Surface skin hemodynamics determination system, surface skin hemodynamics determination method and program

Abstract

To provide a surface skin hemodynamics determination system making it possible to easily, contactlessly determine surface skin hemodynamic state.SOLUTION: A surface skin hemodynamics determination system includes: an area averaging processing unit 102 that determines an area set as a surface skin hemodynamics determination point from an image obtained by capturing the surface skin hemodynamics determination point of a subject, acquires a difference component between a red component and a blue component for the determined area at regular intervals, and averages the acquired difference component in the entire region; a fluctuation acquisition unit 103 for acquiring fluctuation in the difference component averaged by the area averaging processing unit 102; and a determination unit 104 for determining surface skin hemodynamics of the surface skin hemodynamics determination point based on a fluctuation amount acquired by the fluctuation acquisition unit 103, and estimating muscle activity of the corresponding area.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a surface skin blood flow dynamics determination system, a surface skin blood flow dynamics determination method, and a program for estimating muscle activity states and the like.

Background Art

[0002] Estimation of muscle activity states is applied in various places such as rehabilitation and exercise training. Conventionally, estimation of muscle activity states has mainly been performed using electromyogram and infrared thermography. When measuring using electromyogram, since it is necessary to attach electrodes, a large-scale measuring device may be required in some cases. Therefore, there is a problem that some subjects are easily affected by noise. In the measurement using infrared thermography, although the activity amount can be known from the temperature, there is a problem that the measuring device becomes expensive.

[0003] Patent Document 1 describes a technique for estimating autonomic nerve activity by obtaining a difference component (R - B component) between a red component (R) and a blue component (B) of the nose, which is easily affected by the sympathetic nerve, from an image of the face and analyzing the time-series data of the R - B component.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] As described above, the measurement of the conventional muscle activity state has problems such as being unable to be easily measured and the measurement device being expensive, and it has been desired to be able to measure non-contact and with a low-cost device. For this reason, the inventor of the present application utilized the analysis technique of the time-series data of the R-B component described in Patent Document 1 to determine the surface skin blood flow dynamics of a target site such as an arm, and developed the ability to measure the muscle activity state.

[0006] An object of the present invention is to provide a surface skin blood flow dynamics determination system, a surface skin blood flow dynamics determination method, and a program that can easily measure the muscle activity state non-contact.

Means for Solving the Problems

[0007] The surface skin blood flow dynamics determination system of the present invention determines a region set as the surface skin blood flow dynamics determination site from an image obtained by photographing the surface skin blood flow dynamics determination site of a subject, and for the determined region, obtains a difference component between a red component and a blue component at regular intervals, and performs a region averaging process of averaging the obtained difference component over the entire region; a variation acquisition unit that obtains the variation of the difference component averaged by the region averaging unit; and a determination unit that determines the surface skin blood flow dynamics of the surface skin blood flow dynamics determination site based on the variation amount obtained by the variation acquisition unit and estimates the muscle activity of the corresponding region.

[0008] Further, the surface skin blood flow dynamics determination method of the present invention is a surface skin blood flow dynamics determination method for determining the surface skin blood flow dynamics of a subject by arithmetic processing by a computer, and by arithmetic processing by a computer, determines a region set as the surface skin blood flow dynamics determination site from an image obtained by photographing the surface skin blood flow dynamics determination site of the subject, obtains a difference component between a red component and a blue component at regular intervals for the determined region, and performs a region averaging process of averaging the obtained difference component over the entire region; a variation acquisition process of obtaining the variation of the difference component averaged by the region averaging process by arithmetic processing by a computer; and a determination process of determining the surface skin blood flow dynamics of the surface skin blood flow dynamics determination site based on the variation amount obtained by the variation acquisition process by arithmetic processing by a computer and estimating the muscle activity of the corresponding region.

[0009] In addition, the program of the present invention causes a computer to execute each process of the above-described surface skin blood flow dynamics determination method as a procedure.

