KR20230102262A - SKELETON DATA-BASED sarcopenia SEVERE PHASE IDENTIFICATION SYSTEM AND METHOD - Google Patents

Abstract

The present invention relates to a skeleton data-based sarcopenia optimization determination system and method and, specifically, to a skeleton data-based sarcopenia optimization determination system and method including an imaging part and a server. One embodiment of the present invention is to more accurately determine the severe phase of sarcopenia patients by using five detection elements calculated using the characteristics of the sarcopenia patients walking with their knees apart.

Description

Skeleton data-based sarcopenia optimization discrimination system and method {SKELETON DATA-BASED sarcopenia SEVERE PHASE IDENTIFICATION SYSTEM AND METHOD}

The present invention relates to a system and method for determining sarcopenia based on skeleton data, and more specifically, to a system and method for determining sarcopenia based on skeleton data including a photographing unit and a server.

In general, physical therapy refers to a treatment method using various physical materials such as various instruments and mechanical devices, including electricity, light rays, sound, and exercise therapy, rather than surgery or drug therapy, and is developed for treatment purposes and applied to users. This is to alleviate the user's pain and further restore the function. Such physical therapy is widely practiced across various medical fields, including orthopedic surgery, neurosurgery, and rehabilitation medicine, and is also easily performed at home without the help of a doctor.

As a conventional physical therapy device, a physical therapy device using electrical frequency is most widely used. In a conventional physical therapy device using frequency, two electrode terminals consisting of '+' and '-' are brought into contact with the user's skin. After that, by driving the pulse generator so that direct current is transmitted to the user's body, a predetermined therapeutic effect can be obtained through the stimulation transmitted through the skin.

However, since most of the physical therapy devices using electrical frequencies as described above are used to relieve physical pain through high or low frequency, there is a problem in that the treatment range is very limited.

Meanwhile, recently, light therapy is known as a new treatment method. A device for conventional phototherapy generally has a configuration in which a light emitter is installed on a substrate having a shape matching the shape of the affected area to be treated. Such a phototherapy device for phototherapy can be used for the purpose of dermatological treatment and cosmetic treatment as well as treatment of trauma, edema, varicose veins, infectious diseases and other diseases.

The above-described treatment method using electric stimulation or light may also be used for treating sarcopenia. However, research on a non-face-to-face diagnosis system for sarcopenia is still insufficient, and the need for a sarcopenia treatment device and diagnosis device is increasing.

Accordingly, in the present invention, a non-face-to-face diagnosis method and system for sarcopenia will be described.

On the other hand, the above-mentioned background art is technical information that the inventor possessed for derivation of the present invention or acquired during the derivation process of the present invention, and cannot necessarily be said to be known art disclosed to the general public prior to filing the present invention.

An object of one embodiment of the present invention is to provide a system and method for determining sarcopenia based on skeleton data.

를 기준으로 보행자의 각 무릎의 z좌표의 차이의 평균편차/골반길이를 연산하고,

에 기준으로 보행자의 각 발목의 z좌표의 차이의 평균편차/골반길이를 연산하는 연산부 및 상기 보행자의 각 무릎의 z좌표의 차이의 평균편차/골반길이 및 보행자의 각 발목의 z좌표의 차이의 평균편차/골반길이로부터 보행자의 근감소증 상태가 기 설정된 범위내에 포함되는지 판별하는 판별부를 포함하는 스켈레톤 데이터 기반 근감소증 판별 시스템을 포함할 수 있다.In order to solve the above object, one embodiment of the present invention is a photographing unit for photographing the side of the pedestrian when the pedestrian walks forward, and a server for receiving photo data taken by the photographing unit and determining a severity level; The server includes an extraction unit for extracting skeleton data of a pedestrian from photographed photo data;

Calculate the average deviation/pelvic length of the difference between the z coordinates of each knee of the pedestrian based on

Based on the calculation unit for calculating the average deviation / pelvic length of the difference between the z coordinates of each ankle of the pedestrian and the average deviation / pelvic length of the difference between the z coordinates of each knee of the pedestrian and the difference between the z coordinates of each ankle of the pedestrian A skeleton data-based sarcopenia determination system including a determination unit determining whether the sarcopenia state of the pedestrian is within a preset range based on the average deviation/pelvic length may be included.

를 기준으로 보행속도/다리길이를 연산하고, 상기 판별부는 상기 보행속도/다리길이로부터 보행자의 근감소증 상태 상태가 기 설정된 범위내에 포함되는지 판별하는 스켈레톤 데이터 기반 근감소증 판별 시스템을 포함할 수 있다.In addition, the calculation unit from the skeleton data,

The determination unit may include a skeleton data-based sarcopenia determination system that calculates gait speed/leg length based on , and determines whether the sarcopenia state of the pedestrian is within a preset range based on the gait speed/leg length.

를 기준으로 측면에서 바라본 보행중인 보행자 양 발의 평균 발길이/보행자의 발길이를 연산하고, 상기 판별부는 상기 측면에서 바라본 보행중인 보행자 양 발의 평균 발길이/보행자의 발길이로부터 보행자의 근감소증 상태가 기 설정된 범위내에 포함되는지 판별하는 스켈레톤 데이터 기반 근감소증 판별 시스템을 포함할 수 있다.In addition, the calculation unit from the skeleton data,

The average foot length of both feet of a walking pedestrian viewed from the side/foot length of the pedestrian is calculated based on It may include a skeleton data-based sarcopenia determination system that determines whether it is within a predetermined range.

에 기준으로 보행중인 양 무릎의 굽힘 각 평균을 연산하고, 상기 판별부는 보행중인 양 무릎의 굽힘 각 평균으로부터 보행자의 근감소증 상태가 기 설정된 범위내에 포함되는지 판별하는 스켈레톤 데이터 기반 근감소증 판별 시스템을 포함할 수 있다.In addition, the calculation unit from the skeleton data,

The determination unit includes a skeleton data-based sarcopenia discrimination system that calculates the average bending angle of both knees during walking based on , and determines whether the sarcopenia state of the pedestrian is within a preset range from the average bending angle of both knees during walking. can do.

