WO2023079942A1

WO2023079942A1 - Lower extremity control capability measurement device, lower extremity control capability measurement system, lower extremity control capability measurement program, computer-readable recording medium recording lower extremity control capability measurement program, and lower extremity control capability measurement method

Info

Publication number: WO2023079942A1
Application number: PCT/JP2022/038691
Authority: WO
Inventors: 洋平下河内; 晋史郎峯田
Original assignee: 学校法人浪商学園
Priority date: 2021-11-05
Filing date: 2022-10-18
Publication date: 2023-05-11

Abstract

The present invention comprises: a sensor I/F unit 25 that acquires a physical amount detected in a period during which knee flexion exercise is performed by a subject U, with a physical amount detection device 3 for detecting a predetermined physical amount attached to a femoral area of the subject U; a first waveform acquisition unit 211 that acquires a first waveform W1 based on a detected waveform obtained by arranging the physical amount acquired by the sensor I/F unit 25 along a time axis; a second waveform acquisition unit 212 that acquires a second waveform W2 by smoothing the first waveform W1; and an index calculation unit 213 that calculates, as a first index IA indicating the lower extremity control capability of the subject U, the sum of the respective absolute values of differences between the first waveform W1 and the second waveform W2 within a target period Tw.

Description

Lower-limb control ability measuring device, lower-limb control ability measuring system, lower-limb control ability measuring program, computer-readable recording medium recording lower-limb control ability measuring program, and lower-limb control ability measuring method

The present invention provides a lower-limb control ability measuring device for measuring lower-limb control ability, a lower-limb control ability measuring system, a lower-limb control ability measuring program, a computer-readable recording medium recording the lower-limb control ability measuring program, and a lower-limb control ability measuring method. Regarding.

Conventionally, there has been known a technique in which a sensor is attached to a user's body to obtain long-term sensor data, and a neural network is used to estimate a motor function from the long-term sensor data (for example, Patent Document 1. reference.).

Japanese Unexamined Patent Application Publication No. 2018-33949

However, like the technique described in Patent Document 1, in order to estimate motor function using a neural network, it is necessary to prepare a large amount of training data for learning the neural network. Moreover, even if a neural network is trained using such teacher data, it is impossible to know whether or not the neural network will output an appropriate estimation result by trying. Furthermore, it is necessary to verify whether the estimation results output from the neural network are appropriate. Therefore, it takes a lot of time and effort to actually implement the technique described in Patent Document 1, and whether or not an appropriate estimation result can be obtained cannot be known until the technique is actually performed.

An object of the present invention is to provide a lower-limb control ability measuring device, a lower-limb control ability measuring system, a lower-limb control ability measuring program, a computer-readable recording medium recording the lower-limb control ability measuring program, which facilitates measuring lower-limb control ability, and to provide a lower limb control ability measuring method.

A lower-limb control ability measuring device according to one aspect of the present invention is a test subject having a physical quantity detection device that detects at least one physical quantity of angular velocity and acceleration attached to the thigh, and flexes and/or bends the knee joint while resisting a load. Alternatively, a physical quantity acquisition unit that acquires the physical quantity detected by the physical quantity detection device during a period in which the knee flexion motion including the extension motion is performed, and a detection that arranges the physical quantities acquired by the physical quantity acquisition unit along the time axis. A first waveform acquisition unit that acquires a first waveform based on the waveform; a second waveform acquisition unit that acquires a second waveform by smoothing the first waveform; an index calculation unit that calculates a sum of absolute values of differences between the first waveform and the second waveform as a first index representing the lower limb control ability of the subject.

Also, a lower-limb control ability measuring system according to one aspect of the present invention includes the above-described lower-limb control ability measuring device and the physical quantity detection device.

Also, a lower-limb control ability measuring program according to one aspect of the present invention causes a computer to function as the above-described lower-limb control ability measuring device.

Also, a computer-readable recording medium according to one aspect of the present invention records the lower limb control ability measurement program described above.

Further, in the method for measuring lower limb control ability according to one aspect of the present invention, a subject having a physical quantity detection device that detects at least one physical quantity of angular velocity and acceleration attached to the thigh bends the knee joint while resisting a load. and/or a physical quantity acquisition process for acquiring physical quantities detected by the physical quantity detection device during a period in which a knee flexion exercise including an extending motion is performed; and arranging the physical quantities acquired by the physical quantity acquisition process along a time axis. a first waveform acquisition process for acquiring a first waveform based on the detected waveform; a second waveform acquisition process for acquiring a second waveform by smoothing the first waveform; and the first waveform and the second waveform. and an index calculation process of calculating the sum of the absolute values of the differences between and as a first index representing the lower limb control ability of the subject.

Further, the lower limb control ability measuring system according to one aspect of the present invention performs principal component analysis using the above-described lower limb control ability measuring device and the first index IA(z) obtained from a plurality of subjects as variables, a principal component analysis unit that obtains the first principal component of the principal component analysis as the formula (1).

Further, a lower limb control ability measuring system according to one aspect of the present invention includes the lower limb control ability measuring device described above, and the first index IA(x) and the first index IA(z) obtained from a plurality of subjects as variables and a principal component analysis unit that performs a principal component analysis to obtain the first principal component of the principal component analysis as the formula (2).

Further, a leg control ability measuring system according to one aspect of the present invention includes the leg control ability measuring device described above, the first index IA(x) obtained from a plurality of subjects, the first index IA(y), and a principal component analysis unit that performs principal component analysis with the first index IA(z) as a variable and obtains the first principal component of the principal component analysis as the above equation (3).

Further, the lower limb control ability measuring system according to one aspect of the present invention performs principal component analysis using the above-described lower limb control ability measuring device and the second index IB obtained from a plurality of subjects as variables, and the principal component and a principal component analysis unit that obtains the first principal component of the analysis as the above equation (4).

It is an explanatory view showing an example of composition of a leg control ability measuring system concerning one embodiment of the present invention. 2 is a block diagram showing an example of an electrical configuration of the physical quantity detection device shown in FIG. 1; FIG. It is a wave form diagram which shows an example of the 1st waveform W1 and the 2nd waveform W2. It is an explanatory view for explaining the first index IA. 4 is a graph showing an example of angular velocities when a subject wearing the physical quantity detection device shown in FIG. 1 on the thigh performs squat exercise five times. 2 is a flow chart showing an example of a method for generating equations (1) to (4) by the lower limb control ability measurement system shown in FIG. 1; 2 is a flow chart showing an example of a method for generating equations (1) to (4) by the lower limb control ability measurement system shown in FIG. 1; 9 is a flowchart showing an example of first index acquisition processing; FIG. 4 is an explanatory diagram showing a knee joint valgus angle Rk; FIG. 2 is a flow chart showing an example of the operation of an index calculation unit shown in FIG. 1; FIG. 4 is a spectrum diagram of an experimentally obtained angular velocity detection waveform; FIG. Multiple correlation coefficient R obtained by multiple regression analysis for formula (1), multiple contribution rate R ² , partial regression coefficient a, constant (intercept) c, standard partial regression coefficient β, and p value (p value) are shown. It is a table. FIG. 10 is a table showing multiple correlation coefficient R, multiple contribution rate R ² , partial regression coefficients c and d, constant e, standard partial regression coefficient β, and p value obtained by multiple regression analysis for Equation (2). 4 is a table showing multiple correlation coefficient R, multiple contribution rate R ² , partial regression coefficient i, constant j, standard partial regression coefficient β, and p-value obtained by multiple regression analysis for Equation (4).

Hereinafter, embodiments according to the present invention will be described based on the drawings. It should be noted that the same reference numerals in each figure indicate the same configuration, and the description thereof will be omitted. FIG. 1 is an explanatory diagram showing an example of the configuration of a lower limb control ability measuring system according to one embodiment of the present invention. A lower-limb control ability measuring system 1 shown in FIG. 1 includes a lower-limb control ability measuring device 2 and a physical quantity detection device 3 .

The lower limb control ability measurement system 1 is a system for measuring the subject U's lower limb control ability as an index IX. In particular, the index IX can be LEECam Index representing the subject U's lower extremity eccentric control ability (LEEC: Lower Extremity Eccentric Control).

The index IX includes the first index IA(x), the first index IA(y), the first index IA(z), the second index IB, the knee joint valgus peak angle Rkp, and the subject U from a predetermined height. It includes the increment ΔGRF per unit time of the reaction force in the vertical direction from the landing point when landing.

　It is important to maintain and improve the motor function of the lower extremities in order to prevent sports injuries for athletes and improve the QOL (Quality Of Life) of middle-aged and elderly people. The lower-limb control ability measuring system 1 can measure an index IX that is an index of the motor function of the subject's U lower limbs. If the motor function of the lower extremities of the subject U can be grasped as the index IX, it can be used to maintain and improve the motor function of the lower extremities of the subject U.

