WO2020222626A1

WO2020222626A1 - Method of analyzing muscle fatigue by electromyogram measurement

Info

Publication number: WO2020222626A1
Application number: PCT/KR2020/095033
Authority: WO
Inventors: 정윤영; 김영석; 윤인열
Original assignee: 포항공과대학교 산학협력단; 재단법인 나노기반소프트일렉트로닉스연구단
Priority date: 2019-04-29
Filing date: 2020-03-05
Publication date: 2020-11-05
Also published as: KR102151745B1

Abstract

Provided according to one embodiment of the present disclosure is a process for analyzing muscle fatigue via electromyogram measurement by a wearable device, the process comprising the steps of: (a) measuring electromyogram signals by electric signals on a muscle surface of a user, detected at electrodes; (b) obtaining pure electromyogram signals (m[n]) from the measured electromyogram signals; and (c) analyzing muscle fatigue on the basis of the obtained pure electromyogram signals (m[n]), and providing an analysis result to the user.

Description

Method for analyzing muscle fatigue through EMG measurement and wearable device for performing the same

The present disclosure relates to a method for analyzing muscle fatigue through EMG measurement and a wearable device for performing the same, and more particularly, to measure an EMG signal, which is an electrical signal generated from a user's muscle, and include an EMG signal from the measured EMG signal. The present invention relates to a method and apparatus for obtaining only a pure EMG signal from which noise has been removed, and analyzing muscle fatigue with the obtained pure EMG signal, thereby providing more accurate muscle fatigue evaluation information to a user.

For athletes, quantitatively checking muscle fatigue during training and competition is a very necessary skill for physical condition management and long-term muscle training. Several studies have been conducted on the method of quantitatively evaluating muscle fatigue through EMG signals generated during muscle contraction, but wearable devices that quantitatively analyze muscle fatigue by measuring EMG in real time have not been developed. I couldn't.

In general, EMG signals contain various noises, including electrocardiogram signals, which are electrical signals generated by the heart, so in order to analyze muscle fatigue with high accuracy from the EMG signals, it is necessary to distinguish only signals generated from pure muscles. .

Therefore, in order to accurately measure the muscle fatigue of an athlete in real time, it is possible to quantitatively evaluate the muscle fatigue through signal analysis after measuring the EMG and removing noise including the EKG from the measured EMG signal without damage to the pure EMG signal. The demand for technology development for wearable devices is gradually increasing, and a solution to the above-described problems is urgently needed.

The present disclosure is to solve the problems of the prior art described above, by measuring an EMG signal, which is an electrical signal generated from a user's muscle, and obtaining only a pure EMG signal from which noise including an EMG signal has been removed from the measured EMG signal, Its purpose is to provide a user with more accurate muscle fatigue evaluation information in real time by quantitatively analyzing muscle fatigue based on the obtained pure EMG signal.

The objects of the present disclosure are not limited to the above-mentioned objects, and other objects not mentioned will be clearly understood from the following description.

According to an embodiment of the present disclosure for achieving the above object, in a method of analyzing a muscle fatigue level through an electromyography measurement by a wearable device, (a) an EMG signal is transmitted as an electrical signal on a user's muscle surface detected by an electrode. Measuring; (b) obtaining a pure EMG signal (m[n]) from the measured EMG signal; And (c) analyzing muscle fatigue based on the obtained pure EMG signal m[n], and providing the analysis result to a user, a method for analyzing muscle fatigue through EMG measurement is provided.

The step (a) may include measuring an EMG signal c[n] before muscle contraction of the user and an EMG signal s[n] during muscle contraction.

In step (b), (b1) the first heart rate peak point is determined from the measured slope change information of the EMG signal c[n] before muscle contraction, and the muscle is based on the determined first heart rate peak point. Rearranging the EMG signal (c[n]) before contraction; And (b2) determining a second heart rate peak point from the measured slope change information of the EMG signal s[n] during muscle contraction.

