KR102168073B1 - Method and apparatus for evaluating body composition - Google Patents

Abstract

A method and apparatus for evaluating body composition are disclosed. The disclosed body composition evaluation method determines a target graph of the test subject based on the body composition data of other users corresponding to the test subject and a target value previously determined or input from the test subject, and calculates evaluation information on the body composition data of the test subject based on the target graph. Decide.

Description

Body composition evaluation method and apparatus {METHOD AND APPARATUS FOR EVALUATING BODY COMPOSITION}

The following description relates to a body composition evaluation method and apparatus.

It can be said that the main purpose of measuring body composition in general is to motivate users for diet or exercise. However, since the conventional body composition measuring apparatus provides only the measurement result to the user as a simple numerical value, it is difficult for the user to intuitively recognize whether a diet or exercise is properly proceeding.

A method for evaluating body composition according to an embodiment includes determining a target graph of the test subject based on body composition data of other users corresponding to the test subject and a predetermined or target value input from the test subject; And determining evaluation information on the body composition data of the test subject based on the target graph.

In the body composition evaluation method according to an embodiment, the determining of the target graph includes determining a target period and a target change amount of the test subject based on the body composition change amount and the target value of the other users, and based on the target period and target change amount. The target graph can be determined.

In the body composition evaluation method according to an exemplary embodiment, the other users may be selected as users whose difference from the biometric data of the test subject is within a predetermined range from among users whose biometric data is stored in a database.

In the body composition evaluation method according to an embodiment, the biometric data of the test subject may include data on at least one of sex, age, height, skeletal muscle mass, and body fat mass of the test subject.

In the body composition evaluation method according to an embodiment, the target value may be any one of a target period and a target change amount set by the test subject.

In the body composition evaluation method according to an embodiment, the target graph may be determined such that the amount of change in body composition in the first section of the target period of the test subject is greater than or equal to the amount of change in body composition in the last section.

In the body composition evaluation method according to an embodiment, the target graph may be corrected based on the size of body composition data of other users corresponding to the test subject.

In the body composition evaluation method according to an embodiment, the determining of the evaluation information includes setting a plurality of regions according to at least one of a distance from the target graph and a direction away from the target graph, and a point representing the body composition data of the subject. May be located in any one of the plurality of areas.

In the body composition evaluation method according to an embodiment, in each of the plurality of regions, unique visual information corresponding to evaluation information determined based on at least one of a distance from the target graph and a direction away from the target graph may be set.

In the body composition evaluation method according to an embodiment, the determining of the evaluation information includes a first intermediate period from the start of the target period of the test subject to the intermediate time when the test subject last measures the body composition data among the other users, and During the first intermediate period, first partial users corresponding to the body composition change amount of the test subject may be selected, and a first prediction graph for the body composition data of the test subject may be determined based on the body composition data of the first partial users.

In the body composition evaluation method according to an embodiment, the first prediction graph may include body composition data of the test subject predicted at the end of the target period.

In the body composition evaluation method according to an embodiment, the first prediction graph may further include ranking information on the body composition data of the test subject predicted at the end of the target period.

In the body composition evaluation method according to an embodiment, the determining of the evaluation information includes a second intermediate period between two most recent time points when the test subject measures body composition data among the other users and the second intermediate period. The second partial users corresponding to the body composition change amount of the test subject may be selected, and a second prediction graph for the body composition data of the test subject may be determined based on the body composition data of the second partial users.

In the body composition evaluation method according to an embodiment, the second prediction graph may include body composition data of the test subject predicted at the end of the target period of the test subject.

In the body composition evaluation method according to an embodiment, the evaluation information includes a value of the body composition data of the test subject, a value of an intermediate target corresponding to the value, an amount of change between the body composition data of the test subject and the initial body composition data of the test subject, and of the intermediate target. Information on at least one of the amount of change between the numerical value and the initial body composition data of the test subject, the ranking corresponding to the body composition data of the test subject, the description of the body composition data of the test subject, the rate of change corresponding to the body composition data of the test subject, grade, and emoticon It may include.

In the method for evaluating body composition according to an embodiment, the body composition data may be data on one of body fat mass and skeletal muscle mass.

In the body composition evaluation method according to an embodiment, the target graph and the evaluation result may be output from one of the mobile terminal of the subject, a measuring device measuring the body composition data of the subject, and a result sheet.

In the body composition evaluation method according to an embodiment, the body composition evaluation method is performed by any one of a measuring device measuring body composition data of the test subject, a mobile terminal or a server of the test subject receiving body composition data of the test subject from the measuring device. Can be.

