JP7623892B2 - Muscle condition evaluation device - Google Patents

Description

The present invention relates to a muscle condition evaluation device that evaluates the muscle condition of a subject.

It is desirable to be able to easily diagnose the posture of subjects in medical and nursing care facilities and sports gyms in order to manage the subjects' health, for example to prevent and improve lower back pain and stiff shoulders.

Patent Document 1 discloses categorizing the posture of a subject based on the vertical center of gravity line calculated from foot pressure data obtained by measuring the foot pressure of the subject, and the coordinates of diagnostic points indicated in the imaging data obtained by capturing the posture of the subject. It further discloses modeling the subject's figure according to the categorized posture of the subject, and clearly indicating the stretching state of body tissues, including muscle tissue.

JP 2005-224452 A

However, Patent Document 1 only clearly indicates the stretching state of body tissue, including muscle tissue, and does not go so far as to evaluate the more detailed state of a specific muscle. In other words, it does not go so far as to determine whether a specific muscle is in a stiff, overactive state or a weak, inactive state.

The object of the present invention is to provide a muscle condition evaluation device that can evaluate the more detailed condition of a subject's specific muscles.

To achieve the above object, the present invention provides a muscle condition evaluation device for evaluating the muscle condition of a subject, and is characterized by comprising a data acquisition unit that acquires posture image data captured when the subject assumes a specific posture and plantar pressure data measured when the posture image data is captured, and a muscle condition determination unit that determines whether each muscle of the subject is in an overactive or inactive state based on the posture image data and the plantar pressure data.

The present invention provides a muscle condition evaluation device that can evaluate the more detailed condition of a subject's specific muscles.

Overall configuration of the muscle condition evaluation device FIG. 2 is a diagram showing an example of a functional block of a muscle condition evaluation device. FIG. 1 is a diagram showing an example of a processing flow according to a first embodiment; FIG. 1 shows an example of a skeletal pattern. FIG. 1 is a diagram for explaining calculation of center of gravity coordinates. A diagram showing an example of a muscle table FIG. 13 is a diagram showing an example of a judgment formula table. FIG. 13 is a diagram showing an example of a comprehensive judgment table; FIG. 13 is a diagram showing an example of a display screen according to the first embodiment; FIG. 13 is a diagram showing an example of a first intervention menu table. FIG. 13 is a diagram showing an example of a second intervention menu table. FIG. 13 is a diagram showing an example of a process flow of the second embodiment. FIG. 1 is a diagram showing an example of a symptom table. FIG. 1 shows an example of a posture table. A diagram for explaining a case where multiple judgment formulas are selected Diagram explaining switching of judgment formula

Below, an embodiment of the muscle condition evaluation device according to the present invention will be described with reference to the attached drawings. Note that in the following description and the attached drawings, components having the same functional configuration are designated by the same reference numerals, and duplicated explanations will be omitted.

Figure 1 is a diagram showing the hardware configuration of a muscle condition evaluation device 1. The muscle condition evaluation device 1 is configured with a calculation unit 2, memory 3, storage device 4, and network adapter 5 connected by a system bus 6 so as to be capable of transmitting and receiving signals. The muscle condition evaluation device 1 is also connected to a posture imaging device 10 and a foot pressure measuring device 11 via a network 9 so as to be capable of transmitting and receiving signals. Furthermore, a display device 7 and an input device 8 are connected to the muscle condition evaluation device 1. Here, "capable of transmitting and receiving signals" refers to a state in which signals can be transmitted and received between each other or from one device to the other, regardless of whether they are electrically or optically wired or wireless.

The calculation unit 2 is a device that controls the operation of each component, specifically a CPU (Central Processing Unit), MPU (Micro Processor Unit), GPU (Graphics Processing Unit), etc. The calculation unit 2 loads the programs stored in the storage device 4 and data required for program execution into the memory 3 and executes them to evaluate the state of each muscle of the subject. The memory 3 stores the programs executed by the calculation unit 2 and the intermediate progress of the calculation process. The storage device 4 is a device that stores the programs executed by the calculation unit 2 and data required for program execution, specifically a HDD (Hard Disk Drive) or SSD (Solid State Drive), etc. The network adapter 5 is for connecting the muscle condition evaluation device 1 to a network 9 such as a LAN, a telephone line, or the Internet. Various data handled by the calculation unit 2 may be transmitted to and received from outside the muscle condition evaluation device 1 via a network 9 such as a LAN (Local Area Network).

