JP2015104466A - Information processing apparatus, program, and communication system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an apparatus capable of presenting exercise to attain a target with respect to the body compositions which have a large difference from the target out of plural body compositions, on the basis of the body composition and exercise history of another user.SOLUTION: An information processing apparatus comprises: first determination means for determining, from among plural evaluation items, specific items for which the correlation between the current body composition and the target body composition satisfies predetermined conditions, on the basis of first acquisition means for acquiring the current body composition information and the target body composition information; second determination means for determining, from first storage means, the user identification information stored in association with the first body composition information and the second body composition information in which the correlation between the body composition of the specific items in the first body composition information and the body composition of the specific items in the second body composition information satisfies predetermined conditions; second acquisition means for acquiring, from second storage means, the exercise identification information which identifies the exercise conducted by the user indicated by the user identification information determined by the second determination means; and control means for causing the exercise identification information acquired by the second acquisition means to be output by output means.

Description

  The present invention relates to a technical field that presents an exercise performed by a user based on the body composition of the user.

  For example, Patent Document 1 discloses a technique for displaying a target value, a standard value, and an actual measurement value for a plurality of body compositions, and calculating and displaying a training menu to be performed by the user based on the target value and the actual measurement value. It is disclosed.

JP 2008-35876 A

  There is a case where it is desired to perform an exercise for reaching a target for a body composition having a large difference from the target among a plurality of body compositions of the user. At this time, based on the body composition and exercise history of other users, there is a desire to determine the exercise performed when the other user achieves the goal as an exercise recommended to the user. However, with the technique described in Patent Literature 1, it is not possible to present an exercise for reaching a goal for a body composition that has a large difference from the goal based on the body composition of other users and the history of the exercise.

  The present invention has been made in view of the above points, and the exercise for reaching the target for the body composition having a large difference from the target among the plurality of body compositions is made into the body composition of other users and the history of the exercise. It is an object to provide an information processing apparatus, a program, and a communication system that can be presented based on the information.

  In order to solve the above problem, the invention according to claim 1 is characterized in that user identification information for identifying a user, body composition information indicating the body composition of the user, and date information indicating a date on which the body composition is registered. Are associated with each other, the user identification information for identifying the user, the exercise identification information for identifying the exercise performed by the user, and the date information indicating the date on which the exercise was performed A second storage means for storing information, current body composition information indicating the current body composition, current body composition information including a plurality of evaluation items, and target body composition information indicating the target body composition. First body acquisition unit configured to acquire target body composition information configured by a plurality of evaluation items, and the plurality of the plurality of pieces based on the current body composition information and the target body composition information acquired by the first acquisition unit. Current evaluation items A first determining unit that determines a specific item in which a relationship between a body composition and a target body composition satisfies a predetermined condition; and a body composition and a second body of the specific item in the first body composition information from the first storage unit A second determining means for determining the user identification information stored in association with the first body composition information and the second body composition information in which the relationship with the body composition of the specific item in the composition information satisfies the predetermined condition; Second acquisition means for acquiring the exercise identification information for identifying the exercise performed by the user indicated by the user identification information determined by the second determination means from the second storage means, and acquired by the second acquisition means Control means for causing the output means to output the motion identification information.

  According to a second aspect of the present invention, from the date indicated by the date information stored in the first storage means in association with the first body composition information, the first storage in association with the second body composition information. Further comprising third determining means for determining a period until the date indicated by the date information stored in the means, wherein the second acquiring means is associated with the user identification information determined by the second determining means A period in which the date indicated by the date information stored in the second storage unit in association with the exercise identification information among the exercise identification information stored in the second storage unit is determined by the third determination unit The motion identification information included in the information is acquired.

  According to a third aspect of the present invention, there is provided a third storage means for storing exercise identification information for identifying exercise and time information indicating the time during which the exercise is performed at an exercise facility, and the second acquisition means. The apparatus further comprises third acquisition means for acquiring the time information stored in the third storage means in association with the acquired exercise identification information, and the control means is the exercise acquired by the second acquisition means. The identification information and the time information acquired by the third acquisition means are output by the output means.

  According to a fourth aspect of the present invention, when the plurality of specific items are determined by the first determination unit, the second determination unit includes all the body compositions of the plurality of specific items in the first body composition information. In the second body composition information, the user identification information satisfying the predetermined condition is determined by a relationship with all body compositions of the plurality of specific items.

  According to a fifth aspect of the present invention, in the case where a plurality of specific items are determined by the first determination unit, the second determination unit includes the first body composition information for each specific item of the plurality of specific items. The user identification information satisfying the predetermined condition is determined by the relationship between the body composition of the one specific item in the above and the body composition of the one specific item in the second body composition information.

  According to a sixth aspect of the present invention, when the plurality of specific items are determined by the first determination unit, the second determination unit divides the plurality of specific items into a predetermined number of groups, For each group of groups, the relationship between all body compositions of the specific item included in the group in the first body composition information and all body compositions of the specific item included in the group in the second body composition information Determining the user identification information satisfying the predetermined condition.

  According to a seventh aspect of the present invention, the first determining means includes a specific item in which a deviation between a current body composition and a target body composition is equal to or greater than a predetermined value, or a predetermined number of specific items in descending order of the deviation. It is characterized by determining.

  According to an eighth aspect of the present invention, the second determining means is configured so that the body composition of the specific item in the first body composition information and the second body composition information is in the current body composition information and the target body composition information. The user identification information similar to the body composition of the specific item is determined.

  The invention according to claim 9 is current body composition information indicating a current body composition, and is current body composition information composed of a plurality of evaluation items and target body composition information indicating a target body composition. A first acquisition step of acquiring target body composition information composed of a plurality of evaluation items, and a plurality of the plurality of the plurality of target body composition information based on the current body composition information and the target body composition information acquired by the first acquisition step. A first determination step for determining a specific item in which the relationship between the current body composition and the target body composition satisfies a predetermined condition from among the evaluation items, user identification information for identifying the user, and the body composition of the user From the first storage means for storing the body composition information and the date information indicating the date on which the body composition is registered, the body composition of the specific item in the first body composition information and the body composition in the second body composition information Structure of specific items A second determination step for determining the user identification information stored in association with the first body composition information and the second body composition information whose relationship satisfies the predetermined condition, user identification information for identifying a user, The user identification determined by the second determination step from second storage means for storing exercise identification information for identifying the exercise performed by the user and date information indicating the date on which the exercise was performed in association with each other. A second acquisition step of acquiring the exercise identification information for identifying the exercise performed by the user indicated by the information; and a control step of causing the output means to output the exercise identification information acquired in the second acquisition step. It is made to perform.

  The invention according to claim 10 is current body composition information indicating a current body composition, and is current body composition information composed of a plurality of evaluation items, and target body composition information indicating a target body composition. A first acquisition unit configured to acquire target body composition information configured by a plurality of evaluation items; and the plurality of the plurality of the plurality of target body composition information based on the current body composition information and the target body composition information acquired by the first acquisition unit. First identification means for determining a specific item in which the relationship between the current body composition and the target body composition satisfies a predetermined condition from among the evaluation items, and exercise identification information for identifying exercise from the server device via the network A terminal device comprising: receiving means for receiving; and control means for outputting the exercise identification information received by the receiving means by an output means; user identification information for identifying a user; and a body indicating the body composition of the user First storage means for associating and storing the composition information and date information indicating the date on which the body composition is registered, user identification information for identifying the user, and exercise identification information for identifying the exercise performed by the user And second storage means for storing date information indicating the date on which the exercise was performed in association with each other, and the body composition and the second body composition of the specific item in the first body composition information from the first storage means Second determination means for determining the user identification information stored in association with the first body composition information and the second body composition information in which the relationship with the body composition of the specific item in the information satisfies the predetermined condition; The second acquisition means for acquiring the exercise identification information for identifying the exercise performed by the user indicated by the user identification information determined by the second determination means from the second storage means, and acquired by the second acquisition means. Before Transmitting means for transmitting the movement identification information to the terminal device, and a server device comprising a, characterized in that it comprises a.

  According to the invention described in claim 1, 9 or 10, the exercise for reaching the target for the body composition having a large difference from the target among the plurality of body compositions is based on the body composition of other users and the history of the exercise. Can be presented.

  According to the second aspect of the present invention, it is possible to present an appropriate exercise to reach the target for the body composition having a large difference from the target.

  According to the third aspect of the present invention, it is possible to present the time during which the exercise is performed together with the exercise for reaching the goal for the body composition having a large difference from the goal.

  According to invention of Claim 4, the exercise | movement for reaching a target about the composition of a some specific item can be shown.