A method for evaluating body composition according to an embodiment includes the steps of selecting other users whose difference from the biometric data of the subject is within a predetermined range from among users whose biometric data is stored in a database; Determining a target period and a target change amount of the test subject based on the body composition change amount of the other users and a target value determined in advance or input from the test subject; And determining a target graph of the test subject based on the target period and the target change amount.

Body composition evaluation apparatus according to an embodiment of the processor; And a memory including at least one instruction executable by the processor, and when the at least one instruction is executed in the processor, the processor includes body composition data of other users corresponding to the subject and predetermined or the subject Based on the target value input from, the target graph of the test subject is determined, and evaluation information on the body composition data of the test subject is determined based on the target graph.

In the body composition evaluation apparatus according to an embodiment, the processor comprises a target period of the test subject based on the target value and the amount of change in body composition of other users whose difference from the biometric data of the test subject is within a predetermined range among users having biometric data stored in the database. And a target change amount may be determined, and the target graph may be determined based on the target period and the target change amount.

In a computer-readable storage medium recording a program for determining evaluation information on body composition data of a test subject according to an embodiment, the program comprises: body composition data of other users corresponding to the test subject and a predetermined or target value input from the test subject A set of instructions for determining a target graph of the test subject based on the test subject; And an instruction set for determining evaluation information on the body composition data of the test subject based on the target graph.

According to an embodiment, by determining a target graph based on body composition data of other users identical or similar to the test subject through big data analysis, realistic and achievable targets may be presented step by step as a test subject.

According to an embodiment, by providing a prediction graph determined based on the measured body composition data of the test subject, the subject can intuitively recognize the body composition data predicted at the end of the diet or exercise at the current speed and whether the goal has been achieved. have.

1 is a diagram for describing a process of determining a target graph according to an exemplary embodiment.
FIG. 2 is a diagram for describing a process of determining an evaluation result for body composition data of a subject according to an exemplary embodiment.
3 to 5 are diagrams for explaining a process of determining an evaluation result for body composition data of a subject according to another embodiment.
6 is a diagram for explaining a process of selecting another user used when determining a target graph or a prediction graph according to an exemplary embodiment.
7 is a diagram for describing a process of determining an evaluation result for body composition data of a subject based on a speed according to an exemplary embodiment.
8 is a diagram illustrating an example in which body composition data is skeletal muscle mass data according to an embodiment.
9 is a diagram showing a method for evaluating body composition according to an embodiment.
10 is a diagram showing a body composition evaluation apparatus according to an embodiment.
11 to 20 are diagrams showing examples of evaluation information according to an embodiment.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only, and may be changed in various forms and implemented. Accordingly, the embodiments are not limited to a specific disclosure form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical idea.

Although terms such as first or second may be used to describe various components, these terms should be interpreted only for the purpose of distinguishing one component from other components. For example, a first component may be referred to as a second component, and similarly, a second component may be referred to as a first component.

When a component is referred to as being "connected" to another component, it is to be understood that it may be directly connected or connected to the other component, but other components may exist in the middle.

Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate that the described feature, number, step, action, component, part, or combination thereof exists, but one or more other features or numbers, It is to be understood that the presence or addition of steps, actions, components, parts, or combinations thereof, does not preclude the possibility of preliminary exclusion.

Unless otherwise defined, all terms, including technical or scientific terms, used herein have the same meaning as commonly understood by one of ordinary skill in the relevant technical field. Terms as defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this specification. Does not.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. The specific structural or functional descriptions below are exemplified for the purpose of describing the embodiments only, and the scope of the embodiments should not be construed as being limited to the content described in the text. Those of ordinary skill in the related art can make various modifications and variations from these descriptions. In addition, the same reference numerals shown in each drawing denote the same member, and known functions and structures will be omitted.

1 is a diagram for describing a process of determining a target graph according to an exemplary embodiment.

According to an embodiment, when the test subject wants to change body composition through diet or exercise, the test subject may set a target period or a target amount of change. In addition, the test subject may measure body composition data to determine how much body composition has changed during diet or exercise. However, since the measured body composition data represents only absolute values, it may be difficult to recognize whether the test subject is currently on a diet or exercise well with only the body composition data, and whether the final goal can be reached if proceeding. The body composition evaluation device may determine a target graph and evaluation information and provide it to the test subject.

In FIG. 1, an example for explaining a process of determining a target graph of a subject by a body composition evaluation apparatus according to an embodiment is illustrated. The body composition evaluation apparatus determines a target graph of the test subject based on (i) body composition data of other users corresponding to the test subject and (ii) a target value determined in advance or input from the test subject.

