Information processing device, method for controlling information processing device, and information processing system

Abstract

To provide an information processing device or the like for predicting user's exercise ability with high accuracy by a simpler method without requiring time and effort of measurement at a specialized facility of an AT value.SOLUTION: An information processing device includes a first storage part for storing a correspondence relation between a running time corresponding to a running distance and a running ability, a second storage part for storing a lap time of running of each running activity about running activities of a plurality of times performed by a prediction object user, a running ability calculation part for calculating a plurality of running abilities corresponding to a prescribed running distance for each running activity on the basis of the lap time and the correspondence relation, a determination part for determining a representative value of the running ability of each running activity on the basis of the plurality of running abilities, a generation part for generating a regression model with the representative value as an objective variable and with a running distance in a running activity in determining the representative value as an explanatory variable, and a prediction part for predicting a running ability in the case that the prediction object user runs a prescribed distance on the basis of the regression model.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, a control method for an information processing apparatus, and an information processing system.

Background Art

[0002] Conventionally, as an index considered in creating a practice menu for a user who runs, there is an AT value (Anaerobic Threshold). The AT value refers to the exercise load that is the conversion point from aerobic exercise to anaerobic exercise when increasing the intensity of exercise. By knowing an individual's AT value, it becomes possible to predict race records and set running paces according to the purpose of the practice menu. For example, Patent Document 1 discloses calculating the heart rate during exercise based on the pulse wave signal and body movement signal of a user measured while performing a predetermined exercise, and determining the degree of effectiveness of a predetermined exercise in contributing to the user's physical strength based on the lactate value information including the anaerobic threshold (AT value) representing the relationship between the heart rate of the user obtained in advance and the amount of blood lactate, and the heart rate during exercise.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The AT value is estimated based on the relationship between the running speed of the user and the blood lactate concentration, VO2max (maximum oxygen uptake), the race record under maximum effort, or the correspondence between the heart rate and the running speed. Here, values such as blood lactate concentration and VO2max require measurement in a specialized facility, and when measurement under maximum effort is not performed, the measurement accuracy decreases.

[0005] Therefore, there has been a demand for a simpler method to accurately predict the exercise ability of users without the need for the laborious measurements in specialized facilities such as the AT value.

Means for Solving the Problem

[0006] An information processing apparatus according to an aspect of the present invention includes: a first storage unit that stores a correspondence relationship between a running time corresponding to a running distance and a running ability; a second storage unit that stores a lap time of running for each running activity for a plurality of running activities performed by a user to be predicted; a running ability calculation unit that calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence relationship; a determination unit that determines a representative value of the running ability for each running activity based on the plurality of running abilities; a generation unit that generates a regression model with the representative value as an objective variable and the running distance in the running activity when the representative value is determined as an explanatory variable; and a prediction unit that predicts the running ability when the user to be predicted runs a predetermined distance based on the regression model.

[0007] A control method for an information processing apparatus according to an aspect of the present invention includes steps in which the information processing apparatus stores a correspondence relationship between a running time corresponding to a running distance and a running ability in a first storage unit, stores a lap time of running for each running activity for a plurality of running activities performed by a user to be predicted in a second storage unit, calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence relationship, determines a representative value of the running ability for each running activity based on the plurality of running abilities, generates a regression model with the representative value as an objective variable and the running distance in the running activity when the representative value is determined as an explanatory variable, and predicts the running ability when the user to be predicted runs a predetermined distance based on the regression model.

[0008] An information processing system including an information processing device and a terminal of a user to be predicted, wherein the information processing device includes: a first storage unit that stores a correspondence relationship between a running time according to a running distance and a running ability; a second storage unit that stores, for a plurality of running activities performed by the user to be predicted, a lap time of each running activity acquired from the terminal of the user to be predicted; a running ability calculation unit that calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence relationship; a determination unit that determines a representative value of the running ability for each running activity based on the plurality of running abilities; a generation unit that generates a regression model having the representative value as an objective variable and a running distance in the running activity when the representative value is determined as an explanatory variable; and a prediction unit that predicts the running ability when the user to be predicted runs a predetermined distance based on the regression model.

