KR102668988B1 - Method for analyzing body status for relearning workouts - Google Patents

Abstract

According to the present invention, a body condition analysis method performed by a server includes receiving user information of a first user; Receiving at least one body condition measurement data corresponding to the first user from the at least one measurement device, wherein the at least one body condition measurement data is transmitted to an electromyography meter located on at least one body part of the first user. Contains first electromyography data acquired by -; Obtaining second EMG data by performing a first operation corresponding to a first filter on the first EMG data, wherein the first filter includes a noise removal filter and a rectification filter; Obtaining third EMG data by performing a second operation corresponding to a second filter on the second EMG data; Based on the third electromyography data, determining stability of the at least one body part and mobility of the at least one body part; and analyzing the physical condition of the first user based on the stability and mobility.

Description

{METHOD FOR ANALYZING BODY STATUS FOR RELEARNING WORKOUTS}

The present invention relates to a method of analyzing body condition for exercise relearning.

To learn movement, people learn the overall pattern of movement by repeating the same movement. If movements that occur in daily life are learned incorrectly, muscle performance deteriorates and the likelihood of injury and pain increases. Decreased muscle performance, injuries, and pain can cause each other and worsen. Therefore, there is a need to relearn movement in a desirable direction.

The conventional exercise relearning method is conducted by observing the joint range of motion, finding joints with an abnormal range of motion, and performing exercises to strengthen the corresponding area.

Public Patent Publication No. 10-2018-0007162 (2018.01.22.) Public Patent Publication No. 10-2021-0015176 (2021.02.10.) Public Patent Publication No. 10-2019-0015458 (2019.02.13.) Public Patent Publication No. 10-2014-0071739 (2014.06.12.) Registered Patent Publication No. 10-2117708 (2020.06.01.)

Conventional exercise relearning methods only focus on externally visible factors such as joint range of motion, making it difficult to find the root cause of incorrectly learned movements, resulting in low effectiveness of exercise relearning.

The purpose of the present invention is to provide a system that allows efficient exercise re-learning by providing an exercise program taking into account variables such as stability and mobility that are not apparent on the surface.

Additionally, the purpose of the present invention is to provide a method for determining a program for exercise re-learning and precisely analyzing the physical state during the exercise re-learning process.

According to the present invention, a body condition analysis method performed by a server includes receiving user information of a first user; Receiving at least one body condition measurement data corresponding to the first user from the at least one measurement device, wherein the at least one body condition measurement data is transmitted to an electromyography meter located on at least one body part of the first user. Contains first electromyography data acquired by -; Obtaining second EMG data by performing a first operation corresponding to a first filter on the first EMG data, wherein the first filter includes a noise removal filter and a rectification filter; Obtaining third EMG data by performing a second operation corresponding to a second filter on the second EMG data; Based on the third electromyography data, determining stability of the at least one body part and mobility of the at least one body part; and analyzing the physical condition of the first user based on the stability and mobility.

According to an embodiment of the present invention, the body condition analysis method determines the stability of at least one body part and the stability of at least one body part based on data resulting from performing a first operation and a second operation on electromyography data. Mobility By identifying the type of physical problem based on data that is not visible to the naked eye and providing an exercise re-learning program corresponding to the type of identified physical problem, the physical problem can be accurately identified, thus achieving high efficiency. contributes to motor relearning.

1 shows a body condition analysis system according to an embodiment of the present invention.
Figure 2 shows a method performed in a server included in a body condition analysis system according to an embodiment of the present invention.
3A-3C show examples of body condition measurements.
Figure 4 shows data input to an artificial intelligence model generated in a server and data output from the artificial intelligence model, according to an embodiment of the present invention.
Figure 5 shows a method performed in a server included in a body condition analysis system according to an embodiment of the present invention.
Figure 6 shows a method performed in a server included in a body condition analysis system according to an embodiment of the present invention.