The lower limb control ability measuring device 2 is configured using, for example, a so-called personal computer. The lower limb control ability measuring device 2 includes, for example, a control section 21, a display 22, a keyboard 23, a mouse 24, and a sensor I/F section 25 (physical quantity acquisition section). In addition, the lower-limb control ability measuring device 2 is not limited to an example configured using a personal computer, and may be, for example, a smart phone, a tablet terminal, or the like.

The physical quantity detection device 3 is attached to the subject's U thigh using, for example, a belt or adhesive tape. FIG. 2 is a block diagram showing an example of the electrical configuration of the physical quantity detection device 3 shown in FIG. The physical quantity detection device 3 shown in FIG. 2 includes an angular velocity sensor 31 , a storage section 32 , an external I/F (interface) section 33 and a control section 34 .

In addition, the lower limb control ability measuring system 1 uses a small information processing device such as a so-called smartphone equipped with an angular velocity sensor 31 to integrate the lower limb control ability measuring device 2 and the physical quantity detection device 3 into a single device. It may be a system with

The angular velocity sensor 31 is, for example, an angular velocity sensor that detects an angular velocity Ax around the X axis, an angular velocity Ay around the Y axis, and an angular velocity Az around the Z axis in X, Y, Z orthogonal coordinates. Angular velocities Ax, Ay, and Az are collectively referred to as angular velocities A hereinafter.

In addition to the angular velocity sensor 31, or instead of the angular velocity sensor 31, the physical quantity detection device 3 may include an acceleration sensor for detecting X-axis direction acceleration, Y-axis direction acceleration, and Z-axis direction acceleration. good. Angular velocity and acceleration are examples of physical quantities.

Also, the physical quantity detection device 3 is not limited to detecting the angular velocity A and/or the acceleration with the angular velocity sensor 31 and/or the acceleration sensor. The physical quantity detection device 3 includes, for example, a camera (for example, a high-speed camera) that captures the knee bending motion of the subject U, and performs three-dimensional motion analysis from the video, thereby detecting the angular velocity A and/or acceleration. .

Fig. 1 shows a single leg squat exercise as an example of knee flexion exercise. A single leg squat exercise is a knee flexion exercise that involves flexing and extending the knee joint of one leg while resisting a load (gravity).

It should be noted that the knee flexion exercise is not limited to single-leg squat exercise, and may be double-leg squat exercise. The double-leg squat is suitable for assessing lower-limb control ability related to the walking and balance functions of middle-aged and elderly people, and the single-leg squat is suitable for evaluating the lower-limb control ability related to the possibility of injury in athletes.

Also, the knee bending exercise is not limited to the squat exercise. Various exercises can be used as the knee bending exercise. For example, knee flexion motions suitable for evaluation of lower limb control ability of middle-aged and elderly persons include motions of sitting on a chair, motions of standing up, motions of climbing stairs, and motions of running. In addition, for example, as knee flexion exercises suitable for evaluating lower limb control ability related to the possibility of injury occurrence of athletes, double leg vertical jump, single leg vertical jump, stop motion, side step motion, double leg landing motion, drop jump motion, A double-leg rebound jump motion, a single-leg rebound jump motion, and the like are included.

A stop motion is a motion to stop suddenly from a running state. A drop-jump motion is a motion of jumping off a platform. A rebound jump is an action of continuously jumping, such as jumping with a jump rope.

Also, the load in the knee bending motion is not limited to gravity. For example, it may be a load applied to the leg due to motion such as lateral movement, stopping from lateral movement, or the like. Alternatively, for example, in machine training, it may be a load applied to the leg by a spring or a weight.

1 and 2, the X axis of the angular velocity sensor 31 is the long axis direction of the thigh of the subject U, the Y axis of the angular velocity sensor 31 is the longitudinal direction of the thigh of the subject U, and the Z axis of the angular velocity sensor 31 The physical quantity detection device 3 is attached to the thigh of the subject U so that the axis extends along the lateral direction of the thigh of the subject.

The storage unit 32 is a non-volatile storage device configured using, for example, flash memory. The external I/F unit 33 is a communication interface circuit that is connected to the sensor I/F unit 25 of the lower limb control ability measuring device 2 via, for example, an unillustrated cable or the like, and that can transmit data to the lower limb control ability measuring device 2. .

Note that the external I/F unit 33 is not limited to transmitting data to the lower-limb control ability measuring device 2 by wire, and may be a wireless communication circuit that transmits data to the lower-limb control ability measuring device 2 using a radio signal. Alternatively, for example, the storage unit 32 may be configured by a removable storage medium such as a memory card, and the external I/F unit 33 may be a connector or the like that allows the storage medium to be removed.

The control unit 34 is configured using, for example, a so-called microcomputer. The control unit 34 consists of a CPU (Central Processing Unit) that executes predetermined arithmetic processing, a RAM (Random Access Memory) that temporarily stores data, a non-volatile storage unit, a timer circuit, and these peripheral circuits. It is The control unit 34 operates as follows by executing a predetermined control program.

The control unit 34 acquires the angular velocities Ax, Ay, and Az detected by the angular velocity sensor 31 at a preset sampling frequency fs, and cumulatively stores them in the storage unit 32 . Further, the control unit 34 transfers the data stored in the storage unit 32 to the lower limb control ability measuring device when the external I/F unit 33 and the sensor I/F unit 25 are connected, for example, by a cable (not shown) or the like. 2.

FIG. 11 is a spectrum diagram of the detected waveform Wd of the angular velocity Ax obtained experimentally. As shown in FIG. 11, since the main frequency components of the detected waveform Wd are 8 Hz or less, 80 Hz or more can be preferably used as the sampling frequency fs.

The sensor I/F unit 25 shown in FIG. 1 corresponds to an example of a physical quantity acquisition unit that acquires the physical quantity detected by the physical quantity detection device 3. The sensor I/F unit 25 may be, for example, a communication circuit capable of receiving data from the external I/F unit 33 in a wired manner via a cable. It may be a wireless communication circuit capable of receiving data from the external I/F unit 33 via the sensor I/F unit 25 or an interface circuit that reads data from a storage medium in which data is written by the sensor I/F unit 25 .

The control unit 21 of the lower limb control ability measuring device 2 is configured using, for example, a microcomputer. The control unit 21 includes a CPU that executes predetermined arithmetic processing, a RAM that temporarily stores data, non-volatile storage devices such as HDD (Hard Disk Drive) and SSD (Solid State Drive), peripheral circuits thereof, etc. consists of The storage device also functions as an analysis result storage unit 215 . The analysis result storage unit 215 stores equations (1) to (4), etc., which will be described later.

The control unit 21 executes the lower-limb control ability measurement program stored in the storage device described above, for example, to obtain a first waveform acquisition unit 211, a second waveform acquisition unit 212, an index calculation unit 213, and a principal component analysis unit 214. function as

Note that the principal component analysis unit 214 is not limited to being included in the lower limb control ability measuring device 2 . The principal component analysis unit 214 may be configured as a device independent of the lower limb control ability measuring device 2 .

The first waveform acquisition unit 211 acquires a first waveform W1 based on a detected waveform Wd in which at least one of the angular velocities Ax, Ay, and Az (physical quantities) acquired by the sensor I/F unit 25 is arranged along the time axis. to get

Specifically, the first waveform acquisition unit 211 acquires a waveform obtained by filtering the detected waveform Wd with a low-pass filter as the first waveform W1. High-frequency noise components can be removed by filtering. A Butterworth filter, for example, can be used as the low-pass filter. The cutoff frequency of the low-pass filter can be set as appropriate. For example, a frequency of 5 Hz to 10 Hz can be used, more preferably a frequency of 6 Hz to 8 Hz can be used, and 6 Hz is particularly preferable. . As shown in FIG. 11, the main frequency component of the detected waveform Wd is 8 Hz or less, so 6 Hz to 8 Hz is suitable as the cutoff frequency. Also, 6 Hz is the cutoff frequency actually used in the experiments described later, and is suitable as the cutoff frequency of the low-pass filter.

Note that the first waveform acquisition unit 211 may use the detected waveform Wd as it is as the first waveform W1.

The first waveform W1 includes a first waveform W1(x) based on the detected waveform Wd(x), which is the waveform of the angular velocity Ax, and a first waveform W1(y) based on the detected waveform Wd(y), which is the waveform of the angular velocity Ay. ) and a first waveform W1(z) based on the detected waveform Wd(z), which is the waveform of the angular velocity Az. The first waveform acquisition unit 211 may acquire at least one of the first waveform W1(x), the first waveform W1(y), and the first waveform W1(z) according to the purpose.