Effects of the Invention

[0010] According to the present invention, it becomes possible to appropriately determine the surface skin blood flow dynamics of a target region of a subject from an image of the subject and accurately determine the muscle activity of the target region.

Brief Description of the Drawings

[0011]

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Mode for Carrying Out the Invention

[0012] Hereinafter, a surface skin blood flow dynamics determination system, a surface skin blood flow dynamics determination method, and a program according to an embodiment example of the present invention (hereinafter referred to as "this example") will be described with reference to the accompanying drawings.

[0013] [Example of Measurement Environment for Making a Judgment] FIG. 1 shows an example of a measurement environment for making a judgment by the surface skin blood flow dynamics determination system of this example. As shown in FIG. 1, a subject (test subject) 10 for determining the dynamics of surface skin blood flow is seated on a chair 1. Here, the vicinity of the biceps brachii of the right arm 11 of the subject 10 is set as the data acquisition range (surface skin blood flow dynamics determination location), and a green adhesive tape (tape-shaped member) 12 is attached to the skin of the upper arm corresponding to the vicinity of the biceps brachii. The skin color range surrounded by this adhesive tape 12 becomes the data acquisition range. The skin color range surrounded by the adhesive tape 12 is a location where the skin of the upper arm of the right arm 11 of the subject 10 is exposed. At the time of this measurement, the subject 10 holds a weight 13 as a load with the hand at the tip of the right arm 11.

[0014] In such a state, the right arm 11 of the subject 10 is photographed by a camera (imaging unit) built into a smartphone 20 as a surface skin blood flow dynamics determination system. At the time of this photographing, as will be described with reference to FIG. 8 later, as the measurement time, a rest time is first set, then a time when a load is applied is set, and finally a rest time is set again. Then, the camera (imaging unit) built into the smartphone 20 determines the surface skin blood flow dynamics during the first rest period, the surface skin blood flow dynamics during the time of application of the load, and the surface skin blood flow dynamics during the last rest period. However, setting the rest period in this way is just an example, and other measurement time settings may also be used. For example, the rest state before applying the load may be measured first, and then the state with the load applied may be measured, omitting the last rest period.

[0015] The smartphone 20 extracts the data acquisition range surrounded by the adhesive tape 12 from the captured image data, and analyzes the image data of the extracted data acquisition range to determine the surface skin blood flow dynamics of the data acquisition range.

[0016] [Surface Skin Blood Flow Dynamics Determination System] FIG. 2 shows a configuration example of the smartphone 20 as a surface skin blood flow dynamics determination system. The configuration shown in FIG. 2 shows a processing functional unit configured in the RAM 22 by an operation under the control of the CPU 21, which will be described later with reference to FIG. 3. The smartphone 20 as the facial skin blood flow dynamics determination system in this example captures the image data captured by the camera 24 (FIG. 3) provided in the smartphone 20. The image data is of the arm of the subject. The image data captured by the camera 24 is moving image data at a certain frame rate, and the image data of each frame is composed of pixel data of the primary colors red (R), green (G), and blue (B). The frame rate of the image data is, for example, 30 frames / second, and each pixel data within each frame is data indicating the luminance values of red (R), green (G), or blue (B) at a predetermined gradation.

[0017] The image data captured by the smartphone 20 as the facial skin blood flow dynamics determination system is supplied to the RGB component acquisition unit 101, and the red component, green component, and blue component are individually acquired. The red component, green component, and blue component obtained by the RGB component acquisition unit 101 are respectively supplied to the region averaging processing unit 102.

[0018] The area averaging processing unit 102 extracts the area of the skin exposed by the adhesive tape 12 from the image of each frame of the acquired moving image data. Also, the area averaging processing unit 102 obtains an (R - B) component obtained by taking the difference between the red (R) component and the blue (B) component as a color component, and averages (smoothes) the (R - B) component for all pixels within the area. Note that the area surrounded by the tape 12 is divided into five areas as described with reference to FIG. 9, and averaging is performed for each of the five areas. However, dividing into five areas is just an example, and for example, averaging may be performed for the entire area without division.

