KR20210082982A - Method and Apparatus for measuring aging parameter based on motion information - Google Patents

Abstract

Provided is a method for predicting a cardiopulmonary function comprising: a step of measuring, by a movement sensor, a movement of a cardiopulmonary function measurement subject; a step of determining, by a processor, a walking speed of the cardiopulmonary function measurement subject according to the movement of the cardiopulmonary function measurement subject; and a step of predicting, by the processor, a cardiopulmonary function index of the cardiopulmonary function measurement subject based on the walking speed of the cardiopulmonary function measurement subject. Therefore, the present invention is capable of allowing a patient's cardiopulmonary function to be predicted simply and efficiently.

Description

Cardiopulmonary function index measuring device based on motion information {Method and Apparatus for measuring aging parameter based on motion information}

The present invention relates to a method and apparatus for predicting a cardiopulmonary function index of a subject for measuring cardiopulmonary function, and more particularly, to a method and an apparatus for predicting a cardiopulmonary function index based on movement information of a subject for measuring cardiopulmonary function.

Cardiopulmonary function measurement is very important for risk assessment and treatment response evaluation of cardiovascular diseases such as congestive heart failure and pulmonary arterial hypertension, respiratory diseases such as chronic obstructive pulmonary disease and interstitial lung disease, and oncological diseases such as lung cancer requiring lung resection. Do. However, in the exercise load cardiorespiratory function test, etc., it is difficult to conduct a complex cardiopulmonary function test for the elderly or patients with poor physical function because the subject must exercise up to the maximum intensity according to the instructions of the test protocol. In addition, since medical equipment and specialized personnel for cardiopulmonary function tests are available only in some tertiary general hospitals, performing cardiopulmonary function tests in the case of conventional surgery is limited. In addition, there is a difficulty in repeating the time-consuming and expensive professional cardiopulmonary function test periodically for a short period of time as a follow-up test for treatment response.

However, a method of measuring cardiopulmonary function that can be conveniently measured repeatedly can be helpful for accurate and appropriate clinical decision making. Therefore, there is a need for a better method that can easily measure cardiopulmonary function, which can replace the conventional professional cardiopulmonary function test in patients with a normal level.

In the present specification, a method and apparatus for predicting cardiopulmonary function index based on movement information of a subject for cardiopulmonary function measurement are provided. And a system for predicting cardiopulmonary function index based on the movement information of the target cardiopulmonary function measurement is provided. In addition, there is provided a storage medium in which the program is recorded together with a program for performing a method of predicting cardiopulmonary function index.

According to an embodiment, the method comprising: measuring, by a motion sensor, a movement of a cardiopulmonary function measurement target; By the processor, according to the movement of the cardiopulmonary function measurement subject, determining the walking speed of the cardiopulmonary function measurement subject; And, by the processor, based on the walking speed of the cardiopulmonary function measurement subject, a cardiopulmonary function prediction method comprising the step of predicting the cardiopulmonary function index of the cardiopulmonary function measurement subject is provided.

According to one embodiment, a motion sensor for measuring the movement of the target cardiopulmonary function measurement; a memory in which a program including at least one instruction is stored; and a processor for predicting the cardiopulmonary function index of the cardiorespiratory function measurement subject by executing the at least one program, wherein the at least one instruction includes: an instruction for a motion sensor to measure the movement of the cardiopulmonary function measurement object; Instructions for causing the processor to determine at least one of a walking speed and a gait acceleration of the cardiopulmonary function measurement subject according to the movement of the cardiopulmonary function measurement subject; And based on at least one of the walking speed and gait acceleration of the cardiopulmonary function measurement subject to the processor, a cardiopulmonary function prediction device comprising a command to predict the cardiopulmonary function index of the cardiopulmonary function measurement subject is provided do.

According to an embodiment, there is provided a computer program product including instructions for causing each step of the method for predicting cardiopulmonary function to be performed by a computer. Also provided is a computer-readable recording medium in which the computer program product is stored.

According to the method for predicting cardiopulmonary function provided in the present disclosure, the method patient's cardiorespiratory function can be predicted simply and efficiently.

1 illustrates an embodiment of a gait measuring device that determines a cardiopulmonary function index based on a walking ability of a cardiorespiratory function measurement subject.
FIG. 2 shows an embodiment of a cardiopulmonary function indicator measurement system including a gait meter, an oximeter, an external computing device, and a server.
3 illustrates an embodiment of a method for predicting cardiopulmonary function index performed by a gait meter.
4 illustrates an embodiment of a method for predicting a cardiopulmonary function index according to a cardiopulmonary function measurement system including a gait meter and an external computing device.
5 illustrates an embodiment of a method for predicting a cardiopulmonary function index according to a cardiopulmonary function measurement system including a gait meter, an external computing device, and a server.
6 illustrates an embodiment of a method for predicting a cardiopulmonary function index according to a cardiopulmonary function measurement system including a gait meter, an oxygen saturation meter, an external computing device, and a server.
7 illustrates an embodiment of a setting item of a walking speed measurement item displayed on a display in a program executed by a gait meter or an external computing device.
8 illustrates an embodiment of a walking speed measurement item of a walking speed measurement item displayed on a display in a program executed by a walking meter or an external computing device.
9 illustrates an embodiment of a data management item displayed on a display in a program executed by a gait meter or an external computing device.
10 illustrates a method of measuring a walking speed, according to an embodiment.
11 shows a graph showing the walking distance of each group according to each NYHA functional classification according to the 6-minute walking test.
12 is a graph showing the correlation between walking distance, cardiac output, total lung resistance, maximum exercise oxygen consumption, anaerobic threshold, oxygen pulse, and minute ventilation versus carbon dioxide emission slope according to the 6-minute walking test.
13 shows a graph of the Kaplan-Meier survival curves of the two groups according to the 6-minute walk test. The Kaplan-Meier survival curve represents the survival probability over time of the experimental group.
In the following description, the same reference numerals are used for the same elements, and overlapping descriptions are omitted unless described in other drawings.

Advantages and features of the disclosed embodiments, and methods of achieving them, will become apparent with reference to the embodiments described below in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and common knowledge in the art to which the present invention pertains It is only provided to fully inform those who have the scope of the invention.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail.

1 illustrates an embodiment of a gait meter 100 that determines a cardiopulmonary function index based on the walking ability of the cardiorespiratory function measurement target 120 .

The gait meter 100 may include a display 102 , a memory 104 , a motion sensor 106 , a processor 108 , and a communication interface 110 . According to an embodiment, the gait measuring device 100 may additionally include a configuration necessary for measuring the walking ability of the cardiorespiratory function measurement target 120 .

The gait meter 100 may include a display 102 for displaying data related to the measurement and predicted cardiorespiratory function of the cardiorespiratory function measurement target 120 . For example, the display 102 may display cardiopulmonary function such as a step length, a step width, a stride length, a number of steps, a gait speed and a gait acceleration of the cardiorespiratory function measurement target 120 . A value indicating the walking ability of the measurement target 120 may be displayed. The stride length represents the distance from one heel to the other. In addition, the stride length represents the distance from one heel to the same heel after two steps. In addition, the display 102 may display cardiopulmonary function related data for the cardiopulmonary function measurement target (120). Cardiopulmonary function-related data include mean pulmonary arterial blood pressure (mPAP, mean pulmonary arterial pressure), cardiac output (CO, Cardiac Output), total pulmonary resistance (TPR), NYHA classification (New York Heart Association Classification), and maximum exercise oxygen. Peak V _O2 , peak exercise oxygen consumption, anaerobic threshold (AT), Oxygen Pulse, and V _E -V _CO2 slope, regression slope relating minute ventilation to carbon cardiorespiratory function indicators such as dioxide output).

