CN109567799A - EMG Feature Extraction based on smooth small echo coherence - Google Patents
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- 230000006399 behavior Effects 0.000 claims abstract description 7
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- 238000002567 electromyography Methods 0.000 description 33
- 238000004458 analytical method Methods 0.000 description 10
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- 238000012545 processing Methods 0.000 description 4
- 210000003314 quadriceps muscle Anatomy 0.000 description 4
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- 238000011160 research Methods 0.000 description 2
- 208000018737 Parkinson disease Diseases 0.000 description 1
- 230000036982 action potential Effects 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000001270 agonistic effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
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- 210000003141 lower extremity Anatomy 0.000 description 1
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- 201000000585 muscular atrophy Diseases 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000005312 nonlinear dynamic Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 210000002435 tendon Anatomy 0.000 description 1
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- 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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Abstract
本发明公开了一种基于平滑小波相干性的肌电信号特征提取方法，本发明通过肌电信号采集仪采集人体相关肌肉的肌电信号，并采用带通滤波方法进行预处理，对滤波处理后的肌电信号进行小波变换，然后计算两路肌电信号的交叉小波变换，并对交叉小波变换分别进行时间轴和尺度轴上的平滑操作。最后，计算两路肌电信号的平滑小波相干系数，并使用T检验来检验不同行为之间的相关性是否存在统计学差异，得到不同肌肉组合的32级平滑小波相干系数作为特征向量。本发明使用的平滑小波相干性在特征提取方法上具有很大创新，对于后续模式识别具有较高的识别率和可靠性，可以较好地满足多模式识别任务中的特征提取要求，具有广阔的应用前景。
The invention discloses an electromyographic signal feature extraction method based on smooth wavelet coherence. The invention collects electromyographic signals of relevant muscles of the human body through an electromyographic signal acquisition instrument, and adopts a band-pass filtering method for preprocessing. Wavelet transform is performed on the EMG signals, and then the cross wavelet transform of the two EMG signals is calculated, and the smooth operation on the time axis and the scale axis is performed on the cross wavelet transform respectively. Finally, the smooth wavelet coherence coefficients of the two EMG signals were calculated, and the T test was used to test whether the correlation between different behaviors was statistically different, and the 32-level smooth wavelet coherence coefficients of different muscle combinations were obtained as eigenvectors. The smooth wavelet coherence used in the present invention has great innovation in the feature extraction method, has high recognition rate and reliability for subsequent pattern recognition, can better meet the feature extraction requirements in multi-pattern recognition tasks, and has a wide range of application prospects.
Description
Technical field
The invention belongs to electromyography signal process fields, are related to a kind of EMG Feature Extraction, in particular to a kind of
EMG Feature Extraction based on the smooth small echo coherence of myoelectricity.
Background technique
Electromyography signal (Electromyogram, EMG) is the superposition of muscle fibre action potential, is that one kind is faint, non-linear,
Random non-stable bioelectrical signals.Medically, electromyography signal can be used to diagnose and analyze Parkinson's disease, amyotrophia funiculus lateralis
The diseases such as hardening.In engineer application field, the bioelectrical signals such as myoelectricity are used frequently as control signal source.Neural activation bit can
To obtain from electromyography signal, according to the approach for obtaining electromyography signal, it is generally divided into plug-in type electromyography signal and surface myoelectric letter
Number (surface EMG, sEMG).Surface electromyogram signal is not due to needing insertion skin layer hereinafter, being got over to human zero damage
It is used come more researchers, is also had a wide range of applications.
The feature extraction of surface electromyogram signal is the premise and committed step of pattern-recognition and regression forecasting, its purpose exists
In therefrom obtaining useful information and removing the information of redundancy, so that information has better ga s safety degree.According to feature
The mode of extraction can be divided into time domain, frequency domain, time-frequency domain, nonlinear dynamic analysis method.Time-frequency domain method combines time domain
And the characteristics of frequency domain, the characteristic information of description signal that not only can be local, but also will appreciate that the frequency domain information of signal, wavelet transformation is
A kind of main method of time-frequency domain.
Wavelet transformation has a wide range of applications in surface electromyogram signal processing, it is by Decomposition Surface EMG at many
Subband comprising precise information.And coherent analysis, and number are carried out to the relationship in time frequency space between two kinds of echo signals
Common method in word signal analysis especially wavelet transformation.RyotaroImoto et al. uses electromyography signal small echo coherence analysis
The coordinated movement of various economic factors mechanism for having studied agonistic muscle and Opposing muscle has obtained under stable condition there is more high correlation than instability condition
Conclusion.In general, small echo coherence can be used to analyze nonstationary random signal, such as electromyography signal and EEG signals.Surface myoelectric
The coherence of signal can provide a seed coat layer muscle coupling information.Currently based on the research of surface electromyogram signal coherent analysis
It is less, there is more wide research space.
