CN112006686A - Neck muscle fatigue analysis method and system - Google Patents
Neck muscle fatigue analysis method and system Download PDFInfo
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Abstract
A neck muscle fatigue analysis method comprises collecting surface electromyographic signals of left and right sternocleidomastoid muscles and left and right upper trapezius muscles of a tester; preprocessing the collected surface electromyographic signals; carrying out signal separation on the preprocessed surface electromyographic signals to obtain separated signals; carrying out feature extraction on the separated surface electromyographic signals; and carrying out neck muscle fatigue analysis according to the surface electromyographic signal characteristics after the characteristic extraction. The invention also relates to a neck muscle fatigue analysis system. The method is simple to operate, non-invasive to collect, and capable of effectively analyzing the neck muscle fatigue.
Description
Technical Field
The invention relates to a neck muscle fatigue analysis method and system.
Background
Nowadays, the neck bending probability of people in daily life is greatly increased. When the neck is bent, the gravity center of the skull moves forwards, so that the contraction force of neck muscles and the pressure born by the vertebral body are increased by times, and meanwhile, the tensile stress of neck extensors is also obviously increased. Therefore, if the neck is in a poor posture of excessive forward flexion for a long time, the neck muscles will be in a tense state for a long time, so that the neck muscles are overloaded, and muscle strain, muscle spasm and muscle tension decline appear for a long time, and finally the dynamic and static balance of the neck is disordered. Once the dynamic and static balance of the cervical vertebra is damaged and the mechanical property is reduced, the degeneration of the cervical vertebra and cervical muscle can be accelerated, and the occurrence and the development of cervical spondylosis can be caused. Relevant investigation shows that 5000 million cervical spondylosis patients in China are treated, about 100 million new cervical spondylosis patients are newly added every year, and the cervical spondylosis is gradually one of main diseases threatening the health of the population in China. Investigations also indicate that about 70% of people suffer from cervical spondylosis in their lifetime. A series of studies at home and abroad prove that the muscle strength of the neck of a patient with the cervical spondylosis is obviously reduced compared with that of a normal person, the activity of the cervical vertebra is obviously lower than that of the normal person, and meanwhile, muscles around the cervical vertebra are easier to fatigue compared with the normal person. Various movements of the neck are mainly related to cervical spine stabilizing muscles, which are groups of muscles distributed in the cervical spine and trunk parts to maintain cervical spine stability and protect the cervical spine, wherein sternocleidomastoid muscle and upper trapezius muscle play a main stabilizing role. Therefore, the fatigue state of cervical vertebra stable muscles can be known timely, and cervical spondylosis prevention can be effectively performed for people with neck fatigue.
An increasing number of basic and clinical medical findings have revealed that muscles gradually enter a state of fatigue during static tension. For muscle analysis, at present, the myoelectric signals are mostly adopted at home and abroad, and are used as a signal source for early research on muscle movement conditions, the myoelectric signals can well display the potential shift change in the human body muscle movement process, and a large number of researches prove that relevant indexes of muscle fatigue and the myoelectric signals have good correlation, and the muscle fatigue evaluation method has good reliability and effectiveness in the aspects of muscle fatigue evaluation of neck, waist, back and four limbs muscle groups.
Electromyographic signal acquisition generally has two methods: needle electrodes and surface electrodes. The former is inserted into the muscle to collect electromyographic signals, and the latter is placed on the skin to acquire surface electromyographic signals. The needle-shaped muscle electrode can collect myoelectric activity of deep muscles, the number of the involved motion units is very small, pertinence is achieved, potential change generated by a single motion unit or a single muscle fiber can be clearly derived, and therefore the function of a certain bundle of muscle fibers in muscles can be researched. However, since the area of the needle electrode test is very small and cannot reflect the functional state of the whole muscle, and the needle electrode is very critical in terms of invasiveness, position, depth and manipulation of the needle insertion, the requirement for the operator is high, and the needle electrode cannot measure the myoelectric signal during the movement, the needle electrode is generally used for clinical diagnosis and some basic research. Compared with the needle electrode for acquiring the electromyographic signals, the surface electrode has a larger detection range and lower spatial resolution due to the acquisition indirectness and non-specificity, the acquired surface electromyographic signals are more easily interfered, and the signal-to-noise ratio is lower. Its signal quality is susceptible to many factors, such as electrode position, skin condition, hair condition, fat thickness, mental state, electromagnetic interference conditions, and so forth. However, measuring electromyographic signals via surface electrodes is non-invasive, does not require the involvement of a physician, and is a preferred method for acquiring electromyographic signals in a non-clinical setting.
