CN107320097A - The method and apparatus that muscular fatigue feature is extracted using electromyographic signal marginal spectrum entropy - Google Patents
The method and apparatus that muscular fatigue feature is extracted using electromyographic signal marginal spectrum entropy Download PDFInfo
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- CN107320097A CN107320097A CN201710516492.3A CN201710516492A CN107320097A CN 107320097 A CN107320097 A CN 107320097A CN 201710516492 A CN201710516492 A CN 201710516492A CN 107320097 A CN107320097 A CN 107320097A
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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
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
- A61B5/4519—Muscles
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
Abstract
The invention discloses the method and apparatus that a kind of utilization electromyographic signal marginal spectrum entropy extracts muscular fatigue feature, this method comprises the following steps：A, the electromyographic signal collected is stored in array sEMG [CE]；B, using Hilbert-Huang transform Time-Frequency Analysis Method combining information entropy theory, extract marginal spectrum entropy feature to collecting the electromyographic signal in array sEMG [CE]；C, will be in above-mentioned marginal spectrum entropy deposit array HHE [CH], it is a frame data to define HHE [CE], and array HHE [CH] is used for depositing marginal spectrum entropy according to first-in first-out；And d, repeat the above steps a, b and c, is filled with array HHE [CH], the array HHE [CH] after being filled with judges as muscular fatigue feature for muscular fatigue.Muscular fatigue is judged using the above method, the muscular fatigue index based on marginal spectrum entropy calculates quick, data length robustness is good, assessment muscular fatigue is highly reliable, and noiseproof feature is good.
Description
Technical field
Electromyographic signal is utilized the present invention relates to a kind of method that utilization electromyographic signal assesses muscular fatigue, more particularly to one kind
Marginal spectrum entropy extracts the method for muscular fatigue feature and extracts muscular fatigue device.
Background technology
Muscular fatigue is a kind of complicated physiological phenomenon, its genesis mechanism and its complexity, is related to central nervous system, muscle
Oxygen-supplying amount etc. in energetic supersession, blood, serious muscular fatigue can cause the expendable damage of muscle.
The assessment and monitoring of muscular fatigue are significant to muscular fatigue damage caused by prevention excessive movement, use extensively
In fields such as clinical medicine, medical science of recovery therapy, sports science and ergonomicses.
Surface electromyogram signal (sEMG) is a kind of non-stationary time-varying biological electricity for reacting nerve, muscle systems function and state
Signal, its signal characteristic from different aspect, reflect muscular fatigue correlated process in various degree, therefore sEMG is commented in muscular fatigue
Played an important role in estimating.Research shows that the partial feature value of electromyographic signal has significantly along with muscular fatigue
Variation tendency, therefore it is to extract effective special from electromyographic signal to assess one of crucial work of muscular fatigue using electromyographic signal
Value indicative, it is desirable to which this feature value should be able to be along with the generation of muscular fatigue with stable variation tendency.
At present, in the problem that muscular fatigue is assessed using electromyographic signal, electromyographic signal characteristics extraction is concentrated mainly on
Time domain and frequency domain, temporal signatures use root mean square RMS, myoelectric integral value IEMG, average AVG etc.；Intermediate value is used frequency domain character more
Frequency MPF, means frequency MPF.But the algorithm stability and cluster property of temporal signatures and frequency domain character still have the space of lifting.
In addition the NONLINEAR EIGENVALUE such as fuzzy approximation entropy, Sample Entropy, Wavelet Entropy also has grinding applied to muscular fatigue
Study carefully, also have made some progress.But most nonlinear indicator is in the presence of just selection parameter and computation complexity are high in advance at present
Shortcoming, for example, determine algorithm tolerance limit, synchronous signal length, it is necessary to realize using more approximate entropy according to the prior information of signal
Influence can be produced on characteristic value stability.Sample Entropy is to improve to produce on the basis of approximate entropy, steady with more preferable data length
Strong property, but tolerance limit selection error can cause occur the situation without definition in short data.
Fuzzy approximation entropy replaces two-value classification function with fuzzy set, makes it have more preferable reliability, noise immunity, but fuzzy
Approximate entropy must calculate the membership function of each distance vector, add the time complexity of algorithm.
Wavelet Entropy need to select wavelet basis function in advance, and often wavelet basis function has been largely fixed Wavelet Entropy
Effect.Therefore, it is necessary to which a kind of adaptive, stability is high to have both good again in the characteristics extraction problem for muscular fatigue
Algorithm complex feature extracting method.