The second waveform acquisition unit 212 acquires the second waveform W2 by smoothing the first waveform W1 acquired by the first waveform acquisition unit 211. FIG. 3 is a waveform diagram showing an example of the first waveform W1 and the second waveform W2.

Specifically, the second waveform acquisition unit 212 acquires the second waveform W2 by performing a moving average for moving the first waveform W1 along the time axis as smoothing processing. The moving average is performed by calculating the average value of the number of data N detected by the physical quantity detection device 3 during a preset moving average time ta. The moving average time ta can be, for example, 0.5 seconds to 2 seconds, preferably 1 second.

In this case, the number of data N is given by the following formula (A) from the moving average time ta and the sampling frequency fs.

Number of data N = ta x fs + 1 ... (A)

For example, if the moving average time ta is 1 second and the sampling frequency fs is 200 Hz, the number of data N is 201. Therefore, the second waveform acquisition unit 212 averages 201 consecutive data along the time axis in the first waveform W1, and repeats this average while moving the data one by one along the time axis. , to perform a moving average. A waveform obtained as a result of executing the moving average in this way is the second waveform W2.

Note that the smoothing process is not limited to the moving average, and the second waveform acquisition section 212 can perform various smoothing processes.

The second waveform W2 includes a second waveform W2(x) obtained by smoothing the first waveform W1(x), a second waveform W2(y) obtained by smoothing the first waveform W1(y), and a first The waveform W1(z) may include at least one of the smoothed second waveform W2(z). The second waveform acquisition unit 212 may acquire at least one of the second waveform W2(x), the second waveform W2(y), and the second waveform W2(z) according to the purpose.

The index calculation unit 213 calculates the sum of the absolute values of the differences between the first waveform W1 and the second waveform W2 within the preset target period Tw as the first index IA representing the lower limb control ability of the subject U. . Any period can be set as the target period Tw. However, if the knee bending motion is a squat motion, the descending period during which the center of gravity of the subject U (physical quantity detection device 3) descends (the period from the fully extended knee position to the maximum knee flexed position) is set as the target period Tw. is more preferred. The descending period in the squat exercise corresponds to an example of the period during which the knee joint is bent while resisting the load.

FIG. 4 is an explanatory diagram for explaining the first index IA. In FIG. 4, the area of the gray shaded portion corresponds to the first index IA. The entirety of FIG. 4 corresponds to the target period Tw.

When the period during which the knee joint is bent while resisting the load (descent period in squat exercise) is set as the target period Tw, the first index IA and the second index IB, which will be described later, are the leg extensibility. It becomes the control capability index (LEECAmpIndex).

There are three types of muscle contraction patterns: constriction contraction in which the muscle exerts force while its length shortens, isometric contraction in which the muscle exerts force while the length is constant, and force exertion while the muscle lengthens. There is an eccentric contraction that exerts In knee flexion exercise, when the knee joint is flexed while resisting the load, and in squat exercise, when the body's center of gravity is lowered while bending the knee and hip joints, the contraction that occurs in the extensor muscles of the lower extremities is mainly eccentric contraction. .

Eccentric contractions of the extensor muscles of the lower extremities, such as the quadriceps femoris, gluteus maximus, gastrocnemius, and soleus, are involved in daily activities such as shock-absorbing situations where falls and injuries are likely to occur, walking down stairs, and sitting on a chair. used. If a person does not have the ability to slowly lower the center of gravity of the body, the muscles cannot absorb the impact during these movements, so it is believed that the person is more likely to receive a large impact and fall more easily.

In addition, it is known that strength training that emphasizes eccentric contraction (eccentric training) is more effective in improving muscle strength than strength training that only uses other contraction modes (published by Nihon Bungeisha, written by Kazunori Nosaka, " If you sit comfortably, you can walk for the rest of your life!"). Therefore, evaluating the ability to slowly lower the body's center of gravity during squat exercises performed with one's own weight is useful from the perspective of preventing athletes from sports injuries and improving the basic muscle strength for middle-aged and elderly people to safely lead daily lives. From the viewpoint of evaluation, it is effective as an evaluation method.

Therefore, if the descent period is set as the target period Tw and the index IX is set as the lower extensibility control ability index, the index IX is from the viewpoint of preventing sports injuries for athletes, improving the QOL of middle-aged and elderly people, and preventing nursing care. Therefore, it is considered to be particularly effective as an indicator of the ability to control the lower extremities.

FIG. 5 is a graph showing an example of the angular velocity Az when the subject U who has the physical quantity detection device 3 shown in FIG. A graph G1 corresponds to an example of the detected waveform Wd(z) indicating the angular velocity Az, and a graph G2 indicates the inclination angle of the thigh.

The horizontal axis of the graph shown in FIG. 5 is time (sec), the left vertical axis is angular velocity (dps: degree per second) corresponding to graph G1, and the right vertical axis is tilt angle (degree) corresponding to graph G2. . The tilt angle of the thigh, that is, the tilt angle of the X-axis is obtained by integrating the angular velocity Az. The tilt angle indicates that the thigh (X-axis) is vertical at 0 degrees, and the thigh (X-axis) is horizontal at -90 degrees. Angular velocities Ax, Ay (detected waveforms Wd(x), Wd(y)) are substantially the same as angular velocity Az (detected waveforms Wd(z)). Description of y)) is omitted.

As shown in FIG. 5, the graph G2 decreases during the descending period when the subject U bends the leg and the center of gravity descends, and the graph G2 increases during the ascending period when the subject U stretches the leg and the center of gravity rises. Therefore, it is possible to distinguish between the falling period and the rising period based on the inclination angle of the graph G2.

The index calculation unit 213 can calculate the first index IA based on the first waveform W1 and the second waveform W2 of the target period Tw corresponding to one falling period thus determined.

The first index IA includes a first index IA(x) that is the sum of absolute values of the differences between the first waveform W1(x) and the second waveform W2(x), and the first waveform W1(y) and the first waveform W1(y). A first index IA(y) that is the sum of the absolute values of the differences between the two waveforms W2(y), and a sum of the absolute values of the differences between the first waveform W1(z) and the second waveform W2(z). and at least one of the first index IA(z). The index calculator 213 may calculate at least one of the first index IA(x), the first index IA(y), and the first index IA(z) according to the purpose.

The principal component analysis unit 214 performs principal component analysis using the first index IA or the second index IB obtained from a plurality of subjects U as variables, and from the first principal component of the principal component analysis, formula (1 ) to (4), and stored in the analysis result storage unit 215 .

FIGS. 6 and 7 are flowcharts showing an example of a method of generating equations (1) to (4) by the lower limb control ability measuring system 1 shown in FIG. In the following flowcharts, the same step numbers are given to the same processes, and the description thereof will be omitted.

In order to generate equations (1) to (4), first, a first index acquisition process is executed for a plurality of subjects U (step S1).

FIG. 8 is a flowchart showing an example of the first index acquisition process. First, the physical quantity detection device 3 is attached to the thigh on the side of the subject U performing the knee bending exercise (step S21).

Next, subject U performs knee flexion exercise five times. Then, the physical quantity detection device 3 detects the angular velocities Ax, Ay, and Az during the period of knee bending motion (step S22). Specifically, the control unit 34 samples the angular velocities Ax, Ay, and Az detected by the angular velocity sensor 31 of the physical quantity detection device 3 at the sampling frequency fs and stores them in the storage unit 32 . In the following, a case of performing a one-leg squat as a knee bending exercise will be described as an example.

Explain the details of the single leg squat exercise. First, the subject U stands by on one leg with the leg to which the physical quantity detection device 3 is attached as the support leg, and the knee joint of the opposite leg (free leg side) is bent at an arbitrary angle. The subject U performs a one-leg squat at the same time as the start signal is given. Single-leg squats start from a single-legged position, lower the center of gravity until the knee of the opposite leg touches the ground over 5 seconds, and when the knee of the opposite leg touches the ground, extend the knee and perform five consecutive single-leg squats. . Angular velocities Ax, Ay, and Az are detected by performing one set of this for each person.

Next, for example, the physical quantity detection device 3 is removed from the subject U, and the external I/F section 33 of the physical quantity detection device 3 and the sensor I/F section 25 of the lower limb control ability measurement device 2 are connected, for example, with a cable not shown. Then, the sensor I/F unit 25 acquires the angular velocities Ax, Ay, and Az from the physical quantity detection device 3 attached to the left thigh, and the control unit 21 stores them in the storage device (step S23: physical quantity acquisition processing ).

Next, the first waveform acquisition unit 211 arranges the angular velocities Ax, Ay, and Az in the target period Tw for three of the five knee flexion movements excluding the first and last, along the time axis, thereby obtaining the angular velocity Ax. Detected waveform Wd(x), detected waveform Wd(y) of angular velocity Ay, and detected waveform Wd(z) of angular velocity Az are generated three each, and detected waveforms Wd(x), Wd(y), Wd(z) are generated. , the first waveforms W1(x), W1(y), and W1(z) are obtained three times (step S24: first waveform obtaining process).