In addition, the determination unit calculates the gait speed/leg length calculated by the calculation unit, the average deviation/pelvic length of the difference between the z-coordinates of each knee of the pedestrian, and the average deviation of the difference between the z-coordinates of each ankle of the pedestrian/pelvic length. , Determining whether the sarcopenia state of the pedestrian is included within a preset range using the muscle loss determination data, which is the average value of the average foot length of both feet of the walking pedestrian/foot length of the pedestrian and the bending of both knees while walking as viewed from the side The server may include a skeleton data-based sarcopenia determination system including a learning unit for deep learning the sarcopenia determination data.

In addition, deep learning the muscle reduction discrimination data, photographing the side of the pedestrian when the pedestrian walks forward, extracting the pedestrian's skeleton data from the photographed photo data, and determining the muscle reduction from the skeleton data It may include a skeleton data-based sarcopenia determination method including the step of calculating with data and the step of determining whether the sarcopenia of a pedestrian is higher than a predetermined criterion based on the deep-learned sarcopenia determination data.

를 기준으로 보행자의 각 무릎의 z좌표의 차이의 평균편차/골반길이를 연산하는 단계를 포함하고, 상기 제 4단계는 상기 보행자의 각 무릎의 z좌표의 차이의 평균편차/골반길이로부터 기 설정된 기준내에 포함되는지 판별하는 단계를 포함하는 스켈레톤 데이터 기반 근감소증 판별 방법을 포함할 수 있다.In addition, the third step is from the skeleton data,

and calculating an average deviation/pelvic length of differences in z-coordinates of each knee of the pedestrian based on , wherein the fourth step is a predetermined average deviation/pelvis length of differences in z-coordinates of each knee of the pedestrian. It may include a skeleton data-based sarcopenia determination method including the step of determining whether it is within the criteria.

를 기준으로 보행자의 각 발목의 z좌표의 차이의 평균편차/골반길이를 연산하는 단계를 더 포함하고, 제 4단계는 상기 보행자의 각 발목의 z좌표의 차이의 평균편차/골반길이로부터 기 설정된 기준내에 포함되는지 판별하는 단계를 더 포함하는 스켈레톤 데이터 기반 근감소증 판별 방법을 포함할 수 있다.In addition, the third step is from the skeleton data,

and calculating an average deviation/pelvic length of the difference between the z coordinates of each ankle of the pedestrian based on, and the fourth step is a preset average deviation/pelvic length of the difference between the z coordinates of each ankle of the pedestrian. It may include a skeleton data-based sarcopenia determination method further comprising the step of determining whether it is included within the criterion.

를 기준으로 보행속도/다리길이를 연산하는 단계를 더 포함하고, 상기 제 4단계는 상기 보행속도/다리길이로부터 기 설정된 기준내에 포함되는지 판별하는 단계를 더 포함하는 스켈레톤 데이터 기반 근감소증 판별 방법을 포함할 수 있다.In addition, the third step is from the skeleton data,

A method for determining sarcopenia based on skeleton data further comprising calculating gait speed/leg length based on , and the fourth step further comprising the step of determining whether the gait speed/leg length is within a predetermined criterion. can include

를 기준으로 측면에서 바라본 보행중인 보행자 양 발의 평균 발길이/보행자의 발 길이를 연산하는 단계를 더 포함하고, 상기 제 4단계는 상기 측면에서 바라본 보행중인 보행자 양 발의 평균 발길이/보행자의 발 길이로부터 기 설정된 기준내에 포함되는지 판별하는 단계를 더 포함하는 스켈레톤 데이터 기반 근감소증 판별 방법을 포함할 수 있다.In addition, the third step is from the skeleton data,

and calculating the average foot length of both feet of the pedestrian while walking/foot length of the pedestrian viewed from the side based on It may include a skeleton data-based sarcopenia determination method further comprising the step of determining whether it is included within a predetermined criterion.

를 기준으로 보행중인 양 무릎의 굽힘 각 평균을 연산하는 단계를 더 포함하고, 상기 제 4단계는 상기 보행중인 양 무릎의 굽힘 각 평균으로부터 기 설정된 기준내에 포함되는지 판별하는 단계를 더 포함하는 스켈레톤 데이터 기반 근감소증 판별 방법을 포함할 수 있다.In addition, the third step is from the skeleton data,

Skeleton data further comprising calculating an average of each bending angle of both knees during walking based on , and the fourth step further comprising determining whether the average bending angle of both knees during walking is within a predetermined criterion. based sarcopenia discrimination method.

One embodiment of the present invention can discriminate sarcopenia patients only by the appearance of sarcopenia patients while they are walking.

In addition, one embodiment of the present invention can determine the severe stage of sarcopenia in patients with sarcopenia using the average deviation of z-coordinates of each knee of a pedestrian/pelvic length and the average deviation of z-coordinates of each ankle of a pedestrian/pelvic length of a pedestrian. can

In addition, one embodiment of the present invention can determine the severe stage of sarcopenia in a patient with sarcopenia using walking speed/leg length.

In addition, one embodiment of the present invention can determine the severe stage of sarcopenia of a sarcopenia patient using the average foot length of both feet of a walking pedestrian viewed from the side/walker's foot length.

In addition, one embodiment of the present invention can determine the severe stage of sarcopenia of a patient with sarcopenia using the average of each bending of both knees during walking.

In addition, one embodiment of the present invention has an effect that it is possible to more accurately discriminate a patient with sarcopenia by using five detection elements.

Effects obtainable in the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below. .

1 is a schematic diagram of the mechanism by which sarcopenia occurs.
2 is a conceptual diagram illustrating a situation in which a pedestrian walking forward is photographed from the side based on the system for determining sarcopenia based on skeleton data according to an embodiment of the present invention.
3 is a diagram specifically illustrating the configuration of a system for determining sarcopenia based on skeleton data according to an embodiment of the present invention.
FIG. 4 is a flowchart of a method for determining sarcopenia based on skeleton data according to an embodiment of the present invention. FIG. 8 is a flowchart of a method for determining a severe stage of Parkinson's syndrome based on skeleton data according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail so that those skilled in the art can easily practice with reference to the accompanying drawings. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part is said to be “connected” to another part, this includes not only the case of being “directly connected” but also the case of being “electrically connected” with another element in the middle. In addition, when a certain component is said to "include", this means that it may further include other components, not excluding other components unless otherwise stated.