The display device 7 is a device that displays the processing results of the muscle state evaluation device 1, and is specifically a liquid crystal display, a touch panel, etc. The input device 8 is an operation device with which the operator issues operation instructions to the muscle state evaluation device 1, and is specifically a keyboard, a mouse, a touch panel, etc. The mouse may be another pointing device such as a track pad or a track ball.

The posture image capturing device 10 is a device that captures an image of a subject in a predetermined posture, such as a camera. The foot pressure measuring device 11 is a device that measures the pressure distribution of the soles of the feet of a subject in a predetermined posture, such as multiple pressure sensors provided on the inner sole of a shoe or the top surface of a base.

The functional block diagram of the first embodiment will be described with reference to FIG. 2. Each function shown in FIG. 2 may be configured with dedicated hardware using an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or the like, or may be configured with software that runs on the calculation unit 2. In the following description, the case where each function of the first embodiment is configured with software will be described.

Example 1 includes a data acquisition unit 201 and a muscle state determination unit 202. The storage device 4 may also store posture image data, plantar pressure data, and the like. The posture image data is image data captured by the posture image capture device 10 when the subject assumes a specific posture, and preferably includes the subject's entire body. The plantar pressure data is data on the pressure distribution of the soles of the feet measured by the foot pressure measurement device 11 when the posture image data is captured, and is preferably the pressure distribution of both feet. Each component will be described below.

The data acquisition unit 201 acquires posture image data and plantar pressure data. The acquired posture image data and plantar pressure data are transmitted from the posture imaging device 10 and the foot pressure measuring device 11 via the network adapter 5 or the like, or are read from the storage device 4. The data acquisition unit 201 transmits the acquired posture image data and plantar pressure data to the muscle state determination unit 202.

The muscle state determination unit 202 determines whether each muscle of the subject is in an overactive state, that is, in a stiff state, or in an inactive state, that is, in a weak state, based on the posture image data and plantar pressure data transmitted from the data acquisition unit 201. The muscle state determination unit 202 may be generated by machine learning using a large amount of posture image data and plantar pressure data when the state of each muscle is known, or may use a determination formula set based on the empirical rules of an athletic trainer or physical trainer. The determination formula outputs the state of each muscle as a numerical value when a predetermined numerical value calculated from the posture image data and plantar pressure data is input. For example, if each muscle is in an overactive state, a positive value is output, and if it is in an inactive state, a negative value is output, and a larger absolute value is output depending on the degree of the state. The muscle state determination unit 202 may use multiple determination formulas.

Using Figure 3, we will explain an example of the process flow executed in Example 1 step by step.

(S301)
The data acquisition unit 201 acquires posture image data from the posture imaging device 10 and plantar pressure data from the foot pressure measuring device 11. The posture image data and plantar pressure data stored in advance in the storage device 4 may be read out. The posture image data and plantar pressure data acquired by the data acquisition unit 201 are transmitted to the muscle state determination unit 202.

(S302)
The muscle state determining unit 202 calculates a skeletal pattern from the posture image data, and calculates the center of gravity coordinates from the plantar pressure data.

An example of a skeletal pattern will be described with reference to FIG. 4. A skeletal pattern is data generated by connecting feature points of the human body, such as joints, eyes, ears, and the base of the neck, with lines. Note that the skeletal pattern may include coordinate information of the feature points of the human body, and does not necessarily have to be connected with lines. Feature points such as joints are extracted by image analysis of posture image data, or are specified in the posture image data by the operator. In FIG. 4, the coordinates of 18 feature points are illustrated as (X0, Y0), (X1, Y1), ... (X17, Y17). A combination of each coordinate may be registered as a skeletal pattern. The registered skeletal pattern is compared with the skeletal pattern in an ideal posture.