  According to invention of Claim 5, the exercise | movement for reaching a target can be shown for every specific item of a some specific item.

  According to invention of Claim 6, the exercise | movement for reaching a target can be shown for every divided | segmented specific item.

It is a figure showing an example of outline composition of exercise support system 1 of this embodiment. (A) is a figure which shows the structural example of a body composition history table, (B) is a figure which shows the structural example of an exercise | movement history table. (A) is a figure which shows an example of the determination method of a target item, (B) is a figure which shows the structural example of a present similarity list, (C) shows the structural example of a target similarity list. It is a figure and (D) is a figure which shows the structural example of an average similarity list. (A), (D), and (G) are diagrams showing configuration examples of the item division list, and (B), (E), and (H) are diagrams showing configuration examples of the exercise list. C) and (F) are diagrams showing display examples of a recommendation screen. (A) is a flowchart which shows an example of the recommendation control process of CPU51 of the browsing terminal 5, (B) is a flowchart which shows an example of the target determination process of CPU51 of the browsing terminal 5. FIG. (A) is a flowchart which shows an example of the recommendation exercise | movement determination control process of CPU21 of the delivery server 2, (B) is a flowchart which shows an example of the item division | segmentation list production | generation process of CPU21 of the delivery server 2. FIG. (A) is a flowchart which shows an example of the similarity calculation process of CPU21 of the delivery server 2, (B) is a flowchart which shows an example of the similarity calculation process of CPU21 of the delivery server 2. (A) is a flowchart which shows an example of the recommendation possible determination process of CPU21 of the distribution server 2, (B) is a flowchart which shows an example of the exercise | movement possible determination process of CPU21 of the distribution server 2. FIG.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. In addition, embodiment described below is embodiment at the time of applying this invention to the exercise | movement assistance system which assists an exercise | movement.

[1. Configuration of Exercise Support System 1]
First, the configuration of the exercise support system 1 of the present embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating a schematic configuration example of an exercise support system 1 of the present embodiment. As shown in FIG. 1, the exercise support system 1 includes a distribution server 2, one or more output terminals 71, one or more studio terminals 72, a plurality of exercise equipment terminals 82, one or more body composition meters 83, and One or more browsing terminals 5 are included. The distribution server 2, the output terminal 71, the studio terminal 72, the exercise equipment terminal 82, the body composition meter 83, and the browsing terminal 5 can be connected via the network 10. The network 10 includes, for example, the Internet. The distribution server 2 is an example of a server device of the present invention. A database 3 is connected to the distribution server 2. The database 3 is an example of the first storage unit, the second storage unit, and the third storage unit of the present invention. The first storage unit, the second storage unit, and the third storage unit may be the same storage device, or may be different storage devices. Information relating to exercise and information relating to music are registered in the database 3. The distribution server 2 sends the information registered in the database 3 to the output terminal 71, the studio terminal 72, or the viewing terminal 5 periodically or in response to a request from the output terminal 71, the studio terminal 72, or the viewing terminal 5. To deliver. The output terminal 71, the studio terminal 72, the exercise equipment terminal 82, and the body composition meter 83 are installed in the facility 4, for example. The facility 4 may be a sports facility, for example. In the facility 4, there are a studio 7 and a training room 8 as exercise places.

  Studio 7 is a room where studio lessons are held. A studio lesson is an exercise composed of a plurality of exercises by one or more users. In the studio 7, an output terminal 71 and a studio terminal 72 are installed. The output terminal 71 outputs exercise content. The exercise content includes an exercise image and music for supporting exercise. The motion video is a video showing a figure that performs a motion. The figure is a virtual object in the form of, for example, a person, an animal, a virtual creature, or a robot. The output terminal 71 outputs the music by a speaker so that the music and the movement of the figure are synchronized, and displays the motion video on the display. The user can exercise while watching the figure displayed on the display while listening to the music. The studio lesson may be performed without using the output terminal 71. For example, a user may perform exercise under the guidance of an instructor. The studio terminal 72 is a terminal device for registering a user's exercise history in the studio in the database 3. For example, an IC card 63 is given to each user who uses the facility 4. A user ID is recorded on the IC card 63. The user ID is identification information that can identify the user. The user ID is an example of user identification information of the present invention. The user brings the IC card 63 close to the studio terminal 72 when entering the studio and leaving the studio. Then, the studio terminal 72 receives the user ID from the IC card 63 by a communication method such as NFC (Near Field Communication). Details of the process for registering the exercise history will be described later.

  In the training room 8, an exercise equipment 81, an exercise equipment terminal 82, and a body composition meter 83 are installed. The training room 8 is a room for the user to exercise using the exercise equipment 81. The exercise equipment 81 is an instrument or instrument used for exercise. Examples of the exercise equipment 81 include a strength training machine, a running machine, a bike machine, and a bench press machine. An exercise equipment terminal 82 is installed corresponding to each exercise equipment 81. The exercise equipment terminal 82 is a terminal device for registering a user's exercise history using the exercise equipment 81 in the database 3. The user brings the IC card 63 closer to the exercise equipment terminal 82 when the use of the exercise equipment 81 is started and when the use of the exercise equipment 81 is finished. Then, the exercise equipment 81 receives the user ID from the IC card 63. Details of the process for registering the exercise history will be described later. The body composition meter 83 is a device that measures the body composition of the user. The body composition meter 83 can measure the body composition for a plurality of evaluation items. Evaluation items for the body composition of the entire body include, for example, body fat percentage, body fat mass, lean mass, body water content, BMI (Body Mass Index), standard body weight, and basal metabolic rate. In addition, for example, body fat percentage, fat mass, lean mass, and muscle mass are evaluation items of the body composition of each body part of the right foot, left foot, right arm, left arm, and trunk. When measuring the body composition, the user brings the IC card 63 close to the body composition meter 83. Then, the body composition meter 83 receives the user ID from the IC card 63. The body composition meter 83 transmits the body composition information including the measured body composition information and the received user ID to the distribution server 2. Distribution server 2 registers body composition information in a database.

  The browsing terminal 5 is an example of a terminal device of the present invention. The browsing terminal 5 is a terminal device for presenting a recommended exercise to the user. The recommended exercise is called a recommended exercise. The browsing terminal 5 may be, for example, a personal computer, a smartphone, a tablet computer, a mobile phone, or the like. The browsing terminal 5 may be installed in the facility 4 in advance, or each user may own the browsing terminal 5.

[2. Recommendation of exercise based on body composition information and exercise history]
The user can set a target for one or more evaluation items among the plurality of evaluation items of the body composition. At this time, there is a case where it is desired to focus on exercise for reaching the target with respect to the body composition having a large difference from the target. Therefore, the browsing terminal 5 determines a target item from among a plurality of evaluation items. The target item is an evaluation item in which a target body composition is set. The target item is an example of a specific item of the present invention. Next, the distribution server 2 determines a recommended exercise for achieving the target for the body composition of the target item based on the body composition and exercise history of the other users. Then, the browsing terminal 5 outputs recommended exercise information. In the present embodiment, the distribution server 2 and the browsing terminal 5 cooperate to determine the target item and the recommended exercise. However, the target item and the recommended exercise may be determined by only one of the distribution server 2 or the browsing terminal 5. That is, the information processing apparatus of the present invention may be applied to either the distribution server 2 or the browsing terminal 5.

  In order to determine a recommended exercise, the database 3 stores a body composition history table and an exercise history table. The body composition history table is a table in which body composition information is registered. FIG. 2A is a diagram illustrating a configuration example of a body composition history table. As shown in FIG. 2A, the body composition information includes a measurement date and time, a user ID, and a body composition value for each evaluation item. The measurement date and time is the date and time when the body composition was measured by the body composition meter 83. FIG. 2 (A) shows only the date among the date and time of the measurement date and time for convenience. The user ID indicates a user whose body composition has been measured. The body composition value is a value indicating the body composition. Each body composition value is associated with an item ID. The item ID is identification information that can identify the evaluation item.

  The exercise history table is a table in which exercise history is registered. FIG. 2B is a diagram illustrating a configuration example of the exercise history table. As shown in FIG. 2B, the exercise history includes an implementation date, a user ID, an exercise ID, a start time, and an end time. The implementation date indicates the date on which the exercise was performed. The user ID indicates the user who performed the exercise. The exercise ID is identification information that can identify the exercise. The exercise ID is an example of exercise identification information according to the present invention. There are lesson IDs and training IDs as exercise IDs. The lesson ID is identification information that can identify a studio lesson. In other words, the exercises performed in the studio lesson can be identified by the lesson ID. The training ID is identification information that can identify an exercise performed using the exercise equipment 81. The distribution server 2 can identify whether the exercise ID is a lesson ID or a training ID from the exercise ID. The start time is the time when the exercise is started. The end time is the time when the exercise ends.