The body composition evaluation apparatus according to an embodiment may use body composition data of other users to determine a target graph of the test subject. Here, the other users may be selected as users having the same or similar body condition as the body condition of the test subject, for example, among users whose biometric data is stored in the database, in which a difference between the test subject's biometric data is within a predetermined range. The range is a parameter for selecting another user that is the same or similar to the test subject, and may be predetermined. The database may store biometric data of various users and changes in body composition due to diet or exercise. The biometric data may include data on at least one of sex, age, height, skeletal muscle mass, and body fat mass.

Further, the body composition evaluation apparatus may use a target value previously determined or input from the test subject to determine a target graph of the test subject. Here, the target value is any one of a target period set by the test subject and a target change amount, and may be determined in advance by the body composition evaluation apparatus or input from the test subject. For example, a subject who can diet or exercise for 8 weeks may set a target period to 8 weeks. Alternatively, a subject who wants to reduce the amount of body fat by 5 kg may set the target change amount to 5 kg.

The body composition evaluation apparatus according to an embodiment may determine a target period and a target change amount of the test subject based on the change amount of body composition of other users and the target value set by the test subject.

For example, when the target value is a target period set by the test subject, the body composition evaluation apparatus may determine how much body composition data of other users, which are the same or similar to the test subject, change during the same period as the target period, and thus determine the target change amount of the test subject. . For example, the body composition evaluation apparatus may determine the average body composition change amount of other users during the same period as the target period as the target change amount of the test subject. In addition, the body composition evaluation apparatus may determine the target period in the same way as the target period set by the test subject.

Alternatively, if the target value is the target change amount set by the test subject, the body composition evaluation apparatus may determine the target period of the test subject by checking how long it took for other users identical or similar to the test subject to change the body composition data by the target change amount. . For example, the body composition evaluation apparatus may determine the average time required for other users to reduce the body fat mass by 5 kg as the target period of the test subject. In addition, the body composition evaluation apparatus may determine the target change amount equal to the target change amount set by the test subject.

In addition, the body composition evaluation apparatus may determine a target graph based on the determined target period and target change amount. The target period indicates a period from the start point to the end point, and the target change amount may indicate a difference between the body composition data at the start point and the body composition data at the end point.

The target graph may also include an intermediate target corresponding to an intermediate point between the start point and the end point. The test subject can increase the likelihood of reaching the final goal by achieving the intermediate goal included in the target graph in stages. In the target graph, one or more intermediate viewpoints may be set, and intermediate targets at such intermediate viewpoints may be connected to determine a target graph.

The body composition evaluation apparatus may also determine an intermediate target based on changes in body composition of other users. Since the amount of change in body composition of other users is stored in the database, the body composition evaluation apparatus may determine an intermediate goal achieved on average by other users corresponding to the test subject as the intermediate goal of the test subject.

As exemplarily illustrated in FIG. 1, the target graph may have a curve shape. This is a form that reflects that a lot of body composition changes occur initially, but gradually decreases, even if the same effort is made during diet or exercise, and a more realistic goal may be presented through this curve-shaped goal graph. For example, the target graph may be determined such that a body composition change amount of the first section is greater than or equal to the body composition change amount of the last section among a plurality of sections included in the target period.

The table below shows an example of a target graph.

04/28 05/05 05/12 05/19 05/26 06/02 06/09 06/16 06/23 start Interim Goal 1 middle
Goal 2 middle
Goal 3 middle
Goal 4 middle
Goal 5 middle
Goal 6 middle
Goal 7 final
goal Body fat mass (kg) 11.1 10.1 9.2 8.4 7.7 7.1 6.6 6.3 6.2 Change in body fat mass (kg) -One -0.9 -0.8 -0.7 -0.6 -0.5 -0.3 -0.1

According to an embodiment, the target graph may be corrected according to the size of body composition data of other users corresponding to the test subject. For example, when a test subject is in their 20s to 30s and has general biometric data, a target graph suitable for the test subject may be determined based on sufficient body composition data because there are many other users identical or similar to the test subject. On the other hand, if the test subject has unusual biometric data in their 50s and 60s, there may be fewer other users who are the same or similar to the test subject, and a target graph determined with less body composition data may not present an appropriate target to the test subject. Therefore, when the size of the body composition data of other users is small, additional correction processing may be applied to the target graph determined by the body composition data. For example, the correction process may be a process of making the target graph have a smoother shape. In addition, various correction processes may be applied without limitation. The body composition data may be data for either body fat mass or skeletal muscle mass. In FIG. 1, for convenience of explanation, the target graph is described based on the amount of body fat, but this description can be applied to the target graph based on the skeletal muscle mass, and an embodiment based on the skeletal muscle mass will be described later with reference to FIG. do.