Also, the display 102 may include a function of a touch screen for receiving a user's instruction. Accordingly, the user may receive information through the display 102 and input the user's instruction to be performed by the gait measurement device 100 . For example, the gait meter 100 may receive personal information of the cardiopulmonary function measurement target 120 from the user, such as identification information, age, gender, residential area, race, etc., from the display 102 . Alternatively, the gait meter 100 may receive setting information for measuring gait ability from the display 102 .

The gait meter 100 may include other input interfaces in addition to the display 102 . Accordingly, the gait meter 100 may receive information for predicting cardiopulmonary function from another input interface such as a mechanical or electronic button other than the display 102 .

The gait measurement device 100 may include a memory 104 in which a program for driving the gait measurement device 100 is stored. The memory 104 may store at least one program for performing cardiopulmonary function prediction. In addition, the memory 104 may store information obtained from the communication interface 110 or information processed by the processor 108 . In addition, the memory 104 may store information on the correlation between the cardiopulmonary function index and the walking ability necessary for the information processing of the processor 108 .

The gait meter 100 may include a motion sensor 106 . According to the motion sensor 106, how the cardiopulmonary function measurement target 120 moved during the measurement time is sensed. Sound waves or infrared rays are emitted from the motion sensor 106 , and sound waves or infrared rays reflected from the cardiopulmonary function measurement target 120 are sensed by the motion sensor 106 , whereby the position of the cardiopulmonary function measurement target 120 is identified. The motion sensor 106 may be implemented as a one-dimensional sensor capable of measuring only the distance to the measurement object or a two-dimensional sensor capable of measuring the position of the measurement object, but is not limited thereto. A person skilled in the art can readily use a commonly known distance measuring sensor as the motion sensor 106 . When the motion sensor 106 is a two-dimensional sensor capable of measuring the position of the target cardiopulmonary function measurement target 120 in a plane, a vertical component and a horizontal component of the position of the cardiopulmonary function measurement target 120 may be detected. In addition, the vertical and horizontal components of the walking speed and gait acceleration of the cardiopulmonary function measurement target 120 may be calculated according to the vertical component and the horizontal component of the position of the cardiorespiratory function measurement target 120 . In addition, the cardiopulmonary function index of the cardiopulmonary function measurement target 120 may be predicted according to the vertical and/or horizontal component of the walking speed and/or gait acceleration of the cardiopulmonary function measurement target 120 .

The gait meter 100 may include a processor 108 . The processor 108 may execute instructions of at least one program for measuring walking ability and predicting a cardiopulmonary function index stored in the memory 104 . Hereinafter, the function of the processor 108 according to the program for performing the measurement of walking ability and the prediction of the cardiorespiratory function index is described.

By the processor 108, based on the movement information of the cardiopulmonary function measurement subject 120, the cardiopulmonary function measurement subject 120 stride length (step length), stride width (step width), stride length (stride length), It is possible to calculate factors related to the walking ability of the target 120 for cardiopulmonary function measurement, such as the number of steps, a walking distance, a walking speed, and a walking acceleration. In addition, the processor 108 may calculate a factor related to the walking ability of the subject 120 for cardiopulmonary function measurement based on at least one of the set setting values. For example, the setting information includes at least one of a measurement direction, a measurement range, and an effective measurement range.

The measurement direction indicates the movement direction of the cardiopulmonary function measurement subject to be recognized by the motion sensor 106 . The measurement direction may include a forward direction, a backward direction, a left direction, a right direction, etc. with respect to the motion sensor 106 . The motion sensor 106 may record the movement of the cardiopulmonary function measurement target 120 when the cardiopulmonary function measurement target 120 moves in a set measurement direction.

The measurement range indicates the range of the distance between the motion sensor 106 and the cardiopulmonary function measurement target 120 in which the movement of the cardiopulmonary function measurement target can be measured. The motion sensor 106 may generate movement information of the cardiopulmonary function measurement target 120 when it is detected that the cardiopulmonary function measurement target 120 is within the measurement range. The user may input a measurement range into the gait meter 100 . Alternatively, the measurement range may be automatically changed according to the measurement environment of the motion sensor 106 .

The effective measurement range indicates a range of an area in which the motion sensor 106 detects the cardiopulmonary function measurement target 120 in order to generate motion information of the cardiorespiratory function measurement target 120 . Even if the cardiopulmonary function measurement subject 120 walks within the measurement range, if the gait is not completed within the effective measurement range, the processor 108 does not generate movement information of the cardiopulmonary function measurement object 120 . Similar to the measurement range, the user may input an effective measurement range into the gait meter 100 . Alternatively, the effective measurement range may be automatically changed according to the measurement environment of the motion sensor 106 .

The processor 108 performs movement information according to one of a walking speed measurement mode for measuring the walking speed of the cardiopulmonary function measurement subject and a gait analysis mode for additionally acquiring movement information according to the gait analysis of the cardiopulmonary function measurement subject to the walking speed can create

The processor 108 may predict the cardiopulmonary function index of the cardiopulmonary function measurement target 120 based on the movement information of the cardiopulmonary function measurement target 120 . Movement information such as walking speed and gait acceleration is closely related to the cardiopulmonary function of the target 120 for cardiopulmonary function measurement. Accordingly, the processor 108 may calculate the cardiopulmonary function index of the cardiopulmonary function measurement target 120 according to the correlation between the movement information and the cardiopulmonary function index.

The processor 108 may determine a walking speed parameter indicative of a walking speed. The walking speed parameter represents the walking speed of a specific section. For example, the walking speed parameter may be defined for each section having a size of 0.2 m/s. As a specific example, the walking speed parameter may be defined as 1 for a section of 0.4 to 0.6 m/s, and may be defined as 2 for a section of 0.6 to 0.8 m/s. In addition, a unique walking speed parameter may be defined for the remaining 0.2 m/s sections. Similarly, the processor 108 may determine a gait acceleration parameter representative of gait acceleration of a specific section. The above example is merely exemplary, and the value of the walking speed parameter and the corresponding section and the value of the walking acceleration parameter and the corresponding section can be easily changed by a person skilled in the art.

The processor 108 may determine the cardiorespiratory function index from the walking speed based on the correlation between the walking speed and the cardiopulmonary function index indicating the relationship between the walking speed and the cardiopulmonary function index. When the walking speed is expressed as the walking speed parameter, the processor 108 may determine the cardiopulmonary function index from the walking speed parameter according to the correlation of the walking speed-cardiopulmonary function index. The correlation between the walking speed and the cardiorespiratory function index may be determined by regression analysis of statistical data on the walking speed and the cardiopulmonary function index or by machine learning. The correlation between walking speed-cardiopulmonary function index is specifically described in FIGS. 11 to 13 .

Additionally, the processor 108 may calculate the gait acceleration of the cardiopulmonary function measurement target 120 from the movement information of the cardiopulmonary function measurement target 120 . In addition, the processor 108 may obtain the cardiopulmonary function index of the cardiopulmonary function measurement target 120 by analyzing the walking speed and gait acceleration of the cardiopulmonary function measurement target 120 .

The processor 108 further considers personal information such as identification information, actual age, gender, residence area, race, height, weight, etc. of the target person 120 for cardiopulmonary function measurement, cardiopulmonary function index of the target person 120 for cardiopulmonary function measurement can be derived. The processor 108 may predict the probability of occurrence of a cardiopulmonary disease according to the cardiopulmonary function index. Therefore, according to the probability of occurrence of cardiopulmonary-related diseases calculated by the processor 108 , it may be helpful in determining a treatment method for the subject 120 for measuring cardiopulmonary function.

The processor 108 may use motion information as well as other measurements to determine cardiorespiratory function indicators. In order to measure the cardiorespiratory function index, factors other than movement information such as walking speed may be additionally used. For example, data obtained through muscle strength evaluation, muscle mass evaluation, balance sensor evaluation, and the like may be additionally considered. In order to acquire the additional data, separate measuring devices may be installed separately from the walking measuring device 100 . For example, a muscle strength meter may be installed to evaluate muscle strength, a muscle mass meter to evaluate muscle mass, and a balance sensor to evaluate balance.

