Patent Application
20240408451
The present disclosure relates to a method of providing an artificial intelligence (AI) exercise sequence to a user according to a physical condition of the user and a use state of a smart gym.
Various types of weight training machines are provided according to the body parts they are intended to increase the muscle strength for or the purpose for which they are used. The weight training machines are mainly designed to train the upper half or lower half of the body by using the arms or legs. A user may perform an exercise by moving a selected weight by using an exercise structure of an exercise machine.
However, it may be difficult for the user to determine a required exercise machine and exercise load based on his/her physical development state. Also, the user may have difficulties in setting an exercise plan for great exercise effect based on his/her physical characteristics.
According to a desirable embodiment of the present disclosure, there is provided an artificial intelligence (AI) exercise sequence including information about an exercise machine, an exercise load, a use order of exercise machines, etc. required by a user.
According to a desirable embodiment of the present disclosure, there is provided an AI exercise sequence for balanced development of full body muscles of a user according to accumulated exercise records of the user, a rest state of the user, and a physical condition of the user.
According to a desirable embodiment of the present disclosure, there is provided an AI exercise sequence that is generated to be optimized for a user by reflecting an occupancy status of an exercise machine in a smart gym.
According to a desirable embodiment of the present disclosure, an apparatus for providing an artificial intelligence (AI) exercise sequence includes: a communicator configured to communicate with each of exercise machines in a smart gym; a determining unit configured to determine an occupancy status of the exercise machine; an exercise sequence generation unit configured to generate an exercise program including a plurality of exercise machines needed for a user using the smart gym and an exercise sequence including a user order of the plurality of exercise machines; and an AI exercise sequence providing unit configured to provide, to the user in real time, an AI exercise sequence automatically resetting the use order of the plurality of exercise machines included in the exercise sequence according to the occupancy status of the exercise machine in the smart gym.
According to a desirable embodiment of the present disclosure, an apparatus for providing an artificial intelligence (AI) exercise sequence may have the effect of balanced development of full body muscles by providing an exercise sequence including an exercise machine and an exercise program that are appropriate according to the characteristics of an individual user by taking into account an occupancy status of the exercise machine in a smart gym.
According to a desirable embodiment of the present disclosure, there is provided an AI exercise sequence in which an exercise sequence of a user is re-set to maintain exercise efficiency of the user and at the same time to allow a plurality of users to effectively use exercise machines in a smart gym.
According to a desirable embodiment of the present disclosure, an apparatus for providing an artificial intelligence (AI) exercise sequence includes: a communicator configured to communicate with each of exercise machines in a smart gym; a determining unit configured to determine an occupancy status of the exercise machine; an exercise sequence generation unit configured to generate an exercise program including a plurality of exercise machines needed for a user using the smart gym and an exercise sequence including a user order of the plurality of exercise machines; and an AI exercise sequence providing unit configured to provide, to the user in real time, an AI exercise sequence automatically resetting the use order of the plurality of exercise machines included in the exercise sequence according to the occupancy status of the exercise machine in the smart gym.
According to a desirable embodiment of the present disclosure, the apparatus for providing the AI exercise sequence may further include a classifier configured to classify the exercise machines in the smart gym into an aerobic exercise group, a weight exercise group, and a stretching exercise group according to exercise characteristics, wherein the exercise sequence generation unit may further be configured to select the exercise machines from each of the aerobic exercise group, the weight exercise group, and the stretching exercise group.
According to a desirable embodiment of the present disclosure, the AI exercise sequence providing unit may further be configured to set the AI exercise sequence such that an exercise machine included in a different exercise group is used after all of exercise machines included a same exercise group are used.
According to a desirable embodiment of the present disclosure, the AI exercise sequence providing unit may further be configured to set the use order among the one or more exercise groups based on an occupancy rate of each exercise group.
According to a desirable embodiment of the present disclosure, the AI exercise sequence providing unit may further be configured to set the use order such that an exercise machine not occupied from among the exercise machines included in the same group is used first.
According to a desirable embodiment of the present disclosure, the apparatus for providing the AI exercise sequence may further include a classifier configured to classify the exercise machines in the smart gym into exercise groups according to exercise characteristics, wherein the classifier may further be configured to classify the exercise groups into exercise small groups according to body parts.