Advantages of the Invention

[0009] According to one aspect of the present invention, it is possible to provide an information processing device or the like that can accurately predict a user's motor ability with a simpler method without the need for laborious measurements at a specialized facility such as an AT value.

Brief Description of the Drawings

[0010]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Mode for Carrying Out the Invention

[0011] Hereinafter, an aspect of the invention according to the present disclosure (also referred to as the present invention) will be described with reference to the drawings. Note that the drawings are merely examples, and the present invention is not limited to those shown in the drawings. For example, the number of illustrated servers (information processing devices), user terminals (communication devices), database servers, sensor devices, and their size ratios, data sets (tables), display screens, and flowcharts are merely examples, and the present invention is not limited thereto.

[0012] <System Configuration> FIG. 1 is a diagram showing a configuration example of a practice support system according to an aspect of the present invention. The practice support system 600 may be an information processing system that supports a user's (runner's) practice. According to an aspect of the present invention, based on data (information) obtained from the user's daily practice, the running ability of the user when running a predetermined distance is predicted. Note that the "running ability" is an index related to the performance and record of the user when running. For example, the running ability includes VDOT (pseudo VO2max) described later, the race completion time, the AT pace (running pace at an exercise load near the AT value), etc., and these are in a corresponding relationship, and for example, VDOT can be converted into the race completion time or the AT pace.

[0013] Note that the "practice" in this specification includes "training", "exercise", "workout", etc., and may include those independently performed by an individual or those performed while receiving guidance from an expert or a trainer.

[0014] The practice support system 600 may include a server 100, a database server 101, a user's communication terminal (user terminal) 200 (200A, 200B), and sensor devices 301(301B), 302(302B). In FIG. 1, two users A and B are shown, and the letters "A" and "B" are attached to the symbols of their respective user terminals and sensor devices. However, the number of users may be more or less than this, and the number of user terminals and sensor devices may be the same as the number corresponding to the users. Also, unless otherwise distinguished hereinafter, the user terminal and the sensor device are simply referred to as the user terminal 200 and the sensor devices 301, 302, respectively. Although details will be described later, the user terminal 200 and the sensor devices 301, 302 may be communication devices having a position information acquisition function such as (GPS: Global Positioning System).

[0015] The server 100 can execute various processes related to the practice support service realized by the practice support system 600. Also, the server 100 is connected to the user terminal 200 via the network 500. The network 500 may include a wireless network or a wired network. For example, it may be a wireless LAN (WLAN), a wide area network (WAN), ISDNs (Integrated Service Digital Networks), wireless LANs, CDMA (Code Division Multiple Access), LTE (Long Term Evolution), LTE-Advanced, 4th generation communication (4G), 5th generation communication (5G), and a mobile communication system such as 6th generation communication (6G) and later, or a combination thereof.

[0016] Server 100 further transmits and receives various types of data to and from database server 101. Database server 101 stores (stores) various types of data necessary for realizing the functions of the practice support system 600. In FIG. 1, a mode in which server 100 and database server 101 are connected via network 500 is shown. However, server 100 and database server 101 may be connected by a dedicated internal network. Further, in FIG. 1, server 100 and database server 101 are shown separately. However, database server 101 may function as a storage unit (first storage unit, second storage unit) of server 100, which will be described later. In FIG. 1, one server 100 and one database server 101 are shown respectively, but the present invention is not limited to this. That is, each function described as being provided in server 100 may be realized by a plurality of servers, or there may be a plurality of database servers 101. Further, server 100 may be, for example, a distributed server system that cooperates by communicating via a network, or a so-called cloud server. That is, server 100 is not limited to a physical server and may include a virtual server by software.

[0017] User terminal 200 is a communication terminal used by a user, and an application for using the practice support service (hereinafter also referred to as "practice support application") is installed. Although details will be described later, user terminal 200 may be able to transmit information acquired by its own device or sensor devices 301 and 302 to server 100 via the practice support application.

[0018] In FIG. 1, a smartphone is shown as the user terminal 200. However, the user terminal 200 may be any terminal as long as it can realize the functions described in each of the embodiments described hereinafter. For example, the user terminal 200 may be a computer (e.g., a tablet, a desktop personal computer, a notebook personal computer), a handheld computer device (by way of non-limiting example, a wearable terminal (a glasses-type device (smart glasses), a watch-type device (smartwatch), etc.), a smart speaker, or the like. Further, the sensor device 302 may be provided with various functions of the user terminal 200 described hereinafter and function as the user terminal 200.