The processor 108 may perform a calculation for deriving a cardiopulmonary function index based on statistical data. For example, statistical data on the correlation between walking speed and cardiorespiratory function index presented above, walking speed, and statistical data according to the actual age and gender of the target 120 for measuring cardiorespiratory function may be used. In addition, statistical data on other factors that are correlated with cardiorespiratory function indicators may be used. The processor 108 may periodically update the statistical data from an external server and store it in the memory 104 of the gait meter 100 .

The processor 108 does not calculate the cardiopulmonary function index of the cardiopulmonary function measurement target 120, but can obtain the cardiopulmonary function index of the cardiopulmonary function measurement target 120 calculated in the server 122 through the communication interface 110 have. When the cardiopulmonary function index of the cardiopulmonary function measurement target 120 is predicted in the server 122, the size of data and programs required for prediction of the cardiopulmonary function index stored in the memory 104 of the gait meter 100 is reduced, and cardiopulmonary The outflow of movement information and human information required for prediction of functional indicators may be restricted.

The processor 108 may obtain identification information of the user. And when the user's identification information is valid, the processor 108 may perform a function for determining the cardiopulmonary function index. The processor 108 may transmit the user's identification information stored in the external server through the communication interface 110 to determine whether the user's identification information is valid. And when it is determined by the external server that the user's identification information is valid, the processor 108 may allow the user's access to the function for predicting the cardiopulmonary function index. In addition, the processor 108 may store information related to the cardiopulmonary function measurement target 120 in the user's e-mail account.

The processor 108 may generate a time-based graph of at least one of a walking speed and a walking acceleration of the target 120 for cardiopulmonary function measurement. The time-based graph may then be shown on the display 102 . The processor 108 may predict the cardiopulmonary function index of the cardiopulmonary function measurement target 120 according to the gait pattern shown in the time-based graph.

The processor 108 may predict the cardiopulmonary function index of the cardiopulmonary function measurement target 120 by further considering the oxygen saturation of the cardiopulmonary function measurement target 120 . Oxygen saturation refers to the ratio of the number of hemoglobin bound to oxygen to the total number of hemoglobin in the blood. The oxygen saturation level may be measured from the target 120 for cardiopulmonary function measurement before and/or after motion information measurement.

In addition to the processor 108 , the gait meter 100 may include an additional processor for performing a command required for predicting cardiopulmonary function. In addition, the gait measurement device 100 may use a processor external to the gait measurement device 100 to perform a command related to prediction and determination of a cardiopulmonary function index.

The gait meter 100 may include a communication interface 110 to receive and transmit data with an external device. The communication interface 110 may be implemented according to communication standards such as mobile communication, Bluetooth, Wi-Fi, infrared data association standard (IrDA), WiMAX, and the like.

Also, the gait meter 100 may transmit movement information and human information of the cardiopulmonary function measurement target 120 to an external device through the communication interface 110 . In addition, the gait measuring device 100 may acquire the cardiopulmonary function index of the subject 120 for cardiopulmonary function measurement and medical information based on the cardiopulmonary function index from an external device through the communication interface 110 . Also, according to an embodiment, the gait measurement device 100 may acquire a database indicating the relationship between the cardiopulmonary function index and the gait speed from an external device through the communication interface 110 .

The gait measurement device 100 of FIG. 1 is not limited to the above-described embodiment, and those skilled in the art of medical devices for cardiopulmonary disease may easily exclude or modify some configurations of the gait measurement device 100 described in FIG. 1 . Or, it can be implemented by adding a configuration obvious to those skilled in the art to the gait measurement device 100 .

FIG. 2 shows an embodiment of a cardiopulmonary function indicator measurement system 200 including a gait meter 210 , an oxygen saturation meter 230 , an external computing device 250 , and a server 260 .

The cardiorespiratory function index measurement system 200 may include at least one of an oxygen saturation meter 230 , an external computing device 250 , and a server 260 together with the gait meter 210 . The step meter 210 may include a display 212 , a memory 214 , a motion sensor 216 , a processor 218 , and a communication interface 220 . The gait meter 210 may be communicatively connected to the oxygen saturation meter 230 , the external computing device 250 , and the server 260 to transmit/receive data. Also, the gait measurement device 210 of FIG. 2 may perform the function of the gait measurement device 100 of FIG. 1 . Accordingly, the relationship between the gait meter 100 described in FIG. 1 and an external server and an external device becomes clearer by the cardiopulmonary function index measurement system 200 shown in FIG. 2 .

The oxygen saturation meter 230 may include a display 232 , a memory 234 , an oxygen saturation sensor 236 , a processor 238 , and a communication interface 240 .

The display 232 may display the oxygen saturation of the cardiopulmonary function measurement target 120 . In addition, the display 232 may display cardiopulmonary function related data for the cardiopulmonary function measurement target (120).

The memory 234 may store at least one program for measuring oxygen saturation. In addition, the memory 234 may store information obtained from the communication interface 240 or information processed by the processor 238 . In addition, the memory 234 may store information on the correlation between the cardiorespiratory function index and the oxygen saturation level required for information processing by the processor 238 .

According to the oxygen saturation sensor 236 , the oxygen saturation level of the cardiopulmonary function measurement subject 120 may be measured before and/or after motion information measurement.

The processor 238 may execute an instruction of at least one program for measuring oxygen saturation and/or predicting a cardiopulmonary function index stored in the memory 234 . In addition, the processor 238 of the oximeter 230 may perform the function of the processor 108 of the gait meter 100 instead. Accordingly, by the processor 238 of the oxygen saturation meter 230, the cardiopulmonary function index may be predicted from the walking ability and the oxygen saturation level.

According to an embodiment, the oxygen saturation meter 230 may include an additional processor for executing a command required for predicting cardiopulmonary function in addition to the processor 238 . In addition, the oxygen saturation meter 230 may use a processor external to the oxygen saturation meter 230 to perform a command related to prediction and determination of the cardiorespiratory function index.

The communication interface 240 may be implemented according to a communication standard such as mobile communication, Bluetooth, Wi-Fi, infrared data association standard (IrDA), WiMAX, and the like. Through the communication interface 240 , the oxygen saturation information and personal information of the cardiopulmonary function measurement subject 120 may be transmitted to the gait meter 210 , the external computing device 250 , and/or the server 260 . And from the gait meter 210, the external computing device 250, and/or the server 260 through the communication interface 110, the cardiopulmonary function index of the target person 120 for cardiopulmonary function measurement and medical information based on the cardiopulmonary function index is obtained can be In addition, according to an embodiment, the oximeter 230 may receive a relationship between the cardiopulmonary function index and oxygen saturation from the gait meter 210 , the external computing device 250 , and/or the server 260 through the communication interface 240 . It is possible to obtain a cardiopulmonary function prediction database representing

The external computing device 250 may include a display 252 , a memory 254 , a processor 256 , and a communication interface 258 . External computing device 250 may be a personal computer, laptop, mobile device, or wearable device.

The external computing device 250 is communicatively connected to the gait meter 210 and/or the oximeter 230 to receive cardiopulmonary function measurement from the gait meter 210 and/or the oximeter 230 . ) of motion information, human information, and/or oxygen saturation information may be acquired. In addition, the external computing device 250 may predict the cardiopulmonary function index of the cardiopulmonary function measurement target 120 from the movement information, human information, and/or oxygen saturation information of the cardiopulmonary function measurement target 120 . And the predicted cardiorespiratory function indicator may be displayed on the display 252 of the external computing device 250 , or transmitted to the gait meter 210 and/or the oximeter 230 .