In the past, in the research on muscle fatigue, surface electromyographic signals collected by a single muscle are often analyzed, but the signals measured by a single electrode may even originate from a plurality of muscle groups. When the muscle of the human body conducts the electromyographic signals, the equivalent circuit of the muscle is a group of complex volume conductors, and the surface electromyographic signals are easily interfered by a plurality of muscle groups, so that the result has great uncertainty.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a neck muscle fatigue analysis method and a neck muscle fatigue analysis system, which are used for analyzing the neck muscle fatigue of a human body based on a surface electromyogram signal.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for analyzing fatigue state of neck muscles, comprising the following steps:
a. collecting surface electromyographic signals of left and right sternocleidomastoid muscles and left and right upper trapezius muscles of a tester;
b. preprocessing the collected surface electromyographic signals;
c. carrying out signal separation on the preprocessed surface electromyographic signals to obtain separated signals;
d. carrying out feature extraction on the separated surface electromyographic signals;
e. and performing neck muscle fatigue analysis according to the extracted features.
Further, the process of the step a is as follows: the method comprises the steps of adhering surface electromyographic electrodes to left and right chest latus mastoid muscles and left and right upper trapezius muscles of a neck of a tester, collecting surface electromyographic signals in the static stretching process of the neck muscles of the tester, filtering and amplifying the collected analog electromyographic signals, converting the analog electromyographic signals into digital electromyographic signals through an analog-to-digital converter, transmitting the digital electromyographic signals to a main control module, and packaging and sending the converted digital electromyographic signals to a PC (personal computer) end by the main control module.
The preprocessing of the step b comprises band-pass filtering, notch filtering and wavelet denoising.
The process of the step c is as follows: and separating the preprocessed electromyographic signals, and separating four paths of surface electromyographic signals by adopting a signal separation algorithm to eliminate the components of mutual aliasing among the signals.
The process of the step d is as follows: and extracting a root mean square value (RMS), an integral myoelectricity value (IEMG), a Median Frequency (MF) and a Mean Power Frequency (MPF) of the separated electromyographic signals.
The process of the step e is as follows: and performing windowing calculation on the extracted time domain characteristics and frequency domain characteristics, wherein the window length is L, and the step length is n/m window length. And performing linear fitting on each obtained characteristic curve, and analyzing the fatigue trend change through the slope of a linear fitting equation.
A neck muscle fatigue analysis system comprises a surface electromyogram signal acquisition module, a preprocessing module, a signal separation module, a feature extraction module and a fatigue analysis module, wherein the acquisition module is used for acquiring the surface electromyogram signals of related muscles of the neck of a tester; the preprocessing module is used for preprocessing the acquired surface electromyographic signals; the signal separation module is used for separating the preprocessed four-path surface electromyographic signals; the characteristic extraction module is used for extracting the characteristics of the separated surface electromyographic signals; the fatigue analysis module is used for carrying out neck muscle fatigue analysis according to the extracted features.
Furthermore, in the acquisition module, the surface electromyography electrodes are adhered to left and right chest latus mastoid muscles and left and right upper trapezius muscles of the neck of the tester, surface electromyography signals are acquired in the static stretching process of the neck muscles of the tester, the acquired analog electromyography signals are filtered and amplified, and then the analog electromyography signals are converted into digital electromyography signals through an analog-to-digital converter and transmitted to the main control module. And the main control module packs the converted digital electromyographic signals and sends the digital electromyographic signals to a PC (personal computer) end.
In the preprocessing module, preprocessing comprises band-pass filtering, notch filtering and wavelet denoising.
