Evaluation system, evaluation method, and computer program

Abstract

To evaluate a muscular fatigue degree incapable of being statistically evaluated on the basis of an average value of muscular rigidity alone.SOLUTION: An evaluation system for evaluating a muscular fatigue degree comprises: a data acquisition part for acquiring measurement data including a characteristic value related to rigidity of a muscle to be measured, which is measured more than once; a calculation part for calculating a standard deviation of the characteristic value in the measurement data; and an evaluation part for evaluating the fatigue degree of the muscle to be measured, by using the standard deviation calculated from the measurement data acquired after fatigue accumulation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an evaluation system, an evaluation method, and a computer program. [Background technology]

[0002] There are known techniques for assessing muscle fatigue using muscle stiffness values ​​as evaluation indices (see, for example, Patent Documents 1 and 2). The muscle stiffness measuring device described in Patent Document 1 measures the pressure when a probe is pressed against the measurement site, and displays a tomographic image of the thickness of the muscle layer inside the subcutaneous fat at the measurement site on a display. The device measures muscle stiffness by calculating the compressibility of the muscle layer based on the pressure and the amount of change in muscle layer thickness before and after pressing, which is determined from the tomographic image.

[0003] The muscle fatigue measuring device described in Patent Document 2 applies vibrations to the heel of one foot and detects the vibrations from the heel of the other foot. The device measures muscle fatigue by analyzing the correlation between the frequency distribution characteristics of the detected vibrations and measurements taken with a muscle hardness meter using statistical methods such as regression analysis for different age groups, sexes, weights, and builds. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-168063 [Patent Document 2] Japanese Patent Application Laid-Open No. 2006-230861 Summary of the Invention [Problem to be solved by the invention]

[0005] The devices described in Patent Documents 1 and 2 measure muscle fatigue levels using muscle stiffness values. However, when a low-intensity load, such as maintaining a sitting position, is applied to a muscle, the fluctuations in muscle stiffness values ​​in response to fatigue become small. In such cases, when measuring fatigue levels using muscle stiffness values ​​as described in Patent Documents 1 and 2, if the average stiffness value is used as an evaluation index, a statistically significant difference may not be confirmed before and after the accumulation of fatigue. In other words, the technologies described in Patent Documents 1 and 2 may not be able to adequately evaluate muscle fatigue levels.

[0006] The present invention has been made to solve at least part of the above-mentioned problems, and aims to evaluate the degree of muscle fatigue, which cannot be statistically evaluated using only the average value of muscle stiffness. [Means for solving the problem]

[0007] The present invention has been made to solve at least part of the above-mentioned problems, and can be realized in the following forms. An evaluation system for evaluating muscle fatigue levels, comprising: a data acquisition unit that acquires measurement data including characteristic values ​​related to the stiffness of a muscle being measured multiple times; a calculation unit that calculates the standard deviation of the characteristic values ​​in the measurement data; and an evaluation unit that evaluates the fatigue level of the muscle being measured using the standard deviation calculated from the measurement data acquired after fatigue has accumulated, wherein the calculation unit calculates an average value of the characteristic values ​​in the measurement data in addition to the standard deviation, and the evaluation unit compares the standard deviation calculated from the measurement data acquired before fatigue has accumulated with the standard deviation calculated from the measurement data acquired after fatigue has accumulated, and evaluates the fatigue level of the muscle being measured. The present invention can also be realized in the following forms.

[0008] (1) According to one aspect of the present invention, there is provided an evaluation system for evaluating muscle fatigue levels, comprising: a data acquisition unit that acquires measurement data including characteristic values ​​related to the stiffness of a muscle being measured over multiple measurements; a calculation unit that calculates the standard deviation of the characteristic values ​​in the measurement data; and an evaluation unit that evaluates the fatigue level of the muscle being measured using the standard deviation calculated from the measurement data acquired after fatigue has accumulated.

[0009] According to this configuration, the standard deviation of the characteristic values ​​in the measurement data acquired by the data acquisition unit is calculated. The standard deviation of the characteristic values ​​after fatigue has accumulated is used to evaluate the fatigue level of the muscle being measured. When a muscle becomes fatigued, the stiffness of the muscle changes, and a value related to the stiffness of the muscle being measured is calculated. The characteristic values ​​change. With this configuration, the degree of fatigue of the muscle being measured can be evaluated by comparing the change in the characteristic values ​​with the standard deviation, which is an index. In particular, when fatigue accumulates in the muscle at a low load over a long period of time, simply focusing on the average muscle stiffness value itself as an index may not be enough to statistically evaluate the degree of muscle fatigue. In contrast, with this configuration, by using the standard deviation of the muscle stiffness value as an evaluation index, it is possible to evaluate the degree of muscle fatigue that cannot be evaluated using the average stiffness value.