According to an embodiment, the external computing device 250 is communicatively connected to the server 260 , and provides the server 260 with movement information, human information, and/or oxygen saturation information of the target 120 for cardiopulmonary function measurement. can be transmitted. In addition, the external computing device 250 may obtain the cardiopulmonary function index of the cardiopulmonary function measurement target 120 predicted by the server 260 from the server 260 .

The display 252 may display movement information, personal information, oxygen saturation information, and/or a predicted value of a cardiopulmonary function index of the cardiorespiratory function measurement target 120 .

A program for controlling the operation of the external computing device 250 may be stored in the memory 254 . In the memory 254, according to the movement information, personal information, and/or oxygen saturation information of the cardiopulmonary function measurement target 120 obtained from the gait meter 210 and/or the oxygen saturation meter 230, the cardiopulmonary function measurement target ( 120) a program for determining the predicted value of the cardiopulmonary function index may be stored. In addition, the memory 254 may store movement information, personal information, oxygen saturation information, and/or a predicted value of a cardiopulmonary function index of the cardiorespiratory function measurement target 120 .

The processor 256 may execute instructions of a program for controlling the operation of the external computing device 250 . The processor 256 executes the instructions of the program for determining the predicted value of the cardiopulmonary function index of the cardiopulmonary function measurement subject 120 according to the movement information, human information, and/or oxygen saturation information of the cardiorespiratory function measurement subject 120 . can do.

By way of communication interface 258 , external computing device 250 may be communicatively coupled with gait meter 210 , oximeter 230 , and/or server 260 . Accordingly, the external computing device 250 transmits, via the communication interface 258 , setting information for measurement, such as motion information and/or oxygen saturation information, to the gait meter 210 and/or the oximeter 230 . can In addition, the motion information and/or oxygen saturation information generated based on the setting information is transmitted from the gait meter 210 and/or the oxygen saturation meter 230 to the external computing device 250 through the communication interface 258 . can be transmitted.

The server 260 may include a memory 262 , a processor 264 , and a communication interface 266 . According to an embodiment, the server 260 may additionally include a configuration necessary for predicting the cardiopulmonary function index.

A program necessary for driving the server 260 may be stored in the memory 262 . The memory 262 may store at least one program for performing prediction and determination of a cardiopulmonary function prediction database and cardiopulmonary function index including statistical data for prediction and determination of the cardiopulmonary function index. In addition, the memory 262 may store movement information, cardiopulmonary function index, personal information, and oxygen saturation information for the cardiorespiratory function measurement target 120 .

An instruction of at least one program for predicting and determining the cardiopulmonary function index stored in the memory 262 by the processor 264 may be performed. By the processor 264, the cardiopulmonary function measurement target 120 from the movement information, human information and / or oxygen saturation information of the cardiopulmonary function measurement target 120 received from the external computing device 250 through the communication interface 266 A cardiopulmonary function index of may be determined. In addition, the cardiopulmonary function indicator of the cardiopulmonary function measurement target 120 may be transmitted to the external computing device 250 by the processor 264 through the communication interface 266 .

The processor 264 is based on the cardiopulmonary function prediction database stored in the memory 262, the cardiopulmonary function of the cardiopulmonary function measurement target 120 from the movement information, human information and / or oxygen saturation information of the cardiopulmonary function measurement target 120 indicators can be determined. The processor 264 may update the cardiopulmonary function prediction database stored in the memory 262 . Accordingly, the processor 264 may predict the cardiopulmonary function index of the cardiopulmonary function measurement target 120 based on the updated latest cardiopulmonary function prediction database.

Server 260 may include a communication interface 266 to receive and transmit data with external computing device 250 . The communication interface 266 may be implemented according to a communication standard such as mobile communication, Bluetooth, Wi-Fi, infrared data association standard (IrDA), WiMAX, or the like.

The server 260 may receive movement information, personal information, and/or oxygen saturation information of the cardiorespiratory function measurement target 120 from the external computing device 250 through the communication interface 266 . In addition, the server 260 may transmit the cardiopulmonary function index of the target person 120 for cardiopulmonary function measurement and medical information based on the cardiopulmonary function index to the external computing device 250 through the communication interface 266 . The server 260 may also transmit the cardiopulmonary function prediction data set to the external computing device 250 via the communication interface 266 .

In addition to the processor 264 , the server 260 may include an additional processor for performing a command required for predicting cardiopulmonary function. In addition, the server 260 may use a processor external to the server 260 to perform a command regarding cardiopulmonary function index determination.

According to an embodiment, the server 260 may be directly connected to the gait meter 210 and/or the oxygen saturation meter 230 without being connected to the external computing device 250 . Accordingly, the server 260 may directly receive information necessary for the prediction of the cardiorespiratory function index from the gait meter 210 and/or the oxygen saturation meter 230 . In addition, the server 260 may directly transmit the predicted cardiorespiratory function index to the gait meter 210 and/or the oxygen saturation meter 230 .

According to an embodiment, the cardiopulmonary function measurement target 120 by storing information necessary for the prediction of the cardiopulmonary function index and the cardiopulmonary function index of the cardiopulmonary function measurement target 120 only in the server 260 of the cardiopulmonary function measurement system 200 , the cardiopulmonary function measurement target 120 leakage of personal information can be prevented.

According to an embodiment, a person skilled in the art measures cardiopulmonary function in one or more of a gait meter 210 , an oximeter 230 , an external computing device 250 , and a server 260 using the cardiorespiratory function measurement system 200 . The cardiopulmonary function index of the subject 120 can be easily designed to predict. Accordingly, in FIG. 1 , all functions necessary for predicting the cardiopulmonary function index are performed by the gait meter 100 , but in the cardiopulmonary function measuring system 200 of FIG. 2 , in FIG. ), oxygen saturation meter 230 , external computing device 250 , and server 260 .

And those of ordinary skill in the art have the cardiopulmonary function measurement system 200, the information necessary for the prediction of the cardiorespiratory function index of the cardiorespiratory function measurement subject 120 is a gait meter 210, an oxygen saturation meter 230, an external computing device 250. , and can be easily designed to be transmitted between the server 260 . In addition, the cardiorespiratory function measurement system 200 may include an additional device for acquiring information necessary for the prediction of the cardiorespiratory function index.

3 illustrates an embodiment of a method for predicting cardiopulmonary function index performed by the gait meter 100 .

In step S32, the movement of the cardiopulmonary function measurement target is measured by the motion sensor.

According to an embodiment, the motion sensor may be implemented as a 2D LIDAR, and a two-dimensional motion of a subject to be measured for cardiopulmonary function may be measured using the 2D LIDAR.

According to an embodiment, an effective measurement range of the motion sensor may be set. In addition, the movement of the cardiopulmonary function measurement target within the effective measurement range may be measured.

According to an embodiment, one of a gait speed measurement mode for measuring the walking speed of the subject to be measured for cardiopulmonary function and a gait analysis mode for additionally acquiring motion information according to the gait analysis of the subject for measuring cardiorespiratory function may be selected. And according to the selected measurement mode, the movement information of the cardiopulmonary function measurement target is generated.

In step S34, by the processor, according to the movement of the cardiopulmonary function measurement subject, the walking speed of the cardiopulmonary function measurement subject is determined. In addition, the gait acceleration of the cardiopulmonary function measurement subject may be determined according to the cardiopulmonary function measurement subject's walking speed.

According to an embodiment, at least one of a vertical component and a horizontal component of the walking speed and a vertical component and a horizontal component of the walking acceleration may be measured based on the two-dimensional movement of the subject to measure cardiopulmonary function.

According to an embodiment, a time-based graph of at least one of a walking speed and a gait acceleration of the target for measuring cardiopulmonary function may be generated.