As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other elements in addition to the mentioned elements. The various embodiments of this specification and the terms used herein are not intended to limit the technical features described in this specification to specific embodiments, but should be understood to include various changes, equivalents, or replacements of the relevant embodiments. In connection with the description of the drawings, similar reference numbers may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the above items, unless the relevant context clearly indicates otherwise. As used herein, “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B or C”, “at least one of A, B and C”, and “A Each of phrases such as “at least one of , B, or C” may include any one of the items listed together in the corresponding phrase, or any possible combination thereof. Terms such as "first", "second", or "first" or "second" may be used simply to distinguish one element from another, and may be used to distinguish such elements in other respects, such as importance or order) is not limited. One (e.g., first) component is said to be “coupled” or “connected” to another (e.g., second) component, with or without the terms “functionally” or “communicatively.” When mentioned, it means that any of the components can be connected to the other components directly (e.g. wired), wirelessly, or through a third component.

The term “module” used in various embodiments of the present specification may include a unit implemented in hardware, software, or firmware, and is interchangeable with terms such as logic, logic block, component, or circuit, for example. It can be used as A module may be an integrated part or a minimum unit of the parts or a part thereof that performs one or more functions. For example, according to one embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC).

Various embodiments of the present specification may be implemented as software including one or more instructions stored in a storage medium (eg, internal memory or external memory) that can be read by a machine. For example, the processor of the device may call at least one instruction among one or more instructions stored from a storage medium and execute it. This allows the device to be operated to perform at least one function according to the at least one instruction called. The one or more instructions may include code generated by a compiler or code that can be executed by an interpreter. A storage medium that can be read by a device may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' only means that the storage medium is a tangible device and does not contain signals (e.g. electromagnetic waves). This term refers to cases where data is stored semi-permanently in the storage medium. There is no distinction between temporary storage cases.

According to one embodiment, methods according to various embodiments disclosed in this specification may be provided and included in a computer program product. Computer program products are commodities and can be traded between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g. compact disc read only memory (CD-ROM)), or through an application store (e.g. Play Store ^TM ) or on two user devices (e.g. It can be distributed (e.g. downloaded or uploaded) directly between smart phones) or online. In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

According to various embodiments, each component (e.g., module or program) of the above-described components may include a single or plural entity, and some of the plurality of entities may be separately placed in other components. there is. According to various embodiments, one or more of the components or steps described above may be omitted, or one or more other components or steps may be added. Alternatively or additionally, multiple components (eg, modules or programs) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each component of the plurality of components identically or similarly to those performed by the corresponding component of the plurality of components prior to the integration. . According to various embodiments, steps performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the steps may be executed in a different order, or omitted. Alternatively, one or more other steps may be added.

1 shows a system for providing a conditioning program for exercise relearning according to an embodiment of the present invention. Referring to FIG. 1 , a system for providing a conditioning program for exercise relearning includes a server 110, at least one measuring device 120, a trainer device 130, and a user terminal 140. Although FIG. 1 shows one measuring device 120, one trainer device 130, and one user terminal 140, according to various embodiments, a system for providing a conditioning program for exercise relearning may include a plurality of devices. It may include a measuring device 120, a trainer device 130, and a user terminal 140.

The server 110 includes a processor 111, a communication circuit 114, and a memory 115. The communication circuit 114 can transmit information to or receive information from another electronic device, and the type of communication supported by the communication circuit 114 is not limited.

The processor 111 performs an operation based on data received through the communication circuit 114 and/or data stored in the memory 115, and sends at least a portion of the result of the operation to another electronic device through the communication circuit 114. It can be transmitted to or stored in the memory 115.

The processor 111 may include a data learning unit 112 and a data recognition unit 113. As will be described later, the data learning unit 112 may receive user information and the type of user's physical problem, predict improvement progress, and generate artificial intelligence models to output the expected improvement progress. The data recognition unit 113 may preprocess data and provide the preprocessed data to the data learning unit 112 for learning.

At least one of the data learning unit 112 and the data recognition unit 113 is implemented as a dedicated hardware chip for artificial intelligence, or as part of an existing general-purpose processor (e.g., AP or CPU) or a graphics-specific processor. It could be.

