KR102518324B1 - Cloud-based artificail intelligence guide system for muscle strength exercise - Google Patents

Abstract

The present invention objectively evaluates the user's muscular strength to determine muscles to be strengthened, based on this, enables the user to set a desired goal, and objectively evaluates the exercise to achieve the goal to provide an appropriate exercise that meets the goal. It relates to a cloud-based artificial intelligence strength exercise guide system that can be recommended, and more specifically, a user group in which users who have obtained muscle conditions using an electromyogram meter (10) and a personal terminal (20) are classified according to personal characteristics. Statistical information is generated for each muscle condition, and the user's muscle condition is compared and evaluated with the statistical information for each user group, so that the user can set the muscle condition of the desired user group as the goal of strength exercise, and it is suitable for the strength exercise goal. It relates to a cloud-based artificial intelligence strength exercise guide system that selects and recommends appropriate exercises according to fitness after receiving recommendations from a group of experts.

Description

Cloud-based artificial intelligence strength exercise guide system {CLOUD-BASED ARTIFICAIL INTELLIGENCE GUIDE SYSTEM FOR MUSCLE STRENGTH EXERCISE}

The present invention objectively evaluates the user's muscular strength to determine muscles to be strengthened, based on this, enables the user to set a desired goal, and objectively evaluates the exercise to achieve the goal to provide an appropriate exercise that meets the goal. It is about a cloud-based artificial intelligence strength exercise guide system that can be recommended.

Among the human muscles, there are more than 600 skeletal muscles, and there are dozens of major muscles that are interested in health training.

It is impossible without the help of a fitness trainer to determine which type of exercise to perform by comprehensively evaluating which muscle strength is strong and which muscle strength is weak among each of these major muscles.

However, even if you receive a comprehensive evaluation of the major muscles with the help of a health trainer and exercise with a recommendation for an exercise event, you must exercise with personal training that requires time, money, and effort from the health trainer to exercise properly, exercise and Because the effect of exercise can only be obtained through re-evaluation, there are many cases in which it is not possible to exercise continuously.

In addition, even with the help of a fitness trainer, since the muscle condition is evaluated according to the subjective judgment of the fitness trainer, the evaluation may be different for each fitness trainer, and the recommended exercise may also be different. As a result, the goal of muscle condition is ambiguous, the motivation to continue exercising is not sufficient, and there may be doubts about exercise fitness, which may not give reliability to personal training. Due to the lack of professionalism of the Consumer Protection Agency, the number of consumer damage complaints filed with the Consumer Protection Agency is increasing every year.

In addition, the goal of muscle condition may vary from person to person. For example, there may be individuals who want to target the upper level of the same age group, the upper level of the same gender, the upper level of the athlete they are interested in, etc. There may also be individuals. In other words, it is possible to be motivated by setting a goal to reduce individual differences according to a relative evaluation with a specific class of people, but even with the help of a health trainer, it is impossible to set a goal like this. For example, an elderly person who needs exercise to prevent osteoporosis may have the purpose of exercise to maintain appropriate muscle strength, but it is difficult to determine how much appropriate muscle strength to do.

On the other hand, according to the prior art disclosed in Patent Registration No. 10-1687252, Registration Patent No. 10-1715369 and Publication Patent No. 10-2020-0032364, a customized training plan is provided according to individual user information to exercise, By monitoring the exercise status, it guides you to exercise in the way the professional trainer teaches you, and it can provide you with an appropriate customized training plan by collecting training methods from the professional trainer.

However, since the prior art recommends exercises based on user information registered for each individual without objectively evaluating the overall major muscles of each individual, it is difficult to set goals according to objective indicators, and exercises collected through professional trainers After matching and presenting the exercise method by type with user information, it is possible to induce the user to follow the exercise method and exercise in the correct posture, but it is difficult to present an exercise suitable for the overall condition of the muscles, and I can't even suggest exercise.

KR 10-1687252 B1 2016.12.12. KR 10-1715369 B1 2017.03.06. KR 10-2020-0032364 A 2020.03.26.

An object of the present invention is to determine the general state of muscles for each user according to an objective evaluation rather than subjective evaluation by an expert, and to improve the overall state of muscles according to the objective evaluation as desired by the user. It is to provide a cloud-based artificial intelligence strength exercise guide system that can objectively evaluate recommended exercises for each health tray and present exercise items that meet the goal.

In order to achieve the above object, the present invention provides a cloud-based artificial intelligence strength exercise guide system, comprising: an electromyogram meter (10) for detecting an electromyogram signal from each muscle part of a user; Muscle strength comparison screen (300, 310, 320) that obtains the user's muscle condition according to the muscle-specific EMG signal detected by the EMG meter 10 and shows statistical information on the muscle condition for each user group to be compared with the user's muscle condition , 330), and after presenting the user group as a strength exercise target, outputting a goal-oriented recommendation screen (400, 430, 440) showing the target muscle state and at least one recommended exercise that meets the strength exercise target selected by the user a personal terminal 20 that enables; Among the muscle conditions collected from users' personal terminals 20, statistical information is generated on the muscle conditions of user groups grouped according to different personal characteristics of users, and then transmitted to the personal terminal 20 to generate a muscle strength comparison screen (300, 310, 320, 330), and according to the target muscle condition and strength exercise goal selected according to the statistical information of the user group corresponding to the strength exercise goal selected by the user among the strength exercise goals named according to the characteristics of the user group. a cloud server 30 that supports output of goal-oriented recommendation screens 400, 430, and 440 by transmitting at least one selected recommended exercise to the personal terminal 20; includes

According to an embodiment of the present invention, the cloud server 30 displays exercises recommended by a group of experts for each strength exercise target using the individual terminal 20 or a separate expert terminal on a goal-oriented recommendation screen (400, 430, 440). ) is used as a recommended exercise to be printed.

