KR20150041830A - Integrated evaluating system for muscle fatigue and muscle force - Google Patents

Abstract

The present invention relates to a short measuring device for determining whether a short circuit for the outmost layer of the unit cell wherein the outermost separator layer is disposed, and an electrode having the electrode tap and the separator are stacked and formed alternatively. It comprises a conductive part having a conductivity of the conductive tap in contact with the electrode tap extending from the body and conductive body in contact with the outermost separator; and a short measuring unit for determining a short circuit by applying a voltage on the conductive tap of the conductive part with electrode taps of the unit cell.

Description

[0001] The present invention relates to an integrated evaluation system for muscle fatigue and muscle force,

The present invention relates to a system and method for evaluating muscle fatigue and muscle strength, and more particularly, to a system and method for evaluating muscular fatigue and muscle strength in a human body by repeatedly performing EMG (electromyography) To a system and method thereof that can be evaluated.

Regarding the system for evaluating the exercise function, many applications and disclosures have been made in addition to Korean Patent Laid-Open No. 10-2008-0008823 (hereinafter referred to as "prior literature").

The above-mentioned prior art document discloses a exercise device that allows a user to exercise according to a predetermined amount of exercise; A heart rate measuring module for measuring a heart rate at a designated time including the time during which the user exercises using the exercise device and before and after the exercise; A blood pressure measuring module for measuring the blood pressure of the user at a designated time including the time during which the user performs the exercise using the exercise device and before and after the exercise; A subject exercising exercise with the exercising device and performing at least one exercise function including at least one of heart rate, blood pressure value, cardiopulmonary endurance, and strength of the subject using the heart rate measuring module and the blood pressure measuring module A database in which measured values are matched and stored; And a control unit for controlling the motion of the user by comparing the exercise function of the user with the exercise function of the exercise groups stored in the database, controlling the exercise amount of the exercise apparatus, calculating the exercise function of the user from the heart rate and the blood pressure of the user, A main computer for relative evaluation of exercise functions; .

Currently, physiological evaluation methods of human muscles include measuring the physiological response such as heart rate, oxygen consumption, and EMG, etc. according to the amount of work given to the worker, There is a way.

Because heart rate and oxygen consumption are involved in energy metabolism of muscles, the amount of energy consumed during work can be estimated. At this time, heart rate and oxygen consumption are very useful for evaluating the whole body work, but because it is not suitable for the situation of evaluating the load of the local muscle, the EMG is used to evaluate the workload.

EMG signals can be analyzed in time domain and frequency domain. In the time domain, information on muscle use, force information, and frequency fatigue information are provided in the frequency domain.

The main parameters used for EMG analysis in the time domain are the integrated EMG and the root mean square (RMS), and applying the Fourier transform (Fourier transform) to the EMG signal in the time domain, And the fatigue information of the EMG signal can be extracted therefrom.

The most commonly used variables to measure fatigue are mean power frequency (MPF) or mean frequency (MDF) and median frequency (MDF). However, the scale to provide both strength and fatigue information has not existed until now, and there is a great difficulty in estimating fatigue because the quantitative evaluation scale of fatigue is not presented.

SUMMARY OF THE INVENTION The present invention has been conceived in view of the above problems, and it is an object of the present invention to simultaneously evaluate the fatigue and muscle strength of the human body muscles caused by repeated load application through the analysis of the EMG signal during repetitive motion of the human body, It has its purpose.

In order to accomplish the above object, the present invention provides a muscle fatigue and muscle integration evaluation system comprising: a measurement unit for measuring and storing a surface EMG signal of a muscle; A time domain analysis for calculating an average integrated electromyogram value normalized and an average integrated electromyogram value divided by the integrated electromyogram value of each cycle and the execution time of each cycle based on the area of the electromyogram signal measured by the electromyogram measuring unit part; A frequency domain analyzer for generating a frequency spectrum of each cycle by using the electromyogram signal measured through the electromyogram measuring unit, calculating a center frequency value of the generated frequency spectrum, and calculating a reduction rate of the calculated center frequency value; And evaluating the muscle fatigue and strength based on the increase rate of the average integrated electromyogram value and the reduction rate of the center frequency value of the frequency spectrum calculated through the time domain analyzer and the frequency domain analyzer, An evaluation unit for deriving the evaluation value; .

