KR102313044B1 - Method and system for evaluating elderly health status using characteristics of walk-in-place - Google Patents

Abstract

Provided are a method and system for evaluating the health status of the elderly using a standing gait characteristic. A health status evaluation system for the elderly using standing-in-place characteristics according to an embodiment of the present invention includes: a standing-by-step data collection unit for collecting standing-by-step data from a subject; a standing gait characteristic analyzer for analyzing the standing gait data to extract standing gait characteristics; a health state learning unit for machine learning the health state of the subject with respect to the standing-in-place characteristic to construct a health state evaluation model based on the standing-to-walk characteristic; and a health state evaluation unit for evaluating the health state of the new subject by inputting the standing gait characteristic obtained from the new subject into the health state evaluation model.

Description

Method and system for evaluating elderly health status using characteristics of walk-in-place}

The present invention relates to a method and system for evaluating the health condition of an elderly person using a standing gait characteristic, and to a method and system capable of evaluating the health condition of an elderly person based on the standing gait characteristic measured using simple equipment in a limited space.

With the rapid aging of the population, the personal and social burden of medical expenses for the elderly is increasing. It is known that the health status of the elderly affects gait characteristics. Gait is a complex process in which well-coordinated dynamic movements occur simultaneously in several joints of the lower extremities, and high-level cognitive functions such as memory, concentration, and execution are involved as well as motor and sensory abilities. That is, gait characteristics include osteoarthritis of the lower extremities (ankle, knee, hip), neuromuscular diseases (eg, degenerative spinal disease), brain dysfunction (eg, dementia), cerebrovascular It may reflect health conditions such as cerebrovascular disease (eg, stroke), cardiorespiratory diseases, metabolic disturbances, and the like. Therefore, if the health status of the elderly is evaluated using walking characteristics, it can be mainly used for early diagnosis of related diseases, symptom management, and appropriate treatment.

However, in order to analyze and quantify such walking ability, U.S. Patent Publication No. US 2009-0141933 discloses a technique for acquiring an image while walking using a camera and analyzing the movement of the leg to predict motion and predict the gait cycle. suggested. However, this prior art is expensive motion capture system (motion capture system), electromyograph (electromyograph), equipment such as force plate (force plate), etc., free space such as a straight structure space of up to 16 meters in length and experience rich experimentation. self is needed That is, the method for evaluating walking ability using the above-described equipment not only caused high costs due to the input of special equipment and manpower, but also made it difficult for an elderly person with reduced mobility to fully perform the above-described experiment, so it is difficult to take accurate result data. There were difficult drawbacks.

Therefore, there is a need for a method and a system capable of measuring a gait using simple equipment in a limited space and evaluating the health status of an elderly person therefrom.

US Patent Publication US 2009-0141933 A1 (June 04, 2009)

The present invention has been devised to solve the above problems, and specifically, it provides a method and system for measuring the standing gait characteristic of a subject using simple equipment in a limited space and evaluating the subject's health status based on this. .

An elderly health status evaluation system according to an embodiment of the present invention includes: a standing step data collection unit for collecting standing step data from a subject; a standing gait characteristic analyzer for analyzing the standing gait data to extract standing gait characteristics; a health state learning unit for machine learning the health state of the subject with respect to the standing-in-place characteristic to construct a health state evaluation model based on the standing-to-walk characteristic; and a health state evaluation unit for evaluating the health state of the new subject by inputting the standing gait characteristic obtained from the new subject into the health state evaluation model.

The elderly health status evaluation method according to an embodiment of the present invention acquires standing gait data from a plurality of subjects, analyzes the standing gait data to extract standing gait characteristics, and health based on the standing gait characteristics building a state evaluation model; collecting standing gait data from the subject to be evaluated and extracting situation characteristics; and inputting the standing gait characteristic of the subject to be evaluated into the constructed health condition evaluation model to evaluate the health condition of the subject to be evaluated.

