JP2022099894A - Disability risk estimation device, method for estimating risk of disability, and disability risk estimation program - Google Patents

Abstract

To provide a method for running an easy self-check on the state of physical functions of himself/herself.SOLUTION: In a disability risk estimation device, an information acquisition unit 24 receives an input of the result of a test that shows the mobility of each of a plurality of physical sites set to be evaluation targets to correspond to the positional relation between stability joins and mobility joints of a subject. A risk estimation unit 36 estimates the risk of disability for each physical site on the basis of the result obtained by integrating test results of the physical sites. The result output unit 50 outputs a result of estimation about the risk of disability. A risk estimation unit 36 makes an estimation that a disability risk is growing in any of the physical sites if the sum of values showing the result of test for each physical site is at least a predetermined value.SELECTED DRAWING: Figure 5

Description

The present invention relates to a failure risk estimation device. In particular, it relates to a device for diagnosing the physical condition of a subject.

In recent years, while the life span has been extended due to the progress of medicine, the practice of sports has been attracting attention as a solution for the life span to be maintained in good physical and mental health.

Japanese Unexamined Patent Publication No. 2016-051055 Japanese Unexamined Patent Publication No. 2019-185722 Special Table 2015-501700

However, if you play sports without confirming your physical function, there is a risk of injury, which may adversely affect your health. Therefore, it is desirable to establish a method that allows you to easily inspect and confirm your physical condition while making a diagnosis by a doctor, physiotherapist, or athletic trainer.

The present invention has been made in view of these circumstances, and an object of the present invention is to provide a method for easily self-examining the state of one's physical function.

In order to solve the above problems, the disorder risk estimation device of an aspect of the present invention is set as an evaluation target for a plurality of body parts so as to correspond to the positional relationship between the stability joint and the mobility joint in the subject's body. Regarding, an information acquisition unit that accepts input of test results indicating each mobility, a risk estimation unit that estimates the risk of disability for each body part based on the result of integrating the test results for each body part, and an estimation for the risk of disability. It includes a result output unit that outputs a result.

Here, stability joints and mobility joints are names that focus on the role of each joint in a theory known as the joint-by-joint theory. Stability joints are joints that do not move much in various directions, such as the cervical spine, shoulder blades, lumbar spine, elbow joints, knee joints, and pelvis, and have the role of stabilizing body movements. , Chest, hip, wrist, ankle, and other joints that move well in various directions to create body movements. Stability joints and mobility joints basically alternate in the human body and are located so as to sandwich one stability joint between multiple mobility joints. When the movement of the mobility joint is hindered, a pain called compensatory movement may occur in the stability joint adjacent to the upper, lower, left, or right of the mobility joint in an attempt to compensate for the hindrance. The "test result indicating mobility" may be, for example, a value indicating whether or not a predetermined movement is possible in the body part, or may be a value indicating how much the body part can move. The "risk of disability" may broadly include the possibility of injury or pain now or in the future, the possibility of pain in normal times but no pain during exercise, the possibility of no pain but inability to move as expected, and the like.

According to this aspect, the subject can obtain an estimation result regarding the risk of disability simply by inputting the mobility of each body part of the subject. In particular, by inputting the positional relationship of stability joints and mobility joints and the mobility of multiple body parts corresponding to them, the risk of injury due to compensatory movements of parts different from the parts where mobility is actually impaired is predicted. You can also do it.

The risk estimation unit may estimate that there is a high possibility that a risk of disability has occurred in any of the body parts when the total value indicating the test results for each body part is equal to or more than a predetermined value. .. The predetermined value referred to here may be, for example, a numerical value or a ratio corresponding to about 70% of the whole, and the value may be a statistical value derived from a large number of cases or a value determined from an empirical rule. .. According to this aspect, it is possible to easily predict that a risk of injury will occur in any of the parts by a simple process of summarizing the test results regarding the mobility of each body part and comparing it with the reference value.

When the test result of any body part shows a predetermined result, the risk estimation unit identifies the stability joint whose position is adjacent to the mobility joint corresponding to the body part, and corresponds to the identified stability joint. The risk of disability can be estimated for body parts. As this estimation method, for example, an estimation method based on an algorithm for determining the test results regarding mobility in a plurality of body parts in light of predetermined conditions may be used. In this case, the risk of disability can be estimated by a method theorized from a physiological or medical point of view.

Further, a model storage unit may be provided for storing a disorder prediction model that has learned the correlation between the test result for each body part and the body part in which the disorder is occurring. The risk estimation unit may estimate the risk of disability by inputting the test results for each body part into the disability prediction model and predicting the body part in which the disability is occurring. The more case data showing the relationship between the test result for each body part and the damaged part is learned by machine learning, the more accurately the risk of the disorder can be estimated.

The information acquisition unit may further accept the input of the subject's attributes, and the risk estimation unit may estimate the disability risk based on the test result and the subject's attributes. The "attribute" here may be an element that causes a difference in physical function such as age, gender, height, weight, dominant hand, and dominant foot, and may be a cause of obstacles such as work history, sports history, and the sport that is currently being performed. It may be an element that causes a difference in mode. By adding attributes in the estimation of the risk of disability, the risk of disability can be estimated more accurately.