[0019] Then, the result averaged by the area averaging processing unit 102 is supplied to the variation acquisition unit 103, and the variation acquisition unit 103 performs a variation acquisition process on the averaged result. The variation acquisition unit 103 acquires the variation status of the average value at regular time intervals. Specifically, the variation acquisition unit 103 acquires the variation status such as a state where there is almost no change in the average value, a state where the average value increases, and a state where the average value decreases for each area at regular time intervals.

[0020] The information on the variation status of each area acquired by the variation acquisition unit 103 is supplied to the determination unit 104. The determination unit 104 determines the surface skin blood flow dynamics of each area based on the information on the variation status of each area, and estimates muscle activity from this determination result. The surface skin blood flow dynamics determined by the determination unit 104 is output from the output unit 105. The output unit 105 displays, for example, the determined surface skin blood flow dynamics and the estimation result of muscle activity as measurement results. Alternatively, the output unit 105 may transmit the determined surface skin blood flow dynamics and the estimation result of muscle activity to an external terminal.

[0021] [Example of the hardware configuration of a smartphone as a surface skin blood flow dynamics determination system] FIG. 3 shows an example of the hardware configuration when the surface skin blood flow dynamics determination system is configured by a smartphone. The smartphone 20 is configured as a computer which is an information processing device.

[0022] The smartphone (computer) 20 shown in Fig. 3 includes a CPU (Central Processing Unit) 21, a RAM (Random Access Memory) 22, a storage 23, a camera 24, an input unit 25, a display unit 26, and a communication unit 27, which are respectively connected to a bus.

[0023] The CPU 21 is an arithmetic processing unit that reads and executes the program code of software that realizes the functions performed by the surface skin blood flow dynamics determination system from the RAM 22 or the storage 23. By the CPU 21 reading the program code from the RAM 22 or the storage 23 and executing arithmetic processing in the work area of the RAM 22, various processing function units are configured in the RAM 22. For example, in the RAM 22, an RGB component acquisition unit 101, an area averaging processing unit 102, a variation acquisition unit 103, and a determination unit 104 shown in Fig. 2 are configured.

[0024] For the storage 23, for example, a large-capacity information storage medium with non-volatility such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a memory card is used. In the non-volatile storage 23, software that realizes the functions of the surface skin blood flow dynamics determination system and data obtained by the execution of its program are stored.

[0025] The camera 24 is a photographing unit that photographs videos and still images, and the images photographed by the camera 24 are stored in the storage 23. The input unit 25 accepts operations by a touch panel or operation buttons attached to the display unit 26. Also, the input unit 25 accepts operations such as the start and end of the determination operation as the surface skin blood flow dynamics determination system. The display unit 26 is composed of a display device such as a liquid crystal display, and displays images and various information. The display unit 26 also displays the determination result as the surface skin blood flow dynamics determination system. In the communication unit 27, communication with other devices or a base station for a wireless phone is performed by wireless transmission.

[0026] [Explanation of determination from (R - B) component] The surface skin blood flow dynamics determination system of this example will be described with respect to the determination of surface skin blood flow dynamics from the (R - B) component. As already described, the region averaging processing unit 102 obtains the (R - B) component obtained by taking the difference between the red (R) component and the blue (B) component of the captured image. Then, the surface skin blood flow dynamics determination system makes a determination from the (R - B) component.

[0027] This point will be described with reference to FIG. 4. FIG. 4 is a diagram for explaining the difference in the penetration depth of the wavelengths of each primary color into human skin. As shown in FIG. 4, in order of decreasing penetration depth into the skin, there are the red (R) component, the green (G) component, and the blue (B) component. That is, the blue (B) component is the light reflected from the epidermis of the skin and is captured as an image taken by the camera 24. Also, the green (G) component (omitted in FIG. 4) is the light reflected from the dermis deeper than the epidermis and is captured as an image taken by the camera 24. Capillaries are arranged at this depth of the dermis. Furthermore, the red (R) component is the light reflected from a location in the subcutaneous tissue deeper than the dermis and is captured as an image taken by the camera 24. Arteries and veins are arranged at this location in the subcutaneous tissue.