The camera used in the present invention may include a Time of Flight (ToF) module inside the camera. When the camera takes a photograph, the Time of Flight (ToF) module emits light toward the subject, and the time for the emitted light to hit the subject and then return after being reflected may be calculated as a distance.

In the present invention, photo data refers to a frame-by-frame scene of an image of a pedestrian being viewed, photographed using a camera used in the present invention. Skeleton data is a total of 31 characteristics extracted by designating each point of the skeleton of the pedestrian as one point in the photographed data. The 31 characteristics included in the extracted skeleton data include the nose, both eyes, both ears, head, neck, elbows, wrists, chest, waist, pelvis, hips, knees, feet, etc. The example uses only nine attributes, including the points of the pelvis, both hips, both knees, both ankles, and both feet.

Hereinafter, FIG. 1 is a schematic diagram of a mechanism by which sarcopenia occurs, and FIG. 2 is a photograph of a pedestrian walking forward from the side based on a skeleton data-based sarcopenia discrimination system according to an embodiment of the present invention. It is a conceptual diagram showing a situation, and FIG. 3 is a diagram specifically illustrating the configuration of a system for determining sarcopenia based on skeleton data according to an embodiment of the present invention.

Skeleton data-based sarcopenia discrimination system, which is an embodiment of the present invention, is a system for discriminating the sarcopenia patient's x using data taken by photographing the side of a sarcopenia patient walking forward.

A system for determining sarcopenia based on skeleton data according to an embodiment of the present invention may include a photographing unit 100 , a server 200 , and a terminal 300 .

The photographing unit 100 may include a camera that photographs the side of the pedestrian toward the front. The server 200 may include an extraction unit 210 , a calculation unit 220 , a learning unit 230 and a determination unit 240 . The terminal 300 may include a display unit 310 . The display unit 310 may include a display panel displaying information of the sarcopenia patient (p) on a screen. However, the display unit 310 is not limited thereto and may include various components supporting information output. In addition, the photographing unit 100, the server 200, and the terminal 300 are DMR, WiFi, low energy Bluetooth (BLE), Bluetooth, 3G (3rd Generation), 3GPP (3rdGeneration Partnership Project), LTE (Long Term Evolution), LTE-A, 4th generation (4G), and 5th generation (5G) communication modules may perform wireless communication with an external device through at least one communication module.

The extraction unit 210 may extract skeleton data of the pedestrian from photo data captured by the photographing unit 100 . The calculation unit 220 may calculate five detection elements to be used to determine the patient with sarcopenia based on the skeleton data of the pedestrian extracted by the extraction unit. Hereinafter, the calculated five detection elements are referred to as muscle reduction discrimination data. The five detection elements will be described later. The learning unit 230 may perform deep learning from the muscle reduction discrimination data in the calculation unit 230, and the determination unit 240 determines the sarcopenia of the pedestrian through a predetermined criterion based on the deep-learned muscle reduction discrimination data. can be identified. According to an embodiment of the present invention, when the pedestrian p walks forward (x-axis), the side of the pedestrian can be photographed using the camera c.

Specifically, when the sarcopenia patient (p) walks a predetermined distance toward the front, the camera (c) located on the side of the sarcopenia patient (p) captures the walking of the sarcopenia patient (p). From the photographed photo data, only the skeleton data of the lower body to be used for calculation of the sarcopenia patient (p) is extracted. Using the extracted skeleton data, the server 200 calculates muscle reduction determination data to be described later. A process of calculating muscle reduction discrimination data will be described later. The calculated muscle loss discrimination data is subjected to deep learning in the learning unit 230 together with muscle reduction discrimination data of other sarcopenia patients. The deep-learned muscle reduction discrimination data is determined by the determination unit 240 to determine whether or not it is higher than a predetermined criterion, and to determine the sarcopenia of the sarcopenia patient (p). The identified sarcopenia patient (p) is displayed to the sarcopenia patient (p) through the display unit 310, and can be transmitted not only to the sarcopenia patient (p) but also to the medical staff.

이다.

에서

는 왼쪽 무릎의 z좌표를,

는 오른쪽 무릎의 z좌표를 의미한다.

는 보행자가 기 설정된 거리를 보행한 시간을 의미한다.

는 촬영되는 보행자(p)의 골반 길이이다. 보행자(p)의 골반길이로 보행중인 보행자(p)의 각 무릎의 z좌표의 차의 평균편차를 나누는 이유는 촬영되는 보행자(p)마다 키가 커서 보행 속도의 차이가 나기도 하고, 골반이 넓은 것에 의해 기본적으로 무릎이 벌어지는 것에 차이가 나기 때문에 표준화하기 위해서이다. 또한, 상기 검출요소 1을 연산하는 식에서 보행자(p)의 각 무릎의 z좌표만 사용하는 이유는 x좌표는 보행자(p)가 전진하거나 후진하는 것으로 바뀌는 값으로 보행중인 무릎의 특징을 짓기에는 어렵다고 판단하여 포함되지 않았고, 각 무릎의 y 좌표 또한 보행자(p)의 신체좌표가 위 아래로 움직일 때 변화하는 값이므로 x 좌표와 마찬가지로 포함되지 않는다. 근감소증 환자의 보행에서 일반인의 보행과 다른 점은 무릎을 벌리고 걷는다는 특징이 있다. 무릎을 벌리고 걷는 특징을 이용하기 위해서 도2의 표시된 3차원 좌표계에서 z좌표를 이용해야 한다. 앞에서 서술했듯이, x좌표는 보행자가 앞 뒤로 걸을 때 바뀌는 값이며, y좌표는 보행자가 위 아래 움직일 때 변화하는 값이므로 z좌표가 카메라를 기준으로 하여금 가깝거나 먼 거리를 나타내는 값이 된다. 따라서 전면을 향해 보행하는 보행자를 측면에서 촬영하기 때문에 무릎의 벌어지는 값은 z좌표로 나타낼 수 있는 것이다. 따라서 보행자(p)의 각 무릎의 z좌표를 이용하는 것이다.Detection factor 1 is the average deviation/pelvic length of the difference between the z-coordinates of each knee of the pedestrian. The expression for calculating detection element 1 is

am.

at

is the z-coordinate of the left knee,

denotes the z-coordinate of the right knee.