Assess muscular fatigue using electromyographic signal has its special character again compared with other electromyographic signal application fields, utilizes myoelectricity
Signal evaluation muscular fatigue is different from electromyographic signal starting point testing goal and is the mutation value for detecting that certain in electromyographic signal is put；
It is different from and carries out medical diagnosis on disease using electromyographic signal, medical diagnosis on disease is whole section of extraction characteristic value to measured signal, it is therefore an objective to made
Normal signal and pathological signals otherness are maximum.And muscular fatigue is a process in itself, it is necessary to per period of this process
Electromyographic signal carries out characteristics extraction, and muscular fatigue is assessed by the variation tendency of characteristic value, therefore characteristic value is needed with flesh
The generation of meat fatigue has deterministic variation tendency, and requires that variation tendency has good uniformity and repeatability.
The content of the invention
In view of the existing shortcoming for assessing muscular fatigue method of the above, myoelectricity letter is utilized it is an object of the invention to provide one kind
The method that number marginal spectrum entropy extracts muscular fatigue feature so that the variation tendency for the characteristic value extracted have good uniformity and
Repeatability.
Present invention also offers a kind of device for extracting muscular fatigue feature, so that the variation tendency tool for the characteristic value extracted
There is good uniformity and repeatability.
Therefore, one aspect of the present invention provides the side that a kind of utilization electromyographic signal marginal spectrum entropy extracts muscular fatigue feature
Method, comprises the following steps：A, the electromyographic signal collected is stored in array sEMG [CE], wherein, CE be with sampling duration and
The related data length of sample frequency；B, using Hilbert-Huang transform Time-Frequency Analysis Method combining information entropy theory, to collection
Marginal spectrum entropy feature is extracted to the electromyographic signal in array sEMG [CE]；C, by above-mentioned marginal spectrum entropy be stored in array HHE [CH]
In, it is a frame data to define HHE [CE], and array HHE [CH] is used for depositing marginal spectrum entropy, wherein CH tables according to first-in first-out
Show the number of storage electromyographic signal marginal spectrum entropy；And d, repeat the above steps a, b and c, array HHE [CH] is filled with, will be filled with
Array HHE [CH] afterwards judges as muscular fatigue feature for muscular fatigue.
Further, above-mentioned steps d also includes：Also repeated the above steps CN times after array HHE [CH] is filled with, obtain CN
Individual marginal spectrum entropy；CN marginal spectrum entropy is stored in array HHE [CH] according to sequencing, by the array HHE [CH] at first
CN marginal spectrum entropy of deposit overflows, and obtains new array HHE [CH].
Further, above-mentioned CN values are set according to the prior information of different people muscular fatigue.
Further, the above-mentioned marginal spectrum Entropy change trend according to electromyographic signal assesses whether to produce muscular fatigue.
Further, above-mentioned use least square method carries out linear fit to the array HHE [CH] of fill-status, obtains outlet
Property fitting slope, the slope characterizes variation tendency of marginal spectrum entropy when occurring with muscular fatigue.
Further, a length of 0.1s-10s during each sampling of the above-mentioned electromyographic signal collected, sample frequency is
1024Hz-4096Hz。
Further, the above method also includes using Wavelet Denoising Method to the electromyographic signal in deposit array sEMG [CE], with
Remove the white noise in electromyographic signal.
Further, above-mentioned CH values are set to 10~30.
Further, above-mentioned steps b includes following sub-step：The electromyographic signal that will be collected in array sEMG [CE] is carried out
EMD is decomposed, and obtains the IMF of electromyographic signal；Hilbert conversion is carried out to every rank IMF afterwards, the Hilert- of electromyographic signal is obtained
Huang time-frequency spectrums；Time integral is carried out to Hilert-Huang time-frequency spectrums afterwards, Hilbert marginal spectrums are obtained；According to comentropy
It is theoretical that Hilbiet marginal spectrums are carried out to calculate the marginal spectrum entropy for obtaining electromyographic signal.