[0010] (2) The evaluation system of the above aspect may include a data acquisition unit that acquires measurement data including characteristic values ​​related to the stiffness of the muscle being measured multiple times; a calculation unit that calculates the standard deviation of the characteristic values ​​in the measurement data; and an evaluation unit that evaluates the degree of fatigue of the muscle being measured by comparing the standard deviation calculated from the measurement data acquired before fatigue accumulated with the standard deviation calculated from the measurement data acquired after fatigue accumulated. According to this configuration, the level of fatigue of the muscle being measured is evaluated by comparing the standard deviation of the characteristic values ​​before fatigue accumulates with the standard deviation of the characteristic values ​​after fatigue accumulates. The characteristic values ​​related to the stiffness of the muscle being measured change before and after fatigue accumulates. According to this configuration, the level of fatigue of the muscle being measured can be evaluated by comparing the change in characteristic values ​​using standard deviation as an index. According to this configuration, by using the standard deviation of the stiffness of the muscle before and after fatigue accumulation as an evaluation index, it is possible to evaluate the level of fatigue of a muscle that cannot be statistically evaluated using the average stiffness value.

[0011] (3) In the evaluation system of the above aspect, the calculation unit may calculate the average value of the characteristic values ​​in the measurement data in addition to the standard deviation, and the evaluation unit may evaluate the degree of fatigue of the muscle being measured by comparing, in addition to the standard deviation before and after fatigue accumulation, the average value calculated from the measurement data obtained before fatigue accumulation with the average value calculated from the measurement data obtained after fatigue accumulation. With this configuration, the average value of the characteristic values ​​is calculated in addition to the standard deviation of the characteristic values. The calculated standard deviation and average value of the characteristic values ​​are used to evaluate the muscle fatigue level before and after fatigue accumulation. Therefore, with this configuration, the muscle fatigue level can be evaluated more accurately than when evaluating the muscle fatigue level using only the standard deviation of the characteristic values.

[0012] (4) The evaluation system of the above aspect may further include an application unit that applies ultrasound to the muscle to be measured, and a reflected wave acquisition unit that acquires reflected waves, which are the muscle's reaction when the ultrasound is applied, and the calculation unit may calculate the shear wave propagation velocity as the characteristic value using the acquired reflected waves. According to this configuration, ultrasonic waves are applied to the muscle to be measured, and reflected waves are acquired as a response to the applied ultrasonic waves. The calculation unit uses the acquired reflected waves to calculate the shear wave velocity as a characteristic value. Therefore, according to this configuration, even if measurement data of the characteristic value is not prepared in advance, the acquired reflected waves can be used to evaluate the degree of muscle fatigue.

[0013] (5) In the evaluation system of the above aspect, when there are multiple muscles within the range to which the ultrasound can be applied, the reflected wave acquisition unit may acquire the reflected waves from a selected portion of the muscles. With this configuration, when there are multiple muscles that can be measured within the range where ultrasound can be applied, one measurement target is selected and the reflected waves are acquired. Therefore, with this configuration, the fatigue level of the muscle that you want to evaluate can be selected and evaluated.

[0014] (6) In the evaluation system of the above aspect, the reflected wave acquisition unit may acquire the reflected waves from muscles present deeper inside in addition to the superficial layer within the range where the ultrasound can be applied. According to this configuration, reflected waves from muscles present within a range where ultrasonic waves can be applied are acquired. Therefore, with this configuration, the fatigue level of the internal muscles can be selected and evaluated.

[0015] (7) In the evaluation system of the above aspect, the calculation unit may further use the calculated shear wave propagation velocity to calculate the shear stiffness of the muscle being measured as the characteristic value. With this configuration, the standard deviation of muscle shear stiffness other than shear wave velocity is calculated as a characteristic value. Because muscle fatigue is a parameter directly related to muscle shear stiffness, this configuration allows for more accurate evaluation of muscle fatigue.