In the signal separation module, the preprocessed electromyographic signals are separated, four paths of surface electromyographic signals are separated by adopting a signal separation algorithm, and components of mutual aliasing among the signals are eliminated.
The feature extraction module extracts a root mean square value (RMS), an integrated myoelectricity value (IEMG), a Median Frequency (MF) and a Mean Power Frequency (MPF) of the separated electromyographic signals.
In the fatigue analysis module, windowing calculation is carried out on the extracted time domain characteristics and the extracted frequency domain characteristics, the window length is L, the step length is n/m, the window length is L, linear fitting is carried out on each obtained characteristic curve, and fatigue trend change is analyzed through the slope of a linear fitting equation.
According to the invention, electromyographic signals of sternocleidomastoid muscle and upper trapezius muscle related to neck fatigue are collected in a non-invasive mode, time domain parameters and frequency domain parameters expressing muscle characteristics are extracted after signal separation, and time domain and frequency domain parameters are obtained according to time-varying characteristics, so that neck muscle fatigue is analyzed.
The invention has the following beneficial effects: 1. evaluating the current fatigue condition of the muscle according to the variation characteristics of the time domain parameters and the frequency domain parameters; 2. the effectiveness of fatigue analysis is enhanced by using a signal separation method; 3. the neck muscle fatigue is evaluated in an objective and simple manner.
Drawings
FIG. 1 is a flow chart of a method of analyzing neck muscle fatigue according to the present invention;
FIG. 2 is a schematic diagram of a wavelet decomposition tree structure in wavelet denoising of a surface electromyography signal according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of time-domain and frequency-domain parameters of a four-path surface electromyogram signal varying with time according to an embodiment of the present invention, wherein (a) is each characteristic curve before separation, and (b) is each characteristic curve after separation;
fig. 4 is a hardware architecture diagram of the neck muscle fatigue analysis system of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 4, a method for analyzing a fatigue state of neck muscles, the method comprising the steps of:
a (S1), collecting surface electromyographic signals of left and right sternocleidomastoid muscles and left and right upper trapezius muscles of a tester;
b (S2), preprocessing the collected surface electromyographic signals;
c (S3), performing signal separation on the preprocessed surface electromyographic signals to obtain separated signals;
d (S4), performing feature extraction on the separated surface electromyographic signals;
e (S5). performing a neck muscle fatigue analysis based on the extracted features.
And a, collecting surface electromyographic signals of the testee. Specifically, the method comprises the following steps:
in the process of statically stretching neck muscles of a tester, surface electromyographic signals of left and right sternocleidomastoid muscles and left and right upper trapezius muscles of the tester are collected.
Further, the method mainly comprises signal acquisition, transmission and reception. Before the surface muscle electrode is placed, the following treatment needs to be carried out on the surface skin of the electrode, which is in contact with the human body: cleaning hair at a corresponding part, cleaning the surface of skin by using 70% alcohol, injecting a conductive coagulation paste into a metal plate surface muscle electrode, fixing the metal plate surface muscle electrode on the cleaned neck skin surface, and fixing the surface muscle electrode by using a PU (polyurethane) membrane; the tester keeps the head-lowering posture in the body forward bending posture, collects the myoelectric signals of the surfaces of the left and right sternocleidomastoid muscles and the left and right upper trapezius muscles of the neck of the human body, filters and amplifies the analog myoelectric signals, and then converts the analog myoelectric signals into digital myoelectric signals through an analog-to-digital converter and transmits the digital myoelectric signals to the main control module. And the main control module packs the converted digital electromyographic signals and sends the digital electromyographic signals to a PC (personal computer) end.
And in the step b, preprocessing the collected surface electromyographic signals. Specifically, the method comprises the following steps:
the pretreatment of the collected electromyographic signals mainly comprises band-pass filtering, notch filtering and wavelet denoising. In the embodiment, a 20-200Hz band-pass filter is adopted to carry out band-pass filtering on the collected surface electromyogram signals, power frequency interference of 50Hz and 150Hz harmonic components are subjected to power frequency denoising, and then a wavelet spatial filtering method is adopted to eliminate in-band noise.