[0019] The user may include a target user to be predicted, who is a user who predicts his / her running ability when running a predetermined distance, and a reference user who has running data when running a predetermined distance in the past. Here, the predetermined distance may be any numerical value as long as it is the distance that the user wants to complete, and may be, for example, a full marathon (42.195 km), a half marathon (21.0975 km), or the like. In one aspect of the present invention, the running data of the reference user is used to correct the prediction result of the target user to be predicted, contributing to an improvement in prediction accuracy. When there is no particular need to distinguish between the target user to be predicted and the reference user, they are simply referred to as "user".

[0020] The user may run while wearing at least one of the user terminal 200 and the sensor devices 301 and 302 having a position information acquisition function. Note that the sensor device 301 is a motion sensor, and may be a wearable device having a communication function that can acquire information regarding the user's running form and information regarding the moving distance, and transmit the acquired information to the user terminal 200. Further, in addition to measuring the moving distance, the sensor device 302 may include a vital sensor capable of detecting biological information such as the user's body temperature, blood pressure, heart rate, and the number of breaths per unit time. However, in one aspect of the present invention, it is sufficient to acquire information regarding the time and distance that the user has moved, and acquisition of information regarding the running form and biological information is not essential. That is, according to one aspect of the present invention, only the data obtained by running while holding the user terminal 200 is sufficient, and wearing a motion sensor or a vital sensor is not essential.

[0021] Next, with reference to FIG. 2, the hardware configuration and functional configuration of the server 100 and the user terminal 200 will be described.

[0022] <User Terminal> (1) Hardware Configuration of User Terminal The user terminal 200 includes a control unit 210, a communication unit 220, a display unit 230, an input / output unit 240, and a storage unit 270.

[0023] The control unit 210 is typically a processor, and is realized by a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), or the like. The control unit 210 reads out a program stored in the storage unit 270 and executes the code or instruction included in the program, thereby executing the functions and methods shown in each embodiment.

[0024] The control unit 210 controls the communication unit 220, the display unit 230, and the input / output unit 240. Specifically, the control unit 210 controls the communication between the user terminal 200 and the server 100 and the sensor devices 301 and 302 by the communication unit 220, and performs transmission and reception of various data therebetween. For example, the control unit 210 may acquire the travel distance and travel time as the data of the user during travel measured by the sensor devices 301 and 302. Further, the control unit 210 controls the display of data on the display unit 230. For example, the control unit 210 may cause the display unit 230 to display the predicted running ability or the like when traveling a predetermined distance. Furthermore, the control unit 210 controls the transmission of various information to and from an external device via the input / output unit 240. For example, the control unit 210 may transmit various information to each functional unit according to the input operation of the user received by the input device, or transmit information from each functional unit to an output device (not shown) such as a touch panel, a monitor, and a speaker.

[0025] The storage unit 270 stores various programs and various data necessary for the operation of the user terminal 200. For example, the storage unit 270 may store the program of the above-described practice support application. The storage unit 270 includes, for example, a flash memory or the like, and may also include a memory (RAM (Random Access Memory), ROM (Read Only Memory), etc.) that provides a working area for the control unit 210. Further, the lap time described later may be temporarily stored.

[0026] The communication unit 220 is implemented as hardware such as a network adapter, communication software, or a combination thereof, and performs transmission and reception of various data with the server 100 and the sensor devices 301 and 302 via the network 500 according to a predetermined protocol.

[0027] The display unit 230 is a monitor that displays data according to the display data written in the frame buffer, and may be, for example, a touch panel, a touch display, or the like.

[0028] The input / output unit 240 includes an input device for inputting various operations to the user terminal 200 and an output device for outputting the processing results processed by the user terminal 200. The input device may include, for example, a touch panel, a touch display, a camera, and a microphone, and the output device may include, for example, a display, a touch panel, a speaker, etc.