[0016] The present invention can be realized in various forms, for example, a fatigue level measuring device, a fatigue level determining device, a muscle hardness measuring device, a muscle fatigue level measuring device, an evaluation system, an evaluation method, a system including these devices, a computer program for executing these devices, a server device for distributing this computer program, a non-transitory storage medium storing the computer program, etc. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a block diagram of an evaluation system according to an embodiment of the present invention. [Figure 2] FIG. 10 is an explanatory diagram for evaluating muscle fatigue levels. [Figure 3] FIG. 10 is an explanatory diagram for evaluating muscle fatigue levels. [Figure 4] 1 is a flowchart of a method for evaluating muscle fatigue level according to the present embodiment. [Figure 5] FIG. 10 is an explanatory diagram of evaluation of muscle fatigue level in a modified example. [Figure 6] FIG. 10 is an explanatory diagram of evaluation of muscle fatigue level in a modified example. [Figure 7] 10 is a flowchart of a modified method for evaluating muscle fatigue levels. DETAILED DESCRIPTION OF THE INVENTION

[0018] First Embodiment 1 is a block diagram of an evaluation system 100 according to one embodiment of the present invention. The evaluation system 100 of this embodiment evaluates the degree of muscle fatigue after fatigue accumulation. The evaluation system 100 calculates the standard deviation of characteristic values ​​related to the stiffness of the muscle being measured, and evaluates the degree of muscle fatigue by comparing the standard deviations before and after fatigue accumulation.

[0019] As shown in FIG. 1, the evaluation system 100 of this embodiment includes an evaluation device 50 that evaluates muscle fatigue levels, and a probe (application unit) 90 that applies ultrasound. The probe 90 applies ultrasound to the muscle being measured. The probe 90 also acquires reflected waves, which are a reaction to the application of ultrasound. Specifically, the probe 90 applies ultrasound to the muscle being measured, thereby generating shear waves in the muscle. By acquiring the reflected waves, the probe 90 tracks the propagation time of the generated shear waves. The shear wave propagation velocity can then be calculated.

[0020] The evaluation device 50 is a personal computer that controls the probe 90. As shown in Fig. 1, the evaluation device 50 includes a CPU 10, a storage unit 20 that stores various information, an input unit 30 that accepts user input, and a monitor 40 that displays images and sounds. The input unit 30 is composed of a keyboard and a mouse. The storage unit 20 is composed of a hard disk drive (HDD) and the like. The storage unit 20 stores data of the reflected waves acquired by the probe 90 as measurement data.

[0021] The CPU (Central Processing Unit) 10 is connected to a ROM (Read Only Memory) and a RAM (Random Access Memory), not shown, and controls each part of the evaluation device 50 by expanding a computer program stored in the ROM into the RAM and executing it. As shown in FIG. 1, the CPU 10 includes a data acquisition unit 11 that acquires measurement data, It functions as a vibration control unit 12 that controls the ultrasonic waves of the probe 90, an image processing unit 13 that controls the image displayed on the monitor 40, a calculation unit 14, and an evaluation unit 15 that evaluates the fatigue level of the subject to be measured.

[0022] The vibration control unit 12 controls whether or not ultrasonic waves are applied to the muscle to be measured by the probe 90, as well as the frequency of the waves. The data acquisition unit 11 acquires measurement data of reflected waves, which are muscle responses measured multiple times as a result of the ultrasonic waves applied by the probe 90. In this embodiment, when there are multiple muscles that can be measured within the range to which ultrasonic waves can be applied, the data acquisition unit 11 selects some of the muscles as the measurement targets. The data acquisition unit 11 can acquire measurement data of reflected waves from the selected some of the muscles. Furthermore, the data acquisition unit 11 can acquire measurement data of reflected waves from muscles located deeper within the muscles, in addition to the surface layers within the range to which ultrasonic waves can be applied, as some of the muscles selected as the measurement targets. The data acquisition unit 11 stores the acquired measurement data in the memory unit 20. In this embodiment, the probe 90 and the data acquisition unit 11 correspond to a reflected wave acquisition unit.

[0023] The image processing unit 13 uses the measurement data of the acquired reflected waves to create a tomographic image of the muscle being measured and the surrounding area of ​​the muscle. The image processing unit 13 displays the created tomographic image of the muscle on the monitor 40.

[0024] The calculation unit 14 calculates the shear wave velocity of the muscle using the reflected waves as measurement data, and calculates the average value and standard deviation of the shear wave velocity. The calculation unit 14 calculates the shear wave velocity by dividing the distance between each point tracking the propagation of the shear wave in the muscle by the shear wave propagation time. The calculation unit 14 stores the calculated shear wave velocity of the muscle in the storage unit 20. The calculation unit 14 calculates the average value and standard deviation of the calculated data of the shear wave velocity.