According to various embodiments, unlike the data learning unit 112 and the data recognition unit 113 shown in FIG. 2 as being included in the server 110, the data learning unit 112 and the data recognition unit 113 are Each may be mounted on a separate electronic device.

In this case, the data learning unit 112 and the data recognition unit 113 are connected to each other by wire or wirelessly, and the model information generated by the data learning unit 112 is provided to the data recognition unit 113, or the data recognition unit 113 The data input at 113 may be provided to the data learning unit 112 as additional learning data.

At least one of the data learning unit 112 and the data recognition unit 113 may be implemented as a software module. In this case, the software module may be stored in a non-transitory computer-readable recording medium. At least a portion of the software module may be provided by an operating system (OS) or a predetermined application.

Memory 115 may include a physical problem database 116 and a user status database 117. The physical problem database 116 includes the type of physical problem, the physical problem determination condition that is the condition of the physical state measurement information for determining that there is a physical problem, and the physical problem that is the condition of the physical state measurement information to determine that the physical problem is solved. The solution condition, at least one monitoring variable, a weight of the at least one monitoring variable, and an exercise relearning program for improving the physical problem are stored in association with each other. The physical problem determination condition and the physical problem solving condition are composed of at least one condition among the information constituting the body condition measurement information. The monitoring variable is a variable selected to quantitatively evaluate how close the user's body is to solving the physical problem when the physical problem determination condition is satisfied and the physical problem solving condition is not satisfied, and is information that constitutes the body condition measurement information. At least one piece of information may be a monitoring variable. For example, the physical problem determining condition associated with the physical problem determining condition of the problem of reduced mobility of the elbow joint may be that the stability of the elbow joint is lower than grade C while performing the biceps curl movement, and the physical problem solving condition may be The stability of the elbow joint may be grade B or higher while performing the biceps curl movement. At this time, the monitoring variable may be the co-contraction index of the elbow joint or the stability of the elbow joint while performing the biceps curl motion. Depending on the type of physical problem, there may be multiple monitoring variables. When there are multiple monitoring variables, a weight of 0 to 1 may be set for each of the multiple monitoring variables, and the sum of the weights of the multiple monitoring variables is 1. It can be. If there is one monitoring variable, the weight of one monitoring variable can be set to 1. Type of physical problem, physical problem determination conditions that are conditions of physical state measurement information for determining that a physical problem exists, physical problem solving conditions that are conditions of physical state measurement information for determining that a physical problem is resolved, and at least one monitoring The variables, the weight of the at least one monitoring variable, and an exercise re-learning program for improving physical problems may be entered into the database by the operator of the server.

The user status database 117 may store user account information, user information, type of physical problem, trainer account information, physical condition measurement information, and improvement progress in association. User information may include age, gender, region, occupation, and activity time information. Activity time zone information may be a selection of nocturnal, which sleeps during the day and is active at night, and diurnal, which sleeps at night and is active during the day. Trainer account information may be information that specifies the account of a trainer who will receive information related to the user.

Body condition measurement information may be derived by the processor 111 of the server 110 based on at least one body condition measurement value obtained from the measurement device 120. The measuring device 120 may include an inbody measuring device, a body type measuring device, a plantar pressure measuring device, an electromyography measuring device, a muscle tension measuring device, a motion analysis camera, a ground reaction force measuring device, and an isokinetic muscle measuring device. The body condition measurement value, which is the measurement value of each measuring device, may be transmitted to the server 110 and serve as basic data for deriving body condition measurement information from the server 110. For example, the electromyography measuring device may transmit the muscle activity of the agonist muscle and the muscle activity of the antagonist muscle of at least one body part, acquired during repeated exercise movements, to the server 110 as body condition measurement values. For example, the motion analysis camera may transmit motion performance images acquired during repeated exercise motions to the server 110 as body condition measurement values.

The body condition measurement information includes stability of at least one body part, mobility of at least one body part, range of motion of at least one joint, torque value of at least one joint, and fatigue of at least one muscle. , includes at least one of the degree of body imbalance, static center of gravity, dynamic center of gravity, and plantar pressure distribution information.