In step S36, the cardiopulmonary function index of the cardiopulmonary function measurement subject is predicted by the processor based on the walking speed of the cardiopulmonary function measurement subject. In addition, the cardiorespiratory function index may be predicted by further considering the gait acceleration of the cardiorespiratory function measurement target.

According to an embodiment, a walking speed parameter indicating a walking speed may be determined by the processor. Likewise, a gait acceleration parameter indicative of gait acceleration may be determined. The walking speed parameter represents a walking speed of a specific section, and the walking acceleration parameter represents a walking acceleration of a specific section.

And, by the processor, the predicted value of the cardiopulmonary function index may be determined according to the gait speed parameter and/or the gait acceleration parameter. According to the walking speed-cardiopulmonary function correlation function, a predicted value of the cardiopulmonary function index may be determined based on the walking speed parameter. Also, according to the gait acceleration-cardiopulmonary function correlation function, a predicted value of the cardiopulmonary function index may be determined based on the gait acceleration parameter.

According to an embodiment, the processor may determine the predicted value of the cardiopulmonary function index according to at least one of a vertical component and a horizontal component of the walking speed and a vertical component and a horizontal component of the walking acceleration.

According to an embodiment, the cardiopulmonary function index of the cardiopulmonary function measurement target may be predicted by further considering the human information of the cardiopulmonary function measurement target together with movement information such as walking speed.

According to an embodiment, the cardiopulmonary function index of the subject of cardiopulmonary function measurement may be predicted according to the gait pattern shown in the time-based graph of walking speed and/or the time-based graph of gait acceleration.

According to an embodiment, the oxygen saturation level of the subject to be measured for cardiopulmonary function may be measured by the oxygen saturation meter. And further considering oxygen saturation, the cardiopulmonary function index of the target cardiopulmonary function measurement target can be predicted.

4 illustrates an embodiment of a method for predicting a cardiopulmonary function index according to a cardiopulmonary function measurement system including a gait meter 210 and an external computing device 250 .

In step S41 , identification information of the gait meter 210 is transmitted from the gait meter 210 to the external computing device 250 .

In step S42 , it is determined whether the identification information of the gait meter 210 is valid by the external computing device 250 . If the identification information of the gait measurement device 210 is valid, the external computing device 250 and the gait measurement device 210 are communicatively connected.

In step S43 , setting information regarding motion detection of the cardiopulmonary function measurement target 120 is transmitted from the external computing device 250 to the gait meter 210 .

In step S44, the gait meter 210 detects the movement of the cardiopulmonary function measurement target 120 according to the transmitted setting information. The setting information may include a measurement direction, a measurement range, and an effective measurement range. In addition, the gait measurement device 210 generates motion information including at least one of a position, a gait speed, and a gait acceleration.

In step S45 , the movement information of the cardiopulmonary function measurement target 120 is transmitted from the gait meter 210 to the external computing device 250 .

In step S46, the personal information of the cardiopulmonary function measurement target 120 is input to the external computing device (250).

In step S47 , the cardiopulmonary function index of the cardiopulmonary function measurement target 120 is calculated from the movement information and the human information of the cardiopulmonary function measurement target 120 by the external computing device 250 .

In step S48 , the cardiopulmonary function index of the cardiopulmonary function measurement target 120 is displayed by the display 252 of the external computing device 250 .

5 illustrates an embodiment of a method for predicting a cardiopulmonary function index according to a cardiopulmonary function measurement system including a gait meter 210 , an external computing device 250 , and a server 260 .

In step S51 , identification information of the gait meter 210 is transmitted from the gait meter 210 to the external computing device 250 .

In step S52 , it is determined whether the identification information of the gait meter 210 is valid by the external computing device 250 . If the identification information of the gait measurement device 210 is valid, the external computing device 250 and the gait measurement device 210 are communicatively connected.

In step S53 , setting information related to motion detection of the cardiopulmonary function measurement target 120 is transmitted from the external computing device 250 to the gait meter 210 .

In step S54, the gait meter 210 detects the movement of the cardiopulmonary function measurement target 120 according to the transmitted setting information. In addition, the gait measurement device 210 generates motion information including at least one of a position, a gait speed, and a gait acceleration.

In step S55 , the movement information of the cardiopulmonary function measurement target 120 is transmitted from the gait meter 210 to the external computing device 250 .

In step S56, the personal information of the cardiopulmonary function measurement target 120 is input to the external computing device (250).

In step S57, the movement information and the human information of the cardiopulmonary function measurement target 120 from the external computing device 250 to the server 260 is transmitted. Additionally, other information necessary for calculating the cardiorespiratory function index may be transmitted from the external computing device 250 to the server 260 .

In step S58, the cardiopulmonary function index of the cardiopulmonary function measurement target 120 is calculated from the movement information and the human information of the cardiopulmonary function measurement target 120 by the server 260.

In step S59 , the cardiopulmonary function index of the cardiopulmonary function measurement target 120 is transmitted from the server 260 to the external computing device 250 .

In step S60 , the cardiopulmonary function index of the cardiopulmonary function measurement target 120 is displayed by the display 252 of the external computing device 250 .

6 illustrates an embodiment of a method for predicting a cardiopulmonary function index according to a cardiopulmonary function measurement system including a gait meter 210 , an oxygen saturation meter 230 , an external computing device 250 , and a server 260 .

In step S61 , identification information of the gait meter 210 and the oximeter 230 is transmitted from the gait meter 210 and the oximeter 230 to the external computing device 250 .

In step S62 , it is determined whether the identification information of the gait meter 210 and the oxygen saturation meter 230 is valid by the external computing device 250 . If the identification information of the gait meter 210 and the oximeter 230 is valid, the external computing device 250 is communicatively connected with the gait meter 210 and the oximeter 230 .

In step S63 , setting information regarding movement detection and oxygen saturation measurement of the cardiopulmonary function measurement target 120 is transmitted from the external computing device 250 to the gait meter 210 . In addition, setting information regarding the oxygen saturation measurement of the cardiorespiratory function measurement target 120 is transmitted from the external computing device 250 to the oxygen saturation meter 230 .

In step S64, the gait meter 210 detects the movement of the cardiopulmonary function measurement target 120 according to the transmitted setting information. In addition, the gait measurement device 210 generates motion information including at least one of a position, a gait speed, and a gait acceleration.

In step S65 , the movement information of the cardiopulmonary function measurement target 120 is transmitted from the gait meter 210 to the external computing device 250 .

In step S66, the oxygen saturation meter 230 measures the oxygen saturation of the cardiopulmonary function measurement target 120 according to the transmitted setting information. In addition, the oxygen saturation meter 230 generates oxygen saturation information regarding oxygen saturation.

In step S67 , the oxygen saturation information of the cardiopulmonary function measurement target 120 is transmitted from the oxygen saturation meter 230 to the external computing device 250 .

In step S68, the personal information of the cardiopulmonary function measurement target 120 is input to the external computing device (250).

In step S69 , movement information, oxygen saturation information, and human information of the cardiopulmonary function measurement target 120 are transmitted from the external computing device 250 to the server 260 . Additionally, other information necessary for calculating the cardiorespiratory function index may be transmitted from the external computing device 250 to the server 260 .

In step S70 , the cardiopulmonary function index of the cardiopulmonary function measurement target 120 is calculated from the movement information, oxygen saturation information, and human information of the cardiopulmonary function measurement target 120 by the server 260 .

In step S71 , the cardiopulmonary function index of the cardiopulmonary function measurement target 120 is transmitted from the server 260 to the external computing device 250 .

In step S72 , the cardiopulmonary function index of the cardiopulmonary function measurement target 120 is displayed by the display 252 of the external computing device 250 .