Further, as the frequency band range of the surface electromyogram signal is mainly between 10Hz and 500Hz, and the main energy is concentrated between 20Hz and 200Hz, the acquired surface electromyogram signal is subjected to 20-200Hz band-pass filtering processing. 50Hz power frequency interference generated by the voltage 220v in China has great influence on surface myoelectric signals, and therefore, a notch filter is used for filtering the power frequency signals.
Further, in order to filter out in-band noise, a wavelet spatial filtering method is adopted. The wavelet decomposition tree structure of the original signal is shown in fig. 2. Performing 3-layer decomposition on the surface myoelectric signal by using a db5 wavelet to obtain a high-frequency coefficient and a low-frequency coefficient; and then, the purpose of filtering is achieved by utilizing the correlation of corresponding points of the wavelet coefficients on different scales, and then the original signal is reconstructed by a reconstruction algorithm of wavelet transformation, so that the de-noised signal can be obtained.
And c, performing signal separation on the preprocessed surface electromyographic signals. Specifically, the method comprises the following steps:
and (3) performing signal Separation on the preprocessed surface electromyographic signals, and performing de-aliasing Separation on the four paths of surface electromyographic signals by adopting a time Decorrelation Source Separation (TDSEP) in a signal Separation algorithm.
Furthermore, when the muscle of the human body conducts the electromyographic signals, the equivalent circuit of the muscle is a group of complex volume conductors, and the surface electromyographic signals are easily interfered by a plurality of muscle groups. The four-way surface electromyographic signals are separated by using time decorrelation Technology (TDSEP).
And d, performing feature extraction on the surface electromyographic signals after signal separation. Specifically, the method comprises the following steps:
this embodiment extracts Root Mean Square (RMS) and Integrated myoelectric (IEMG) values in the time domain, Mean Power Frequency (MPF) and Median Frequency (media Frequency, MF) in the Frequency domain of the surface electromyographic signal after the signal separation. The surface electromyogram signal root mean square value represents the overall signal energy level, the integral electromyogram value reflects the change of the signal intensity along with time, and the average power frequency and the median frequency reflect the frequency domain change of the signal.
And e, performing muscle fatigue analysis according to the electromyographic signals after the characteristic extraction.
The characteristics before and after signal separation are calculated by using a windowing method, the window length is set to be 2000 sampling points (the sampling rate is 1000Hz, namely 2-second time window), the step length is set to be one tenth of the window length, and the obtained characteristic curves can not only keep the detail change of the surface electromyogram signals, but also reflect the trend change of the fatigue process. Please refer to fig. 3, wherein (a) is the characteristic curves before separation, and (b) is the characteristic curves after separation.
Further, linear fitting is carried out on each characteristic curve before and after separation, and a linear fitting equation of each characteristic curve is obtained.
Further, the slope of a linear fitting equation of each characteristic curve of the myoelectricity in the fatigue period is compared, and the fatigue state of the neck muscle of the tester is analyzed according to the trend change conditions of the time domain and frequency domain indexes. The time domain index surface electromyogram signal root mean square value and the integral electromyogram value can be increased along with the increase of the fatigue degree, and the frequency domain index median frequency and the average power frequency can be reduced along with the increase of the fatigue degree.
Through analysis of characteristic parameters of surface electromyographic signals of a tester during a low head fatigue test in a posture of body forward flexion, changes of muscle fatigue indexes with time are found, and as shown in the following table, ch1 is right sternocleidomastoid muscle, ch2 is right upper oblique muscle, ch3 is left sternocleidomastoid muscle, and ch4 is left upper oblique muscle.