Summary of the invention
The present invention proposes a kind of based on smooth small echo coherence point for deficiency existing for existing myoelectricity feature extracting method
The EMG Feature Extraction of analysis can effectively extract the feature of surface electromyogram signal.It is adopted by electromyographic signal collection instrument
Collect the electromyography signal of human body related muscles, and pre-processed using band-pass filtering method, to the electromyography signal after filtering processing
Wavelet transformation is carried out, the cross wavelet analysis of two-way electromyography signal is then calculated, and the time is carried out respectively to cross wavelet analysis
Smooth operation on axis and scale axis.Finally, calculating the smooth wavelet coherence of two-way electromyography signal, and examine using T
It examines the correlation between different behaviors with the presence or absence of statistical difference, obtains 32 grades of wavelet coherences of different muscle combinations
As feature vector.
In order to achieve the goal above, the method for the present invention mainly comprises the steps that
Step 1 obtains related electromyography signal sample data, acquires human body correlation flesh by electromyographic signal collection instrument first
Then the electromyography signal of meat is pre-processed using band-pass filtering method.
Step 2 carries out wavelet transformation to the electromyography signal after filtering processing.
Wx(a, b) is wavelet coefficient, and x (t) is electromyography signal to be analyzed,It is Morlet mother wavelet function, a is small
Wave scale factor, b are time shifts, and t is local time's origin.
P is frequency parameter, and σ is the parameter for controlling small wave attenuation.
Step 3 calculates two-way electromyography signal x (t), the cross wavelet analysis of y (t):
Step 4, to Wxy(a, b) is smoothed.First calculate the smooth operation on time shaft.
c1It is normalisation coefft, * represents convolution algorithm.Then the smooth operation on scale axis is calculated.
Sa(Wx(a, b))=Wx(a,b)*c2Π(0.6a)
c2It is also normalisation coefft, Π is rectangular function.
Step 5 calculates the smooth wavelet coherence of two-way electromyography signal.
S is smooth function, S (W)=Sa[St(W)]。
Step 6 obtains maximum average wavelet coherence R when taking 32 scalexy(32,b).Then it is examined using T to check
Whether there were significant differences in different behaviors for the wavelet coherence of different muscle combinations, will finally have maximum average small echo phase
32 multi-scale wavelet coherence factors of the muscle combination of responsibility number are as feature vector.
The EMG Feature Extraction based on myoelectricity small echo coherence that the present invention designs, has a characteristic that
The smooth small echo coherence that the present invention uses has significantly validity in terms of feature extraction, and propose
Feature extracting method is broken down into 32 scales using wavelet transformation to electromyography signal, wavelet coefficient is recycled to calculate small echo
Coherence factor examines correlation between different behaviors with the presence or absence of statistical difference using T inspection later, then by different fleshes
Method of 32 grades of wavelet coherences as feature vector of meat combination, for follow-up mode identify discrimination with higher with
Reliability can better meet the requirements for extracting features in multimode recognition task, have broad application prospects.
Detailed description of the invention
Fig. 1 is implementation flow chart of the invention；
Fig. 2 (a) is original semitendinosus surface electromyogram signal figure；
Fig. 2 (b) is the semitendinosus surface electromyogram signal figure after noise reduction；
Fig. 3 (a) is that the coherence of rectus femoris and semitendinosus over time and frequency schemes；
Fig. 3 (b) is that the coherence of rectus femoris and gastrocnemius over time and frequency schemes；
Fig. 3 (c) is that the coherence of rectus femoris and tibialis anterior over time and frequency schemes；
Fig. 3 (d) is that the coherence of semitendinosus and gastrocnemius over time and frequency schemes；
Fig. 3 (e) is that the coherence of semitendinosus and tibialis anterior over time and frequency schemes；
Fig. 3 (f) is that the coherence of gastrocnemius and tibialis anterior over time and frequency schemes；
Fig. 4 (a), which falls, occurs the wavelet coherence average value of all muscle combinations under different scale；
Fig. 4 (b) moves the wavelet coherence average value of all muscle combinations under different scale upstairs；
The wavelet coherence average value of all muscle combinations under Fig. 4 (c) road-work different scale；
Fig. 4 (d), which goes downstairs, moves the wavelet coherence average value of all muscle combinations under different scale；
The wavelet coherence average value of all muscle combinations under Fig. 4 (e) stance different scale；
The wavelet coherence average value of all muscle combinations under Fig. 4 (f) walking movement different scale；
Specific embodiment
As shown in Figure 1, the present embodiment includes the following steps:
Step 1 obtains related electromyography signal sample data, specifically: passing through DELSYS TrignoWireless
The electromyography signal of related muscles when System electromyographic signal collection instrument acquires human body lower limbs movement, the experiment movement taken are station
It stands, walk, run, go upstairs, go downstairs and falls, the related muscles taken are gastrocnemius, tibialis anterior, rectus femoris and half tendon
Flesh.After being collected data, Signal Pretreatment is carried out using band-pass filtering method.Semitendinosus surface myoelectric letter after original and noise reduction
Number such as Fig. 2 (a), shown in 2 (b).