In the step (b), (b3) the first heart rate peak point of the EMG signal c[n] before muscle contraction and the second heart rate peak point of the EMG signal s[n] during muscle contraction Setting a time difference value (n ₀ ) by comparing and calculating a median value or an average value; (b4) The first heart rate peak point of the pre-muscular contraction EMG signal (c[n]) by moving the rearranged pre-muscular contraction EMG signal (c[n]) by a time difference value (n ₀ ) on the time axis And matching the second heart rate peak point of the EMG signal (s[n]) when the muscle contracts; And (b5) removing the rearranged EMG signal (c[n]) from the EMG signal (s[n]) during muscle contraction to obtain the pure EMG signal (m[n]). Can include.

In the step (b5), the rearranged EMG signal (c[n]) before muscle contraction is removed from the time axis (i) or (ii) converts the time axis signal into a frequency axis signal through fast Fourier transform. It may include the step of removing using any one of the methods of removing.

In the step (c), muscle fatigue is quantitatively compared by comparing the ratio of the total energy spectral density to the spectral density below a specific frequency using a spectrum evaluation method based on the obtained pure EMG signal (m[n]). It may include the step of analyzing.

The step (c) may include (i) providing the muscle fatigue analysis result in the form of voice information or visual information through an information providing module, or (ii) transmitting the muscle fatigue analysis result to a user terminal through a wireless communication module. have.

On the other hand, according to another embodiment of the present disclosure, an electrode for measuring an EMG signal using an electrical signal detected from a user's muscle surface; A processor for obtaining a pure EMG signal (m[n]) from the measured EMG signal and analyzing muscle fatigue based on the obtained pure EMG signal (m[n]); An information providing module that provides a result of muscle fatigue analysis in the form of voice information or visual information; And a wireless communication module for transmitting the muscle fatigue analysis result to a user terminal through wireless communication.

The electrode may measure an EMG signal c[n] before muscle contraction of the user and an EMG signal s[n] during muscle contraction.

The processor determines a first heart rate peak point from the measured slope change information of the pre-muscular contraction EMG signal c[n], and based on the determined first heart rate peak point, the EMG signal c [n]) may be rearranged, and the second heart rate peak point may be determined from the measured slope change information of the EMG signal s[n] during muscle contraction.

The processor compares the first heart rate peak point of the EMG signal c[n] before muscle contraction and the second heart rate peak point of the EMG signal s[n] during muscle contraction, and the median value or average value By calculating, a time difference value (n ₀ ) is set, and the rearranged EMG signal before muscle contraction (c[n]) is moved by a time difference value (n ₀ ) on the time axis, and the EMG signal before muscle contraction ( The first heart rate peak point of c[n]) coincides with the second heart rate peak point of the EMG signal s[n] during muscle contraction, and the EMG signal s[n] during muscle contraction The pure EMG signal m[n] may be obtained by removing the EMG signal c[n] before the rearranged muscle contraction.

The processor is a method of removing the rearranged EMG signal (c[n]) before muscle contraction by (i) removing it from the time axis or (ii) converting the time axis signal into a frequency axis signal through fast Fourier transform It can be removed using any one of the methods.

The processor can quantitatively analyze muscle fatigue by comparing the ratio of the total energy spectral density to the spectral density below a specific frequency using a spectrum evaluation method based on the obtained pure EMG signal (m[n]). have.

On the other hand, according to another embodiment of the present disclosure, in a wearable device, an EMG signal measurement function for measuring an EMG signal using an electric signal on a user's muscle surface detected by an electrode; A pure EMG acquisition function for obtaining a pure EMG signal (m[n]) from the measured EMG signal; And a computer-readable recording medium in which a program for executing a muscle fatigue analysis result providing function that analyzes muscle fatigue based on the obtained pure EMG signal m[n] and provides the analysis result to the user is provided. do.

According to an embodiment of the present disclosure, the analysis of muscle fatigue is performed by classifying only the electrical signal generated by muscle movement from the EMG signal measured through the electrode, so that a more accurate analysis of the muscle fatigue is possible. .