Another aspect of the present invention is a method for estimating the risk of disability. In this method, the computer accepts input of test results showing the mobility of multiple body parts set as evaluation targets so as to correspond to the positional relationship between the stability joint and the mobility joint in the subject's body. It includes a process, a process in which a computer estimates the risk of disability for each body part based on the result of integrating the test results for each body part, and a process in which the computer outputs an estimation result regarding the risk of disability.

According to this aspect, the subject can obtain an estimation result regarding the risk of disability simply by inputting the mobility of each body part of the subject. In particular, by inputting the positional relationship of stability joints and mobility joints and the mobility of multiple body parts corresponding to them, the risk of injury due to compensatory movements of parts different from the parts where mobility is actually impaired is predicted. You can also do it.

It should be noted that any combination of the above components, or the components and expressions of the present invention are mutually replaced between a method, a device, a program, a temporary or non-temporary storage medium for storing the program, a system, and the like. Is also effective as an aspect of the present invention.

According to the present invention, it is possible to provide a method for easily self-examining the state of one's physical function.

It is a figure which shows typically the positional relationship between a body part and a joint. It is a figure which shows the screen example which the subject inputs the answer to the examination item in the physical function self-examination program. It is a figure which shows the screen example which displays the test result for each test item, the evaluation for each body part, and the risk of disability. It is a basic block diagram of a failure risk estimation system. It is a functional block diagram of a failure risk estimation system. It is a figure which shows the relationship between the comprehensive evaluation by the physical function self-examination program, and the intensity of pain. It is a figure which shows the test result for each body part and the case list of the body part with pain. It is a flowchart which shows the process of estimating the failure risk.

The physical function self-examination program will be described below as an example. In the physical function self-examination program in the present embodiment, the whole body is divided into eight body parts, and the muscle strength, flexibility, and stability of each body part are evaluated in a combined manner. The subject can perform a self-inspection by himself / herself without the need for measuring instruments, and can easily obtain an evaluation result.

FIG. 1 schematically shows the positional relationship between a body part and a joint. FIG. 1 (a) shows each body part divided into eight in the human body model seen from the front side, and FIG. 1 (b) shows the position of each joint in the human body model seen from the front side, and FIG. 1 (c) shows. ) Indicates the position of each joint in the human body model seen from the back side. In FIGS. 1 (b) and 1 (c), the joints indicated by diagonal circles are stability joints, and the joints indicated by white circles are mobility joints.

As shown in FIG. 1 (a), the eight body parts include the neck 110, the upper right body 112, the upper left body 114, the core 116, the right hip 118, the left hip 120, the right lower limb 122, and the left lower limb 124. Consists of.

In FIG. 1 (b), the cervical spine 130, the right elbow joint 135, the left elbow joint 136, the right knee joint 138, and the left knee joint 140 indicated by diagonal circles are stability joints. The right shoulder joint 142, the left shoulder joint 144, the right hip joint 148, the left hip joint 150, the right wrist joint 152, the left wrist joint 154, the right ankle joint 156, and the left ankle joint 158, which are indicated by white circles, are mobility joints.

In FIG. 1 (c), the cervical vertebrae 130, the right shoulder blade 132, the left shoulder blade 133, the right elbow joint 135, the left elbow joint 136, the lumbar vertebra 146, the right knee joint 138, and the left knee joint 140, which are indicated by diagonal circles, are stable. It is a joint. The right shoulder joint 142, the left shoulder joint 144, the thoracic spine 134, the right hip joint 148, the left hip joint 150, the right wrist joint 152, the left wrist joint 154, the right ankle joint 156, and the left ankle joint 158 indicated by white circles are mobility joints.

Stability joints and mobility joints are located so as to be arranged in an alternating manner, and one stability joint is sandwiched between a plurality of mobility joints. For example, the cervical spine 130 is sandwiched between the right shoulder joint 142 and the left shoulder joint 144, the right elbow joint 135 is sandwiched between the right shoulder joint 142 and the right wrist joint 152, and the left elbow joint 136 is sandwiched between the left shoulder joint 144 and the left wrist. It is sandwiched between joints 154. The right knee joint 138 is sandwiched between the right hip joint 148 and the right ankle joint 156, and the left knee joint 140 is sandwiched between the left hip joint 150 and the left ankle joint 158. The left scapula 133 is sandwiched between the left shoulder joint 144 and the thoracic vertebra 134, and the right scapula 132 is sandwiched between the right shoulder joint 142 and the thoracic vertebra 134. The lumbar vertebrae 146 are sandwiched between the thoracic vertebrae 134, the right hip joint 148, and the left hip joint 150. For example, when pain occurs in the lower back, the cause is deterioration of the mobility of the thoracic spine 134, the right hip joint 148, and the left hip joint 150 that sandwich the lumbar spine 146, and the compensation for the lumbar spine 146 trying to compensate for the movement is forced. The movement may cause pain in the lumbar spine 146.