According to another aspect of the present invention there is provided a kind of device for extracting muscular fatigue feature, including processor, storage
Be stored with computer program on device, the memory, and the processor realizes following steps when performing the computer program：
A, the electromyographic signal collected is stored in array sEMG [CE], wherein, CE is the number related with sample frequency with sampling duration
According to length；B, using Hilbert-Huang transform Time-Frequency Analysis Method combining information entropy theory, to collecting in array sEMG [CE]
Electromyographic signal extract marginal spectrum entropy feature；C, will be in above-mentioned marginal spectrum entropy deposit array HHE [CH], defining HHE [CE] is
One frame data, array HHE [CH] is used for depositing marginal spectrum entropy according to first-in first-out, and wherein CH represents storage electromyographic signal side
Compose the number of entropy in border；And d, repeat the above steps a, b and c, is filled with array HHE [CH], the array HHE [CH] after being filled with makees
For muscular fatigue feature, judge for muscular fatigue.
The present invention extracts the limit of electromyographic signal using Hilbert-Huang transform Time-Frequency Analysis Method combining information entropy theory
Compose entropy feature, the characteristics of empirical mode decomposition has signal adaptive, it is to avoid algorithm parameter selection.Pass through the change of marginal spectrum entropy
Rate, can assess muscular fatigue by doctor.Root is it was found that marginal spectrum entropy algorithm is quicker than median frequency and approximate entropy, for tired
Labor has more preferable reliability, and noiseproof feature is stronger, therefore this method can be used preferably compared to approximate entropy and median frequency
In assessment muscular fatigue.Marginal spectrum entropy is characterized with muscle using the slope in fixed duration per period marginal spectrum entropy linear fit
Variation tendency when fatigue occurs.This method can integrate multi-period marginal spectrum entropy, it is to avoid the big singular point of dispersion degree is done
Disturb variation tendency.
Muscular fatigue is judged using the above method, the muscular fatigue index based on marginal spectrum entropy calculates quick, data length
Robustness is good, and assessment muscular fatigue is highly reliable, and noiseproof feature is good.
In addition to objects, features and advantages described above, the present invention also has other objects, features and advantages.
Below with reference to figure, the present invention is further detailed explanation.
Brief description of the drawings
The Figure of description for constituting the part of the application is used for providing a further understanding of the present invention, and of the invention shows
Meaning property embodiment and its illustrate be used for explain the present invention, do not constitute inappropriate limitation of the present invention.In the accompanying drawings：
Fig. 1 is the method that utilization electromyographic signal marginal spectrum entropy according to an embodiment of the invention extracts muscular fatigue feature
Flow chart；And
Fig. 2 is the flow chart for the method that utilization electromyographic signal according to an embodiment of the invention assesses muscular fatigue.
Embodiment
It should be noted that in the case where not conflicting, the feature in embodiment and embodiment in the application can phase
Mutually combination.Describe the present invention in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Figures 1 and 2 show that according to some embodiments of the present invention.
Per period marginal spectrum entropy of extract real-time electromyographic signal of the present invention, it is marginal per the period in fixed duration by calculating
The slope of entropy linear fit is composed, variation tendency when marginal spectrum entropy occurs with muscular fatigue is characterized, specifically, muscular fatigue is special
Extracting method is levied to comprise the following steps：
S101：By the electromyographic signal collected deposit array sEMG [CE]；
S103, electromyographic signal progress empirical mode decomposition (the Empirical Mode to collecting array sEMG [CE]
Decomposition, EMD), the intrinsic modal components (Intrinsic Mode Function, IMF) of electromyographic signal are obtained, are gone
Except discrepance, Hilbert transform (Hilbert transform) is carried out to every rank IMF respectively afterwards, electromyographic signal is obtained
Hilbert-Huang time-frequency spectrums, to the Hilbert-Huang time-frequency spectrums in time-domain upper integral, obtain Hilbert marginal spectrums,
According to the definition of comentropy, marginal spectrum entropy is calculated using Hilbert marginal spectrums.
S105：It will calculate in obtained marginal spectrum entropy, deposit array HHE [CH], it is a frame data, number to define HHE [CE]
Group HHE [CH] is used for depositing the marginal spectrum entropy for collecting electromyographic signal every time, and wherein CH represents to deposit electromyographic signal marginal spectrum
The number of entropy.
S107：Repeat the above steps, until array HHE [CH] is filled with, array HHE [CH] is carried out using least square method
Linear fit, draws the slope a1 of first linear fit, if can judge muscular fatigue trend using slope a1, terminates weight
It is multiple, otherwise continue to repeat the above steps, update array HHE [CH], reject CN data for entering array HHE [CH] at first, remain
Under data CN forward, it is last CN addition latest computed CN marginal spectrum entropy, using least square method to renewal after
Array HHE [CH] carries out linear fit, the slope a2 of linear fit is drawn, if can judge that muscular fatigue becomes using slope a2
Gesture, then terminate, and otherwise repeats down.