In this embodiment, the slope of the linear fitting equation of the time domain characteristic curves before and after the separation of the surface electromyographic signals during the low head fatigue test is shown in table 1:
wherein, 1: p >0.05,2: p <0.01
TABLE 1
The slope of the linear fitting equation of each characteristic curve of the frequency domain before and after the separation of the surface electromyographic signals in the period of the low head fatigue experiment is shown in the table 2:
wherein, 1: p >0.05,2: p <0.01,3:0.01< p <0.05
TABLE 2
According to experimental data in an analysis table, in the period of a low head fatigue experiment, the slope of a linear fitting equation of the root mean square value and the integral myoelectric value of the surface myoelectric signals of the right-side trapezius muscle and the left-side trapezius muscle of each testee is a positive value, the slope of the linear fitting equation of the root mean square value and the integral myoelectric value shows a slow ascending trend along with the increase of the low head time, the slope of the linear fitting equation of the median frequency and the average power frequency curve is a negative value, and the median frequency and the average power frequency show a slow descending trend along with the increase of the low head; and the time domain and frequency domain indexes of the surface electromyographic signals of the right sternocleidomastoid muscle and the left sternocleidomoid muscle have no obvious trend change. It is shown that the trapezius muscle is in tension during the lowering of the head, and fatigue is generated, and the sternocleidomastoid muscle is not obviously fatigued. The time domain and frequency domain index differences before and after the separation of the electromyographic signals of the surfaces of the right sternocleidomastoid muscle and the left sternocleidomastoid muscle are not obvious, after the electromyographic signals of the surfaces of the right trapezius muscle and the left trapezius muscle are separated by a time decorrelation method, the change trends of a root mean square value curve, an integral electromyographic value curve and an average power frequency curve are enhanced remarkably, and the change trend of a median frequency curve is enhanced remarkably. Therefore, the change of the slope of the root mean square value, the integral myoelectric value and the mean power frequency curve linear fitting equation of the surface myoelectric signal after being separated by the time decorrelation method can be used as a more effective fatigue analysis index.
Referring to fig. 4, a hardware architecture diagram of the neck muscle fatigue analysis system 10 of the present invention is shown. The system comprises: the system comprises an acquisition module 101, a preprocessing module 102, a signal separation module 103, a feature extraction module 104 and a fatigue analysis module 105.
The acquisition module 101 is used for acquiring surface electromyogram signals of a tester. Specifically, the method comprises the following steps:
the acquisition module 101 acquires the electromyographic signals of the left and right sternocleidomastoid muscles and the left and right upper trapezius muscles of the neck of the tester during the static stretching period.
Further, the acquisition module 101 mainly includes signal acquisition, transmission and reception. Before the surface muscle electrode is placed, the following treatment needs to be carried out on the surface skin of the electrode, which is in contact with the human body: cleaning hair at a corresponding part, cleaning the surface of skin by using 70% alcohol, injecting a conductive coagulation paste into a metal plate surface muscle electrode, fixing the metal plate surface muscle electrode on the cleaned neck skin surface, and fixing the surface muscle electrode by using a PU (polyurethane) membrane; the tester keeps the head-lowering posture in the body forward bending posture, collects the myoelectric signals of the surfaces of the left and right sternocleidomastoid muscles and the left and right upper trapezius muscles of the neck of the human body, filters and amplifies the analog myoelectric signals, and then converts the analog myoelectric signals into digital myoelectric signals through an analog-to-digital converter and transmits the digital myoelectric signals to the main control module. And the main control module packs the converted digital electromyographic signals and sends the digital electromyographic signals to a PC (personal computer) end.
The preprocessing module 102 is configured to preprocess the collected surface electromyogram signal. Specifically, the method comprises the following steps:
the preprocessing module 102 is used for preprocessing the collected electromyographic signals and mainly comprises band-pass filtering, notch filtering and wavelet denoising. In the embodiment, a 20-200Hz band-pass filter is adopted to carry out band-pass filtering on the collected surface electromyogram signals, power frequency interference of 50Hz and 150Hz harmonic components are subjected to power frequency denoising, and then a wavelet spatial filtering method is adopted to remove in-band noise.
Furthermore, because the frequency band range of the surface electromyogram signal is mainly between 10Hz and 500Hz, the main energy is concentrated between 20Hz and 200Hz, the power frequency interference 50Hz generated by the voltage 220v in China has a great influence on the surface electromyogram signal, and therefore the power frequency signal needs to be filtered. And the acquired surface electromyogram signals are subjected to 20-200Hz band-pass filtering processing.
