KR102335210B1 - Measurement method and visualization device for muscle fatigue using multi-channel near-infrared spectroscopy - Google Patents

Abstract

_{The present invention provides a method for measuring muscle fatigue using oxidized hemoglobin (HbO 2} ) and reduced hemoglobin (HHb or HbR) levels measured in muscle through multi-channel near-infrared spectroscopy (NIRS), and an EMG capable of measuring muscle fatigue It provides a device for measuring fatigue for each muscle part and visualizing (expressing) a distribution map (or depth map) through correlation analysis with the MDF (median frequency). According to the present invention, there is provided a procedure for attaching a sensor patch having a channel composed of a plurality of light source-detector pairs to the skin of a muscle region to be measured for muscle fatigue; a procedure of acquiring measurement data for each channel by measuring the multi-channel signals obtained from the sensor patch by near-infrared spectroscopy; A method for measuring muscle fatigue using multi-channel near-infrared spectroscopy is provided, including a procedure for analyzing muscle fatigue using the levels of _{oxidized hemoglobin (HbO 2} ) and reduced hemoglobin (HbR) from the acquired measurement data for each channel.

Description

{Measurement method and visualization device for muscle fatigue using multi-channel near-infrared spectroscopy}

The present invention relates to a method for measuring muscle fatigue according to muscle area and depth by analyzing the EMG MDF correlation between oxidized hemoglobin and reduced hemoglobin through multi-channel near-infrared spectroscopy and to a display (visualization) device.

Existing muscle-related measurements, such as electromyography (EMG), muscle vibration sensor (MMG), and muscle hardness sensor, are used. This is impossible.

Near-infrared spectroscopy (NIRS) is an improved muscle measurement method, but in this case, it is a single-channel or mammary device, so it is inconvenient to measure the subject's muscle activity in various areas, and multi-channel Real-time monitoring is difficult.

And, conventionally, only the surface of the muscle is used to measure and evaluate muscle fatigue for each muscle depth information according to the near-infrared transmittance.

The present inventors have found that, when multi-channel near-infrared spectroscopy is used, movement noise is relatively very small, and depth information according to the degree of penetration of near-infrared can be obtained, so that activity can be measured according to the internal depth of the muscle, so that more effective customized muscle strength reinforcement The present invention is proposed based on the fact that training becomes possible. If the muscle fatigue measurement method using the proposed multi-channel near-infrared spectrometer is used, more complex muscle fatigue measurement research becomes possible.

_{Therefore, the present invention is a method for measuring muscle fatigue using oxidized hemoglobin (HbO 2} ) and reduced hemoglobin (HHb or HbR) levels measured in muscle through multi-channel near-infrared spectroscopy (NIRS), and it is possible to measure muscle fatigue An object of the present invention is to provide a device for measuring fatigue for each muscle region and visualizing (expressing) a distribution map (or depth map) through correlation analysis with MDF (median frequency) of an electromyogram.

In order to solve the above problems, according to the present invention, the following means are provided.

- Muscle fatigue measurement process of near-infrared spectroscopy using correlation analysis with EMG MDF

- Derivation of correlation between oxidized hemoglobin and reduced hemoglobin using linear regression analysis with MDF of electromyography

- Visualization of muscle fatigue using measured muscle fatigue data and distribution map

- Visualization of muscle fatigue using muscle fatigue data and depth map by measured muscle depth

More specifically, according to one aspect of the present invention, a procedure of attaching a sensor patch having a channel composed of a plurality of light source-detector pairs to the skin of a muscle region to be measured for muscle fatigue; a procedure of acquiring measurement data for each channel by measuring the multi-channel signals obtained from the sensor patch by near-infrared spectroscopy; A method for measuring muscle fatigue using multi-channel near-infrared spectroscopy is provided, including a procedure for analyzing muscle fatigue using the levels of _{oxidized hemoglobin (HbO 2} ) and reduced hemoglobin (HbR) from the acquired measurement data for each channel.