[0025] The evaluation unit 15 evaluates the degree of muscle fatigue by comparing the average value and standard deviation of the shear wave velocity in a healthy state before fatigue accumulates with the average value and standard deviation of the shear wave velocity in a fatigued state after fatigue accumulates, before and after fatigue accumulates in the muscle. In other words, the evaluation unit 15 evaluates the degree of muscle fatigue by comparing the average value and standard deviation of the shear wave velocity calculated from the measurement data acquired in the healthy state with the average value and standard deviation of the shear wave velocity calculated from the measurement data acquired in the fatigued state.

[0026] Figures 2 and 3 are explanatory diagrams for evaluating muscle fatigue. In Figure 2, the subject sat in a chair without a backrest for one hour and maintained the seated position for one hour. The measurement data for the healthy state before sitting is shown as "Before" on the horizontal axis. Figure 2 also shows the measurement data for the fatigued state after maintaining the seated position for one hour as "After" on the horizontal axis. The plot data for the healthy state and fatigued state shown in Figure 2 are measurement data for the shear wave velocity (vertical axis) of the target muscle, measured 30 times per day for six days. The plot data are distributed across a specified width to prevent overlapping. The length l of the vertical rectangle shown on the "After" side of Figure 2 indicates the standard deviation of the plot data. μ indicates the mean value of the plot data. a indicates the width of the 95% significance level in Welch's t-test. The rectangles shown on the "Before" side and the lines inside the rectangles correspond to the width of the standard deviation l, the mean value μ, and the significance level a, just like on the "After" side.

[0027] In Figure 3, the subject sat in a chair with a backrest for one hour and maintained the seated position. The measured data for the healthy state before sitting is shown as "Before." Figure 3 also shows the measured data for the fatigued state after maintaining the seated position for one hour as "After." The plot data for the healthy state and fatigued state shown in Figure 3 are measurement data of the shear wave propagation velocity of the target muscle, measured 25 times a day for four days. In Figure 3, as in Figure 2, the standard deviation width l, mean value μ, and significance level a are indicated by rectangles and lines within the rectangles. The subjects for the fatigue evaluations in Figures 2 and 3 were the same individuals, who reported fatigue after maintaining a sitting position for one hour and reported that they were more tired when sitting in a position without a backrest. In other words, the subjects reported that fatigue accumulated when sitting in a chair without a backrest and a chair with a backrest, and that the degree of fatigue without a backrest was greater than the degree of fatigue when sitting in a chair with a backrest.

[0028] It is known that muscle stiffness increases as muscle fatigue accumulates, causing the muscle to become stiff. When a muscle contracts, due to the principle of size, small, low-threshold motor neurons are recruited, so stiffness changes occur first in muscles that begin to contract early (muscles that fatigue quickly). As a result, there is variance in stiffness values ​​measured multiple times, and as shown in Figures 2 and 3, the standard deviation of the shear wave velocity in the muscle varies more after fatigue has accumulated. As shown in Figures 2 and 3, it can be seen that the shear wave velocity is higher in a fatigued state than in a healthy state.

[0029] In the case of the seat without a backrest shown in Figure 2, a Welch t-test was performed on the mean value μ of the shear wave velocity of the measurement data at a significance level of 0.05, and a significant difference was confirmed between before and after fatigue accumulation. In other words, by comparing the mean value μ of the shear wave velocity before and after fatigue accumulation, the degree of fatigue in the fatigued state could be evaluated. On the other hand, in the case of the seat with a backrest shown in Figure 3, a Welch t-test was performed on the mean value μ of the shear wave velocity of the measurement data at a significance level of 0.05, and the p-value was 0.077 (>0.05), and no significant difference was confirmed between before and after fatigue accumulation. In other words, comparing the mean value μ of the shear wave velocity before and after fatigue accumulation did not allow for evaluation of the degree of fatigue in the fatigued state. However, as shown in Figure 3, the standard deviation of the shear wave velocity is larger in the fatigued state than in the healthy state. Therefore, the evaluation unit 15 can evaluate the subject's fatigue level by comparing the standard deviation of the shear wave velocity before and after fatigue accumulation. When discussing differences in the average values ​​of data with variation, it is necessary to perform statistical testing to determine whether there is a probabilistic difference, including the degree of variation in the data. In contrast, in this embodiment, by focusing on the standard deviation, which is the magnitude of the variation in the data itself, it is possible to directly compare the variation in the data itself without performing statistical testing of the average values. In other words, the standard deviation of the shear wave velocity can be used to evaluate the fatigue level reported by the subject. Furthermore, the average value μ and standard deviation of the shear wave velocity can be used to evaluate the level of fatigue of the subject.

