KR101031939B1 - Method and apparatus of analyzing a walking - Google Patents

Abstract

The present invention provides a gait analysis method and apparatus, which can provide joint evaluation indexes of measurement patients calculated using the main gait variables used in each joint of the lower extremity, to be normalized to joint evaluation indexes of a normal person. To this end, the present invention comprises the steps of the control unit of the gait analysis device for receiving the measurement information on the kinematic changes and mechanical changes for each of the joints of the lower limbs of the normal person and the measurement patient to store in the storage unit; Calculating gait variables for each of the joints using the measurement information; Calculating joint evaluation indices for each of the joints of the measurement patient and joint evaluation indices for each of the joints of the normal person using the gait variables; And normalizing joint evaluation indices for each of the joints of the measurement patient with respect to the joint evaluation indices for each of the joints of the normal person.

Gait, joint, exponent, normalization

Description

METHOD AND APPARATUS OF ANALYZING A WALKING}

The present invention relates to a method and apparatus for analyzing a walking state of a patient and outputting the result.

Techniques for enhancing delivery power through the compression of complex data have been studied in various ways. In particular, in the field of statistics and pattern recognition, techniques for evaluating differences between groups using average values or deviations and calculating distances in space have been well known. In addition, in the field of motion analysis, kinematic data (relative angle of the joint) or mechanical data (joint force, moment) generated in the joint has been a criterion for determining abnormality through the analysis of human body motion.

With regard to gait analysis, L.M. Schutte et al. (2000) selected 16 variables of major clinical value among the kinematic data of the lower extremity joints obtained from gait analysis and performed data compression using them.Principal Component Analysis was used as the technique. It was. In particular, the analysis of the key elements normalizes the patient's data using the mean and the deviation of the normal person, and defines the difference in the spatial distance between the normal person and the patient in the process as the NI (Normalcy Index). . This technique was also used by M. Romei et al. In 2003 as a tool for evaluating patients with lateral or bilateral cerebral palsy.

Although these studies have value as a study for judging problems across the lower extremities, they have limitations in that they cannot find problems occurring in each joint. Also, these studies have only shown numerical results and expert systems, No visualization techniques have been proposed for use in decision-making systems.

An object of the present invention for solving the above problems, can be provided by normalizing the joint evaluation index of the measurement patient calculated by using the main gait parameters used in each joint of the lower limbs with respect to the joint evaluation index of normal people In addition, the present invention provides a gait analysis method and apparatus.

The present invention for achieving the above object, the control unit of the gait analysis device for receiving the measurement information on the kinematic change and mechanical changes for each of the joints of the lower limbs of the normal person and the measurement patient to store in the storage unit; Calculating gait variables for each of the joints using the measurement information; Calculating joint evaluation indices for each of the joints of the measurement patient and joint evaluation indices for each of the joints of the normal person using the gait variables; And normalizing joint evaluation indices for each of the joints of the measurement patient with respect to the joint evaluation indices for each of the joints of the normal person.

The present invention provides the joint evaluation index of the measurement patient calculated by using the main gait variables used in each joint of the lower extremity, by normalizing the joint evaluation indexes of the normal person, and thus the complexity of the analysis of the data obtained through the gait analysis. Can be solved.

In addition, the present invention can classify the symptoms of the patient through the data of the normal person, and can provide an indicator for evaluating the degree of the patient's symptoms, so that the results of gait analysis that can be made only by experts until now, Can be provided as an interpretable figure.

That is, the three joint evaluation indexes obtained through the present invention include a technique for compressing the data obtained in the gait analysis, which may enable the non-expert to evaluate the gait analysis data that could be analyzed only by an expert. . In addition, if the exercise analysis expert system is applied, the technical ripple effect in the clinical field is expected to be great.

In addition, the current exercise analysis expert system has the limitation that the presented result itself has the complexity of analysis, and when the artificial neural network or other artificial intelligence method is applied, the clinical results show only the results, not the reference data. In addition, the results presented through the present invention can be used as a major reference for clinical diagnosis, and also have an advantage of ensuring effective delivery power to non-specialized patients or their families. This technical basis is expected to be used as a major technology for the expansion of the exercise analysis expert system.

In addition, the exploration method and urbanization method presented through the present invention has a variety of applications, such as ergonomics, sports biomechanics, as well as clinical, and thus has the potential to be applied as a base technology to such a solution.