The stability of at least one body part may be determined based on the co-contraction index, which is a value obtained by dividing the muscle activity of an antagonist muscle involved in bending the corresponding body part by the muscle activity of an agonist muscle. For example, the processor 111 may check a graph of the co-contraction index based on the muscle activity of the agonist and antagonist muscles of the upper arm while performing a plurality of biceps curl movements, and based on the pattern of the graph, By checking the point corresponding to one repetitive motion, the graph can be divided into repetition cycles, and the average value of the co-contraction index during the repetition cycle located in the middle temporally among the plurality of divided repetition cycles is calculated as the biceps curl. It can be defined as the stability of the elbow joint while performing an action. For example, when the biceps curl action is performed 5 times, the processor 111 uses the average value of the co-contraction index during the third repetition cycle as the stability of the elbow joint while performing the biceps curl action. ) can be determined. In another example, when the biceps curl action is performed 6 times, the processor 111 calculates the average value of the co-contraction index during the third and fourth repetition cycles as the state of the elbow joint while performing the biceps curl action. It can be decided by stability. According to various embodiments, the processor 111 may determine stability as a grade according to a section in which the average value of the co-contraction index during a specific repetition cycle is located. For example, if the average value of the co-contraction index during a specific repetition cycle is less than 60%, the stability can be determined as grade A, if it is more than 60% but less than 100%, it can be determined as grade B, and if it is more than 100%, it can be graded as C. At this time, boundary values such as 60% and 100%, which are the standards for class classification, may be determined in advance by the operator of the server 110.

The mobility of at least one body part may be determined based on the co-contraction index and the motion performance image. The processor 111 can analyze the motion performance image to calculate the range of motion of the joint over time and derive the point in time when the range of motion is maximum during the time the exercise motion is performed. The processor 111 may determine the mobility of the corresponding body part based on the co-contraction index and the maximum range of motion at the point where the range of motion is maximum. The lower the co-contraction index at the point where the range of motion is maximum, the higher the mobility can be determined, and the greater the maximum range of motion, the higher the mobility can be determined. Like stability, mobility can also be defined as a grade according to the section to which the co-contraction index at the point when the range of motion is at its maximum and the section to which the maximum range of motion belongs, and the boundary value that serves as the basis for class classification is the server 110. It can be determined in advance by the operator. If the maximum range of motion is the same, the lower the co-contraction index at the point where the range of motion is maximum, the higher the mobility can be evaluated. Additionally, if the co-contraction index at the point where the range of motion is maximum is the same, the higher the maximum range of motion, the higher the mobility can be evaluated.

For example, while performing a biceps curl, a specific first point on the elbow joint, a specific second point on the shoulder, and a specific third point on the wrist connect the first and second points. When the angle formed by the first straight line and the second straight line connecting the first and third points is θ, the range of motion can be defined as 90°-θ. In other words, when the distance between the first straight line and the second straight line forms 90°, the range of motion is defined as 0°, and as the biceps muscle contracts while performing the biceps curl motion, the second straight line rotates around the first point. While doing this, the angle formed by the second straight line and the first straight line becomes smaller, and the angular displacement of the second straight line can be defined as the range of motion. The maximum range of motion may be the range of motion at the point when maximum contraction of the biceps curl motion occurs, that is, the minimum value of the range of motion while performing the biceps curl motion. In this example, the mobility of the elbow joint can be determined as shown in Table 1.

According to various embodiments, at least one of stability and mobility may be calculated based on data resulting from performing various operations on first electromyography data acquired by an electromyography measuring device. The processor 111 may obtain second EMG data by performing a first operation corresponding to the first filter on the first EMG data. Here, the first filter may include a noise removal filter and a rectification filter. Thereafter, the processor 111 may obtain third EMG data by performing a second operation corresponding to the second filter on the second EMG data. Here, the second filter may be a filter that extracts only specific frequency components from the second EMG data. Thereafter, the processor 111 may determine stability and mobility based on the third EMG data.