Further, in order to filter out in-band noise, a wavelet spatial filtering method is adopted. The wavelet decomposition tree structure of the original signal is shown in fig. 2. Performing wavelet 3-layer decomposition on the surface myoelectric signal by using a db5 wavelet to obtain a high-frequency coefficient and a low-frequency coefficient; and then, the purpose of filtering is achieved by utilizing the correlation of corresponding points of the wavelet coefficients on different scales, and then the original signal is reconstructed by a reconstruction algorithm of wavelet transformation, so that the de-noised signal can be obtained.
The signal separation module 103 is configured to perform signal separation on the preprocessed surface electromyogram signal. Specifically, the method comprises the following steps:
the signal Separation module 103 performs signal Separation on the preprocessed surface electromyogram signals, and performs de-aliasing Separation on the four paths of surface electromyogram signals by using a time Decorrelation Source Separation (TDSEP) in a signal Separation algorithm.
Furthermore, when the muscle of the human body conducts the electromyographic signals, the equivalent circuit of the muscle is a group of complex volume conductors, and the surface electromyographic signals are easily interfered by a plurality of muscle groups. The four-way surface electromyographic signals are separated by using time decorrelation Technology (TDSEP).
The feature extraction module 104 is configured to perform feature extraction on the electromyographic signals after signal separation. Specifically, the method comprises the following steps:
in this embodiment, the feature extraction module 104 extracts Root Mean Square (RMS) and Integrated myoelectric (IEMG) values in the time domain, Mean Power Frequency (MPF) and Median Frequency (media Frequency, MF) in the Frequency domain of the surface electromyogram after the signal separation. The surface electromyogram signal root mean square value represents the overall signal energy level, the integral electromyogram value reflects the change of the signal intensity along with time, and the average power frequency and the median frequency reflect the frequency domain change of the signal.
The fatigue analysis module 105 is configured to perform muscle fatigue analysis according to the surface electromyogram signal after feature extraction. The method comprises the following specific steps:
the characteristics before and after signal separation are calculated by using a windowing method, the window length is set to be 2000 sampling points (the sampling rate is 1000Hz, namely 2-second time window), the step length is set to be one tenth of the window length, and the obtained characteristic curves can not only keep the detail change of the surface electromyogram signals, but also reflect the trend change of the fatigue process. Referring to fig. 3, fig. 3 (a) shows the characteristic curves before separation, and fig. 3 (b) shows the characteristic curves after separation.
Further, linear fitting is carried out on each characteristic curve before and after separation, and a linear fitting equation of each characteristic curve is obtained.
Further, the slope of a linear fitting equation of each characteristic curve of the myoelectricity in the fatigue period is compared, and the fatigue state of the neck muscle of the tester is analyzed according to the trend change conditions of the time domain and frequency domain indexes. The time domain index surface electromyogram signal root mean square value and the integral electromyogram value can be increased along with the increase of the fatigue degree, and the frequency domain index median frequency and the average power frequency can be reduced along with the increase of the fatigue degree.
Through analysis of characteristic parameters of surface electromyographic signals of a tester during a low head fatigue test in a posture of body forward flexion, changes of muscle fatigue indexes with time are found, and as shown in the following table, ch1 is right sternocleidomastoid muscle, ch2 is right upper oblique muscle, ch3 is left sternocleidomastoid muscle, and ch4 is left upper oblique muscle.
The neck muscle fatigue analysis method adopts an objective, scientific, simple and quick mode to analyze the neck muscle fatigue, analyzes the neck muscle fatigue by collecting the electromyographic signals of the left and right sternocleidomastoid muscles for maintaining the neck stability and the surface electromyographic signals of the left and right upper trapezius muscles and adopting a Time Decorrelation (TDSEP) signal separation algorithm system device, can enhance the fatigue characteristic change, guides the neck fatigue-prone crowd to change the current neck posture, and relieves the neck muscle fatigue condition through neck exercise.
Although the present invention has been described with reference to the presently preferred embodiments, it will be understood by those skilled in the art that the foregoing description is illustrative only and is not intended to limit the scope of the invention, as claimed.
