A computational method for identifying stimulated muscle compartments during exercise

Abstract

The present invention discloses a calculation method for identifying stimulated muscle compartments during exercise, which belongs to the field of medical detection. The technical key points are: calculating the recruitment rate of the 1st to Mth muscle compartments at the exercise time ti, which is characterized by the cross-sectional area ratio of the 1st to Mth muscle compartments. As for the cross-sectional area ratio of any Pth muscle compartment, it is the ratio of the area of ​​the cross-sectional area corresponding to the recruited state in the Pth muscle compartment to the area of ​​the two-dimensional image of the Pth muscle compartment. The calculation method for identifying stimulated muscle compartments during exercise of the present application is used to evaluate the effect of rehabilitation exercise.

Description

Technical Field

[0001] The invention belongs to the technical field of rehabilitation medicine, and in particular relates to a calculation method for identifying stimulated muscle compartments during exercise. Background Art

[0002] Regular exercise training has a positive effect on muscle strength, with improvements in muscle mass in specific muscle compartments as muscle strength increases. The selective recruitment of muscles as motor units prior to exercise training, which activates the muscles, enables them to better exert strength and function.

[0003] Generally speaking, the recruitment of motor units develops in the order of low threshold to high threshold. This selective recruitment can produce strength grading, stimulate motor units with higher thresholds, and thus enhance muscle strength. This way of recruiting muscles is not only present in the warm-up phase, but in fact it can run through your entire training process.

[0004] However, effective exercise requires professional guidance throughout the entire process. During personal training, there is a lack of direct observation tools for monitoring the muscles recruited during exercise, which creates a huge demand for real-time monitoring of the muscles recruited during exercise.

[0005] Generally, tomographic imaging techniques, such as magnetic resonance imaging (MRI) and X-ray computed tomography (CT), can measure muscle contraction rate (e.g., CN101080645A) to understand the physiological induced response after exercise. However, these methods cannot be used in community and home settings due to their large size and inconvenience in mobility.

[0006] Ultrasound imaging (UI) can usually be used during exercise, but the field of view of UI is small, and it is very difficult to maintain the stability of the ultrasound probe during exercise, so it is impossible to observe the physiological induced responses of multiple muscle compartments during exercise (for example: Lin Shanshan, Zheng Yiyi, Wang Chuhuai. Effect of real-time ultrasound imaging on the contraction efficiency of multifidus muscles in healthy adults [J]. Chinese Journal of Rehabilitation Medicine 2022.4).

[0007] To overcome the shortcomings of conventional tomography, surface electromyography (sEMG) is often used during exercise to measure the electromyographic signals generated by action potentials in contracting muscles and present them in the form of a line graph. sEMG provides a real-time, non-invasive, painless, simple and safe method (e.g., Chen Qingjie, Wu Ying, Wu Xie. Kinematic characteristics of rowing athletes' paddle pulling technique and sEMG analysis of related muscles [J]. Journal of Shanghai Institute of Physical Education, 2012, 36(005):81-85). However, the disadvantages of sEMG are increased complexity and signal crosstalk, so the reliability of evaluating effects during exercise is insufficient.

[0008] In order to overcome the above difficulties of traditional tomography, electrical impedance tomography (EIT) has attracted much attention due to its simple operation, non-invasiveness, low cost, and real-time tomography. EIT can be used to monitor body fluids, bladder fluid volume, pulmonary edema, bleeding, and blood congestion. In this context, the R&D team developed frequency difference electrical impedance tomography (fd-EIT) for real-time monitoring of recruited muscle compartments during exercise. The intensity of muscle contraction also increases with the increase in the intensity of exercise. This is because the blood flowing into the muscle is sensitive to the intensity of muscle contraction. According to this explanation, different trends in the response of physiologically induced electrical properties during exercise can be monitored or observed through EIT.

[0009] Therefore, it becomes possible to use EIT technology to monitor and identify the stimulated muscle compartments during exercise. Summary of the invention

[0010] The purpose of the present invention is to solve the problems existing in the above-mentioned prior art and to provide a calculation method for identifying stimulated muscle compartments during exercise.