The positional relationship between each body part in FIG. 1 (a) and each joint in FIGS. 1 (b) and 1 (c) is as follows. That is, the neck 110 includes the cervical spine 130, which is a stability joint. The right upper body portion 112 includes a mobility joint, the right shoulder joint 142, a right wrist joint 152, and a thoracic spine 134, and a stability joint, the right elbow joint 135, and the right scapula 132. The left upper body portion 114 includes a mobility joint, the left shoulder joint 144, a left wrist joint 154, and a thoracic vertebra 134, and a stability joint, the left elbow joint 136, and the left shoulder blade 133. The core portion 116 includes the lumbar spine 146, which is a stability joint. The right hip portion 118 includes a right hip joint 148, which is a mobility joint. The left hip portion 120 includes a left hip joint 150, which is a mobility joint. The right lower limb portion 122 includes a right knee joint 138, which is a stability joint, and a right ankle joint 156, which is a mobility joint. The left lower limb portion 124 includes a left knee joint 140, which is a stability joint, and a left ankle joint 158, which is a mobility joint.

FIG. 2 shows an example of a screen in which a subject inputs an answer to an examination item in a physical function self-examination program. First, the subject registers identification information (name, user name, etc.) and attribute information indicating an individual on a start screen (not shown). The attribute information is, for example, information such as age, gender, and the type of sport to be performed. After the identification information and the attribute information have been registered, the screen transitions to the screen shown in FIG. The physical function self-examination program in the present embodiment is roughly divided into 11 items from A to K. Each inspection item is a format for answering whether or not the subject can perform various body movements, and is an inspection that does not require measuring instruments. In the examination, explanations of various body movements are displayed on the screen, and the subject tries to perform the movements, and if possible, answers "1", and if not, "0". To answer. By repeating the implementation and response of such movements, the subject can input the test results regarding the mobility of each part. In addition, each inspection item does not always correspond to one joint or body part. That is, the mobility of one or a plurality of joints is inspected for each inspection item, and one or a plurality of body parts are inspected for each inspection item.

In the example of this figure, the answer columns for the six sub-items included in the inspection of "neck movement" of the inspection item "A" are displayed in a check box format. The "neck movement" of the inspection item "A" is an inspection of whether or not the subject can move the neck without difficulty. For example, A1) can be tilted forward, A2) can be tilted backward, A3) can be tilted 45 degrees to the left, A4) can be tilted 45 degrees to the right, A5) can be turned to the left, or A6) can be tilted to the right. There are 6 items to turn.

The first check box 211 is configured to select the answer of "1" when the subject can tilt the neck forward, and select the answer of "0" when the subject cannot tilt the neck as the inspection item A1. The second check box 212 is configured to select the answer of "1" when the subject can tilt his / her neck backward as the inspection item A2, and select the answer of "0" when he / she cannot tilt his / her neck. The third check box 213 is configured to select the answer of "1" when the subject can tilt the neck at an angle of 45 degrees to the left as the inspection item A3, and select the answer of "0" when the subject cannot tilt the neck at an angle of 45 degrees to the left. To. The fourth check box 214 is configured to select the answer of "1" when the subject can tilt the neck at an angle of 45 degrees to the right as the inspection item A4, and select the answer of "0" when the subject cannot tilt the neck at an angle of 45 degrees. To. The fifth check box 215 is configured to select the answer of "1" when the subject turns his / her head to the left side as the inspection item A5, and selects the answer of "0" when he / she does not turn his / her head to the left. The sixth check box 216 is configured to select the answer of "1" when the subject turns his / her head to the right side as the inspection item A6, and selects the answer of "0" when he / she does not turn his / her head to the right.

Below each of the first check box 211, the second check box 212, the third check box 213, the fourth check box 214, the fifth check box 215, and the sixth check box 216 are the first detail button 221 and the second. The detail button 222, the third detail button 223, the fourth detail button 224, the fifth detail button 225, and the sixth detail button 226 are displayed. When any one of the first detail button 221 to the sixth detail button 226 is pressed, a detailed explanation column 220 is displayed below the detailed explanation button 220, and a detailed explanation about the inspection method of the corresponding inspection item is displayed together with an image or a moving image. In the example of the figure, as the display content of the detailed explanation column 220 when the third detail button 223 is pressed, the detailed explanation of the inspection item A3 is a character string of "Can the neck be tilted at an angle of 45 degrees to the left?" , An image of a person whose neck is tilted at an angle of 45 degrees to the left is displayed.

Although not shown, the 10 items from the inspection items "B" to "K" are also configured so that the subject can input the answer of each inspection item on the same screen as in FIG.

FIG. 3 shows an example of a screen displaying the test results for each test item, the evaluation for each body part, and the risk of disability. In the inspection result column 204, 11 self-inspection items from A to K and their inspection results are displayed. For each inspection item, 1 point is added if the specified movement is possible, and 0 point is added if the specified movement is not possible, that is, 1 point is scored for each item.

A's "neck movement" is a test to see if the subject can move his neck without difficulty. For example, A1) can be tilted forward, A2) can be tilted backward, A3) can be tilted 45 degrees to the left, A4) can be tilted 45 degrees to the right, A5) can be turned to the left, or A6) can be tilted to the right. There are 6 items, and the total score is 6 points.