According to another aspect of the present invention, as an apparatus for visualizing muscle fatigue data analyzed by the muscle fatigue measurement method using the above-described multi-channel near-infrared spectroscopy, 3D human body modeling data produced for each upper and lower limb muscles, and the analyzed muscle fatigue data A multi-channel near-infrared spectroscopy comprising a muscle fatigue data display unit expressing different colors or brightness for each pixel, and an attachment position display unit of the multi-channel sensor patch displayed at a position on the 3D human body modeling data corresponding to the muscle attachment position of the sensor patch An apparatus for visualizing muscle fatigue measurement data used is provided.

The configuration and operation of the present invention introduced above will be clearer through specific embodiments described later with drawings.

According to the present invention, a multi-channel muscle fatigue index for each muscle region can be measured using multi-channel near-infrared spectroscopy (NIRS) and expressed as a distribution map or a depth map. While the conventional muscle fatigue measurement using EMG uses surface EMG to measure fatigue, it is impossible to measure muscle fatigue according to the depth of the muscle, whereas the multi-channel near-infrared spectroscopy of the present invention measures oxidized hemoglobin and reduced hemoglobin for each muscle depth according to the near-infrared transmittance. It is possible to provide a multi-channel muscle fatigue index for each muscle depth as a depth map. Therefore, it becomes possible to measure muscle fatigue by muscle depth by measuring oxidized hemoglobin and reduced hemoglobin according to muscle depth, which could not be measured previously.

By using oxidized hemoglobin and reduced hemoglobin measured using multi-channel near-infrared spectroscopy equipment, muscle fatigue can be expressed as a distribution map or a depth map, so it can be used for customized strength training, muscular endurance training, and rehabilitation training. Since it provides a distribution map and depth map analysis for real-time muscle fatigue by using a near-infrared spectrometer, it can be used in the field of muscle strength measurement and customized training systems.

1 is an embodiment of a method for measuring muscle fatigue using multi-channel near-infrared spectroscopy (NIRS);
2 is an arrangement of optical elements (light source and photodetector) of NIRS and channel configuration in case of 8 channels
3 is a muscle fatigue prediction process included in the multi-channel NIRS muscle fatigue measurement method according to the present invention;
4 is an exemplary diagram of a predicted time visualization user interface for adjusting the amount of training when entering the 70% muscle fatigue section shown in FIG. 3
5 is a result of applying a muscle fatigue algorithm by normalizing HbO₂ and HbR data of 40 regions to which DOT is applied.
6 is an example diagram of muscle fatigue data visualization by size and color
An example of muscle modeling applied with the bipad rigging technique of FIG.
8 is an example diagram of 3D model objectification for each lower extremity muscle
9 is an NIRS patch attachment positioning process on 3D human body modeling.
10 is an example of a muscle fatigue distribution map by color
11 is an exemplary view of a numerical graph of muscle fatigue
12 is an example of a UI of a muscle fatigue monitoring program

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the detailed description in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in a variety of different forms, only this embodiment allows the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully inform the person of the scope of the invention, and the present invention is defined by the description of the claims.

On the other hand, the terms used herein are for the purpose of describing the embodiment, not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” or “comprising” refers to the presence or absence of one or more other components, steps, operations and/or elements other than the stated elements, steps, acts and/or elements. addition is not excluded.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In adding reference numerals to the components of each drawing, the same components are given the same reference numerals as much as possible even if they are shown in different drawings, and in explaining the present invention, detailed descriptions of related known components or functions In the case where the gist of the present invention may be obscured, a detailed description thereof will be omitted.