Means the time the pedestrian walked a preset distance.

is the length of the pelvis of the pedestrian p being photographed. The reason for dividing the average deviation of the difference between the z-coordinates of each knee of the walking pedestrian (p) by the pelvis length of the pedestrian (p) is that each pedestrian (p) being photographed is tall, so there is a difference in walking speed, and the pelvis is wide. This is to standardize it because there is a difference in how the knee is basically widened by it. In addition, the reason why only the z-coordinate of each knee of the pedestrian p is used in the equation for calculating the detection element 1 is that the x-coordinate is a value that changes when the pedestrian p moves forward or backward, and it is difficult to characterize the walking knee. The y-coordinate of each knee is also a value that changes when the body coordinate of the pedestrian (p) moves up and down, so it is not included like the x-coordinate. The difference between the gait of patients with sarcopenia and that of ordinary people is that they walk with their knees apart. In order to use the characteristic of walking with the knees apart, the z-coordinate should be used in the 3-dimensional coordinate system shown in FIG. As described above, the x-coordinate is a value that changes when the pedestrian walks back and forth, and the y-coordinate is a value that changes when the pedestrian moves up and down, so the z-coordinate is a value representing a distance closer or farther from the camera. Therefore, since the pedestrian walking toward the front is photographed from the side, the widening value of the knee can be expressed as the z-coordinate. Therefore, the z-coordinate of each knee of the pedestrian p is used.

이다. 검출요소 1과 비슷하지만 다른 점은 검출요소 1은 보행자(p)의 무릎의 z좌표를 사용하는 식이고, 검출요소 2는 보행자(p)의 발목의 z 좌표를 사용한다. 상기 식의

에서

는 왼쪽 발목의 z좌표를

는 오른쪽 발목의 z좌표를 의미한다.

는 보행자가 기 설정된 거리를 보행한 시간을 의미한다. 상기 식의

는 촬영되는 보행자(p)의 골반 길이이다. 검출요소 2를 연산하는 식에서

로

를 나누는 이유도 검출요소 1를 연산하는 식에서 보행중인 보행자(p)의 각 무릎의 z좌표의 평균편차를 보행자(p)의 골반 길이로 나누는 이유와 동일하게 촬영되는 보행자 마다 키가 커서 보행 속도의 차이가 나고, 골반이 넓은 것에 의해 기본적으로 무릎이 벌어져 양 발목 사이가 멀어지는 것에서 차이가 나기 때문에 표준화하기 위해서이다. 앞서 서술했듯이 근감소증 환자와 일반인과의 보행에서 다른 점으로는 무릎을 벌리고 걷는 특징이 있다. 따라서 무릎이 벌리고 걸어, 양 발목의 사이도 멀어진다. 보행자(p)의 양 발목 사이의 거리를 측정하기 위해서는 보행자(p)의 측면에서 촬영하는 카메라(c)를 기준으로 원근을 나타낼 수 있는 z좌표를 이용해야 한다.Detection factor 2 is the average deviation/pelvic length of the difference between the z-coordinates of each ankle of the pedestrian. The expression for calculating detection factor 2 is

am. It is similar to detection element 1, but the difference is that detection element 1 uses the z-coordinate of the knee of the pedestrian p, and detection element 2 uses the z-coordinate of the ankle of the pedestrian p. of the above expression

at

is the z-coordinate of the left ankle

is the z-coordinate of the right ankle.

Means the time the pedestrian walked a preset distance. of the above expression

is the length of the pelvis of the pedestrian p being photographed. In the equation for calculating detection factor 2

as

The reason for dividing is the same as the reason for dividing the average deviation of the z-coordinate of each knee of the walking pedestrian (p) by the pelvis length of the walking pedestrian (p) in the equation for calculating the detection factor 1. This is to standardize because there is a difference, and the difference is that the knee is basically wide apart due to the wide pelvis and the distance between the ankles. As mentioned earlier, the difference between sarcopenia patients and the general public is that they walk with their knees apart. Therefore, the knees are wide apart, and the distance between the ankles is also wide. In order to measure the distance between the ankles of the pedestrian p, a z-coordinate capable of representing perspective should be used based on the camera c taking pictures from the side of the pedestrian p.

이다.

에서

는 보행자(p)의 허리의 x 좌표를 의미하고,

는 보행자가 기 설정된 거리를 보행한 시간을 의미한다. 또한,

는 보행자(p)의 다리 길이를 의미한다. 앞서 서술했듯이, x좌표는 보행자가 앞 뒤로 이동할 때 변화하는 값으로 보행자가 시작지점에서 끝지점까지 보행한 거리를 나타내는 좌표가 되는 것이다. 따라서 검출요소 3을 연산하는 식

에서

는 보행자가 촬영 중 보행한 총 거리를 보행한 시간으로 나눈것이므로 보행자가 보행한 속도를 의미한다.

를

로 나누는 이유는 다리 길이에 따라서 보폭에 크기가 달라져 같은 거리를 걷더라도 속도가 달라지기 때문에 표준화하기 위해서이다. Detection factor 3 is walking speed/leg length. The expression for calculating detection factor 3 is

am.

at

Means the x-coordinate of the waist of the pedestrian p,

Means the time the pedestrian walked a preset distance. also,

Means the leg length of the pedestrian (p). As described above, the x-coordinate is a value that changes when the pedestrian moves back and forth, and becomes a coordinate indicating the distance the pedestrian has walked from the starting point to the ending point. Therefore, the equation for calculating detection factor 3

at

Since is the total distance the pedestrian walked during the shooting divided by the walking time, it denotes the walking speed of the pedestrian.

cast

The reason for dividing by is to standardize because the size of the stride varies depending on the length of the leg, so even if you walk the same distance, the speed changes.