The calculation of the center of gravity coordinates will be described with reference to Fig. 5. Fig. 5 shows an example of the arrangement of eight pressure sensors provided on the insole of each shoe, and an equation for calculating the center of gravity coordinates from the coordinates and pressure values of each pressure sensor. That is, the center of gravity coordinates G _L (x _GL , y _GL ) and G _R (x _GR , y _GR ) of the left and right feet are calculated by the following equations.
x _GL = (x _L1 m _L1 +x _L2 m _L2 +...+x _L8 m _L8 )/(m _L1 +m _L2 +...+m _L8 )... (Formula 1)
y _GL = (y _L1 m _L1 +y _L2 m _L2 +...+y _L8 m _L8 )/(m _L1 +m _L2 +...+m _L8 )... (Formula 2)
x _GR = (x _R1 m _R1 +x _R2 m _R2 +...+x _R8 m _R8 )/(m _R1 +m _R2 +...+m _R8 )... (Formula 3)
y _GR = (y _R1 m _R1 +y _R2 m _R2 +...+y _R8 m _R8 )/(m _R1 +m _R2 +...+m _R8 )... (Formula 4)
Here, the coordinates of each pressure sensor are _L1 ( _xL1 , _yL1 ), _L2 ( _xL2 , yL2), ... _L8 ( _xL8 , _yL8 ) for the left foot, and _R1 ( _xR1 , _yR1 ), _R2 ( _xR2 , _yR2 ), ... _R8 ( _xR8 , _yR8 ) for the right foot. Also, the pressure values of each pressure sensor are _mL1 , _mL2 , ... _{mL8 for} the left foot, and _mR1 , _mR2 , ... _mR8 for the right foot.

The coordinates of the center of gravity G ( _xG , _yG ) of both feet are calculated using the coordinates and pressure values of each pressure sensor, similarly to the coordinates of the centers of gravity G _L ( _xGL , _yGL ) and G _R ( _xGR , _yGR ) of each foot. The calculated coordinates of the center of gravity are compared with the coordinates of the center of gravity in an ideal posture.

(S303)
The muscle state determination unit 202 determines whether each muscle is in an overactive state or an inactive state based on the skeletal pattern and the center of gravity coordinates calculated in S302. When the muscle state determination unit 202 determines the state of each muscle using a determination formula, it may use the muscle table 600, the determination formula table 700, and the overall determination table 800. Each table will be described below.

An example of the muscle table 600 will be described with reference to FIG. 6. The muscle table 600 has items for muscle ID, muscle name, and judgment formula ID. The muscle ID column stores an identifier for identifying each muscle. The muscle name column stores the name of each muscle, for example, adductor muscle, piriformis muscle, etc. The judgment formula ID column stores the identifier of the judgment formula used to determine the state of each muscle. That is, the muscle table 600 associates each muscle with a judgment formula. In the example of FIG. 6, muscle A with muscle ID 1 is associated with judgment formulas with judgment formula IDs A, B, C, D, ..., and muscle B with muscle ID 2 is associated with judgment formulas with judgment formula IDs A, D, F, H, ....

An example of the judgment formula table 700 will be described with reference to FIG. 7. The judgment formula table 700 has items for a judgment formula ID, posture, judgment formula, and judgment result. The judgment formula ID column stores an identifier that specifies the judgment formula. The posture column stores the type of posture corresponding to the judgment formula ID, such as overhead squats, hands up, standing, etc. The judgment formula column stores the judgment formula corresponding to the judgment formula ID. The judgment result column stores the type of compensatory movement determined by the judgment formula and the names of overactive and inactive muscles. Types of compensatory movement include, for example, movements with the knee inverted or everted. A muscle ID may be stored instead of the muscle name.

That is, in the judgment formula table 700, judgment formula IDs, judgment formulas, and judgment results are associated with each other. In the example of FIG. 7, a judgment formula with a judgment formula ID of A assumes that one-fifth of the difference between the X coordinate X13 of the left ankle and the X coordinate X10 of the right ankle is the foot width wd, and judges whether the X coordinate X9 of the right knee is smaller than X10-wd/2, that is, whether there is inversion as a compensatory movement. A judgment formula with a judgment formula ID of B judges whether X9 is larger than X10+wd/2, that is, whether there is eversion.

An example of the overall judgment table 800 will be described with reference to Figure 8. In the overall judgment table 800, for each muscle ID, the numerical values output from each judgment formula, the sum of these numerical values, and an overall judgment based on the sum are stored. The types of muscles corresponding to the muscle ID may include soleus, lateral gastrocnemius, biceps femoris, tensor fasciae latae, peroneus, adductor, vastus lateralis, piriformis, external oblique, teres major, gluteus medius, gluteus minimus, gracilis, popliteus, sartorius, tibialis anterior, tibialis posterior, gluteus maximus, medial gastrocnemius, multifidus, rotator cuff, rhomboid, coracobrachialis, vastus medialis, hip flexors, abdominal muscles, erector spinae, latissimus dorsi, hamstrings, rectus abdominis, pectoralis major/pectoralis minor, trapezius, deltoid, medial hamstrings, spinal trunk stabilizers, levator scapulae, cervical flexors, upper trapezius, lower trapezius, etc.

