
| CN110151203B (en) * | 2019-06-06 | 2021-11-23 | 常熟理工学院 | Fatigue driving identification method based on multistage avalanche convolution recursive network EEG analysis |
| CN110232976B (en) * | 2019-07-01 | 2023-05-02 | 上海电机学院 | Behavior identification method based on waist and shoulder surface myoelectricity measurement |
| CN112006686A (en) * | 2020-07-09 | 2020-12-01 | 浙江大学 | Neck muscle fatigue analysis method and system |
| CN114081513B (en) * | 2021-12-13 | 2023-04-07 | 苏州大学 | Electromyographic signal-based abnormal driving behavior detection method and system |
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