[0028] Here, since the region averaging processing unit 102 of this example obtains the (R - B) component by taking the difference between the red (R) component and the blue (B) component, it is considered that the range indicated by the arrow as the (R - B) component value in FIG. 4 includes information on the entire blood vessels from the epidermis to the dermis and subcutaneous tissue. Note that when the region averaging processing unit 102 obtains the (G - B) component by taking the difference between the green (G) component and the blue (B) component, it is considered that the (G - B) component value includes information on the entire capillaries.

[0029] Therefore, it is considered that the region averaging processing unit 102 can obtain information on the blood flow volume of the entire blood vessels by obtaining the (R - B) component. The surface skin blood flow dynamics determination system in this example acquires information on the blood flow volume of the entire blood vessel in this way, and from the change in this blood flow volume, determines the dynamics of the surface skin blood flow of the subject (test subject) to obtain an estimation result of muscle activity.

[0030] For a more detailed explanation of obtaining the blood flow volume of blood vessels, it is considered that the light incident deep into the skin is absorbed in the blood through the blood vessels, and the unabsorbed part appears on the face surface as reflected light. Therefore, assuming that the values of each color component obtained from the captured actual image are the amounts of reflected light, it is necessary to know how much the light incident deep into the skin is absorbed in the blood. It is considered that the component that absorbs light in the blood is hemoglobin. Based on this idea, the characteristics of blood that absorbs more light can be captured from the light absorption rates of hemoglobin for each color of light.

[0031] For example, in the wavelength range of 610 nm to 780 nm, which is the wavelength of red light, deoxygenated hemoglobin (Hb) has a higher light absorption rate than oxygenated hemoglobin (HbO2). From this, red incident light is not absorbed much when the blood flow volume from arteries is large, and the amount of reflected light increases. On the other hand, red incident light is more absorbed when the blood flow volume from veins is large, and the reflected light decreases.

[0032] Also, in the wavelength range of 500 nm to 570 nm, which is the wavelength of green light, both oxygenated hemoglobin and deoxygenated hemoglobin have approximately the same light absorption rate. Regarding capillaries, it is considered that the surface skin blood flow dynamics determination system in this example measures the total blood flow volume flowing through the capillaries, rather than the ratio of the blood flow volume from arteries and the blood flow volume from veins. When the blood flow volume in the capillaries increases, it is considered that the absorption amount of green incident light increases and the reflection amount decreases. Conversely, when the blood flow volume in the capillaries decreases, the absorption amount decreases and the reflection amount increases. Utilizing these principles, the surface skin blood flow dynamics determination system in this example acquires the (R - B) component, detects the blood flow volume when looking at the entire blood vessel, determines the surface skin blood flow dynamics, and estimates muscle activity.

[0033] [Example of the determination area (right arm) and load of the subject] The surface skin blood flow dynamics determination system in this example estimates the muscle activity of the upper arm of the right arm of the subject. FIG. 5 shows a state in which a green adhesive tape 12 is attached to the upper arm of the right arm 11, which is the area for measuring muscle activity. As shown in FIG. 5, the adhesive tape 12 surrounds the upper arm in a vertically long rectangular shape. The measurement range surrounded by this adhesive tape 12 becomes the estimation range of the muscle activity state.

[0034] FIG. 6 shows a state in which a weight 13 of a predetermined weight is suspended by the hand at the tip of the right arm 11 during the determination of the surface skin blood flow dynamics. That is, FIG. 6 shows an example (Example 1) of a state in which a load is applied to the upper arm of the right arm 11. FIG. 7 shows a state in which a weight 13 of a predetermined weight is grasped by the hand at the tip of the right arm 11 during the determination of the surface skin blood flow dynamics. That is, FIG. 7 also shows an example (Example 2) of a state in which a load is applied to the upper arm of the right arm 11, similar to FIG. 6.