The time domain analyzer may further include: a rectifier module for performing full wave rectification of the electromyogram signal measured through the electromyogram measuring unit; An EMG calculation module for calculating an integral EMG value of each cycle by calculating an area of a signal rectified through the rectification module, normalizing the integral EMG value of each cycle by dividing the integrated EMG value by each cycle execution time, and calculating an average integrated EMG value; And an increasing rate calculating module for calculating an increasing rate of an average integrated electromyogram calculated by the electromyography calculating module; And a control unit.

The frequency domain analyzer may further include a transform module for performing fast Fourier transform (FFT) on the electromyogram signal measured through the electromyogram measuring unit to generate a frequency spectrum of each cycle; A center frequency value calculation module for calculating a center frequency value of the frequency spectrum generated through the conversion module; And a reduction rate calculation module for calculating a reduction rate of the center frequency value of the frequency spectrum calculated through the center frequency value calculation module; And a control unit.

The evaluating unit is characterized in that, in the evaluation of muscle fatigue, when the average integrated electromyogram value converges to a constant value regardless of the load load, it is evaluated that muscle fatigue due to repeated load is accumulated.

The evaluating section is characterized in that, in the evaluation of muscle fatigue, when the center frequency value decreases regardless of the load load, it is evaluated that muscle fatigue due to the repeated load is accumulated.

The evaluating unit is characterized in that, in the muscle strength evaluation, when the initial value of the mean integrated EMG is increased and the rate of increase is decreased, the evaluation unit evaluates that the muscle fatigue reducing the strength due to the repeated load is accumulated .

The present invention relates to a muscle fatigue and muscle integration evaluation method, and more particularly, to (a) a process of measuring a surface EMG signal of a muscle of an EMG measuring unit; (b) calculating an increase rate of an average integrated electromyogram value and an average integrated electromyography value normalized by dividing the integrated electromyogram value of each cycle, the execution time of each cycle, based on the area of the electromyogram signal measured through the electromyogram measuring unit; (c) generating a frequency spectrum of each cycle by using the electromyogram signal measured through the electromyogram measuring unit, calculating a center frequency value of the generated frequency spectrum, and calculating a reduction rate of the calculated center frequency value; And (d) evaluating muscle fatigue and muscle strength based on the rate of increase of the mean integrated EMG value and the frequency of the frequency spectrum calculated through the time domain analyzer and the frequency domain analyzer, As an evaluation index; .

The step (b) further includes: (b-1) full-wave rectifying the electromyogram signal measured by the time domain analyzing step (a); (b-2) calculating an integrated electromyogram value of each cycle by calculating an area of a signal rectified through the time domain analysis step (b-1); (b-3) calculating the average integrated EMG value by normalizing the integral EMG value of each cycle by dividing the integrated EMG value by each cycle execution time; And (b-4) calculating an increase rate of an average integrated EMG value calculated through the time domain analyzing step (b-3); And a control unit.

The step (c) includes the steps of: (c-1) performing a fast Fourier transform on the electromyogram signal measured through the frequency domain analyzing step (a) to generate a frequency spectrum of each cycle; (c-2) calculating a center frequency value of the frequency spectrum generated through the frequency domain analyzing step (c-1); And (c-3) calculating a reduction rate of a center frequency value of the frequency spectrum in which the frequency domain analysis unit is calculated; And a control unit.

According to the present invention, the fatigue and muscle strength of the local muscles can be evaluated at the same time through the EMG signal analysis. Furthermore, fatigue life prediction can be performed to prevent human injury including fatigue fractures and ligament injuries, Can be minimized.

Brief Description of the Drawings Fig. 1 is an overall view conceptually showing an integrated muscle fatigue and muscle strength evaluation system according to the present invention. Fig.
2 is an overall flow diagram of an integrated muscle fatigue and muscle strength assessment method in accordance with the present invention.
FIG. 3 is a graph showing the results of EMG signals, integral EMG analysis, and cycle times according to fatigue accumulation of the bicep of the upper arm caused by cyclic loading of the dumbbell curl according to the present invention.
FIG. 4 is a graph showing an average integrated EMG analysis result obtained by normalizing integral EMG values to neglect the influence of the operation time of each cycle according to the present invention.
FIG. 5 is a graph showing the result of fast Fourier transform analysis of the biceps biceps frequency region according to the dumbbell curl cyclic loading load according to the present invention.
FIG. 6 is a graph showing an analysis result of the center frequency of biceps biceps according to the dumbbell curl cyclic load according to the present invention, and is used as an evaluation measure of muscle fatigue from the frequency domain signal (FIG. 5) Graph showing center frequency.
Fig. 7 is an example of an integrated index of muscular fatigue and strength evaluation of biceps brachiocephalic due to repeated load of dumbbell curl according to the present invention. Fig.