1 shows an embodiment of a method for measuring muscle fatigue using multi-channel near-infrared spectroscopy (NIRS). By performing NIRS in multiple channels, _{muscle fatigue is measured using the levels of oxidized hemoglobin (HbO 2} ) and reduced hemoglobin (HbR) measured in the muscle. The sensor 10 is attached to the skin of the muscle part to be measured, and the multi-channel NIRS measurement according to the present invention is performed on the signal obtained from the sensor 10 (20), and muscle fatigue analysis (40) is performed from the measurement data obtained for each channel. Thus, the muscle fatigue information is output (50) and fed back to the examinee or medical staff. Meanwhile, in the muscle fatigue analysis apparatus or step 40 , analysis performance and reliability may be improved through the pilot test 25 by the multi-channel NIRS measurement 20 .

2 shows the arrangement of optical elements (light source and photodetector) of NIRS and the channel configuration in the case of 8 channels. The left side shows that the sensor 11 in the form of a patch attached to the skin consists of light sources S1 and S2 and detectors D1, 2, 3, and 4, and the right side shows that the sensor on the left consists of 8 channels. A depth group was composed of a depth 1 group (12) and a depth 2 group (13), and 4 channels were configured for each depth group. In depth 1 group 12, channel Ch 1 is about 2.5 cm between S1 and D1, channel 2 is about 2 cm from S1-D2, channel 3 is about 2.5 cm from S2-D1, and channel 4 is S2 -D2 is about 2 cm. In the depth 2 group 13, channel 1 is about 3.35 cm from S1-D3, channel 2 is about 3 cm from S1-D4, channel 3 is about 3.35 cm from S2-D3, and channel 4 is about 23 m from S2-D24. . By implementing the multi-channel NIRS in this way, unlike the conventional NIRS, HbO ₂ , HbR can be estimated for the depth of each channel by comparing the average values of measurements for all channels.

The sensor is encased in an opaque sleeve of black cloth to avoid interference with ambient light, the signal is sampled at 8Hz by computer software, and the data is filtered at 10-second intervals.

Hereinafter, the multi-channel NIRS muscle fatigue measurement method according to the present invention will be described in detail.

1. Considerations in muscle fatigue measurement method

First, in the muscle fatigue measurement algorithm, it is necessary to correct the relative difference between optical elements (light source and photodetector) for NIRS. Measured data may be different depending on the optical element arrangement (distance, location) of the devices of the multi-channel NIRS, so it means data normalization to reduce the relative difference. To this end, in the multi-channel muscle fatigue measurement algorithm according to the optical device arrangement for multi-channel NIRS of the present invention, a correction technique for each sensor attachment position/depth is applied. _{In the case of multi-channel NIRS, since the HbO 2} and HbR values that can be obtained depending on the arrangement of the optical devices can be changed relatively depending on the distance and location of the optical device, DOT (Diffuse Optical Tomography) technology is applied to determine the relative difference between the optical devices. _{By applying an HbO 2} , HbR-based muscle fatigue measurement algorithm to 40 areas, correction was made for each sensor attachment location/depth. When DOT is applied, a change occurs in the unit of the absolute value between _{HbO 2} and HbR data measured in 8 channels, and a normalization process is required for this.

_{To this end, a regularization algorithm was developed through regression analysis of 8-channel HbO 2} and HbR data and HbO₂ and HbR data to which DOT of the same time period was applied using the 1-minute data average of rest time before exercise. For the applied HbO₂ and HbR 40 domain data, a normalization algorithm was developed through linear regression analysis with the average data of 8-channel HbO₂ and HbR before DOT application. The normalization algorithm for each depth HbO₂ and HbR is as follows.

① HbO₂ regularization algorithm of Depth1

· As a result of linear regression analysis, the R value was .988, which was very high, the HbO₂ coefficient was -.19 and the constant was 0, all with a significance probability of .000.

· Based on the results of linear regression analysis, the following normalization algorithm for HbO₂ of Depth1 was derived.

..............(1)

..............(One)

② HbO₂ regularization algorithm of Depth2

· As a result of linear regression analysis, the R value was .552, the HbO₂ coefficient was -.033, and the constant was .001, indicating a significance probability of .000.