According to another embodiment of the present disclosure, by measuring an electrical signal on a user's muscle surface in contact with an electrode by wearing a wearable device by the user in real time, obtaining a pure EMG signal from the measured EMG signal, and based on the obtained signal. Since the analysis result information is provided to the user by analyzing the muscle fatigue, it has the advantage of providing real-time analysis information on the muscle fatigue to the user.

According to another embodiment of the present disclosure, analysis information or an alarm is provided to the user only when it is determined that the analyzed muscle fatigue is equal to or greater than a preset predetermined threshold, and provides information on EMG measurement and muscle fatigue analysis. It can increase power efficiency.

The effects of the present disclosure are not limited to the above effects, and should be understood to include all effects that can be deduced from the configuration of the invention described in the detailed description or claims of the present disclosure.

1 is a block diagram illustrating a configuration of a wearable device for analyzing muscle fatigue through EMG measurement according to an embodiment of the present disclosure.

FIG. 2 is a flow chart illustrating a process of analyzing muscle fatigue through EMG measurement by a wearable device according to an embodiment of the present disclosure.

3 is a flowchart illustrating a process of obtaining a pure EMG signal from an electrical signal measured by an electrode according to an embodiment of the present disclosure.

4 is a flowchart illustrating a process of evaluating muscle fatigue based on a pure EMG signal according to an embodiment of the present disclosure.

Hereinafter, the present disclosure will be described with reference to the accompanying drawings. However, the present disclosure may be implemented in various different forms, and thus is not limited to the exemplary embodiments described herein. In addition, in the drawings, parts not related to the description are omitted in order to clearly describe the present disclosure, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only "directly connected" but also "indirectly connected" with another member interposed therebetween. . In addition, when a part "includes" a certain component, it means that other components may be further provided, rather than excluding other components unless specifically stated to the contrary.

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating a configuration of a wearable device 100 for analyzing muscle fatigue through EMG measurement according to an embodiment of the present disclosure.

Referring to FIG. 1, a wearable device 100 according to an embodiment of the present disclosure may include an electrode 110, a processor 120, an information providing module 130, and a wireless communication module 140.

The electrode 110 is in direct or indirect contact with the user's skin or directly attached to a muscle, so that an electrical signal can be detected on the user's muscle surface, and an EMG signal can be measured with an electrical signal detected on the user's muscle surface have.

The EMG signal may be a signal measured including an electrocardiogram signal, which is an electrical signal generated from the heart, and various noises.

The electrode 110 may measure the EMG signal c[n] before muscle contraction of the user and the EMG signal s[n] during muscle contraction with respect to the EMG signal.

The electrode 110 transmits the measured EMG signal c[n] before muscle contraction and the EMG signal s[n] during muscle contraction to the processor 120 to obtain a pure EMG signal (m[n]). You can do it.

The pure EMG signal m[n] may mean an electrical signal generated only by movement of a muscle among electrical signals generated on a muscle surface.

The processor 120 may obtain a pure EMG signal m[n] from the measured EMG signal, and analyze muscle fatigue based on the obtained pure EMG signal m[n].

Specifically, the processor 120 may obtain a pure EMG signal m[n] based on the measured EMG signal c[n] before muscle contraction and the EMG signal s[n] during muscle contraction. have.

The processor 120 determines a first heart rate peak point from the measured slope change information of the EMG signal c[n] before muscle contraction, and the measured EMG before muscle contraction based on the determined first heart rate peak point. The signal c[n] can be rearranged.

The processor 120 may determine the second heart rate peak point from the measured slope change information of the EMG signal s[n] during muscle contraction.

The processor 120 compares the first heart rate peak point for the determined EMG signal c[n] before muscle contraction and the second heart rate peak point for the determined muscle contraction EMG signal s[n] A value or average value may be calculated, and a time difference value (n ₀ ) may be set as the calculated median value or average value.

The processor 120 moves the rearranged EMG signal c[n] before muscle contraction by the time difference value n ₀ on the time axis, so that the first EMG signal c[n] before muscle contraction is The heart rate peak point and the second heart rate peak point for the EMG signal s[n] during muscle contraction may be matched.