Specific features and advantages of the present invention will become more apparent from the following detailed description based on the accompanying drawings. It is to be noted that the detailed description of known functions and constructions related to the present invention is omitted when it is determined that the gist of the present invention may be unnecessarily blurred.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings.

The system and method for evaluating muscle fatigue and muscle strength according to the present invention will now be described with reference to FIGS. 1 to 7. FIG.

FIG. 1 is an overall diagram conceptually showing an integrated muscle fatigue and muscle strength evaluation system S according to the present invention. As shown in FIG. 1, the EMG measuring unit 100, the time domain analyzer 200, (300) and an evaluation unit (400).

The EMG measuring unit 100 measures and stores surface EMG signals of the muscles. Specifically, the electromyogram measuring unit 100 is an apparatus including an electromyogram sensor, an electrode, and the like, and can measure a change in the electromyogram signal according to the repeated load of the subject.

In order to analyze muscle strength information, the time domain analyzer 200 divides the integrated electromyogram value of each cycle and the execution time of each cycle based on the area of the EMG signal measured through the EMG measuring unit 100, The EMG calculation module 220 and the increase rate calculation module 230 as shown in FIG. 1, as shown in FIG. 1.

Specifically, the rectification module 210 performs full-wave rectification of the electromyogram signal measured through the electromyography measuring unit 100. [

The EMG calculation module 220 calculates an integrated EMG value of each cycle by calculating an area of a signal rectified through the rectification module 210, normalizes the integrated EMG value by dividing the integrated EMF value of each cycle by each cycle execution time, Calculate EMG value.

The increasing rate calculating module 230 calculates an increasing rate of the average integrated electromyogram calculated through the electromyography calculating module 220.

The frequency domain analyzing unit 300 generates a frequency spectrum of each cycle using the EMG signal measured through the EMG measuring unit 100 to confirm the fatigue information of the muscle, And includes a conversion module 310, a center frequency value calculation module 320, and a reduction rate calculation module 330 as shown in FIG. 1, as shown in FIG. 1 do.

Specifically, the conversion module 310 performs fast Fourier transform on the electromyogram signal measured through the electromyogram measuring unit 100 to generate a frequency spectrum of each cycle.

The center frequency value calculation module 320 calculates the center frequency value of the frequency spectrum generated through the conversion module 310 and observes the frequency shift degree. This frequency transition phenomenon can be caused by a large amplitude increase in the low frequency band due to the accumulation of fatigue.

The reduction rate calculation module 330 calculates a reduction rate of the center frequency value of the frequency spectrum calculated through the center frequency value calculation module 320. [

The evaluation unit 400 calculates muscle fatigue and muscle strength based on the increase rate of the average integrated electromyogram value and the reduction rate of the center frequency value of the frequency spectrum calculated through the time domain analysis unit 200 and the frequency domain analysis unit 300, 7, and the muscle fatigue and muscle strength can be confirmed through the derived evaluation indexes.

 At this time, as muscle fatigue due to cyclic loading is accumulated, the mean integral electromyogram value increases and the center frequency value decreases.

Meanwhile, the evaluating unit 400 can predict the magnitude of the load load through the difference in the rate of increase in the average integrated electromyogram value.

Specifically, in the evaluation of muscle fatigue, the mean integral electromyogram converges to a constant value regardless of the load, when the muscle fatigue accumulates due to cyclic loading, and the center frequency value is constant regardless of the load load. 40% decrease. Here, the time point at which the initial center frequency value decreases to about 60% is the point at which the power begins to decrease due to accumulation of muscle fatigue. This means that power is reduced due to fatigue accumulation. Also, in the evaluation of muscle strength, the initial value of the mean integral electromyogram increases according to the load load, and the rate of increase decreases. That is, the initial value of the mean integral EMG increases, and when the rate of increase decreases, it is estimated that the muscle fatigue, which is caused by the decrease in the force due to the repeated load, is accumulated.

Then, the evaluation unit 400 can output audiovisual information on the evaluation information, the indicators, and the like in real time and generate an alarm.