The elderly health status evaluation method and system according to this embodiment do not use the expensive gait analysis system that was used in the past because it is based on the standing gait data, and evaluate the health status of the subject in a method that requires less professional manpower and physical space. It is possible to collect gait data that can be used for

Time-consuming and labor-intensive processes required for processing and feature engineering of input data can be minimized by analyzing the gait characteristics in a simple and automated way and using it as an input for an artificial neural network model.

It can provide opportunities for early diagnosis, symptom management, and appropriate treatment of related diseases by monitoring health conditions related to senility, muscle loss (fall risk), cognitive dysfunction, etc. have.

1 is a block diagram of a health status evaluation system for the elderly using a standing gait characteristic according to an embodiment of the present invention.
2A is a diagram exemplarily illustrating a subject participating in a health condition evaluation system for the elderly and a sensor attached to the subject according to an embodiment of the present invention.
2B is an exemplary diagram illustrating a schematic configuration of a standing step data collection unit according to an embodiment of the present invention.
3 is an exemplary graph of a signal measured by attaching a three-axis accelerometer to the foot when walking in place.
4A and 4B are exemplary views in which the signal of FIG. 3 is converted into a spectrogram for analyzing the characteristics of walking in place.
5 illustrates an exemplary machine learning model of the health state learning unit of the health state evaluation system for the elderly according to an embodiment of the present invention.
6 is a flowchart of a method for evaluating the health status of the elderly using a standing gait characteristic according to an embodiment of the present invention.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION The detailed description set forth below in conjunction with the appended drawings is intended to describe exemplary embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. The following detailed description includes specific details in order to provide a thorough understanding of the present invention. However, one of ordinary skill in the art will recognize that the present invention may be practiced without these specific details. Specific terms used in the following description are provided to help the understanding of the present invention, and the use of these specific terms may be changed to other forms without departing from the technical spirit of the present invention.

1 is a block diagram of a health status evaluation system for the elderly using a standing gait characteristic according to an embodiment of the present invention. 2A is a diagram exemplarily illustrating a subject participating in a health condition evaluation system for the elderly and a sensor attached to the subject according to an embodiment of the present invention. 2B is an exemplary diagram illustrating a schematic configuration of a standing step data collection unit according to an embodiment of the present invention. 3 is an exemplary graph of a signal measured by attaching a three-axis accelerometer to the foot when walking in place. 4A and 4B are exemplary views in which the signal of FIG. 3 is converted into a spectrogram for analyzing the characteristics of walking in place. 5 illustrates an exemplary machine learning model of the health state learning unit of the health state evaluation system for the elderly according to an embodiment of the present invention.

Referring to FIG. 1 , the elderly health status evaluation system 10 includes a standing step data collection unit 100 , a standing still character analysis unit 110 , a health condition learning unit 120 , a health condition evaluation unit 130 , and data. and a base portion 140 .

An elderly health status evaluation system according to embodiments may be entirely hardware, or may have aspects that are partly hardware and partly software. For example, the health status evaluation system for the elderly of the present specification and each unit included therein may collectively refer to a device for exchanging data in a specific format and content in an electronic communication method and software related thereto. As used herein, terms such as “unit”, “module”, “server”, “system”, “device” or “terminal” refer to a combination of hardware and software driven by the hardware. it is intended to be For example, the hardware herein may be a data processing device including a central processing unit (CPU) or other processor. In addition, software driven by hardware may refer to a running process, an object, an executable file, a thread of execution, a program, and the like.

In addition, each part constituting the elderly health status evaluation system is not necessarily intended to refer to physically distinct and distinct components. In FIG. 1 , the standing step data collection unit 100 , the standing step characteristic analysis unit 110 , the health state learning unit 120 , the health state evaluation unit 130 , and the database unit 140 are separate blocks separated from each other. Although shown as , this is only functionally dividing the devices constituting the health status evaluation system for the elderly by the operations executed by the devices. Therefore, depending on the embodiment, the standing step data collection unit 100, the standing step characteristic analysis unit 110, the health state learning unit 120, the health state evaluation unit 130, and the database unit 140 are partially or All of them may be integrated in the same single device, one or more may be implemented as separate devices physically separated from other units, or components may be communicatively connected to each other in a distributed computing environment.