[0011] The present invention is achieved through the following technical solutions:

[0012] A computational method for identifying stimulated muscle compartments during exercise comprises the following steps:

[0013] The first step is to divide the two-dimensional electric field area Ω of the entire tissue cross section into n grids to obtain the conductivity distribution matrix of the tissue when the tissue moves for a duration of ti.

[0014] In the second step, the two-dimensional electric field area of ​​the tissue is divided into four types: skin, fat, bone and muscle; for the matrix For example, the conductivity of the skin, fat, and bone regions is assigned a value of 0, thereby obtaining the conductivity matrix of the muscle region at any exercise duration ti.

[0015] The third step is to use Image of the imaged muscle area: the muscle area is divided into M muscle compartments; after the image is divided into M muscle compartments, the corresponding conductivity matrix of the 1st to Mth muscle compartments at the movement duration ti is calculated

[0016] The fourth step is to calculate the conductivity matrix corresponding to the 1st to Mth muscle compartments during the exercise duration ti Binarization is performed to obtain the binary conductivity matrix corresponding to the 1st to Mth muscle compartments during the exercise duration ti.

[0017] The conductivity matrix corresponding to any P-th muscle compartment during exercise duration ti Binarization is performed to obtain the corresponding binary conductivity matrix of any P-th muscle compartment during the exercise duration ti The way is:

[0018] matrix When an element in is less than the threshold ε, the element is assigned a value of 0;

[0019] matrix When an element in is greater than or equal to the threshold ε, the element is assigned a value of 1.

[0020] Step 5: According to the matrix Individual muscle compartments were binarized.

[0021] Step 6: Calculate the recruitment rate of the 1st to Mth muscle compartments at exercise time ti: the cross-sectional area ratio of the 1st to Mth muscle compartments Represents the recruitment rate of the 1st to Mth muscle compartments at movement time ti;

[0022] Among them, the cross-sectional area ratio of any P-th muscle compartment at exercise time ti is:

[0023]

[0024] in, represents the cross-sectional area corresponding to the recruited state in the Pth muscle compartment at the exercise time ti;

[0025] in, Represents the area of ​​the 2D image of the Pth muscle compartment at movement time ti.

[0026] Further, in the second step, solve The method is: take the lowest value of muscle area conductivity As the threshold, the conductivity of the skin, fat, and bone areas is filtered to obtain the conductivity matrix of the muscle area That is, for Every element in the matrix that is greater than or equal to The value of the element remains unchanged when it is less than This element is assigned the value 0.

[0027] Further, in the second step, solve Another method is: according to the conductivity distribution matrix of the tissue at the exercise time ti obtained in the first step An image is generated by the frequency difference conductivity imaging method. The conductivity values corresponding to each pixel point in the image are different (from low to high), and the colors of the pixel points are also different (from light to dark). The image is imported into image processing software. Using the lightest color in the muscle area as the lowest limit, the colors of the skin, fat, and bone areas are filtered and set to blank. Then, the remaining muscle area is divided into 1 to M muscle compartments by the boundary extraction algorithm.

[0028] Further, in the fourth step, ε takes the optimal threshold corresponding to the Pth muscle compartment.

[0029] The solution method is as follows:

[0030]

[0031] Among them, N P is the total number of grids in the Pth muscle compartment;

[0032] Among them, represents: the number of grids corresponding to the unrecruited state in the Pth muscle compartment at the movement time ti, which is a numerical value;

[0033] Among them, means that when σ P-u ≥ε, the number of grids corresponding to the recruited state in the Pth muscle compartment at the movement time ti, which is a numerical value;

[0034] Among them, is a function in matlab to find the maximum value of the independent variable threshold ε, which is the optimal threshold

[0035] Among them, x represents the step size, 1 < x < X, and X is user-defined;

[0036] Among them, ε is the threshold, 0 < ε = xΔε < 1, and Δε is user-defined;

[0037] Among them, γ(ε) is the intensity variance when applying the threshold ε, which is calculated from the parameters α0(ε), β0(ε), α1(ε) and β1(ε);

[0038] Among them, represents the sum of the squares of the differences between each element in the matrix and α1(ε);

[0039] Among them, represents the sum of the squares of the differences between each element in the matrix and α0(ε).