The embodiment of FIGS. 3 to 6 is only an example, and the method for predicting cardiopulmonary function index according to the present disclosure is not limitedly interpreted according to the embodiment of FIGS. 3 to 6 . Those skilled in the art of cardiopulmonary disease refer to the functions and operations performed by the gait meter 100 and the cardiopulmonary function index prediction system 200 described in FIGS. 1 and 2 to facilitate the embodiment of FIGS. 3 to 6 . can be changed to

7 shows an embodiment of the setting item 710 of the walking speed measurement item 700 shown on the displays 102 and 252 in a program executed by the gait meter 100 or the external computing device 250 . .

The setting item 710 includes a measurement range setting item 720 and a measurement method setting item 730 .

In the measurement range setting item 720 , setting values related to the measurement ranges of the motion sensors 106 and 216 are displayed. Specifically, the measurement range setting item 720 includes motion sensor information 721 , measurement direction information 722 , distance offset information 723 , effective measurement range length information 724 indicating the length and width of the effective measurement range, and Effective measurement range width information 725 may be included.

The motion sensor information 721 indicates the type of the motion sensors 106 and 216 . In addition, the measurement range of the motion sensors 106 and 216 is determined according to the type of the motion sensors 106 and 216 . The measurement range represents the maximum range within which the motion sensors 106 and 216 can detect the motion of the object.

An effective measurement range in which the movement of the cardiopulmonary function measurement target 120 is recorded within the measurement range is determined. If all movements in the measurement range are recorded, noise may occur in the movement information. Therefore, by setting the effective measurement range and allowing the cardiopulmonary function measurement target 120 to walk within the effective measurement range, movement information of the cardiopulmonary function measurement target 120 can be efficiently generated.

The effective measurement range may be determined according to the measurement direction information 722 , the distance offset information 723 , the effective measurement range length information 724 , and the effective measurement range width information 725 . In the present disclosure, the effective measurement range is set as a rectangle, but may be set in a different shape according to an embodiment. The measurement direction information 722 , the distance offset information 723 , the effective measurement range length information 724 , and the effective measurement range width information 725 may be modified by a user input.

The measurement direction information 722 indicates the measurement direction of the motion sensors 106 and 216 . The distance offset information 723 indicates the minimum vertical distance between the motion sensors 106 and 216 and the effective measurement range. Also, the effective measurement range length information 724 and the effective measurement range width information 725 indicate the length and width of the effective measurement range, respectively. An effective measurement range according to the measurement direction information 722 , the distance offset information 723 , the effective measurement range length information 724 , and the effective measurement range width information 725 is checked in the data confirmation window 750 .

The measurement method setting item 730 represents a method of determining movement information from the movement of the cardiopulmonary function measurement target 120 . Specifically, the measurement method setting item 730 may include a measurement mode item 731 , a walking speed measurement method item 732 , and a measurement start option item 740 .

The moving distance calculation method item 731 represents a calculation method of the moving distance of the cardiorespiratory function measurement target 120 . In the present disclosure, in the measurement mode item 731, the walking speed measurement mode for measuring the walking speed of the subject 120 for cardiopulmonary function measurement and motion information according to the gait analysis of the cardiopulmonary function measurement subject 120 are additionally acquired to the walking speed. One of the gait analysis modes can be selected. According to the gait analysis mode, movement information such as stride length, stride width, stride length, number of steps, gait distance, gait speed, and gait acceleration is calculated according to the gait analysis of the cardiorespiratory function measurement target 120 . According to the walking speed measurement mode, only the walking speed of the cardiopulmonary function measurement target 120 is measured.

The walking speed measurement method item 732 represents the walking pattern of the cardiopulmonary function measurement subject 120 for measuring the walking speed. In the present disclosure, one of the one-way measurement method item 733 and the round trip measurement method item 736 in the walking speed measurement method item 732 may be selected.

According to the one-way measurement method, the time for the cardiopulmonary function measurement target 120 to move a predetermined measurement distance in one direction is measured. In addition, the walking speed of the subject 120 for cardiopulmonary function measurement is measured from the walking speed measurement section in which the change in walking speed is small. Therefore, a section in which the magnitude of the walking acceleration is greater than a predetermined acceleration threshold is excluded from the walking speed measurement section. The predetermined measurement distance and the predetermined acceleration threshold may be determined according to the measurement distance item 734 and the acceleration threshold value item 735, and the measurement distance item 734 and the acceleration threshold value item 735 are the user's It can be determined according to the input.

The round trip measurement method item 736 includes a movement distance-based measurement method item 737 and a time-based measurement method item 738 . According to the movement distance-based measurement method, the walking speed is measured based on the time it takes for the cardiorespiratory function measurement target 120 to complete walking of a predetermined round-trip movement distance. According to the time-based measurement method, the walking speed is measured based on the reciprocating distance that the cardiorespiratory function measurement target 120 walks for a predetermined measurement time. The movement distance-based measurement method item 737 indicates a predetermined round trip movement distance, and the time-based measurement method item 738 indicates a predetermined measurement time. In addition, the predetermined reciprocating movement distance and the predetermined measurement time may be determined according to a user's input.

The measurement start option item 740 indicates a condition for starting measurement. In the present disclosure, one of an immediate start option, a motion detection start option, and a specific distance start option may be selected in the measurement start option item 740 . According to the immediate start option, when the user activates the start 770 , the measurement of the movement of the cardiopulmonary function measurement target 120 starts. According to the motion detection start option, when the movement of the cardiorespiratory function measurement target 120 is detected, the cardiopulmonary function measurement target 120 begins to measure the movement. According to the specific distance start option, when it is sensed that the cardiopulmonary function measurement target 120 is located at a specific distance, movement measurement of the cardiopulmonary function measurement target 120 is started.

According to an embodiment, the setting item 710 may include a motion information display 750 . The movement information display 750 displays the movement of the cardiopulmonary function measurement target 120 .

According to an embodiment, the setting item 710 may further display a block 760 and a start 770 . When the start 770 is activated by the user, the movement of the cardiopulmonary function measurement target 120 is recorded in the gait meter 100 or the external computing device 250 . When the blocking 760 is activated by the user, the connection between the external computing device 250 and the walking meter 210 is terminated.

8 is an embodiment of the walking speed measurement item 810 of the walking speed measurement item 700 shown on the displays 102 and 252 in a program executed by the walking meter 100 or the external computing device 250 . indicates

The walking speed measurement item 810 may include a walking distance-time graph 820 , a personal information item 830 , and a measurement result item 840 .

The walking distance-time graph 820 represents the walking distance of the cardiopulmonary function measurement target 120 over time. According to the walking distance-time graph 820 , the walking pattern of the cardiopulmonary function measurement target 120 is visually provided.

The personal information item 830 represents personal information of the cardiopulmonary function measurement target 120 . The personal information of the cardiorespiratory function measurement target 120 may be determined by a user's input.

The measurement result item 840 represents movement information according to the gait test of the subject 120 for cardiopulmonary function measurement. The measurement result item 840 may indicate a total walking distance, a total time, and a walking speed. In addition, a measurement result necessary for predicting cardiopulmonary function may be displayed in the measurement result item 840 .

The analysis result item 850 represents the analysis result of the movement information of the cardiopulmonary function measurement target 120 . The analysis result item 850 may indicate an average stride length, an average stride width, an average stride length, an average number of steps, and a walking speed. In addition, an analysis result necessary for predicting cardiopulmonary function may be displayed in the analysis result item 850 .

In FIG. 8 , the measurement result item 840 and the analysis result item 850 are separated, but according to another embodiment, only one item in which the measurement result item 840 and the analysis result item 850 are combined is walking speed. It may be displayed in the measurement item 810 .

According to an embodiment, the walking speed measurement item 810 may include a motion information display 860 . The movement information display 860 displays the movement result of the cardiopulmonary function measurement target 120 . Also, the gait speed measurement item 810 may include a gait analysis display 860 . The gait analysis display 860 displays the gait of the cardiopulmonary function measurement target 120 .

