KR20210063567A - Method of Scoliosis classification and joint damage prediction - Google Patents

Method of Scoliosis classification and joint damage prediction Download PDF

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KR20210063567A
KR20210063567A KR1020190151841A KR20190151841A KR20210063567A KR 20210063567 A KR20210063567 A KR 20210063567A KR 1020190151841 A KR1020190151841 A KR 1020190151841A KR 20190151841 A KR20190151841 A KR 20190151841A KR 20210063567 A KR20210063567 A KR 20210063567A
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김영국
조재성
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알바이오텍 주식회사
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Abstract

The present invention relates to a method for predicting scoliosis and joint damage, which analyzes a gait accurately and quickly through machine learning to predict diseases. According to the present invention, the method comprises the following steps: attaching inertial sensors to the waist and the thighs, shins, and feet of both legs to measure a three-dimensional (3D) Euler angle through walking; analyzing a 3D joint angle with 18 graphs for the left and right rotation angles of the hip, knee, and ankle joints on the basis of gait information measured through the inertial sensors and analyzing speed, a stride length, a left and right deviation, and spatiotemporal parameters of a stance phase and a swing phase; subdividing the gait of both legs into 72 dimensions on the basis of the spatiotemporal parameters of the 18 graphs of the 3D joint angle to classify the disease-bearing and normal pedestrians through machine learning; and comparing a spatial distance on the basis of parameters between the classified disease-bearing group and normal pedestrian group.

Description

척추 측만증 및 관절 손상 예측 방법{Method of Scoliosis classification and joint damage prediction}Method of Scoliosis classification and joint damage prediction

본 발명은 척추 측만증 및 관절 손상 예측 방법에 관한 것으로, 더욱 상세하게는 보행분석 데이터의 기계학습을 통한 척추 측만증 및 관절 손상 예측 방법에 관한 것이다.The present invention relates to a method for predicting scoliosis and joint damage, and more particularly, to a method for predicting scoliosis and joint damage through machine learning of gait analysis data.

일반적으로 중추신경계, 척추, 관절, 근육 등 여러 가지 질환이 보행 이상으로 표출되어 지고 있다. 보행에서의 속도, 보폭, 좌우편차, 입각기, 유각기의 일반형 파라미터부터 고관절, 슬관절, 족관절의 좌우 회전각도 총 18개 그래프의 시계열 파라미터를 이용하여 척추 측만증 및 관절 손상을 분석하여 이상 유무를 판단하여 치료하고 있다.In general, various diseases such as the central nervous system, spine, joints, and muscles are expressed as abnormal walking. Determination of abnormalities by analyzing scoliosis and joint damage using time series parameters of a total of 18 graphs, from general parameters of walking speed, stride length, left-right deviation, stance phase and swing phase to hip, knee, and ankle rotation angles is being treated

일반적으로 보행 분석 방법은 적외선 카메라 기반 시스템을 이용하거나 전자 각도기를 이용하고 있다. In general, a gait analysis method uses an infrared camera-based system or an electronic protractor.

그러나, 기존의 보행 분석 방법은 정확성이 떨어지며, 공간의 제약이 있어 정해진 장소에서만 측정을 해야 하는 문제점이 있었다. However, the existing gait analysis method has a problem in that accuracy is poor and the measurement has to be performed only in a fixed place due to space limitations.

상기와 같은 문제점을 해결하기 위한 본 발명의 목적은 보행을 기계학습을 통해 정확하고 신속하게 분석하여 질환을 예측할 수 있는 척추 측만증 분류 및 관절 손상 예측 방법을 제공하는 데 있다. An object of the present invention for solving the above problems is to provide a method for classifying scoliosis and predicting joint damage that can predict a disease by accurately and quickly analyzing a gait through machine learning.