When the first heart rate peak point and the second heart rate peak point match, the processor 120 removes the rearranged muscle contraction pre-EMG signal c[n] from the muscle contraction EMG signal s[n]. A pure EMG signal (m[n]) may be obtained.

According to an embodiment of the present disclosure, the processor 120 is a method of removing the rearranged EMG signal c[n] before muscle contraction in the time axis, and the rearrangement in the EMG signal s[n] when the muscle contracts. The pure EMG signal m[n] may be obtained by removing the EMG signal c[n] before the muscle contraction.

According to another embodiment of the present disclosure, the processor 120 converts a signal of a time axis into a signal of a frequency axis through fast Fourier transform to remove the rearranged EMG signal c[n] before muscle contraction. During contraction, a pure EMG signal m[n] may be obtained by removing the rearranged EMG signal c[n] before muscle contraction from the EMG signal s[n].

The processor 120 quantitatively measures the muscle fatigue of the user by comparing the ratio of the total energy spectral density to the spectral density below a specific frequency using a spectrum evaluation method based on the obtained pure EMG signal (m[n]). Can be analyzed. Here, the processor 120 may determine a specific frequency used for quantitative analysis of muscle fatigue according to a preset method. For example, according to the first method, in a normal state in which the processor 120 does not feel fatigue, the reference frequency without the influence of noise in the PSD (power spectral density) result of the electromyography (EMG) signal (for example, 20 Hz) or more, the frequency value of the peak point with the largest magnitude can be determined as a specific frequency. As another example, according to the second method, the processor 120 obtains a normalized cumulative power distribution (NCPD) from the PSD result of the EMG signal in a normal state where there is no fatigue, and then the cumulative power is a specific ratio (eg: 25%, 50%, 25% to 50%) can be determined as a specific frequency.

The present disclosure is not to be interpreted as limiting the method of determining a specific frequency to the first method or the second method. According to the present disclosure, the processor 120 may obtain a more accurate result by quantitatively analyzing muscle fatigue using a specific frequency determined according to the first method or the second method. As the muscle fatigue increases, less energy in the high-frequency region may be observed, and the processor 120 performs a quantitative analysis with higher accuracy according to the first method or the second method by using such muscle characteristics. can do.

The information providing module 130 may mean that it is provided in the wearable device 100 in the form of a speaker or a display.

Accordingly, the information providing module 130 may convert or display the muscle fatigue information analyzed by the processor 120 into voice information or visual information, and provide it to the user.

The wireless communication module 140 may transmit the muscle fatigue analysis result derived from the processor 120 to a user terminal (not shown) through wireless communication and provide it to the user, and wirelessly communicate not only to the user but also to the user's related terminal. Information can be transmitted through the network so that information can be shared.

The user terminal (not shown) is any kind of handheld-based wireless communication device equipped with a touch screen panel such as a mobile phone, a smart phone, a personal digital assistant (PDA), a portable multimedia player (PMP), a tablet PC, etc. In addition to this, a device on which a basis for installing and executing applications is provided, such as a desktop PC, a tablet PC, a laptop PC, and an IPTV including a set-top box may be included.

In the user terminal (not shown), a muscle fatigue analysis program according to EMG measurement may be pre-installed and stored in a memory of the user terminal (not shown).

The user terminal (not shown) may be implemented as a terminal such as a computer operating through a computer program for realizing the functions described herein.

The wearable device 100 may be implemented in a form attached or worn on a user's body.

The wearable device 100 may have a program for EMG measurement and muscle fatigue analysis previously installed, and a separate memory function may be provided so that a user's setting function or measurement information and analysis information may be stored.

FIG. 2 is a flow chart illustrating a process of analyzing muscle fatigue through EMG measurement by the wearable device 100 according to an embodiment of the present disclosure.