B's "shoulder range of motion" is a test to see if the subject can comfortably turn his left and right hands on his back. For example, there are two items, B1) the left hand is attached to the right shoulder blade, and B2) the right hand is attached to the left shoulder blade, and the total score is 2 points.

The "movement of the scapula" of C is a test as to whether or not the movement of the left and right scapulas of the subject is sufficient. For example, C1) can the left arm pass overhead while holding the right earlobe with the left hand, or C2) can the right arm pass overhead while holding the left earlobe with the right hand? Yes, the total score is 2 points.

D's "movement of the thoracic spine" is a test to see if the subject can rotate the upper body without difficulty. For example, sitting in a chair with your back facing the wall in front of the wall, with a space of two fists between the wall and your back, D1) your left arm will stick to the wall, or D2) your right arm will touch the wall. Or D3) the left fingertip sticks to the wall over the right shoulder, D4) the right fingertip sticks to the wall over the left shoulder, D5) the left elbow touches the wall with both arms lightly crossed in front of the chest. There are 6 items, D6) whether the right elbow is attached to the wall with both arms lightly crossed in front of the chest, and the total score is 6 points.

E's "shoulder stability and strength" is a test of whether the subject can sufficiently support the body with the upper body. For example, E1) stand with both hands and legs spread out to about shoulder width, and hold your hands on the wall for 10 seconds from the position of 4 feet from the wall, or E2) spread both hands and legs to shoulder width, and put both hands and knees on the floor 10 Can you hold it for a second? E3) Spread your hands and legs about the width of your shoulders and keep your hands and toes on the floor for 10 seconds. There are 4 items, whether you can keep your right hand and foot for 5 seconds, and finally raise your left hand on the floor for 3 seconds, and the total score will be 4 points.

F's "hip joint mobility" is a test to see if the subject can move the hip joint reasonably. For example, F1) Can you stand against the wall with your left knee attached to the wall (hip flexion, internal rotation) and touch the outer ankle of your left foot with your left hand? F2) Attach your right knee to the wall (hip flexion) (Internal rotation) Can you stand against the wall and touch the outer ankle of your right foot with your right hand? F3) Put your left knee on the wall (hip flexion, internal rotation) and stand toward the wall. Can you touch the inner ankle with your right hand? F4) Can you put your right knee on the wall (hip flexion, internal rotation) and stand toward the wall and touch the inner ankle of your right foot with your left hand? F5) Left hand Can you attach it to the wall (hip flexion, internal rotation) and stand toward the wall and touch the inner ankle of your left foot with your right hand on the back side? F6) Attach your right hand to the wall (hip flexion, internal rotation) ) Can you stand against the wall and touch the inner ankle of your right foot with your left hand on your back? F7) With your left hand on the wall (hip flexion, internal rotation), stand against the wall and your right foot on your back Can you touch the outer ankle with your right hand? F8) Can you put your right hand on the wall (hip flexion, internal rotation), stand toward the wall, and touch the outer ankle of your left foot with your left hand on your back? It is an item, and the total score is 8 points.

G's "hip and spine flexibility" is a test to see if the subject can reasonably bend forward and backward. For example, G1) can you bend forward from the bent part of your foot to one fist up, G2) can you bend forward to the bent part of your foot, G3) can you bend forward until your fingertips touch the floor, G4) your foot from the wall Can you stand with your back to the wall at the position of one length and bend backward until your fingertips touch the wall? G5) Stand with your back to the wall at the position of two feet from the wall and your fingertips to the wall There are 6 items, 6 items, whether you can bend back until you touch the wall, or G6) stand with your back to the wall at the position of 3 feet from the wall and bend back until your fingertips touch the wall. It will be a score.

H's "stability of the lower body and trunk" is a test to see if the subject can comfortably attach the elbows and knees. For example, H1) bend the left knee and raise the left leg while keeping the body vertical, and hold the left elbow on the left knee for 3 seconds, or H2) bend the right knee while keeping the body vertical and the right leg. There are two items, whether you can raise your right elbow and keep it for 3 seconds with your right knee, and you will get a total of 2 points.

I's "trunk muscle strength" is a test of whether a subject can perform various levels of abdominal exercises. For example, I1) Can you lift your head to shoulder blades from the floor for 5 seconds with your knees upright and your hands fastened? Can you lift your head to shoulder blades from the floor for 5 seconds with your arms tightened? I3) Stretch your legs on your back and cross your arms in front of your chest for 5 seconds from your head to your back. Can be lifted from the floor, or I4) Can the head to back be lifted from the floor for 5 seconds with both legs extended and the head held by both arms, for a total of 4 items. It will be a perfect score.

J's "muscle strength of the legs and hips" is a test to see if the subject can stand up from various conditions. For example, J1) Can you stand up from the state where you put your hand on the knee with the left one knee standing to the left one leg standing, or J2) Can you stand up from the state where you put your hand on the knee with the right one leg standing? J3) Can you stand up from the state where you put your hand on the waist with the left one-knee standing to the left one-leg standing? J4) Can you stand up from the state where you put your hand on the waist with the right one-leg standing? J5) Can you stand up from sitting on a chair with both arms folded to the left one-legged stand, or J6) sitting on a chair and standing up from the state with both arms folded to the right one-legged standing, J7) sitting on the chair and crossing the left leg? 8) Can you stand up to the right one leg standing with the left leg folded from the arm folded state, or can you stand up to the left one leg standing with the right leg folded from the state where you sit on the chair and cross the right leg and both arms folded? It is an item, and the total score is 8 points.