Hereinafter, the method of evaluating muscle fatigue and muscle strength integrated using the system will be described with reference to FIG.

FIG. 2 is an overall flowchart of an integrated muscle fatigue and muscle strength evaluation method according to the present invention. As shown in FIG. 2, the EMG measuring unit 100 measures and stores surface EMG signals of muscles (S10).

The rectification module 210 of the time domain analysis unit 200 performs full wave rectification of the electromyogram signal measured through the electromyogram measuring unit 100 at step S20 and the electromyography calculation module 220 calculates the electromyogram (S30). The integrated EMG value of each cycle is divided by the cycle execution time (S30) and the average integrated electromyogram value is calculated (S40).

Then, the increase rate calculation module 230 of the time domain analysis unit 200 calculates an increase rate of the average integrated electromyogram calculated through the EMG calculation module 220 (S50).

Meanwhile, the transform module 310 of the frequency domain analyzer 300 performs a fast Fourier transform on the electromyogram signal measured through the electromyogram measurer 100 to generate a frequency spectrum of each cycle (S60).

Then, the center frequency value calculation module 320 calculates the center frequency value of the frequency spectrum generated through the conversion module 310 (S70), and observes the frequency transition degree.

In addition, the reduction rate calculation module 330 calculates a reduction rate of the center frequency value of the frequency spectrum calculated through the center frequency value calculation module 320 (S80).

It is to be understood that steps S20 through S50 through the time domain analysis unit 200 are performed simultaneously with steps S60 through S80 through the frequency domain analysis unit 300. [

Based on the rate of increase of the average integrated electromyogram value and the rate of decrease of the center frequency value of the frequency spectrum calculated through the time domain analyzer 200 and the frequency domain analyzer 300, The muscle strength is evaluated (S90) and derived as an evaluation index using the evaluated information (S100).

According to the present invention, since fatigue of a muscle causes a decrease in power of the muscle, a time point at which a given speed can not be followed for a given repetitive motion is determined as a criterion of muscle fatigue accumulation.

The subject performed a dumbbell curl operation at a rate of 3 sec / cycle for 20%, 35%, 50%, and 75% MVC (Maximum voluntary contraction) loading conditions. At this time, the biceps brachii EMG signals were recorded.

The recorded electromyogram signal is obtained by finding the integral EMG value in the time domain for each cycle and calculating the center frequency value by Fourier transform in the frequency domain. In order to ignore the influence of the operation time, the integrated EMG values calculated in the above step were divided by the respective operation execution times to obtain a normalized mean integral EMG value.

FIG. 3 is a graph showing the results of EMG signals, integral EMG analysis, and respective cycle times according to fatigue accumulation of the biceps brachium according to the dumbbell curl cyclic load according to the present invention. FIG. 3 (a) This is the EMG signal of the biceps biceps, showing the change as muscle fatigue due to load (20% MVC) accumulates.

As the weight of the dumbbell increases, the amplitudes of the EMG signals of the biceps brachii increases. This increase in amplitude is due to the increased activity of muscle fibers as more muscle is used for larger load loads.

Also, it can be confirmed that the amplitude of the EMG signal gradually increases as the muscle fatigue due to the repetitive motion accumulates. This is the result of synchronization and recruitment of the exercise units as the muscles become tired and the contraction capacity of the muscle fibers decreases.

FIG. 3 (b) is a graph showing the relationship between the use of biceps according to the repetition frequency of the load and the integral EMG calculated from the area of the extracted signal to estimate the force exerted by the muscles from the EMG signal of FIG. IEMG).

This integrated EMG value represents the total momentum of each exercise cycle. It can be seen from this graph that as the load load increases, the integrated EMG value increases due to an increase in the muscle fiber activity potential. This result is consistent with the result shown in FIG. 3 (a).

In addition, it can be seen that the integrated EMG value increases as the muscle fatigue accumulates due to the cyclic loading. Such an increase in the integrated EMG value results from the fact that as the muscle fatigue accumulates as shown in FIG. 3 (a) This is because the amplitude of the EMG increases due to the synchronization and increment of units.

Meanwhile, FIG. 3 (c) shows the cycle time of each cycle. As shown in the graph, it can be confirmed that the proposed operation time was not maintained after about the 70th cycle.