In addition, the present invention can be applied to motion capture equipment, clinic using gait analysis, rehabilitation center, exercise analysis expert system.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of an embodiment of a gait analysis apparatus according to the present invention.

The present invention relates to a method for exploring the kinematic / mechanical change of the lower extremity of a patient using a three-dimensional imaging device, the kinematic (relative angle) / mechanical (force Moment change, create a reference model using the kinematic / mechanical changes of normal joints during walking, which is already built into the database, and use statistical techniques to measure changes in joints of normal and patient. It is compressed to the value of, and normalized the compressed value of the patient to that of a normal person (Normalization) to provide a value to classify the symptoms of the patient and to evaluate the degree of symptoms.

Therefore, the present invention can solve the complexity of the analysis of the data obtained through the gait analysis, can classify the symptoms of the patient through the data of the normal person, and can provide an indicator that can evaluate the degree of the patient symptoms, The results of gait analysis, which could be done only by experts, can be provided as numerical values that can be interpreted by non-experts.

On the other hand, the gait analysis apparatus 10 according to the present invention to which the gait analysis method according to the present invention is applied, as shown in Figure 1, the interface 11, the control unit 12, the input unit 13, the storage unit 14 ) And an output unit 15. As the gait analyzer 10, the above-described three-dimensional imaging apparatus may be used.

The interface 11 is for communicating with an external device through a network, and may be used when downloading various data from a server or uploading data to a server through a network such as the Internet.

The input unit 13 is for inputting various pieces of information necessary for gait analysis according to the present invention, and for example, a keyboard may be used. That is, the user inputs various data or control signals through the keyboard. In addition, the input unit includes various types of cameras, and may receive image information about a walking state of a patient. In addition, the input unit includes various devices for measuring the kinematic (relative angle) / mechanical (force / moment) change as described above, and performs a function of receiving a variety of data measured by the various devices. On the other hand, other devices except the keyboard, for example, a camera or various devices for measuring kinematic (relative angle) / dynamic (force / moment) change may be connected via an interface. However, hereinafter, it is described that the devices are included in the input unit for convenience of description.

The storage unit 14 stores a variety of data or image information input through the input unit.

The output unit 15 is for outputting various types of information under the control of the controller, and a monitor or a printer may be used.

The controller 12 analyzes the gait of the patient using image information and various data input through the input unit, and calculates the result and outputs the result through the output unit. That is, the gait analysis method according to the present invention is executed by the controller.

2 is a flowchart illustrating an example of a gait analysis method according to an embodiment of the present invention. In addition, Figure 3 is an exemplary view showing the definition of each section of the gait analysis applied to the gait analysis method according to the present invention, it shows the definition of each section of the gait analysis for calculating the joint evaluation index. In addition, Figure 4 is a graph showing the gait parameters applied to the gait analysis method according to the present invention, it shows the gait parameters used to calculate the joint evaluation index for the three joints (Hip, Knee, Ankle) constituting the leg. In addition, Figure 5 is an exemplary view showing a state showing three joint evaluation index applied to the gait analysis method according to the present invention, the present invention is three by using the kinematic / mechanical data obtained through three-dimensional gait analysis While calculating the joint evaluation index of the is shown as shown in FIG.

In the gait analysis method according to the present invention, as illustrated in FIG. 2, an experimental step 102 for constructing an experimental database, an experimental step 104 for testing a measurement patient requiring actual evaluation, and data defined in FIG. 3. Computing a gait variable for calculating the joint evaluation index defined in FIG. 4 through the classification of each section of the step (106), and then using the gait parameters of the measurement patient and a normal person joint evaluation index for each joint below Calculating the joint evaluation index according to the method mentioned in the process of calculating the joint normalcy index (108), and then using the average and the maximum deviation of the joint evaluation index of each joint of the settlement person, Dividing the joint evaluation index (normalization) (110) and the step 112 showing the results in the graph and table shown in FIG.

The first step 102 of the present invention is to build a database through experiments, by using a three-dimensional gait analysis technique to receive the input of the kinematic / mechanical changes of the lower extremity joints of the existing patient and normal people through the input unit The step of storing in the storage unit. That is, personal information (age, gender, etc.) about the patient input through the input unit and measurement information input through the camera and various devices of the input unit are stored in the storage unit.