이다. 검출요소 4를 연산하는 식에서

는 왼발의 x좌표이고

는 왼 발목의 x좌표를 의미한다. 또한,

는 오른발의 x좌표를 의미하고

는 오른발목의 x좌표를 의미한다. 또한, 상기 검출요소 4를 연산하는 식에서

는 보행자의 발길이를 의미하고,

는 보행자가 기 설정된 거리를 보행한 시간을 의미한다. 정상적인 일반인이 걸었을 때 측면에서 바라본 발길이는 본인의 발길이와 동일하지만 근감소증 환자는 무릎을 벌리고 걷기 때문에 발목이 밖을 향하여 걷는다 따라서 본인의 발길이보다 측면에서 바라본 발길이가 더 짧을 수 있다. 검출 요소 4를 구하는 식에서

는, 측면에서 바라본 양 발의 발길이를 구하여 2로 나눈 것이다. 구체적으로, 측면에서 바라본 왼발의 발길이는

로 연산 할 수 있다. 측면에서 바라본 보행중인 보행자의 왼발의 앞 지점(

에서 왼발의 발목 지점(

을 빼면 측면에서 바라본 왼발의 발길이가 나온다. 또한, 측면에서 바라본 오른발의 발길이는

로 연산할 수 있다. 측면에서 바라본 보행중인 보행자의 오른발의 앞 지점(

)에서 오른발의 발목 지점(

)을 빼면 측면에서 바라본 오른발의 발길이가 나온다.Detection factor 4 is the average foot length of both feet of a walking pedestrian/pedestrian's foot length as seen from the side. The expression for calculating the detection factor 4 is

am. In the equation for calculating the detection factor 4

is the x-coordinate of the left foot

means the x-coordinate of the left ankle. also,

means the x-coordinate of the right foot

means the x-coordinate of the right ankle. In addition, in the equation for calculating the detection element 4

is the foot length of a pedestrian,

Means the time the pedestrian walked a preset distance. When a normal person walks, the foot length seen from the side is the same as the person's foot length, but sarcopenia patients walk with their knees apart, so their ankles walk outward. Therefore, the foot length seen from the side may be shorter than the person's foot length. From the equation for finding the detection factor 4

is the length of both feet viewed from the side and divided by 2. Specifically, the foot length of the left foot viewed from the side is

can be computed with The front point of the left foot of a walking pedestrian viewed from the side (

At the ankle point of the left foot (

If you subtract , you get the foot length of the left foot viewed from the side. Also, the foot length of the right foot viewed from the side is

can be computed with The front point of the right foot of a walking pedestrian viewed from the side (

) at the ankle point of the right foot (

) to get the foot length of the right foot viewed from the side.

이다. 상기 검출요소 5를 연산하는 식에서

는 왼쪽 엉덩이, 왼 무릎, 왼 발목의 x좌표와 y좌표로 삼각형 세변의 길이를 계산 후 cos법칙을 이용하여 연산 한 왼 다리의 무릎의 굽힘 각을 의미한다. 또한,

는 오른쪽 엉덩이, 오른 무릎, 오른 발목의 x좌표와 y좌표로 삼각형 세변의 길이를 계산 후 cos법칙을 이용하여 연산한 오른다리의 무릎의 굽힘 각을 의미한다.

는 보행자가 기 설정된 거리를 보행한 시간을 의미한다.Detection factor 5 is the average bending angle of both knees during walking. The expression for calculating detection factor 5 is

am. In the equation for calculating the detection factor 5

means the bending angle of the knee of the left leg calculated using the cos law after calculating the lengths of the three sides of the triangle with the x-coordinates and y-coordinates of the left hip, left knee, and left ankle. also,

means the bending angle of the knee of the right leg calculated using the cos law after calculating the lengths of the three sides of the triangle with the x-coordinates and y-coordinates of the right hip, right knee, and right ankle.

Means the time the pedestrian walked a preset distance.

Patients with sarcopenia walk with their knees wide apart as the severity worsens. The more you spread your knees apart, the more your ankles point outward and the more your knees bend. Walking speed is also very slow. The five detection elements described above are used as indicators to determine the severe stage of sarcopenia patients using these characteristics.

On the other hand, in the skeleton data-based sarcopenia determination method according to an embodiment of the present invention, when the pedestrian walks forward, the first step of photographing the side of the pedestrian (s200), the skeleton data of the pedestrian is obtained from the photographed photo data. A second step (s300) of extracting and calculating the muscle loss discrimination data from the skeleton data, determining whether the pedestrian's muscle loss discrimination data is higher than a predetermined criterion based on the deep-learning muscle loss discrimination data to determine the severity of the sarcopenia of the pedestrian A third step (s400) may be included.

When the pedestrian walks forward, the first step (s200) of photographing the side of the pedestrian is a step of capturing the side of the pedestrian with a camera including a ToF module when the pedestrian walks a preset distance.

The second step (s300) of extracting the pedestrian's skeleton data from the captured photo data and calculating the muscle reduction determination data from the skeleton data may include extracting the pedestrian's skeleton data from the captured photo data (s310). can In addition, in the second step (s300) of extracting the skeleton data of the pedestrian from the photographed photo data and calculating the muscle reduction determination data from the skeleton data, the step of calculating the detection element 1 (s321) and the detection element 2 Step s322, calculating the detection element 3 (s323), calculating the detection element 4 (s324), and calculating the detection element 5 (s325) may be included.

를 기준으로 보행자의 각 무릎의 z좌표의 차이의 평균편차/골반길이를 연산하는 단계이다. 검출요소 1을 연산하는 식에서

는 왼 무릎의 z좌표를 의미하고

는 오른 무릎의 z좌표를 의미한다. 또한,

는 보행자의 골반길이를 의미한다. 보행자의 골반길이로 보행중인 보행자의 각 무릎의 z좌표의 차의 평균편차를 나누는 이유는 촬영되는 보행자마다 키가 커서 보행 속도의 차이가 나기도 하고, 골반이 넓은 것에 의해 기본적으로 무릎이 벌어지는 것에 차이가 나기 때문에 표준화하기 위해서이다. 근감소증 환자는 무릎 및 허리 등의 통증으로 인하여 보행할 때, 무릎을 벌리고 걷는다. 일반인들은 보행할 때 무릎이 벌려 걷지 않기 때문에 보행하는 일반인의 양 무릎의 z좌표를 측정하여 검출요소 1을 연산하면 양 무릎의 차가 일정할 것이다. 하지만 근감소증 환자의 보행을 촬영하면 무릎을 계속 벌리고 걷고, 통증 때문에 걸음걸이가 일정하지 않는다. 따라서 보행중인 보행자의 무릎의 z좌표를 이용하여 연산된 검출요소1에서 보행자의 골반길이 대비 보행자의 무릎의 벌림 정도를 알 수 있다.The step of calculating the detection element 1 (s321) is from the skeleton data,