Muscular fatigue is judged using the above method, the muscular fatigue index based on marginal spectrum entropy calculates quick, data length
Robustness is good, and assessment muscular fatigue is highly reliable, and noiseproof feature is good.
Wherein, the myoelectricity sample frequency in above-mentioned steps S101 may be configured as 1024 hertz to 4096 hertz, duration of sampling
It can be 0.1 second to 10 seconds.The number of CH storages electromyographic signal marginal spectrum entropy may be configured as 10 to 30 in above-mentioned steps S105.On
State and CN data are rejected in step S107 may be configured as 1, it is 1 that as frame, which is moved, can be according to the prior information of different people muscular fatigue
CN values are set.
Embodiment one
As shown in Fig. 2 the utilization electromyographic signal marginal spectrum entropy of the present invention extracts the method for muscular fatigue feature including following
Step：
At the belly of muscle that electrode slice is attached to by skin, rejecting hair the 1st, at cleaning left arm musculus extensor carpi radialis longus patch electrode slice,
It is connected by conducting wire with myoelectricity collecting device, sample frequency is set to 2048 hertz, a length of 0.5 second during sampling, is turned by AD
The array sEMG [1024] changed in deposit processor, using Wavelet Denoising Method, the white noise in signal is removed, spy to be extracted is obtained
The electromyographic signal sEMG [1024] of value indicative.
2nd, marginal spectrum entropy feature, the meter of this section of marginal spectrum entropy are extracted to collecting the electromyographic signal in array sEMG [2048]
It is specific as follows：
A) EMD decomposition is carried out to signal x (t) first, obtains n rank IMF components and residual volume：
X (t) is signal, c in formulai(t) it is natural mode of vibration parametric component (IMF), rn(t) it is residual volume.
B) Hilbert transform is carried out to each IMF components：
H (c in formulai(t)) the Hilbert transform for being IMF.
C) tectonic knot signal：
Then signal x (t) can be expressed as：
The real part for the number of winning the confidence, ai (t), f (t),Instantaneous amplitude, instantaneous frequency and instantaneous phase are represented respectively.
D) Hilbert-Huang time-frequency spectrums, the time of representation signal amplitude and frequency distribution are defined：
E) the Hilbert marginal spectrums that time integral obtains signal are carried out to above formula (5)：
By the definition of marginal spectrum, for discrete Frequency point f=i Δ f, then have：
Wherein, n is frequency-distributed points of the signal in analysis frequency range.
F) then according to the definition of comentropy, HHT marginal spectrum entropys are represented by：
In formula, pi=h (i)/∑ h (i) represents the probability of i-th of frequency correspondence amplitude.
For entropy normalization in the range of [0,1], is then had：
N is h (i) sequence length.
This section of marginal spectrum entropy is stored in array HHE [CH], it is a frame data to define HHE [CH], array HHE [CH] is used
To deposit the marginal spectrum entropy for collecting electromyographic signal every time, wherein CH represents to deposit the number of electromyographic signal marginal spectrum entropy, tool
In body embodiment, CH is 30.
3rd, judge whether the full flag bit DataFullFlag of array element is 1, is then to update array HHE [30], rejects most
It is introduced into array HHE [30] 1 data, remaining data 1 forward, 1 marginal spectrum of last 1 addition latest computed
Entropy；Otherwise the 4th step is carried out.
4th, the marginal spectrum entropy of calculating is stored in array HHE [30] in order, and element count variable adds 1, DL=
DL+1。
5th, judge whether counting variable is more than default element number value CH, more than then array element flag bit
DataFullFlag=1, otherwise repeatedly 2~3 step.
6th, linear fit is carried out to the array HHE [30] after renewal using least square method, draws the slope of linear fit
a。
7th, assess whether occur muscular fatigue according to array HHE variation tendency and slope a, occur then program stopped, it is no
Then repeat 2,3,6 and 7 steps.
Present invention also offers a kind of information processor, the computer journey for being stored with executable on the information processor
Sequence, the computer program is used to realize each step shown in Fig. 1 or Fig. 2 upon execution, to realize muscular fatigue feature extraction.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.Within the spirit and principles of the invention, that is made any repaiies
Change, equivalent substitution, improvement etc., should be included in the scope of the protection.