[0030] The width l of the standard deviation after fatigue accumulation without a backrest shown in FIG. 2 was 1.53 (m / s). The width l of the standard deviation after fatigue accumulation with a backrest shown in FIG. 3 was 1.01 (m / s). The evaluation unit 15 of this embodiment assumes that the fatigue level without a backrest is 100% and calculates the fatigue level with a backrest using the normalization calculation formula (1) below. As a result, the fatigue level with a backrest was 32%. Note that x in the following formula (1) min is the average value of the width of the standard deviation without a backrest before fatigue accumulation and the width of the standard deviation with a backrest before fatigue accumulation.

[0031]

number

[0032] The evaluation unit 15 displays on the monitor 40 the graphs of the shear wave propagation velocity in the muscle before and after the accumulation of fatigue shown in Figures 2 and 3. The evaluation unit 15 also displays on the monitor 40 the numerical value of 32%, which is the relative degree of fatigue with a backrest compared to without a backrest, calculated by the above formula (1).

[0033] 4 is a flowchart of the muscle fatigue evaluation method of this embodiment. The evaluation flow in FIG. 4 includes a stiffness measurement flow from steps S1 to S6 for measuring muscle stiffness values ​​and evaluating muscle fatigue, and a tomographic image creation flow from steps S21 to S24 for creating and displaying muscle tomographic images (echo images). The stiffness measurement flow and the tomographic image creation flow may be executed simultaneously in parallel, or only one of them selected by the user may be executed. Furthermore, one flow may be executed after the other in an order selected by the user (or a predetermined order).

[0034] In the stiffness measurement flow, the probe 90 applies ultrasound to the muscle to be measured in accordance with the control of the vibration control unit 12 in order to generate transverse waves in the muscle (step S1). When ultrasound is applied to the muscle, vibrations associated with the transverse waves generated in the muscle are generated within the biological tissue. The data acquisition unit 11 performs a data acquisition step in which it acquires measurement data of the muscle to be measured multiple times via the probe 90 (step S2). The data acquisition unit 11 stores the reflected waves from the muscle as the acquired measurement data in the memory unit 20.

[0035] The calculation unit 14 calculates the shear wave velocity in the muscle as measurement data (step S3). The calculation unit 14 stores the calculated shear wave velocity of the measurement object in the storage unit 20. The calculation unit 14 also calculates the average value μ and standard deviation of the shear wave velocity using the calculated shear wave velocities of the measurement object (step S4). The processes of steps S3 and S4 correspond to a calculation step.

[0036] The evaluation unit 15 performs an evaluation step of evaluating the degree of muscle fatigue after fatigue accumulation by comparing the mean value μ and standard deviation of the shear wave propagation velocity of the muscle being measured before and after fatigue accumulation (step S5). The evaluation unit 15 displays the evaluation results of the muscle fatigue level shown in Figures 2 and 3 on the monitor 40 (step S6), and the stiffness measurement flow ends.

[0037] In the tomographic image creation flow, the probe 90 applies ultrasound to the muscle to be measured in accordance with control by the vibration control unit 12 to create a tomographic image of the muscle (step S21). The data acquisition unit 11 acquires measurement data of the muscle to be measured and the muscles surrounding the muscle via the probe 90 (step S22). The image processing unit 13 uses the measurement data acquired by the data acquisition unit 11 to create a tomographic image of the muscle to be measured and the surrounding area of ​​the muscle (step S23). The image processing unit 13 displays the created tomographic image of the muscle on the monitor 40 (step S24), and the tomographic image creation flow ends.

[0038] As described above, in the evaluation system 100 of this embodiment, the calculation unit 14 calculates the shear wave velocity of the muscle using the measurement data acquired by the data acquisition unit 11 and calculates the standard deviation of the shear wave velocity. The evaluation unit 15 evaluates the degree of muscle fatigue by comparing the standard deviation of the shear wave velocity in a healthy state before fatigue accumulation with the standard deviation of the shear wave velocity in a fatigued state after fatigue accumulation, before and after fatigue accumulation in the muscle. That is, in this embodiment, the degree of fatigue of the muscle being measured is evaluated by comparing the standard deviation of the shear wave velocity before fatigue accumulation with the standard deviation of the shear wave velocity after fatigue accumulation. When a muscle fatigues, the stiffness of the muscle changes. Therefore, the shear wave velocity, which is related to the stiffness of the muscle being measured, changes before and after fatigue accumulation. According to this embodiment, the degree of fatigue of the muscle being measured can be evaluated by comparing the change in shear wave velocity with the standard deviation, which is an index. In particular, when muscle fatigue accumulates over a long period of time at a low load, such as when the seat is equipped with a backrest as shown in Fig. 3, simply comparing the average value of the shear wave velocity before and after fatigue accumulation may not be enough to statistically evaluate the degree of muscle fatigue. In contrast, in this embodiment, by using the standard deviation, which is not the average value of the shear wave velocity itself, as the evaluation index, it is possible to evaluate the degree of muscle fatigue that cannot be evaluated using the average value of the shear wave velocity.