  Details of the exercise history registration will be described. The database 3 stores a lesson schedule table. The lesson schedule table is a table in which schedule information of studio lessons is stored. The schedule information includes a lesson ID, a date, and a time zone. The lesson ID indicates a studio lesson to be held. The date indicates the date when the studio lesson is held. The time zone includes the time when the lesson starts and the time when the lesson ends. The distribution server 2 distributes the schedule table to the studio terminal 72 periodically, for example.

  When the user enters or exits the studio 7, the IC card 63 is brought close to the studio terminal 72. Thereby, the studio terminal 72 acquires a user ID. Next, the studio terminal 72 acquires today's date and current time. From the schedule table, the studio terminal 72 acquires a lesson ID corresponding to a time zone including the current time among exercise lesson IDs corresponding to today's date as an exercise ID. The studio terminal 72 transmits the acquired date, current time, user ID, and exercise ID to the distribution server 2.

  Each exercise equipment terminal 82 stores, for example, a training ID of exercise performed by the exercise equipment 81 associated with the exercise equipment terminal 82 in advance. When the user starts using the exercise equipment 81 or ends the use, the IC card 63 is brought close to the exercise equipment terminal 82. Thereby, the exercise equipment terminal 82 acquires the user ID. The exercise equipment terminal 82 acquires today's date and current time, and acquires a training ID stored in advance as an exercise ID. The exercise equipment terminal 82 transmits the acquired date, current time, user ID, and exercise ID to the distribution server 2.

  When the distribution server 2 receives the date, current time, user ID, and exercise ID from the studio terminal 72 or the exercise equipment terminal 82, the distribution server 2 sets the received date as the implementation date. Then, the distribution server 2 searches the exercise history table for an exercise history including the received implementation date, the implementation date that matches the user ID and the exercise ID, the user ID, and the exercise ID. When the corresponding exercise history is not searched, the distribution server 2 determines the received current time as the start time. Then, the distribution server 2 registers the exercise history including the implementation date, the user ID, the lesson ID, and the current time in the exercise history table. When the corresponding exercise history is searched, the distribution server 2 determines the received current time as the end time. Then, the distribution server 2 adds the end time to the searched exercise history.

  When determining the recommended exercise, the browsing terminal 5 acquires the current body composition information and target body composition information of the user to be recommended. The current body composition information is body composition information indicating the current body composition of the user. The target body composition information is information indicating the body composition targeted by the user. In order to acquire such information, the browsing terminal 5 transmits the user ID of the user to be recommended to the distribution server 2. The distribution server 2 acquires, from the body composition history table, body composition information with the latest measurement date and time as body composition information among body composition information including the user ID of the user to be recommended. In the database 3, model body composition information is registered. The model body composition information is information indicating the body composition set as a model of the target body composition. Model body composition information is registered for each model. The model body composition information includes a user's height, age, and sex corresponding to the model, and a body composition value that is a model for each evaluation item. The distribution server 2 acquires model body composition information corresponding to the height, age, and sex of the user to be recommended from the database 3. Distribution server 2 transmits current body composition information and model body composition information to browsing terminal 5.

  The browsing terminal 5 sets the body composition of the model indicated by the model body composition information received from the distribution server 2 as the target body composition at the present time. Then, the viewing terminal 5 determines that the relationship between the current body composition of the user to be recommended and the target body composition is a predetermined target from among a plurality of evaluation items based on the current body composition information and the model body composition information. Determine the target item that satisfies the setting conditions. For example, the target setting condition may be that a difference between the current body composition and the target body composition satisfies a predetermined condition.

  For example, the predetermined condition may be that a deviation rate between the current body composition and the target body composition is a predetermined value or more. Further, for example, the predetermined condition is that when the rank is determined for a plurality of evaluation items in descending order of the deviation rate between the current body composition and the target body composition, the determined rank is equal to or higher than the predetermined rank. Also good. For example, the predetermined condition may be that the deviation rate is the largest among the plurality of evaluation items. Further, for example, the predetermined condition may be that a difference value between the current body composition and the target body composition is equal to or greater than a predetermined value. Further, for example, the predetermined condition may be that when the rank is determined for a plurality of evaluation items in descending order of the difference value, the determined rank is equal to or higher than the predetermined rank. For example, the predetermined condition may be that the difference value is the largest among the plurality of evaluation items. The predetermined condition may be determined such that only one evaluation item among the plurality of evaluation items is determined as the target item, or the predetermined condition may be determined so that two or more target items are determined. Good.

  FIG. 3A is a diagram illustrating an example of a method for determining a target item. For example, the browsing terminal 5 calculates the deviation rate between the current body composition value and the target body composition value for each evaluation item. Specifically, when the current body composition value is larger than the target body composition value, the viewing terminal 5 calculates the deviation rate by dividing the current body composition value by the target body composition value. When the current body composition value is less than or equal to the target body composition value, the browsing terminal 5 calculates the deviation rate by dividing the target body composition value by the current body composition value. At this time, for example, as shown in FIG. 3A, the browsing terminal 5 calculates the deviation rate only for the evaluation items of each body part, and does not have to calculate the deviation rate for each evaluation item of the whole body. . For example, the browsing terminal 5 determines a predetermined number of target items in descending order of the deviation rate among the plurality of evaluation items. At this time, the browsing terminal 5 may extract, for example, an evaluation item having the highest divergence rate among body fat percentage, fat mass, lean mass, and muscle mass for each body part. For example, as shown in FIG. 3A, the evaluation item having the highest divergence rate on the right foot is the lean mass. Therefore, the browsing terminal 5 extracts the lean mass of the right foot. The browsing terminal 5 similarly extracts evaluation items for the left foot, right arm, left arm, and trunk. The browsing terminal 5 determines a predetermined number of target items in descending order of the deviation rate among the extracted evaluation items. For example, in the example of FIG. 3A, the rate of divergence between the muscle mass of the left foot and the muscle mass of the right arm is the largest. Next, the rate of divergence increases in the order of lean body mass on the right foot, body fat percentage on the trunk, and muscle mass on the left arm. For example, when three target items are determined, as shown in FIG. 3A, the browsing terminal 5 determines the left leg muscle mass, right arm muscle mass, and right leg lean mass as target items. Note that the browsing terminal 5 may determine one, two, four or more target items, for example. Moreover, the browsing terminal 5 may determine the evaluation item whose deviation rate is a predetermined value or more as a target item, for example.

  The target item and the target body composition of the target item may be changeable based on the user's operation on the viewing terminal 5. In this case, the browsing terminal 5 displays a target body composition information display / edit screen. Information on the current body composition and target items is displayed on the target body composition information display / edit screen. For example, the browsing terminal 5 displays information indicating the current body composition for each evaluation item. For example, the browsing terminal 5 may display a standardized body composition value. For example, the average and standard deviation of body composition values are set in advance for each evaluation item. The browsing terminal calculates the following formula.

Standardized body composition value = (body composition value−average) / standard deviation The viewing terminal 5 may display the standardized body composition value of each evaluation item, for example, on a radar chart. The browsing terminal 5 displays the item name of the target item and the difference between the current body composition and the target body composition in the target item. On the target body composition information display / edit screen, the user can change the target item and the target body composition value of the target item. In addition, the user sets the target period days. The target period days is the number of days during which the user exercises to achieve the target. The browsing terminal 5 generates target body composition information including the body composition value of the target item after the change based on the user's operation. Then, the browsing terminal 5 transmits the target body composition information, the target period days, and the user ID of the user to the distribution server 2.

  When the target item is determined, the distribution server 2 determines the user ID of the model user. The model user is another user who serves as a model for the target user to achieve the target. The distribution server 2 determines the exercise performed by the model user as a recommended exercise. Specifically, the distribution server 2 extracts all combinations of two body composition information associated with the same user ID from the body composition history table. At this time, the distribution server 2 does not extract the body composition information associated with the user ID of the user to be recommended. Of the body composition information included in the combination, the body composition information with the older measurement date is referred to as first body composition information, and the body composition information with the new measurement date is referred to as second body composition information. The distribution server 2 corresponds to the first body composition information and the second body composition information in which the relationship between the body composition of the target item in the first body composition information and the body composition of the target item in the second body composition information satisfies the target setting condition. In addition, the user ID registered in the database 3 is determined. In other words, the distribution server 2 determines the user ID of another user who is considered to have achieved the recommendation target user's goal. When a plurality of target items are determined, for example, the distribution server 2 includes all body compositions of the plurality of target items in the first body composition information and all body compositions of the plurality of target items in the second body composition information. The user ID associated with the first body composition information and the second body composition information whose relationship satisfies the target setting condition may be determined.