In the example of Figure 8, for a muscle with a muscle ID of 1, formula A outputs 1, which indicates an overactive muscle, formula B outputs 2, which indicates an overactive muscle, and formula N outputs -1, which indicates an underactive muscle, and the sum of these is 2, so the overall judgment is "slightly overactive." Similarly, for a muscle with a muscle ID of 2, formula A outputs 3, which indicates an overactive muscle, formula B outputs -1, which indicates an underactive muscle, and formula N outputs 3, which indicates an overactive muscle, and the sum of these is 5, so the overall judgment is "overactive."

By using the muscle table 600, judgment formula table 700, and overall judgment table 800 described above, the muscle state judgment unit 202 can judge the state of each muscle based on the sum of the numerical values output from multiple judgment formulas. By making an overall judgment based on the sum of the output values of each judgment formula, it is possible to improve the judgment accuracy of the overall judgment even when the judgment accuracy of each judgment formula is not high.

(S304)
The result determined in S303 is displayed on the display device 7. An example of a display screen displayed on the display device 7 will be described with reference to Fig. 9. The display screen 900 illustrated in Fig. 9 has nine display areas: an overactive/underactive muscle map 910, an overactive muscle 920, an underactive muscle 930, a skeletal pattern 940, a posture determination 950, a compensatory movement 960, a plantar pressure distribution 970, a center of gravity determination 980, and an intervention menu 990.

In the overactive/underactive muscle map 910, a map 911 simulating the entire body of the subject is displayed, and the locations of overactive and underactive muscles are distinguished on the map 911, for example, by color coding. The degree of overactive or underactive muscles may be indicated by the shade of color, for example, "slightly overactive" locations may be indicated in light red and "underactive" locations in dark blue. Numerical values indicating the degree of overactive or underactive muscles may also be displayed.

Overactive muscles 920 displays the names of the muscles that have been determined to be overactive. In the example of Figure 9, muscle A is shown to be an overactive muscle.

Inactive muscles 930 display the names of muscles that have been determined to be inactive. In the example of Figure 9, muscle B is shown to be an inactive muscle.

The skeletal pattern 940 displays a line diagram 941 showing the subject's skeletal pattern. In addition to the line diagram 941, an ideal skeletal pattern may be displayed, or the difference from the ideal skeletal pattern may be clearly indicated.

Posture determination 950 displays the numerical values calculated as a result of the posture determination. In the example of FIG. 9, the squatting depth when performing overhead squats and the length of the line segments connecting the feature points included in the skeletal pattern are displayed as a ratio to a reference value, and the angle between the line segments is also displayed. These numerical values may be displayed in comparison with average values for each age and gender, or may be displayed as a graph showing changes over time for the same subject. By displaying the comparison with the average value or the changes over time, it is possible to quantitatively evaluate the posture of the subject, making it easier to understand the effects of a training menu or stretching menu.

The type of compensatory movement determined by the muscle state determination unit 202 is displayed in the compensatory movement 960. In the example of FIG. 9, it is shown that compensatory movement B and compensatory movement D have been performed.

The plantar pressure distribution 970 displays the average plantar pressure distribution 971 of both feet calculated from the plantar pressure data, the center of gravity of the left plantar pressure 972, the center of gravity of the right plantar pressure 973, and the center of gravity of the plantar pressure of both feet 974. Also displayed are the standard position 975 of the center of gravity of the plantar pressure of both feet and the deviation 976 of the center of gravity of the plantar pressure of both feet 974 from the standard position 975.

In the center of gravity determination 980, the positions of the center of gravity 974 of both foot sole pressure, the center of gravity 972 of the left foot sole pressure, and the center of gravity 973 of the right foot sole pressure relative to the standard positions are displayed as the result of the center of gravity determination.

The intervention menu 990 displays an intervention menu, which is a training menu or stretching menu that is preferable for the subject to undertake. The intervention menu includes sumo squats, Romanian deadlifts, self-myofascial release stretching, bent-over rows, and the like. The intervention menu may be displayed in order of difficulty, load, and required time. In addition, thumbnail images or videos of representative postures may be displayed to assist in carrying out the intervention menu.