According to an embodiment of the present invention, the cloud server 30 determines the matching rate according to the suitability of the recommended workout, and then outputs the recommended workout selected according to the matching rate ranking on the goal-oriented recommendation screens 400, 430, and 440. Use it as a recommended exercise to do.

According to an embodiment of the present invention, the cloud server 30 generates statistical information for each muscle and supports the output of a muscle strength comparison screen to compare for each muscle.

According to an embodiment of the present invention, the cloud server 30 may use a group of athletes or a group of health trainers for each sport event among users as a user group.

According to an embodiment of the present invention, the cloud server 30 selects only the user group matching the user's personal characteristics or the statistical information of the user group pre-determined by the user, and the muscle strength comparison screen (300, 310, 320, 330 ) can support the output of

The present invention configured as described above evaluates muscle strength without absolute standards relative to other people's muscle strength, objectively judges the level of muscle strength, diversifies the group of people to be evaluated relative to each other, and presents different evaluation criteria for each person, Since the goal of strength exercise can be clearly set according to the relative evaluation, it can serve as a guide to achieve a personalized goal by performing strength exercise according to the individual strength level.

In addition, since the present invention determines the suitability of an exercise recommended by an expert and recommends an exercise suitable for a muscle strength exercise goal, the exercise effect for achieving the goal can be increased.

Therefore, the present invention self-renews the evaluation criteria of the muscle strength level, selects the optimal exercise for the recommended exercise, evaluates the user's muscle strength level objectively, and presents customized goals and customized exercises according to the user's needs. An artificial intelligence-based exercise guide service can be provided.

1 is a use state diagram of a cloud-based artificial intelligence strength exercise guide system according to an embodiment of the present invention.
FIG. 2 is a cross-sectional view of the EMG measuring device 10 and the electrode patch 100 to be connected to the EMG measuring device 10 in FIG. 1 .
Figure 3 is a state of use of the cloud-based artificial intelligence strength exercise guide system using smart clothing (200) instead of the electrode patch (100).
FIG. 4 is a cross-sectional view of a portion to which the EMG meter 10 is connected among the smart clothes 200 worn by the user in FIG. 2 .
5 is an electrical signal block diagram of a cloud-based artificial intelligence strength exercise guide system according to an embodiment of the present invention.
6 is a configuration diagram of a basic screen 300 among muscle strength comparison screens output to the user interface 22 by the muscle state evaluation unit 21c in the personal terminal 20;
FIG. 7 is a configuration diagram of detailed screens 310, 320, and 330 outputted after screen conversion according to user selection in FIG. 6;
8 is a screen configuration in which a strength exercise target is selected through a drop-down 410 using a goal-oriented recommendation screen 400 output to the user interface 22 by the exercise recommendation unit 21d in the personal terminal 20. do.
9 is a configuration diagram of a goal-oriented recommendation screen 400 after selecting a strength exercise target in FIG. 8;
FIG. 10 is a configuration diagram of detailed screens 430 and 440 outputted after screen conversion according to user selection in FIG. 9;

Hereinafter, specific and various examples will be shown and described so that those skilled in the art can easily practice the embodiments of the present invention with reference to the accompanying drawings. However, since it is clear that the embodiments of the present invention can be implemented through various changes or modifications within the scope of the present invention, it is not limited to the described embodiments. In addition, the embodiments of the present invention will not be described in detail because those of ordinary skill in the art can add and implement well-known components, circuits, functions, methods, and typical details.

Embodiments of the present invention may be implemented in the form of a combination of software and hardware, and the form of software and hardware may be described as parts, modules, parts, etc., and in the form of computer-readable program codes implemented in a recording medium. can be implemented as

The connection between components may be a wired connection or a wireless connection to transfer data, signals, information, and the like, and may include an indirect connection with another component in between.

'Including' a certain component means that other components may be further included, not excluding other components unless otherwise stated.

1 is a state diagram showing the use of a cloud-based artificial intelligence strength exercise guide system according to an embodiment of the present invention.

Referring to FIG. 1 , the cloud-based artificial intelligence strength exercise guide system includes an electromyography meter (10), a personal terminal (20), and a cloud server (30).

The cloud-based artificial intelligence strength exercise guide system according to an embodiment of the present invention grasps the condition of the user's muscles and compares and evaluates the condition of the muscles with quantitative and objective statistical indicators (indices obtained according to the statistics of muscle conditions for each user group). When selecting a target for strength exercise according to the statistical index of the group desired by the user, an appropriate exercise is recommended according to the type of muscle to be strengthened.

Therefore, since the user's various muscle conditions should be identified first, the use state diagram illustrated in FIG. 1 shows a situation in which a flat barbell bench press is performed and the EMG of the pectoralis major and deltoid muscles are detected, but the pectoralis major and EMG can be detected while performing exercises that can detect EMG of muscles other than the deltoid. That is, it is assumed that electromyography is detected for each of the main muscles that are of interest and management in health training.

In addition, since the electromyogram measuring device 10 and the personal terminal 20 are used by each user, they are shown as being used by one person in FIG. 1, but are assumed to be used by a plurality of users with various personal characteristics.

Illustratively, the major muscles include some or all of the pectoralis major muscle, serratus anterior muscle, oblique muscle, rectus abdominis muscle, trapezius muscle, deltoid muscle, biceps brachii muscle, triceps brachii muscle, brachioradialis muscle, quadriceps muscle, tibialis anterior muscle, and triceps lower leg muscle, or other types of muscles. can also be added.

Illustratively, the muscle state includes all of the maximum muscle strength, endurance, and balance (imbalance of the left and right muscle states of the body) that can be obtained by analyzing the detected EMG, but is not limited thereto, and may include only some, Alternatively, other types of muscle conditions that can be obtained by analyzing EMG can be added.