The improvement progress may be determined by monitoring variables corresponding to the type of physical problem the user has. The processor 111 refers to the physical problem database 116 to check the monitoring variables, weights of the monitoring variables, and physical problem solving conditions corresponding to the type of physical problem the user has, and improves the user's physical condition based on the measurement information. The rate of progress can be determined.

When there is one monitoring variable, the improvement progress rate at time t can be defined as Equation 1.

In equation 1 is the value of the monitoring variable included in the user's physical condition measurement information at time t, is the value of the monitoring variable included in the first body condition measurement information, means the value of the monitoring variable included in the physical problem solving conditions. For example, if the monitoring variable is the co-contraction index of the agonist and antagonist muscles of the elbow joint while performing a biceps curl movement, and the initial body condition measurement information indicates that the co-contraction index is 100%. The value of is 100%, and if the condition for solving the physical problem is that the co-contraction index of the agonist and antagonist muscles of the elbow joint is less than 60 while performing the biceps curl movement, is 60%. When the user's body condition measurement information at time t indicates that the co-contraction index of the agonist and antagonist muscles of the elbow joint is 90% while performing the biceps curl movement. is 90%. In this case, the improvement progress is |(90%-100%)|/|(60%-100%)|*100=25%.

On the other hand, when the monitoring variable includes stability or mobility, stability or mobility is defined as a grade rather than a number, so the score corresponding to each grade is predefined, and the improvement progress can be calculated using the defined score. You can.

When there are multiple monitoring variables, the improvement progress rate at time t can be defined as the weighted average of the multiple monitoring variables. In other words, the improvement progress rate can be defined as the sum of the values calculated by Equation 1 for each monitoring variable multiplied by the weight of each monitoring variable.

The user status database 117 may cumulatively store not only the most recently acquired body condition measurement information, but also past body condition measurement information and the point in time at which each body condition measurement information was acquired as a history. The user state database 117 may also cumulatively store a history of improvement progress in correspondence to the history of body condition measurement information.

The user status database 117 may further store the user's exercise attendance record in correspondence with user information.

Figure 2 shows a method performed on a server included in a system for providing a conditioning program for exercise relearning, according to an embodiment of the present invention.

In operation 210, the server 110 may receive user information of the first user through the communication circuit 114. The server 110 may receive user information from the trainer device 130 and store the account logged in to the trainer device 130 as trainer account information in the user status database. Alternatively, the server 110 may receive user information from the user terminal 140 and trainer account information from the user terminal 140 and store them in the user status database.

In operation 220, the server 110 may receive at least one body condition measurement value corresponding to the first user from at least one measurement device 120.

In operation 230, the server 110 may confirm body condition measurement information based on at least one body condition measurement value by the processor 111. The body condition measurement information includes stability of at least one body part, mobility of at least one body part, range of motion of at least one joint, torque value of at least one joint, and fatigue of at least one muscle. , may include at least one of muscle tension, degree of body imbalance, static center of gravity, dynamic center of gravity, and plantar pressure distribution information. For example, the processor 111 determines the degree of body imbalance, the static condition based on the body condition measurement value received from the body shape measurement device, the body condition measurement value received from the plantar pressure measurement device, and the body condition measurement value received from the motion analysis camera. The center of gravity and dynamic center of gravity can be determined. For example, the processor 111 may determine the range of motion of at least one joint based on the body condition measurement value received from the motion analysis camera. For example, the processor 111 may determine a torque value of at least one joint based on body condition measurements received from an isokinetic muscle strength meter, a motion analysis camera, and a motion analysis camera. For example, the processor 111 calculates the difference between the average power frequency at the beginning of exercise and the average power frequency after repeated movements have occurred a predetermined number of times, based on the muscle activity waveform of the muscle, which is a body condition measurement value received from the electromyograph. Based on this, muscle fatigue can be determined.

In operation 240, the processor 111 may refer to the physical problem database 115 and check the type of physical problem of the first user based on the physical condition measurement information.