According to a desirable embodiment of the present disclosure, the AI exercise sequence providing unit may further be configured to, when a plurality of users use a same smart gym, determine an exercise group to be assigned to each of the plurality of users and set the AI exercise sequence such that different exercise small groups are assigned to users to which a same exercise group is assigned.
According to a desirable embodiment of the present disclosure, the apparatus for providing the AI exercise sequence may further include an exercise plan unit configured to set a training day and a rest day per user, wherein the exercise sequence generation unit may further be configured to generate the exercise sequence by referring to the rest day and an exercise record performed before the rest day per user.
Hereinafter, various embodiments of the present disclosure are described in detail with reference to the accompanying drawings, so that one of ordinary skill in the art may easily implement the present disclosure.
A smart gym system 100 may include a smart gym server 110, one or more exercise machines 100A, 100B, 100C, . . . , and 100N, at least one user terminal 120, and a manager terminal 130.
Referring to
According to an embodiment, a smart gym refers to a physical space in which a user may provide an exercise record using an exercise machine to the smart gym server 110, and the smart gym server 110 may learn and analyze the exercise record of the user and provide an appropriate exercise plan to the user. The smart gym may be realized as a fitness center, a health center, a place having an exercise machine, etc.
Users (for example, USER A, USER B, USER C, . . . , USER N visiting the smart gym to exercise may enter the smart gym after an identification process. For example, the user may enter the smart gym after verifying membership by tagging the user terminal 120 to an unmanned terminal, such as a kiosk at an entrance of the smart gym, via near-field communication (NFC) or radio frequency identification (RFID), or may enter into the smart gym after verifying membership through biometric identification such as facial recognition through the unmanned terminal, etc.
When the user uses the one or more exercise machines 100A, 100B, 100C, . . . , and 100N in the smart gym system 100, the user may use the user terminal 120 to communicate via tagging such as NFC or RFID or may perform an identification process by using a physical characteristic of the user. When the identification process of the user is completed, the smart gym server 110 may transmit user data to an exercise machine tagged by the user. Information about a user may also be referred to as the user data and includes at least one or all of a gender, an age, a weight, a height, a body mass index (BMI), and a body fat percentage of the user.
The information about the user whose membership is verified may be transmitted from the smart gym server 110 to the at least one of the exercise machines 100A, 100B, 100C, . . . , and 100N through a network. For example, the smart gym server 110 may transmit the information about the user to an exercise machine to which the user tags the user terminal 120.
When the user tags the user terminal 120 to an exercise machine, the smart gym server 110 may determine that the tagged exercise machine is occupied. Referring to
The smart gym server 110 may guide each of the first user USER A and the second user USER B respectively using the exercise machines 100A and 100B arranged in the smart gym 112 about an exercise method, an exercise intensity, an exercise sequence, etc. appropriate for each user. Also, the smart gym server 110 may provide values, such as a target weight, a recommended use speed, etc. of each of the exercise machines 100A and 100B. Also, the smart gym server 110 may receive exercise records of the first user USER A and the second user USER B respectively using the exercise machines 100A and 100B. Also, the smart gym server 110 may further receive, from the user terminal 120, user's health information, such as a health rate, a blood pressure, and a pulse, and user's log information.
The smart gym server 110 may be realized in the form of a cloud server. The smart gym server 110 may manage information collected from each of exercise machines in smart gym health centers at different locations in an integrated way. For example, the smart gym server 110 may manage a record of the first user using an exercise machine in the smart gym 112 at a first location and a record of the first user using an exercise machine in the smart gym 114 at a second location in an integrated way.
According to an embodiment, the one or more exercise machines 100A, 100B, 100C, . . . , and 100N may be stretching exercise machines, weight training machines, or aerobic exercise machines. The weight training machines may include a free weight machine and machine equipment. The one or more exercise machines 100A, 100B, 100C, . . . , and 100N may provide an appropriate exercise guide to the user through a display coupled to the exercise machines or a display capable of communicating with the exercise machines in a wired or wireless fashion. For example, in the case of a stretching machine, an exercise guide related to a stretching exercise to be performed by the user may be provided through a smart mirror capable of wired or wireless communication with the stretching machine. However, the disclosure is not limited thereto, and the exercise guide may be provided by using various output methods, such as a speaker, vibration, etc.