[0035] FIG. 8 shows the time distribution during one measurement in the surface skin blood flow dynamics determination system of this example. As shown in FIG. 8, first, a rest time d1 is provided, then a load time d2 in a state of holding the load 13 is provided, and finally a rest time d3 is provided again. Here, the first rest time d1 is 60 seconds, the load time d2 is 45 seconds, and the second rest time d3 is 60 seconds. However, these times are just examples and are not limited to this time setting.

[0036] FIG. 9 shows an example of dividing the area determined by the area averaging processing unit 102. As already described, the area determined by the area averaging processing unit 102 is the range surrounded by the adhesive tape 12 within the image of the right arm 11 photographed by the camera 24. Here, when the region averaging processing unit 102 determines a region, the region averaging processing unit 102 divides the longitudinal direction of the right arm 11 into five regions A1, A2, A3, A4, and A5 at equal intervals in parallel with the longitudinal direction of the right arm 11. Then, the region averaging processing unit 102 averages each divided region A1, A2, A3, A4, and A5. Further, the region averaging processing unit 102 also averages the entire region A0 formed by combining the five regions A1, A2, A3, A4, and A5.

[0037] [Flow of determination process of surface skin blood flow dynamics by surface skin blood flow dynamics determination system] FIG. 10 is a flowchart showing the flow of the determination process of the surface skin blood flow dynamics by the surface skin blood flow dynamics determination system of this example. First, the region averaging processing unit 102 extracts the region surrounded by the adhesive tape 12 from the captured image (step S11). Then, the region averaging processing unit 102 divides the image data of the extracted region into five regions A1 to A5 equally divided in parallel with the longitudinal direction of the right arm 11 as described with reference to FIG. 9 (step S12).

[0038] Thereafter, the region averaging processing unit 102 acquires the values (luminance values) of the red (R), green (G), and blue (B) components for each pixel for all the pixels within the five regions A1 to A5 (step S13). Furthermore, the region averaging processing unit 102 acquires the component value of the difference (R - B) between the red (R) component and the blue (B) component for each pixel, and performs a region averaging process of acquiring the average of the luminance values in each region of the (R - B) component value (step S14). The average of the luminance values in each region is acquired for each frame of the image, but it may be possible to acquire the average of the images at regular intervals (such as several seconds). Also, the region averaging processing unit 102 acquires the average of the entire region A0 obtained by summing the averages in each of the regions A1 to A5.

[0039] Next, the variation acquisition unit 103 performs a variation acquisition process of acquiring the average variation in each of the regions A1 to A5 and the entire region A0 (step S15). The determination unit 104 performs a determination process of determining the dynamics of the superficial skin blood flow from the average changes in each of the regions A1 to A5 and the entire region A0 obtained in this step S15. Through this determination process, the determination unit 104 estimates the muscle activity of the biceps brachii of the right arm 11 of the subject 10 from the dynamics of the superficial skin blood flow, and obtains a measurement result of the muscle activity.

[0040] [Examples of Changes in (R - B) Component Values and Determination States] Next, with reference to FIGS. 11 to 14, detection examples (Example 1 and Example 2) of changes in the (R - B) component values in each of the regions A1 to A5 and the entire region A0, and the measurement status of muscle activity based on the detection examples will be described. In FIGS. 11 to 14, the horizontal axis represents time (seconds), and the vertical axis represents the (R - B) component value. However, the (R - B) component values on the vertical axes of FIGS. 12 and 14 indicate the amount of change from the reference time point. Also, A1 to A5 in the graphs of FIGS. 11 to 14 indicate the changes in the average (R - B) component values in each of the regions A1 to A5, and A0 indicates the change in the average (R - B) component value of the entire five regions A1 to A5.

[0041] FIG. 11 shows the changes in the (R - B) component values during the rest time d1, the loading time d2, and the rest time d3 shown in FIG. 8 in a state (Example 1) where a load is applied to the right arm 11 of the subject 10 by hanging the weight 13 as shown in FIG. 6. As can be seen from FIG. 11, in the section of the loading time d2, the (R - B) component value has significantly decreased from the first rest time d1, capturing the muscle activity. Also, when changing from the loading time d2 to the rest time d3, the (R - B) component value has increased significantly again. The decrease in the (R - B) component value when such a load is applied captures the fact that the blood that was flowing normally at rest has decreased due to being dispersed to the muscles, indicating that the muscle activity is being accurately measured.