This step calculates the average deviation/pelvic length of the difference between the z coordinates of each knee of the pedestrian based on . In the formula for calculating the detection factor 1

is the z-coordinate of the left knee

denotes the z-coordinate of the right knee. also,

is the pelvic length of the pedestrian. The reason for dividing the average deviation of the difference between the z-coordinates of each knee of the walking pedestrian by the pedestrian's pelvic length is that each pedestrian being photographed is tall, so there is a difference in walking speed, and a wide pelvis basically causes a difference in the spread of the knee. It is to standardize it because it comes out. Patients with sarcopenia walk with their knees apart when walking due to pain in the knee and back. Since ordinary people do not walk with their knees spread apart when they walk, if the detection factor 1 is calculated by measuring the z-coordinates of both knees of ordinary people walking, the difference between both knees will be constant. However, when the gait of sarcopenia patients is filmed, they walk with their knees constantly apart, and their gait is irregular due to pain. Therefore, the degree of abduction of the pedestrian's knee relative to the pedestrian's pelvis length can be known from the detection element 1 calculated using the z-coordinate of the knee of the pedestrian.

In the third step (s400) of determining the sarcopenia of the pedestrian according to the present invention, which will be described later, the detection element 1 calculated in the step (s321) of calculating the detection element 1 can be used to determine the sarcopenia of the pedestrian. Unlike normal people's gait, the gait of patients with sarcopenia has the characteristic of walking with their knees apart. Detection factor 1 calculated using the z-coordinate of the knee of a walking pedestrian contains the degree of abduction between the knees compared to the pelvic length. Therefore, it is possible to determine the sarcopenia of a pedestrian in the severe stage of sarcopenia (s400) with the degree of ablation between the knees compared to the pelvic length included in detection element 1.

를 기준으로 보행자의 각 발목의 z좌표의 차이의 평균편차/골반길이를 연산하는 단계이다. 검출요소 2를 연산하는 식에서

는 왼 발목의 z좌표를 의미하고

는 오른 발목의 z좌표를 의미한다. 또한,

는 보행자의 골반길이를 의미한다.

로

를 나누는 이유도 검출요소 1를 연산하는 식에서 보행중인 보행자의 각 무릎의 z좌표의 평균편차를 보행자의 골반 길이로 나누는 이유와 동일하게 촬영되는 보행자마다 키가 커서 보행 속도의 차이가 나고, 골반이 넓은 것에 의해 기본적으로 무릎이 벌어져 양 발목 사이가 멀어지는 것에서 차이가 나기 때문에 표준화하기 위해서이다. 앞서 서술했듯이 근감소증 환자와 일반인과의 보행에서 다른 점으로는 무릎을 벌리고 걷는 특징이 있다. 따라서 무릎이 벌리고 걸어, 양 발목의 사이도 멀어진다. 따라서 보행중인 보행자의 발목의 z좌표를 이용하여 연산된 검출요소2에서 보행자의 골반길이 대비 보행자의 발목 사이의 벌림 정도를 알 수 있다.Calculating the detection element 2 (s322) from the skeleton data,

This is a step of calculating the average deviation/pelvic length of the difference between the z-coordinates of each ankle of the pedestrian based on . In the equation for calculating detection factor 2

is the z-coordinate of the left ankle

is the z-coordinate of the right ankle. also,

is the pelvic length of the pedestrian.

as

The reason for dividing is the same as the reason for dividing the average deviation of the z-coordinate of each knee of a walking pedestrian by the length of the pelvis in the equation for calculating detection factor 1. This is to standardize because the difference is that the knee is basically spread apart by the wide one and the distance between the ankles is different. As mentioned earlier, the difference between sarcopenia patients and the general public is that they walk with their knees apart. Therefore, the knees are wide apart, and the distance between the ankles is also wide. Therefore, the degree of abduction between the ankles of the pedestrian compared to the length of the pelvis of the pedestrian can be known from the calculated detection element 2 using the z-coordinate of the ankle of the walking pedestrian.

In the third step (s400) of determining the sarcopenia of the pedestrian according to the present invention, which will be described later, the detection element 2 calculated in the step (s322) of calculating the detection element 2 can be used to determine the sarcopenia of the pedestrian. Unlike normal people's gait, the gait of patients with sarcopenia is characterized by walking with their knees apart. Because you walk with your knees apart, the distance between your ankles also increases. Therefore, the detection factor 2 calculated using the z-coordinate of the ankle of the walking pedestrian contains the degree of abduction between the ankles compared to the pelvic length. Therefore, it is possible to determine the sarcopenia of a pedestrian in the severe stage of sarcopenia (s400) with the degree of ablation between the ankles compared to the pelvic length included in the detection element 2.

를 기준으로 보행속도/다리길이를 연산하는 단계이다.

에서

는 보행자(p)의 허리의 x 좌표를 의미하고,

는 보행자가 기 설정된 거리를 보행한 시간을 의미한다. 또한,

는 보행자(p)의 다리 길이를 의미한다.