[0039] Furthermore, in this embodiment, the calculation unit 14 calculates the mean value μ of the shear wave velocities before and after fatigue accumulation, in addition to the standard deviation of the shear wave velocities. The evaluation unit 15 evaluates the degree of muscle fatigue by comparing the mean value and standard deviation of the shear wave velocities in a healthy state before fatigue accumulation with the mean value and standard deviation of the shear wave velocities in a fatigued state after fatigue accumulation, before and after fatigue accumulation in the muscle. That is, in this embodiment, the mean value μ of the shear wave velocities is calculated in addition to the standard deviation of the shear wave velocities. The calculated standard deviation and mean value μ of the shear wave velocities are used to evaluate the degree of muscle fatigue before and after fatigue accumulation. Therefore, in this embodiment, the degree of muscle fatigue can be evaluated more accurately than when evaluating the degree of muscle fatigue using only the standard deviation of the shear wave velocities.

[0040] Furthermore, the probe 90 of this embodiment applies ultrasonic waves to the muscle to be measured and acquires the reflected waves from the applied ultrasonic waves. The calculation unit 14 calculates the shear wave velocity of the muscle using the reflected waves as measurement data. Therefore, according to this embodiment, even if measurement data of characteristic values ​​related to muscle stiffness, such as shear wave velocity, is not prepared in advance, the acquired reflected waves can be used to evaluate the degree of muscle fatigue.

[0041] Furthermore, in this embodiment, when there are multiple muscles that can be measurement targets within the range where ultrasound can be applied, the data acquisition unit 11 selects some of the muscles as measurement targets. The data acquisition unit 11 can acquire measurement data of the reflected waves from some of the selected muscles. Therefore, in this embodiment, when there are multiple muscles that can be measurement targets within the range where ultrasound can be applied, one measurement target is selected and the reflected waves are acquired. As a result, according to this embodiment, it is possible to select and evaluate the fatigue level of the muscle whose fatigue level is to be evaluated.

[0042] Furthermore, the data acquisition unit 11 of this embodiment can acquire measurement data of reflected waves from muscles located deeper within the body as well as from the surface layer within the range where ultrasound can be applied, as part of the muscles selected for measurement. For example, fatigue measurement using a surface electromyography device can only evaluate fatigue in superficial muscles near the skin. In contrast, this embodiment can select and evaluate the fatigue level of various muscles present in the body, regardless of whether they are superficial or deep muscles.

[0043] <Modifications of the above embodiment> The present invention is not limited to the above-described embodiment, and can be implemented in various forms without departing from the spirit of the present invention, including, for example, the following modifications: In the above-described embodiment, part of the configuration realized by hardware may be replaced by software, and conversely, part of the configuration realized by software may be replaced by hardware.

[0044] <Variation 1> The evaluation system for evaluating muscle fatigue level can be modified to the extent that it evaluates muscle fatigue level using the standard deviation calculated from measurement data before and after fatigue accumulation in the muscle being measured. The evaluation system 100 of the above embodiment evaluated muscle fatigue level using the average value μ of the measurement data in addition to the standard deviation of the measurement data, but fatigue level may also be evaluated using only the standard deviation.

[0045] The configuration of the evaluation system 100 of the above embodiment and the control contents executed are merely examples and can be modified. For example, the evaluation system 100 may function as a data acquisition unit 11, a calculation unit 14, and an evaluation unit. Therefore, the evaluation system 100 may not include the probe 90, the memory unit 20, the input unit 30, and the output unit, and may not function as the vibration control unit 12 and the image processing unit 13. For example, the evaluation system 100 acquires measurement data of the muscle's reflected waves by directly measuring them using the probe 90, but may also acquire previously measured measurement data from a server or the like different from the evaluation system 100. The evaluation system 100 may not generate a tomographic image and may not display the tomographic image on the monitor 40. The evaluation result of the muscle fatigue level evaluated by the evaluation unit 15 may be transmitted to another server or the like via wireless communication. It may be transmitted to the device.