상기와 같은 목적을 달성하기 위한 본 발명에 따른 척추 측만증 및 관절 손상 예측 방법은 관성센서를 허리와 양 다리의 허벅지, 정강이, 발에 각각 부착하여 보행을 통해 3차원 오일러 각을 측정하는 단계; 상기 관성센서를 통해 측정한 보행 정보를 기반으로 고관절, 슬관절, 족관절의 좌우 회전 각도에 대해 18개 그래프로 3차원 관절각을 분석하고, 속도, 보폭, 좌우편차, 입각기, 유각기의 시공간 파라미터를 분석하는 단계; 상기 3차원 관절각 18개 그래프를 시공간 파라미터 기반으로 양 다리 걸음걸이를 72차원으로 세분화하여 기계학습을 통해 질환 보유자와 정상 보행자를 분류하는 단계; 및 상기 분류된 질환 보유자 군집과 정상 보행자 그룹간의 파라미터 기반으로 공간상의 거리를 비교하는 단계;를 포함하는 것을 특징으로 한다.The method for predicting scoliosis and joint damage according to the present invention for achieving the above object includes the steps of attaching an inertial sensor to the thigh, shin, and foot of the waist and both legs, respectively, and measuring the three-dimensional Euler angle through walking; Based on the gait information measured through the inertial sensor, the three-dimensional joint angle was analyzed with 18 graphs for the left and right rotation angles of the hip, knee, and ankle joints, and the spatiotemporal parameters of speed, stride length, left and right deviation, stance phase, swing phase analyzing the; classifying the disease-bearing and normal pedestrians through machine learning by subdividing the gait of both legs into 72 dimensions based on the spatiotemporal parameters of the 18 three-dimensional joint angle graphs; and comparing the spatial distance based on the parameter between the classified disease-bearing group and the normal pedestrian group.

상기 양 다리 걸음걸이의 72차원의 세분화는 초기 양하지 지지지, 단하지 지지지, 말기 양하지 지지지 및 입각기에서 고관절의 굽힘/폄, 내전/외전 및 내회전/외회전과, 슬관절의 굽힘/폄, 내반/외반 및 내회전/외회전과, 족관절의 배측굴곡/저측굴국, 내번/외번 및 내회전/외회전으로 분류할 수 있다.The 72-dimensional subdivision of the gait of both legs includes initial biceps support, short lower extremity support, late biceps support, hip flexion/extension, adduction/abduction and internal rotation/external rotation, and knee joint bending/extension, varus It can be classified into /valgus and internal rotation/external rotation, dorsiflexion/plantar flexion of the ankle joint, internal rotation/external rotation and internal rotation/external rotation.

상기와 같이, 본 발명에 따르면 보행분석 데이터의 기계학습을 통해 척추 측만증 질환 여부와 정상인과 대비해 유의한 차이가 나는 손상된 관절을 예측하여 예방과 재활치료를 조기에 권고 할 수 있다.As described above, according to the present invention, through machine learning of gait analysis data, it is possible to predict the presence of scoliosis disease and the damaged joint that has a significant difference compared to the normal person to recommend prevention and rehabilitation treatment early.

또한, 본 발명에 따르면 청소년뿐 만 아니라 고령자의 조기진단에 유용하게 활용되어 환자의 삶의 질 향상에 기여할 수 있다.In addition, according to the present invention, it can be usefully used for early diagnosis of the elderly as well as adolescents, thereby contributing to the improvement of the quality of life of patients.

도 1은 본 발명에 따른 척추 측만증 및 관절 손상 예측 방법의 순서도이다. 1 is a flowchart of a method for predicting scoliosis and joint damage according to the present invention.

아래에서는 첨부한 도면을 참고로 하여 본 발명의 실시예에 대하여 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자가 용이하게 실시할 수 있도록 상세히 설명한다. 그러나 본 발명은 여러 가지 상이한 형태로 구현될 수 있으며 여기에서 설명하는 실시예에 한정되지 않는다. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art may easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein.

그러면 본 발명에 따른 척추 측만증 분류 및 관절 손상 예측 방법의 바람직한 실시예에 대하여 자세히 설명하기로 한다.Next, a preferred embodiment of the scoliosis classification and joint damage prediction method according to the present invention will be described in detail.

도 1은 본 발명에 따른 척추 측만증 및 관절 손상 예측 방법의 순서도이다.1 is a flowchart of a method for predicting scoliosis and joint damage according to the present invention.