In this case, the present invention is defined by the gait analysis for each section as shown in Figure 3 to calculate the joint evaluation index, the definition of each section of the gait analysis applied to the present invention, Jacquelin Perry in 1992 Gait analysis, published by the Institute, refers to eight major sections presented in the Normal and Pathological function. In general, each section has a predetermined time period, and by using this, data existing on the time series is divided by sections, and a gait variable for calculating each joint evaluation index defined in FIG. 4 is calculated. That is, the first step is a step of receiving a database of various data measured for each gait analysis section as shown in FIG.

The second step 104 of the present invention is a step of testing a measurement patient requiring actual evaluation, and receives and stores various data measured for each gait analysis section as in the first step with respect to the measurement patient, which is a measurement target. Step.

The third step 106 of the present invention is the step of calculating the walking parameter as shown in FIG. That is, the third step is to calculate values for various gait variables as shown in FIG. 4 using various data of the existing patient, the normal person, and the measured patient measured in the first and second steps.

At this time, the present invention, as shown in Figure 4, out of the 50 main gait parameters presented by Sebastian Wolf in 2006, 34 main gait variables extracted by arranging only the parts corresponding to the relative angle of the hip joint / knee joint / ankle joint I use it. Here, the hip joint is a joint between the pelvis and the femur, which is represented by Hip in FIG. 4, and the knee joint is a joint that performs the function of bending the leg backward from the knee, and is represented by Knee, and the ankle joint refers to the ankle joint. It is expressed as Ankle. That is, the third step is to extract gait variables (14 hip joints, 4 knee joints, 16 ankle joints) from the kinematic / mechanical changes of joints during the 1-cycle gait of existing patients, normal subjects and measured patients.

On the other hand, in Figure 4, FlexExt means Flextion and Extension movement, AbAdd means Abduction and Adduction movement, Rot is Internal Rotation and External Rotation ) Std is the standard deviation and Std is the deviation of the angular movement during the main pedestrian interval. Vel is Velocity and it is the angular velocity.

The fourth step 108 of the present invention is to obtain a joint normalcy index for each joint of the measurement patient, and each of the three joints (hip, knee, and ankle) Computing the joint evaluation index for.

First, the theoretical background for obtaining the joint normalcy index for each joint of the measurement patient will be described. That is, the present invention calculates three joint evaluation indexes through principal component analysis using 34 gait parameters.

As a first theoretical background, the present invention uses the 34 gait parameters as follows. That is, the 34 gait variables (evaluation variables) (14 hip joints, 4 knee joints, and 16 ankle joints) applied to the present invention include each joint (hip joint / knee joint) of a normal person and a measurement patient who participated in the gait analysis through the third step. It is extracted based on the change of relative angle / force / moment occurring in the / ankle joint). In general, gait analysis shows data based on one cycle (100%) of the experimenter, and the clinical analyst is in charge of evaluating the various graphs generated in each joint. However, the present invention was summarized as shown in Figure 4 by extracting only 34 major variables corresponding to each joint of the 50 major evaluation factors for gait analysis presented by Sebastian Wolf et al. In 2006, each of the joints 14, knee joint Four values in and 16 values in the ankle joint were used as walking parameters for evaluation of normal and patients.

As a second theoretical background, the present invention uses a principal component analysis as follows. Here, principal component analysis is a multivariate statistical analysis method that is widely used to reveal the structure of data from high-dimensional data or to reduce the dimension of data. That is, the principal component analysis method may be defined as a method of projecting data of an n-dimensional input space to data of an m-dimensional output space. Where m <n, which is to obtain a dimension reduction to keep most of the internal information of the input data. Principal component analysis is a technique for reducing variables into a smaller number of latent variables that contain as much information as possible about the original variables by representing relevant variables among the observed input data. In the analysis, it is called Principal Component. The mathematical functions necessary for understanding principal component analysis and their mathematical background are as follows.

First, Standard Deviation and Variance are applied as shown in [Equation 1]. The distribution of data that the mean cannot represent can be expressed in terms of standard deviation and variance. The statistical significance of the variances shows how far the observed data are from the mean. Variance is defined as the mean of the square of the deviations, and the standard deviation, the square root of the variance, is used more often.