[0040] The advantages of the technical solution of the present invention are mainly reflected in:

[0041] This application proposes a method for identifying stimulated muscle compartments during exercise. The basic idea is:

[0042] S1, obtain the conductivity distribution matrix of the tissue at the exercise duration ti

[0043] S2, from Solve the conductivity matrix corresponding to the muscle area when the exercise duration is ti

[0044] S3, divide the muscle area into M muscle compartments; solve the corresponding conductivity matrix of the 1st to Mth muscle compartments when the exercise duration is ti

[0045] S4, Binarization is performed to obtain the binary conductivity matrix corresponding to the 1st to Mth muscle compartments during the exercise duration ti.

[0046] S5, according to the matrix Individual muscle compartments were binarized.

[0047] S6, calculate the recruitment rate of the 1st to Mth muscle compartments at exercise time ti: the cross-sectional area ratio of the 1st to Mth muscle compartments Represents the recruitment rate of the 1st to Mth muscle compartments at movement time ti;

[0048] Among them, the cross-sectional area ratio of any P-th muscle compartment at exercise time ti is:

[0049]

[0050] in, represents the cross-sectional area corresponding to the recruited state in the Pth muscle compartment at the exercise time ti;

[0051] in, Represents the area of ​​the 2D image of the Pth muscle compartment at movement time ti. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described in detail below in conjunction with the embodiments in the accompanying drawings, but this does not constitute any limitation to the present invention.

[0053] Figure 1 This is a schematic diagram of a wearable system for identifying stimulated muscle compartments during exercise according to the present invention.

[0054] Figure 2 It is a front view of the side away from the skin of a wearable EIT sensor in a system for identifying stimulated muscle compartments during exercise of the present invention.

[0055] Figure 3 This is a back view of the side of a wearable EIT sensor close to the skin in a system for identifying stimulated muscle compartments during exercise according to the present invention.

[0056] Figure 4 This is an EIT electrode arrangement diagram for identifying stimulated muscle compartment systems in the upper arm and thigh during exercise according to the present invention.

[0057] Figure 5 The present invention is a schematic diagram of the division of the muscle compartments of the upper arm and thigh in a system for identifying stimulated muscle compartments during exercise.

[0058] Figure 6 The present invention is a schematic diagram of the conductivity matrix of the upper arm and thigh muscle compartment partitions of a system for identifying stimulated muscle compartments during exercise.

[0059] Figure 7 The invention discloses a high-intensity binary image of the local muscle cross-sectional area in the muscle compartments of the upper arm and thigh for identifying the stimulated muscle compartment system during exercise.

[0060] Figure 8 yes Explanatory diagram of .

[0061] Fig. 9 It is a data comparison diagram of the spatial average conductivity value and the fat infiltration rate tested by a system for identifying stimulated muscle compartments during exercise according to the present invention.

[0062] The accompanying drawings are as follows:

[0063] 1: Electrical impedance tomography system to monitor muscle compartments recruited during exercise;

[0064] 2: Node controller;

[0065] 3: Coaxial wire with snap-on connector;

[0066] 4: Sixteen-channel robust multiplexer;

[0067] 5: Wearable EIT sensor;

[0068] 501-516: sixteen electrodes;

[0069] 6: A computer equipped with a frequency difference electrical impedance (fd-EIT) distribution image reconstruction algorithm. DETAILED DESCRIPTION

[0070] The purpose, advantages and features of the present invention will be explained by the non-limiting description of the following preferred embodiments. These embodiments are only typical examples of the application of the technical solution of the present invention, and any technical solution formed by equivalent replacement or equivalent transformation falls within the scope of protection claimed by the present invention.

[0071] <Basic Concept>

[0072] The basic concept of the present application is: adopting the electrical impedance tomography (EIT) method, using a wearable electrical impedance tomography (EIT) system to monitor the recruitment rate of each muscle compartment during the subject's exercise, and obtaining a frequency difference electrical impedance (fd-EIT) distribution image that reflects the conductivity change data of the upper arm or thigh before and after the subject performs effective exercise, and processing the image by muscle area, so as to monitor the recruitment rate of each muscle compartment during exercise and evaluate the effectiveness of the exercise.