  Specifically, the distribution server 2 determines that the body composition of the target item in the first body composition information and the second body composition information is the body of the target item in the current body composition information and the target body composition information of the user to be recommended, for example. A user ID similar to the composition may be determined. More specifically, the distribution server 2 uses the body composition information registered in the body composition history table as the first body composition information, the body composition value of the target item included in the first body composition information, and the current body composition information. The similarity with the body composition value of the target item included is calculated. For example, the distribution server 2 may calculate the cosine similarity. At this time, a standardized body composition value is used as a parameter for calculating the cosine similarity. The method for calculating the standardized body composition value has already been described. The cosine similarity is calculated by the following formula.

Cosine similarity = x · y / | x | * | y |
In the above formula, x and y are vectors. x · y is the inner product of the vectors x and y. | X | is the magnitude of the vector x, and | y | is the magnitude of the vector y. Here, x is the body composition of the first body composition information, and y is the body composition of the current body composition information. The number of target items is n. Moreover, let xi be the body composition value of the i-th target item in the first body composition information. Further, yi is the body composition value of the i-th target item in the current body composition information. In this case, x · y and | x | are calculated by the following equations.

x · y = x1 * y1 + x2 * y2 + ... + xn * yn
| X | = sqrt (x1 ² + x2 ² +... + Xn ² )
In the above formula, sqrt represents a square root. | Y | is calculated in the same manner as | x |. As described above, when a plurality of target items are determined, the distribution server 2 calculates the similarity using the body composition values of all the target items. When the similarity is calculated, the distribution server 2 generates a current similarity list. The current similarity list is a list of similarities calculated using the first body composition information and the current body composition information. FIG. 3B is a diagram illustrating a configuration example of the current similarity list. A plurality of current similarity records are registered in the current similarity list. A current similarity record is registered for each first body composition information. As shown in FIG. 3B, each current similarity record stores a record ID, a user ID, a similarity, and a measurement date. The record ID is identification information that can identify the current similarity record. The distribution server 2 stores the user ID and measurement date / time included in the first body composition information and the calculated similarity in the current similarity record.

  When the current similarity list is generated, the distribution server 2 uses the body composition information registered in the body composition history table as the second body composition information, and the body composition value of the target item included in the second body composition information. The similarity with the body composition value of the target item included in the target body composition information is calculated. The method of calculating the similarity is the same as that for the first body composition information. Then, the distribution server 2 generates a target similarity list. The target similarity list is a list of similarities calculated using the second body composition information and the target body composition information. FIG. 3C is a diagram illustrating a configuration example of the target similarity list. A plurality of target similarity records are registered in the target similarity list. A target similarity record is registered for each second body composition information. As shown in FIG. 3C, each target similarity record stores a record ID, a user ID, a similarity, and a measurement date. The record ID is identification information that can identify the target similarity record. The distribution server 2 stores the user ID and measurement date / time included in the second body composition information and the calculated similarity in the target similarity record.

  Next, the distribution server 2 extracts and combines the current similarity record and the target similarity record having the same user ID from the current similarity list and the target similarity list by an internal combination algorithm using the user ID as a key. That is, the distribution server 2 specifies the first body composition information and the second body composition information that have the same user ID. For example, the distribution server 2 acquires the user ID and the measurement date / time from a certain current similarity record in the current similarity list. The distribution server 2 stores a target similarity record that stores a user ID that matches the user ID acquired from the current similarity record and stores a measurement date and time that is newer than the measurement date and time acquired from the current similarity list. Search from the degree list. Then, the distribution server 2 combines a certain current similarity record with the target similarity record retrieved from the target similarity list. When a plurality of target similarity records are searched, the distribution server 2 combines each of the searched target similarity records with the current similarity record. The distribution server 2 executes these processes for all current similarity records registered in the current similarity list.

  The distribution server 2 calculates the average similarity for each extracted combination. The average similarity is an average of the similarity stored in the current similarity record and the similarity stored in the target similarity record. The average similarity is the similarity between the body composition of the target item in the first body composition information and the second body composition information and the body composition of the target item in the current body composition information and the target body composition information of the user to be recommended. Show. The higher the average similarity is, the higher the probability that the relationship between the body composition of the target item in the first body composition information and the body composition of the target item in the second body composition information satisfies the target setting condition. When the average similarity is calculated, the distribution server 2 generates an average similarity list. FIG. 3D is a diagram illustrating a configuration example of the average similarity list. In the average similarity list, an average similarity record is registered for each extracted combination. Each average similarity record stores record ID, user ID, average similarity, start date / time, end date / time, and training date / time. The record ID is identification information that can identify the average similarity record. The distribution server 2 stores the user ID stored in the combination current similarity record or target similarity record in the average similarity record. The distribution server 2 stores the measurement date and time currently stored in the similarity record as the start date and time in the average similarity record. Further, the distribution server 2 stores the measurement date and time stored in the target similarity record as the end date and time in the average similarity record. Further, the distribution server 2 stores the number of days from the start date / time to the end date / time in the average similarity record as the number of training period days. In the average similarity record, the higher the average similarity, the higher the probability that the user indicated by the user ID has achieved the target of the recommended user in the exercise performed during the training period from the start date to the end date. Indicates.

  When the average similarity list is generated, the distribution server 2 determines, as the user ID of the model user, the user ID having the highest corresponding average similarity among the user IDs registered in the average similarity list. At this time, the distribution server 2 selects the user ID having the highest average similarity from the user IDs having the corresponding exercise history during the training period indicated by the start date and time and the end date and time. The ID may be determined. That is, the user who performed some exercise during the training period becomes the model user. Thereby, the exercise | movement which the model user performed during the training period can be determined as a recommendation exercise | movement. Therefore, it is possible to present an appropriate exercise for the recommended user to achieve the purpose. Specifically, the distribution server 2 determines that the exercise date and time of the exercise history corresponding to the user ID that matches the user ID stored in the average similarity record is within the training period stored in the average similarity record. A certain exercise history is searched from the exercise history table. If more than one exercise history is retrieved, there is a corresponding exercise history. The distribution server 2 may delete an average similarity record whose average similarity is a predetermined value or less from the average similarity list so that a user ID having a low average similarity is not determined as the user ID of the model user. . Further, the distribution server 2 may delete an average similarity record in which the difference between the training period days and the target period days of the user to be recommended is a predetermined value or more from the average similarity list.

  When the user ID of the model user is determined, the distribution server 2 acquires the exercise ID of the recommended exercise from the exercise history table. Specifically, the distribution server 2 acquires the exercise ID, the start time, and the end time from the exercise history searched from the exercise history table. Next, the distribution server 2 generates an exercise list. The exercise list is a list of exercise IDs of recommended exercises. FIG. 4B is a diagram illustrating a configuration example of the exercise list. A record is registered in the exercise list for each exercise ID of the recommended exercise. As shown in FIG. 4B, each record stores a record ID, an exercise ID, an implementation time, and an implementation impossibility flag. The record ID is identification information that can identify a record. The implementation time is the length of time that the recommendation exercise is performed by the model user. The execution time can be calculated from the start time and the end time. Details of the impossibility flag will be described later. The distribution server 2 transmits the exercise list and the item ID of the target item to the browsing terminal 5. The browsing terminal 5 displays a recommendation screen on the display based on the received information. FIG. 4C is a diagram illustrating a display example of the recommendation screen. As shown in FIG. 4C, the item name of the target item, the exercise name of the recommended exercise, and the like are displayed on the recommendation screen. The exercise name is an example of exercise identification information of the present invention. Here, the browsing terminal 5 may display exercise names in the order of long execution time.