[0046] In the above embodiment, when there are multiple muscles that can be measured within the range where ultrasound can be applied, the data acquisition unit 11 selects some of the muscles as measurement targets, but it may also acquire reflected waves from all of the muscles. Also, in the above embodiment, the data acquisition unit 11 acquires measurement data of reflected waves from muscles that are located deeper than the surface layer within the range where ultrasound can be applied, but it may also automatically acquire reflected waves from the muscle that is located in the most superficial layer that can be measured.

[0047] <Variation 2> In the above embodiment, the standard deviation of the shear wave velocity of the muscle is calculated as a characteristic value in the measurement data, but the characteristic value for which the standard deviation is calculated may be other than the shear wave velocity. The calculation unit 14 may use the calculated shear wave velocity to calculate the shear stiffness of the muscle being measured as a characteristic value for calculating the standard deviation. Shear wave velocity V s ,If the density of the muscle being measured is ρ, the shear stiffness G of the muscle can be calculated using the following equation (2).

[0048]

number

[0049] In this modification, the muscle shear stiffness G is used as a characteristic value other than the muscle shear wave velocity. Because muscle fatigue is a parameter directly related to muscle shear stiffness G, this modification allows for a more accurate evaluation of muscle fatigue.

[0050] <Variation 3> The evaluation system 100 of the above embodiment evaluates the level of fatigue after fatigue accumulation by comparing the magnitude of the standard deviation of the shear wave velocity in the muscle before and after fatigue accumulation, but the method of evaluating the level of fatigue can be modified. For example, the evaluation system may set the standard deviation of the shear wave velocity measured before fatigue accumulation as a threshold. The evaluation system may also evaluate the level of fatigue by comparing the standard deviation of the shear wave velocity measured after fatigue accumulation with the set threshold. In this case, the evaluation unit in the evaluation system can evaluate the level of fatigue by using only the standard deviation of the shear wave velocity measured multiple times after fatigue accumulation, as in the above embodiment, without using the standard deviation of the shear wave velocity before fatigue accumulation.

[0051] The evaluation unit of this modified example can evaluate the level of fatigue by using only the standard deviation of the shear wave velocity measured multiple times after fatigue accumulation, as in the above embodiment, without using the standard deviation of the shear wave velocity before fatigue accumulation. Therefore, in this modified example, the level of muscle fatigue can be evaluated by using the change in the characteristic value related to muscle stiffness as the standard deviation of the shear wave velocity. In particular, when muscle fatigue accumulates over a long period of time at a low load, simply focusing on the average muscle stiffness itself as an index may not be enough to evaluate the level of muscle fatigue. In contrast, in this modified example, by using the standard deviation, which is not the average muscle stiffness itself, as an evaluation index, it is possible to evaluate the level of muscle fatigue that cannot be evaluated using the average stiffness.

[0052] 5 and 6 are explanatory diagrams for evaluating muscle fatigue levels in a modified example. Figures 5 and 6 show tables that associate the mean value μ and standard deviation of muscle shear wave velocity with the fatigue level of shear wave velocity. The evaluation system of this modified example creates in advance the table of Figure 5, which shows the relationship between the difference in mean value μ and standard deviation of shear wave velocity measured before and after fatigue accumulation and the muscle fatigue level. The evaluation system can evaluate the fatigue level by using the difference in mean value μ of shear wave velocity measured before and after fatigue accumulation and the difference in standard deviation of shear wave velocity measured before and after fatigue accumulation, and the table of Figure 5.

[0053] The evaluation system also creates in advance the table shown in Figure 6, which shows the relationship between the mean value μ and standard deviation of the measured shear wave velocity after fatigue accumulation and the degree of muscle fatigue. The evaluation system can evaluate the degree of fatigue by using the acquired mean value μ of the shear wave velocity after fatigue accumulation and the standard deviation of the shear wave velocity after fatigue accumulation, as well as the table shown in Figure 6. By using the table shown in Figure 6, the evaluation system can evaluate the degree of muscle fatigue without using the mean value μ and standard deviation of the shear wave velocity before fatigue accumulation.

[0054] <Variation 4> Fig. 7 is a flowchart of a modified example of a method for evaluating muscle fatigue. In the evaluation flow shown in Fig. 7, data acquisition unit 11 performs a data acquisition step of acquiring measurement data obtained by measuring a muscle of interest multiple times after fatigue has accumulated (step S11). Calculation unit 14 performs a calculation step of calculating the shear wave velocity of the muscle as measurement data and calculating the standard deviation of the shear wave velocities using the calculated shear wave velocities of the muscle of interest (step S12). Evaluation unit 15 performs an evaluation step of evaluating the muscle fatigue level after fatigue has accumulated by comparing the standard deviations of the shear wave velocities of the muscle of interest after fatigue has accumulated (step S13).