The intervention menu is selected according to the location of the overactive or inactive muscles and the type of compensatory movement determined by the muscle condition determination unit 202. When selecting an intervention menu, the muscle condition determination unit 202 may use the first intervention menu table 1000 or the second intervention menu table 1100. Each table will be described below.

An example of the first intervention menu table 1000 will be described with reference to FIG. 10. In the first intervention menu table 1000, each muscle ID is associated with an intervention menu for overactive muscles and an intervention menu for inactive muscles. The muscle state determination unit 202 selects an intervention menu from the first intervention menu table 1000 based on the sum of the numerical values output from a plurality of determination formulas. For example, the muscle ID with the largest absolute value of the sum of the numerical values output from a plurality of determination formulas is extracted, and the intervention menu associated with the extracted muscle ID is read from the first intervention menu table 1000. Note that the intervention menu for overactive muscles is mainly a menu for increasing muscle flexibility, and the intervention menu for inactive muscles is mainly a menu for strengthening muscles.

An example of the second intervention menu table 1100 will be described with reference to FIG. 11. In the second intervention menu table 1100, the type of compensatory movement is associated with an intervention menu. The muscle state determination unit 202 selects an intervention menu from the second intervention menu table 1100 based on the type of compensatory movement determined by the determination formula.

The process flow described above determines whether each muscle is overactive or inactive based on posture image data and plantar pressure data, making it possible to evaluate the condition of specific muscles of the subject in more detail. In addition, an intervention menu according to the condition of each muscle is displayed, allowing the subject to learn a more appropriate training or stretching menu.

The display screen 900 illustrated in FIG. 9 is a screen that displays the judgment results for a specific posture, but the judgment results during a specific movement may also be displayed in real time. By displaying the judgment results during the movement in real time, the subject can instantly correct the posture during the movement while looking at the screen, making it possible to carry out a more appropriate training menu or stretching menu.

In Example 1, it was described how each muscle of a subject is judged to be in an overactive or inactive state based on posture image data and plantar pressure data, and in particular, when multiple judgment formulas are used, an overall judgment is made based on the sum of the output values of each judgment formula. In Example 2, it is described how the judgment formula is switched based on plantar pressure data in order to judge the state of each muscle using a more appropriate judgment formula.

Using Figure 12, we will explain an example of the process flow executed in Example 2 step by step.

(S1201)
The data acquisition unit 201 receives symptoms that the subject wishes to improve. The symptoms that the subject wishes to improve are, for example, stiff shoulders, lower back pain, etc., and are input by the operator via the input device 8.

(S1202)
The data acquiring unit 201 displays the posture corresponding to the symptom received in S1201 on the display device 7. The posture to be displayed may be selected from the symptom table 1300.

An example of the symptom table 1300 will be described with reference to FIG. 13. In the symptom table 1300, a posture ID, posture name, and performance difficulty are stored for each symptom. A plurality of posture IDs, posture names, and performance difficulties may be stored for each symptom, as illustrated in FIG. 13. The posture ID column stores an identifier that identifies the posture. The posture name column stores the name of each posture, such as overhead squats, raising both hands, standing, etc. The performance difficulty column stores a numerical value indicating the difficulty of each posture. In the example of FIG. 13, posture A with a posture ID of 1, posture B with a posture ID of 2, and posture C with a posture ID of 3 are stored for symptom A, and it is shown that the difficulty of posture A is 10, which is the highest, and the difficulty of posture C is 1.

The data acquisition unit 201 selects a posture corresponding to the symptom received in S1201 from the symptom table 1300 and displays it on the display device 7. In the symptom table 1300, if multiple postures correspond to the symptom received in S1201, all the corresponding postures may be displayed, or one or more postures selected according to a predetermined criterion may be displayed. For example, the predetermined criterion may be the difficulty of performance, and postures with a difficulty of performance higher than a predetermined threshold or postures with the highest difficulty of performance are displayed. When the subject takes the posture displayed on the display device 7, the posture is photographed by the posture photographing device 10, and the pressure distribution of the subject's soles is measured by the foot pressure measuring device 11.

(S1203)
The data acquisition unit 201 acquires posture image data from the posture imaging device 10 and plantar pressure data from the foot pressure measuring device 11 .

(S1204)
As in S302, the muscle state determining unit 202 calculates a skeletal pattern from the posture image data, and calculates the center of gravity coordinates from the plantar pressure data.