In addition, the target of the elderly health status evaluation system 10 according to an embodiment of the present invention may be an elderly person. Here, the elderly means a person who is older than a certain age, and may be defined as a person who is 65 years of age or older under the Elderly Welfare Act, but is not limited thereto. Subjects of the present invention may be composed of subjects younger than the above age, or may be composed of subjects above the age.

The standing step data collection unit 100 may collect standing step data from the subject. The elderly health status evaluation system 10 may include one or more sensors 101 for measuring the standing gait data.

Here, the sensor 101 may include an accelerometer or a gyroscope of a small size that is easily removable and attached to the body of the subject H and is portable.

When the kinematic data of the gait motion is collected using an accelerometer or a gyroscope, the subject H participating in the elderly health status evaluation system 10 is equipped with a sensor 101 on the body as shown in FIG. 2A . will do The body part of the subject H to which the sensor 101 is mounted may be a body part directly related to the gait motion of the subject H. Illustratively, the body part of the subject H to which the sensor 101 is mounted may be at least one of a pelvis, a thigh, a shank, and a foot of the subject H, The present invention is not limited thereto.

The accelerometer may measure the acceleration of a moving object or the strength of an impact, and the gyroscope may measure the rotation angle and inclination of the rotating object. That is, the accelerometer and the gyroscope attached to the body segment of the subject H may provide quantitative data on the motion occurring in the body joints during the standing-still motion to the standing-step data collection unit 100 .

That is, the sensor 101 performs a tool role in evaluating the health status of the subject H using the standing gait characteristic. The standing step data collection unit 100 may receive the standing step data of the subject H from each sensor 101 . The standing step data collection unit 100 and the sensor 101 may be configured to wirelessly transmit and receive signals.

In some embodiments, the elderly health status assessment system 10 may further include a camera 102 and a pressure mat 103 , as shown in FIG. 2B . The camera 102 may measure the user's body joint position and angle. The pressure mat 103 corresponds to a limited space in which the subject's standing gait is performed. In addition, the pressure mat 103 may measure the plantar pressure of the user. The standing step data (the user's body joint position and angle) collected through the camera 102 and the standing step data (the plantar pressure of the user) collected through the pressure mat 103 are collected by the standing step data collection unit 100 . can be provided on That is, the standing step data may include at least one of the measured kinematic data, the measured body joint position and angle of the subject, and the measured plantar pressure of the subject.

The standing walking characteristic analyzer 110 analyzes the standing walking data received from the standing still data collecting unit 100 and extracts the standing walking characteristics. Specifically, the standing gait characteristic analyzer 110 extracts parameters related to spatiotemporal gait factors, joint angles, and changes in plantar pressure. Also, the standing walking characteristic analyzer 110 may acquire images related to spatiotemporal gait factors, joint angles, and changes in plantar pressure. That is, the standing gait characteristic may include at least one of a spatiotemporal gait factor, parameters related to joint angle and plantar pressure change, and an image related to spatiotemporal gait factor, joint angle and plantar pressure change. Here, the image may be a spectrogram showing a change in frequency and energy of a signal over time. The standing-still characteristic analyzer 110 may convert the time-domain signal into a spectrogram through a Fourier transform or a wavelet transform.

FIG. 4A exemplarily shows a spectrogram generated by applying a short-time Fourier transform (STFT) to the signal of FIG. 3, and FIG. 4B is a continuous wavelet transform (CWT). The spectrogram generated by applying it is shown as an example. The image representing the kinematic data of the standing motion in the standing walking characteristic analyzer 110 may be a spectrogram, but is not limited thereto. The health state learning unit 120 machine-learns the health state of the subject with respect to the standing-still characteristic, and builds a health state evaluation model based on the standing-still characteristic. Here, the standing gait characteristic may include a spatiotemporal gait factor analyzed by the standing gait characteristic analyzer 110 , parameters related to joint angle and plantar pressure change, and an image related to spatiotemporal gait element, joint angle, and plantar pressure change.