This decrease in shortening velocity is due to the decrease in muscular power with muscle fat accumulation. Therefore, for this subject, the fatigue life (N _{f, 20%} ) is shown to be 70 cycles for a 20% MVC load. Similar tendency of the integrated EMG was confirmed under different load (35%, 50%, 75% MVC) conditions and the fatigue life was 22 cycles, 10 cycles, and 6 cycles for each load load condition.

On the other hand, in the case of the 75% MVC, the motion execution time (3 sec) shown from the first cycle was exceeded because the 75% MVC condition was the overload condition for the subject.

FIG. 4 shows mean IEMG analysis results obtained by normalizing the integral electromyogram (IEMG) value to neglect the influence of the operation execution time of each cycle according to the present invention.

As can be seen from FIG. 4, although the influence of each cycle time was neglected, it was confirmed that the mean integral EMG value increased as the muscle fatigue accumulates. As the load load increased, the initial value increased and the increase in the mean integral EMG Was decreased. Also, except for the 75% MVC load condition, it was confirmed that the final value of fatigue accumulation had similar values for all load loads. Therefore, it can be said that when the muscle fatigue is accumulated, the same mean integrated EMG value is obtained regardless of the load load condition.

Meanwhile, FIG. 5 is a graph showing the results of fast Fourier transform analysis in the frequency domain of the biceps brachium by the dumbbell curl cyclic loading load according to the present invention, and shows a result of Fourier transform of the EMG signal to obtain muscle fatigue information .

It was confirmed that the amplitude of the power spectrum increases as the load load increases and the amplitude increases as the muscle fatigue increases. In particular, when the muscle fatigue accumulates, the amplitude increases significantly in the low frequency band (15 to 45 Hz), and this frequency transition phenomenon is known to be related to the propagation velocity of the action potential . In addition, this decrease in the action potential transfer rate is related to accumulation of metabolites due to muscle fatigue.

FIG. 6 is a graph showing the results of analysis of the mean frequency of the biceps brachypis according to the dumbbell curl cyclic load according to the present invention. From the frequency domain signal (FIG. 5) It represents the center frequency used as a measure.

According to FIG. 6, as the fatigue accumulates, the center frequency decreases and it is confirmed that about 40% of the initial value is reduced in all the load load conditions. Therefore, the point at which muscle fatigue accumulates can be seen when the center frequency is reduced by 40% regardless of load load.

The present invention can simultaneously evaluate fatigue and muscle strength of human body muscles through mean integrated EMG (mean IEMG) and central frequency tendency according to the accumulation of muscle fatigue.

As shown in FIG. 7, as the muscle fatigue accumulates, it is confirmed that the fatigue index converges to a fatigue point and occurs at a point where the fatigue index decreases to 60% of the initial center frequency value.

On the other hand, the magnitude of the load load can be predicted by the difference in the rate of increase of the mean integral EMG (Mean IEMG). Therefore, these parameters can be applied as an integrated index that can simultaneously evaluate muscle fatigue and muscle strength. It was confirmed that this integrated index for muscle fatigue and muscle strength evaluation was valid for all subjects.

Hereinafter, the present invention will be described in more detail with reference to preferred embodiments. However, the following examples are provided to aid understanding of the present invention, and the scope of the present invention should not be construed as being limited thereby.

[Example 1]

One 26 - year - old man measured the EMG signal, which is the activation level of the biceps biceps according to the cyclic load during dumbbell curl operation, and measured 20%, 35%, 50% and 75% MVC load conditions. When measuring the EMG, the distance between the electrodes was set to 2 cm in the direction of the muscle fiber, and the ground electrode was placed outside or inside.

The electrodes were attached to the lower one third of the biceps femoris muscle, which was accelerated when the muscles were contracted, because the electrode could be detached depending on the position of the muscle due to muscle contraction. To remove dirt from the skin, It was sterilized with alcohol cotton before attachment.

The EMG sensor was connected to the EMG module, and one channel EMG signal was collected at a sampling frequency of 1500 Hz, a low pass filter of 500 Hz, and a high pass filter of 10 Hz.

The subject performed the dumbbell curl operation with only the right arm while keeping the standing posture with the leg open at the shoulder width. The dumbbell was lifted at a constant speed for each load, and the EMG signal was measured until the time when the subject could no longer observe the proposed action time (3 seconds).

After storing the measured EMG signals, signal processing and analysis were performed through the integrated EMF and fast Fourier transforms in the time domain and the frequency domain, respectively, from the stored signals.