[0029] (2) Functional configuration of the user terminal The user terminal 200 includes a position information acquisition unit 211 and a conversion unit 212 as functions realized by the control unit 210. The position information acquisition unit 211 acquires position information regarding the current position of the own terminal. The position information acquisition unit 211 acquires, for example, the latitude and longitude information of the user terminal 200 as the position information of the current position of the user terminal 200 using GPS (Global Positioning System). Note that the position information acquisition unit 211 may acquire position information by any method, for example, using wireless LAN, IMES (Indoor MEssaging System), RFID (Radio Frequency Identifier), BLE (Bluetooth Low Energy) (registered trademark), geomagnetism, etc.

[0030] The conversion unit 212 converts the amount of movement per unit time of the user acquired by the position information acquisition unit 211 into a lap time for each predetermined section. The predetermined section may be, for example, 1000 m, but is not limited thereto. When the user wears the sensor devices 301 and 302 and travels, and the sensor devices 301 and 302 are provided with a function to convert to a lap time, the conversion to a lap time may be performed by the sensor devices 301 and 302. Alternatively, the conversion to a lap time may be performed by the server 100 described later.

[0031] <Server> (1) Hardware configuration of the server The server 100 includes a control unit 110, a communication unit 120, a display unit 130, and a storage unit 170.

[0032] The memory unit 170 is typically realized by various recording media such as HDD (Hard Disc Drive), SSD (Solid State Drive), flash memory, etc., and has a function of storing various programs and data required for the operation of the server 100. Further, the memory unit 170 includes a memory (such as RAM, ROM, etc.) that provides a working area for the control unit 110.

[0033] The control unit 110 is typically a processor, which is realized by a central processing unit (CPU), MPU, GPU, etc. The control unit 110 may execute the functions and methods shown in each embodiment by reading the programs stored in the memory unit 170 and executing the codes or instructions included in the programs.

[0034] The communication unit 120 is implemented as hardware such as a network adapter, communication software, and combinations thereof. The communication unit 120 may transmit and receive various data to and from the user terminal 200 via the network 500 using any communication protocol.

[0035] (2) Functional Configuration of the Server As functions realized by the control unit 110, the server 100 includes an acquisition unit 111, a calculation unit 112, a determination unit 113, a generation unit 114, a prediction unit 115, a correction unit 116, and an output unit 117. In FIG. 2, functional units that are not essential in each embodiment described hereinafter may be omitted. Also, the functions or processes of each functional unit may be realized by machine learning or AI within the achievable range.

[0036] The acquisition unit 111 acquires, from the database server 101, the lap times for each running activity among a plurality of running activities performed by the user to be predicted. Fig. 3 shows an example of the lap times stored in the database server 101. In Fig. 3, the lap time table TB10A shows the lap times of the user to be predicted (User A), and the tables TB10B and TB10C show the lap times of the reference users (User B and User C). Hereinafter, when there is no need for particular distinction, these lap time tables will be simply referred to as TB10. The lap time table TB10 may record the lap time measured in the running activity for an identifier (activity ID (Identifier)) that identifies one running activity. In the example of the lap time table TB10, User A performed a running activity of "7001m" on "May 1, 2023", and the lap times every 1000m are recorded. Also, User A performed a running activity of "4001m" on "May 4, 2023", and the lap times every 1000m are recorded. These data may be calculated based on the data measured by the user terminal 200 and the sensor devices 301 and 302.

[0037] The calculation unit 112 calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the correspondence between the lap time and the running time and running ability corresponding to the running distance. That is, the calculation unit 112 functions as a running ability calculation unit. Here, the "correspondence between the running time corresponding to the running distance and the running ability" may be, for example, a data set according to a calculation table based on VDOT proposed by Jack Daniels (hereinafter also referred to as the "Daniels' data set"). VDOT is a pseudo VO2max (maximum oxygen uptake) and is an index indicating the current running power level of the user.

[0038] FIG. 4(b) shows an example of a data set indicating the correspondence between running time and running ability according to the running distance. From Table TB20, theoretically, if the running ability (VDOT) is "30", 1500 m can be completed in "8 minutes and 30 seconds", and a full marathon (42.195 km) can be completed in "4 hours, 49 minutes and 17 seconds". Also, if the running ability is "32", 3000 m can be completed in "16 minutes and 59 seconds", and a full marathon can be completed in "4 hours, 34 minutes and 59 seconds". In one aspect of the present invention, VDOT is obtained as the running ability and converted into an AT pace or a completion time. Note that the numerical values surrounded by the broken line in Table TB20 will be described later.