In this way, the body composition evaluation apparatus determines a target graph that can be achieved on average by other users having the same or similar biometric data as the test subject based on big data analysis, so that the test subject can effectively present a goal that can be achieved realistically.

FIG. 2 is a diagram for describing a process of determining an evaluation result for body composition data of a subject according to an exemplary embodiment.

According to an embodiment, the body composition evaluation apparatus determines evaluation information on the body composition data of the subject based on the target graph. The evaluation information may be information that enables the test subject to intuitively know how much diet or exercise is proceeding as planned. For example, the evaluation information is information indicating whether the body composition data measured at the current time point (i.e., the intermediate time point) is considered superior or inferior to other users, and when considering the amount of change in the body composition of the subject from the start to the current time point. It may include body composition data of the subject predicted at the end time, body composition data of the test subject predicted at the end point when considering the amount of change in body composition measured at the last two time points, ranking information on the body composition data of the test subject predicted at the end time, etc. . Hereinafter, evaluation information will be described in detail with reference to FIGS. 2 to 4.

In FIG. 2, an example for explaining a process of determining evaluation information on body composition data of a subject by using a plurality of regions 210 to 240 by the body composition evaluation apparatus according to an embodiment is illustrated. In FIG. 2, four regions 210 to 240 are illustrated for convenience of description, but a variety of other regions may be applied without limitation.

The target graph may be determined by a target period and target change amount that can be achieved based on representative values of other users that are the same or similar to the test subject. Therefore, if the body fat mass of the test subject is measured to be higher than the target graph, performance lower than the representative values of other users may be displayed. Conversely, if the body fat mass of the test subject is measured to be lower than the target graph, it may exhibit higher performance than the representative values of other users. In other words, evaluation information on the body composition data of the test subject may be determined as excellent or inferior according to a direction away from the target graph. Here, the representative value is a value representing other users that are the same or similar to the test subject, and may include, for example, an average value, a median value, a mode value, and the like. Hereinafter, for convenience of explanation, the description is based on the average value, but this description does not limit the representative value.

In addition, even if the body fat amount of the test subject is equally measured to be higher than the target graph, a case that is far away from the target graph may exhibit a lower performance than a case where there is less fall. Alternatively, even if the body fat amount of the test subject is equally measured to be lower than the target graph, a case that is far away from the target graph may exhibit higher performance than a case where there is less fall. In other words, the degree of superiority or inferiority of evaluation information for body composition data may be determined according to the degree of distance from the target graph.

In consideration of the above-described matters, the body composition evaluation apparatus may set the plurality of regions 210 to 240 based on at least one of a distance from the target graph and a direction away from the target graph. For example, evaluation information on the regions 210 and 220 located in the lower direction of the target graph may be determined as excellent. Among them, since the area 210 is farther away from the target graph than the area 220, it may be determined that evaluation information on the area 210 is better than the area 220. Also, the evaluation information on the regions 230 and 240 located in the upper direction of the target graph may be determined as inferior. Among these, since the region 240 has a greater degree of distance from the target graph than the region 230, it may be determined that evaluation information for the region 240 is inferior to the region 230.

In addition, the body composition evaluation apparatus may locate a point 200 representing body composition data of a subject in any one of the plurality of regions 210 to 240. By checking the area where the point 200 is located, the test subject can easily recognize the evaluation information on the body composition data.

Unique visual information is set in the plurality of areas 210 to 240 so that the test subject can intuitively recognize the evaluation information set in the corresponding area. In this case, the unique visual information may correspond to evaluation information determined based on evaluation information determined based on at least one of a distance from the target graph and a direction away from the target graph. In FIG. 2, for convenience of explanation, visual information of the regions 210 to 240 is divided into hatching and grid dots, but various visual information such as different colors may be applied without limitation.

3 to 5 are diagrams for explaining a process of determining an evaluation result for body composition data of a subject according to another embodiment.

FIG. 3 illustrates an example for explaining a process of determining evaluation information on body composition data of a test subject by using a prediction graph based on a start time point and an intermediate time point by the body composition evaluation apparatus according to an embodiment.

For convenience of explanation, it is assumed that the test subject measured body composition data at the start and middle points 1, 2, and 3. In FIG. 3, a solid line represents an actual graph connecting body composition data of an actual measured subject, and a dotted line represents a prediction graph determined based on body composition data at a start point and an intermediate point 3. Point 310 represents the body composition data of the test subject measured at the start time, point 320 represents the body composition data of the test subject measured at the intermediate time point 3, and point 330 represents the body composition data of the test subject predicted at the end time. Can be indicated. Here, the intermediate time point 3 may be a time point at which the test subject last measures the body composition data.