In this way, in order to grasp the state of each muscle of the user, the EMG signal is detected from each muscle part of the user using the electromyogram measuring device 10 .

2 is a cross-sectional view of the EMG measuring device 10 and the electrode patch 100 to be connected to the EMG measuring device 10 .

The EMG measuring device 10 includes at least two or more connectors 11 to be connected to the electrode patch 100 attached to the user's skin.

As is well known, the electrode patch 100 has the electrode 110 and the adhesive layer 130 on the surface to be in contact with the user's skin, and the connector connecting member 120 electrically connected to the electrode 110 is in contact with the user's skin. It is provided on a surface that is not used to detect the electromyographic potential signal detected by the bioelectrode 110 in contact with the skin in a state of being attached to the skin by the adhesive layer 130 through the connector connecting member 120.

In general, since the connector connecting member 120 of the electrode patch 100 is formed in the form of a protrusion to be connected in a snap button manner, the connector 11 is fitted into the protruding connector connecting member 120, the groove 11a. ), and at least two are provided to detect the EMG signal, and after attaching at least two electrode patches 100 to the skin, they are connected one-to-one to the connector 11 to detect the EMG. Of course, an electrode patch having a plurality of electrical configurations including the electrode 110 and the connector connection member 120 may be used.

The EMG measuring device 10 may be installed so that two or more connectors 11 are partially exposed outside of a case containing a signal processing unit 12 and a short-distance communication module 13 described later. At this time, between the parts of the case where the connector 11 is installed, it can be bent or stretched by having flexibility.

However, since the EMG signal can be detected as a sufficiently accurate signal even with two bioelectrodes, it will be assumed that two connectors 11 are provided when the embodiment of the present invention is described.

3 is a state diagram showing the use of a cloud-based artificial intelligence strength exercise guide system using smart clothing 200 instead of the electrode patch 100.

4 is a cross-sectional view of a portion of the smart clothing 200 worn by the user to which the EMG meter 10 is to be connected.

The smart clothing 200 combines and integrates a plurality of bioelectrodes 220 with the clothing 210 worn by a person, and selectively fixes the electrode support 230 to the bioelectrode 220 at the site to measure EMG It is a garment that allows the EMG meter 10 to be connected afterwards.

The bioelectrode 220 is a detection electrode 221 that can detect an EMG potential signal by contacting the body skin on the inner surface of the clothing 210, and a detection electrode 221 with the clothing 210 in between A terminal 222 having an attachment member 222a that is exposed on the outer surface of the clothing 210 and is capable of detachably attaching and detaching the electrode support 230, and the detection electrode 221 penetrating the clothing 210 ) and a through member 223 electrically connecting the terminal 222 to each other. Here, the detection electrode 221, the terminal 222, and the penetrating member 223 may be physically integrated as a conductive material.

The detection electrode 221 may be composed of any one of a conductive metal electrode, a conductive rubber electrode, a conductive fiber electrode, and a conductive silicon electrode, and is formed to slightly protrude from the inner surface of the clothing 210 or considering the hair of the body skin A plurality of protrusions 221a may be provided.

The bioelectrodes 220 configured as described above are distributed and installed on the clothing 210 so that at least two pairs are placed at each muscle position when the user wears the clothing 210 . As illustrated in FIG. 3, the smart clothing 200 uses the bioelectrode 220 according to the positions of the pectoralis major muscle, serratus anterior muscle, oblique muscle, rectus abdominis muscle, trapezius muscle, deltoid muscle, biceps brachii muscle, triceps brachii muscle, and brachioradialis muscle among major muscles. Although it is made of two tops, it is also possible to make a bottom with two bioelectrodes 220 installed in accordance with the positions of the quadriceps femoris muscle, tibialis anterior muscle, and triceps lower leg muscle. Two bioelectrodes 220 may be installed at each muscle position, or two additional bioelectrodes 220 may be additionally installed at positions of other muscles.

The electrode support 230 has a connector connection member 231 connected to the connector 11 of the EMG meter 10 in a snap button manner on one side thereof, and is detachable from the terminal 222 of the bioelectrode 220. It is configured in the form of a plate having a split attachment member 232 on the other side. That is, the electrode support 230 is provided with the same connector connecting member 231 as the connector connecting member 120 of the electrode patch 100, and the electromyography measuring instrument 10 by a snap button method in the same way as the electrode patch 100. , and can be attached to and detached from the bioelectrode 220 by the attachment member 222a of the terminal 222 of the bioelectrode 220 and the detachable attachment member 232. After attaching to the bioelectrode 220 placed in position, the EMG 10 can be connected and used.

Here, the attachment member 222a of the bioelectrode 220 and the attachment member 232 of the electrode support 230 may be configured to be detachable in any one of velcro coupling, snap button coupling, and magnet coupling. there is. When using the Velcro coupling method, it can be composed of a conductive Velcro hook and a conductive Velcro loop. In the case of using the snap button method, it can be composed of conductive grooves and protrusions. In the case of using the magnetic coupling method, it may be composed of conductive magnets having different polarities of the surfaces to be contacted, or may be fitted into conductive grooves and protrusions, and the periphery of the grooves and protrusions may be constituted by magnets.

Whenever a user wearing the smart clothing 200 configured as described above performs an exercise capable of electrically activating each muscle, the electrode support 230 is coupled to the bioelectrode 220 installed on the muscle part, and then the electrode support 230 By connecting the EMG meter 10 to the EMG signal of the muscle can be detected.

5 is an electrical signal block configuration diagram of a cloud-based artificial intelligence strength exercise guide system according to an embodiment of the present invention.