Second, covariance as shown in [Equation 2] is applied. When two or more variables are given, the correlation between each variable is called covariance, and the covariance of the two data is calculated by the following equation (2).

Third, a covariance matrix as shown in [Equation 3] is applied. A matrix representing each correlation between multidimensional data is called a covariance matrix. The covariance matrix is generated by the correlation of the variables of the joints affecting walking. When using a covariance matrix, it is difficult to understand the properties of the matrix and the computation is complicated. Therefore, variables can be correlated using eigenvalues and eigenvectors of the covariance matrix.

Fourth, an eigen vector and an eigen value are applied. Eigenvectors and eigenvalues represent an important feature of matrices as well as analytical tools used in the fields of physics and mechanics as well as in multivariate statistical analysis. A vector that does not change its direction except length after being transformed by a transformation matrix (consisting of eigenvectors in the principal component analysis method) is called an eigenvector of the transformation. When eigenvectors are perpendicular to each other, they can be used as basis vectors. There are n eigenvectors in an n * n matrix, and an eigenvector of length 1 is called a normal eigenvector. After the transformation is performed by the transformation matrix, there is a set of eigenvectors corresponding to one eigenvalue. The variance of the eigenvectors and the amount of information are expressed as eigenvalues. The eigenvalues are a constant feature no matter what value you multiply the eigenvectors before the transformation. On the other hand, in the principal component analysis method, the average value of the eigenvalues is 1, and in the present invention, Kaiser's rule is used in the method of selecting the principal component. The main component has the following five purposes and four characteristics.

The five objectives of the principal components are: first, exploratory data analysis that analyzes multivariate data by identifying covariance matrix structure; second, abbreviate the multidimensional data into a few principal components; and third, identify and interpret the original variables through the obtained principal components. In addition, the principal components are plotted to examine the normality of the original data and to determine outliers. Fifth, the obtained principal components are used as input data for further statistical analysis.

The four characteristics of the main component are as follows. First, the principal component is an artificial variable made by linear combination of the original variables, that is, the principal component is simply a condensed sum of the original variables, not a factor or potential cause that is separate from the original variables. . Second, the principal components extracted from the same data have no correlation with each other. That is, the information implied by each of the main components is independent of each other without overlapping. Third, the principal component is that the size of the dispersion decreases in the order of extraction. Variance in the principal component means the amount of information that the original data has. Therefore, the first principal component implies the most information contained in the original data, and the second and third principal components describe the remaining information that cannot be explained in the first principal component, and the size of the information decreases. Fourth, the maximum number of principal components that can be extracted from the original data is equal to the number of data. That is, the main component may not have additional information other than the information of the original data. Therefore, the number of principal components cannot be greater than the number of original data.

Hereinafter, the practical process 108 for obtaining the joint normalcy index for each joint will be described in detail. That is, the present invention can calculate the joint evaluation index for each of the three joints (Hip, Knee, Ankle) by repeating the process to be performed three times below, or perform the following processes in parallel for each joint By doing so, three joint evaluation indices can be calculated simultaneously. In the following, a method of calculating three joint evaluation indices by separate processes is described as an example of the present invention. That is, when the three joints are expressed as the first joint (Hip), the second joint (Knee), and the third joint (Ankle), the first joint evaluation index and the second joint evaluation The index, the third joint evaluation index can be calculated, and the following describes the process of calculating the first joint evaluation index, particularly for the first joint. Therefore, the second joint evaluation index and the third joint evaluation index may also be calculated through the same process as the following process.

)과 표준편차(

)를 구한다. 즉, 제1관절에 해당되는 14개의 보행 변수들에 대하여 평균과 표준편차가 산출된다. First, the mean of walking parameters for the first joint (hip joint) measured in the normal group through the first to third stages (

) And standard deviation (

). That is, the average and standard deviation are calculated for 14 walking variables corresponding to the first joint.

Second, the measured gait variables of the first joint are standardized using the Z distribution formula shown in [Equation 4] because the difference in units and digits is large. In Equation 4, N means the number of normal people participating in the experiment. That is, the various walking variables shown in FIG. 4 have various units such as speed, angle, and force, and various sizes thereof. The present invention standardizes each walking variable to a certain level through Equation 4.