[0042] FIG. 12 shows the amount of change in the average (R - B) component value in each of the regions A1 to A5 shown in FIG. 11. The example in FIG. 12 shows the amount of change in the (R - B) component value from the start timing, with the start timing of the rest time d1 in FIG. 11 being set to 0. As shown in FIG. 12, in any of the five regions A1 to A5, a decrease in the (R - B) component value occurs during the load time d2, indicating that muscle activity is being captured. However, there are fluctuations in the actual muscle activity situation in each region, and there are changes in the decrease situation in each of the regions A1 to A5. Therefore, when determining muscle activity, it is conceivable that the determination unit 104 determines from the detection situation of the entire A0, or from the detection situation of the region A5 where the blood decrease is the largest, etc.

[0043] FIG. 13 shows the changes in the (R - B) component value during the rest time d1, load time d2, and rest time d3 shown in FIG. 8 in the state (Example 2) where a load is applied by holding the weight 13 by hand as shown in FIG. 7. Also in the case of the example in FIG. 13, in the section of the load time d2, the (R - B) component value has decreased significantly from the first rest time d1, similar to the example in FIG. 11. Then, when changing from the load time d2 to the rest time d3, the (R - B) component value increases significantly again, accurately capturing the situation of muscle activity. However, in the case of Example 2 shown in FIG. 13, the situation where a decrease appears in the five regions A1 to A5 has changed. This indicates that due to the different ways of applying the load, the situation of muscle activity is different.

[0044] FIG. 14 shows the amount of change in the average (R - B) component value in each of the regions A1 to A5 shown in FIG. 13. The example in FIG. 14 shows the amount of change in the (R - B) component value from the start timing, with the start timing of the rest time d1 in FIG. 13 being set to 0, similar to FIG. 12. In the case of Example 2 shown in FIG. 14, for region A1, no decrease in the (R - B) component value has occurred, but in the characteristics of the other regions A2 to A5 and the entire region A0, a decrease in the (R - B) component value has occurred. Therefore, the determination unit 104 can accurately determine the situation of muscle activity.

[0045] In addition, from the differences in the change situations in the five regions A1 to A5, it is also possible to finely determine the muscle activity situation at each location in each of the five regions A1 to A5, and it is also possible to measure a more detailed muscle activity situation.

[0046] [Modification Example] In the above-described embodiment example, in order to show that the muscle activity situation is captured, after applying a load, a rest time and a load time are set and measured. However, in actual measurement, various loads are assumed. Therefore, as long as it is possible to measure the rest state and the load state, the setting of the measurement time may be a setting other than the above-described three steps. For example, first measure the rest state, and then perform the measurement in the load state, and determine the muscle activity situation from the comparison between the initial rest state and the subsequent load state.

[0047] Also, in order to extract the region to be detected, an example is that an adhesive tape is pasted on the target location of the subject. As long as the same region of the target location can be extracted, other methods may be used for extraction. For example, the same region may always be extracted by image recognition processing from an image taken of the arm, leg, etc. Alternatively, it may be drawn in the same way as the tape with a paint that can be easily washed off, or a member other than the tape-shaped member may be attached to a part around the target location. Taking the biceps brachii of the upper arm of the arm as the target part is also an example, and it may be applied to the determination of the superficial skin blood flow dynamics at various locations such as the leg and other parts. When determining the muscle activity of the leg, similar to the example shown in FIG. 9, the region may be set by dividing the leg in the longitudinal direction into a plurality of parts, and the muscle activity of each region may be finely determined.

[0048] Also, in the above-described embodiment example, all the processing as the superficial skin blood flow dynamics determination system is performed in the smartphone 20. However, in the smartphone 20, only a part of the processing such as shooting is executed, and the captured image data or the data of each region extracted from the image data is transmitted to an external server or the like, and the muscle activity determination processing may be performed on the server side.