Referring to FIG. 5 , the EMG meter 10 includes a signal processing unit 12 that obtains an EMG signal through two connectors 11 and a short-range communication module that wirelessly transmits the detected EMG signal to the personal terminal 20. (13) and a power supply unit 14 to supply power for obtaining EMG signals and power for wireless transmission.

It is well known that the signal processing unit 12 amplifies the EMG potential signal detected through the two connectors 11 and performs differential operation to obtain the EMG signal, and may include various circuit configurations for suppressing noise. omit the explanation.

The short-distance communication module 13 wirelessly transmits the EMG signal detected by the signal processing unit 12 and transmits it to the personal terminal 20. For example, it may be configured as a Bluetooth module or a Wi-Fi module. It is not limiting.

The personal terminal 20 includes a user interface 22 for user input and output of information to be displayed to the user, a memory 23 capable of storing data, and short-range wireless communication with the EMG meter 10. A peripheral device including a communication module 24 and a communication module 25 capable of data communication with the cloud server 30 through a communication network, a user registration unit 21a, an EMG analyzer 21b, and a muscle condition evaluation unit 21c, a processor 21 including programmatic function blocks including an exercise recommendation unit 21d, an exercise monitoring unit 21e, and an expert recommendation unit 21f. Of course, a power supply unit for supplying power required for the processor 21 to perform each function using peripheral devices is also provided.

Here, the processor 21 is a component called an application processor (AP), a central processing unit (CPU), a micro controller unit (MCU), and the like, and the user registration unit 21a, the EMG analysis unit (21b), the user registration unit ( 21a), EMG analysis unit 21b, muscle condition evaluation unit 21c, exercise recommendation unit 21d, exercise monitoring unit 21e, and expert recommendation unit 21f are provided as functional blocks to perform calculation, data processing, and result Operations such as storage and peripheral device control can be performed.

The personal terminal 20 configured as described above can be configured as a smart phone or tablet PC that is carried and used and can perform short-range wireless communication with the EMG meter 10 through Bluetooth communication or Wi-Fi communication.

The user registration unit 21a transmits personal characteristic information including gender, age, whether or not an athlete, and in the case of an athlete whether or not an exercise event and fitness trainer, and user information including expert participation intention to the cloud server 30. Register to create an account.

That is, the user information registered in the cloud server 30 includes information capable of classifying users by gender, grouping by age group, athlete group by sport event, and fitness trainer group, as will be described later. , as will be described later, it is possible to form an expert group to recommend exercise. Here, expert participation may be allowed to a user registered as an athlete or fitness trainer, or may be permitted according to a procedure such as registration of related qualifications to users who have relevant qualifications in sports medicine and thus have expertise in recommending exercises.

The EMG analyzer 21b analyzes the EMG signal transmitted from the EMG meter 10 to obtain a muscle state, stores the muscle state in the memory 23 so as to classify it by muscle, and stores it in the cloud server 30. to provide.

To this end, the EMG analysis unit 21b allows the user to input the name of the muscle of the body part wearing the EMG meter 10 through the user interface 22, or specifies the muscle according to the event the user exercises By guiding the worn part of the EMG meter 10, it is possible to distinguish which muscle the EMG signal is from which the EMG signal transmitted from the EMG meter 10 is.

Here, the muscle state may include maximum muscle strength, endurance, and balance, and may be provided to the cloud server 30 when muscle states for a predetermined number of muscles are stored, or when a muscle state of a new muscle is acquired. Alternatively, when the muscle condition is changed, it may be provided to the cloud server 30 .

Techniques for estimating muscle strength by analyzing EMG signals and estimating the timing and frequency of muscle relaxation and contraction are known techniques. In an embodiment of the present invention, maximum muscle strength, endurance, and balance are calculated according to muscle strength and number of times (exercise time).

Maximum muscle strength (1RM: one repetition maximum) is the muscle strength that can be performed only once as a complete exercise, but it can also be calculated according to the maximum number of repetitions with less than the maximum muscle strength. It can be calculated according to the measured muscle strength and number of times.

Endurance guides strength training to be continued at a constant speed and intensity, and can be calculated according to the measured strength and number of times (or duration).

The number of balances can be calculated according to the difference in maximum strength or endurance of the muscles that exist symmetrically on the left and right sides of the body. When both the maximum muscle strength difference and the endurance difference are applied, the weighted average of the difference values can be calculated.

The muscle state evaluation unit 21c receives statistical information from the cloud server 30 and outputs muscle strength comparison screens 300 , 310 , 320 , and 330 for comparison with the user's muscle state.

As will be described later, the statistical information received from the cloud server 30 is statistics of muscle conditions generated for each user group after the cloud server 30 groups users according to various criteria. Statistical information for each user group is subdivided for each muscle group so that it can be compared for each muscle group.

The muscle state evaluation unit 21c first outputs a basic screen for comparison with statistical information of a basic user group pre-determined programmatically among statistical information for each user group, and statistical information for various user groups according to user selection on the basic screen. By switching to a detailed screen to be compared with, user convenience can be promoted.

Here, the basic user group for comparison output on the basic screen is pre-determined among user groups that match the user's personal characteristics, and allows the user to change to another user group, and the user group for comparison output on the detailed screen is also determined by the user. By making it possible to provide a user-customized service.

In the detailed screen, it is subdivided by muscle so that the muscles that differ from the user group can be identified.

Specific muscle strength comparison screens 300, 310, 320, and 330 output by the muscle condition evaluation unit 21c will be described with reference to FIGS. 6 and 7.

6 is a configuration diagram of the basic screen 300 among the muscle strength comparison screens 300, 310, 320, and 330 output to the user interface 22 by the muscle condition evaluation unit 21c.

FIG. 7 is a configuration diagram of detailed screens 310, 320, and 330 outputted after screen conversion according to user selection in FIG. 6. Referring to FIG.