The health state learning unit 120 is a set of machine learning algorithms that attempts high-level abstraction (summarizing core contents or functions in large amounts of data or complex data) through a combination of several nonlinear transformation methods. A health state evaluation model having a structure as shown in FIG. 5 may be constructed using deep learning. The deep learning model is a convolutional neural network that forms a connection pattern between neurons similar to the structure of the visual cortex of an animal, a recurrent neural network that builds up a neural network every moment over time, and a probability distribution for an input set It may be based on a restricted Boltzmann machine that can learn , but is not limited thereto.

An input used for learning by the health state learning unit 120 is a standing gait characteristic of each of a plurality of subjects, and an output is a health state of each of the plurality of subjects.

Here, the health status of the subject includes a degree of senility of the subject, a degree of muscle weakness of the subject, and/or a degree of cognitive dysfunction of the subject. The subject's degree of frailty may be dichotomized into non-frailty or frailty, or triangulated into non-frailty, pre-frailty, or frailty. The health state learning unit 120 uses the standing walking characteristics (parameters and/or images) generated by the standing still characteristics analyzing unit 110 as an input value, and using the degree of senescence of the subject as an output value to obtain a first health state An evaluation model can be built.

In addition, the degree of sarcopenia of the subject may be bisected into non-sarcopenia or sarcopenia. The health state learning unit 120 may build a second health state evaluation model by using the standing gait characteristic generated by the standing gait characteristic analysis unit 110 as an input value and using the subject's muscle loss degree as an output value. have.

In addition, the degree of cognitive impairment of the subject is dichotomized into non-cognitive impairment (non-CI) or cognitive impairment (CI), or non-cognitive impairment, mild cognitive impairment (mild CI), or moderate or higher cognitive impairment ( moderate-to-severe CI). The health state learning unit 120 may build a third health state evaluation model by using the standing gait characteristic generated by the standing still characteristic analysis unit 110 as an input value and using the degree of cognitive dysfunction as an output value. .

The health state evaluation model built in the health state learning unit 120 includes a first health state evaluation model for determining the degree of senescence of the subject, a second health state evaluation model for determining the degree of muscle loss of the subject, and cognition of the subject At least one of the third health status evaluation models for determining the degree of functional impairment may be included.

The health state evaluation model built in the health state learning unit 120 may be stored in the database unit 140 . The database unit 140 may be configured to temporarily store or store information such as the health status evaluation model and the standing gait data and/or the standing gait characteristics based thereon.

The health state evaluation unit 130 may evaluate the health state of a new subject by using the health state evaluation model built in the health state learning unit 120 . Here, the new subject may mean another subject not used in the construction of the model.

The standing gait characteristics of the new subject acquired through the standing gait data collection and standing gait characteristics analysis process may be provided to the health condition evaluation unit 130 and input to the health condition evaluation model, and the health status of the new subject may be output. have. Specifically, the health state evaluation unit 130 uses the degree of senescence of the new subject using the first health state evaluation model, the degree of muscle loss of the new subject using the second health state evaluation model, or the third health state evaluation model. Thus, it is possible to evaluate the degree of cognitive dysfunction in new subjects.

The health status evaluation unit 130 divides the degree of senility of a new subject into non-senescence or senility by using the first health status evaluation model, or compares the degree of senility of the new subject using the first health status evaluation model. It can be judged by triangulation into senility, pre-senescence, or senility. The health state evaluation unit 130 may determine the degree of muscle loss of the new subject by bisecting it into sarcopenia or sarcopenia by using the second health state evaluation model. In addition, the health state evaluation unit 130 determines the degree of cognitive dysfunction of the new subject by dividing it into non-cognitive impairment or cognitive impairment using the third health state evaluation model, or uses the third health state evaluation model to determine the new subject. The degree of cognitive impairment of the subject can be divided into three categories: non-cognitive impairment, mild cognitive impairment, or moderate or higher cognitive impairment.

The health state evaluation unit 130 may provide the corresponding subject with the results output from each health state evaluation model, and health conditions related to senescence, muscle loss (fall risk), cognitive dysfunction, etc. of the elderly based on the gait characteristic monitoring can be provided. Accordingly, it is possible to provide the subject with an opportunity for early diagnosis of related diseases, symptom management, and appropriate treatment.