As shown in FIG. 5, the first squat exercise immediately after the start of the squat exercise has a different rhythm of the squat exercise compared to the second and subsequent times, and the tendencies of the angular velocity tend to differ, such as a longer falling period. In addition, since the angular velocity waveforms of other squat exercises are connected before and after the second to fourth times, the period of one squat exercise can be easily determined. On the other hand, it is difficult to determine the start timing of the squat exercise for the first time, and it is difficult to determine the end timing of the squat exercise for the last fifth time. Therefore, the index calculator 213 preferably acquires the angular velocities Ax, Ay, and Az in the squat exercise excluding the first and last squat exercises among multiple squat exercises.

It should be noted that the angular velocities Ax, Ay, and Az in the initial and final squat motions do not necessarily have to be excluded. Also, the number of squat exercises is not limited to 5 times, and may be 6 times or more, 4 times or less, or 1 time.

Next, the second waveform acquisition unit 212 smoothes the three first waveforms W1(x), W1(y), and W1(z) to obtain the three second waveforms W2(x) , W2(y), and W2(z) are obtained (step S25: second waveform obtaining process).

Next, the index calculator 213 calculates the absolute values of the differences between the first waveforms W1(x), W1(y), W1(z) and the second waveforms W2(x), W2(y), W2(z). are calculated as the first indexes IA(x), IA(y) and IA(z). By repeating this three times, the three first indices IA(x), IA(y), and IA(z) of the subject U are calculated (step S26: index calculation processing).

Next, the index calculation unit 213 averages the first index IA(x) for three times to obtain a new first index IA(x), and averages the first index IA(y) for three times to obtain a new first index IA(x). The final first index IA (x ), IA(y), and IA(z) are obtained (step S27). In the following processing, final first indices IA(x), IA(y), and IA(z) are to be processed.

By averaging the first indices IA(x), IA(y), and IA(z) for multiple times to obtain the final first indices IA(x), IA(y), and IA(z), the human can compensate for random errors and random motion variations that occur in motion measurements. Note that it is not always necessary to average the first indices IA(x), IA(y), and IA(z) for a plurality of times. In steps S24 to S26, the first waveforms W1(x), W1(y), W1(z) and the second waveforms W2(x), W2(y), W2(z) obtained from one knee flexion exercise ) are used as the final first indices IA(x), IA(y), IA(z), and step It is not necessary to execute S27.

Alternatively, the first waveforms W1(x), W1(y), W1(z) for a plurality of times obtained in step S24 are averaged, and in step S25, the averaged first waveforms W1(x), W1( y), W1(z) to acquire the second waveforms W2(x), W2(y), W2(z), and in step S26, the averaged first waveforms W1(x), W1(y) , W1(z) and the second waveforms W2(x), W2(y), W2(z), the final first indices IA(x), IA(y), and IA(z) are calculated. , step S27 may not be executed.

By executing steps S21 to S27 for a plurality of subjects U, a plurality of first indices IA(x), IA(y), and IA(z) are obtained (step S1). Note that in steps S21 to S27, the knee bending exercise described above may be performed instead of the one-leg squat exercise.

Next, the principal component analysis unit 214 executes principal component analysis using the first index IA(z) obtained from a plurality of subjects U as variables (step S2: principal component analysis processing).

Next, the principal component analysis unit 214 stores the first principal component obtained by the principal component analysis in the analysis result storage unit 215 as the following formula (1), the coefficient a, and the constant b (step S3: main component analysis processing).

Knee joint valgus peak angle Rkp=aIA(z)+b (1)

FIG. 9 is an explanatory diagram showing the knee joint valgus angle Rk. The knee joint valgus angle Rk is an acute angle formed by the extension line L1 of the long axis U1 of the thigh and the long axis U2 of the lower leg when viewing the subject U in a standing state from the front. A case in which the major axis U2 rotates outward with respect to the extension line L1 is referred to as knee valgus. The knee joint valgus peak angle Rkp is the maximum value of the knee joint valgus angle Rk within the target period Tw in one knee flexion exercise.

An increase in the knee joint valgus peak angle Rkp is known to be a risk factor for typical knee joint injuries such as anterior cruciate ligament injury, medial collateral ligament injury, and lateral meniscus injury. Therefore, the knee joint valgus peak angle Rkp can be used as an index representing the lower limb control ability, and if the knee joint valgus peak angle Rkp can be obtained from the knee flexion movement, the lower limb control ability of the subject U can be measured. easier to do.

The present inventors performed the first index acquisition processing in steps S21 to S27 by performing a one-leg squat exercise on 50 university student athletes, and performed the first index IA(x), IA(y), IA( z) was obtained. Then, from the principal component analysis results of the first index IA(z) for 50 subjects, the coefficient a=0.110 and the constant b=−4.119 of formula (1) were calculated as the first principal component.

Therefore, coefficient a=0.110 and constant b=-4.119 can be preferably used as coefficient a and constant b. Note that coefficient a = 0.110 and constant b = -4.12 with three significant digits, or coefficient a = 0.11 and constant b = -4.1 with two significant digits. A coefficient a=0.1 and a constant b=-4 may be set in one digit. However, the greater the number of significant digits of the coefficient a and the constant b, the more preferable the accuracy of predicting the knee joint valgus peak angle Rkp from the first index IA(z) improves.

From equation (1), coefficient a, and constant b, if the first index IA(z) increases, the knee joint valgus peak angle Rkp also increases, so the first index IA(z) itself can be used as an index showing the lower limb control ability of the subject U. Since the first index IA(z) is easier to measure than directly measuring the knee joint valgus peak angle Rkp, it becomes easier to measure the controllability of the lower limbs.

The principal component analysis unit 214 stores the formula (1), the coefficient a and the constant b in the analysis result storage unit 215, so that the lower limb control ability measuring device 2 measures the first index IA(z) of the new subject U. Based on the results, it is possible to predict the knee joint valgus peak angle Rkp, which represents the controllability of the lower extremity. Validity of equation (1), coefficient a and constant b will be described later.

Next, the principal component analysis unit 214 executes principal component analysis using the first indices IA(x) and IA(z) obtained from a plurality of subjects U as variables (step S4: principal component analysis processing).

Next, the principal component analysis unit 214 stores the first principal component obtained by the principal component analysis in the analysis result storage unit 215 as the following formula (2), coefficients c and d, and constant e (step S5 : principal component analysis processing).

ΔGRF=cIA(z)+dIA(x)+e (2)

△GRF is the amount of increase per unit time of the reaction force in the vertical direction from the landing point when the subject U lands from a predetermined height. ΔGRF is an index that indicates how rapidly the floor reaction force is generated.

A large increase in the vertical floor reaction force per unit time (ΔGRF) means that a greater vertical floor reaction force suddenly acts after landing. It is known that the aforementioned knee joint injury occurs immediately after contacting the ground within about 40 msec after contacting the ground. Therefore, the greater the knee joint valgus peak angle Rkp and the greater the ΔGRF, the greater the risk of knee joint injury. That is, ΔGRF is an index that can evaluate the degree of factors causing knee joint injury.

Therefore, ΔGRF can be used as an index representing the controllability of the lower limbs, and if ΔGRF can be obtained from the knee bending motion, it becomes easier to measure the controllability of the lower limbs of the subject U.

From the principal component analysis results of the first indexes IA(x) and IA(z) for 50 university student athletes, the present inventors determined that the coefficient c of formula (2) = 0.007 as the first principal component. , the coefficient d=−0.003, and the constant e=0.868.

Therefore, coefficient c=0.007, coefficient d=-0.003, and constant e=0.868 can be preferably used as coefficients c, d, and constant e. Note that the coefficient c=0.007, the coefficient d=-0.003, and the constant e=0.87 with two significant digits, or the coefficient c=0.007 with one significant digit, and the coefficient d=-0. 003, and constant e=0.9. However, it is more preferable to increase the number of significant digits of the coefficients c and d and the constant e in that the accuracy of predicting ΔGRF from the first indices IA(x) and IA(z) improves.

From equation (2), coefficients c, d, and constant e, ΔGRF increases as the first index IA(z) increases, and ΔGRF decreases as the first index IA(x) increases. , the first indices IA(x) and IA(z) themselves can be used as indices indicating the lower limb control ability related to the degree of causes of the knee joint injury of the subject U. Since the first indices IA(x) and IA(z) are easier to measure than the direct measurement of ΔGRF, it becomes easier to measure the controllability of the lower extremities.