Claims (10)
1. a kind of method that utilization electromyographic signal marginal spectrum entropy extracts muscular fatigue feature, it is characterised in that comprise the following steps：
A, the electromyographic signal collected is stored in array sEMG [CE], wherein, CE is related to sampling duration and sample frequency
Data length；
B, using Hilbert-Huang transform Time-Frequency Analysis Method combining information entropy theory, to collecting the flesh in array sEMG [CE]
Electric signal extracts marginal spectrum entropy feature；
C, will be in above-mentioned marginal spectrum entropy deposit array HHE [CH], it is a frame data to define HHE [CE], and array HHE [CH] is used for
Marginal spectrum entropy is deposited according to first-in first-out, wherein CH represents to deposit the number of electromyographic signal marginal spectrum entropy；And
D, repeat the above steps a, b and c, is filled with array HHE [CH], and the array HHE [CH] after being filled with is used as muscular fatigue special
Levy, judge for muscular fatigue.
2. the method that utilization electromyographic signal marginal spectrum entropy according to claim 1 extracts muscular fatigue feature, its feature exists
In the step d also includes：
After array HHE [CH] is filled with, also repeat the above steps CN times, obtain CN marginal spectrum entropy；According to sequencing by CN
Individual marginal spectrum entropy deposit array HHE [CH], CN marginal spectrum entropy being stored at first in the array HHE [CH] is overflowed, obtained
New array HHE [CH], as muscular fatigue feature, judge for muscular fatigue.
3. the method that utilization electromyographic signal marginal spectrum entropy according to claim 2 extracts muscular fatigue feature, its feature exists
In the CN values are set according to the prior information of different people muscular fatigue.
4. utilization electromyographic signal marginal spectrum entropy according to claim 1 or 2 extracts the method for muscular fatigue feature, its feature
It is, is assessed whether to produce muscular fatigue according to the marginal spectrum Entropy change trend of electromyographic signal.
5. the method that utilization electromyographic signal marginal spectrum entropy according to claim 3 extracts muscular fatigue feature, its feature exists
In carrying out linear fit to the array HHE [CH] of fill-status using least square method, draw the slope of linear fit, this is oblique
Rate characterizes variation tendency when marginal spectrum entropy occurs with muscular fatigue.
6. the method that utilization electromyographic signal marginal spectrum entropy according to claim 1 extracts muscular fatigue feature, its feature exists
In, a length of 0.1s-10s during each sampling of the electromyographic signal collected, sample frequency is 1024Hz-4096Hz.
7. the method that utilization electromyographic signal marginal spectrum entropy according to claim 1 extracts muscular fatigue feature, its feature exists
In, in addition to Wavelet Denoising Method is used to the electromyographic signal in deposit array sEMG [CE], to remove the white noise in electromyographic signal.
8. the method that utilization electromyographic signal marginal spectrum entropy according to claim 1 extracts muscular fatigue feature, its feature exists
In the CH values are set to 10~30.
9. the method that utilization electromyographic signal marginal spectrum entropy according to claim 1 extracts muscular fatigue feature, its feature exists
In the step b includes following sub-step：The electromyographic signal that will be collected in array sEMG [CE] carries out EMD decomposition, obtains flesh
The IMF of electric signal；Hilbert conversion is carried out to every rank IMF afterwards, the Hilert-Huang time-frequency spectrums of electromyographic signal are obtained；It
Time integral is carried out to Hilert-Huang time-frequency spectrums afterwards, Hilbert marginal spectrums are obtained；According to information entropy theory to Hilbiet
Marginal spectrum calculate the marginal spectrum entropy for obtaining electromyographic signal.
10. a kind of device for extracting muscular fatigue feature, it is characterised in that including processor, memory, deposited on the memory
Computer program is contained, the processor realizes following steps when performing the computer program：
A, the electromyographic signal collected is stored in array sEMG [CE], wherein, CE is related to sampling duration and sample frequency
Data length；
B, using Hilbert-Huang transform Time-Frequency Analysis Method combining information entropy theory, to collecting the flesh in array sEMG [CE]
Electric signal extracts marginal spectrum entropy feature；
C, will be in above-mentioned marginal spectrum entropy deposit array HHE [CH], it is a frame data to define HHE [CE], and array HHE [CH] is used for
Marginal spectrum entropy is deposited according to first-in first-out, wherein CH represents to deposit the number of electromyographic signal marginal spectrum entropy；And
D, repeat the above steps a, b and c, is filled with array HHE [CH], and the array HHE [CH] after being filled with is used as muscular fatigue special
Levy, judge for muscular fatigue.
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