According to an embodiment, the body composition evaluation apparatus may select some users having a body composition change amount similar to that of the test subject based on the start time point and the intermediate time point 3 from among other users whose biometric data is the same as or similar to the test subject. In other words, the body composition evaluation apparatus may select some users corresponding to the amount of change in body composition of the test subject during the intermediate period from the start time to the intermediate time point 3 when the test subject last measured the body composition data among other users.

In addition, the body composition evaluation apparatus may determine a prediction graph for the body composition data of the subject based on the selected body composition data of some users. For example, the body composition evaluation apparatus may determine average body composition data of some users at the end time based on body composition data of some users, and determine the average body composition data as predicted body composition data of the subject at the end time. In addition, the body composition evaluation apparatus may determine the prediction graph using points 310 and 320 representing measured body composition data at the start and middle time points 3 and points 330 representing predicted body composition data at the end time.

The prediction graph may include body composition data of the subject predicted at the end point. In addition, the prediction graph may further include ranking information on the body composition data of the subject predicted at the end point. Here, the ranking information may be information indicating what percentage of the subject's predicted body composition data corresponds to the top percentage among other users. For example, if the predicted body composition data of the test subject is the same as the body composition data on the target graph at the end point, the ranking information may be determined as the top 50%. Alternatively, if the subject's predicted body composition data is higher than the body composition data on the target graph at the end point, the ranking information may be determined to be greater than the top 50.

The prediction graph may allow the subject to intuitively recognize the body composition data that is predicted to reach the end point when continuing with the average diet or exercise speed in the meantime. In the example of FIG. 3, the examinee who checks the prediction graph will not be able to reach the initially set goal with the average diet or exercise speed so far, and thus can easily recognize that greater effort is required.

4 illustrates an example for explaining a process in which the body composition evaluation apparatus according to an embodiment determines evaluation information on body composition data of a subject by using a prediction graph based on two recent times.

For convenience of explanation, it is assumed that the test subject measured body composition data at the start and middle points 1, 2, and 3. In FIG. 3, a solid line represents an actual graph connecting body composition data of an actual measured subject, and a dotted line represents a prediction graph determined based on body composition data at intermediate times 2 and 3. Point 410 represents the body composition data of the test subject measured at the intermediate time point 2, point 420 represents the body composition data of the test subject measured at the intermediate time point 3, and point 430 is the body composition data of the test subject predicted at the end time. Can represent. Here, the intermediate time point 3 may be a time point at which the test subject last measures the body composition data.

According to an embodiment, the body composition evaluation apparatus may select some users having a body composition change amount similar to that of the test subject based on the intermediate time points 2 and 3 from among other users whose biometric data is the same as or similar to the test subject. For example, among other users, the body composition evaluation apparatus is a part corresponding to the change in body composition of the test subject during the intermediate period and the intermediate period between the two most recent time points (i.e., intermediate time points 2 and 3) when the subject measured body composition data among other users. Users can be selected.

In addition, the body composition evaluation apparatus may determine a prediction graph for the body composition data of the subject based on the selected body composition data of some users. For example, the body composition evaluation apparatus may determine average body composition data of some users at the end time based on body composition data of some users, and determine the average body composition data as predicted body composition data of the subject at the end time. In addition, the body composition evaluation apparatus may determine the prediction graph using points 410 and 420 representing measured body composition data at intermediate times 2 and 3 and points 430 representing predicted body composition data at the end time. The prediction graph may include body composition data of the subject predicted at the end time. Alternatively, the prediction graph may further include ranking information on the body composition data of the subject predicted at the end time.

The prediction graph may allow a subject to intuitively recognize body composition data predicted to reach an end point when continuing with a recent diet or exercise speed. In the example of FIG. 4, the examinee who checks the prediction graph will not be able to reach the initially set goal even with a recent diet or exercise speed, and thus can easily recognize that a greater effort is required.

FIG. 5 illustrates an example for explaining a process in which the body composition evaluation apparatus according to an embodiment determines evaluation information on body composition data of a subject using a plurality of prediction graphs. By simultaneously displaying the prediction graph based on the start and middle time points described above and the prediction graph based on the two most recent times, the body composition at the end point predicted according to the recent diet or exercise speed as well as the average diet or exercise speed for the test subject. Data can be intuitively recognized.

6 is a diagram for explaining a process of selecting another user used when determining a target graph or a prediction graph according to an exemplary embodiment.