Claims (10)
1. A method for analyzing fatigue state of neck muscles, which is characterized by comprising the following steps:
a. collecting surface electromyographic signals of left and right sternocleidomastoid muscles and left and right upper trapezius muscles of a tester;
b. preprocessing the collected surface electromyographic signals;
c. carrying out signal separation on the preprocessed surface electromyographic signals to obtain separated signals;
d. carrying out feature extraction on the separated surface electromyographic signals;
e. and performing neck muscle fatigue analysis according to the extracted features.
2. The method for analyzing fatigue status of neck muscles according to claim 1, wherein the process of step a is as follows: the method comprises the steps of adhering surface electromyographic electrodes to left and right chest latus mastoid muscles and left and right upper trapezius muscles of a neck of a tester, collecting surface electromyographic signals in the static stretching process of the neck muscles of the tester, filtering and amplifying the collected analog electromyographic signals, converting the analog electromyographic signals into digital electromyographic signals through an analog-to-digital converter, transmitting the digital electromyographic signals to a main control module, and packaging and sending the converted digital electromyographic signals to a PC (personal computer) end by the main control module.
3. A method for analyzing fatigue status of neck muscles as claimed in claim 1 or 2, wherein the preprocessing of step b comprises band-pass filtering, notch filtering and wavelet de-noising.
4. A method for analyzing fatigue status of neck muscles as claimed in claim 1 or 2, wherein said step c comprises the steps of: and separating the preprocessed electromyographic signals, and separating four paths of surface electromyographic signals by adopting a signal separation algorithm to eliminate the components of mutual aliasing among the signals.
5. A method for analyzing fatigue status of neck muscles as claimed in claim 1 or 2, wherein said step d comprises the steps of: and extracting the root mean square value RMS, the integral myoelectric value IEMG, the median frequency MF and the average power frequency MPF of the separated myoelectric signal.
6. A method for analyzing fatigue status of neck muscles as claimed in claim 1 or 2, wherein said procedure of step e is: and performing windowing calculation on the extracted time domain characteristics and frequency domain characteristics, wherein the window length is L, and the step length is n/m window length. And performing linear fitting on each obtained characteristic curve, and analyzing the fatigue trend change through the slope of a linear fitting equation.
7. The system realized by the neck muscle fatigue analysis method according to claim 1, comprises a surface electromyogram signal acquisition module, a preprocessing module, a signal separation module, a feature extraction module and a fatigue analysis module, wherein the acquisition module is used for acquiring the surface electromyogram signals of the related muscles of the neck of the tester; the preprocessing module is used for preprocessing the acquired surface electromyographic signals; the signal separation module is used for separating the preprocessed four-path surface electromyographic signals; the characteristic extraction module is used for extracting the characteristics of the separated surface electromyographic signals; the fatigue analysis module is used for carrying out neck muscle fatigue analysis according to the extracted features.
8. The system of claim 7, wherein in the collecting module, the surface electromyographic electrodes are adhered to left and right sternocleidomastoid muscles and left and right upper trapezius muscles of the neck of the tester, the surface electromyographic signals are collected in the static stretching process of the neck muscles of the tester, the collected analog electromyographic signals are filtered and amplified, then the analog electromyographic signals are converted into digital electromyographic signals through an analog-to-digital converter and transmitted to the main control module, and the main control module packs the converted digital electromyographic signals and transmits the digital electromyographic signals to the PC terminal.
9. The system of claim 7 or 8, wherein in the preprocessing module, the preprocessing includes band-pass filtering, notch filtering and wavelet denoising; in the signal separation module, the preprocessed electromyographic signals are separated, four paths of surface electromyographic signals are separated by adopting a signal separation algorithm, and components of mutual aliasing among the signals are eliminated.
10. The system according to claim 7 or 8, wherein the feature extraction module extracts the root mean square value RMS, the integrated electromyogram value IEMG, the median frequency MF and the mean power frequency MPF of the separated electromyogram signal;
in the fatigue analysis module, windowing calculation is carried out on the extracted time domain characteristics and the extracted frequency domain characteristics, the window length is L, the step length is n/m, the window length is L, linear fitting is carried out on each obtained characteristic curve, and fatigue trend change is analyzed through the slope of a linear fitting equation.
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