The one or more exercise machines 100A, 100B, 100C, . . . , and 100N may perform wired or wireless communication with the smart gym server 110, the user terminal 120, and the manager terminal 130.
According to an embodiment, the user terminal 120 may be realized in the form of a smartphone, a smart watch, a hand-held device, a wearable device, etc. Also, the user terminal 120 may install an application for using the smart gym system. The user terminal 120 may receive, from the smart gym server 110, an exercise program, exercise sequence information, artificial intelligence (AI) exercise sequence information, or the like.
According to a desirable embodiment of the present disclosure, the exercise program may include information about target weights, the numbers of repetitions, Reps, exercise volumes, etc. of a plurality of exercise machines selected by taking into account a physical strength and exercise competence of a user.
Referring to
The exercise sequence refers to a use order of the exercise machines selected for the user “Exercise Kim.” For example, it refers to the stated use order of the seated row machine 510, the lat pull down machine 520, the back extension machine 530, the inner out thigh-out machine 540, the leg press machine 550, and the stair climber machine 560.
The AI exercise sequence refers to an automatically reset use order of the plurality of exercise machines included in the exercise sequence according to an occupancy status of the exercise machines in a smart gym. Referring to
The AI exercise sequence providing unit may identify that the exercise program to be next used by the user “Exercise Kim” is the inner out thigh-out machine 540 according to the order included in the exercise sequence. Also, information about whether or not the inner out thigh-out machine 540 is occupied may be obtained through a determining unit. When it is determined that the inner out thigh-out machine 540 is occupied, the AI exercise sequence providing unit may identify, through the determining unit, an exercise machine not occupied from among the remaining exercise sequences 540, 550, and 560 and may reset the exercise sequence such that the non-occupied exercise machine is to be used first instead of the occupied exercise machine. For example, when it is identified that the leg press machine 550 and the stair climber machine 560 are not occupied, the AI exercise sequence providing unit may guide the user having finished the exercise of the back extension machine 530 to the leg press machine 550 as Step 16 based on the AI exercise sequence. With respect to the configuration in which the AI exercise sequence providing unit resets and guides the exercise sequence,
According to a desirable embodiment of the present disclosure, an exercise machine 300 in a smart gym may communicate with a smart gym server 380, a user terminal 390, and an external server 388.
According to a desirable embodiment of the present disclosure, the exercise machine 300 may include a processor 310, a sensor 320, a communicator 340, an exercise guide unit 360, and a display 370. Also, the exercise machine 300 may further include a camera 330 and an image processor 350. The processor 310 may further include an AI processor 312 according to necessity.
Further referring to
A sensor 220 may be mounted on a frame structure 213 of an exercise body 210. The frame structure 213 may include a base frame 213a, a guide rail 213b, and a connection line 213c. The sensor 220 may irradiate a laser beam toward a pin structure 215, receive the laser beam reflected, and measure a distance D S220 from the sensor 220 to the pin structure 215 in real time or in units of a preset time period t. By doing so, the sensor 220 may detect, in real time, at least one of a location, a movement speed, and a moving direction of a weight member 211 selected by the fin structure 215. Also, when a user pushes a handle 212 of the exercise machine 200 to move a weight plate, the sensor 220 may measure the distance D S220 to the fin structure 215 stuck in the weight plate and may detect an exercise trajectory based on the measured distance.
The communicator 340 may receive a user input through a display 230 or may transmit and receive user data to and from a user database (DB) 382 of the smart gym server 380. Also, the communicator 340 may communicate with the external server 388.
The exercise guide unit 360 may provide, to the user, user data received from the smart gym server 380 and information about a target weight of an exercise machine, a movement speed guide of the exercise machine, a respiration guide when using the exercise machine, an exercise sequence, an AI exercise sequence, etc. The exercise guide unit 360 may display, on the display 370, the exercise sequence or the AI exercise sequence received from the smart gym server 380.