[0049] Furthermore, it is conceivable to record a program for realizing all or part of the functions of the surface skin blood flow dynamics determination system shown in FIG. 2 on a computer-readable recording medium. Then, the program recorded on the recording medium may be read into a computer (a terminal such as a smartphone) and executed to configure the surface skin blood flow dynamics determination system. Here, the program is a program that executes the processing procedure shown in the flowchart of FIG. 10.

Explanation of Signs

[0050] 1…Chair, 10…Subject, 11…Right arm, 12…Adhesive tape, 13…Load, 20…Smartphone, 21…CPU, 22…RAM, 23…Storage, 24…Camera, 25…Input unit, 26…Display unit, 27…Communication unit, 101…RGB component acquisition unit, 102…Region averaging processing unit, 103…Fluctuation acquisition unit, 104…Determination unit, 105…Output unit

Claims

1. From an image obtained by photographing a surface skin blood flow dynamic determination location of a subject, a region set as the surface skin blood flow dynamic determination location is determined, and for the determined region, a difference component between a red component and a blue component is obtained at regular intervals, and the obtained difference component is averaged over the entire region; a region averaging processing unit; A variation acquisition unit that obtains a variation in the difference component averaged by the region averaging processing unit; A determination unit that determines the surface skin blood flow dynamics of the surface skin blood flow dynamic determination location based on the variation amount obtained by the variation acquisition unit and estimates the muscle activity of the corresponding region, comprising: A surface skin blood flow dynamic determination system.

2. The image acquired by the region averaging processing unit is an image continuously photographed in a state where a predetermined load is applied to the vicinity of the region of the subject and in a state where no load is applied. The variation acquisition unit obtains a variation amount between the difference component obtained from the image photographed in the state where the load is applied and the difference component obtained from the image photographed in the state where no load is applied. The surface skin blood flow dynamic determination system according to claim 1.

3. The region determined by the region averaging processing unit is a plurality of regions divided and set along the longitudinal direction of the arm or leg of the subject. The determination unit estimates the muscle activity state in each of the divided and set regions from the change in the variation amount obtained in the plurality of divided and set regions. The surface skin blood flow dynamic determination system according to claim 2.

4. A tape-shaped member is attached around the region of the subject. The region averaging processing unit extracts a range surrounded by the tape-shaped member by image analysis. The surface skin blood flow dynamic determination system according to claim 2.

5. A surface skin blood flow dynamic determination method for determining the surface skin blood flow dynamics of a subject by arithmetic processing by a computer. In the operation by the computer, from the image obtained by photographing the surface skin blood flow dynamic determination part of the subject, the area set as the surface skin blood flow dynamic determination part is determined, and for the determined area, a difference component between the red component and the blue component is obtained at regular time intervals, and an area averaging process of averaging the obtained difference component over the entire area is performed. In the operation by the computer, a variation acquisition process of obtaining the variation of the difference component averaged by the area averaging process is performed. In the operation by the computer, based on the variation amount obtained by the variation acquisition process, the surface skin blood flow dynamic of the surface skin blood flow dynamic determination part is determined, and a determination process of estimating the muscle activity of the corresponding area is included. A method for determining surface skin blood flow dynamics.

6. A program for causing a computer to execute an operation for determining the surface skin blood flow dynamics of a subject. From the image obtained by photographing the surface skin blood flow dynamic determination part of the subject, the area set as the surface skin blood flow dynamic determination part is determined, and for the determined area, a difference component between the red component and the blue component is obtained at regular time intervals, and an area averaging procedure of averaging the obtained difference component over the entire area is performed. A variation acquisition procedure of obtaining the variation of the difference component averaged by the area averaging procedure is performed. Based on the variation amount obtained by the variation acquisition procedure, the surface skin blood flow dynamic of the surface skin blood flow dynamic determination part is determined, and a determination procedure of estimating the muscle activity of the corresponding area is caused to be executed by the computer. A program.

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