The database 610 according to an embodiment may include biometric data and body composition data of a plurality of users. For example, a plurality of users is a user whose body composition data has been measured in the past, or a user who has used a body composition evaluation device in the past, and the database 610 may be updated whenever the body composition data is measured or the body composition evaluation device is used. .

When determining the target graph, other users 620 having the same or similar biometric data as the test subject may be selected among users having biometric data stored in the database 610. Other users 620 may be selected based on biometric data such as gender, age, height, skeletal muscle mass, and body fat mass. In addition, when determining the prediction graph, some users 630 from among other users 620 may be selected. Some users 630 may be selected based on the amount of change in body composition during the same or similar intermediate period and the intermediate period as the test subject.

7 is a view for explaining a process of determining an evaluation result for body composition data of a subject based on a change rate according to an exemplary embodiment.

According to an embodiment, the body composition evaluation apparatus may determine sections A and B based on the measured body composition data of the subject, and determine the change rate of the subject as the evaluation information based on the sections A and B. Section A may represent a section from the start point to the middle point 2 at which the test subject measured body composition data, and section B may indicate the section from the start point to the middle point 1 of the target graph corresponding to the measured subject's body composition data. . The rate of change can be determined as "B/A".

As illustrated in FIG. 7, when the section A is longer than the section B, the rate of change has a value less than 1, which may indicate an evaluation result that is inferior to other average users. Conversely, when section A is shorter than section B, the rate of change has a value greater than 1, which may represent an evaluation result superior to the average of other users.

8 is a diagram illustrating an example in which body composition data is skeletal muscle mass data according to an embodiment.

Although the above descriptions are based on body composition data indicating body fat mass, body composition data indicating skeletal muscle mass as shown in FIG. 8 may be used in some embodiments. Unlike body fat mass, since skeletal muscle mass has a property that increases with diet or exercise, a target graph and a prediction graph from the lower left to the upper right may be determined as shown in FIG. 8.

According to another embodiment, the y-axis of the graph may indicate the progress rate. For example, a target graph and a prediction graph may be shown with the y value at the start time being 0% and the y value at the end time being 100%.

9 is a diagram showing a method for evaluating body composition according to an embodiment.

The body composition evaluation method according to an exemplary embodiment may be performed by a processor provided in the body composition evaluation apparatus.

In step 910, the body composition evaluation apparatus determines a target graph of the test subject based on the body composition data of other users corresponding to the test subject and a target value previously determined or input from the test subject.

The body composition evaluation apparatus according to an embodiment may determine a target period and a target change amount of the test subject based on the body composition change amount and target value of other users, and determine a target graph based on the target period and target change amount. Here, other users may be selected as users whose biometric data is stored in the database and have a difference between the biometric data of the subject within a predetermined range. The target graph may be determined such that the amount of change in body composition in the first section during the target period of the test subject is greater than or equal to the change in body composition in the last section.

In step 920, the body composition evaluation apparatus determines evaluation information on the body composition data of the subject based on the target graph.

The body composition evaluation apparatus according to an embodiment sets a plurality of areas according to at least one of a degree away from the target graph and a direction away from the target graph, and stores a point representing the body composition data of the subject within any one of the plurality of areas. Can be located. In each of the plurality of regions, unique visual information corresponding to evaluation information determined based on at least one of a degree away from the target graph and a direction away from the target graph may be set.

In addition, the body composition evaluation apparatus is a first intermediate period from the start of the target period of the test subject to the intermediate time when the test subject last measures the body composition data among other users, and the first intermediate period corresponding to the change in body composition of the test subject during the first intermediate period. Some users may be selected, and a first prediction graph for the body composition data of the subject may be determined based on the body composition data of the first partial users.

In addition, the body composition evaluation apparatus selects second partial users corresponding to the amount of change in body composition of the test subject during the second intermediate period and the second intermediate period between the two most recent time points when the test subject measured body composition data from among other users, The second prediction graph for the body composition data of the subject may be determined based on the body composition data of the second partial users.

Since the above-described matters through FIGS. 1 to 8 are applied to each of the steps shown in FIG. 9 as they are, a more detailed description will be omitted.

10 is a diagram showing a body composition evaluation apparatus according to an embodiment.

Referring to FIG. 10, the body composition evaluation apparatus 1000 according to an embodiment includes a memory 1010 and a processor 1020. The memory 1010 and the processor 1020 may communicate with each other through a bus 1030.

The memory 1010 may include instructions that can be read by a computer. The processor 1020 may perform the aforementioned operations as instructions stored in the memory 1010 are executed by the processor 1020. The memory 1010 may be a volatile memory or a nonvolatile memory.