As the muscle fatigue due to cyclic loading increased, mean IEMG increased and the center frequency decreased. When the initial center frequency decreased to about 60% of the initial center frequency, And it began to decrease.

[Example 2]

A 26-year-old male was measured for electromyogram signaling, which is the activation of the deltoid of the subject according to the cyclic load during side lateral operation, and the 10%, 30%, 50%, and 70% MVC load Conditions.

Electromyography was performed using a Noraxon dual electrode (Noraxon, USA). The distance between the electrodes was 2 cm in the direction of the myofiber, and the ground electrode was placed on the outside or inside.

When measuring the EMG, the distance between the electrodes was set to 2 cm in the direction of the muscle fiber, and the ground electrode was placed outside or inside.

Since the electrode may be detached according to the change of the position of the muscle due to the muscle contraction, the attachment position of the electrode is mounted at the lower third of the triceps muscle facilitated when the muscle is contracted to the maximum. In order to remove the dirt from the skin, It was sterilized with alcohol cotton before attachment.

The EMG sensor was connected to the EMG module and the EMG signals of one channel were collected at a sampling frequency of 1500 Hz, a low pass filter of 500 Hz, and a high pass filter of 10 Hz.

The subject performed a lateral lateral movement with only his right arm while keeping his legs wide open and standing posture. The dumbbell was lifted at a constant speed for each load, and the EMG signal was measured until the point at which the subject could no longer observe the proposed action time (5 seconds).

After storing the measured EMG signals, signal processing and analysis were performed through the integrated EMF and fast Fourier transforms in the time domain and the frequency domain, respectively, from the stored signals.

As the muscle fatigue due to cyclic loading increased, mean IEMG increased and center frequency decreased, and when the initial center frequency decreased to about 60% of the initial center frequency, And it began to decrease.

[Example 3]

One 25-year-old male measured the EMG signal, which is the activation level of the rectus femoris, in response to repetitive load during pelvic limb operation. The EMG signal was measured at 20%, 40%, 60%, and 80% MVC load The load conditions were measured.

When measuring the EMG, the distance between the electrodes was set to 2 cm in the direction of the muscle fiber, and the ground electrode was placed outside or inside.

Since the electrode may be detached depending on the position of the muscle due to the contraction of the muscle due to the contraction of the muscle, the electrode is mounted at the lower third of the muscle of the femoral rectus, which is promoted when the muscle is contracted to the maximum. Was sterilized with alcohol.

The EMG sensor was connected to the EMG module, and EMG signals of one channel were collected at 1500 Hz frequency, 500 Hz low pass filter, and 10 Hz high pass filter.

The dumbbell was lifted at a constant speed for each load, and the EMG signal was measured until the time when the subject could no longer observe the prescribed action time (4 seconds).

After storing the measured EMG signals, signal processing and analysis were performed through the integrated EMF and fast Fourier transforms in the time domain and the frequency domain, respectively, from the stored signals.

As the muscle fatigue due to cyclic loading increased, mean IEMG increased and the center frequency decreased. When the initial center frequency decreased to about 60% of the initial center frequency, And it began to decrease.

As described above, the system and method for evaluating muscle fatigue and muscle strength according to the present invention can simultaneously evaluate the degree of muscle fatigue and muscle strength that have not existed before, and by presenting an integrated evaluation index, the fatigue fracture and ligament injury And can increase the work efficiency and has a characteristic advantage that a database for evaluating muscle fatigue and muscle strength can be constructed by storing measured EMG signals and evaluation results.

While the present invention has been particularly shown and described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. It will be appreciated by those skilled in the art that numerous changes and modifications may be made without departing from the invention. Accordingly, all such appropriate modifications and changes, and equivalents thereof, should be regarded as within the scope of the present invention.

S: Integrated fatigue and muscle strength assessment system
100: EMG measurement unit 200: Time domain analysis unit
300: frequency domain analyzing unit 400:
210: rectification module 220: electromyography calculation module 230: increase rate calculation module 310: conversion module 320: center frequency value calculation module 330: reduction rate calculation module

Claims (9)