를

로 나누는 이유는 다리 길이에 따라서 보폭에 크기가 달라져 같은 거리를 걷더라도 속도가 달라지기 때문에 표준화하기 위해서이다. 앞서 서술했듯이, 근감소증 환자는 허리, 무릎 등 통증으로 인하여 무릎을 벌리고 걷는 특징을 가지고 있다. 하지만 무릎을 벌리고 걷는 특징 외에도 근감소증 환자는 통증으로 인하여 빠르게 걸을 수가 없다. 그렇기 때문에 보폭이 큰 근감소증 환자여도 같은 보폭을 가지고 있는 일반인과는 걷는 속도에서 차이가 날 수 있다.The step of calculating the detection element 3 (s323) is from the skeleton data,

This step calculates the walking speed/leg length based on .

at

Means the x-coordinate of the waist of the pedestrian p,

Means the time the pedestrian walked a preset distance. also,

Means the leg length of the pedestrian (p).

cast

The reason for dividing by is to standardize because the size of the stride varies depending on the length of the leg, so even if you walk the same distance, the speed changes. As described above, patients with sarcopenia have the characteristic of walking with their knees apart due to back and knee pain. However, apart from the characteristic of walking with the knees apart, patients with sarcopenia cannot walk quickly due to pain. Therefore, even patients with sarcopenia with a large stride length may differ in walking speed from normal people who have the same stride length.

In the third step (s400) of determining the sarcopenia of the pedestrian according to the present invention, which will be described later, the detection element 3 calculated in the step of calculating the detection element 3 (s323) can be used to determine the sarcopenia of the pedestrian. Unlike normal people's gait, the gait of patients with sarcopenia is slow. Therefore, it is possible to determine the pedestrian's sarcopenia at the severe stage of the pedestrian's sarcopenia (s400) with the walking speed compared to the leg length included in the detection element 3.

를 기준으로 측면에서 바라본 보행중인 보행자의 발길이/보행자의 발 길이를 연산하는 단계이다. 검출요소 4를 연산하는 식에서

는 왼발의 x좌표이고

는 왼 발목의 x좌표를 의미한다. 또한,

는 오른발의 x좌표를 의미하고

는 오른발목의 x좌표를 의미한다. 또한, 상기 검출요소 4를 연산하는 식에서

는 보행자의 발길이를 의미하고,

는 보행자가 기 설정된 거리를 보행한 시간을 의미한다. 정상적인 일반인이 걸었을 때 측면에서 바라본 발길이는 본인의 발길이와 동일하지만 근감소증 환자는 무릎을 벌리고 걷기 때문에 발목이 밖을 향하여 걷는다 따라서 본인의 발길이보다 측면에서 바라본 발길이가 더 짧을 수 있다.The step of calculating the detection element 4 (s324) is from the skeleton data,

This is a step of calculating the foot length of the walking pedestrian/foot length of the pedestrian viewed from the side based on . In the equation for calculating the detection factor 4

is the x-coordinate of the left foot

means the x-coordinate of the left ankle. also,

means the x-coordinate of the right foot

means the x-coordinate of the right ankle. In addition, in the equation for calculating the detection element 4

is the foot length of a pedestrian,

Means the time the pedestrian walked a preset distance. When a normal person walks, the foot length seen from the side is the same as the person's foot length, but sarcopenia patients walk with their knees apart, so their ankles walk outward. Therefore, the foot length seen from the side may be shorter than the person's foot length.

In the fourth step (s400) of determining the sarcopenia of the pedestrian according to the present invention, which will be described later, the detection element 4 calculated in the step (s324) of calculating the detection element 4 can be used to determine the sarcopenia of the pedestrian. The length of a foot viewed from the side of an ordinary person's gait is the same as that of an ordinary person. However, in the gait of sarcopenia patients, the foot length seen from the side is different from the foot length of sarcopenia patients. When walking using this feature, the detection element 4 is calculated using the foot length viewed from the side and the foot length of the pedestrian. Therefore, it is possible to determine the pedestrian's sarcopenia in the pedestrian's sarcopenia severe stage (s400) with the foot length seen from the side when walking compared to the foot length included in the detection element 4.

를 기준으로 보행중인 양 무릎 굽힘 각 평균을 연산하는 단계이다. 상기 검출요소 5를 연산하는 식에서

는 왼쪽 엉덩이, 왼 무릎, 왼 발목의 x좌표와 y좌표로 삼각형 세변의 길이를 계산 후 cos법칙을 이용하여 연산 한 왼 다리의 무릎의 굽힘 각을 의미한다. 또한,

는 오른쪽 엉덩이, 오른 무릎, 오른 발목의 x좌표와 y좌표로 삼각형 세변의 길이를 계산 후 cos법칙을 이용하여 연산한 오른다리의 무릎의 굽힘 각을 의미한다.

는 보행자가 기 설정된 거리를 보행한 시간을 의미한다. 앞서 서술했듯이 근감소증 환자와 일반인과의 보행에서 다른 점으로는 무릎을 벌리고 걷는 특징이 있다. 따라서 근감소증 환자는 중증단계가 심해질수록 무릎을 양쪽으로 더 많이 벌리면서, 무릎도 더 많이 굽히게 된다. 따라서 무릎의 굽힘 각 평균을 이용하여 연산된 검출요소5에서 보행할 때 보행자가 무릎을 굽힌 정도를 알 수 있다. The step of calculating the detection element 5 (s325) is from the skeleton data,

This is a step of calculating the average of each bending of both knees during walking based on . In the equation for calculating the detection factor 5

means the bending angle of the knee of the left leg calculated using the cos law after calculating the lengths of the three sides of the triangle with the x-coordinates and y-coordinates of the left hip, left knee, and left ankle. also,

means the bending angle of the knee of the right leg calculated using the cos law after calculating the lengths of the three sides of the triangle with the x-coordinates and y-coordinates of the right hip, right knee, and right ankle.

Means the time the pedestrian walked a preset distance. As mentioned earlier, the difference between sarcopenia patients and the general public is that they walk with their knees apart. Therefore, patients with sarcopenia spread their knees more to both sides and bend their knees more as the severity of the disease progresses. Therefore, it is possible to know the degree of bending of the knee by the pedestrian when walking in the detection element 5 calculated using the average of each bending angle of the knee.

In the third step (s400) of determining the sarcopenia of the pedestrian according to the present invention, which will be described later, the detection element 5 calculated in the step (s325) of calculating the detection element 5 can be used to determine the sarcopenia of the pedestrian. When normal people walk, they do not walk with their knees fully extended. However, compared to patients with sarcopenia, there is a difference in the bending angle of the knee. Therefore, it is possible to determine the sarcopenia of the pedestrian with the angle formed by the hip, knee, and ankle of the patient with sarcopenia when walking, which is included in the detection element 5.