The processor 1020 determines a target graph of the test subject based on the body composition data of other users corresponding to the test subject and a target value previously determined or input from the test subject, and calculates evaluation information on the body composition data of the test subject based on the target graph. Decide.

According to an embodiment, the body composition evaluation apparatus 1000 may be any one of a measuring device measuring body composition data of the test subject, a mobile terminal or a server of the test subject receiving body composition data of the test subject from the measuring device. In addition, the target graph and the evaluation result may be output from any one of a mobile terminal of the test subject, a measuring device measuring body composition data of the test subject, and a result sheet. In addition, the body composition data may be automatically input after measurement into the body composition evaluation apparatus 1000, handwritten by a user, or input through a link (eg, barcode, QR code, etc.) included in the measurement result.

In addition, with respect to the body composition evaluation device 1000, the above-described operation can be processed.

11 to 20 are diagrams showing examples of evaluation information according to an embodiment.

According to an embodiment, the evaluation information includes a value of the body composition data of the test subject, a value of an intermediate target corresponding to the value, an amount of change between the body composition data of the test subject and the initial body composition data of the test subject, the value of the intermediate target and the test subject. It may include information on at least one of an amount of change between initial body composition data of, a ranking corresponding to the body composition data of the test subject, a description of the body composition data of the test subject, a rate of change corresponding to the body composition data of the test subject, grades, and emoticons. And examples thereof are shown in FIGS. 11 to 20.

FIG. 11 shows an example in which points and values representing body composition data of a test subject are shown together with a target graph, and FIG. 12 corresponds to intermediate time point 2 when body composition data of the test subject is measured, as well as points and values representing body composition data of the test subject. It may represent an example in which the numerical value of the intermediate target is shown together with the target graph.

In addition, FIG. 13 shows an example in which the amount of change between the body composition data of the test subject and the initial body composition data of the test subject (that is, the change amount of the body composition of the test subject from the initial time point to the intermediate time point 2 when the body composition data of the test subject is measured) is shown together with the target graph. I can. 14 shows not only the amount of change between the body composition data of the test subject and the initial body composition data of the test subject, but also the amount of change between the intermediate target corresponding to the intermediate time point 2 and the initial body composition data of the test subject (that is, the amount of change in body composition on the target graph from the initial time point to the intermediate time point 2). ) May also represent an example shown with a target graph.

In addition, FIG. 15 may represent an example in which a ranking corresponding to body composition data of a subject is shown along with a target graph. Here, the ranking may be information indicating what percentage of the subject's body composition data corresponds to the top percentage among other users. 16 illustrates an example in which rankings corresponding to body composition data of a subject are displayed in a different manner, and one or more ranking graphs indicating a specific ranking may be shown based on a target graph. The test subject can check his or her ranking based on the distance between the points representing his body composition data and the ranking graph.

In addition, FIG. 17 may show an example in which a description corresponding to the body composition data of a subject is shown together with a target graph. Here, the description may analyze the body composition data of the test subject or include a comment on the body composition data. 18 may show an example in which the rate of change of the body composition data of a subject is displayed together with a target graph. Since the change speed has been described in detail in FIG. 7, a more detailed description will be omitted. 19 may show an example in which scores corresponding to body composition data of a subject are shown together with a target graph. The grade may be a ranking of the subject's body composition data as a grade. 20 may show an example in which an emoticon corresponding to body composition data of a subject is shown together with a target graph. The emoticon may represent the ranking of the subject's body composition data with an expression. For example, a smiley emoticon may be displayed when the subject's body composition data is ranked within the top 10%, and a crying emoticon may be displayed when the subject's body composition data is above 70%.

The example of the evaluation information on the body composition data of the test subject is not limited to the above descriptions, and various other embodiments may be applied to the evaluation information.

The embodiments described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices, methods, and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions, such as one or more general purpose computers or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodyed in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of the program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

Components described in the embodiments are programmable logic elements such as one or more digital signal processors (DSPs), processors, controllers, application specific integrated circuits (ASICs), and field programmable gate arrays (FPGAs). Logic Element), other electronic devices, and hardware components including one or more of a combination thereof. At least some of the functions or processes described in the embodiments may be implemented by software, and the software may be recorded on a recording medium. Components, functions, and processes described in the embodiments may be implemented by a combination of hardware and software.