· Based on the results of linear regression analysis, the following normalization algorithm for HbO₂ of Depth2 was derived.

..............(2)

..............(2)

③ HbR regularization algorithm of Depth1

· As a result of the linear regression analysis, the R value was found to be very high at 0.994, the HbR coefficient was -0.013 and the constant was -6.63E-5, all with a significance probability of .000.

· Based on the results of linear regression analysis, the following normalization algorithm for HbR of Depth1 was derived.

..............(3)

...............(3)

④ HbR regularization algorithm of Depth2

· As a result of the linear regression analysis, the R value was very high, 0.981, the HbR coefficient was -.017, and the constant was -6.345E-5, showing the significance probabilities of .000 and .002.

· Based on the results of linear regression analysis, the following normalization algorithm for HbR of Depth2 was derived.

..........(4)

..........(4)

Second, to complement the multi-channel muscle fatigue measurement algorithm of the present invention for each individual and exercise-specific integrated application, customized exercise of Isometric Task, Incremental Load Exercise (GXT) Task, Isokinetic Task, and Wingate Task technology was developed. In the case of these isometric task, incremental load exercise task, isokinetic task, and Wingate task, the NIRS signal pattern appears differently, and accordingly, different analysis methods are required depending on the short-term fatigue-inducing training and the long-term fatigue-inducing training.

In the case of short-term fatigue-induced training, the average change of HbO₂ and HbR of NIRS was evident, whereas in the case of long-term fatigue-induced training, the amplitude change of each signal was observed rather than the average change of HbO₂ and HbR. Accordingly, rather than regression analysis with EMG (electromyography) using the mean value, regression analysis using RMS (Root Mean Square) that can confirm the amplitude change is applied.

Therefore, by supplementing the individual muscle fatigue measurement algorithm suitable for customized training, it can be applied according to the training type.

(A) NIRS muscle fatigue measurement algorithm for isometric training

- Linear bivariate regression was performed on the MDF average extracted from the EMG signal and the RMS average of HbO₂ and HbR of NIRS to derive correlation coefficients and constants.

- As a result of the linear regression analysis, the R value was high as .888, HbO₂ was 773.054, HHb was 2409.466, and the constant was 49.865, all showing significant probabilities of .000, .580, and .015.

- Based on the results of the linear regression analysis, the following NIRS-based muscle fatigue measurement algorithm was derived.

........ (5)

........ (5)

- High reliability (ICC=.584 (95% CI -.238~) as a result of intra-class correlation coefficient analysis between EMG-based muscle fatigue and NIRS-based muscle fatigue by applying the derived correlation coefficients and constants to HbO₂ and HHb .860), p = .056).

(B) Muscle fatigue measurement algorithm for NIRS for isokinetic training

- Linear bivariate regression was performed on the mean of MDF extracted from EMG signals and the mean of HbO₂ and HbR of NIRS to derive correlation coefficients and constants.

- As a result of linear regression analysis, the R value was as high as .758, HbO₂ was -915.063, HHb was -1366.841, and the constant was 63.182, all with a significance probability of .000.

- Based on the results of linear regression analysis, the following NIRS-based muscle fatigue measurement algorithm was developed.

......... (6)

......... (6)

- High reliability (ICC=.844 (95% CI .646~) as a result of intra-class correlation coefficient analysis between EMG-based muscle fatigue and NIRS-based muscle fatigue by applying the derived correlation coefficients and constants to HbO₂ and HHb. 931), p = .000).

(C) Muscle fatigue measurement algorithm for NIRS for incremental load training (GXT) training

- Linear bivariate regression was performed on the MDF average extracted from the EMG signal and the RMS average of HbO₂ and HbR of NIRS to derive correlation coefficients and constants.

- As a result of linear regression analysis, the R value was .201, which was very low, and the significance probabilities were .813 and .236.