K's "ankle mobility" is a test of whether the subject's ankle is sufficiently flexible. For example, K1) You can hold the distance from the wall to the tip of your toes while standing on your left knee near the wall, and you can put your left knee on the wall with the sole of your foot on the floor as one fist, or K2) your right knee near the wall. While standing, the distance from the wall to the tip of the toes is grasped, and the right knee can be attached to the wall with the sole of the foot attached to the floor as one fist, and the total score is 2 points.

In the body part evaluation column 206, the results of each test item in the test result column 204 are divided into eight body parts from 1 to 8 and re-aggregated and displayed as evaluation results. It is necessary to evaluate the whole body separately for each body part because dysfunction of joints and the like causes compensatory movements and may turn into pain by putting an excessive burden on another part of the body. Each test item does not necessarily correspond to one joint or body part, but the mobility of one or more joints is tested for each test item, and one or more for each test item. The body part will be inspected. Therefore, in order to evaluate each body part, the scores for each test item are recounted as in the body part evaluation column 206. Hereinafter, the relationship between each inspection item and the body part will be described.

In the body part evaluation column 206, the total score of the "neck movement" of the inspection item A is directly reflected in the "neck" which is the first body part. The second body part, the "upper right body", reflects the total score related to the upper right body among the test items B, C, D, and E. The third body part, the "upper left body", reflects the total score related to the upper left body among the test items B, C, D, and E. The total score of the inspection items G, H, and I is reflected as it is in the "core part" which is the fourth body part. The fifth body part, the "right hip", reflects the total score related to the right hip among the test items F and J. The sixth body part, the "left hip", reflects the total score related to the left hip among the test items F and J. The seventh body part, the "right lower limb", reflects the total score related to the right lower limb among the test items J and K. The eighth body part, the "left lower limb", reflects the total score related to the left lower limb among the test items J and K.

The scores of the 1st to 8th body parts in the body part evaluation column 206 are also displayed on each body part in the human body model 200. In the figure of the human body model 200 in which the human body is divided into eight body parts, the score for each body part is displayed. For example, the first body part "neck" is 4 out of 6 points, and the second body part "upper right body" is 2 out of 7 points, and the third. It is shown that the "upper left body part", which is the body part of the body, is 4 out of 7 points. The fourth body part, the "core part," is 8 out of 12 points, and the fifth body part, the "right waist," is 4 out of 6 points, and the sixth body. The "left hip", which is the site, is shown to be 5 out of 6 points. It is shown that the 7th body part "right lower limb part" is 3 points out of 3 points, and the 8th body part "left lower limb part" is 2 points out of 3 points. Is done. The total score is 32 points, which is the sum of these scores. In the total evaluation value column 202, the total score and the evaluation result regarding the risk of disability are displayed. In the present embodiment, when the total score is 35 points or more out of 50 points, it is determined that there is no failure risk, and when it is less than 35 points, it is determined that there is a failure risk. Since the score is less than 35 in the example of this figure, it is determined that there is a risk of failure, and that fact is displayed in the total evaluation value column 202.

FIG. 4 is a basic configuration diagram of a failure risk estimation system. The failure risk estimation system 100 is composed of a plurality of user terminals 10 connected via a network 18 such as the Internet and a failure risk estimation server 20. Each of the plurality of subjects operates the user terminal 10, accesses the failure risk estimation server 20 using a dedicated program or an Internet browser, and displays the content transmitted from the failure risk estimation server 20 to display the body. Implement a functional self-inspection program. The user terminal 10 and the failure risk estimation server 20 are computers including a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), an auxiliary storage device, a communication device, and the like. May be configured. However, a failure risk estimation system can be realized with a single device having all the functions of the user terminal 10 and the failure risk estimation server 20, and the subject can implement the physical function self-examination program by directly operating the device. It may be configured as follows. The single device is not limited to a personal computer, and may be provided in the form of an application installed on a mobile terminal such as a smartphone or a tablet terminal. Alternatively, it may be realized as a terminal installed in a sports gym and operated by a trainer.

FIG. 5 is a functional block diagram of the failure risk estimation system. In this figure, functional blocks focusing on functions are drawn, and these functional blocks can be realized in various forms by hardware, software, or a combination thereof. The user terminal 10 includes an information display unit 12, an information input unit 14, and a communication unit 16. The subject operates the user terminal 10 to input an answer for each inspection item. The information display unit 12 receives the image and information of the inspection item screen transmitted from the failure risk estimation server 20 via the communication unit 16 and displays them on the screen. The information input unit 14 transmits information such as an answer input by the subject to the failure risk estimation server 20 via the communication unit 16.

The failure risk estimation server 20 includes a communication unit 22, an information acquisition unit 24, an inspection result storage unit 26, a risk estimation unit 36, a model storage unit 40, a learning processing unit 42, and a result output unit 50.