[0073] <Electrical Impedance Tomography System Introduction>

[0074] The developed electrical impedance tomography imaging system 1 for monitoring the muscle compartments recruited during exercise is used to effectively visualize and quantitatively monitor the recruitment rate of each muscle compartment after exercise.

[0075] like Figure 1-3 As shown, the developed electrical impedance tomography imaging system 1 for monitoring the recruited muscle compartments during movement includes: a node controller 2, a coaxial wire 3 with a snap connector, a sixteen-channel robust multiplexer 4, a wearable EIT sensor 5 (with sixteen electrodes 501-516 built in, evenly arranged in a circular pattern) and a computer 6 equipped with a frequency difference electrical impedance (fd-EIT) distributed image reconstruction algorithm.

[0076] The four ports (Lc, Lp, Hp and Hc) of the node controller 2 are expanded into sixteen ports through the sixteen-channel robust multiplexer 4, and the 16 channels are respectively connected to the sixteen electrodes 501-516 of the wearable EIT sensor 5 using the coaxial wire 3 with the snap connector, that is, the node controller 2 is connected to the sixteen-channel robust multiplexer 4 through the coaxial wire 3 with the snap connector.

[0077] The node controller 2 and the computer 6 installed with the frequency difference electrical impedance (fd-EIT) distribution image reconstruction algorithm perform data transmission via wireless / wired means.

[0078] The wearable EIT sensor 5 can be integrated with the node controller 2, the coaxial wire 3 with the snap connector, and the sixteen-channel robust multiplexer 4 to ensure that the sixteen electrodes 501-516 are in stable contact with the human body and to prevent the movement of the subject from interfering with the operation of the entire system during the test.

[0079] <Example 1: A method for identifying stimulated muscle compartments during exercise>

[0080] A method for identifying stimulated muscle compartments during exercise, comprising the following steps:

[0081] S1, wearing an EIT sensor 5 on the leg or arm;

[0082] S2, perform a measurement at the movement duration t0, t1, ... tm to obtain the conductivity distribution matrix of the overall two-dimensional electric field area Ω at the movement duration t0, t1, ... tm The two-dimensional electric field area Ω is discretized into n grid points, and the movement duration tm is freely determined according to the required movement, t0 = 0 min.

[0083] Among them, for the conductivity distribution matrix of the movement duration ti The method of obtaining is as follows:

[0084] Using adjacent current input and adjacent voltage measurement methods (i.e., adjacent excitation and adjacent measurement methods), low frequency f1=500 Hz and higher frequency f2=1000 Hz are applied respectively, and constant current (1 mA) is injected, which is applied to the sixteen electrodes 501-516 of the wearable EIT sensor 5 through a sixteen-channel robust multiplexer 4 via a coaxial line 3 with a snap-on connector to stimulate the subject's upper arm or thigh.

[0085] Get the electrical impedance data matrix at two different frequencies f1 and f2 and (The acquisition of the impedance data matrix belongs to the prior art such as CN114519708A, which will not be repeated here). The impedance matrix data at two different frequencies are and The data is transmitted back to the node controller 2 via the coaxial line 3 with a snap-on connector and the 16-channel robust multiplexer 4, and then transmitted to the computer 6 equipped with the frequency difference electrical impedance (fd-EIT) distribution image reconstruction algorithm by wireless / wired means. The computer 6 equipped with the frequency difference electrical impedance (fd-EIT) distribution image reconstruction algorithm obtains the frequency difference electrical impedance (fd-EIT) distribution image reconstruction algorithm according to the frequency difference electrical impedance (fd-EIT) distribution image reconstruction algorithm. At this time, the two-dimensional electric field area Ω of the cross section of the upper arm and thigh is discretized into n grid points respectively.