  The distribution server 2 may determine whether or not the recommendation exercise can be performed. Then, the distribution server 2 may cause the browsing terminal 5 to output information indicating whether or not it can be implemented. In the exercise list shown in FIG. 4B, the impossibility flag is information indicating whether the recommended exercise cannot be performed. When the exercise ID of the recommended exercise is a lesson ID, the distribution server 2 acquires lesson information corresponding to the lesson ID from the lesson schedule table. When the distribution server 2 acquires one or more lesson information whose start date is today and the start time is after the current time, the distribution server 2 sets the execution disabled flag to FALSE. On the other hand, when the start date is today and there is no lesson information whose start time is after the current time, the distribution server 2 sets the execution disabled flag to TRUE. That is, the distribution server 2 determines whether or not a studio lesson can be taken in the future time today. When the exercise ID of the recommended exercise is a training ID, for example, the distribution server 2 searches the exercise history corresponding to the exercise ID from the exercise history table for an exercise history whose end time is not yet stored. When the end time is not stored, the exercise equipment 81 corresponding to the exercise ID is currently used by any user. The distribution server 2 counts the number of retrieved exercise histories as the number of exercise devices 81 currently in use. Then, when the number of the exercise equipment 81 currently in use is less than the total number of exercise equipment 81 corresponding to the exercise ID, the distribution server 2 sets the execution disabled flag to FALSE. On the other hand, when the number of the exercise equipments 81 currently in use matches the total number, the distribution server 2 sets the implementation disabled flag to TRUE. That is, the distribution server 2 determines whether or not the exercise equipment 81 is currently available. The browsing terminal 5 displays information indicating whether or not implementation is possible based on the implementation impossibility flag. For example, when the exercise ID is a lesson ID and the infeasibility flag is FALSE, “There is a lesson today” is displayed. When the infeasibility flag is TRUE, “No lesson for today” is displayed. . In addition, when the exercise ID is a training ID and the execution impossible flag is FALSE, “available” is displayed, and when the execution impossible flag is TRUE, “currently in use” is displayed. For example, the distribution server 2 may cause the viewing terminal 5 to output the date, start time, end time, and the like of the recommended exercise studio lesson.

  As described above, when a plurality of target items are determined, the distribution server 2 determines all body compositions of the plurality of target items in the first body composition information and all of the plurality of target items in the second body composition information. The user ID whose relationship with the body composition satisfies the target setting condition is determined as the user ID of the model user. As described above, if all of the plurality of target items are used as conditions, there may be no user ID that satisfies the conditions. Further, the user indicated by the user ID satisfying the condition may not be exercising in the facility 4 during the training period. In these cases, the recommendation exercise cannot be determined. Therefore, the distribution server 2 may divide a plurality of target items into a predetermined number of groups when the recommended exercise cannot be determined. The distribution server 2 then, for each group of the plurality of groups, the body composition of all target items included in the group in the first body composition information and the body of all target items included in the group in the second body composition information. A user ID whose relationship with the composition satisfies the target setting condition may be determined.

  In order to divide the target item, the distribution server 2 sets the division number. The number of divisions indicates the number by which a plurality of target items are divided. The initial value of the number of divisions is 1. When the division number is 1, it indicates that the plurality of target items are not divided. The distribution server 2 generates an item division list based on the division number. FIG. 4A, FIG. 4D, and FIG. 4G are diagrams each showing a configuration example of the item division list. Records for the number of divisions are registered in the item division list. Each record stores a record ID and an item list. The record ID is identification information that can identify a record. The item list is a list of item IDs of the divided target items. The distribution server 2 includes, for each record, all body compositions indicated by item IDs included in the item list in the first body composition information, and all body compositions indicated by item IDs included in the item list in the second body composition information. The user ID satisfying the target setting condition is determined. FIG. 4A is an example of an item division list when the number of divisions is one. Assume that left leg muscle mass, right arm muscle mass, and right foot lean mass are determined as target items. When the number of divisions is 1, as shown in FIG. 4A, item IDs of all target items are stored in one record. If the recommended exercise can be determined when the number of divisions is 1, for example, an exercise list is generated as shown in FIG. 4B, and a recommendation screen is displayed as shown in FIG. Is done.

  If the recommendation exercise cannot be determined, the distribution server 2 increases the number of divisions. For example, the distribution server 2 may add 1 to the number of divisions. Then, the distribution server 2 regenerates the item division list. FIG. 4D is an example of an item division list when the number of divisions is two. As shown in FIG. 4D, in the item division list, for example, a record for storing an item ID for right foot lean mass and a record for storing an item ID for left leg muscle mass and right arm muscle mass are registered. . In this case, the distribution server 2 determines a user ID that satisfies the target setting condition based on the relationship between the body composition of the right foot lean mass in the first body composition information and the body composition of the right foot lean mass in the second body composition information. Further, the distribution server 2 targets the relationship between all body compositions of the left leg muscle mass and right arm muscle mass in the first body composition information and all body compositions of the left leg muscle mass and right arm muscle mass in the second body composition information. A user ID satisfying the setting condition is determined. Further, the distribution server 2 determines a recommended exercise for each determined user ID and generates an exercise list. FIG. 4E is a diagram illustrating a configuration example of an exercise list when the number of divisions is two. Further, the distribution server 2 causes the viewing terminal 5 to output the target item and the recommended exercise for each determined user ID. FIG. 4F is a display example of a recommendation screen when the number of divisions is two. That is, the distribution server 2 determines a recommended exercise for each divided target item.

  If the recommendation exercise cannot be determined, the distribution server 2 regenerates the item division list by setting the division number to 3, for example. FIG. 4G shows an example of the item division list when the number of divisions is three. As shown in FIG. 4G, the item division list includes, for example, a record storing an item ID of right foot lean mass, a record storing an item ID of left foot muscle mass, and an item ID of right arm muscle mass. Records to be stored are registered. FIG. 4H is a diagram showing a configuration example of an exercise list when the number of divisions is 3. The illustration of the display example of the recommendation screen when the number of divisions is 3 is omitted.

  In addition, for example, the distribution server 2 includes, for each target item of the plurality of target items, the body composition of one target item in the first body composition information and the body composition of one target item in the second body composition information. A user ID whose relationship satisfies the target setting condition may be determined as the user ID of the model user.

[3. Configuration of each device]
Next, the configuration of each device included in the exercise support system will be described with reference to FIG.

[3-1. Configuration of distribution server 2]
As shown in FIG. 1, the distribution server 2 includes a CPU 21, a ROM 22, a RAM 23, a bus 24, an I / O interface 25, a display control unit 26, a disk drive 28, a network communication unit 30, and an HDD (hard disk drive) 37. . The CPU 21 is connected to the ROM 22, the RAM 23, the bus 24, and the I / O interface 25 via the bus 24. The CPU 21 has a clock function and a timer function. The CPU 21 controls each unit of the distribution server 2 by executing a program stored in the ROM 22 or the HDD 37. Connected to the I / O interface 25 are a database 3, a display control unit 26, a disk drive 28, a network communication unit 30, a keyboard 31, a mouse 32, and an HDD 37. The CPU 21 accesses the database 3 via the I / O interface 25. The display control unit 26 outputs a video signal to the monitor 27 based on the control of the CPU 21. The disk drive 28 writes data to and reads data from the recording medium 29. The network communication unit 30 performs control for the distribution server 2 to connect to the network 10. The HDD 37 stores an OS, various control programs, and the like.

  The database 3 stores a body composition history table, an exercise history table, and a lesson schedule table. In the database 3, model body composition information is registered. In the database 3, the number of exercise equipment 81 used for the exercise indicated by the training ID is registered for each training ID. In the database 3, user information, target body composition information, and target period days are registered in association with the user ID. User information includes the height, age, and gender of the user.

  The HDD 37 stores various programs such as an OS and a server program. The server program is a program for determining a recommendation exercise. The server program causes the CPU 21 as a computer to execute at least the second determination step. Various programs may be downloaded from a predetermined server device via the network 10, for example. Various programs may be recorded on the recording medium 29 and read via the disk drive 28.

[3-2. Configuration of browsing terminal 5]
As shown in FIG. 1, the browsing terminal 5 includes a CPU 51, a ROM 52, a RAM 53, a bus 54, an I / O interface 55, a display control unit 56, a memory 57, a card drive 58, a display 59, a network communication unit 60, and an operation unit. 61 and a signal receiving unit 62. The CPU 51 is connected to the ROM 52, the RAM 53, and the I / O interface 55 via the bus 54. The CPU 51 controls each unit of the browsing terminal 5 by executing a program stored in the ROM 52 or the memory 57. A display control unit 56, a memory 57, a network communication unit 60, an operation unit 61, and a signal reception unit 62 are connected to the I / O interface 55. The display control unit 56 outputs a video signal to the display 59 based on the control of the CPU 51. The display 59 displays a video based on the video signal. The display 59 is an example of output means of the present invention. The card drive 58 writes and reads data to and from the memory card. The operation unit 61 receives an operation from a user. The signal receiving unit 62 receives a user ID from the IC card 63 by a communication method such as NFC, for example.

  The memory 57 stores model body composition information and the like distributed from the distribution server 2. The memory 57 stores various programs such as an OS and a terminal program. The terminal program is a program for outputting recommended exercise information. The terminal program causes the CPU 51 as a computer to execute at least a first acquisition step, a first determination step, and a control step. Various programs may be downloaded from the server such as the distribution server 2 via the network 10, for example. Various programs may be recorded on a memory card and read via the card drive 58.