A measuring unit 100 for measuring and storing a surface EMG signal of a muscle;
(Mean IEMG) normalized by dividing the total EMG (Integrated EMG) value of each cycle and the execution time of each cycle based on the area of the EMG signal measured by the EMG measuring unit 100, A time domain analyzer 200 for calculating a value and an increasing rate of an average integrated electromyogram value;
A frequency spectrum analyzing unit for generating a frequency spectrum of each cycle by using the electromyogram signal measured by the electromyography measuring unit 100, calculating a center frequency value of the generated frequency spectrum, and calculating a reduction rate of the calculated center frequency value (300); And
The muscle fatigue and muscle strength are evaluated based on the increase rate of the average integrated electromyogram value and the reduction rate of the center frequency value of the frequency spectrum calculated through the time domain analyzer 200 and the frequency domain analyzer 300, An evaluation unit (400) for deriving the information as an evaluation index using information; An integrated muscle fatigue and muscle strength assessment system.
The method according to claim 1,
The time domain analyzer 200 may include:
A rectifying module 210 for performing full wave rectification of an EMG signal measured through the EMG measuring unit 100;
An area of a signal rectified through the rectifier module 210 is calculated to calculate an integral electromyogram value of each cycle, an integral electromyogram value of each cycle is divided by each cycle execution time, and an average electromyography value Calculation module 220; And
An increasing rate calculating module 230 for calculating an increasing rate of the average integrated electromyogram calculated through the electromyography calculating module 220; And an integrated muscle fatigue and muscle strength evaluation system.
The method according to claim 1,
The frequency domain analyzer (300)
A transform module 310 for performing fast Fourier transform (FFT) on the electromyogram signal measured through the electromyogram measuring unit 100 to generate a frequency spectrum of each cycle;
A center frequency value calculation module 320 for calculating a center frequency value of a frequency spectrum generated through the conversion module 310; And
A reduction rate calculation module 330 for calculating a reduction rate of the center frequency value of the frequency spectrum calculated through the center frequency value calculation module 320; And an integrated muscle fatigue and muscle strength evaluation system.
The method according to claim 1,
The evaluation unit (400)
Wherein the muscle fatigue evaluation system evaluates that the muscle fatigue due to the repeated load is accumulated when the mean integral electromyogram value converges to a constant value regardless of the load load.
The method according to claim 1 or 4,
The evaluation unit (400)
The evaluation system of muscle fatigue and muscle strength evaluation system according to claim 1, wherein the evaluation of muscle fatigue is based on accumulation of muscle fatigue due to repeated load when the center frequency value decreases regardless of the load.
The method according to claim 1,
The evaluation unit (400)
The muscle fatigue evaluation method according to claim 1, wherein, in the evaluation of muscle strength, the initial value of the mean integral electromyogram is increased, and when the increase rate is decreased, evaluation is made that the muscle fatigue, system.
(a) a process in which the EMG measuring unit 100 measures a surface EMG signal of a muscle;
(b) calculating an increase rate of an average integrated electromyogram and an average integrated electromyography value normalized by dividing the integrated electromyogram value of each cycle and the execution time of each cycle based on the area of the electromyogram signal measured through the electromyogram measuring unit 100 process;
(c) generating a frequency spectrum of each cycle by using the electromyogram signal measured by the electromyography measuring unit 100, calculating a center frequency value of the generated frequency spectrum, and calculating a reduction rate of the calculated center frequency value process; And
(d) The muscle fatigue and muscle strength are evaluated based on the rate of increase of the average integrated electromyogram value and the reduction rate of the center frequency value of the frequency spectrum calculated through the time domain analyzer 200 and the frequency domain analyzer 300, respectively , And deriving the information as an evaluation index using the evaluated information; ≪ / RTI >
8. The method of claim 7,
The step (b)
(b-1) full-wave rectifying the electromyogram signal measured in the step (a) by the time domain analyzer 200;
(b-2) calculating the integral EMG value of each cycle by calculating the area of the signal rectified through the step (b-1);
(b-3) calculating the average integrated EMG value by normalizing the integral EMG value of each cycle by dividing the integrated EMG value by each cycle execution time; And
(b-4) calculating the rate of increase in the average integrated electromyogram calculated in the step (b-3) by the time domain analyzer 200; And an integrated muscle strength evaluation method.
8. The method of claim 7,
The step (c)
(c-1) generating a frequency spectrum of each cycle by performing fast Fourier transform on the electromyogram signal measured through the frequency domain analyzing unit 300;
(c-2) calculating the center frequency value of the frequency spectrum generated in step (c-1) by the frequency domain analyzer 300; And
(c-3) calculating the reduction rate of the center frequency value of the calculated frequency spectrum by the frequency domain analyzer 300; And an integrated muscle strength evaluation method.

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