- As a result of linear regression analysis, the development of the NIRS-based muscle fatigue measurement algorithm did not yield significant results.

(D) Development of muscle fatigue measurement algorithm for NIRS for Wingate training

- Linear bivariate regression was performed on the mean of MDF extracted from EMG signals and the mean of HbO₂ and HbR of NIRS to derive correlation coefficients and constants.

- As a result of the linear regression analysis, the R value was found to be very high as 0.864, HbO₂ was -3323.992, HHb was -9547.833, and the constant was 42.907, all with a significance probability of .008.

- Based on the results of linear regression analysis, the following NIRS-based muscle fatigue measurement algorithm was derived.

...... (7)

...... (7)

- High reliability (ICC=.922 (95% CI .685~) as a result of intra-class correlation coefficient analysis between EMG-based muscle fatigue and NIRS-based muscle fatigue by applying the derived correlation coefficients and constants to HbO₂ and HHb. 981), p = .000).

2. Multi-channel muscle fatigue prediction process

The multi-channel NIRS muscle fatigue measurement method according to the present invention includes a muscle fatigue prediction process. This multi-channel muscle fatigue prediction process is shown in FIG. 3 . Based on the exercise-specific muscle fatigue measurement algorithm constructed by reflecting the above considerations, this process sets the individual exercise start point as a baseline (60) and calculates the amount of change per unit time for the initial data (62) to 70% of the initial value It predicts the time of occurrence of fatigue (64) so that the training intensity can be adjusted (68). The predicted time visualization 66 for adjusting the amount of training when entering the 70% muscle fatigue section shown in FIG. 3 may be provided through a user interface as shown in FIG. 4 .

Next, a method for expressing muscle fatigue according to another object of the present invention (visualisation, visualization) will be described. Basically, it is a visualization method using 3D human body modeling. Implement visualization with the following configuration.

A) Visualization of multi-channel muscle fatigue measurement data through 3D human modeling data for each major muscle of upper and lower limbs

In general, there are approximately 640 skeletal muscles, and most of them are bilateral muscles, and there are approximately 320 pairs of muscles, and data is produced based on this. In addition, 3D human body modeling data is produced for each major muscle (rectus femoris, great lateral muscle, vastus medial muscle, etc.) (refer to human body modeling in FIG. 4 ).

As for the data to be expressed, the muscle fatigue data output of the multi-channel NIRS device of the present invention is extracted as double data and consists of 40 areas, which means each pixel, and is visualized by classifying a color level for each pixel. Although individual deviations may occur, to visualize the degree of muscle fatigue induction, the baseline is set to 0 based on the experimental start data, and the average of the incremental load exercise (GXT) that can induce maximum fatigue during the test experiment The algorithm that can visualize from green to blue based on the change amount of -10 N_MF (NIRS_MuscleFatigue) was reflected in the method for measuring muscle fatigue using multi-channel near-infrared spectroscopy (NIRS) of the present invention.

Figure 5 is the result of applying the muscle fatigue algorithm by normalizing HbO₂ and HbR data of 40 areas to which DOT is applied. An example of muscle fatigue data visualization in 40 areas using DOT for depth 2 (1.5 cm). For example, when the patch acquisition area of the multi-channel NIRS is acquired at 4 mm per pixel, the muscle fatigue expression size is defined as 2 cm × 3.2 cm by 40 area coordinates (5 × 8).

In the visualization user interface of the present invention, when the user selects the attachment location of the multi-channel sensor patch that is different for each user in 3D human body modeling, the muscle fatigue data set by calculating the center of the selected muscle location is visualized. This is shown in FIG. 6 .