(S1205)
The muscle state determination unit 202 determines whether the center of gravity coordinates calculated in S1204 are within a normal range. If the center of gravity coordinates are within the normal range, the process proceeds to S1206, and if not, the process proceeds to S1207. The normal range is determined in advance, and may be, for example, a circle centered on the standard position 975 of the center of gravity and having a radius of a predetermined threshold value, for example, 3 cm.

In S1205, the subject may be notified by a change in sound or light whether the center of gravity coordinates are within the normal range. By being notified whether the center of gravity coordinates are within the normal range, the subject is more likely to adopt a desirable posture.

(S1206)
The muscle state determining unit 202 selects a determination formula to be used for state determination. The determination formula may be selected using the posture table 1400 together with the muscle table 600.

An example of a posture table 1400 will be described with reference to FIG. 14. In the posture table 1400, associated muscles, which are muscles related to the posture, are associated with the posture ID and posture name. In the example of FIG. 14, it is shown that the muscles related to posture A, which has a posture ID of 1, are muscle A and muscle B, and that the muscles related to posture B, which has a posture ID of 2, are muscle C and muscle D.

The muscle state determination unit 202 extracts the relevant muscles corresponding to the posture displayed in S1202 from the posture table 1400, and selects the determination formula to be used for state determination by reading the determination formula corresponding to the extracted relevant muscles from the muscle table 600. There may be multiple determination formulas selected.

A case where multiple judgment formulas are selected will be described with reference to FIG. 15. FIG. 15 shows an example of an overall judgment using judgment formula A, judgment formula B, judgment formula C, ..., judgment formula N. Judgment formula A and judgment formula B are judgment formulas used for the overhead squat posture, and judgment formula A outputs +2, indicating "overactive muscles," while judgment formula B outputs -1, indicating "slightly underactive muscles." Judgment formula C, which is used for the standing posture, outputs +3, indicating "overactive muscles." From there, +5 is calculated by adding up the output values from each judgment formula up to judgment formula N, and the overall judgment result shows "overactive muscles."

The overall judgment is not limited to the simple addition illustrated in FIG. 15. For example, weighted addition may be used in which the output of each judgment formula is multiplied by a weighting coefficient that is set in advance according to the importance of each judgment formula. The importance of each judgment formula is set according to the type of posture.

(S1207)
The muscle state determination unit 202 determines whether or not to change the posture. If the posture is to be changed, the posture is changed and the process returns to S1202, and if the posture is not to be changed, the process proceeds to S1208.

Whether or not to change posture is determined based on, for example, the degree to which the center of gravity coordinates deviate from the normal range. In other words, if the degree to which the center of gravity coordinates deviate from the normal range is greater than a predetermined threshold, it is determined that the subject cannot assume the posture, and the posture is changed. The posture may be changed based on the degree of difficulty of implementation in the symptom table 1300. For example, if the posture before the change was the one with the highest degree of difficulty of implementation, the posture after the change is selected to be the one with the next highest degree of difficulty of implementation.

(S1208)
The muscle state determination unit 202 switches the determination formula. The switching of the determination formula will be described with reference to Fig. 16. In Fig. 16, whether or not the center of gravity coordinate calculated from the plantar pressure data is within the normal range is determined by the determination formula A. When the center of gravity coordinate is within the normal range, the determination formula B, the determination formula C, the determination formula D, and the determination formula N are used for the overall determination, and when the center of gravity coordinate is not within the normal range, only the determination formula B is used for the overall determination. That is, the determination formula used for the overall determination is switched based on the plantar pressure data.

For example, if the center of gravity coordinates of a subject in an overhead squat position are too far forward from the standard position, the heels will rise and the squat will become too deep, resulting in inaccurate assessment of the muscle condition. Therefore, when the center of gravity coordinates are not within the normal range, the condition of each muscle can be more accurately assessed by using only judgment formula B for the upper body, without using judgment formulas C, D, and N for the squat depth.

(S1209)
The muscle state determination unit 202 determines the state of each muscle using the determination formula selected in S1206 or the determination formula switched in S1208, and displays the determination result on the display device 7. The screen displayed on the display device 7 may be the display screen 900 exemplified in Fig. 9. In other words, an intervention menu according to the determination result may be displayed on the display device 7.

The above-described processing flow allows the system to switch to a more appropriate judgment formula based on plantar pressure data, so the condition of each muscle can be determined more accurately.

In addition to switching the judgment formula based on the plantar pressure data, the judgment formula may be switched based on the posture image data, or the posture displayed in S1202 may be changed.