First, the wearable device 100 may measure an EMG signal by sensing an electrical signal from the user's muscle surface (S210). In this case, the measured EMG signal may be a signal measured by including an EMG signal and noise, which are electrical signals generated from the heart, in addition to the EMG signal generated during muscle exercise.

Thereafter, the wearable device 100 classifies and obtains only pure EMG signals (m[n]) from the measured EMG signals in order to perform muscle fatigue analysis with improved accuracy with EMG signals generated during muscle exercise. Can be (S220).

When the pure EMG signal m[n] is obtained, the wearable device 100 may perform a quantitative analysis on muscle fatigue based on the obtained pure EMG signal m[n] (S230), and the analysis It is possible to provide the result information on the muscle fatigue to the user (S240).

The analyzed result information on the muscle fatigue may be provided to the user in the form of audio information or visual information through a speaker or display device provided in the wearable device 100, and for this purpose, the wearable device 100 Transformation for information provision may be further performed.

According to an embodiment of the present disclosure, the wearable device 100 determines whether or not the muscle fatigue analyzed through a separate algorithm exceeds a predetermined threshold based on the analyzed result information on the muscle fatigue level. The power efficiency can be improved by providing the analysis result to the user only when the analyzed muscle fatigue level exceeds a predetermined threshold value. In this case, the predetermined threshold may be set in advance by a user.

According to another embodiment of the present disclosure, the wearable device 100 provides result information on the analyzed muscle fatigue to the user as soon as the analysis on the muscle fatigue is completed, but the analyzed muscle fatigue exceeds a predetermined threshold. Only when it is determined that the alarm can be additionally provided in the form of audio information or visual information.

3 is a flowchart illustrating a process of obtaining a pure EMG signal m[n] from an electrical signal measured by the electrode 110 according to an embodiment of the present disclosure.

First, the wearable device 100 may measure an EMG signal of a user through the provided electrode 110.

Specifically, in order to obtain a pure EMG signal (m[n]) generated only by muscle movement from the measured EMG signal, and to analyze an accurate muscle fatigue level based on the signal, the wearable device 100 includes the electrode 110 Through this, the user's EMG signal c[n] before muscle contraction may be measured (S211), and the EMG signal s[n] during muscle contraction may be measured (S212).

Thereafter, the wearable device 100 may determine the first heart rate peak point from the slope change information of the EMG signal c[n] before muscle contraction through the processor 120 (S221), and the determined first heart rate peak Based on the point, the EMG signal c[n] before muscle contraction may be rearranged (S222).

In addition, the wearable device 100 may determine the second heart rate peak point from the slope change information of the EMG signal s[n] when the muscle contracts through the processor 120 (S223).

The wearable device 100 compares the first heart rate peak point for the EMG signal c[n] before muscle contraction and the second heart rate peak point for the EMG signal s[n] during muscle contraction, and has a median value. Alternatively, by calculating the average value, the calculated median value or the average value may be set as the time difference value n ₀ (S224).

When the setting of the time difference value (n ₀ ) is completed, the wearable device 100 moves the EMG signal (c[n]) before the rearranged muscle contraction by a time difference value (n ₀ ) on the time axis to contract the muscle. The first heart rate peak point for the entire EMG signal c[n] and the second heart rate peak point for the EMG signal s[n] during muscle contraction may be matched (S225).

As the wearable device 100 matches the first heart rate peak point and the second heart rate peak point, the re-arranged EMG signal c[n] in the EMG signal s[n] when the muscle contracts. ), a pure EMG signal m[n] can be obtained (S226).

According to an embodiment of the present disclosure, after matching the first heart rate peak point and the second heart rate peak point, the wearable device 100 removes the rearranged muscle contraction EMG signal c[n] on the time axis. The pure EMG signal m[n] can be obtained by removing the EMG signal c[n] before the rearranged muscle contraction from the EMG signal s[n] during muscle contraction by using a method of .

According to another embodiment of the present disclosure, after matching the first heart rate peak point and the second heart rate peak point, the wearable device 100 uses a method of converting a time-axis signal into a frequency-axis signal through fast Fourier transform. Thus, the pure EMG signal m[n] can be obtained by removing the rearranged EMG signal c[n] before muscle contraction from the EMG signal s[n] during muscle contraction.