도 1을 참조하면, 본 발명에 따른 척추 측만증 및 관절 손상 예측 방법은 먼저, 가속, 각속도, 지자기를 측정할 수 있는 관성센서를 허리와 왼쪽 및 오른쪽 다리의 허벅지, 정강이, 발에 각각 부착하여 보행을 통해 무선으로 정보를 전달하여 3차원 오일러 각(euler angle)을 측정한다(S100). Referring to FIG. 1, in the method for predicting scoliosis and joint damage according to the present invention, first, an inertial sensor capable of measuring acceleration, angular velocity, and geomagnetism is attached to the waist and thighs, shins, and feet of the left and right legs, respectively. The three-dimensional Euler angle is measured by wirelessly transmitting information through (S100).

이어서, 상기 관성센서를 통해 측정한 보행 정보를 기반으로 고관절(Hip), 슬관절(Knee), 족관절(Ankle)의 좌우 회전 각도에 대해 18개 그래프로 3차원 관절각을 분석하고, 속도, 보폭, 좌우편차, 입각기, 유각기의 시공간 파라미터를 분석한다(S200). 여기서, 상기 18개 그래프는 고관절, 슬관절, 족관절의 좌우 회전 각도를 각각 시상면(Sagittal plane), 관상면(Coronal plane), 황단면(Transverse plane)에 대해 분석하여 나타낸 것이다.Then, based on the gait information measured through the inertial sensor, the three-dimensional joint angle was analyzed with 18 graphs for the left and right rotation angles of the hip, knee, and ankle joints, speed, stride length, Analyze spatiotemporal parameters of left and right deviation, stance phase, swing phase (S200). Here, the 18 graphs show the left and right rotation angles of the hip joint, knee joint, and ankle joint by analyzing the sagittal plane, coronal plane, and transverse plane, respectively.

다음으로, 상기 3차원 관절각 18개 그래프를 시공간 파라미터 기반으로 표 1과 같이 양 다리의 걸음걸이를 72차원으로 세분화하여 기계학습을 통해 척추 측만증 및 관절 손상된 질환 보유자와 정상 보행자를 분류한다(S300). Next, the gait of both legs is subdivided into 72 dimensions as shown in Table 1 based on the spatiotemporal parameters of the 18 graphs of the three-dimensional joint angle, and the scoliosis and joint damaged disease bearers and normal pedestrians are classified through machine learning (S300 ).

Figure pat00001
Figure pat00001

여기서, 상기 양 다리 걸음걸이의 72차원 세분화는 초기 양하지 지지지(Initial Double Support Phase; IDS), 단하지 지지지(Single Support Phase; SS), 말기 양하지 지지지(Terminal Double Support Phase; TDS) 및 입각기(Stance Phase; SP)에서 고관절(Hip)의 굽힘/폄(flexion/extension), 내전/외전(adduction /abduction) 및 내회전/외회전(internal/external rotation)과, 슬관절(Knee)의 굽힘/폄, 내반/외반(varus/valgus) 및 내회전/외회전과, 족관절(Ankle)의 배측굴곡/저측굴국(dorsiflexion/plantarflexion), 내번/외번(inversion/eversion), 내회전/외회전으로 분류한 것이다. Here, the 72-dimensional segmentation of the gait of both legs includes an initial double support phase (IDS), a single support phase (SS), a terminal double support phase (TDS), and a stance. In the Stance Phase (SP), flexion/extension, adduction/abduction and internal/external rotation of the hip joint, and flexion/extension of the knee joint (Knee) , varus/valgus and internal rotation/external rotation, and dorsiflexion/plantarflexion of the ankle joint, inversion/eversion, and internal rotation/external rotation.

다음에, 상기 분류된 질환 보유자 군집과 정상 보행자 그룹간의 파라미터 기반으로 공간상의 거리(Euclidean Distance)를 비교하여 척추 측만증 및 관절 손상을 예측한다(S400).Next, scoliosis and joint damage are predicted by comparing the spatial distance (Euclidean Distance) based on the parameters between the classified disease-bearing group and the normal pedestrian group (S400).