[0055] This aspect has been described above based on embodiments and modifications. However, the above-described embodiments are intended to facilitate understanding of this aspect and are not intended to limit this aspect. This aspect may be modified or improved without departing from the spirit and scope of the claims, and equivalents thereof are included in this aspect. Furthermore, if a technical feature is not described as essential in this specification, it may be deleted as appropriate. [Explanation of symbols]

[0056] 10...CPU 11...Data acquisition unit (reflected wave acquisition unit) 12...Vibration control unit 13...Image processing unit 14...Calculation section 15...Evaluation Department 20...Storage section 30...Input section 40...Monitor 50...Evaluation device 90... Probe (applying part, reflected wave acquiring part) 100...rating system G...shear stiffness V s …Transverse wave propagation velocity a…Significance level l...Width of standard deviation of shear wave propagation velocity μ...Average shear wave propagation velocity

Claims

1. An evaluation system for evaluating muscle fatigue, a data acquisition unit that acquires measurement data including characteristic values ​​related to the stiffness of the muscle being measured multiple times; a calculation unit that calculates a standard deviation of the characteristic value in the measurement data; an evaluation unit that evaluates the degree of fatigue of the muscle being measured using the standard deviation calculated from the measurement data acquired after fatigue has accumulated; Equipped with the calculation unit calculates an average value of the characteristic values ​​in the measurement data in addition to the standard deviation; The evaluation unit comparing the standard deviation calculated from the measurement data acquired before fatigue accumulates with the standard deviation calculated from the measurement data acquired after fatigue accumulates; An evaluation system that evaluates the degree of fatigue of the muscle being measured by comparing the average value calculated from the measurement data acquired before fatigue accumulates with the average value calculated from the measurement data acquired after fatigue accumulates.

2. The evaluation system according to claim 1, further comprising: an applying unit that applies ultrasound to the muscle to be measured; a reflected wave acquiring unit that acquires a reflected wave, which is a muscle response when the ultrasonic wave is applied; Equipped with The calculation unit calculates a shear wave propagation velocity as the characteristic value using the acquired reflected wave.

3. 3. The evaluation system according to claim 2, The reflected wave acquisition unit acquires the reflected waves from a selected portion of muscles when multiple muscles are present within a range where the ultrasound can be applied.

4. The evaluation system according to claim 3, The reflected wave acquisition unit acquires the reflected waves from muscles located deeper inside in addition to the surface layer within the range to which the ultrasound can be applied.

5. The evaluation system according to any one of claims 2 to 4, The calculation unit further calculates the shear stiffness of the muscle being measured as the characteristic value using the calculated shear wave propagation velocity, in this evaluation system.

6. An evaluation method for evaluating a muscle fatigue level, comprising: a data acquisition step of acquiring measurement data including characteristic values ​​related to the stiffness of the muscle being measured multiple times; a calculation step of calculating a standard deviation of the characteristic value in the measurement data; an evaluation step of evaluating the degree of fatigue of the muscle to be measured using the standard deviation calculated from the measurement data acquired after fatigue has accumulated; Run the calculating step calculates an average value of the characteristic values ​​in the measurement data in addition to the standard deviation; The evaluation step includes: comparing the standard deviation calculated from the measurement data acquired before fatigue accumulates with the standard deviation calculated from the measurement data acquired after fatigue accumulates; An evaluation method for evaluating the degree of fatigue of the muscle being measured by comparing the average value calculated from the measurement data acquired before fatigue accumulates with the average value calculated from the measurement data acquired after fatigue accumulates.

7. A computer program for assessing muscle fatigue, a data acquisition function for acquiring measurement data including characteristic values ​​related to the stiffness of the muscle being measured multiple times; a calculation function for calculating the standard deviation of the characteristic value in the measurement data; an evaluation function for evaluating the degree of fatigue of the muscle to be measured using the standard deviation calculated from the measurement data acquired after fatigue has accumulated; This is realized by a computer, the calculation function calculates an average value of the characteristic value in the measurement data in addition to the standard deviation; The evaluation function is comparing the standard deviation calculated from the measurement data acquired before fatigue accumulates with the standard deviation calculated from the measurement data acquired after fatigue accumulates; A computer program that evaluates the degree of fatigue of the muscle being measured by comparing the average value calculated from the measurement data obtained before fatigue accumulates with the average value calculated from the measurement data obtained after fatigue accumulates.

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