As described above, although the embodiments have been described by the limited drawings, a person of ordinary skill in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Claims (21)

Determining a target graph of the test subject based on body composition data of other users corresponding to the test subject and a target value determined in advance or input from the test subject; And
Determining evaluation information on the body composition data of the test subject based on the target graph
Including,
The step of determining the target graph
If any one of the target period and the target change amount is set by the test subject, the other one of the target period and the target change amount is determined based on the change in body composition data of the other users and the value set by the test subject, and the set or determined Determining the target graph based on the target period and the target change amount,
The body composition evaluation method in which the other users are selected as users whose biometric data is stored in a database and a difference between the biometric data of the test subject is within a predetermined range. delete delete The method of claim 1,
The biometric data of the subject is
The body composition evaluation method comprising data on at least one of the sex, age, height, skeletal muscle mass, and body fat mass of the test subject. The method of claim 1,
Above target value
The body composition evaluation method, which is any one of a target period and a target change amount set by the test subject. The method of claim 1,
The target graph is
The body composition evaluation method, wherein the body composition change amount in the first section of the target period of the test subject is determined to be greater than or equal to the body composition change amount in the last section. The method of claim 1,
The target graph is
Body composition evaluation method, which is corrected based on the size of body composition data of other users corresponding to the test subject. The method of claim 1,
The step of determining the evaluation information
Body composition evaluation, in which a plurality of areas are set according to at least one of a degree away from the target graph and a direction away from the target graph, and a point representing the body composition data of the test subject is located in any one of the plurality of areas Way. The method of claim 8,
Each of the plurality of regions
The body composition evaluation method, wherein unique visual information corresponding to evaluation information determined based on a degree away from the target graph and a direction away from at least one of the target graphs is set. The method of claim 1,
The step of determining the evaluation information
Among the other users, a first intermediate period from the start of the target period of the test subject to the intermediate time when the test subject last measured body composition data, and a first part corresponding to the change in body composition of the test subject during the first intermediate period Selecting users and determining a first prediction graph for the body composition data of the test subject based on the body composition data of the first partial users. The method of claim 10,
The first prediction graph is
Body composition evaluation method comprising body composition data of the test subject predicted at the end of the target period. The method of claim 11,
The first prediction graph is
The body composition evaluation method further comprising ranking information on the body composition data of the test subject predicted at the end of the target period. The method of claim 1,
The step of determining the evaluation information
Among the other users, a second intermediate period between the two most recent times when the test subject measured body composition data and a second partial user corresponding to the change in body composition of the subject during the second intermediate period are selected, and the second 2 A body composition evaluation method of determining a second prediction graph for the body composition data of the test subject based on the body composition data of some users. The method of claim 13,
The second prediction graph is
Body composition evaluation method comprising the body composition data of the test subject predicted at the end of the target period of the test subject. The method of claim 1,
The above evaluation information is
The value of the body composition data of the test subject, the value of the intermediate target corresponding to the value, the amount of change between the body composition data of the test subject and the initial body composition data of the test subject, the amount of change between the value of the intermediate target and the initial body composition data of the test subject, the test subject A body composition evaluation method comprising information on at least one of a ranking corresponding to the body composition data of, a description of the body composition data of the test subject, a rate of change corresponding to the body composition data of the test subject, a grade, and an emoticon. The method of claim 1,
The body composition data is
A method for evaluating body composition, which is data on any one of body fat mass and skeletal muscle mass. The method of claim 1,
The target graph and the evaluation result are
The body composition evaluation method output from any one of the mobile terminal of the test subject, a measuring device measuring the body composition data of the test subject, and a result sheet. The method of claim 1,
The body composition evaluation method
The body composition evaluation method performed by any one of a measuring device measuring the body composition data of the test subject, and a mobile terminal or a server of the test subject receiving the body composition data of the test subject from the measuring device. Selecting other users whose biometric data is within a predetermined range from among users whose biometric data is stored in the database;
Determining a target period and a target change amount of the test subject based on the body composition change amount of the other users and a target value determined in advance or input from the test subject; And
Determining a target graph of the test subject based on the target period and target change amount
Including,
The step of determining the target period and the target change amount
When any one of the target period and the target change amount is set by the test subject, the other one of the target period and the target change amount is determined based on the change in body composition data of the other users and a value set by the test subject. Processor; And
Memory containing at least one instruction executable by the processor
Including,
When the at least one command is executed by the processor, the processor determines a target graph of the test subject based on body composition data of other users corresponding to the test subject and a predetermined or target value input from the test subject, and the target Determine evaluation information for the body composition data of the test subject based on the graph,
The processor is
If any one of the target period and the target change amount is set by the test subject, the other one of the target period and the target change amount is determined based on the change in body composition data of the other users and the value set by the test subject, and the set or determined Determining the target graph based on the target period and the target change amount,
The other users are selected as users whose difference between the biometric data of the subject and the biometric data of the subject is within a predetermined range from among users whose biometric data is stored in a database,
Body composition evaluation device. delete

Images