The smart gym server 380 may include the user DB 382, a machine learning processor 384, and an exercise guide processor 386. The user DB 382 may store and manage the user data. The user data may include a user's gender, age, weight, height, BMI, and body fat percentage. The machine learning processor 384 may learn and process big data including the user data obtained from the user DB, exercise records of exercise machines used by users using the smart gym, etc. by analyzing and accumulating the big data. An exercise guide processor 386 may process information about the exercise program, the exercise sequence, the AI exercise sequence, etc. to be provided to the user. In addition, the movement speed guide of the exercise machine, the respiration guide when using the exercise machine, etc. may further be processed.
The smart gym server 380 may calculate, based on the user data, an estimated muscular strength value of a certain muscular strength as represented by Equation 1. An example of the certain muscular strength may be grasping power.
An estimated muscle strength value=A*gender+B*age+C*body fat percentage+D*BMI+E [Equation 1]
In Equation 1, A, B, C, D, and E indicate preset values.
The smart gym server 380 may pre-store percentile rank information with respect to relative grasping power calculated through the Equation 1. Table 1 shows the percentile rank of the relative grasping power of adult males and Table 2 shows the percentile rank of the relative grasping power of adult females.
Also, the smart gym server 380 may contain a mapping table storing personal maximum weight (PMW)estimation percentile rank information generated based on PMW data obtained from a population for each exercise machine as shown in Table 3. Table 3 shows the PMWestimation percentile rank information for a leg extension exercise machine.
According to a desirable embodiment of the present disclosure, the PMW refers to the muscular strength that can be exerted by an individual against the resistance of a weight with maximum effort. Examples of the PMW may include 1RM, 4RM, etc. The PMWestimation indicates an estimated PMW value of a user based on the estimated muscular strength value calculated based on Equation 1. The smart gym server 380 may determine the PMWestimation for each exercise machine. The smart gym server 380 may determine the PMWestimation of an exercise machine to be used by a user, based on the estimated muscular strength value and the PMWestimation percentile rank information. Also, the smart gym server 380 may automatically set an initial target weight of the exercise machine to be used by the user based on the PMWestimation.
For example, when an estimated muscular strength value of a 32-year-old male is 55.9 and a percentile rank value to which the estimated muscular strength value belongs is 40, the PMWestimation percentile rank value 40 corresponding thereto, that is, 66.5 kg, is estimated as the leg extension PMW of the 32-year-old male. According to a desirable embodiment of the present disclosure, the smart gym server 380 may pre-store the PMWestimation percentile rank information for each exercise machine as shown in Table 1.
The exercise guide processor 386 may set an initial target weight of each exercise machine based on the PMWestimation. In this case, the exercise guide processor 386 may set the initial target weight according to an exercise intensity or an exercise objective. The exercise intensity may be determined according to an exercise volume to be provided by an exercise sequence in a preset period unit. For example, the preset unit period is assumed to be one week. When the exercise volume to be assigned to each muscle during a week is determined, the exercise guide processor 386 may set the exercise intensity according to a rest period and accumulated exercise volumes during the unit period for each muscle. The exercise volume may denote a value obtained by multiplying a weight, the number of repetitions, and a set.
When the exercise intensity is a low intensity, the exercise guide processor 386 may set the weight as a % of the PMWestimation, when the exercise intensity is an intermediate intensity, the exercise guide processor 386 may set the weight as b % of the PMWestimation, and when the exercise intensity is a high intensity, the exercise guide processor 386 may set the weight as c % of the PMWestimation. For example, a may be set as from 20 to 40, b may be set as from 40 to 60, and c may be set as from 60 to 80. However, it is only an example and it should be noted that various modifications are possible.
The exercise guide processor 386 may provide the initial target weight of the exercise machine to be used by the user to the exercise guide unit 360 of the exercise machine 300, and the exercise guide unit 360 may display the initial target weight of the exercise machine to be used by the user on the display 370.
According to another embodiment of the present disclosure, the smart gym server 380 may predict the maximum muscular strength value, PMWpersonal, reflecting an objectification index for each individual user. Also, the smart gym server 380 may update the target weight of the exercise machine based on the predicted PMWpersonal. In more detail, the smart gym server 380 may set the initial target weight of the exercise machine based on the PMWestimation and when the objectification index for each individual user is obtained during a preset period, may predict the PMWpersonal by reflecting the objectification index for each individual user. Thereafter, the target weight of the exercise machine may be updated based on the PMWpersonal. The exercise guide processor 386 may display the target weight updated based on the PMWpersonal, on the exercise machine to be used by the user.