[0086] S3, solve the muscle area conductivity matrix for any exercise duration ti

[0087] The two-dimensional electric field area Ω composed of skin, fat, bone and muscle is divided into N area elements according to muscle and other areas; N1 represents the skin area, N2 represents the fat area, N3 represents the bone area, and N4 represents the muscle area;

[0088] like Figure 6 As shown, the conductivity of the muscle area is higher than that of other areas, with the lowest conductivity in the muscle area. As the threshold, the conductivity of the remaining areas is filtered, and finally only the conductivity matrix of the muscle area is retained, that is, the conductivity matrix of the muscle area at any exercise duration ti

[0089] S4, using Image of the imaged muscle area: the muscle area is divided into M muscle compartments; after the image is divided into M muscle compartments, the corresponding conductivity matrix of the 1st to Mth muscle compartments at the movement duration ti is calculated (The process from conductivity matrix to imaging, and from image to conductivity are all existing technologies and will not be elaborated here).

[0090] The following relationship exists:

[0091] For example, the two-dimensional electric field area Ω of the upper arm muscle section composed of skin, fat, bone and muscle is divided into N regional elements according to the muscle and the remaining area, and the area outside the muscle is defined as the empty field The upper arm muscles are divided into three compartments: 1) biceps brachii AM1; 2) brachialis AM2; 3) triceps brachii AM3.

[0092] The two-dimensional electric field area Ω of the transverse thigh muscle composed of skin, fat, bones and muscles is divided into N regional elements according to the muscles and the remaining areas, and the area outside the muscles is defined as the empty field σ0; the transverse thigh muscle area can be divided into five compartments: 1) biceps femoris BM1; 2) sartorius, adductor longus, adductor magnus, gracilis, semimembranosus and semitendinosus BM2; 3) vastus medialis BM3; 4) rectus femoris BM4; 5) vastus intermedius and vastus lateralis BM5.

[0093] S5, the conductivity matrix corresponding to the 1st to Mth muscle compartments at exercise duration ti Binarization is performed to obtain the binary conductivity matrix corresponding to the 1st to Mth muscle compartments during the exercise duration ti.

[0094] The conductivity matrix corresponding to any P-th muscle compartment during exercise duration ti Binarization is performed to obtain the corresponding binary conductivity matrix of any P-th muscle compartment during the exercise duration ti The way is:

[0095] matrix The elements in are less than the threshold When , the element is assigned a value of 0;

[0096] Matrix the elements in are greater than or equal to the threshold when this element is assigned a value of 1

[0097] For the optimal threshold of the Pth muscle compartment The solution method is as follows

[0098] In the fourth step, ε takes the optimal threshold corresponding to the Pth muscle compartment

[0099] The solution method is as follows

[0100]

[0101] where N P is the total number of grids in the Pth muscle compartment

[0102] where represents: the number of grids corresponding to the unrecruited state in the Pth muscle compartment at the movement time ti, which is a numerical value

[0103] where means that when σ P-u ≥ε, the number of grids corresponding to the recruited state in the Pth muscle compartment at the movement time ti, which is a numerical value

[0104] where is a function in matlab to find the maximum value of the independent variable threshold ε, which is the optimal threshold

[0105] where x represents the step size, 1 < x < X, and X is user-defined

[0106] where ε is the threshold, 0 < ε = xΔε < 1, and Δε is user-defined

[0107] where γ(ε) is the intensity variance when applying the threshold ε, which is calculated from the parameters α0(ε), β0(ε), α1(ε) and β1(ε)

[0108] where represents the sum of the squares of the differences between each element in the matrix and α1(ε)

[0109] where represents the sum of the squares of the differences between each element in the matrix and α0(ε)

[0110] S6, such as Figure 7As shown, the corresponding binary conductivity matrix of the 1st to Mth muscle compartments at the exercise duration ti is Each muscle compartment at the movement time ti is imaged by binary imaging. The matrix area is set to a blank field σ0, that is, the skin area, fat area, and bone area are blank fields σ0.

[0111] S7, such as Figure 8 As shown, the recruitment rate of the 1st to Mth muscle compartments at the exercise time ti is calculated: the cross-sectional area ratio of the 1st to Mth muscle compartments Represents the recruitment rate of the 1st to Mth muscle compartments at movement time ti;

[0112] Among them, the cross-sectional area ratio of any P-th muscle compartment at exercise time ti is:

[0113]

[0114] in, represents the cross-sectional area corresponding to the recruited state in the Pth muscle compartment at the exercise time ti;

[0115] in, Represents the area of ​​the 2D image of the Pth muscle compartment at movement time ti.