In operation 250, the processor 111 may associate the type of physical problem of the first user with the user information of the first user and store it in a user state database.

In operation 260, the processor 111 may refer to the physical problem database 115 and transmit an exercise re-learning program corresponding to the identified physical problem to the trainer device.

3A-3C show examples of body condition measurements. Measurement of physical condition can be made with an electromyography measuring device attached to the location of the muscle, as shown in FIG. 3A. As shown in FIG. 3B, repeating certain movements, such as transitioning from the first posture 310 to the second posture 320 and transitioning from the second posture 320 to the first posture 310, several times. During the procedure, body condition measurements may be obtained. Figure 3C shows an example of body condition measurements. As shown in FIG. 3C, a motion performance image and a graph of muscle activity of various muscles while the exercise motion is performed may be included in the physical condition measurement value.

Figure 4 shows data input to an artificial intelligence model generated in a server and data output from the artificial intelligence model, according to an embodiment of the present invention. The processor 111 may generate a first artificial intelligence model that receives user information and the type of physical problem and outputs the user's expected improvement progress. The first artificial intelligence model may be learned based on user information in the user status database, type of physical problem, and improvement progress rate. If a new improvement progress rate is not entered for more than a predetermined period of time, the processor 111 determines the most recently input improvement progress rate as the final improvement progress rate for the user, and determines the user information of several users, the type of physical problem, and the final improvement progress rate. The first artificial intelligence model may be trained based on the improvement progress.

When the user's expected improvement progress is confirmed, the processor 111 may store the confirmed expected improvement progress in the user status database 117.

When the user's expected improvement progress is confirmed, the processor 111 may refer to the user status database 117 and transmit the confirmed expected improvement progress to the trainer device corresponding to the user's trainer account information.

The learning method for creating the first artificial intelligence model is not limited. For example, the first artificial intelligence model, the second artificial intelligence model, and the third artificial intelligence model may be created using various machine learning techniques. For example, at least one of a Recurrent Neural Network (RNN), a Convolution Neural Network (CNN), an Artificial Neural Network (ANN), and a Transformer model may be used for learning to generate the first artificial intelligence model.

Figure 5 shows a method performed in a server included in a system for providing a conditioning program for exercise relearning, according to an embodiment of the present invention. In operation 510, the processor 111 may check the improvement progress of the first user. This value is a calculated percentage of improvement progress based on the most recently obtained body condition measurement. Before performing operation 510, the processor 111 may calculate the expected improvement progress of the first user using the first artificial intelligence model and store it in the user state database 117.

In operation 520, the processor 111 may determine whether the first user's improvement progress rate is higher than the expected improvement progress rate or whether the difference between the first user's improvement progress rate and the expected improvement progress rate is greater than a predetermined first value.

If it is determined in operation 520 that the improvement progress rate of the first user is higher than the expected improvement progress rate, or that the difference between the improvement progress rate of the first user and the expected improvement progress rate is greater than a predetermined first value, the processor 111 performs the first predetermined value in operation 530. 1 The communication circuit 114 may be controlled to transmit motivation information at a first frequency to a user terminal associated with the user's user account information. The motivation information may include a cheering message written by a second user whose improvement progress rate is more than a predetermined second value while at least one of the user information is the same as that of the first user, an exercise video of the second user, and an exercise record of the second user. You can.

If it is determined in operation 520 that the improvement progress rate of the first user is less than the expected improvement progress rate and that the difference between the first user's improvement progress rate and the expected improvement progress rate is smaller than the first predetermined value, the processor 111 performs the first predetermined value in operation 540. The communication circuit 114 may be controlled to transmit motivation information at a second frequency to a user terminal associated with the user's user account information. The second frequency may be a higher frequency than the first frequency.

Figure 6 shows a method performed in a server included in a system for providing a conditioning program for exercise relearning, according to an embodiment of the present invention.

In operation 610, the processor 111 may receive an exercise attendance record from the user terminal 140 associated with the user account information of the first user. The exercise attendance record may be created using the exercise attendance record interface on the user terminal 140 and includes information on the date and time the first user attended.