An example of the objectification index may include an exercise record, and the exercise record may include a weight of the exercise machine, the number of repetitions (Reps), the number of sets, an exercise trajectory, a movement speed, the regularity of the number of repetitions (Reps) in a set unit, etc. identified when the exercise machine is used.
The machine learning processor 384 may learn and process the objectification index including the exercise records of the exercise machines used by the user in the smart gym. The machine learning processor 384 may update the PMWestimation as the PMWpersonal based on the objectification index of the exercise machines that are used by the user in the smart gym for a certain period. When the objectification index is greater than or equal to a first reference value, the machine learning processor 384 may update the PMWpersonal to be a value greater than the PMWestimation, and when the objectification index is less than or equal to a second reference value, the machine learning processor 384 may update the PMWpersonal to be a value less than the PMWestimation.
Through this, even when the PMWestimation of each of a first user and a second user, for whom the gender, the age, the weight, the height, the BMI, and the body fat percentage of the user are identical, is estimated to be identical, the machine learning processor 384 may learn and predict the PMWpersonal appropriate for each of the first user and the second user for each exercise machine used by the first user and the second user, by further reflecting the personal objectification index including the exercise records of the first user and the second user, etc.
An example of generating, via the machine learning processor 384, the objectification index is described below. For example, the degree of completion of an exercise trajectory may be determined by using at least one of an ascending start time point, a descending start time point, an ascending section speed V1, a descending section speed V2, an ascending section average speed, a descending section average speed, and a height H which are identified in the exercise trajectory detected while a user uses a shoulder press machine, and the degree of completion of the exercise trajectory may be converted into a numerical value and used as the objectification index. For example, when the ascending start time point is 0 to ta seconds, it may be determined as appropriate, and when the ascending start time point deviates from ta, it may be determined as late. Also, a grade of good, fair, and poor or a score in the range of 1 point to 10 points may be assigned to with respect to the degree of completion, according to a preset reference appropriate for the ascending start time point. Likewise, the ascending section speed V1 may be determined to be appropriate, fast, and slow, respectively, when the ascending section speed V1 is between Va and Vb, exceeds Vb, and falls short of Va, according to a preset reference, and the grade of good, fair, and poor or the score in the range of 1 point to 10 points may be assigned to each determination result of appropriate, fast, or slow. Based on this method, the machine learning processor 384 may generate the objectification index based on the exercise records of the user.
According to another desirable embodiment of the present disclosure, when the machine learning processor 384 uses the number of repetitions (Reps) as the objectification index, the machine learning processor 384 may determine a degree of completion of the repetitions (Reps) based on the regularity between respective exercise trajectories of the total repetitions forming one set and the execution time in which the total number of repetition times forming the one set is executed, and may use, as the objectification index, the degree of completion of the repetitions (Reps) converted into a numerical value.
The apparatus 400 for providing the AI exercise sequence may include a communicator 410, a determining unit 420, a classifier 430, an exercise sequence generation unit 440, and an AI exercise sequence providing unit 450. The exercise sequence generation unit 440 may further include an exercise plan unit.
The communicator 410 may perform communication with each of exercise machines in a smart gym. The determining unit 420 may determine occupancy statuses of the exercise machines in the smart gym. The classifier 430 may classify the exercise machines in the smart gym into a plurality of exercise groups according to the exercise characteristics.
According to a desirable embodiment of the present disclosure, the classifier 430 may classify the exercise machines in the smart gym into an aerobic exercise group, a weight exercise group, and a stretching exercise group. Also, the classifier 430 may further classify the exercise machines included in a predetermined exercise group into small exercise groups by using at least one reference, such as a human body, a joint, muscle, etc.
For example, the exercise machines included in the weight exercise group may be classified into an upper body small exercise group, a main body small exercise group, and a lower body small exercise group based on the human body. As another example, the exercise machines in the smart gym may be classified according to the number of joints used when using the exercise machines. For example, it is assumed that there are a seated leg press machine, a long pull machine, a chest press machine, a shoulder press machined, a seated row machine, and a leg extension machine in a smart gym. The classifier 430 may classify the seated leg press machine and the long pull machine as the three-joint exercise machine. As the two-joint exercise machine, the chest press machine and the shoulder press machine are classified. Also, as the single-joint exercise machine, the seated row machine and the leg extension machine of an exercise small group are classified.