[0116] <Result Comparison>

[0117] Ultrasound fat infiltration rate (uFI) is defined as the ratio between gray pixels (representing adipose tissue) and black pixels (representing muscle tissue) in ultrasound images. Ultrasound is a traditional method for assessing muscle mass and is the gold standard for assessing muscle recruitment rate. The lower the uFI value, the less fat infiltration in the muscle, the higher the muscle mass, and the higher the muscle recruitment rate.

[0118] Therefore, uFI and the cross-sectional area ratio of muscle compartments calculated by this application were compared. The exercise time was the result when ti = 23 minutes.

[0119] Experimental situation: Taking the upper arm as an example, 14 subjects were recruited and the fat infiltration rate of each muscle compartment was calculated. Fig. 9 The grey bars in the graph show the values ​​of ultrasonic fat infiltration (uFI), which increase linearly from the youngest to the oldest subjects, with older subjects having more fat in their muscles than younger subjects.

[0120] The ratio of the cross-sectional areas of each muscle compartment in the upper arm of the 14 subjects is the muscle recruitment rate of each muscle compartment, and the muscle recruitment rate of each muscle compartment is Fig. 9 Comparison (Square Line Chart), (Circular Line Chart), (Triangular line chart). The values ​​of decreased linearly from the youngest to the oldest subjects. The highest, because the M1 muscle compartment is a type II muscle, which is characterized by containing more and larger muscle fibers for producing muscle force. The lowest, because the M3 muscle compartment is a type I muscle, which is characterized by having fewer muscle fibers. For comparison, the cross-sectional area of ​​each muscle compartment in the upper arm is There is good consistency between the uFI and the MRI in assessing muscle recruitment rate.

[0121] Physical parameters in this application:

[0122] t0, t1, ...tm: exercise duration.

[0123] : They respectively represent the conductivity distribution matrices corresponding to the overall two-dimensional electric field area Ω at the motion durations t0, t1, …tm.

[0124] n: grid points of the overall two-dimensional electric field area Ω.

[0125] N: The area of ​​the overall two-dimensional electric field area Ω, N = N1 + N2 + N3 + N4.

[0126] N1: Skin area.

[0127] N2: Fatty area.

[0128] N3: Skeletal region.

[0129] N4: Muscle area.

[0130] : Muscle area conductivity matrix at arbitrary exercise duration ti.

[0131] M: overall 2D electric field area of ​​the muscle compartment.

[0132] : Respectively represent the corresponding conductivity matrices of the 1st to Mth muscle compartments during exercise duration ti.

[0133] ε: threshold.

[0134] : Optimal threshold for the Pth muscle compartment.

[0135] It is a function in Matlab that finds the maximum value of the independent variable threshold ε, which is the optimal threshold

[0136] x: step length, which satisfies: 1 <x<X。

[0137] X: Custom parameters.

[0138] ε: parameter, set by yourself.

[0139] γ(ε): The intensity variance when the threshold ε is applied, calculated from the parameters α0(ε), β0(ε), α1(ε) and β1(ε).

[0140] α0(ε), β0(ε), α1(ε), β1(ε): parameters.

[0141] N P is the number of grid cells in the Pth muscle compartment.

[0142] It means: the number of grids corresponding to the unrecruited state in the Pth muscle compartment at the exercise time ti is a numerical value.

[0143] means that when σ P-u ≥ε, the number of grids corresponding to the recruited state in the Pth muscle compartment at exercise time ti is a value.

[0144] Representation Matrix The sum of the squares of the differences between each element in and α1(ε).

[0145] Representation Matrix The sum of the squares of the differences between each element in and α0(ε).

[0146] : The cross-sectional area ratio of the 1st to Mth muscle compartments at exercise time ti, which represents the recruitment rate of the 1st to Mth muscle compartments at exercise time ti;

[0147] : The cross-sectional area ratio of any Pth muscle compartment at exercise time ti.

[0148] : The cross-sectional area corresponding to the recruited state in the Pth muscle compartment at exercise time ti.