Hereinafter, a health condition evaluation method according to an embodiment of the present invention will be described.

6 is a flowchart of a health status evaluation method according to an embodiment of the present invention. The method may be performed in the system of FIGS. 1 to 5 described above, and FIGS. 1 to 5 may be referred to for explanation in this embodiment.

Referring to FIG. 6 , in the method for evaluating the health of the elderly using the standing-in-place characteristic according to an embodiment of the present invention, the step of constructing a health condition evaluation model based on the standing-in-place characteristic (S100), and collecting standing-up data from the subject to be evaluated and extracting the standing gait characteristics (S110) and inputting the standing gait characteristics of the subject to be evaluated into the health condition evaluation model to evaluate the health condition of the subject to be evaluated (S120).

Building a health status evaluation model based on the standing gait characteristics (S100).

First, a plurality of subjects (H) performs a standing step for a predetermined time while the sensor 101 is mounted on a body part, and the sensor 101 provides standing step data to the standing step data collection unit 100 . can do. That is, the sensor 101 performs a tool role in evaluating the physical state of the subject H according to the standing gait. The standing step data collection unit 100 may receive standing step data from each sensor 101 . The sensor 101 may include an accelerometer or gyroscope of a small size that is easy to attach to and detach from the body of the subject H and is portable. In some embodiments, the elderly health status assessment system 10 may further include a camera 102 and a pressure mat 103 , as shown in FIG. 2B . The camera 102 may measure the user's body joint position and angle. The pressure mat 103 corresponds to a limited space in which the subject's standing gait is performed. In addition, the pressure mat 103 may measure the plantar pressure of the user. The standing step data (the user's body joint position and angle) collected through the camera 102 and the standing step data (the plantar pressure of the user) collected through the pressure mat 103 are collected by the standing step data collection unit 100 . can be provided on That is, the standing step data may include at least one of the measured kinematic data, the measured body joint position and angle of the subject, and the measured plantar pressure of the subject.

Next, the standing gait characteristics are extracted by analyzing the standing gait data.

The standing walking characteristic analyzer 110 analyzes the standing walking data received from the standing still data collecting unit 100 and extracts the standing walking characteristics. Specifically, the standing gait characteristic analyzer 110 extracts parameters related to spatiotemporal gait factors, joint angles, and changes in plantar pressure. Also, the standing walking characteristic analyzer 110 may acquire images related to spatiotemporal gait factors, joint angles, and changes in plantar pressure. That is, the standing gait characteristic may include at least one of a spatiotemporal gait factor, parameters related to joint angle and plantar pressure change, and an image related to spatiotemporal gait factor, joint angle and plantar pressure change. Here, the image may be a spectrogram showing a change in frequency and energy of a signal over time. The standing still characteristic analyzer 110 may convert the time domain signal into a spectrogram through a Fourier transform or a wavelet transform.

A health status evaluation model based on the standing-to-walk characteristics is constructed by machine learning the health state of the subject for the standing-to-walk characteristics.

The health state learning unit 120 machine-learns the health state of the subject with respect to the standing-still characteristic, and builds a health state evaluation model based on the standing-still characteristic. Here, the standing gait characteristic may include a spatiotemporal gait factor analyzed by the standing gait characteristic analyzer 110 , parameters related to joint angle and plantar pressure change, and an image related to spatiotemporal gait element, joint angle, and plantar pressure change.

The health state learning unit 120 builds a health state evaluation model of the structure shown in FIG. 5 using deep learning, which is a set of machine learning algorithms that attempt high-level abstraction through a combination of several non-linear transformation methods. can The deep learning model is a convolutional neural network that forms a connection pattern between neurons similar to the structure of the visual cortex of an animal, a recursive neural network that builds a neural network every moment over time, and a limit that can learn probability distributions for input sets It may be based on a Boltzmann machine or the like, but is not limited thereto.