The information acquisition unit 24 acquires the test results of each test item in the physical function self-test program from the user terminal 10 via the communication unit 22 together with the identification information and attribute information of the subject, and stores them in the test result storage unit 26. .. The attributes of the subject may be factors that cause physical functional differences such as age, gender, height, weight, dominant hand, and dominant foot, and may be the cause of disorders such as work history, sports history, and current sports event. It may be an element that causes a difference in mode. As a modification, the information acquisition unit 24 may further acquire information indicating whether or not any body part has pain as input information based on the declaration by the subject.

The risk estimation unit 36 includes an aggregation unit 28, an aggregation result storage unit 30, a condition determination unit 32, and a prediction unit 34. The totaling unit 28 distributes the scores, which are the test results for each test item stored in the test result storage unit 26, to each body part, totals them as the test results for each body part, and stores them in the totaling result storage unit 30. The totaling unit 28 sends the test result for each body part to the result output unit 50 as an evaluation value for each body part. The totaling unit 28 aggregates the evaluation values of all body parts and sends the total value as a comprehensive evaluation result to the result output unit 50. The aggregation unit 28 estimates the presence or absence of a failure risk depending on whether or not the total value is equal to or higher than a predetermined reference value. For example, when the total value is 50 points, it is estimated that there is a low possibility that a failure risk has occurred if it is 35 points or more, and if it is less than 35 points, it is highly likely that a failure risk has occurred. , The estimation result is sent to the result output unit 50. As the reference value, a value derived based on a large number of cases and findings is used.

The condition determination unit 32 estimates the presence or absence of a disability risk and the body part at a disability risk depending on whether or not the test result of the test result for each body part shows a predetermined result based on a predetermined risk estimation algorithm. The risk estimation algorithm defines the correspondence between the test results for each body part, the presence or absence of a disability risk, and the location where the disability risk occurs. That is, the correspondence between the body part where the mobility problem occurs and the body part where the risk of disability is likely to occur in that case is defined. This correspondence is defined based on a number of cases and the positional relationship between mobility and stability joints. For example, if a mobility joint has mobility problems, there may be a risk of injury to the stability joint that is adjacent to that mobility joint. The condition determination unit 32 identifies a body part that is likely to have a risk of disability based on the test results for each body part, and outputs information on the possibility of risk of disability and the body part to the result output unit 50. send. The risk estimation algorithm includes not only the relationship between the test results for each body part and the risk of disability, but also attribute information such as age, gender, height, weight, dominant hand, dominant foot, work history, sports history, and the type of sport currently being conducted. The relationship with may be further defined. In that case, the condition determination unit 32 estimates the risk of disability based on the attribute information of the subject and the test result for each body part. By adding attributes in the estimation of the risk of disability, the risk of disability can be estimated more accurately.

The model storage unit 40 stores a failure prediction model in which the correlation between the test result for each body part and the body part in which the disorder is occurring is machine-learned. The prediction unit 34 inputs the test results for each body part of the subject into the failure prediction model stored in the model storage unit 40 to predict the body part in which the disorder is occurring, thereby causing a risk of failure. The presence or absence of possibility and the body part at risk of disability are estimated, and the estimation result is sent to the result output unit 50. The learning processing unit 42 machine-learns the test results for each body part and the data indicating the body part where the disorder occurs as teacher data to generate a failure prediction model and stores it in the model storage unit 40.

In addition to the test results for each body part and data indicating the part of the body where the disorder is occurring, the learning processing unit 42 includes age, gender, height, weight, dominant hand, dominant foot, work history, sports history, and sports currently being carried out. Attribute information such as the item of the item may be further machine-learned as teacher data to generate a failure prediction model. In that case, the prediction unit 34 further inputs the attribute information of the subject into the failure prediction model and estimates the failure risk. By adding attributes in the estimation of the risk of disability, the risk of disability can be estimated more accurately.

The result output unit 50 evaluates each body part aggregated by the aggregation unit 28, comprehensive evaluation, presence / absence of failure risk based on the reference value, failure risk estimation result by the condition determination unit 32, and failure risk estimation by the prediction unit 34. The result is output to the user terminal 10 via the communication unit 22. However, in the modified example, only one or two of the aggregation and comprehensive evaluation by the aggregation unit 28, the estimation of the failure risk by the condition determination unit 32, and the estimation of the failure risk by the prediction unit 34 are executed, and the result is output. It may be a specification. In particular, for the estimation of the failure risk by the condition determination unit 32 and the estimation of the failure risk by the prediction unit 34, the one with higher accuracy may be adopted by accumulating cases.

FIG. 6 shows the relationship between the comprehensive evaluation by the physical function self-examination program and the intensity of pain. In the graph of this figure, the horizontal axis is the comprehensive evaluation by the physical function self-examination program in this embodiment, and the vertical axis is the intensity of the pain when pain occurs in any of the body parts. It shows the relationship between evaluation and pain intensity. The comprehensive evaluation by the physical function self-examination program is a score out of 50 points, and the cases shown in this figure are plotted from 24 points to 50 points. Pain intensity is shown on a 10-step scale of NRS (Numeric Rating Scale), which is known as one of the methods for expressing the intensity and degree of pain in the body. In NRS, it is "0" when there is no pain, and the larger the pain, the larger the number. A relatively strong negative correlation of R = -0.624 and p <0.001 was found between the overall evaluation and the pain intensity, and the smaller the evaluation value, the greater the pain intensity, and the evaluation value. The greater the value, the smaller the intensity of pain. For example, pain exceeds "5" when the evaluation value is "35" or less, which can be one of the grounds for setting the reference value for disability risk estimation based on the comprehensive evaluation by the tabulation unit 28 to "35".