)을 구한다. N X N 행렬로 나타나는 공분산행렬의 ij성분은 i번째와 j번째 변수간의 상관계수를 나타낸다. 즉, 본 과정에서는 둘째 과정을 통해 표준화된 보행 변수들을 서로 연결관계를 갖는 행렬로 나타낸다.Third, the covariance matrix for the standardized N variables

) The ij component of the covariance matrix represented by the NXN matrix represents the correlation coefficient between the i th and j th variables. In other words, in this process, the standardized walking variables are represented as matrices connected with each other through the second process.

)과 고유벡터(Eigen vector:

)를 구한다. 각 고유값에 대한 고유벡터는 선형결합으로 표현되는 해당 주성분에서 변수간의 상대적 가중치를 나타낸다. 즉, 본 과정에서는 제1관절의 14개의 보행 변수들이 벡터적으로 어떠한 특징을 가지고 있는지가 산출될 수 있다.Fourth, the eigen value of the covariance matrix calculated in the third process (Eigen value:

) And eigenvectors:

). The eigenvectors for each eigenvalue represent the relative weights between the variables in the corresponding principal component, expressed as a linear combination. That is, in this process, it is possible to calculate what characteristics the fourteen walking variables of the first joint have as a vector.

으로 나누어 각 주성분을 편차로 표준화한다.Fifth, new variables with standardization and independence are created as shown in Equation 5 below. Where the principal component is the square root of the eigenvalue

Divide by to standardize each principal component as a deviation.

Meanwhile, when the sum of the values calculated through Equation 5 is obtained, the first joint evaluation index for the first joint of the normal person is calculated, and the present invention provides the first joint evaluation index of the normal person through the five processes. Will be calculated.

)과 표준편차(

)가 이용된다. 따라서, [수학식 6]는 [수학식 4]과 대응되는 형태를 갖는다.Sixth, to calculate the joint evaluation index (JNI) of the measurement patient, the gait variables needed for calculation are extracted from the measurement patient data, and using the mean and standard deviation obtained from the gait variables of the normal person, [Equation 6] Normalize the gait variable as shown (110). In other words, the gait variables of the measurement patients are performed by performing the same process as the first and second processes.

) And standard deviation (

) Is used. Therefore, Equation 6 has a form corresponding to Equation 4.

를 생성한다. 여기서,

와

는 정상인의 데이터에서 추출된 것을 사용한다. 즉, 일곱째 과정은 세번째 내지 다섯번째에 해당되는 과정을 측정환자의 데이터에 대하여 수행하는 과정으로서, [수학식 7]은 [수학식 5]와 대응되는 형태를 갖는다.Seventh, as in the third to fifth process, a new variable for the patient through Equation 7

. here,

Wow

Uses extracted from normal data. That is, the seventh process is a process of performing the process corresponding to the third to fifth data on the patient's data, [Equation 7] has a form corresponding to [Equation 5].

의 합을 이용하여 제1관절에 대한 제1관절평가지수(JNI)가 산출된다. Eighth, as shown in [Equation 8],

Using the sum, the first joint evaluation index (JNI) for the first joint is calculated.

That is, the present invention can calculate the first joint evaluation index for the normal person and the measurement patient through the above processes, and to calculate the second joint evaluation index and the third joint evaluation index for the normal person and the measurement patient through the same process do. On the other hand, the joint evaluation index (J NI) calculated through the above process represents the square of the distance between the average of the normal person and the walking patient data in the newly defined independent coordinate system.

The fifth step 110 of the present invention is to normalize each joint evaluation index of the measurement patient. That is, the data (joint evaluation index) of the measured patient calculated through the above process is divided using the calculated joint evaluation index (mean + deviation) of the normal person. The reason for doing this is to represent the result of comparing the patient's data by making the area of the normal person in the unit triangle area on the graph in the visualization process of the sixth step to be described below. That is, the fifth step is to calculate the relative size of the three joint evaluation index of the measurement patient when each of the three joint evaluation index of the normal person is 1, and the following [Table 1] and It is run using the same program.

The sixth step 112 of the present invention is a step of outputting the normalized joint evaluation index of the measurement patient, which is calculated through the fifth step, as a graph and a table through an output unit, and the controller outputs a graph and a table as shown in FIG. 5. Done.

That is, FIG. 5 is an exemplary view showing a state of the joint evaluation index applied to the gait analysis method according to the present invention. In the present invention, the joint evaluation index is calculated using kinematic / mechanical data obtained through three-dimensional gait analysis. It is calculated and shown as shown in FIG. 5.