[4. Operation of Exercise Support System 1]
Next, the operation of the exercise support system 1 will be described with reference to FIGS. FIG. 5 is a flowchart illustrating an example of processing executed by the CPU 51 of the browsing terminal 5. The CPU 51 executes the process shown in FIG. 5 according to the program stored in the memory 57. 6 to 8 are flowcharts showing an example of processing executed by the CPU 21 of the distribution server 2. The CPU 21 executes the processes shown in FIGS. 6 to 8 according to the program stored in the HDD 37. FIG. 5A is a flowchart illustrating an example of the recommendation control process of the CPU 51 of the browsing terminal 5. For example, when the user brings the IC card 63 close to the browsing terminal 5, the signal receiving unit 62 receives the user ID of the recommended user from the IC card 63. At this time, the CPU 51 executes a recommendation control process. As shown in FIG. 5A, the CPU 51 acquires the user ID from the signal receiving unit 62 and stores it in the RAM 53 (step S1).

  Next, the CPU 51 acquires current body composition information from the distribution server 2 (step S2). Specifically, the CPU 51 transmits an information request including the acquired user ID to the distribution server 2. The CPU 21 of the distribution server 2 acquires, from the body composition history table, the body composition information with the latest measurement date and time as the current body composition information from the body composition information corresponding to the user ID included in the information request. In addition, the CPU 21 acquires target body composition information corresponding to the user ID included in the information request from the database 3. In addition, the CPU 21 acquires user information corresponding to the user ID included in the information request from the database 3. The CPU 21 transmits the acquired current body composition information, target body composition information, and user information to the browsing terminal 5. The CPU 51 of the browsing terminal 5 receives the information transmitted from the distribution server 2. When there is no corresponding current body composition information, the distribution server 2 does not transmit the current body composition information. Further, even when there is no corresponding target body composition information, the distribution server 2 does not transmit the target body composition information.

  Next, the CPU 51 determines whether or not there is current body composition information in the received information (step S3). At this time, if the CPU 51 determines that there is currently no body composition information (step S3: NO), the process proceeds to step S4. In step S <b> 4, the CPU 51 causes the display 59 to display a screen that prompts the body composition meter 83 to measure the body composition. After finishing step S4, the CPU 51 ends the recommendation control process.

  On the other hand, if the CPU 51 determines that there is currently body composition information (step S3: YES), the CPU 51 proceeds to step S5. In step S5, the CPU 51 determines whether there is target body composition information in the received information. At this time, if the CPU 51 determines that there is target body composition information (step S5: YES), the CPU 51 proceeds to step S7. On the other hand, if the CPU 51 determines that there is no target body composition information (step S5: NO), the CPU 51 proceeds to step S6. In step S6, the CPU 51 executes target determination processing.

  FIG. 5B is a flowchart illustrating an example of target determination processing of the CPU 51 of the browsing terminal 5. As illustrated in FIG. 5B, the CPU 51 acquires the height, age, and sex from the user information received from the distribution server 2. And CPU51 acquires the model body composition information corresponding to the acquired height, age, and sex from the memory 57 (step S11). Next, the CPU 51 calculates the deviation rate between the current body composition value and the target body composition value for each evaluation item using the body composition indicated by the model body composition information as the target body composition (step S12). Next, the CPU 51 determines, as target items, evaluation items that rank third or higher in the magnitude of the deviation rate among the plurality of evaluation items (step S13). Next, the CPU 51 calculates the average of the deviation rates of the target items. Then, the CPU 51 determines whether or not the calculated average is equal to or less than a preset threshold value (step S14). At this time, when the CPU 51 determines that the average is equal to or less than the threshold (step S14: YES), the CPU 51 proceeds to step S15. In step S15, the CPU 51 subtracts a predetermined value from the age acquired from the user information. Then, the CPU 51 acquires from the memory 57 model body composition information corresponding to the height, age, and sex after lowering the age. Next, the CPU 51 proceeds to step S12. The reason for lowering the age of the model body composition information is to raise the deviation rate of the target item to a threshold value or more. The older the model body composition information is, the smaller the body fat percentage and fat mass of the model, and the greater the lean mass and muscle mass.

  On the other hand, when the CPU 51 determines that the average is not equal to or less than the threshold (step S14: NO), the CPU 51 proceeds to step S16. In step S16, the CPU 51 causes the display 59 to display a target body composition information display / edit screen based on the current body composition information, model body composition information, and target items. The user uses the operation unit 61 to perform an operation of changing the target item and the body composition value of the target item as necessary. In addition, the user sets the target period days. CPU51 changes a target item and a target item based on a user's operation (Step S17). Then, the CPU 51 generates body composition information including the body composition value of the target item after the change. CPU51 complete | finishes a target determination process, after finishing step S17.

  When the goal determination process is completed, as shown in FIG. 5A, the CPU 21 distributes an exercise recommendation request including a user ID, item ID of the goal item, goal body composition information, goal period days, and current body composition information. 2 (step S7). The distribution server 2 that has received the exercise recommendation request executes a recommended exercise determination control process. In the recommended exercise determination control process, the distribution server 2 acquires the user ID of the model user and the exercise ID of the recommended exercise, and transmits the item division list and exercise list of the target items to the browsing terminal 5. Details of the recommended exercise determination control process will be described later. When receiving the item division list and exercise list from the distribution server 2 (step S8), the CPU 51 displays a recommendation screen on the display 59 based on the received list (step S9). Specifically, the CPU 51 determines, for each record registered in the item division list, the item name of the target item indicated by the item ID included in the record and the exercise of exercise indicated by the exercise ID included in the exercise list corresponding to the record. The name is displayed on the display 59. At this time, the CPU 51 causes the display 59 to display information indicating whether or not the exercise indicated by the exercise ID can be performed based on the exercise disabled flag included in the exercise list. When finishing the process of step S9, the CPU 51 ends the recommendation control process.

  FIG. 6A is a flowchart illustrating an example of recommended exercise determination control processing of the CPU 21 of the distribution server 2. The CPU 21 registers the target body composition information and the user ID included in the exercise recommendation request received from the viewing terminal 5 in the database 3 in association with each other. Next, as shown in FIG. 6A, the CPU 21 sets the division number to 1 (step S21). Next, the CPU 51 executes item division list generation processing (step S22).

  FIG. 6B is a flowchart illustrating an example of the item division list generation process of the CPU 21 of the distribution server 2. As shown in FIG. 6B, the CPU 21 determines the ranking of the target items based on the item ID and the deviation rate of the target items included in the exercise recommendation request (step S31). Specifically, the CPU 21 rearranges the item IDs in descending order of the deviation rate. Then, the CPU 21 generates an item division list in which no record is registered. Next, the CPU 21 sets the number of divisions for the number N and sets the number of target items for the rank R (step S32). Next, the CPU 21 determines whether or not the number N is 1 (step S33). At this time, if the CPU 21 determines that the number N is not 1 (step S33: NO), the CPU 21 proceeds to step S34. In step S <b> 34, the CPU 21 generates a record including the item ID of the target item whose rank is R among all the target items. Then, the CPU 21 adds the generated record to the item division list. Next, the CPU 21 subtracts 1 from the number N and subtracts 1 from the rank R (step S35). Next, the CPU 21 proceeds to step S33.

  On the other hand, when the CPU 21 determines that the number N is 1 (step S33: YES), the CPU 21 proceeds to step S36. In step S <b> 36, the CPU 21 generates a record including the item IDs of the target items whose ranks are 1st to Rth. Then, the CPU 21 adds the generated record to the item division list. CPU21 complete | finishes an item division | segmentation list | wrist production | generation process, after finishing step S36.

  When the item division list generation process is completed, the CPU 21 executes the similarity calculation control process as shown in FIG. 6A (step S23). FIG. 7A is a flowchart illustrating an example of similarity calculation control processing of the CPU 21 of the distribution server 2. As shown in FIG. 7A, the CPU 21 specifies the first record from the item division list (step S41). Next, the CPU 21 determines the target item indicated by all the item IDs included in the specified record as the target item for calculating the similarity (step S42). Next, the CPU 21 executes similarity calculation processing (step S43).

  FIG. 7B is a flowchart illustrating an example of similarity calculation processing of the CPU 21 of the distribution server 2. As shown in FIG. 7B, the CPU 21 generates a current similarity list (step S61). Specifically, the CPU 21 calculates the similarity of all body compositions of the determined target item between the current body composition information included in the exercise recommendation request and each body composition information registered in the body composition history table. To do. At this time, CPU21 calculates a similarity degree about body composition information other than the user of recommendation object. Next, the CPU 21 acquires the user ID and the measurement date / time from the body composition information for each body composition information registered in the body composition history table. Next, the CPU 21 generates a current similarity record including the user ID, the measurement date and time, and the calculated similarity. Then, the CPU 21 generates a current similarity list including all generated current similarity records.