Above, several embodiments of the present invention have been described. Note that the present invention is not limited to the above embodiments, and the components can be modified and embodied without departing from the gist of the invention. Furthermore, the multiple components disclosed in the above embodiments may be combined as appropriate. Furthermore, some components may be deleted from all the components shown in the above embodiments.

1: Muscle condition evaluation device, 2: Calculation unit, 3: Memory, 4: Storage device, 5: Network adapter, 6: System bus, 7: Display device, 8: Input device, 10: Posture imaging device, 11: Foot pressure measuring device, 201: Data acquisition unit, 202: Muscle condition judgment unit, 600: Muscle table, 700: Judgment formula table, 800: Overall judgment table, 900: Display screen, 910: Overactive/underactive muscle map, 911: Map, 920: Overactive muscles, 930: Underactive muscles , 940: Skeleton pattern, 941: Diagram, 950: Posture determination, 960: Compensatory movement, 970: Sole pressure distribution, 971: Average sole pressure distribution, 972: Center of gravity of left sole pressure, 973: Center of gravity of right sole pressure, 974: Center of gravity of both sole pressures, 975: Standard position, 976: Misalignment, 980: Center of gravity determination, 990: Intervention menu, 1000: First intervention menu table, 1100: Second intervention menu table, 1300: Symptom table, 1400: Posture table

Claims (12)

A muscle condition evaluation device for evaluating a muscle condition of a subject, comprising:
A data acquisition unit that acquires posture image data captured when the subject is in a predetermined posture and plantar pressure data measured when the posture image data is captured;
a state determination unit that determines whether each muscle of the subject is in an overactive state or an inactive state based on the posture image data and the plantar pressure data ;
The muscle condition evaluation device is characterized in that the condition determination unit uses a determination formula that outputs the condition of each muscle as a numerical value when a numerical value calculated from the posture image data and the plantar pressure data is input. The muscle condition evaluation device according to claim 1 ,
The muscle condition evaluation device, characterized in that the state determination unit uses a plurality of the determination formulas and determines whether each of the muscles is in an overactive state or an inactive state based on the numerical values output from each of the determination formulas. The muscle condition evaluation device according to claim 2 ,
The muscle condition evaluation device, wherein the condition determination unit uses a sum of numerical values output from each of the determination equations. The muscle condition evaluation device according to claim 2 ,
The muscle state evaluation device, wherein the state determination unit accumulates values obtained by multiplying a numerical value output from each of the judgment formulas by a weighting coefficient that is set in advance according to the importance of the judgment formula. The muscle condition evaluation device according to claim 4 ,
The muscle state evaluation device according to claim 1, wherein the weighting coefficient is set according to the type of posture. The muscle condition evaluation device according to claim 1 ,
A muscle condition evaluation device characterized in that the numerical values calculated from the posture image data are the coordinates of the subject's feature points, and the numerical values calculated from the plantar pressure data are the coordinates of the subject's center of gravity. The muscle condition evaluation device according to claim 6 ,
The muscle condition evaluation device is characterized in that the condition determination unit switches the determination formula when the center of gravity coordinates are not within a normal range. The muscle condition evaluation device according to claim 6 ,
The muscle condition evaluation device, wherein the condition determination unit notifies whether the center of gravity coordinates are within a normal range. The muscle condition evaluation device according to claim 6 ,
The muscle state evaluation device, wherein the state determination unit displays a skeletal pattern generated by connecting the feature points with line segments in comparison with a skeletal pattern in an ideal posture. The muscle condition evaluation device according to claim 1,
The muscle condition evaluation device is characterized in that the condition determination unit displays an intervention menu, which is a training menu or stretching menu for the subject to undertake, depending on whether each muscle is in an overactive or inactive state. A muscle condition evaluation device for evaluating a muscle condition of a subject, comprising:
A data acquisition unit that acquires posture image data captured when the subject is in a predetermined posture and plantar pressure data measured when the posture image data is captured;
a state determination unit that determines whether each muscle of the subject is in an overactive state or an inactive state based on the posture image data and the plantar pressure data ;
The muscle condition evaluation device, characterized in that the data acquisition unit accepts symptoms of the subject and displays a posture corresponding to the symptoms. The muscle condition evaluation device according to claim 11 ,
A muscle condition evaluation device characterized in that, when a plurality of postures correspond to the symptom, the data acquisition unit selects a posture to be displayed based on a difficulty level of performance that is preset for each posture.

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