In the third step (s400) of determining the severe stage of sarcopenia of the pedestrian, the skeleton data of the pedestrian is extracted from the photographed photo data, and the muscle calculated in the second step (s300) of calculating the muscle reduction discrimination data from the skeleton data. Based on the decrease determination data, it is possible to determine whether the pedestrian has sarcopenia by determining whether it is higher than a predetermined criterion. The five detection elements described above are also used as indicators for determining the severity of the pedestrian's sarcopenia in the third step (s400) of determining the severity of the pedestrian's sarcopenia. In addition, in the third step (s400) of determining the severe stage of sarcopenia of the pedestrian, it is possible to determine the severe stage of sarcopenia of the pedestrian by using all five detection elements.

For example, there is a sarcopenia patient (p). The sarcopenia patient (p) walks forward a predetermined distance. At this time, the side of the sarcopenia patient (p) is photographed. Skeleton data of the patient with sarcopenia (p) is extracted from the photographed data. At this time, only nine characteristics of the skeleton located in the lower body are used. Patients with sarcopenia walk with their knees wide apart when the severity is severe. The more you spread your knees, the more your ankles point out, the more you bend your knees, and the slower you walk. In the skeleton data extracted from the photographed picture data, five detection elements that can recognize the characteristics of the knee, ankle, and speed are designated and calculated. The five computed detection elements of the sarcopenia patient (p) are compared with the detection elements of other sarcopenia patients and determined whether they are higher than a preset standard to determine the sarcopenia patient (p)'s .

The above description of the present invention is for illustrative purposes, and those skilled in the art can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and equivalent concepts should be construed as being included in the scope of the present invention. do.

100: shooting unit
200: server
210: extraction unit
220: calculation unit
230: learning unit
240: determination unit
300: Terminal
310: display unit
c: camera
p: Pedestrian

Claims (12)

When the pedestrian walks forward, the photographing unit for photographing the side of the pedestrian; and
a server that receives the photographing data captured by the photographing unit and determines a severity level;
The server includes an extraction unit for extracting skeleton data of a pedestrian from photographed photographing data;

a calculation unit for calculating the average foot length of both feet of a walking pedestrian viewed from the side/foot length of the pedestrian; and
Skeleton data-based sarcopenia determination system including a determination unit for determining whether the Parkinsonian syndrome state of the pedestrian is within a preset range from the average foot length of both feet of the walking pedestrian/pedestrian's foot length viewed from the side. According to claim 1,
The calculation unit,
From the skeleton data,

Skeleton data-based sarcopenia discrimination system for calculating gait speed/leg length based on , and determining whether the Parkinson's syndrome state of the pedestrian is within a preset range from the gait speed/leg length. According to claim 2,
The calculation unit,
From the skeleton data,

Based on , the average deviation/pelvic length of the z-coordinate difference of each knee of the pedestrian is calculated, and the determination unit calculates the Parkinson's syndrome condition of the pedestrian based on the average deviation/pelvic length of the z-coordinate difference of each knee of the pedestrian. Skeleton data-based sarcopenia discrimination system that determines whether it is within the range According to claim 3,
The calculation unit,
From the skeleton data,

Based on , the average deviation/pelvic length of the z-coordinate difference of each ankle of the pedestrian is calculated, and the determination unit calculates the Parkinson's syndrome condition of the pedestrian from the average deviation/pelvic length of the z-coordinate difference of each ankle of the pedestrian. Skeleton data-based sarcopenia discrimination system that determines whether it is within the range According to claim 4,
The calculation unit,
From the skeleton data,

Skeleton data-based sarcopenia discrimination system that calculates an average of each bending of the knee while walking based on, and the determination unit determines whether the Parkinson's syndrome state of the pedestrian is included within a preset range from the average of each bending of the knee during walking. The determination unit,
The gait speed/leg length calculated by the calculation unit, the average deviation/pelvic length of the z-coordinate difference of each knee of the pedestrian, the average deviation/pelvic length of the z-coordinate difference of each ankle of the pedestrian, viewed from the side Using Parkinson's discrimination data, which is the average value of the average foot length of both feet of a walking pedestrian/walker's foot length and bending of the knee while walking, it is determined whether the Parkinson's syndrome state of the pedestrian is within a preset range, and the server determines the Parkinson's syndrome. Skeleton data-based Parkinson's syndrome severe stage discrimination system including a learning unit for deep learning data A first step of deep learning Parkinson's discrimination data; A second step of photographing the side of the pedestrian when the pedestrian walks forward; A third step of extracting skeleton data of the pedestrian from the photographed data and calculating the Parkinsonian discrimination data from the skeleton data; and a fourth step of determining whether the pedestrian has Parkinson's syndrome by determining whether the parkinson's syndrome is higher than a predetermined criterion based on the deep-learning Parkinson's discrimination data. Parkinson's syndrome severe stage discrimination method based on gait data including According to claim 7,
The third step is
From the skeleton data,

and calculating the average foot length of both feet of the walking pedestrian viewed from the side/foot length of the pedestrian while walking viewed from the side, based on Skeleton data-based sarcopenia determination method further comprising the step of determining whether it is included within a predetermined criterion According to claim 8,
The third step is
From the skeleton data,

and calculating the gait speed/leg length based on , and the fourth step further comprising the step of determining whether the gait speed/leg length is within a predetermined criterion. According to claim 9,
The third step is
From the skeleton data,

and calculating an average deviation/pelvic length of differences in z-coordinates of each knee of the pedestrian based on , wherein the fourth step is based on the average deviation/pelvic length of differences in z-coordinates of each knee of the pedestrian. Skeleton data-based sarcopenia determination method further comprising the step of determining whether it is within the set criteria According to claim 10,
The third step is
From the skeleton data,

and calculating an average deviation/pelvic length of differences in z coordinates of each ankle of the pedestrian based on , wherein the fourth step is based on the average deviation/pelvic length of differences in z coordinates of each ankle of the pedestrian. Skeleton data-based sarcopenia determination method further comprising the step of determining whether it is within the set criteria According to claim 11,
The third step is
From the skeleton data,

and calculating an average of each bending angle of the walking knee based on the skeleton data based root How to identify sarcopenia

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