FIG. 7 shows test results for each body part and a list of cases of painful body parts. The list in this figure shows cases for eight people. The first column 250 shows the case number, and the second column 252 shows the sports event currently performed by the subject. The third column 254 shows the test results for each of the eight body parts. Column 4 256 shows the body part with great pain and the intensity of the pain. Column 5 258 shows the next most painful body part and the intensity of the pain. The sixth column 260 indicates the presence or absence of the possibility of pain caused by compensatory movement.

The first case is a freestyle skier. The large pain is the "waist" corresponding to the fourth body part, the pain intensity is "7", the small pain is the "right shoulder" corresponding to the second body part, and the pain intensity is " 5 ". In the case of this athlete, the test results of the upper right body part, the upper left body part, and the right waist part, which are the second, third, and fifth body parts, are in a state requiring attention (indicated by a thin diagonal line), and are the first body parts. The examination result of the neck is in a dangerous state (indicated by a dark diagonal line), and the remaining 4th, 6th, 7th, and 8th body parts are in a good state (no diagonal line). However, it is not the body part shown by the diagonal line that causes great pain, but the core part (thick frame) of the fourth body part that is in good condition. Thus, in the first case, there is a high possibility that the pain due to the compensatory movement is occurring.

The first case is a freestyle skier. The large pain is the "waist" corresponding to the fourth body part, the pain intensity is "7", the small pain is the "right shoulder" corresponding to the second body part, and the pain intensity is " 5 ". In the case of this athlete, the test results of the upper right body part, the upper left body part, and the right hip part, which are the second, third, and fifth body parts, are in a state requiring attention (indicated by a thin diagonal line), and are the first body parts. The test results of the neck are in a dangerous state (indicated by dark diagonal lines), and the test results of the core, left hip, right lower limb, and left lower limb, which are the remaining 4th, 6th to 8th body parts, are in good condition. (No diagonal line). However, it is not the body part shown by the diagonal line that causes great pain, but the core part (thick frame) of the fourth body part that is in good condition. Thus, in the first case, there is a high possibility that the pain due to the compensatory movement is occurring.

The second case is a discus thrower. The major pain is the "right wrist" corresponding to the second body part, and the pain intensity is "7". In the case of this athlete, the test results of the first, third, fifth to eighth body parts are in a state requiring attention, the test results of the second body part are in a dangerous state, and the remaining fourth body parts are in a dangerous state. The test results are in good condition. In this case, it is considered that the major pain is caused in the second body part which was in a dangerous state in the test result, and in the second case, it is not the pain due to the compensatory movement.

The third case is a hammer thrower. The large pain is the "left wrist" corresponding to the third body part, the pain intensity is "7", the small pain is the "right shoulder" corresponding to the second body part, and the pain intensity is It is "5". In the case of this athlete, the test results of the 7th body part are in a state requiring attention, the test results of the 1st to 3, 5 and 6 body parts are in a dangerous state, and the remaining 4th and 8th bodies are in a dangerous state. The test results of the site are in good condition. In this case, it is considered that the large pain is caused in the third body part which was in a dangerous state in the test result, and in the third case, it is not the pain due to the compensatory movement.

The fourth case is a hammer thrower. The large pain is the "waist" corresponding to the fourth body part, the pain intensity is "8", the small pain is the "back" corresponding to the second and third body parts, and the pain intensity is It is "7". In the case of this athlete, the test results of the 8th body part are in a state requiring attention, the test results of the 1st to 3, 5 and 6 body parts are in a dangerous state, and the remaining 4th and 7th bodies are in a dangerous state. The site is in good condition. However, it is not the body part indicated by the diagonal line that causes great pain, but the fourth body part that is in good condition. Thus, in the fourth case, there is a high possibility that the pain due to the compensatory movement is occurring.

The fifth case is a rugby player. The large pain is the "waist" corresponding to the fourth body part, the pain intensity is "7", the small pain is the "right shoulder" corresponding to the second body part, and the pain intensity is " 6 ". In the case of this athlete, the test results of the 2nd and 3rd body parts are in a state requiring attention, the test results of the 1st, 5th and 6th body parts are in a dangerous state, and the remaining 4th, 7th and 8th are in a dangerous state. Body parts are in good condition. However, it is not the body part indicated by the diagonal line that causes great pain, but the fourth body part that is in good condition. Thus, in the fifth case, there is a high possibility that pain due to compensatory movement is occurring.