The principal component analysis unit 214 stores the equation (2), the coefficients c and d, and the constant e in the analysis result storage unit 215, so that the lower limb control ability measuring device 2 obtains the new first index IA(x ), and IA(z), it is possible to predict ΔGRF, which represents the ability to control the lower extremities. Validity of equation (2), coefficients c and d, and constant e will be described later.

Next, the principal component analysis unit 214 executes principal component analysis using the first indices IA(x), IA(y), and IA(z) obtained from a plurality of subjects U as variables (step S6: principal component analysis processing).

Next, the principal component analysis unit 214 stores the first principal component obtained by the principal component analysis in the analysis result storage unit 215 as the following formula (3) and coefficients f, g, h (step S7: main component analysis processing).

Second index IB=fIA(x)+gIA(y)+hIA(z) (3)

As will be described later, the present inventors have found that the second index IB has a correlation with the knee joint valgus peak angle Rkp, and if the second index IB increases, the knee joint valgus peak angle Rkp also increases. It was found that there is a correlation that Therefore, the second index IB can be used as an index showing the subject's U lower limb control ability. Since the second index IB is easier to measure than directly measuring the knee joint valgus peak angle Rkp, it becomes easier to measure the controllability of the lower limbs.

From the principal component analysis results of the first indices IA(x), IA(y), and IA(z) for 50 university student athletes described above, the present inventors calculated the coefficient of formula (3) as the first principal component f=0.487, coefficient g=0.465, coefficient h=0.352 were calculated.

Therefore, the coefficient f=0.487, the coefficient g=0.465, and the coefficient h=0.352 can be preferably used as the coefficients f, g, and h. In addition, the coefficient f = 0.49, the coefficient g = 0.47, and the coefficient h = 0.35 with two significant digits, and the coefficient f = 0.5, the coefficient g = 0.5, with one significant digit. A coefficient h=0.4 may be used.

In addition, from the viewpoint of using the second index IB itself as an evaluation index, the ratio of the coefficients f, g, and h is f: g: h = 487: 465: 352 in three significant digits, and f: g in two significant digits. :h=49:47:35, f:g:h=5:5:4 with one significant digit.

However, the greater the number of significant digits of the coefficients f, g, and h, the more preferable the accuracy of the correlation between the second index IB and the knee joint valgus peak angle Rkp is.

The principal component analysis unit 214 stores the equation (3) and the coefficients f, g, and h in the analysis result storage unit 215, so that the lower limb control ability measuring device 2 obtains the new first index IA(x) of the subject U , IA(y), and IA(z), it is possible to calculate the second index IB having a correlation with the knee joint valgus peak angle Rkp. Validity of equation (3) and coefficients f, g, and h will be described later.

Next, the principal component analysis unit 214 calculates the multiple subjects U is calculated (step S11).

Next, the principal component analysis unit 214 executes principal component analysis using the plurality of second indices IB obtained from the plurality of subjects U as variables (step S12: principal component analysis processing).

Next, the principal component analysis unit 214 stores the first principal component obtained by the principal component analysis in the analysis result storage unit 215 as the following formula (4), coefficient i and constant j (step S13: principal component analytical processing).

Knee joint valgus peak angle Rkp=iIB+j (4)

As described above, the knee joint valgus peak angle Rkp can be used as an index representing the control ability of the lower limbs, and if the knee joint valgus peak angle Rkp can be obtained from the knee flexion movement, it is possible to control the lower limbs of the subject U. Ability can be easily measured.

From the principal component analysis results of the second index IB for 50 university student athletes described above, the present inventors determined that the first principal component is the coefficient i = 1.666 and the constant j = -1.664 in formula (4). was calculated.

Therefore, coefficient i=1.666 and constant j=-1.664 can be preferably used as coefficient i and constant j. In addition, coefficient i=1.67 and constant j=-1.66 may be set with three significant digits, or coefficient i=1.7 and constant j=-1.7 may be set with two significant digits. A coefficient i=2 and a constant j=-2 may be set in one digit. However, the greater the number of significant digits of the coefficient i and the constant j, the more preferable in that the accuracy of predicting the knee joint valgus peak angle Rkp from the second index IB improves.

From the equation (4), the coefficient i and the constant j, if the second index IB increases, the knee joint valgus peak angle Rkp also increases. It can be used as an indicator of ability. Since the second index IB is easier to measure than directly measuring the knee joint valgus peak angle Rkp, it becomes easier to measure the controllability of the lower limbs.

It should be noted that the lower limb control ability measuring device 2 does not have to include the principal component analysis unit 214 and does not need to execute steps S1 to S13.

Next, the lower limb control ability measurement process of the subject U by the lower limb control ability measuring device 2 shown in FIG. 1 will be described. The lower-limb control ability measuring device 2 uses the first index IA(x), IA(y), IA(z), the second index IB, the knee joint valgus peak angle Rkp, and ΔGRF of the subject U as the index IX. By calculating at least one of the indices, the subject's U lower extremity control ability is measured.

FIG. 10 shows the first index IA(x), IA(y), IA(z), the second index IB, the knee joint valgus peak angle Rkp, and ΔGRF measured by the lower limb control ability measuring device 2 shown in FIG. 4 is a flow chart showing an example of a method of calculating each index;

First, the test subject U who intends to measure each index is subjected to the same kind of knee bending exercise as that performed when calculating the formulas, coefficients, and constants stored in the analysis result storage unit 215, in steps S21 to The first index acquisition process of S27 is executed to acquire the first indices IA(x), IA(y), IA(z) of the subject U (step S31).

As described above, the first indices IA(x), IA(y), and IA(z) are correlated with the knee joint valgus peak angle Rkp and ΔGRF, which are risk factors for knee joint injury. If the first indices IA(x), IA(y), and IA(z) can be obtained from the flexion movement, the first index IA(x) can be used as an index indicating the control ability of the lower extremity of the subject U that affects the knee joint injury. , IA(y), IA(z) can be measured.

Next, the index calculation unit 213 substitutes the first index IA(z) obtained from the subject U into Equation (1) to calculate the knee joint valgus peak angle Rkp (step S32: index calculation processing). . The index calculator 213 notifies the user of the knee joint valgus peak angle Rkp by, for example, displaying it on the display 22 .

As described above, an increase in the knee joint valgus peak angle Rkp is known to be a risk factor for knee joint injury. The knee joint valgus peak angle Rkp can be measured as an index indicating the control ability of the subject's U lower limbs that affects the joint injury.

Next, the index calculation unit 213 substitutes the first indices IA(x) and IA(z) obtained from the subject U into Equation (2) to calculate ΔGRF (step S33: index calculation processing). The index calculator 213 notifies the user of ΔGRF by, for example, displaying it on the display 22 .

As described above, it is known that the greater the ΔGRF, the greater the risk of knee joint injury. ΔGRF can be measured as an index showing the controllability of

Next, the index calculation unit 213 substitutes the first indices IA(x), IA(y), and IA(z) obtained from the subject U into the equation (3) to calculate the second index IB ( step S34: index calculation processing). The index calculator 213 notifies the user of the second index IB by, for example, displaying it on the display 22 .

As described above, the second index IB has a correlation with the knee joint valgus peak angle Rkp, which is a risk factor for knee joint injury. A second index IB can be measured as an index indicating the control ability of the lower extremities of the subject U that affects the .

Next, the index calculation unit 213 substitutes the second index IB obtained from the subject U into Equation (4) to calculate the knee joint valgus peak angle Rkp (step S35: index calculation processing). The index calculator 213 notifies the user of the knee joint valgus peak angle Rkp by, for example, displaying it on the display 22 .

Next, the validity of equations (1) to (4), coefficients a, c, d, f, g, h, and i, and constants b, e, and j will be explained. The present inventors performed a one-leg landing task with the same 50 athletes who acquired the first indices IA(x), IA(y), and IA(z) by the above-mentioned single-leg squat as subjects U. Knee joint valgus peak angle Rkp was measured using a table infrared camera at a sampling frequency of 240 Hz. Also, ΔGRF was measured at a sampling frequency of 1000 Hz using a force plate.

The one-leg landing task is to stand on one leg on a 30 cm platform and land on the force plate installed on the floor with one leg at the same time as the signal is given. In the one-foot landing task, the maximum knee joint valgus angle Rk in the analysis interval from when the foot hits the ground until the body's center of gravity reaches the lowest point was measured as the knee joint valgus peak angle Rkp. In addition, the floor reaction force at the time of landing was measured with a force plate at a sampling frequency of 1000 Hz. ΔGRF, which is The one-leg landing task was performed with the right leg for the subject U who performed the above-described single-leg squat with the right leg, and with the left leg for the subject U who performed the above-described single-leg squat with the left leg.

For 50 subjects U, multiple regression analysis was performed on the relationship between the knee joint valgus peak angle Rkp thus obtained and the first index IA(z).