Referring to the basic screen 300 of FIG. 6 , the muscle condition evaluation unit 21c divides the muscle condition into maximum muscle strength, endurance, and balance, and determines the user's muscle condition for the maximum muscle strength and endurance in a basic user group (user's Relative evaluation guideline (301, 303) that informs the muscle condition of the group corresponding to gender and age) and which level it is compared to individually, and absolute evaluation guideline (305) that absolutely evaluates the user's muscle condition for balance and buttons (302, 304, 306, 308) for viewing in detail the contents of the comprehensive evaluation guide (307) and each guide (301, 303, 305, 307), which were comprehensively evaluated according to the evaluation results for each muscle condition. ) is output.

Here, the basic user group is preset as a group to which the user belongs among user groups selected by gender and age group, and as illustrated in FIG. do. Although a drawing is not attached, after outputting a list of various user groups selected by the cloud server 30, the user can select and change a desired group.

Relative evaluation guides 301 and 303 for maximum muscle strength and endurance among muscle conditions can be created according to the comprehensive evaluation of the difference values for each muscle shown in the detailed screens 310 and 320 of FIG. 7 (a, b). For example, it is possible to guide which level it is according to the average value of the difference value for each muscle.

Referring to the detailed screens 310 and 320 of FIG. 7 (a, b), the results of comparison with the user group for 'left biceps', 'right biceps' and 'left trapezius' are exemplarily shown, but by scrolling the screen Comparative results of other muscles are also shown. Exemplarily, the statistical information of the user group is the average value (312, 322) of 'all' users, the average value (313, 323) of '40's' to which the user belongs, and the 'top 10' of '40' to which the user belongs. By showing the average values (314, 324) of '%' together with the user's measured values (311, 321), the user can compare and view them. As an example, a value different from the statistics of the user group may be output, and the user group may also be arbitrarily changed by the user.

Among the muscle conditions, balance represents the difference in muscular strength or endurance of the muscles that exist symmetrically on the left and right sides of the body, so it is good that there is no such difference. Therefore, as illustrated in FIG. 6 , the average value of the user's muscle-specific balance shown in FIG. 7 (c) can be displayed as an absolute evaluation guide 305 .

Referring to the detailed screen 330 of FIG. 7(c), illustratively, measurement values of the left and right muscles of 'biceps', 'triceps', 'trapezius', and 'trapezius' are shown in a bar graph 331, and the left and right muscles Although the difference in measured values is shown as a percentage (332), the balance of other muscles can also be shown according to the scrolling of the screen. In FIG. 6 , the absolute evaluation guide 305 for balance may inform the average value of balance errors for each muscle type.

The comprehensive evaluation guide 307 contains the comprehensive evaluation results output by the relative evaluation guides 301 and 303 and the absolute evaluation guide 305 with maximum muscle strength, endurance, and balance as evaluation items for muscle conditions. The comprehensive evaluation guide 307 may include a recommended exercise for reaching a target muscle state by setting a target muscle state corresponding to a basic user group as an exercise goal among target muscle states described below. When the button 308 attached to the comprehensive evaluation guide 307 is clicked, as shown in the goal-oriented recommendation screen 400 output in FIG. 9 below, the recommended exercise 426 and matching rate 427 can be displayed. there is.

In this way, by comparing the user's muscle condition with that of various user groups and showing the screen of the result of relative evaluation, the user can easily understand the level of his or her muscle condition by comparing it with different interest groups for each user. You can also clearly select the goal of strength training according to your muscle condition level.

The exercise recommendation unit 21d selects a muscle strength exercise goal according to each user's interests, shows a detailed relative evaluation corresponding to the selected muscle strength exercise goal, and identifies the state of muscles to be strengthened, along with a matching rate. Authorizes the user to select a desired recommended exercise from among at least one suggested exercise.

For this purpose, the exercise recommendation unit 21d transmits a plurality of different strength exercise goals, a target muscle state for each muscle that meets the strength exercise target, at least one recommended exercise, and a matching rate for each recommended exercise to the cloud server ( 30), and outputs a goal-oriented recommendation screen.

8 is a screen configuration diagram in which a strength exercise target is selected through a drop-down 410 on the goal-oriented recommendation screen 400.

9 is a configuration diagram of a goal-oriented recommendation screen 400 after selecting a strength exercise target.

FIG. 10 is a configuration diagram of detailed screens 430 and 440 outputted after screen conversion according to user selection in FIG. 9 .

As shown in FIG. 8 , the exercise recommendation unit 21d presents a plurality of different strength exercise goals using a drop-down 410 provided on the goal-oriented recommendation screen 400 . At this time, the proposed strength exercise goals are classified into professional athlete goals 411 and general people goals 413 including detailed goals 412 and 414, respectively. Here, the detailed goals 412 of the professional player goal 411 are 'golf pro player', 'soccer player', 'marathon player', 'basketball player', 'swimmer' and 'health trainer', but this There is no need to limit it, and as will be described later, it can be determined according to user information of users who have created accounts by registering in the cloud server 30 . Although the detailed target 414 of the target 413 for the general public consists of a difference in gender and a difference in age, it is not necessary to be limited thereto, and various modifications are possible, such as including an average of gender or an average of age.

For example, if 'professional golf player' is selected from the professional player goal 411, the muscle condition of a professional golf player is selected as a goal, and if 'top 10% of age' is selected from the general goal 413, the user and gender While this is the same, we aim for the muscle condition of the top 10% of the same age group.

When the user selects a strength exercise goal from the drop-down 410, as illustrated in FIG. 9, a goal-oriented recommendation screen 400 displaying content corresponding to the strength exercise goal is displayed.