[0039] Next, with reference to FIG. 4, the processing of the calculation unit 112 will be described. The table TB10A' in FIG. 4(a) is a data set of the lap times of a single running activity identified by the activity ID "act_01" in the lap time table TB10A of the prediction target user (user A) shown in FIG. 3. Based on the data in the table TB10A', the calculation unit 112 calculates the respective fastest paces when running 1500 m, 3000 m, and 5000 m as the predetermined running distances. Here, 1500 m, 3000 m, and 5000 m are the running distances for which the correspondence with the running ability is shown in the above-described Daniels data set. In the example of the table TB10A', since the total running distance is 7001 m, the fastest paces up to 5000 m are calculated. However, if the total running distance is 10 km or more, the fastest pace for 10 km may be calculated.

[0040] Here, the respective fastest paces when running 1500 m, 3000 m, and 5000 m were "8 minutes and 29 seconds", "17 minutes and 9 seconds", and "30 minutes and 77 seconds", as shown in the table TB11 of FIG. 4(c). The calculation unit 112 compares the fastest pace with Daniels' dataset, and for each running distance, takes the running ability in which the pace closest to the fastest pace is stored as the user's running ability when running that running distance. In the examples of FIGS. 4(a) and 4(b), the running ability "30" in which the pace "8 minutes and 30 seconds", which is closest to the fastest pace "8 minutes and 29 seconds" when running 1500 m, is stored may be calculated as the running ability of user A when running 1500 m. Similarly, for 3000 m and 5000 m, the running abilities shown in the table TB11 of FIG. 4(c) are calculated.

[0041] The determination unit 113 determines a representative value of the running ability for each running activity based on a plurality of running abilities. For example, the determination unit 113 may determine the maximum value among the plurality of running abilities calculated by the calculation unit 112 as the representative value. In the example of the table TB11 in FIG. 4(c), the representative value of the running ability of the running activity identified by the activity ID "act_01" is "32". The database server 101 stores the running distance and the running ability in the running activity when the representative value is determined in association with each other. In the case of the example in FIG. 4, the representative value "32" of the running ability of the running activity identified by the activity ID "act_01" and the total running distance "7001 m" may be associated and stored as the table TB12. Since the calculation unit 112 and the determination unit 113 perform the above-described processing for each running activity identified by the activity ID, a dataset composed of the representative value of the running ability and the total running distance is generated for the number of running activities.

[0042] The generation unit 114 generates a regression model with the representative value as the target variable and the running distance in the running activity when the representative value is determined as the explanatory variable. This will be described with reference to FIG. 5. FIG. 5 is an example of a graph with the running distance (total running distance) on the horizontal axis and the representative value of the running ability on the vertical axis, plotting the data for each running activity collected from one user. Based on these data sets, the generation unit 114 may generate a regression curve shown by the solid line in the figure as a regression model using an existing statistical method such as the least squares method. For example, when the regression curve is represented by y(x) = β1 logx + β2, the generation unit 114 obtains the coefficients β1 and β2. Note that the regression curve is not limited to this. Also, a regularization term (penalty term) may be included to ensure robustness. Alternatively, the generation unit 114 may generate a regression model by machine learning using the data sets. Also, as the explanatory variable, not only the running distance but also a plurality of parameters may be used.

[0043] The prediction unit 115 predicts the running ability of the user to be predicted when running a predetermined distance based on the regression model generated by the generation unit 114. For example, when the predetermined distance is 42.195 km of a full marathon, the prediction unit 115 may substitute x = 42.195 into the generated regression curve y(x) to predict the running ability.

[0044] <Server control flowchart> The control method of the server 100 described above will be described with reference to the flowchart of FIG. 6. First, the first storage unit stores the correspondence between the running time corresponding to the running distance and the running ability (step S11). The correspondence may be the above-described Daniels' dataset shown in the table TB20 of FIG. 4(b). Also, the second storage unit stores the lap time of each running activity for a plurality of running activities performed by the user to be predicted (step S12). The lap time stored in the second storage unit may be the one stored in the lap time table TB10 of FIG. 3 described above. Note that the first storage unit and the second storage unit may be the database server 101. The acquisition unit 111 may acquire these data stored in the first storage unit and the second storage unit as needed and use them for prediction processing. The calculation unit 112 calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence (step S13). The determination unit 113 determines a representative value of the running ability for each running activity based on the plurality of running abilities (step S14). The generation unit 114 generates a regression model having the representative value as the objective variable and the running distance in the running activity when the representative value is determined as the explanatory variable (step S15). The prediction unit 115 predicts the running ability when the user to be predicted runs a predetermined distance based on the regression model (step S16).