The determining unit 420 may determine an occupancy rate for each exercise group or each exercise small group classified by the classifier 430. For example, when there are 10 exercise machines in the aerobic exercise group and 8 of them are being used, the classifier 430 may determine the occupancy rate as 80%. Also, an occupancy rate of each of the upper body small exercise group, the main body small exercise group, and the lower body small exercise group from among the exercise machines included in the weight exercise group may be determined. According to a desirable embodiment of the present disclosure, the determining unit 420 may determine the occupancy rates of the aerobic exercise group, the weight exercise group, and the stretching exercise group, when a user enters into the smart gym and undergoes a personal identification process. Alternatively, the determining unit 420 may determine the occupancy rate of the exercise group or the exercise small group, when a user has performed an exercise program assigned to an exercise machine currently used by the user by a degree to satisfy a preset rate according to an exercise sequence.
The exercise sequence generation unit 440 may generate an exercise program including a plurality of exercise machines needed for a user using the smart gym and an exercise sequence including a use order of the plurality of exercise machines.
The exercise sequence generation unit 440 may select at least one exercise machine from each of the plurality of exercise groups classified by the classifier 430. Also, the exercise sequence generation unit 440 may generate the exercise program with respect to each of the selected exercise machines. Thereafter, the exercise sequence generation unit 440 may generate the use order of the selected exercise machines and provide the use order to the user.
For example, the exercise sequence generation unit 440 may select an exercise machine from each of an aerobic exercise group, a weight exercise group, and a stretching exercise group and may generate the exercise program and the exercise sequence with respect to the selected exercise machines.
The exercise sequence generation unit 420 may select one or more exercise machines based on a physical condition of the user and generate the exercise program with respect to each of the one or more exercise machines. The exercise program may include information about a target weight, the number of repetitions, an exercise volume, etc. of each exercise machine.
The physical state of the user may be analyzed based on at least one of a PMW, a maximum oxygen intake (VO2Max), a user's physical development state, an exercise rest period, a user's subjective sense of exercise fatigue, a user's exercise record, and user's accumulated exercise records during a predetermined period.
An example of analyzing the user's physical condition by using the PMW is described below. The exercise sequence generation unit 420 may determine the physical development state of a muscle used by a corresponding exercise machine by using PMW_estimation and PMW_personal identified for each exercise machine. For example, development of quadriceps muscle of thigh corresponding to an exercise target muscle of a leg extension machine may be determined based on the PMW_personal and the PMW_estimation with respect to the leg extension machine. When the PMW_personal is greater than or equal to the PMW_estimation, it is determined that the quadriceps muscle of thigh is developed, and when the PMW_personal is less than the PMW_estimation, it is determined that the quadriceps muscle of thigh is insufficient. When the development of the quadriceps muscle of thigh is determined to be insufficient, the exercise sequence generation unit 420 may generate the exercise sequence such that the user's physical development is achieved in a balanced way by increasing a use frequency for selecting an exercise machine to develop the quadriceps muscle of thigh to be higher than a preset reference.
The exercise sequence generation unit 420 may generate at least one exercise sequence in a preset period unit for the balanced development of full body muscle of the user by using information about main muscles and auxiliary muscles activated when the user uses an exercise machine. The preset exercise period may be a day, a week, a month, etc. The exercise sequence generation unit 420 may differently generate the exercise sequence according to the exercise period.
The exercise sequence generation unit 440 may further include an exercise plan unit to set training days and rest days for each user. Also, the exercise sequence generation unit 440 may generate the exercise sequence of a predetermined period by referring to the rest days and exercise records performed before the rest days for each user. The exercise sequence generation unit 440 may set the rest days according to an exercise volume of the user in the preset period. When it is determined that the user has not had a sufficient rest, the exercise sequence generation unit 440 may decrease the exercise volume and intensity of the exercise program generated after the rest days. Alternatively, when it is determined that the user has been provided with enough rest days, the exercise sequence generation unit 440 may increase the exercise volume and intensity of the exercise program generated after the rest days.