The sixth case is a rugby player. The large pain is the "left elbow" corresponding to the third body part, the pain intensity is "7", the small pain is the "waist" corresponding to the fourth body part, and the pain intensity is " 5 ". In the case of this athlete, the test results of the 2nd to 4th and 6th body parts are in a state requiring attention, the test results of the 1st body part are in a dangerous state, and the remaining 5th, 7th and 8th bodies are in a dangerous state. The site is in good condition. However, it is not the dangerous body part that causes great pain, but the third body part that requires attention. Thus, in the sixth case, it is highly possible that the pain due to the compensatory movement is occurring.

The seventh case is a long jump player. The large pain is the "left hamstrings" corresponding to the 8th body part, the pain intensity is "5", and the small pain is the "right ankle" corresponding to the 7th body part, the pain intensity. Is "4". In the case of this athlete, the test results of the 3rd and 8th body parts are in a state requiring attention, the test results of the 2nd body part are in a dangerous state, and the remaining 1st, 4th to 7th body parts are in a dangerous state. It is in good condition. However, it is not the dangerous body part that causes great pain, but the eighth body part that requires attention. Thus, in the seventh case, it is highly possible that the pain due to the compensatory movement is occurring.

In addition to generating a failure prediction model by machine learning the test results for each body part and data on the painful body part in a large number of subjects as in the above cases, based on the findings of these cases. A risk estimation algorithm can be generated.

FIG. 8 is a flowchart showing a process of estimating the risk of failure. The information acquisition unit 24 acquires the inspection results for each inspection item from the user terminal 10 of the subject (S10), and the aggregation unit 28 aggregates the inspection results for each inspection item as the inspection results for each body part and aggregates them. It is determined as an evaluation value for each body part (S12), and the evaluation value for each body part is aggregated to determine a comprehensive evaluation (S14). The tabulation unit 28 estimates that the risk of disability is small if the value of the comprehensive evaluation is 35 points or more, which is the reference value, and estimates that the risk of disability may have occurred if the value is less than 35 points (S16). .. The condition determination unit 32 estimates the presence or absence of a disability risk and the body part having a disability risk based on the risk estimation algorithm (S18). The prediction unit 34 inputs the test results for each body part into the failure prediction model generated by machine learning, and estimates the failure risk by predicting the presence or absence of the failure risk and the body part having the failure risk (S20). The result output unit 50 evaluates each body part aggregated by the aggregation unit 28, comprehensive evaluation, presence / absence of failure risk based on the reference value, failure risk estimation result by the condition determination unit 32, and failure risk estimation by the prediction unit 34. The result is output to the user terminal 10 via the communication unit 22 (S22).

The present invention has been described above based on the embodiments. It is understood by those skilled in the art that this embodiment is an example, and that various modifications are possible for each of these components and combinations of each processing process, and that such modifications are also within the scope of the present invention. be. A modified example is shown below.

10 User terminal, 20 Failure risk estimation server, 24 Information acquisition unit, 26 Inspection result storage unit, 28 Aggregation result storage unit, 30 Aggregation result storage unit, 32 Condition judgment unit, 34 Prediction unit, 36 Risk estimation unit, 40 Model storage unit, 42 Learning processing unit, 50 Result output unit, 100 Failure risk estimation system.

Claims (7)

An information acquisition unit that accepts input of test results indicating the mobility of multiple body parts set as evaluation targets so as to correspond to the positional relationship between stability joints and mobility joints in the subject's body.
A risk estimation unit that estimates the risk of disability for each body part based on the result of integrating the test results for each body part,
A result output unit that outputs the estimation result regarding the failure risk, and
A failure risk estimation device characterized by being equipped with. The risk estimation unit estimates that there is a high possibility that a risk of injury has occurred in any of the body parts when the total value of the values indicating the test results for each body part exceeds a predetermined value. The failure risk estimation device according to claim 1. The risk estimation unit identifies a stability joint whose position is adjacent to the mobility joint corresponding to the body part when the test result of any body part shows a predetermined result, and the identified stability joint is used. The disorder risk estimation device according to claim 1, wherein the disorder risk can be estimated for a corresponding body part. Further provided with a model storage unit for storing a failure prediction model that has learned the correlation between the test result for each body part and the body part in which the disorder is occurring.
The first aspect of claim 1, wherein the risk estimation unit estimates the risk of disability by inputting test results for each body part into the disability prediction model and predicting the body part where the disability is occurring. Failure risk estimator. The information acquisition unit further accepts the input of the subject's attributes,
The failure risk estimation device according to claim 3 or 4, wherein the risk estimation unit estimates the failure risk based on the test result and the attributes of the subject. The process by which the computer accepts input of test results showing the mobility of multiple body parts set as evaluation targets so as to correspond to the positional relationship between the stability joint and the mobility joint in the subject's body.
The process by which the computer estimates the risk of disability for each body part based on the total result of the test results for each body part, and
The process by which the computer outputs the estimation result regarding the failure risk,
A method of estimating the risk of disability, which is characterized by the provision of. A function that accepts input of test results showing the mobility of multiple body parts set as evaluation targets so as to correspond to the positional relationship between stability joints and mobility joints in the subject's body.
A function to estimate the risk of disability for each body part based on the result of integrating the test results for each body part, and
The function to output the estimation result regarding the failure risk and
A failure risk estimation program characterized by having a computer run.

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