  Next, the CPU 21 generates a target similarity list (step S62). Specifically, the CPU 21 calculates the similarity of all body compositions of the determined target item between the target body composition information included in the exercise recommendation request and each body composition information registered in the body composition history table. To do. At this time, CPU21 calculates a similarity degree about body composition information other than the user of recommendation object. Next, the CPU 21 acquires the user ID and the measurement date / time from the body composition information for each body composition information registered in the body composition history table. Next, the CPU 21 generates a target similarity record including the user ID, the measurement date and time, and the calculated similarity. Then, the CPU 21 generates a target similarity list including all the generated target similarity records.

  Next, the CPU 21 generates an average similarity list (step S63). Specifically, the CPU 21 extracts and combines a current similarity record and a target similarity record with the same user ID from the current similarity list and the target similarity list. Next, for each combination, the CPU 21 calculates an average similarity between the similarity included in the current similarity record and the similarity included in the target similarity record. Further, the CPU 21 calculates the number of days from the measurement date / time included in the current similarity record to the measurement date / time included in the target similarity record as the number of training period days. Further, the CPU 21 acquires the user ID and the measurement date and time as the start date and time from the current similarity list, and acquires the measurement date and time as the end date and time from the target similarity list. Next, the CPU 21 generates a record including the average similarity, the number of training period days, the user ID, the start date and time, and the end date and time. And CPU21 produces | generates the average similarity list containing all the produced | generated records.

  Next, the CPU 21 deletes a record whose similarity stored in the record is less than a preset similarity threshold from the average similarity list (step S64). Next, the CPU 21 deletes a record in which the difference between the training period days stored in the record and the target period days is greater than or equal to a preset days threshold from the average similarity list (step S65). Next, the CPU 21 sorts the records registered in the average similarity list in descending order of the average similarity (step S66). CPU21 complete | finishes a similarity calculation process, after finishing the process of step S66.

  When the similarity calculation processing is completed, the CPU 21 determines whether or not one or more records are registered in the average similarity list as shown in FIG. 7A (step S44). At this time, if the CPU 21 determines that no record is registered in the average similarity list (step S44: NO), the similarity calculation control process is terminated. On the other hand, if the CPU 21 determines that one or more records are registered in the average similarity list (step S44: YES), the CPU 21 proceeds to step S45. In step S45, the CPU 21 specifies the first record from the average similarity list. Next, the exercise history corresponding to the user ID stored in the record specified from the average similarity list is searched from the exercise history table. And CPU21 searches the exercise history which stores the implementation date and start time which are in the range from the start date to the end date stored in the specified record from the searched exercise history (step S46). .

  Next, the CPU 21 determines whether or not there is an exercise history that stores the execution date and time within the range from the start date and time to the end date and time (step S47). At this time, if the CPU 21 determines that there is no exercise history (step S47: NO), the CPU 21 proceeds to step S48. In step S48, the CPU 21 determines whether or not the record specified from the average similarity list is the last record in the average similarity list. At this time, if the CPU 21 determines that the identified record is the last record (step S48: YES), the similarity calculation control process is terminated. On the other hand, if the CPU 21 determines that the identified record is not the last record (step S48: NO), the CPU 21 proceeds to step S49. In step S49, the CPU 21 specifies the next record from the average similarity list. Next, the CPU 21 proceeds to step S46.

  If the CPU 21 determines in step S47 that there is an exercise history (step S47: YES), the process proceeds to step S50. In step S50, the CPU 21 generates an exercise list. Specifically, the CPU 21 acquires an exercise ID, a start time, and an end time from the searched exercise history. Next, the CPU 21 calculates an execution time from the start time to the end time for each exercise ID. Next, the CPU 21 generates a record including the exercise ID and the execution time for each exercise ID. Next, the CPU 21 generates an exercise list including all the generated records. Then, the CPU 21 stores the exercise list in the RAM 23 in association with the record specified from the item division list. Thereby, CPU21 determines the user ID stored in the record specified from the average similarity list as the user ID of the model user.

  Next, the CPU 21 determines whether or not the record specified from the item division list is the last record in the item division list (step S51). At this time, if the CPU 21 determines that the identified record is not the final record (step S51: NO), the process proceeds to step S52. In step S52, the CPU 21 specifies the next record from the item division list. Next, the CPU 21 proceeds to step S42. On the other hand, when the CPU 21 determines that the identified record is the last record (step S51: YES), the CPU 21 ends the similarity calculation control process.

  When the similarity calculation control process is completed, the CPU 21 executes a recommendation possibility determination process as shown in FIG. 6A (step S24). FIG. 8A is a flowchart illustrating an example of a recommendation possibility determination process performed by the CPU 21 of the distribution server 2. As shown in FIG. 8A, the CPU 21 specifies the first record from the item division list (step S71). Next, the CPU 21 determines whether or not there is an exercise list associated with the identified record (step S72). At this time, if the CPU 21 determines that there is no exercise list (step S72: NO), the process proceeds to step S73. In step S73, the CPU 21 sets a recommendation impossibility flag to TRUE. CPU21 complete | finishes the recommendation possible determination process, after finishing step S73.

  On the other hand, if the CPU 21 determines that there is an exercise list (step S72: YES), the CPU 21 proceeds to step S74. In step S <b> 74, the CPU 21 acquires an exercise list associated with the specified record from the RAM 23. Next, the CPU 21 executes exercise determination processing. FIG. 8B is a flowchart illustrating an example of the exercise determination process performed by the CPU 21 of the distribution server 2. As shown in FIG. 8B, the CPU 21 specifies the first record from the acquired exercise list (step S81). Next, the CPU 21 determines whether or not the exercise ID stored in the identified record is a training ID (step S82). At this time, if the CPU 21 determines that the exercise ID is a training ID (step S82: YES), the CPU 21 proceeds to step S83. In step S83, the CPU 21 searches the exercise history table that does not include the end time from the exercise history corresponding to the exercise ID stored in the specified record. Then, the CPU 21 calculates the number of retrieved exercise histories. Next, the CPU 21 acquires the total number of exercise equipment 81 corresponding to the exercise ID stored in the identified record from the database 3. Then, the CPU 21 determines whether or not the number of exercise histories matches the total number of exercise equipment 81 (step S84). At this time, if the CPU 21 determines that the number of exercise histories matches the total number of exercise equipment 81 (step S84: YES), the CPU 21 proceeds to step S85. In step S85, the CPU 21 sets the execution flag stored in the identified record to TRUE. Next, the CPU 21 proceeds to step S92. On the other hand, if the CPU 21 determines that the number of exercise histories does not match the total number of exercise equipment 81 (step S84: NO), the CPU 21 proceeds to step S86. In step S86, the CPU 21 sets the execution flag stored in the identified record to FALSE. Next, the CPU 21 proceeds to step S92.

  In step S82, if the CPU 21 determines that the exercise ID is not a training ID (step S82: NO), the process proceeds to step S87. In this case, the exercise ID is a lesson ID. In step S87, the CPU 21 acquires the date and start time corresponding to the lesson ID stored in the specified record from the lesson schedule table. Next, the CPU 21 acquires the current date and time from the clock function. Then, the CPU 21 determines whether or not the date of the event matches today's date (step S88). At this time, if the CPU 21 determines that the date does not match today's date (step S88: NO), the CPU 21 proceeds to step S90. On the other hand, if the CPU 21 determines that the date of the event coincides with today's date (step S88: YES), the CPU 21 proceeds to step S89. In step S89, the CPU 21 determines whether or not the start time is after the current time. At this time, if the CPU 21 determines that the start time is not after the current time (step S89: NO), the process proceeds to step S90. In step S90, the CPU 21 sets the execution flag stored in the identified record to TRUE. Next, the CPU 21 proceeds to step S92. On the other hand, when determining that the start time is after the current time (step S89: YES), the CPU 21 proceeds to step S91. In step S91, the CPU 21 sets the execution flag stored in the identified record to FALSE. Next, the CPU 21 proceeds to step S92.

  In step S92, the CPU 21 determines whether or not the identified record is the last record in the exercise list. At this time, if the CPU 21 determines that the specified record is not the final record (step S92: NO), the process proceeds to step S93. In step S93, the CPU 21 specifies the next record from the exercise list. Next, the CPU 21 proceeds to step S82. On the other hand, when the CPU 21 determines that the specified record is the last record (step S92: YES), the exercise possibility determination process is terminated.