Here, each of the three axes represents the joint evaluation index occurring in the hip, knee, and ankle joints, and the area scaled to [1, 1, 1] in the center is the normal subject's joint using the average + maximum deviation of the joint evaluation index. It is a unit triangle that normalizes the joint evaluation index, and is a joint evaluation index region of a normal person. That is, in the case of a normal person, a numerical value always exists inside the unit circle (1, 1, 1).

In addition, the part indicated with a solid line means the joint evaluation index of the measurement patient normalized to the maximum value of a normal person. Meanwhile, in the present invention, as shown in FIG. 5, the joint evaluation index generated in the lower left and lower right is displayed in two graphs and tables. In the table shown in FIG. 5, a balance scale using the left and right joint evaluation indexes is displayed. The balance scale is calculated by dividing the joint evaluation index in the right lower extremity by the joint evaluation index in the left lower extremity. These results are used as indicators for the inequality of the left and right lower extremities. In addition, the reference on the far right of the table of FIG. 5 represents a mean and a deviation of a balance scale of a normal person, and may be used as reference data for comparative evaluation of a balance scale of a measurement patient.

On the other hand, the present invention as described above is to calculate the joint evaluation index (Joint Normalcy Index) by applying the principal component analysis technique, which is a statistical method based on the kinematic / mechanical data obtained through the gait analysis, the contents of the present invention To recap, simply summarize:

That is, the present invention first compresses the data as the main parameter used in the clinic using the kinematic / mechanical data of the human body generated during walking, and then compresses it into three joint evaluation indexes using a statistical technique. It is about a method of urbanization.

In addition, the present invention has a technical feature of acquiring the kinematic / mechanical data generated during walking of the patient and compressing it into primary and secondary to overcome the complexity of data analysis and numerically present the results.

In addition, the present invention relates to a method for exploring the kinematic / mechanical changes of the lower extremities of the patient using a three-dimensional gait analysis device and to a method for urbanization, through such analysis and urbanization, obtained through the gait analysis The analysis of gait analysis, which can only be performed by experts until now, can solve the complexities of data analysis, classify patient's symptoms through the data of normal people, and provide indicators to evaluate the degree of patient's symptoms. Can be provided as a numerical value that can be interpreted by non-experts.

Brief description of the difference between the present invention and the prior art as described above is as follows.

In other words, the gait analysis and related technologies to date do not take into account the complexity of biomechanical data analysis, and thus the focus is on analysis using a neural network or a database of a normal person. However, for the commercialization and efficient analysis of such walking analysis system and delivery to non-experts, it should be quantified simply and an evaluation method should be presented to illustrate. That is, the present invention focuses on a method for commercializing and commercializing an analysis system, unlike the techniques up to now.

In addition, among the conventional techniques, the process of extracting parameters from the kinematic data of the upper body and the lower body and the kinematic data of the foot is mostly, but in the present invention statistically using the main parameters of the gait analysis proposed in existing clinical studies We present a single numerical value using data compression techniques and an effective method to compare and compare it with a normal person.

In addition, among the conventional techniques, 16 evaluation elements (analysis elements) were proposed in the entire lower limb for gait analysis, and some methods were proposed to quantify the same. In order to overcome this limitation, we propose a method for simultaneously providing distance and azimuth indexes, a method for evaluating anomalies for each joint, and a method for urbanizing these results.

Those skilled in the art will appreciate that various changes and modifications can be made without departing from the technical spirit of the present invention. Therefore, the technical scope of the present invention should not be limited to the contents described in the detailed description of the specification, but should be defined by the claims.

1 is a block diagram of an embodiment of a gait analysis apparatus according to the present invention.

Figure 2 is a flow diagram of one embodiment of a gait analysis method according to the invention.

Figure 3 is an exemplary view showing the definition of each section of the gait analysis applied to the gait analysis method according to the present invention.

Figure 4 is a graph showing the gait parameters applied to the gait analysis method according to the invention.

5 is an exemplary view showing a state showing three joint evaluation index applied to the gait analysis method according to the present invention.