Step 2 carries out wavelet transformation to the electromyography signal after filtering processing.
Wx(a, b) is wavelet coefficient, and x (t) is electromyography signal to be analyzed,It is Morlet mother wavelet function, a is small
Wave scale factor, b are time shifts, and t is local time's origin.
P is frequency parameter, this example is that 6, σ is the parameter for controlling small wave attenuation.
Step 3 calculates two-way electromyography signal x (t), the cross wavelet analysis of y (t):
Step 4, to Wxy(a, b) is smoothed.First calculate the smooth operation on time shaft.
c1It is normalisation coefft, * represents convolution algorithm.Then the smooth operation on scale axis is calculated.
Sa(Wx(a, b))=Wx(a,b)*c2Π(0.6a)
c2It is also normalisation coefft, Π is rectangular function.
Step 5 calculates the smooth wavelet coherence of two-way electromyography signal.
S is smooth function, S (W)=Sa[St(W)]。
Step 6 obtains maximum average wavelet coherence R when taking 32 scalexy(32,b).Then it is examined using T to check
Whether there were significant differences in different behaviors for the wavelet coherence of different muscle combinations, will finally have maximum average small echo phase
32 multi-scale wavelet coherence factors of the muscle combination of responsibility number are as feature vector.Different muscle combinations are over time and frequency
Shown in coherence such as Fig. 3 (a)-(f)；Size distribution of the wavelet coherence in different daily behaviors such as Fig. 4 (a)-(f) institute
Show.
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Cited By (7)
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| CN110151176A (en) * | 2019-04-10 | 2019-08-23 | 杭州电子科技大学 | A method for continuous motion estimation of upper limb elbow joint based on electromyographic signals |
| CN111563581A (en) * | 2020-05-27 | 2020-08-21 | 杭州电子科技大学 | A method for building brain muscle function network based on wavelet coherence |
| CN112036357A (en) * | 2020-09-09 | 2020-12-04 | 曲阜师范大学 | Upper limb action recognition method and system based on surface electromyogram signal |
| CN112932508A (en) * | 2021-01-29 | 2021-06-11 | 电子科技大学 | Finger activity recognition system based on arm electromyography network |
| CN116189909A (en) * | 2023-03-06 | 2023-05-30 | 佳木斯大学 | Clinical medical discrimination method and system based on recommendation algorithm |
| CN116595290A (en) * | 2023-07-17 | 2023-08-15 | 广东海洋大学 | Method for identifying key factors affecting chlorophyll change of marine physical elements |
| CN120277373A (en) * | 2025-06-09 | 2025-07-08 | 四川物通科技有限公司 | Electroencephalogram driving behavior identification method based on coherent network characteristic dynamic evolution |
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|---|---|---|---|---|
| CN110151176A (en) * | 2019-04-10 | 2019-08-23 | 杭州电子科技大学 | A method for continuous motion estimation of upper limb elbow joint based on electromyographic signals |
| CN111563581A (en) * | 2020-05-27 | 2020-08-21 | 杭州电子科技大学 | A method for building brain muscle function network based on wavelet coherence |
| CN111563581B (en) * | 2020-05-27 | 2023-08-18 | 杭州电子科技大学 | Brain muscle function network construction method based on wavelet coherence |
| CN112036357A (en) * | 2020-09-09 | 2020-12-04 | 曲阜师范大学 | Upper limb action recognition method and system based on surface electromyogram signal |
| CN112036357B (en) * | 2020-09-09 | 2023-05-12 | 曲阜师范大学 | Upper limb action recognition method and system based on surface electromyographic signals |
| CN112932508A (en) * | 2021-01-29 | 2021-06-11 | 电子科技大学 | Finger activity recognition system based on arm electromyography network |
| CN116189909A (en) * | 2023-03-06 | 2023-05-30 | 佳木斯大学 | Clinical medical discrimination method and system based on recommendation algorithm |
| CN116189909B (en) * | 2023-03-06 | 2024-02-20 | 佳木斯大学 | Clinical medical discrimination method and system based on recommendation algorithm |
| CN116595290A (en) * | 2023-07-17 | 2023-08-15 | 广东海洋大学 | Method for identifying key factors affecting chlorophyll change of marine physical elements |
| CN120277373A (en) * | 2025-06-09 | 2025-07-08 | 四川物通科技有限公司 | Electroencephalogram driving behavior identification method based on coherent network characteristic dynamic evolution |
| CN120277373B (en) * | 2025-06-09 | 2025-08-22 | 四川物通科技集团有限公司 | Electroencephalogram driving behavior identification method based on coherent network characteristic dynamic evolution |
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