  When the exercise determination process ends, the CPU 21 determines whether or not the specified record is the last record in the item division list, as shown in FIG. 8A (step S76). At this time, if the CPU 21 determines that the specified record is not the last record (step S76: NO), the process proceeds to step S77. In step S77, the CPU 21 specifies the next record from the item division list. Next, the CPU 21 proceeds to step S72. On the other hand, if the CPU 21 determines that the identified record is the last record (step S76: YES), the CPU 21 proceeds to step S78. In step S78, the CPU 21 sets the recommendation impossibility flag to FALSE. CPU21 complete | finishes a recommendation possible determination process, after finishing step S78.

  When the recommendation possibility determination process is finished, the CPU 21 determines whether or not the recommendation impossibility flag is TRUE as shown in FIG. 6A (step S25). At this time, if the CPU 21 determines that the recommendation disable flag is TRUE (step S25: YES), the CPU 21 proceeds to step S26. In step S26, the CPU 21 determines whether or not the number of target items matches the number of divisions. At this time, if the CPU 21 determines that the number of target items matches the number of divisions (step S26: YES), the recommended exercise determination control process is terminated. On the other hand, if the CPU 21 determines that the number of target items does not match the number of divisions (step S26: NO), the CPU 21 proceeds to step S27. In step S27, the CPU 21 adds 1 to the division number. Next, the CPU 21 proceeds to step S22.

  If the CPU 21 determines in step S25 that the recommendation impossibility flag is not TRUE (step S25: NO), the process proceeds to step S28. In step S <b> 28, the CPU 21 transmits the generated item division list and exercise list to the browsing terminal 5. CPU21 complete | finishes a recommendation exercise | movement determination control process, after finishing step S28.

  As described above, according to the present embodiment, the browsing terminal 5 determines a target item that satisfies a predetermined target setting condition among a plurality of evaluation items, based on the current body composition information and the target body composition information. The distribution server 2 also includes first body composition information and second body composition information in which the relationship between the body composition of the target item in the first body composition information and the body composition of the target item in the second body composition information satisfies the target setting condition. The user ID registered in the database 3 is determined in association with. Further, the distribution server 2 acquires an exercise ID corresponding to the determined user ID from the exercise history table. Further, the browsing terminal 5 causes the display 59 to output the exercise name indicated by the acquired exercise ID. Therefore, the exercise | movement for reaching a target can be shown based on the body composition of another user, and the log | history of exercise | movement about the body composition with a big difference with a target among several body compositions.

1 exercise support system 2 distribution server 3 database 5 browsing terminals 21, 51 CPU
22, 52 ROM
23, 53 RAM
37 HDD
30, 60 Network communication unit 56 Display control unit 57 Memory 59 Display

Claims (10)

First storage means for storing user identification information for identifying a user, body composition information indicating the body composition of the user, and date information indicating a date on which the body composition is registered;
Second storage means for storing user identification information for identifying a user, exercise identification information for identifying exercise performed by the user, and date information indicating a date on which the exercise was performed;
Current body composition information indicating the current body composition, current body composition information composed of a plurality of evaluation items, and target body composition information indicating a target body composition, composed of a plurality of evaluation items First acquisition means for acquiring target body composition information;
Based on the current body composition information and the target body composition information acquired by the first acquisition means, a relationship between the current body composition and the target body composition among the plurality of evaluation items satisfies a predetermined condition First determining means for determining a specific item;
The first body composition information and the second condition that the relationship between the body composition of the specific item in the first body composition information and the body composition of the specific item in the second body composition information satisfies the predetermined condition from the first storage means. Second determining means for determining the user identification information stored in association with body composition information;
Second acquisition means for acquiring the exercise identification information for identifying the exercise performed by the user indicated by the user identification information determined by the second determination means from the second storage means;
Control means for outputting the motion identification information acquired by the second acquisition means by an output means;
An information processing apparatus comprising: From the date indicated by the date information stored in the first storage means in association with the first body composition information, the date information stored in the first storage means in association with the second body composition information is Further comprising third determining means for determining a period until the indicated date;
The second acquisition means is associated with the exercise identification information among the exercise identification information stored in the second storage means in association with the user identification information determined by the second determination means. 2. The information processing apparatus according to claim 1, wherein the motion identification information included in a period determined by the third determination unit is acquired by the date indicated by the date information stored in the second storage unit. Third storage means for storing exercise identification information for identifying exercise and time information indicating a time at which the exercise is performed in an exercise facility in association with each other;
Further comprising third acquisition means for acquiring the time information stored in the third storage means in association with the exercise identification information acquired by the second acquisition means;
The control means causes the output means to output the motion identification information acquired by the second acquisition means and the time information acquired by the third acquisition means. The information processing apparatus described.   When the plurality of specific items are determined by the first determination unit, the second determination unit includes all body compositions of the plurality of specific items in the first body composition information and the second body composition information. The information processing apparatus according to any one of claims 1 to 3, wherein the user identification information is determined such that a relationship with all body compositions of a plurality of specific items satisfies the predetermined condition.   When the plurality of specific items are determined by the first determination unit, the second determination unit determines the body composition of one specific item in the first body composition information for each specific item of the plurality of specific items. The user identification information according to any one of claims 1 to 3, wherein the user identification information satisfying the predetermined condition is determined by a relationship between the second body composition information and the body composition of the one specific item. The information processing apparatus described.   The second determining unit divides the plurality of specific items into a predetermined number of groups when the first determining unit determines a plurality of specific items, and for each group of the predetermined number of groups, the second determining unit The user who satisfies the predetermined condition that the relationship between all body compositions of the specific items included in the group in one body composition information and all body compositions of the specific items included in the group in the second body composition information The information processing apparatus according to any one of claims 1 to 3, wherein identification information is determined.   The first determining means determines a specific item whose deviation between a current body composition and a target body composition is a predetermined value or more, or a predetermined number of specific items in descending order of the deviation. The information processing apparatus according to any one of 1 to 6.   In the second determination means, the body composition of the specific item in the first body composition information and the second body composition information is similar to the body composition of the specific item in the current body composition information and the target body composition information. The information processing apparatus according to claim 1, wherein the user identification information is determined. Current body composition information indicating the current body composition, current body composition information composed of a plurality of evaluation items, and target body composition information indicating a target body composition, composed of a plurality of evaluation items A first acquisition step of acquiring target body composition information;
Based on the current body composition information and the target body composition information acquired in the first acquisition step, a relationship between the current body composition and the target body composition among the plurality of evaluation items satisfies a predetermined condition. A first determination step for determining a specific item;
From first storage means for storing user identification information for identifying a user, body composition information indicating the body composition of the user, and date information indicating a date on which the body composition is registered, in association with each other. The relationship between the body composition of the specific item in the information and the body composition of the specific item in the second body composition information is stored in association with the first body composition information and the second body composition information satisfying the predetermined condition A second determination step of determining user identification information;
From second storage means for storing user identification information for identifying a user, exercise identification information for identifying exercise performed by the user, and date information indicating a date on which the exercise was performed, in association with each other, A second acquisition step of acquiring the exercise identification information for identifying the exercise performed by the user indicated by the user identification information determined by the determination step;
A control step of causing the output means to output the motion identification information acquired in the second acquisition step;
A program that causes a computer to execute. Current body composition information indicating the current body composition, current body composition information composed of a plurality of evaluation items, and target body composition information indicating a target body composition, composed of a plurality of evaluation items First acquisition means for acquiring target body composition information;
Based on the current body composition information and the target body composition information acquired by the first acquisition means, a relationship between the current body composition and the target body composition among the plurality of evaluation items satisfies a predetermined condition First determining means for determining a specific item;
Receiving means for receiving exercise identification information for identifying exercise from the server device via a network;
Control means for causing the output means to output the motion identification information received by the receiving means;
A terminal device comprising:
First storage means for storing user identification information for identifying a user, body composition information indicating the body composition of the user, and date information indicating a date on which the body composition is registered;
Second storage means for storing user identification information for identifying a user, exercise identification information for identifying exercise performed by the user, and date information indicating a date on which the exercise was performed;
The first body composition information and the second condition that the relationship between the body composition of the specific item in the first body composition information and the body composition of the specific item in the second body composition information satisfies the predetermined condition from the first storage means. Second determining means for determining the user identification information stored in association with body composition information;
Second acquisition means for acquiring the exercise identification information for identifying the exercise performed by the user indicated by the user identification information determined by the second determination means from the second storage means;
Transmitting means for transmitting the exercise identification information acquired by the second acquisition means to the terminal device;
A server device comprising:
A communication system comprising:

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