9 shows an embodiment of a data management item 900 displayed on the displays 102 and 252 in a program executed by the gait meter 100 or the external computing device 250 .

The data management item 900 includes a data field 910, a human information item 920, a measurement result item 930, an analysis result item 940, a walking distance-time graph 950, and a walking path image 960. may include

In the data field 910 , data sets representing the results of the gait test of the subject 120 for measuring cardiorespiratory function are arranged. The data set of the data field 910 includes the name of the subject 120 for cardiopulmonary function measurement, a gait test identification number, walking speed, total walking distance, walking time, and measurement time.

A data set of the data field 910 may be selected by the user's input. According to an embodiment, the human information item 920 , the measurement result item 930 , the walking distance-time graph 940 , and the walking path image 950 indicating data for the selected data set are the data fields 910 . displayed on the right.

The personal information item 920 represents personal information of the cardiopulmonary function measurement target 120 . The personal information of the cardiorespiratory function measurement target 120 may be modified by the user's input.

The measurement result item 930 represents movement information according to the gait test of the cardiorespiratory function measurement subject 120 . The measurement result item 840 may indicate a total walking distance, a total time, a walking speed, a walking speed calculation method, an acceleration threshold, and a measurement time. In addition, a measurement result required for predicting cardiopulmonary function may be displayed in the measurement result item 930 .

The analysis result item 940 represents an analysis result of motion information according to the gait test of the subject 120 for cardiopulmonary function measurement. The analysis result item 940 may include a measurement mode, stride length, stride width, stride length, number of steps, and the like. In addition, an analysis result required for predicting cardiopulmonary function may be displayed in the analysis result item 930 .

In FIG. 9 , the measurement result item 930 and the analysis result item 940 are separated, but according to another embodiment, only one item in which the measurement result item 930 and the analysis result item 940 are combined is data management may be displayed in item 900 .

Like the walking distance-time graph 820 of FIG. 8 , the walking distance-time graph 950 represents the walking distance of the cardiopulmonary function measurement target 120 over time. According to the walking distance-time graph 950 , the walking pattern of the cardiopulmonary function measurement target 120 is visually provided.

The walking path image 960 visually represents the walking path of the cardiopulmonary function measurement subject 120 .

An embodiment of the program described in FIGS. 7 to 9 is only an example, and the program in which the cardiopulmonary function prediction method of the present disclosure is implemented is not limited to the embodiment of FIGS. 7 to 9 . In addition, a person skilled in the art can easily change the configuration of the program of FIGS. 7 to 9 based on the cardiopulmonary function prediction method and apparatus of FIGS. 1 to 6 .

10 illustrates a method of measuring a walking speed, according to an embodiment.

According to the one-way measurement method 1100 , the cardiopulmonary function measurement target 120 walks the total walking section 1002 . In addition, the walking speed of the subject 120 for cardiopulmonary function measurement is measured from the walking speed measurement section 1006 in which the change in walking speed is small. Therefore, the acceleration section 1004 in which the magnitude of the walking acceleration is greater than the predetermined acceleration threshold is not included in the walking speed measurement section 1006 . Accordingly, the walking speed may be determined according to the walking time and the walking distance of the walking speed measuring section 1006 .

According to the round-trip measurement method 1010 , the cardiopulmonary function measurement target 120 travels back and forth in a specific section. In the round trip measurement method 1010 , the walking speed may be calculated based on a moving distance or a walking time.

According to the movement distance-based measurement method, the cardiopulmonary function measurement target 120 walks a round-trip section of a predetermined distance. In addition, the walking speed of the target person 120 for measuring cardiopulmonary function is calculated according to the time when the round trip section walking of the predetermined distance is completed. According to the time-based measurement method, the cardiopulmonary function measurement target 120 walks the round-trip section for a predetermined time. And according to the length of the round-trip section walked for a predetermined time, the walking speed of the cardiopulmonary function measurement subject 120 is calculated.

11 shows a graph showing the walking distance of each group according to each NYHA functional classification according to the 6-minute walking test. The NYHA functional classification is a method of classifying the severity of heart failure. According to the NYHA functional classification, patients are divided into four groups (NYHA Class I, NYHA Class II, NYHA Class III, NYHA Class VI) according to the degree of heart failure. NYHA Class I includes patients without symptoms of heart failure, and patients with severe symptoms of heart failure are classified into a higher class.

According to the graph of FIG. 11 , it appears that patients classified as the control group, NYHA Class I, walked 600 meters-700 meters for 6 minutes. And it appears that patients classified as NYHA Class II walked 400-500 meters in 6 minutes. And it appears that patients classified as NYHA Class III walked about 300 meters in 6 minutes. In addition, patients classified as NYHA Class VI with the most severe heart failure walked about 100 meters in 6 minutes.

Therefore, it can be seen that heart failure and walking speed are closely related. Therefore, depending on the walking speed, the degree of heart failure can be predicted.

12 shows walking distance, cardiac output (CO, Cardiac Output), total lung resistance (TPR), peak exercise oxygen consumption (Peak V _O2 , peak exercise oxygen consumption), and anaerobic threshold (AT) according to the 6-minute walking test. , anaerobic threshold), oxygen pulse, and _{graphs showing the correlation of minute ventilation to carbon dioxide output slope (V E} -V _CO2 slope, regression slope relating minute ventilation to carbon dioxide output).

The first graph 1200 shows the relationship between the walking distance and cardiac output (CO, Cardiac Output) according to the 6-minute walking test. Cardiac output is the amount of blood pumped out of the heart in 1 minute. According to the first graph 1200 , it can be seen that the cardiac output statistically increases as the walking distance increases. Therefore, it can be predicted that the greater the walking speed, the greater the cardiac output.

The second graph 1202 shows the relationship between the walking distance and total lung resistance (TPR) according to the 6-minute walking test. Total pulmonary resistance refers to the pressure difference between the oral cavity and the pleural surface of the lungs to cause airflow in the oral cavity. According to the second graph 1202, it can be seen that as the walking distance increases, the total lung resistance decreases statistically. Therefore, it can be predicted that the higher the walking speed, the smaller the total lung resistance.

The third graph 1204 shows the relationship between the walking distance and the peak exercise oxygen consumption (Peak V _O2 , peak exercise oxygen consumption) according to the 6-minute walking test. The maximum exercise oxygen consumption means the maximum amount of oxygen that the human body can consume during exercise. According to the third graph 1204, it can be seen that the maximum exercise oxygen consumption increases statistically as the walking distance increases. Therefore, it can be predicted that the greater the walking speed, the greater the maximum exercise oxygen consumption.

The fourth graph 1206 shows the relationship between the walking distance and oxygen pulse according to the 6-minute walking test. Oxygen pulse refers to the amount of oxygen absorbed by the lungs in 1 minute divided by the number of pulses in 1 minute. According to the fourth graph 1206, it can be seen that as the walking distance increases, the oxygen pulse statistically increases. Therefore, it can be predicted that the greater the walking speed, the greater the oxygen vein.

The fifth graph 1208 shows the relationship between the walking distance and anaerobic threshold (AT) according to the 6-minute walking test. The anaerobic threshold refers to the amount of oxygen consumed at exercise intensity at which anaerobic metabolism begins. According to the fifth graph 1208, it can be seen that the anaerobic threshold statistically increases as the walking distance increases. Therefore, it can be predicted that the greater the walking speed, the greater the anaerobic threshold.

The sixth graph 1210 shows the relationship between walking distance and minute ventilation versus carbon dioxide emission slope (V _E -V _CO2 slope, regression slope relating minute ventilation to carbon dioxide output) according to the 6-minute walking test. The minute ventilation versus carbon dioxide emission slope refers to the magnitude of the ratio of the amount of carbon dioxide emitted out of the body to the amount of air circulated for 1 minute. According to the sixth graph 1210, it can be seen that as the walking distance increases, the minute ventilation versus carbon dioxide emission slope statistically decreases. Therefore, it can be predicted that the greater the walking speed, the greater the gradient of minute ventilation versus carbon dioxide emission.