Specifically, the first index IA (z) of each subject U is used as an independent variable for multiple regression analysis, and the knee joint valgus peak angle Rkp is used as a dependent variable to represent a multiple regression model (1), coefficient a = 0 .110, constant b=-4.119, the correlation with the knee joint valgus peak angle Rkp obtained by the above-mentioned one-leg landing task was verified by multiple regression analysis. Between the knee joint valgus peak angle Rkp obtained by the one-leg landing task and the knee joint valgus peak angle Rkp obtained by Equation (1), coefficient a = 0.110, constant b = -4.119 If there is a significant relationship, the first index IA(z) is considered appropriate as an index representing the subject's U lower-limb control ability.

FIG. 12 shows the multiple correlation coefficient R, multiple contribution ratio R ² , partial regression coefficient a, constant (intercept) b, standard partial regression coefficient β, and p value (p value).

According to the multiple regression analysis results for the knee joint valgus peak angle ^Rkp shown in FIG. This means that 20.2% of the variation in can be explained by the knee joint valgus peak angle Rkp calculated by Equation (1).

That is, the knee joint valgus peak angle Rkp calculated by the formula (1) has a correlation with the knee joint valgus peak angle Rkp obtained by the one-leg landing task. It shows that the actual knee joint valgus peak angle Rkp can be estimated. Therefore, it was confirmed that the formula (1), the coefficient a=0.110 and the constant b=-4.119, are appropriate.

Also, according to the multiple regression analysis results regarding the knee joint valgus peak angle Rkp shown in FIG. 12, the p-value of the first index IA(z) is 0.001, which is smaller than 0.05. In the multiple regression analysis, if the p-value is smaller than 0.05, it is determined to be significant, so the first index IA(z) can be determined to be significant from the results of the multiple regression analysis.

Here, the first index IA(z) obtained from the single-leg squat is significant from the results of the multiple regression analysis for the formula (1) regarding the knee joint valgus peak angle Rkp. It means that the knee joint valgus peak angle Rkp of the subject U can be estimated by substituting the index IA(z) into the formula (1) and calculating the knee joint valgus peak angle Rkp.

Therefore, for example, formula (1), coefficient a = 0.110, constant b = -4.119 are stored in advance in the analysis result storage unit 215, and the index calculation unit 213 calculates the first index calculated in step S31. By substituting IA(z) into the formula (1) stored in the analysis result storage unit 215, the knee joint valgus peak angle Rkp is used as an index showing the control ability of the lower extremity of the subject U that affects the knee joint injury. can be calculated as

In addition, the present inventors set the first indices IA (x) and IA (z) of each subject U as independent variables for multiple regression analysis, and used ΔGRF as the dependent variable to express the multiple regression model (2), the coefficient For c=0.007, coefficient d=−0.003, and constant e=0.868, the correlation with ΔGRF obtained by the above-mentioned one-foot landing task was verified by multiple regression analysis. Significant association between the ΔGRF obtained from the one-foot landing task and the ΔGRF obtained from Equation (2), coefficient c = 0.007, coefficient d = -0.003, constant e = 0.868 If so, the first indices IA(x) and IA(z) are considered appropriate as indices representing the subject U's ability to control the lower extremities.

FIG. 13 shows multiple correlation coefficient R, multiple contribution ratio R ² , partial regression coefficients c and d, constant e, standard partial regression coefficient β, and p value obtained by multiple regression analysis for formula (2). there is

According to the multiple regression analysis results for ΔGRF shown in FIG. 13, the heavy contribution rate ^R2 is 0.204, which accounts for 20.4% of the variation in ΔGRF obtained from the one-foot landing task. This means that it can be explained by ΔGRF calculated by Equation (2).

That is, the ΔGRF calculated by the formula (2) has a correlation with the ΔGRF obtained by the one-leg landing task, and the actual ΔGRF of the subject U can be estimated by the formula (2). is shown. Therefore, it was confirmed that the formula (2), the coefficient c=0.007, the coefficient d=-0.003, and the constant e=0.868, is appropriate.

Further, according to the multiple regression analysis results for ΔGRF shown in FIG. 13, the p-value of the first index IA(z) is 0.001, and the p-value of the first index IA(x) is 0.035. , are both less than 0.05. In the multiple regression analysis, if the p-value is smaller than 0.05, it is determined to be significant, so the first indicators IA(x) and IA(z) can be determined to be significant from the results of the multiple regression analysis. .

Here, the first indices IA(x) and IA(z) obtained from the single-leg squat are significant from the results of the multiple regression analysis on the formula (2) regarding ΔGRF, which is the first It means that the ΔGRF of the subject U can be estimated by substituting the indices IA(x) and IA(z) into the equation (2) to calculate ΔGRF.

Therefore, for example, formula (2), the coefficient c=0.007, the coefficient d=−0.003, and the constant e=0.868 are stored in advance in the analysis result storage unit 215, and the index calculation unit 213 performs step S31 By substituting the first indices IA(x) and IA(z) calculated in the equation (2) stored in the analysis result storage unit 215, ΔGRF is calculated as It can be calculated as an index showing the ability to control the lower extremities.

In addition, the present inventors used the second index IB of each subject U as an independent variable in multiple regression analysis, and the knee joint valgus peak angle Rkp as a dependent variable to represent a multiple regression model (4), coefficient i = 1 0.666 and a constant j=-1.664, the correlation with the knee joint valgus peak angle Rkp obtained by the above-mentioned one-leg landing task was verified by multiple regression analysis. Between the knee joint valgus peak angle Rkp obtained by the one-foot landing task and the knee joint valgus peak angle Rkp obtained by Equation (4), coefficient i = 1.666, constant j = -1.664 If there is a significant relationship, the second index IB is considered appropriate as an index representing the subject's U lower limb control ability.

FIG. 14 shows the multiple correlation coefficient R, the multiple contribution ratio R ² , the partial regression coefficient i, the constant j, the standard partial regression coefficient β, and the p-value obtained by multiple regression analysis for Equation (4).

According to the multiple regression analysis results for the knee joint valgus peak angle ^Rkp shown in FIG. This means that 12.6% of the variation in can be explained by the knee joint valgus peak angle Rkp calculated by Equation (4).

That is, the knee joint valgus peak angle Rkp calculated by the formula (4) has a correlation with the knee joint valgus peak angle Rkp obtained by the one-leg landing task, and the subject U's It shows that the actual knee joint valgus peak angle Rkp can be estimated. Therefore, it was confirmed that the formula (4), the coefficient i=1.666 and the constant j=-1.664, are appropriate.

Also, according to the multiple regression analysis results regarding the knee joint valgus peak angle Rkp shown in FIG. 14, the p-value of the second index IB is 0.011, which is smaller than 0.05. In the multiple regression analysis, if the p-value is smaller than 0.05, it is determined to be significant, so the second indicator IB can be determined to be significant from the results of the multiple regression analysis.

Here, the fact that the second index IB obtained from the single-leg squat is significant from the results of the multiple regression analysis on the formula (4) regarding the knee joint valgus peak angle Rkp indicates that the second index IB of the subject U is It means that the knee joint valgus peak angle Rkp of the subject U can be estimated by substituting it into Equation (4) and calculating the knee joint valgus peak angle Rkp.

Therefore, for example, the equation (4), the coefficient i=1.666, and the constant j=−1.664 are stored in advance in the analysis result storage unit 215, and the index calculation unit 213 calculates the second index calculated in step S34. By substituting IB into the formula (4) stored in the analysis result storage unit 215, the knee joint valgus peak angle Rkp is calculated as an index indicating the control ability of the lower extremity of the subject U that affects the knee joint injury. be able to.

Furthermore, if the second index IB is significant, the equation (3) used to calculate the second index IB, coefficient f = 0.487, coefficient g = 0.465, coefficient h = 0.352 It can also be judged to be appropriate.

Note that the principal component analysis unit 214 does not need to execute steps S6 and S7, does not need to execute steps S4 and S5, and does not need to execute steps S2 and S3. Further, the index calculation unit 213 may not execute step S35, may not execute step S34, may not execute step S33, and may not execute step S32.

That is, in the lower limb control ability measuring device according to one aspect of the present invention, a subject having a physical quantity detection device that detects at least one physical quantity of angular velocity and acceleration attached to the thigh bends the knee joint while resisting a load. and/or a physical quantity acquisition unit that acquires the physical quantity detected by the physical quantity detection device during a period in which the knee flexion motion including the extension motion is performed, and the physical quantity acquired by the physical quantity acquisition unit are arranged along the time axis. A first waveform acquisition unit that acquires a first waveform based on the detected waveform, a second waveform acquisition unit that acquires a second waveform by smoothing the first waveform, and a preset target period, an index calculation unit that calculates a sum of absolute values of differences between the first waveform and the second waveform as a first index representing the lower limb control ability of the subject.