이와 같이, 본 발명에 따르면 병원에만 의지 하지 않고, 학교 혹은 보건소 등에에 비치해 조기 진단 및 예방에 유용하게 활용될 수 있다.As described above, according to the present invention, the present invention can be usefully utilized for early diagnosis and prevention by providing it to a school or a public health center without relying solely on a hospital.

이상에서 본 발명의 실시예에 대하여 상세하게 설명하였지만 본 발명의 권리범위는 이에 한정되는 것은 아니고 다음의 청구범위에서 정의하고 있는 본 발명의 기본 개념을 이용한 당업자의 여러 변형 및 개량 형태 또한 본 발명의 권리범위에 속하는 것이다.Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concept of the present invention defined in the following claims are also provided. It belongs to the scope of rights.

Claims (2)

관성센서를 허리와 양 다리의 허벅지, 정강이, 발에 각각 부착하여 보행을 통해 3차원 오일러 각을 측정하는 단계;
상기 관성센서를 통해 측정한 보행 정보를 기반으로 고관절, 슬관절, 족관절의 좌우 회전 각도에 대해 18개 그래프로 3차원 관절각을 분석하고, 속도, 보폭, 좌우편차, 입각기, 유각기의 시공간 파라미터를 분석하는 단계;
상기 3차원 관절각 18개 그래프를 시공간 파라미터 기반으로 양 다리 걸음걸이를 72차원으로 세분화하여 기계학습을 통해 질환 보유자와 정상 보행자를 분류하는 단계; 및
상기 분류된 질환 보유자 군집과 정상 보행자 그룹간의 파라미터 기반으로 공간상의 거리를 비교하는 단계;를 포함하는 것을 특징으로 하는 척추 측만증 및 관절 손상 예측 방법. measuring a three-dimensional Euler angle through walking by attaching an inertial sensor to each of the thighs, shins, and feet of the waist and both legs;
Based on the gait information measured through the inertial sensor, the three-dimensional joint angle was analyzed with 18 graphs for the left and right rotation angles of the hip, knee, and ankle joints, and the spatiotemporal parameters of speed, stride length, left and right deviation, stance phase, swing phase analyzing the;
classifying the disease-bearing and normal pedestrians through machine learning by subdividing the gait of both legs into 72 dimensions based on the spatiotemporal parameters of the 18 three-dimensional joint angle graph; and
Comparing the spatial distance based on the parameter between the classified disease-bearing group and the normal pedestrian group; scoliosis and joint damage prediction method comprising: a. 제1항에 있어서,
상기 양 다리 걸음걸이의 72차원의 세분화는 초기 양하지 지지지, 단하지 지지지, 말기 양하지 지지지 및 입각기에서 고관절의 굽힘/폄, 내전/외전 및 내회전/외회전과, 슬관절의 굽힘/폄, 내반/외반 및 내회전/외회전과, 족관절의 배측굴곡/저측굴국, 내번/외번 및 내회전/외회전으로 분류하는 것을 특징으로 하는 척추 측만증 및 관절 손상 예측 방법. The method of claim 1,
The 72-dimensional subdivision of the gait of both legs includes initial biceps support, short lower extremity support, late biceps support, hip flexion/extension, adduction/abduction and internal rotation/external rotation, and knee joint bending/extension, varus A method for predicting scoliosis and joint damage, characterized in that it is classified into /valgus and internal rotation / external rotation, dorsiflexion / plantar flexion of the ankle joint, inversion / lateral rotation and internal rotation / external rotation.
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KR102536431B1 (en) * 2022-10-12 2023-05-26 주식회사 사이클룩스 Method And Systems for Managing Clinical Trial Based on Gait Analysis

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WO2022245077A1 (en) 2021-05-17 2022-11-24 주식회사 엘지화학 Resin and method for producing same
KR102536431B1 (en) * 2022-10-12 2023-05-26 주식회사 사이클룩스 Method And Systems for Managing Clinical Trial Based on Gait Analysis

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