Referring to the goal-oriented recommendation screen 400, relative evaluation guides 421 and 423 that inform you of which level you are at for each detailed item of muscle condition compared to the user group corresponding to the strength exercise goal, and each relative evaluation guide The matching rate for the buttons 422 and 424 for viewing the contents of 421 and 423 in detail, the recommended exercise guide 425, a plurality of recommended exercises 426, and each recommended exercise 426 ( 427) is output. Compared to the basic screen 300 among the strength comparison screens 300, 310, 320, and 330, relative evaluation guides 421 and 423 are output by limiting the user group that meets the strength exercise goal as the comparison target, and recommended exercise is that it specifically presents The absolute evaluation guide for balance was not shown, but it can be shown.

In the embodiment of the present invention, since the detailed items to be compared among muscle conditions are maximum muscle strength and endurance, when the detailed buttons 422 and 424 are clicked, as shown in FIG. 10, the user's measurement values 432 , 442), the target muscle state (433, 443), and the difference (434, 444) between the user measurement value (432, 442) and the target muscle state (433, 443). The muscle types are provided with sorting buttons 431 and 441 to output in order according to various criteria. Easy to figure out. Compared with the detail screens 310 and 320 among the muscle strength comparison screens 300, 310, 320, and 330, the comparison target user group is limited according to the strength exercise goal.

The plurality of recommended exercises 426 are arranged according to the size order of the matching rate 427, so that the recommended exercise 426 with the largest matching rate 427 is recommended if possible, but there is room for the user to select. Here, the matching rate 427 is a value indicating how much the recommended exercise 426 meets the strength exercise goal, and can be said to mean the degree of contribution to reaching the target muscle state determined according to the strength exercise goal.

Here, the recommended exercise 426 is displayed as content to strengthen a specific body part or muscle, but may also specifically show an exercise event that can be strengthened when the user selects it.

The exercise monitoring unit 21e selects one recommended exercise 426 from the goal-oriented recommendation screen 400 by the exercise recommendation unit 21d and transmits information from the EMG meter 10 when exercising. The muscle condition is obtained in real time by analyzing the EMG signal, and the progress is displayed on the screen by comparing it with the target muscle condition according to the strength exercise goal. Of course, it can be shown subdivided by muscle. Here, the degree of progress may be a graph comparing the difference between the target muscle state and the current user muscle state in real time.

Meanwhile, in order to accurately evaluate the muscle condition by the muscle condition evaluation unit 21c and to recommend a suitable exercise by the exercise recommendation unit 21d, the user's muscle condition must be grasped for all major muscles.

Accordingly, in the process of operating the muscle state evaluation unit 21c and the exercise recommendation unit 21d, when the number or type of muscles whose muscle state is determined is not sufficient for evaluation or recommendation, the muscle state is additionally determined. By guiding a muscle, an EMG signal of the corresponding muscle may be detected to determine a muscle state.

The expert recommendation unit 21f categorizes users registered as athletes, fitness trainers, or sports medicine-related qualifications among users who have created an account by registering user information in the cloud server 30 as an expert group, and makes exercise recommendations. enable them to participate

The exercise recommendation method may be performed by inputting an exercise to be recommended for each strength exercise target. In order to recommend an exercise in a personalized manner, a user-specific muscle condition may be viewed, and then a recommended exercise may be individually input for each muscle strength exercise target according to each user's muscle condition.

When inputting an exercise to be recommended, a degree of fitness indicating how much it meets the strength exercise target may be input together.

The recommended exercise or fitness input for each strength exercise goal is registered in the cloud server 30 .

The cloud server 30 registers user information through the database unit 36 and the personal terminal 20 to create an account and group the user information according to the user manager 31 and the personal terminal 20. A user data collection unit 32 that collects the muscle condition of each muscle for each user and also collects exercise recommendations registered by experts and stores them in the database unit 36; Statistical unit 33 that stores in 36, recommended exercise evaluation unit 34 that evaluates exercise recommendations registered by experts and prioritizes recommended exercises for each strength exercise goal, and user-customized statistical information and user-customized It includes a user support unit 35 that transmits exercise recommendations to each personal terminal 20 .

The cloud server 30 configured as described above renews the evaluation criteria of the muscle strength level by itself, selects the optimal exercise for the recommended exercise, evaluates the user's muscle strength level objectively, and then sets a goal customized according to the user's request. And it is possible to provide an artificial intelligence-based exercise guide service that presents a customized exercise.

The user management unit 31 stores in the database unit 36 information obtained by grouping users who have created accounts according to personal characteristics included in user information. That is, group information of users extracted according to gender, age, and whether they are athletes is stored. Athletes may be subdivided and grouped according to exercise events, and fitness trainers may also be grouped into separate groups. Of course, since there are differences in muscle conditions between men and women, groups by age group, athlete groups by sport event, and fitness trainer groups are grouped by gender. A group classified only by gender can also be used, and in this case, the group may exclude athletes and fitness trainers.

The user management unit 31 names and assigns the names of strength exercise targets to various user groups according to the characteristics of each user group. That is, as in the example shown in the dropdown 410 of FIG. 8 , names of strength training goals may be assigned to professional golf players, soccer players, marathon players, basketball players, swimmers, fitness trainers, and the like.

Pre-set group information and the name of a pre-set strength training target may be used for a group of ordinary people excluding athletes and fitness trainers among users. That is, as illustrated in FIG. 8 , the name of the strength exercise target is assigned to the upper group by gender or age group and used. Of course, the gender average or the average by age group may be used as the name of the strength exercise target.

The user management unit 31 allows users who are willing to participate in experts to perform verification procedures, such as uploading relevant documents that can prove that they are experts when registering as experts. You can decide whether or not to include them in a group.