For example, when the exercise period is a week from the first day of November to the seventh day of November, four exercise sequences and the rest period of three days may be provided. When the user has not performed all of the four exercise sequences and has performed only some of the four exercise sequences or when an exercise load, etc. of an exercise machine used for each exercise sequence does not match the exercise sequence provided, the exercise sequence after the eighth day of November may be generated by reflecting the performance rate of the exercise sequence of the user. For example, the exercise sequence generation unit 440 may select the exercise machine to increase the use frequency of the exercise machine related to the muscle for which the exercise volume is insufficient, by reflecting the exercise records from the first to the seventh day of November and the performance state of the exercise sequence.
The AI exercise sequence providing unit 450 may reset the exercise sequence generated by the exercise sequence generation unit 440 in real time according to an occupancy status of the exercise machine in the smart gym. The AI exercise sequence providing unit 450 may automatically assign a different exercise machine from among exercise machines included in the exercise sequence, when a user enters into a smart gym and tries to perform an exercise according to a given exercise sequence, but there are many people using the corresponding exercise machine. Here, the AI exercise sequence providing unit 450 may reset the exercise sequence such that the user may maintain the exercise efficiency, while a plurality of users may effectively use the exercise machines in the smart gym.
According to a desirable embodiment of the present disclosure, the AI exercise sequence providing unit 450 may identify an exercise sequence to be assigned to a corresponding user at a time point at which the user tags to an entrance when the user enters into the smart gym. Then, an occupancy rate of each of exercise groups included in the exercise sequence may be identified. It is assumed that two exercise machines included in an aerobic exercise group, ten exercise machines included in a weight exercise group, and one exercise machine included in a stretching exercise group are assigned to a user, Exercise Kim. The AI exercise sequence providing unit 450 may identify the occupancy rate of each of the aerobic exercise group, the weight exercise group, and the stretching exercise group and may preferentially assign the exercise group having a lowest occupancy rate. Also, the AI exercise sequence providing unit 450 may automatically reset the use order of exercise machines such that exercise machines included in a different exercise group may be used after a user has used all of exercise machines included in an exercise group. For example, the use order may be set such that exercise machines included in the aerobic exercise group or the stretching exercise group may be used after all of the ten exercise machines included in the weight exercise group are used. Also, while the exercise machines included in the weight exercise group are being used, the use order may be reset such that other exercise machines are used when it is identified that an exercise machine to be next used by the user, Exercise Kim, from among the ten exercise machines, is being occupied. In other words, the AI exercise sequence providing unit 450 may automatically reset the use order such that an exercise machine not occupied from among the exercise machines included in the same exercise group may be used first.
According to a desirable embodiment of the present disclosure, when a plurality of users use the same smart gym during a predetermined time section, the AI exercise sequence providing unit 450 may determine an exercise group to be assigned to each of the plurality of users. Also, for example, when the weight exercise group is assigned to both of a first user and a second user, the use order of the exercise machines may be reset such that exercise small groups of the first user and the second user may be different from each other, by referring to the weight exercise machines included in the exercise sequence of the first user and the second user. For example, an upper body exercise small group may be assigned to the first user, and a lower body exercise small group may be assigned to the second user. However, it is assumed that the exercise machines included in the exercise sequence of the first user correspond to the upper body exercise small group and the exercise machines included in the exercise sequence of the second user correspond to the lower body exercise small group.
By referring to
The methods according to an embodiment of the present disclosure may be implemented in the form of a program command executable by various computer devices and may be recorded on a computer-readable medium. The computer-readable medium may include a program command, a data file, a data structure, etc. individually or in a combined fashion. The program command recorded on the medium may be specially designed and configured for the present disclosure or may be well known to and usable by one of ordinary skill in the art.
The present disclosure is described with reference to limited embodiments and drawings as above. However, the present disclosure is not limited to the embodiment above, and from the description, various modifications and alterations are possible for one of ordinary skill in the art.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2021-0190373 | Dec 2021 | KR | national |
| 10-2022-0183489 | Dec 2022 | KR | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/KR2022/021304 | 12/26/2022 | WO |