In this case, the method of converting the signal on the time axis into a signal on the frequency axis and removing the rearranged pre-muscular contraction EMG signal c[n] from the muscle contraction EMG signal s[n] is a fast Fourier transform. It may be performed through at least one of a discrete-time Fourier transform, discrete Fourier transform, and a spectrum evaluation method in which autocorrelation and fast Fourier transform are combined.

4 is a flowchart illustrating a process of evaluating muscle fatigue based on a pure EMG signal m[n] according to an embodiment of the present disclosure.

First, the wearable device 100 performs transformation through a spectrum evaluation method based on the pure EMG signal m[n] obtained through the processor 120 to determine the energy spectral density (M[k]) of the frequency axis. It can be calculated (S231).

The wearable device 100 may calculate a ratio r of the energy spectral density below a specific frequency to the total energy spectral density based on the calculated energy spectral density M[k] on the frequency axis (S232). In this case, the specific frequency may be set differently according to the user's setting.

Thereafter, the wearable device 100 uses the ratio (r) of the calculated total energy spectral density to the energy spectral density below a specific frequency and the calculated energy spectrum at a specific frequency or less compared to the calculated total energy spectral density before muscle exercise. By comparing the density ratio (r ₀ ), the degree of reduction can be determined, and muscle fatigue can be analyzed through this (S233).

Through the above-described muscle fatigue evaluation algorithm, compared to a method of analyzing muscle fatigue using a simple frequency filter, a pure EMG signal (m[n]) can be detected without damage.

In addition, since the above-described muscle fatigue evaluation algorithm is an efficient calculation method that can be implemented even in low-spec hardware, it is easy to put it into practical use due to its wide device application range.

As described above, according to an embodiment of the present disclosure, the analysis of muscle fatigue is performed by classifying only the electrical signal generated by muscle movement from the EMG signal measured through the electrode, and a more accurate analysis of the muscle fatigue is possible. It works.

In addition, according to an embodiment of the present disclosure, by measuring in real time an electrical signal of a user's muscle surface in contact with an electrode by wearing a user's wearable device, a pure EMG signal is obtained from the measured EMG signal, and the obtained signal is It analyzes muscle fatigue as a basis and provides analysis result information to the user, which has the advantage of providing real-time analysis information on muscle fatigue to the user.

In addition, according to an embodiment of the present disclosure, analysis information or an alarm is provided to the user only when it is determined that the analyzed muscle fatigue is equal to or greater than a predetermined threshold, and information on EMG measurement and muscle fatigue analysis is provided. Power efficiency can be increased.

The above description of the present disclosure is for illustrative purposes only, and those of ordinary skill in the art to which the present disclosure pertains will be able to understand that it is possible to easily transform it into other specific forms without changing the technical spirit or essential features of the present disclosure. will be. Therefore, it should be understood that the embodiments described above are illustrative and non-limiting in all respects. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present disclosure is indicated by the claims to be described later, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present disclosure.

Claims

In the method of analyzing the muscle fatigue through the wearable device, EMG measurement,

(a) measuring an EMG signal from an electrical signal on a user's muscle surface detected by the electrode;

(b) obtaining a pure EMG signal (m[n]) from the measured EMG signal; And

(c) Analyzing muscle fatigue based on the obtained pure EMG signal (m[n]) and providing the analysis result to a user.
The method of claim 1,

The step (a),

Including the step of measuring the EMG signal (c[n]) before the user's muscle contraction and the EMG signal (s[n]) during muscle contraction, muscle fatigue analysis method through EMG measurement.
The method of claim 2,

The step (b),

(b1) A first heart rate peak point is determined from the measured slope change information of the EMG signal c[n] before muscle contraction, and the EMG signal before muscle contraction (c[) based on the determined first heart rate peak point reordering n]); And