[0149] : The area of ​​the two-dimensional image of the Pth muscle compartment at movement time ti.

[0150] The above embodiments are preferred implementation modes of the present invention and are only used to facilitate the description of the present invention. They are not intended to limit the present invention in any form. Any person with ordinary knowledge in the relevant technical field, if they do not depart from the scope of the technical features of the present invention, can make equivalent embodiments by partial changes or modifications to the technical contents disclosed in the present invention, and they still fall within the scope of the technical features of the present invention without departing from the technical features of the present invention.

Claims

1. A computational method for identifying stimulated muscle compartments during exercise, characterized in that: Including the following steps: The first step is to obtain the conductivity distribution matrix of the tissue during the exercise duration ti The second step is to solve the muscle area conductivity matrix when the exercise duration is ti The third step is to divide the muscle area into M muscle compartments and calculate the corresponding conductivity matrix of the 1st to Mth muscle compartments during the exercise duration ti. The fourth step is to calculate the conductivity matrix corresponding to the 1st to Mth muscle compartments during the exercise duration ti Binarization is performed to obtain the binary conductivity matrix corresponding to the 1st to Mth muscle compartments during the exercise duration ti. Among them, the conductivity matrix corresponding to any P-th muscle compartment during exercise duration ti is Binarization is performed to obtain the corresponding binary conductivity matrix of any P-th muscle compartment during the exercise duration ti The way is: matrix When an element in is less than the threshold ε, the element is assigned a value of 0; matrix When an element in is greater than or equal to the threshold ε, the element is assigned a value of 1; Step 5: According to the matrix Binarized imaging of each muscle compartment; Step 6, calculating the recruitment rate of the 1st to Mth muscle compartments at the movement time ti: the cross-sectional area ratio of the 1st to Mth muscle compartments represents the recruitment rate of the 1st to Mth muscle compartments; wherein, the cross-sectional area ratio of any Pth muscle compartment at the movement time ti: in, represents the cross-sectional area corresponding to the recruited state in the Pth muscle compartment at the exercise time ti; in, Represents the area of ​​the 2D image of the Pth muscle compartment at movement time ti.

2. A computational method for identifying stimulated muscle compartments during exercise according to claim 1, characterized in that: In the second step, solve The method is: take the lowest value of muscle area conductivity As the threshold, the conductivity of the skin, fat, and bone areas is filtered to obtain the conductivity matrix of the muscle area That is, for Every element in the matrix that is greater than or equal to The value of the element remains unchanged when it is less than This element is assigned the value 0.

3. A computational method for identifying stimulated muscle compartments during exercise according to claim 1, characterized in that: In the second step, solve The method is: according to the conductivity distribution matrix of the tissue at the exercise time ti obtained in the first step The image is generated by the frequency difference conductivity imaging method. The conductivity value corresponding to each pixel in the image is different, and the color of the pixel is also different. The image is imported into the image processing software. The lightest color of the muscle area is used as the minimum limit, and the colors of the skin, fat, and bone areas are filtered and set to blank. The remaining muscle area is then divided into 1 to M muscle compartments using a boundary extraction algorithm.

4. A computational method for identifying stimulated muscle compartments during exercise according to claim 1, characterized in that: In the fourth step, ε takes the optimal threshold value corresponding to the Pth muscle compartment The solution is as follows: Among them, N P is the total number of grids in the Pth muscle compartment; in, It means: the number of grids corresponding to the unrecruited state in the Pth muscle compartment at the exercise time ti, which is a value; in, means that when σ P-u ≥ε, the number of grids corresponding to the recruited state in the Pth muscle compartment at the movement time ti is a value; in, It is a function in Matlab that finds the maximum value of the independent variable threshold ε, which is the optimal threshold where x represents the step size, 1 < x < X, and X is user-defined; where ε is the threshold, 0 < ε = xΔε < 1, and Δε is user-defined; where γ(ε) is the intensity variance when applying the threshold ε, and is calculated from the parameters α0(ε), β0(ε), α1(ε) and β1(ε); in, Representation Matrix The sum of the squares of the differences between each element in and α1(ε); in, Representation Matrix The sum of the squares of the differences between each element in and α0(ε).

Images