An input used for learning by the health state learning unit 120 is a standing gait characteristic of each of a plurality of subjects, and an output is a health state of each of the plurality of subjects.

Here, the health status of the subject includes a degree of senility of the subject, a degree of muscle weakness of the subject, and/or a degree of cognitive dysfunction of the subject. The health state evaluation model built in the health state learning unit 120 includes a first health state evaluation model for determining the degree of senescence of the subject, a second health state evaluation model for determining the degree of muscle loss of the subject, and cognition of the subject At least one of the third health status evaluation models for determining the degree of functional impairment may be included.

Next, the standing step data is collected from the subject to be evaluated, and the standing step characteristics are extracted (S110).

At least one of the sensor 101 , the camera 102 , and the pressure mat 103 may be used to acquire data of the subject's walking in place. The standing step data collection unit 100 receives the standing step data of the subject to be evaluated from at least one of the sensor 101, the camera 102, and the pressure mat 103, and the standing step characterization unit 110 is the standing step By analyzing the data, the gait characteristics can be extracted.

The health status of the subject to be evaluated is evaluated (S120).

The standing gait characteristic obtained from the new subject may be provided to the health state evaluation unit 130 and provided as an input value of the constructed health state evaluation model, and the health state evaluation model provides the health state of the new subject as an output value. can That is, the health state evaluation unit 130 may evaluate the health state of the new subject based on the standing gait characteristic of the new subject. Specifically, the health state evaluation unit 130 uses the degree of senescence of the new subject using the first health state evaluation model, the degree of muscle loss of the new subject using the second health state evaluation model, or the third health state evaluation model. Thus, it is possible to evaluate the degree of cognitive dysfunction in new subjects. The health status evaluation unit 130 may provide a result according to each health status evaluation model to the subject, and based on the standing gait characteristic, the health status related to senescence, muscle loss (fall risk), cognitive dysfunction, etc. of the elderly monitoring can be provided. Accordingly, it is possible to provide the subject with an opportunity for early diagnosis of related diseases, symptom management, and appropriate treatment.

As such, the elderly health condition evaluation method according to the present embodiment can acquire the subject's gait characteristics in a way that requires less professional manpower and physical space without using the expensive gait analysis system used in the past.

Time-consuming and labor-intensive processes required for processing input data and generating features can be minimized by analyzing the gait characteristics in a simple and automated way and using it as an input for an artificial neural network model.

It can provide opportunities for early diagnosis, symptom management, and appropriate treatment of related diseases by monitoring health conditions related to senility, muscle loss (fall risk), cognitive dysfunction, etc. have.

The operation by the method for evaluating the health condition of the elderly using the standing gait characteristic according to the embodiments described above may be at least partially implemented as a computer program and recorded in a computer-readable recording medium. A program for implementing the operation by the method for evaluating the health of the elderly using the standing-in-place characteristic according to the embodiments is recorded and the computer-readable recording medium includes all kinds of recording devices in which data readable by the computer is stored. include Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. In addition, the computer-readable recording medium may be distributed in a network-connected computer system, and the computer-readable code may be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present embodiment may be easily understood by those skilled in the art to which the present embodiment belongs.

Although described above with reference to the embodiments, the present invention should not be construed as being limited by these embodiments or drawings, and those skilled in the art will appreciate the spirit and scope of the present invention described in the claims below. It will be understood that various modifications and variations of the present invention can be made without departing from the scope of the present invention.

10: Elderly Health Status Assessment System
100: stationary step data collection unit
110: stand-by step characteristic analysis unit
120: Health State Learning Department
130: health status evaluation department
140: database unit

Claims (15)