According to these configurations, the physical quantity detection device is attached to the subject's thigh, and the first index representing the subject's lower limb control ability is calculated from the waveform of the physical quantity acquired when the subject performs a knee flexion exercise. Therefore, it is easy to measure the control ability of the lower extremities.

Further, it is preferable that the first waveform acquisition unit acquires, as the first waveform, a waveform obtained by filtering the detected waveform with a low-pass filter.

According to this configuration, noise components can be removed from the first waveform, so the accuracy of the first index representing the subject's lower limb control ability is improved.

Further, it is preferable that the second waveform acquisition unit performs the smoothing by performing a moving average on the first waveform while moving along the time axis.

A moving average is suitable as a smoothing process.

Further, the physical quantity detection device detects the physical quantity at a preset sampling frequency, and in the moving average, the number of data detected by the physical quantity detection device during a preset moving average time period. It is preferable to calculate the average value of

According to this configuration, the degree of smoothing by the moving average can be made constant, so the value of the first index is stabilized and relative comparison between multiple first indices becomes easy.

Also, in the smoothing, the moving average may be repeated multiple times.

By repeating the moving average multiple times, the degree of smoothing increases.

Further, the knee bending exercise includes an operation of bending the knee joint while resisting the load, and the target period is a period during which the knee bending exercise is performing the operation of bending the knee joint while resisting the load. A period is preferred.

During the period during which the knee joint is flexed while resisting the load, the contraction morphology that occurs in the extensor muscles of the lower extremities of the subject is mainly eccentric contraction. As will be described later, eccentric contractile ability is particularly important from the viewpoints of injury prevention for athletes, improvement of QOL for middle-aged and elderly people, and prevention of nursing care. Therefore, by setting the period during which the eccentric contraction exercise is performed as the target period for calculating the first index, the ability to control the lower extremities is particularly beneficial from the perspectives of injury prevention for athletes, improvement of QOL for middle-aged and elderly people, and prevention of nursing care. can be measured.

Further, it is preferable that the knee flexion exercise is a one-leg squat, and the physical quantity detection device is attached to the thigh on the side where the one-leg squat is performed.

When the knee flexion exercise is a one-leg squat, it is preferable that the physical quantity detection device is attached to the thigh on the side where the one-leg squat is performed.

The angular velocity is about at least one of an X-axis extending in the longitudinal direction of the thigh, a Y-axis extending in the front-rear direction of the thigh, and a Z-axis extending in the left-right direction of the thigh. and the acceleration is acceleration in at least one axial direction of the X-axis, the Y-axis, and the Z-axis.

The X-axis extending in the longitudinal direction of the thigh, the Y-axis extending in the front-rear direction of the thigh, and the Z-axis extending in the left-right direction of the thigh are axial directions that are closely related to the ability to control the lower limbs in knee flexion motion. Therefore, the first waveform representing the control ability of the lower extremity of the subject is obtained from the physical quantity corresponding to at least one of the X-axis, Y-axis, and Z-axis, and is represented by the first index. The accuracy of the control ability of the subject's lower extremities is improved.

Further, the first waveform acquisition unit obtains a first waveform W1(x) based on the detected waveform, which is a waveform of angular velocity about the X-axis, and the detected waveform, which is a waveform of angular velocity about the Y-axis. obtaining at least one of a first waveform W1(y) based on the waveform and a first waveform W1(z) based on the detected waveform, which is a waveform of angular velocity about the Z axis, and obtaining the second waveform; The acquisition unit obtains a second waveform W2(x) obtained by smoothing the first waveform W1(x), a second waveform W2(y) obtained by smoothing the first waveform W1(y), and the Obtaining at least one of a second waveform W2(z) obtained by smoothing one waveform W1(z), the index calculation unit calculates the first waveform W1(x) and the second waveform W2(x ), and a first index IA (x) that is the sum of the absolute values of the differences between the first waveform W1 (y) and the second waveform W2 (y). At least one of IA(y) and a first index IA(z) that is the sum of absolute values of differences between the first waveform W1(z) and the second waveform W2(z) is It is preferable to calculate as an index.

According to this configuration, the first index IA(x) corresponding to at least one axial direction of the X-axis, Y-axis, and Z-axis, which is closely related to the ability to control the lower limbs in knee bending motion, and the first index IA At least one of (y) and the first index IA(z) can be calculated as the first index.

Further, the index calculation unit calculates the first index IA(z) as the first index, and the index calculation unit further calculates the following formula (1) from the first index IA(z): is preferably used to calculate the knee joint valgus peak angle Rkp of the subject. Knee joint valgus peak angle Rkp=aIA(z)+b (1), where a is a coefficient and b is a constant.

According to this configuration, it is possible to calculate the subject's knee joint valgus peak angle Rkp from the first index IA(z) without directly measuring the subject's knee joint valgus peak angle Rkp.

Also, it is preferable that the coefficient a is 0.1 in one significant digit, and the constant b is -4 in one significant digit.

The inventors have found that the coefficient a is preferably 0.1 in one significant digit, and the constant b is preferably -4 in one significant digit.

Further, the index calculation unit calculates the first index IA(x) and the first index IA(z) as the first index, and the index calculation unit further calculates the first index IA(x) And from the first index IA (z), using the following formula (2), the amount of increase per unit time in the vertical reaction force from the landing point when the subject lands from a predetermined height It is preferable to calculate ΔGRF. ΔGRF=cIA(z)+dIA(x)+e (2), where c and d are coefficients and e is a constant.

According to this configuration, it is possible to calculate the subject's ΔGRF from the first index IA(x) and the first index IA(z) without directly measuring the subject's ΔGRF.

Further, it is preferable that the coefficient c is 0.007 with one significant digit, the coefficient d is -0.003 with one significant digit, and the constant e is 0.9 with one significant digit.

The present inventors have found that the coefficient c is preferably 0.007 in one significant digit, the coefficient d is -0.003 in one significant digit, and the constant e is preferably 0.9 in one significant digit. .

Further, the index calculation unit calculates the first index IA(x), the first index IA(y), and the first index IA(z) as the first index, and the index calculation unit Furthermore, it is preferable to calculate the second index IB using the following formula (3). Second index IB=fIA(x)+gIA(y)+hIA(z) (3), where f, g and h are coefficients.

According to this configuration, the second index IB is the first index IA(x), IA(y) of all three axes, the X-axis, Y-axis, and Z-axis, which are closely related to the ability to control the lower limbs in knee flexion exercise. , IA(z) are reflected. As a result, the second index IB is a comprehensive index representing the control ability of the subject's lower extremities.

Also, it is preferable that the ratio of the coefficients f, g, and h is f:g:h=5:5:4 with one significant figure.

The inventors found that the ratio of coefficients f, g, and h is preferably f:g:h=5:5:4 with one significant figure.

Also, the coefficient f is 0.5 in one significant digit, the coefficient g is 0.5 in one significant digit,

The coefficient h is preferably 0.4 with one significant figure.

The present inventors have found that the coefficient f is preferably 0.5 with one significant digit, the coefficient g is preferably 0.5 with one significant digit, and the coefficient h is preferably 0.4 with one significant digit. I found out.

Further, it is preferable that the index calculation unit further calculates the subject's knee joint valgus peak angle Rkp from the second index IB using the following formula (4). Knee joint valgus peak angle Rkp=iIB+j (4), where i is a coefficient and j is a constant.

According to this configuration, it is possible to calculate the subject's knee joint valgus peak angle Rkp from the second index IB without directly measuring the subject's knee joint valgus peak angle Rkp.

The inventors have found that the coefficient i is preferably 2 with one significant digit, and the constant j is preferably -2 with one significant digit.

According to this configuration, the above formula (1) can be obtained based on the first index IA(z) obtained from a plurality of subjects.

According to this configuration, the above equation (2) can be obtained based on the first indices IA(x) and IA(z) obtained from a plurality of subjects.

According to this configuration, the above formula (3) can be obtained based on the first indices IA(x), IA(y), and IA(z) obtained from a plurality of subjects.

According to this configuration, the above formula (4) can be obtained based on the second index IB obtained from a plurality of subjects.

The lower limb control ability measuring device, the lower limb control ability measuring system, the lower limb control ability measuring program, the computer-readable recording medium recording the lower limb control ability measuring program, and the lower limb control ability measuring method configured as described above are the control ability of the lower limbs. is easy to measure.