B) Production of 3D human body modeling capable of expressing more than 20 major motor muscles

In order to express the contraction and relaxation of each muscle according to the type of exercise, three layers of skin/muscle/bone are divided and a polygon 3D human body model is produced to express this. For example, we create 3D skin polygon modeling for male and female 2 types, and muscle 3D human body polygon modeling for 2 male and female types. And for rigging for motion animation (cycles, etc.), 3D human body polygon modeling with bones and muscles for male and female 2 formulas is produced. 3D model production for rigging for motion animation can give motion functions by applying bipad rigging technology to 3D human body modeling data lower extremity muscles, and when performing specific motions (eg, cycling), Apply a volume change. The left side of FIG. 7 shows muscle modeling to which the bipad rigging technique is applied, and the right side shows muscle modeling to which motion functions are applied.

C) Implementation of Show/Hide function for each muscle

By objectifying all modeling data, the Show/Hide function is applied through the SetActive function of the On/Off concept for each layer of skin, muscle, and bone. In order to apply the visible effect and real-time muscle fatigue change according to the movement, declare the object as a 3D object (object) and apply the Transparent function. In this way, the Visible function (transparency control function) of the 3D object can be given for the naturalness of the object. For example, if you adjust to 'Skin 100% Visible', only 100% skin is displayed, and if you adjust to 'Skin 21% Visible', only the skin with 21% transparency is shown and the muscle tissue inside the skin is expressed.

D) Matching the attachment position of the NIRS sensor patch and the 3D human body modeling position

This is the content to match the muscle fatigue data to the 3D human body modeling part according to the attachment position of the multi-channel NIRS sensor patch of the present invention. First, each muscle is objectified (FIG. 8 shows 3D model objectification of each lower extremity muscle). At this time, since the attachment location of the NIRS sensor patch for each user/training type is different, the virtual NIRS sensor is attached to the actual sensor attachment location on the 3D human body modeling muscle during initial execution so that it can be visualized. And, when selecting the relevant muscle in the main training (or exercise) area, the relevant muscle is activated and the name of the corresponding muscle is explained. Then, by using the lighting method, the muscle fatigue visualization area defined by the size of about 2 cm × 3.2 cm is displayed at the central point corresponding to the muscle selected by the user. Next, the pixel information composed of the 40 3D histograms (Array) mentioned above is projected onto the muscle surface material on the screen to position the corresponding values.

9 is to briefly show the process of matching the position of the NIRS patch attachment on the 3D human body model described above. As in (a), when attaching the patch to the area to be measured for muscle fatigue, click and select the attachment location as shown in (b), (c) go through lighting and projection, and (d) draw the muscle fatigue visualization area at the corresponding location of the muscle. Mark the position of the data.

E) Visualization through DOT (Diffuse Optical Tomography) for muscle fatigue

By applying DOT technology to correct the distance and position arrangement of optical devices of multi-channel NIRS, the relative difference between optical devices is eliminated, and the muscle fatigue of the HbO₂ and HbR values for 40 regions (pixels) of the relevant muscle area is measured by color difference or brightness. Real-time visualization of the car (refer to FIGS. 5 and 10). 10 shows an example of a muscle fatigue distribution map by these colors.

F) Display of muscle fatigue numerical graph for major muscles

The expression range of colors (or brightness, etc.) expressing muscle fatigue in the form of FIG. 5 obtained by applying DOT is divided into 0 to 10 steps and expressed as a bar-type numerical graph. Indicate the numerical value (green to blue) for each color. 11 is a numerical graph of muscle fatigue, which can be visualized together with a muscle fatigue distribution map as shown in the lower part of FIG. 10 .

G) Customizable training amount adjustment visualization function through muscle fatigue prediction simulator and comparative analysis

For 3D visualization of motion changes during training and exercise, bones and muscles are contained in the bipad during animation movements so that the corresponding objects can move physically when each joint changes, and the above-mentioned individual and exercise-specific muscle fatigue prediction technology By applying , the time to reach 70% of muscle fatigue is presented to realize the visualization of training amount adjustment so that individual training intensity can be adjusted.