DESCRIPTION OF THE RELATED ART [0002]

10: walk analysis device 11: interface

12 control unit 13 input unit

14: storage unit 15: output unit

Claims (18)

A control unit of the gait analyzing apparatus receiving and storing measurement information about kinematic change and mechanical change of each of the lower limb joints of a normal person and a measurement patient, in a storage unit; Calculating gait variables for each of the joints using the measurement information; Calculating joint evaluation indices for each of the joints of the measurement patient and joint evaluation indices for each of the joints of the normal person using the gait variables; And And normalizing the joint evaluation indexes for each of the joints of the measurement patient with respect to the joint evaluation indexes for each of the joints of the normal person, and outputting the joint evaluation indexes. The measurement information, Calculated for each section by dividing the walking section of one cycle into 8 sections Gait analysis method. The method of claim 1, The measurement information is a gait analysis method characterized in that it is calculated using a three-dimensional gait analysis technique. The method of claim 1, The joint evaluation indexes, Gait analysis method, characterized in that calculated for the hip joint, knee joint, ankle joint. The method of claim 3, wherein The gait variables, Gait analysis method characterized in that the variables corresponding to the relative angle of the hip joint, knee joint, ankle joint. delete The method of claim 1, Computing the joint evaluation indexes, (a) matrixing gait variables for a first joint of the lower limb joints and calculating a covariance matrix using the same; (b) calculating eigenvalues and eigenvectors of the covariance matrix; (c) calculating a first joint evaluation index for the joint using the eigenvalue and the eigenvector; And (d) repeating the steps (a) to (c) for each joint of the measurement patient and the normal person, and the joint evaluation indexes for each joint of the measurement patient and the joint evaluation indexes for each joint of the normal person. Gait analysis method comprising the step of calculating. The method of claim 1, Normalizing and outputting the joint evaluation indexes, And the control unit outputs the joint evaluation indices of the measurement patient normalized based on the joint evaluation indices of the normal person on a X-axis, a Y-axis, and a Z-axis to output a graph. The method of claim 1, Normalizing and outputting the joint evaluation indexes, And the control unit outputs a balance scale calculated by dividing a joint evaluation index generated in the lower right leg of the measurement patient by a joint evaluation index generated in the lower left leg. The method of claim 8, And the controller calculates and outputs a balance scale of a normal person together with the balance scale of the measurement patient. An input unit for receiving measurement information on kinematic and mechanical changes of the lower extremity joints of a normal person and a measurement patient; The gait variables for each of the joints are calculated using the measurement information, and the joint evaluation index for each of the joints of the measurement patient and the joint evaluation index for each of the joints of the normal person are calculated using the gait variables. A control unit for calculating joint evaluation indexes for each of the joints of the measurement patient with respect to the joint evaluation indexes for each of the joints of the normal person; And An output unit for outputting the information under the control of the controller, The measurement information, Calculated for each section by dividing the walking section of one cycle into 8 sections Gait Analysis Device. 11. The method of claim 10, And the measurement information is calculated using a three-dimensional gait analysis technique. 11. The method of claim 10, The joint evaluation indexes, Gait analysis device, characterized in that calculated for the hip joint, knee joint, ankle joint. 13. The method of claim 12, The gait variables, Gait analysis device characterized in that the variables corresponding to the relative angle of the hip joint, knee joint, ankle joint. delete 11. The method of claim 10, The control unit, (e) matrixing the walking parameters for the first joint of the lower limb joints, calculating a covariance matrix using the same, (f) calculating the eigenvalues and eigenvectors of the covariance matrix, and (g) the eigenvalues The first joint evaluation index for the joint is calculated using the value and the eigenvector, and (h) (e) to (g) are repeated for each joint of the measurement patient and the normal person, Gait analysis device characterized in that for calculating the joint evaluation index for each joint and the joint evaluation index for each joint of the normal person. 11. The method of claim 10, The control unit, The gait analyzer, characterized in that for controlling the output unit, displaying the joint evaluation indices of the measurement patient normalized based on the joint evaluation indices of the normal person on the X-axis, the Y-axis, and the Z-axis to output a graph. . 11. The method of claim 10, The control unit, And controlling the output unit to output a balance scale calculated by dividing the joint evaluation index generated in the lower right leg of the patient by the joint evaluation index generated in the lower left leg. The method of claim 17, The control unit, And a control unit configured to control the output unit to calculate and output a balance scale of a normal person together with the balance scale of the measurement patient.

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