This application is based on Japanese Patent Application No. 2021-180865 filed on November 5, 2021, the contents of which are included in this application. It should be noted that the specific embodiments or examples described in the section for carrying out the invention merely clarify the technical content of the present invention, and the present invention is based on such specific examples. It should not be interpreted narrowly by limiting only

1 Lower Limb Control Ability Measuring System 2 Lower Limb Control Ability Measuring Device 3 Physical Quantity Detector 21 Control Unit 22 Display 23 Keyboard 24 Mouse 25 Sensor I/F Unit (Physical Quantity Acquisition Unit)
31 Angular velocity sensor 32 Storage unit 33 External I/F unit 34 Control unit 211 First waveform acquisition unit 212 Second waveform acquisition unit 213 Index calculation unit 214 Principal component analysis unit 215 Analysis result storage unit A, Ax, Ay, Az Angular velocity IA , IA(x), IA(y), IA(z) First index IB Second index L1 Extension line N Number of data R Multiple correlation coefficient R ^Double contribution rate Rk Knee joint valgus angle Rkp Knee joint valgus peak Angle Tw Target period U Subject U1 Long axis of thigh U2 Long axis of lower leg W1, W1(x), W1(y), W1(z) First waveform W2, W2(x), W2(y), W2 (z) Second waveforms Wd, Wd(x), Wd(y), Wd(z) Detected waveforms a, c, d, f, g, h, i Coefficients b, e, j Constant fs Sampling frequency ta Moving average Time β Standard partial regression coefficient

Claims

The period during which the subject, whose thigh is equipped with a physical quantity detection device that detects at least one physical quantity of angular velocity and acceleration, performs a knee flexion exercise that includes flexion and/or extension of the knee joint while resisting the load. a physical quantity acquisition unit that acquires the physical quantity detected by the physical quantity detection device;
a first waveform acquisition unit that acquires a first waveform based on a detected waveform in which the physical quantities acquired by the physical quantity acquisition unit are arranged along the time axis;
a second waveform acquisition unit that acquires a second waveform by smoothing the first waveform;
an index calculation unit that calculates the sum of the absolute values of the differences between the first waveform and the second waveform within a preset target period as a first index representing the lower limb control ability of the subject. Ability measuring device.
The lower limb control ability measuring device according to claim 1, wherein the first waveform acquisition unit acquires, as the first waveform, a waveform obtained by filtering the detected waveform with a low-pass filter.
3. The leg control ability measurement according to claim 1 or 2, wherein the second waveform acquisition unit performs the smoothing by performing a moving average that averages the first waveform while moving it along the time axis. Device.
The physical quantity detection device detects the physical quantity at a preset sampling frequency,
4. The lower limb control ability measuring device according to claim 3, wherein the moving average calculates an average value for the number of data detected by the physical quantity detecting device during a period of a preset moving average time.
The lower limb control ability measuring device according to claim 4, wherein in the smoothing, the moving average is repeated multiple times.
The knee bending motion includes an action of bending the knee joint while resisting the load,
The lower-limb control ability measuring device according to any one of claims 1 to 5, wherein the target period is a period during which an action of bending the knee joint while resisting the load is being performed in the knee bending exercise.
the knee flexion exercise is a single leg squat;
The lower limb control ability measuring device according to any one of claims 1 to 6, wherein the physical quantity detection device is attached to the thigh on the side where the one-leg squat is performed.
The angular velocity is an angular velocity around at least one of an X-axis extending in the longitudinal direction of the thigh, a Y-axis extending in the front-rear direction of the thigh, and a Z-axis extending in the left-right direction of the thigh. and
The lower limb control ability measuring device according to any one of claims 1 to 7, wherein the acceleration is acceleration in at least one axial direction of the X-axis, the Y-axis, and the Z-axis.
The first waveform acquisition unit,
a first waveform W1(x) based on the detected waveform, which is a waveform of angular velocity about the X axis;
a first waveform W1(y) based on the detected waveform, which is a waveform of angular velocity about the Y axis;
obtaining at least one of a first waveform W1(z) based on the detected waveform, which is a waveform of angular velocity about the Z axis;
The second waveform acquisition unit,
a second waveform W2(x) obtained by smoothing the first waveform W1(x);
a second waveform W2(y) obtained by smoothing the first waveform W1(y);
obtaining at least one of the smoothed second waveform W2(z) of the first waveform W1(z);
The index calculation unit
a first index IA(x) that is the sum of the absolute values of the differences between the first waveform W1(x) and the second waveform W2(x);
a first index IA(y) that is the sum of the absolute values of the differences between the first waveform W1(y) and the second waveform W2(y);
At least one of a first index IA(z), which is a sum of absolute values of differences between the first waveform W1(z) and the second waveform W2(z), is calculated as the first index. 9. The lower limb control ability measuring device according to 8.
The index calculation unit calculates the first index IA(z) as the first index,
10. The lower limb control ability measurement according to claim 9, wherein the index calculation unit further calculates a knee joint valgus peak angle Rkp of the subject from the first index IA(z) using the following formula (1). Device.
Knee joint valgus peak angle Rkp=aIA(z)+b (1)
a is a coefficient, b is a constant
The coefficient a is 0.1 with one significant figure,
11. The lower limb control ability measuring device according to claim 10, wherein the constant b is -4 in one significant figure.
The index calculation unit calculates the first index IA(x) and the first index IA(z) as the first index,
The index calculation unit further uses the following formula (2) from the first index IA(x) and the first index IA(z) to calculate the landing point when the subject lands from a predetermined height. The lower limb control ability measuring device according to any one of claims 9 to 11, which calculates ΔGRF, which is the amount of increase per unit time of the reaction force in the vertical direction from.
ΔGRF=cIA(z)+dIA(x)+e (2)
c and d are coefficients, e is a constant
The coefficient c is 0.007 with one significant figure,
The coefficient d is -0.003 with one significant figure,
13. The lower limb control ability measuring device according to claim 12, wherein the constant e is 0.9 with one significant figure.
The index calculation unit calculates the first index IA(x), the first index IA(y), and the first index IA(z) as the first index,
The lower limb control ability measuring device according to any one of claims 9 to 13, wherein the index calculator further calculates a second index IB using the following formula (3).
Second index IB=fIA(x)+gIA(y)+hIA(z) (3)
f, g and h are coefficients
The ratio of the coefficients f, g and h is
f:g:h=5:5:4 with one significant digit
15. The lower limb control ability measuring device according to claim 14.
The coefficient f is 0.5 with one significant figure,
The coefficient g is 0.5 with one significant figure,
16. The lower limb control ability measuring device according to claim 15, wherein the coefficient h is 0.4 with one significant figure.
17. The lower limb control ability measuring device according to claim 16, wherein the index calculation unit further calculates a knee joint valgus peak angle Rkp of the subject from the second index IB using the following formula (4).
Knee joint valgus peak angle Rkp=iIB+j (4)
i is a coefficient, j is a constant
The coefficient i is 2 in one significant digit,
18. The lower limb control ability measuring device according to claim 17, wherein the constant j is -2 in one significant figure.
a lower limb control ability measuring device according to claim 10 or 11;
a principal component analysis unit that performs principal component analysis using the first index IA(z) obtained from a plurality of subjects as a variable, and obtains the first principal component of the principal component analysis as the formula (1). Lower extremity control ability measurement system.
a lower limb control ability measuring device according to claim 12 or 13;
A principal component analysis is performed using the first index IA (x) and the first index IA (z) obtained from a plurality of subjects as variables, and the first principal component of the principal component analysis is expressed by the formula (2) A lower limb control ability measurement system including a principal component analysis part obtained as
a lower limb control ability measuring device according to any one of claims 14 to 18;
Principal component analysis is performed using the first index IA (x), the first index IA (y), and the first index IA (z) obtained from a plurality of subjects as variables, and the principal component analysis A lower limb control ability measuring system including a principal component analysis unit that obtains the first principal component of as the above equation (3).
A lower limb control ability measuring device according to claim 17 or 18;
a principal component analysis unit that performs principal component analysis using the second index IB obtained from a plurality of subjects as a variable, and obtains the first principal component of the principal component analysis as the above equation (4). measurement system.
a lower limb control ability measuring device according to any one of claims 1 to 18;
A lower limb control ability measuring system including the physical quantity detection device.
A lower-limb control ability measuring program that causes a computer to function as the lower-limb control ability measuring device according to any one of claims 1 to 18.
A computer-readable recording medium recording the lower limb control ability measuring program according to claim 24.
The period during which the subject, whose thigh is equipped with a physical quantity detection device that detects at least one physical quantity of angular velocity and acceleration, performs a knee flexion exercise that includes flexion and/or extension of the knee joint while resisting the load. a physical quantity acquisition process for acquiring the physical quantity detected by the physical quantity detection device during
a first waveform acquisition process for acquiring a first waveform based on a detected waveform in which the physical quantities acquired by the physical quantity acquisition process are arranged along the time axis;
a second waveform acquisition process for acquiring a second waveform by smoothing the first waveform;
An index calculation process of calculating a sum of absolute values of differences between the first waveform and the second waveform as a first index representing the lower limb control ability of the subject.