An example of designing a user interface (UI) of a muscle fatigue monitoring program based on the muscle fatigue information visualization method using the above 3D human body modeling will be described. In order to visualize various monitoring program functions and 3D human body modeling expression layout UI, position-matched muscle fatigue part enlargement/reduction function, patch attachment location selection function, motion animation selection button, muscle fatigue numerical graph, muscle fatigue gradient recording function, 3D Human modeling data, skin/muscle/bone Show/Hide confirmation button, male/female selection, depth layer (Depth1, Depth2) expression button, muscle fatigue prediction simulation contents, etc. can be configured to be visualized.

12 shows an example of a UI of a muscle fatigue monitoring program. An example in which the following functions are implemented is shown.

70: a function selection unit including a male and female selection button, a skin/muscle/bone selection button, an animation selection button, and a record (record) button

72: patch attachment location selection unit

74: muscle fatigue gradient recording function

76: Current muscle fatigue notification message ("Current fatigue is 83%.")

78: 3D Human Body Modeling (Cycling Situation Animation)

80: human body modeling zoom function

82: Cycling grade (intensity), time, current muscle fatigue display

84: depth layer 1 (Depth1) muscle fatigue distribution map

86: depth 1 muscle fatigue numerical graph

88: Depth layer 2 (Depth2) muscle fatigue distribution map

90: depth 2 muscle fatigue numerical graph

The present invention described above can be implemented in terms of an apparatus or a method. In particular, the function or process of each component of the present invention is a digital signal processor (DSP), a processor, a controller, and an application-specific (ASIC). IC), a programmable logic device (FPGA, etc.), may be implemented as a hardware element including at least one of other electronic devices, and a combination thereof. In addition, it can be implemented as software in combination with a hardware element or independently, and the software can be stored in a recording medium.

As mentioned above, although the configuration of the present invention has been described in detail through preferred embodiments of the present invention, those of ordinary skill in the art to which the present invention pertains will appreciate that the present invention is disclosed in the present specification without changing its technical spirit or essential features. It will be understood that the present invention may be implemented in a specific form other than the above. It should be understood that the embodiments described above are illustrative in all respects and not restrictive. The protection scope of the present invention is defined by the claims described below rather than the above detailed description, and all changes or modifications derived from the claims and their equivalent concepts should be construed as being included in the technical scope of the present invention. .

Claims (8)

a procedure of attaching a sensor patch having a channel composed of a plurality of light source-detector pairs to the skin of a muscle region to be measured for fatigue;
a procedure of acquiring measurement data for each channel by measuring the multi-channel signals obtained from the sensor patch by near-infrared spectroscopy;
A procedure of analyzing muscle fatigue using the levels of _{oxidized hemoglobin (HbO 2} ) and reduced hemoglobin (HbR) from the acquired measurement data for each channel,
In the above sensor patch attachment procedure
The channel is separately configured for each of a plurality of depth groups,
The muscle fatigue analysis procedure is
Setting the individual exercise starting point as a baseline, calculating the amount of change per unit time for the initial value of muscle fatigue, and predicting the time of occurrence of muscle fatigue by 70% compared to the initial value A method for measuring muscle fatigue using channel near-infrared spectroscopy. The method of claim 1, wherein in the muscle fatigue analysis procedure,
In order to correct the relative position difference between the light source and detector of each channel of the sensor patch, DOT (Diffuse Optical Tomography) technology is applied and HbO₂ and HbR data are normalized for HbO₂ and HbR through regression analysis. A method for measuring muscle fatigue using near-infrared spectroscopy. The method of claim 1, wherein in the muscle fatigue analysis procedure,
A method for measuring muscle fatigue using multi-channel near-infrared spectroscopy, which includes a procedure of applying regression analysis using RMS (Root Mean Square) to confirm the amplitude change of the multi-channel signal obtained from the sensor patch in the case of long-term muscle fatigue induced training. delete delete delete delete delete

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