The cardiopulmonary function indicators introduced in FIGS. 11 and 12 are important in the diagnosis of cardiopulmonary disease. Accordingly, according to the relationship between the cardiopulmonary function index and the walking speed, the cardiopulmonary function index may be predicted from the walking speed. Although not introduced in FIGS. 11 and 12 , other cardiopulmonary function indicators having statistical correlation with walking speed may also be predicted from walking speed.

13 shows a graph of the Kaplan-Meier survival curves of the two groups according to the 6-minute walk test. The Kaplan-Meier survival curve represents the survival probability over time of the experimental group.

In FIG. 13 , according to the 6-minute walking test, patients with primary pulmonary hypertension (PPH) were classified into an upper walking distance group and a lower walking distance group. The top walking distance group had a greater than 90% chance of surviving at 50 months. However, the lower walking distance group has a sharp decrease in survival probability over time. In particular, the survival probability of the lower walking distance group rapidly decreases to 20% after 20 months. Therefore, by measuring the walking speed of patients with primary pulmonary arterial hypertension, the survival probability of patients is significantly predicted.

As shown in FIG. 13 between the survival probability and walking speed according to primary pulmonary arterial hypertension, a correlation between the survival probability and walking speed according to other cardiopulmonary diseases may be derived. Therefore, the survival probability of patients with other cardiopulmonary diseases can also be predicted according to walking speed.

Hereinafter, with reference to the accompanying drawings, the embodiments will be described in detail so that those of ordinary skill in the art to which the present invention pertains can easily implement them. And in order to clearly explain the present invention in the drawings, parts irrelevant to the description will be omitted.

Meanwhile, the above-described embodiments of the present invention can be written as a program that can be executed on a computer, and can be implemented in a general-purpose digital computer that operates the program using a computer-readable recording medium.

The terms used in the present specification have been selected as currently widely used general terms as possible while considering the functions in the present invention, but these may vary depending on the intention or precedent of a person skilled in the relevant field, the emergence of new technology, and the like. In addition, in specific cases, there are also terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the corresponding invention. Therefore, the term used in the present invention should be defined based on the meaning of the term and the overall content of the present invention, rather than the name of a simple term.

References in the singular herein include plural expressions unless the context clearly dictates that the singular is singular. When a part "includes" a certain element throughout the specification, this means that other elements may be further included, rather than excluding other elements, unless otherwise stated.

While the present invention has been described with reference to a particular preferred embodiment, other inventions in which alternatives, modifications and variations are applied will be apparent to those skilled in the art in light of the foregoing description. That is, the claims are to be construed to cover all such alternatives, modifications and modified inventions. Therefore, all contents described in this specification and drawings should be interpreted in an illustrative and non-limiting sense.

Claims (15)

Measuring the movement of the cardiopulmonary function measurement target by the motion sensor;
By the processor, according to the movement of the cardiopulmonary function measurement subject, determining the walking speed of the cardiopulmonary function measurement subject; and
By the processor, based on the walking speed of the target cardiopulmonary function measurement, cardiopulmonary function prediction method comprising the step of predicting the cardiopulmonary function index of the target cardiopulmonary function measurement.
According to claim 1,
Predicting the cardiopulmonary function index,
determining, by the processor, a walking speed parameter representing the walking speed; and
Cardiopulmonary function prediction method comprising the step of predicting, by the processor, a cardiopulmonary function index according to the walking speed parameter.
3. The method of claim 2,
The walking speed parameter is a cardiopulmonary function prediction method, characterized in that representative of the walking speed of a specific section.
3. The method of claim 2,
The walking speed parameter is a cardiopulmonary function prediction method, characterized in that determined by a walking speed-cardiopulmonary function index correlation function indicating a relationship between the walking speed and the cardiopulmonary function index.
According to claim 1,
Measuring the movement of the target cardiopulmonary function measurement step,
Characterized in measuring the two-dimensional movement of the target cardiopulmonary function measurement,
The step of determining the walking speed of the subject to measure the cardiopulmonary function,
Based on the two-dimensional movement of the cardiopulmonary function measurement subject, measuring the vertical component and the horizontal component of the walking speed,
Predicting the cardiopulmonary function index,
Cardiopulmonary function prediction method, characterized in that predicting the cardiopulmonary function index of the cardiopulmonary function measurement subject according to the vertical component and the horizontal component of the walking speed of the cardiopulmonary function measurement subject.
According to claim 1,
Predicting the cardiopulmonary function index,
Cardiopulmonary function prediction method, characterized in that for predicting the cardiopulmonary function index of the cardiopulmonary function measurement target by further considering the personal information of the cardiopulmonary function measurement target.
According to claim 1,
The step of determining the walking speed of the subject to measure the cardiopulmonary function,
Characterized in generating a time-based graph of the walking speed of the cardiopulmonary function measurement subject,
Predicting the cardiopulmonary function index,
According to the walking pattern shown in the time-based graph of the walking speed, the cardiopulmonary function prediction method comprising the step of predicting the cardiopulmonary function index of the subject to be measured.
According to claim 1,
The cardiopulmonary function prediction method,
Further comprising the step of measuring the oxygen saturation of the target cardiopulmonary function measurement,
Predicting the cardiopulmonary function index,
In consideration of the oxygen saturation, cardiopulmonary function prediction method, characterized in that for predicting the cardiopulmonary function index of the subject to measure the cardiorespiratory function.
According to claim 1,
Measuring the movement of the target cardiopulmonary function measurement step,
setting an effective measurement range of the motion sensor; and
Cardiopulmonary function prediction method comprising the step of measuring the movement of the target cardiopulmonary function measurement within the effective measurement range.
According to claim 1,
Measuring the movement of the target cardiopulmonary function measurement step,
selecting one of a walking speed measurement mode for measuring the walking speed of the cardiopulmonary function measurement subject and a gait analysis mode for additionally acquiring motion information according to the walking speed analysis of the cardiopulmonary function measurement subject; and
According to the selected measurement mode, cardiopulmonary function prediction method comprising the step of measuring the movement of the target cardiopulmonary function measurement.
According to claim 1,
The step of determining the walking speed of the subject to measure the cardiopulmonary function,
Comprising the step of determining the gait acceleration of the cardiopulmonary function measurement subject from the walking speed of the cardiopulmonary function measurement subject,
Predicting the cardiopulmonary function index,
According to the walking speed and gait acceleration, cardiopulmonary function prediction method comprising the step of predicting the cardiopulmonary function index of the subject to be measured.
a motion sensor for measuring the movement of a subject for cardiopulmonary function measurement;
a memory in which a program including at least one instruction is stored; and
A processor for predicting the cardiopulmonary function index of the target cardiopulmonary function measurement target by executing the at least one program,
The at least one command is
Command for the motion sensor to measure the movement of the target cardiopulmonary function measurement;
Instructions for causing the processor to determine at least one of a walking speed and a gait acceleration of the cardiopulmonary function measurement subject according to the movement of the cardiopulmonary function measurement subject; and
Cardiopulmonary function prediction device, characterized in that it comprises a command to the processor, based on at least one of the walking speed and gait acceleration of the cardiopulmonary function measurement subject, predicting the cardiopulmonary function indicator of the cardiopulmonary function measurement subject.
13. The method of claim 12,
The motion sensor is
Cardiopulmonary function prediction device, characterized in that it is a 2D lidar.
A computer program product comprising instructions for causing each step of the method for predicting cardiopulmonary function of claim 1 to be performed by a computer.
A computer-readable recording medium in which the computer program of claim 14 is stored.

Images