The user data collection unit 32 stores muscle conditions for each muscle transmitted from the personal terminal 20 for each user in the database unit 36 . In the embodiment of the present invention, maximum muscular strength and muscular endurance are items to be evaluated relative to each other during muscle conditions, and balance is an item to be evaluated absolutely for each user. You can consider it when you do, so collect and save. Of course, balance may be treated as an item to be evaluated relative to each other.

The user data collection unit 32 stores in the database unit 36 an exercise recommended by a group of experts for each muscular strength exercise target through the personal terminal 20 . Here, even if the expert group is not an expert using the EMG measuring device 10 and the personal terminal 20, it is possible to recommend an exercise using a separate expert terminal.

When the expert recommends an exercise, the degree of fitness indicating the degree of help in reaching the muscular strength exercise goal may be input together with the recommended exercise, and stored in the user data collection unit 32 . By allowing the expert to recommend a plurality of exercises matched with the degree of fitness, the number of sports items to be selected by the user may be increased.

A user-customized exercise prescription that recommends an appropriate exercise according to each user's muscle condition may be allowed. To this end, the muscle condition for each muscle of each user can be browsed anonymously, and then an exercise that meets each muscle strength exercise goal is recommended for each user. Alternatively, the muscle conditions of each muscle group collected by user are classified by type, and the muscle conditions of each muscle group representing each type are selected. let it

The statistical unit 33 generates statistical information for each muscle with respect to the muscle condition of the user group and stores it in the database unit 36 .

Statistical information by group is statistical information subdivided by muscle for each user group by generating the muscle conditions of users in the user group by muscle and generating them according to the probability distribution by muscle. average value, etc. As a specific example, for each age group classified by gender, the top 10% value, the top 30% value, and the average value of the group are calculated for each muscle to be statistical information for each muscle within the group, and the athlete group and For the fitness trainer group, an average value may be calculated for each muscle mass to be used as statistical information for each muscle within the group.

In other words, since the muscle condition to be relative evaluated includes maximum muscular strength and muscular endurance as detailed items, the statistical information of each group includes statistical information of maximum muscular strength and statistical information of muscular endurance for each muscle.

However, since athletes have a better specific muscle state than ordinary people, which is different depending on the exercise event, only statistical information on a specific muscle may be generated for the athlete group for each exercise event.

In addition, the statistical unit 33 selects target muscle states for each strength exercise goal and stores them in the database unit 36 . Of course, the target muscle condition is also subdivided by muscle.

The target muscle state is selected according to the statistical information of the group corresponding to the strength exercise target, and may be, for example, the average value of the muscle state of the corresponding group. As a specific embodiment, as illustrated in FIG. 8 , for the strength exercise target for each exercise event, the target muscle state can be selected according to the statistical information of the muscle condition of the athlete group for each exercise event, and each group subdivided into the general public group Also, the target muscle state is selected according to the statistical information of the muscle state of the corresponding group.

Meanwhile, since the target muscle state may be included in the above-mentioned statistical information for each group, it may be extracted from the statistical information for each group. However, since the target muscle state is a muscle state targeted for strength training, the value of the lower muscle state or the upper muscle state value within the user group corresponding to the strength exercise target is excluded according to a predetermined criterion, so it is set too low or too low. Corrections such as not being set too high can be made. Accordingly, the target muscle state may be calculated separately.

The recommended exercise evaluator 34 evaluates the recommended exercise for each muscle strength exercise goal recommended by the expert group, and determines the priority of the exercise to be recommended to the user.

The priority of the exercise to be recommended may be determined according to the ranking (size) of the matching rate by calculating the matching rate for the exercise recommended by the expert group.

Here, the matching rate may be the number of times recommended for each exercise. That is, since the exercises recommended by each expert in the expert group may differ according to the expert's recommendation tendency, the frequency of recommendation for the recommended exercise is calculated, and even if the recommendation tendency is different for each expert, the exercise with a high recommendation frequency is given priority. to make recommendations to users.

Otherwise, the matching rate may be determined according to the distribution of fitness registered by each expert belonging to the expert group by matching with the recommended exercise. That is, since the expert can evaluate how suitable the recommended exercise is to reach the strength exercise goal based on experience or knowledge of the strength exercise, the matching rate can be calculated according to the fitness level input and registered by the expert. At this time, the matching rate may be the fitness registered by relatively many experts according to the probability distribution of the fitness.

On the other hand, when the user data collection unit 32 reads the muscle condition of each user and then recommends an exercise according to the muscle condition of each user, the recommended exercise for each muscular strength exercise for each user is stored. Accordingly, the recommended exercise evaluation unit 34 prioritizes the recommended exercise for each user according to the matching rate ranking and stores the exercise as matched to the user's individual account, and recommends the exercise through the personal terminal 20 in the following. can be used at the time.

The user support unit 35 supports the personal terminal 20 to output muscle strength comparison screens 300 , 310 , 320 , and 330 . To this end, statistical information for each user group is transmitted to the personal terminal 20 so that the muscle strength comparison screens 300, 310, 320, and 330 can be output. Statistical information transmitted at this time is statistical information subdivided by muscle for the maximum muscular strength and endurance to be relative evaluated during the muscle condition, so that the maximum muscular strength and endurance can be relative evaluated for each muscle.

Meanwhile, only statistical information of a user group matching the personal characteristics of the user of the personal terminal 20 or a user group selected by the user may be selected and transmitted to the personal terminal 20 . That is, as described for the muscle state evaluation unit 21c, statistical information of a basic user group that meets the user's personal characteristics and statistical information of a user group selected with interest by the user are selected, and the basic screen 300 and Statistical information to be displayed on the detailed screens 310, 320, and 330 may be delivered.