A limited space in which a subject's standing gait is performed, comprising: a pressure mat for measuring plantar pressure according to the subject's standing gait;
a standing step data collection unit for collecting the plantar pressure of the subject measured from the pressure mat as standing step data;
a standing gait characteristic analyzer for analyzing the standing gait data to extract standing gait characteristics;
a health state learning unit for machine learning the health state of the subject with respect to the standing-in-place characteristic to construct a health state evaluation model based on the standing-to-walk characteristic; and
A health status evaluation system for the elderly using standing-in-place characteristics, comprising: a health status evaluation unit for evaluating the health status of the new subject by inputting the standing-in-place characteristics obtained from the new subject into the health condition evaluation model. According to claim 1,
The health condition evaluation model includes a first health condition evaluation model for determining the degree of senescence of the subject, a second health condition evaluation model for determining the degree of muscle loss of the subject, and a third health condition for determining the degree of cognitive dysfunction of the subject. at least one of the condition evaluation models;
The health status evaluation unit uses the first health status evaluation model to determine the degree of senescence of the new subject, the muscle loss degree of the new subject using the second health status evaluation model, or the third health status evaluation model for the new subject. A health status evaluation system for the elderly using gait characteristics, characterized in that it evaluates the degree of cognitive dysfunction of According to claim 1,
The health state learning unit is a health state evaluation system for the elderly using a standing gait characteristic for generating the health state evaluation model using deep learning. According to claim 1,
a sensor attached to the subject's body to measure kinematic data according to the subject's gait motion in place; And further comprising a camera for measuring the body joint position and angle of the subject,
The standing gait data is a health status evaluation system for the elderly using the standing gait characteristics further comprising the measured kinematic data and the measured body joint position and angle of the subject. 5. The method of claim 4,
The standing gait characteristic includes at least one of spatiotemporal gait factor, parameters related to joint angle and plantar pressure change, and an image related to spatiotemporal gait factor, joint angle and plantar pressure change. 6. The method of claim 5,
The image is a local Fourier transform-based spectrogram, a health status evaluation system for the elderly using a standing gait characteristic. 6. The method of claim 5,
The image is a continuous wavelet transformation-based spectrogram, a health status evaluation system for the elderly using a standing gait characteristic. A method for evaluating the health of the elderly using the standing-in-place characteristics performed in the elderly health condition evaluation system using the standing-in-place, the method comprising:
collecting standing gait data from a plurality of subjects, analyzing the standing gait data to extract standing gait characteristics, and constructing a health status evaluation model based on the standing gait characteristics;
collecting standing gait data from the subject to be evaluated and extracting situation characteristics; and
Comprising the step of evaluating the health status of the subject to be evaluated by inputting the standing gait characteristics of the subject to be evaluated into the constructed health condition evaluation model,
The standing gait data includes plantar pressure of the subject provided from a pressure mat, wherein the pressure mat is a limited space in which the subject's situation is performed. A method of assessing the health status of the elderly using 9. The method of claim 8,
The health condition evaluation model includes a first health condition evaluation model for determining the degree of senescence of the subject, a second health condition evaluation model for determining the degree of muscle loss of the subject, and a third health condition for determining the degree of cognitive dysfunction of the subject. at least one of the condition evaluation models;
The step of evaluating the health status of the subject to be evaluated,
The degree of senility of the new subject using the first health status evaluation model, the degree of muscle loss of the new subject using the second health status evaluation model, or the degree of cognitive dysfunction of the new subject using the third health status evaluation model A method of assessing health status of the elderly using gait characteristics, comprising evaluating 9. The method of claim 8,
The step of constructing a health condition evaluation model based on the standing gait characteristic includes:
A method for evaluating the health of the elderly using the gait characteristic of building the health status evaluation model using deep learning. 9. The method of claim 8,
The standing gait data includes kinematic data provided from a sensor attached to the subject's body and the position and angle of the subject's body joints provided from a camera. 12. The method of claim 11,
The standing gait characteristic includes at least one of a spatiotemporal gait factor, parameters related to joint angle and plantar pressure change, and an image related to spatiotemporal gait factor, joint angle, and plantar pressure change. 13. The method of claim 12,
The image is a spectrogram based on a local Fourier transform, a method for evaluating the health of the elderly using a standing gait characteristic. 13. The method of claim 12,
The image is a continuous wavelet transformation-based spectrogram, a method for evaluating the health of the elderly using a standing-in-place characteristic. A computer program stored in a medium to be combined with hardware to execute the elderly health condition evaluation method using the gait characteristic of any one of claims 8 to 14.

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