(b2) A method of analyzing muscle fatigue through EMG measurement, comprising the step of determining a second heart rate peak point from information on a slope change of the EMG signal s[n] when the muscle contracts.
The method of claim 3,

The step (b),

(b3) comparing the first heart rate peak point of the EMG signal c[n] before muscle contraction and the second heart rate peak point of the EMG signal s[n] during muscle contraction to obtain a median value or an average value. By calculating, setting a time difference value (n 0 );

(b4) The first heart rate peak point of the pre-muscular contraction EMG signal (c[n]) by moving the rearranged pre-muscular contraction EMG signal (c[n]) by a time difference value (n 0 ) on the time axis And matching the second heart rate peak point of the EMG signal (s[n]) when the muscle contracts; And

(b5) further comprising the step of obtaining the pure EMG signal (m[n]) by removing the rearranged EMG signal (c[n]) from the EMG signal (s[n]) during muscle contraction. How to analyze muscle fatigue through EMG measurement
The method of claim 4,

The step (b5),

Any one of the method of removing the rearranged EMG signal (c[n]) before muscle contraction by (i) removing the time axis from the time axis or (ii) converting the time axis signal into a frequency axis signal through fast Fourier transform A method for analyzing muscle fatigue through EMG measurement, comprising the step of removing using a method.
The method of claim 1,

The step (c),

Comprising the step of quantitatively analyzing muscle fatigue by comparing the ratio of the spectral density below a specific frequency to the total energy spectral density using a spectrum evaluation method based on the obtained pure EMG signal (m[n]), A method of analyzing muscle fatigue through EMG measurement.
The method of claim 1,

The step (c),

Muscle fatigue analysis through EMG measurement, including the step of (i) providing the result of the muscle fatigue analysis in the form of audio or visual information through an information providing module, or (ii) transmitting it to a user terminal through a wireless communication module Way.
An electrode for measuring an EMG signal with an electrical signal detected from the user's muscle surface;

A processor for obtaining a pure EMG signal (m[n]) from the measured EMG signal and analyzing muscle fatigue based on the obtained pure EMG signal (m[n]);

An information providing module that provides a result of muscle fatigue analysis in the form of voice information or visual information; And

A wearable device comprising a wireless communication module for transmitting the muscle fatigue analysis result to a user terminal through wireless communication.
The method of claim 8,

The electrode,

A wearable device that measures an EMG signal (c[n]) before a user's muscle contraction and an EMG signal (s[n]) when a muscle contraction.
The method of claim 9,

The processor,

A first heart rate peak point is determined from the measured slope change information of the EMG signal c[n] before muscle contraction, and the EMG signal before muscle contraction (c[n]) based on the determined first heart rate peak point Rearrange the

A wearable device for determining a second heart rate peak point from the measured slope change information of the EMG signal s[n] during muscle contraction.
The method of claim 10,

The processor,

By comparing the first heart rate peak point of the EMG signal c[n] before muscle contraction and the second heart rate peak point of the EMG signal s[n] during muscle contraction to calculate a median value or an average value, Set the time difference value (n 0 ),

The first heart rate peak point of the pre-muscular contraction EMG signal (c[n]) and the muscle by moving the rearranged pre-muscular contraction EMG signal (c[n]) by a time difference value (n 0 ) on the time axis Coincide with the second heart rate peak point of the EMG signal s[n] during contraction,

A wearable device for obtaining the pure EMG signal m[n] by removing the rearranged EMG signal c[n] from the EMG signal s[n] during muscle contraction.
The method of claim 11,

The processor,

The re-arranged EMG signal (c[n]) before muscle contraction is removed using any one of (i) a method of removing from the time axis or (ii) a method of converting it to a frequency axis through fast Fourier transform and removing it. A wearable device.
The method of claim 8,

The processor,

A wearable device for quantitatively analyzing muscle fatigue by comparing a ratio of a spectral density below a specific frequency to a total energy spectral density using a spectrum evaluation method based on the obtained pure EMG signal (m[n]).
A computer program stored in a recording medium to implement the method of any one of claims 1 to 7.