In operation 620, the processor 111 may store the first user's exercise attendance record in the user state database.

In operation 630, the processor 111 may calculate a conscientiousness score for each user based on the exercise attendance records of a plurality of users including the first user. Integrity scores can be compiled over a predetermined period of time. For example, conscientiousness scores may be aggregated monthly. The sincerity score may be determined by accumulating scores corresponding to the amount of rainfall in the area included in the user information of the first user based on weather information each time an exercise attendance record is received from the user terminal. The server 110 can obtain weather information from another server, and the processor 111 can refer to the acquired weather information to check the date and time included in the exercise attendance record and the amount of rainfall corresponding to the area included in the user information. . Thereafter, the processor 111 may accumulate a predetermined score corresponding to the confirmed rainfall amount to the existing sincerity score. For example, the score corresponding to rainfall less than 10 mm per hour is 1, the score corresponding to rainfall more than 10 mm per hour but less than 100 mm is 2, the score corresponding to rainfall more than 100 mm per hour but less than 200 mm is 3, and the score corresponding to rainfall more than 200 mm per hour is 3. It could be 4. For example, if the conscientiousness score is 10 points and a user living in Daejeon transmits the exercise attendance record to the server 110 using the exercise attendance record interface at 3 PM on Tuesday, and the rainfall in Daejeon at 3 PM on Tuesday is 50 mm, the processor (111) increases the user's sincerity score by 2, thereby updating the user's sincerity score to 12.

In operation 640, the processor 111 calculates the conscientiousness score ranking of users of the same age as the first user, the conscientiousness score ranking of users of the same gender as the first user, and the conscientiousness score ranking of users of the same region as the first user. It can be transmitted to the user terminal associated with the user's user account information. The processor 111 may perform operation 640 whenever the aggregation period of the sincerity score ends.

Above, embodiments of the present invention have been described with reference to the attached drawings, but those skilled in the art will understand that the present invention can be implemented in other specific forms without changing its technical idea or essential features. You will be able to understand it. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

Claims (5)

In the body condition analysis method performed by the server,
Receiving user information of a first user;
Receiving at least one body condition measurement data corresponding to the first user from at least one measurement device;
Obtaining second EMG data by performing a first operation corresponding to a first filter on the first EMG data;
Obtaining third EMG data by performing a second operation corresponding to a second filter on the second EMG data;
A first value obtained by performing the first operation and the second operation on the muscle activity of the agonist muscle among the third EMG data, and a second value obtained by performing the first operation and the second operation on the muscle activity of the antagonist muscle Check the value, and divide the first value by the second value, and the average value during the repetition cycle corresponding to the repetitive motion is a grade according to the section belonging to three or more first sections in advance, and at least one body Determining the stability of the area;
Based on the third electromyography data, the value obtained by dividing the first value by the second value at the point in the maximum range of motion is determined by the section belonging to three or more predefined second sections and the maximum range of motion. determining mobility of at least one body part as a rating; and
Confirming the type of physical problem of the first user by comparing the stability and mobility with pre-stored physical problem determination conditions,
The at least one body condition measurement data includes a motion performance image acquired during a repeated exercise movement and the first electromyography data acquired by an electromyography measuring device located on at least one body part of the first user, The first electromyography data includes muscle activity of an agonist muscle and muscle activity of an antagonist muscle of at least one body part,
The first filter includes a noise removal filter and a rectification filter,
How to analyze body condition. According to paragraph 1,
The second filter is configured to extract only specific frequency components from the second EMG data. According to paragraph 1,
storing the type of physical problem of the first user in a user status database by associating it with the user information of the first user; and
A physical condition analysis method further comprising transmitting an exercise relearning program corresponding to the type of the identified physical problem to a trainer device. delete The method of claim 1, wherein
A physical condition analysis method further comprising correlating user account information, user information, type of physical problem, trainer account information, body condition measurement information, and improvement progress and storing them in a user condition database.