In addition, the user support unit 35 supports the personal terminal 20 to output the goal-oriented recommendation screens 400 , 430 , and 440 . To this end, a list of strength exercise goals allocated for each user group is delivered to the personal terminal 20 to give the user the authority to select a strength exercise goal, and a user group corresponding to the strength exercise goal selected by the user in the personal terminal 20 The target muscle condition for each muscle selected according to the statistical information of , a recommended exercise that meets the strength exercise goal, and a matching rate calculated for the recommended exercise are transmitted to the personal terminal 20 .

Accordingly, as described above with reference to FIGS. 8 to 10 , the personal terminal 20 shows a list of strength exercise goals in a drop-down 410 to allow the user to select a strength exercise goal, and select the selected strength exercise goal. It is possible to output a goal-oriented recommendation screen 400 showing a result of comprehensive evaluation by comparing the target muscle state for each muscle corresponding to the muscle state for each muscle of the user and a plurality of recommended exercises listed according to the matching rate ranking, Detailed screens 430 and 440 showing the result of comparison for each category can also be output.

From the point of view of the user of the personal terminal 20, the muscle condition of an athlete or fitness trainer of an event of interest may be the target of muscle strength exercise, and a desired level among muscle conditions of ordinary people other than professional athletes including athletes and fitness trainers Since the muscle condition of the muscle can be a target for strength exercise, it is possible to set different goals for strength exercise for each user. In addition, since the user can recognize the lack of muscle strength compared to the strength exercise goal as a quantitative value, it can be used to make an exercise plan by predicting the amount of exercise required according to the matching rate in exercising suitable for the user's living conditions.

Although the above has been shown and described as specific embodiments to illustrate the technical idea of the present invention, the present invention is not limited to the same configuration and operation as the specific embodiments as described above, and various modifications are within the scope of the present invention. can be carried out in Accordingly, such modifications should be regarded as belonging to the scope of the present invention, and the scope of the present invention should be determined by the claims described below.

10: EMG meter
11: connector 11a: groove
12: signal processing unit 13: short-range communication module
14: power supply
20: personal terminal
21: Processor
21a: user registration unit 21b: EMG analysis unit
21c: muscle condition evaluation unit 21d: exercise recommendation unit
21e: exercise monitoring unit 21f: expert recommendation unit
22: user interface 23: memory
24: short-distance communication module 25: communication module
30: cloud server
31: user management unit 32: user data collection unit
33: statistics unit 34: recommended exercise evaluation unit
35: user support unit 36: database unit
100: electrode patch
110: electrode 120: connector connecting member
130: adhesive layer
200: smart clothing
210: clothes
220: bio-electrode 221: detection electrode
221a: projection 222: terminal
222a: attachment member 223: penetrating member
230: electrode support 231: connector connecting member
232: attachment member

Claims (11)

an electromyogram meter (10) for detecting an electromyogram signal from each muscle part of the user;
Muscle strength comparison screen (300, 310, 320) that obtains the user's muscle condition according to the muscle-specific EMG signal detected by the EMG meter 10 and shows statistical information on the muscle condition for each user group to be compared with the user's muscle condition , 330), and after presenting the user group as a strength exercise target, outputting a goal-oriented recommendation screen (400, 430, 440) showing the target muscle state and at least one recommended exercise that meets the strength exercise target selected by the user a personal terminal 20 that enables;
Among the muscle conditions collected from users' personal terminals 20, statistical information is generated on the muscle conditions of user groups grouped according to different personal characteristics of users, and then transmitted to the personal terminal 20 to generate a muscle strength comparison screen (300, 310, 320, 330), and according to the target muscle condition and strength exercise goal selected according to the statistical information of the user group corresponding to the strength exercise goal selected by the user among the strength exercise goals named according to the characteristics of the user group. a cloud server 30 that supports output of goal-oriented recommendation screens 400, 430, and 440 by transmitting at least one selected recommended exercise to the personal terminal 20;
including,
The cloud server 30 generates statistical information for each muscle and supports output of a muscle strength comparison screen to compare for each muscle,
The personal terminal 20 compares the progress of the user's muscle state with the target muscle state according to the strength exercise goal, but can output a screen for subdividing and comparing each muscle
Cloud-based artificial intelligence strength exercise guide system. According to claim 1,
The cloud server 30 uses the exercise recommended by the expert group for each strength exercise goal using the individual terminal 20 or a separate expert terminal as a recommended exercise to be output on the goal-oriented recommendation screen 400, 430, 440
Cloud-based artificial intelligence strength exercise guide system. According to claim 2,
The cloud server 30 determines the matching rate according to the suitability of the recommended exercise, and then uses the recommended exercise selected according to the matching rate ranking as a recommended exercise to be output on the target-oriented recommendation screens 400, 430, 440
Cloud-based artificial intelligence strength exercise guide system. According to claim 3,
The cloud server 30 uses the recommendation frequency of experts for each recommended exercise as the value of the matching rate.
Cloud-based artificial intelligence strength exercise guide system. According to claim 3,
The cloud server 30 selects the value of the matching rate according to the distribution of the fitness of the recommended exercise registered by the expert
Cloud-based artificial intelligence strength exercise guide system. delete delete According to claim 1,
The cloud server 30 uses a user group having gender and age as personal characteristics.
Cloud-based artificial intelligence strength exercise guide system. According to claim 1,
The cloud server 30 uses a group of athletes or a group of health trainers for each exercise event among users as a user group
Cloud-based artificial intelligence strength exercise guide system. According to claim 1,
The cloud server 30 selects only statistical information of a user group matching the user's personal characteristics or a user group predefined by the user to support the output of the muscle strength comparison screens 300, 310, 320, 330
Cloud-based artificial intelligence strength exercise guide system. According to claim 1,
The muscle condition includes maximum muscle strength and muscle endurance
Cloud-based artificial intelligence strength exercise guide system.

Images