[0045] As described above, according to one aspect of the present invention, without the need to measure biological information such as the user's VO2max, the running ability when running a predetermined distance is predicted using only the lap time obtained through the user's daily practice. Also, the lap time of the user to be predicted does not have to be data measured at maximum effort, and furthermore, there does not have to be running data for a long distance such as a full marathon. Therefore, the running ability can be predicted more easily. Also, by predicting the running ability, it becomes possible to appropriately distribute the pace during practice and during a race, so that the practice efficiency can be improved.

[0046] In the above description, the determination unit 113 determined the maximum value among the plurality of running capabilities calculated by the calculation unit 112 as the representative value. In this case, it is possible to predict the running ability close to the user's personal best. However, the representative value is not limited to the maximum value, and may be the median, average value, minimum value, mode, etc. When the median or mode is used as the representative value, if there is a bias in the distribution of user data in the graph 30, the prediction will be closer to the actual measurement. Also, when the minimum value is used as the representative value, the prediction may be lower than the actual measurement.

[0047] Also, each time the user runs, the lap time for each run may be measured and the lap time table TB10 may be updated (accumulated). Then, a prediction may be made by further adding the newly accumulated lap time. Thereby, the prediction accuracy can be improved.

[0048] <Display of Prediction Results> The output unit 117 may cause the user terminal 200 to display information regarding the time required to travel a predetermined distance. That is, based on the prediction result when traveling a predetermined distance, the output unit 117 may cause the user terminal 200 to display information regarding a practice menu recommended to the user to complete the predetermined distance, an AT pace, VO2max, a target time, a predicted completion time, etc. FIGS. 7(a) and (b) show an example of a prediction result screen displayed on the user terminal 200. Note that the figures are merely examples, and the displayed mode and language are not limited thereto. The screen 10 in FIG. 7(a) is an initial prediction result after starting practice, and as the practice menu 11, "10 km pace run at 5:30 / km" is displayed. Further, on the screen 10, as information regarding the running ability predicted at the current time, for example, a predicted time 12 required to complete the target competition is displayed. Here, since running data has not been accumulated at the initial stage of starting practice and the prediction accuracy is low, it may be shown that the predicted time 12 includes an error of plus or minus 20 minutes. Next, the screen 20 in FIG. 7(b) is such that the user has continued practice for six months and the running ability has improved, and as the practice menu 21, a more demanding "10 km pace run at 4:30 / km" is displayed. Also, the predicted time 22 is predicted earlier than the predicted time 12 at the initial stage of starting practice, and since the user's running data has been accumulated, the prediction accuracy has improved to about plus or minus 10 minutes in terms of error.

[0049] As described above, according to one aspect of the present invention, since information regarding the training ability expected to be obtained by the user is provided to the user, an effect of maintaining the user's motivation can be expected.

[0050] <Correction process> According to one aspect of the present invention, the prediction result of the prediction target user may be corrected based on data obtained from a plurality of running activities performed by a reference user having running data when running a predetermined distance in the past. As shown in FIG. 3, the database server 101 further stores the lap times of running for each running activity (lap time tables TB10B and TB10C) for a plurality of running activities performed by a reference user having running data when running a predetermined distance in the past. The calculation unit 112, the determination unit 113, the generation unit 114, and the prediction unit 115 predict the running ability of the reference user when running a predetermined distance by the same process as the above-described prediction target user based on the lap time of the reference user's running. The correction unit 116 corrects the prediction result of the running ability of the prediction target user by the prediction unit 115 using the prediction result of the running ability of the reference user and the actual running ability of the reference user based on the running data when the reference user ran a predetermined distance in the past.

[0051] Here, the actual running ability may be the running ability corresponding to the completion time when the reference user runs a predetermined distance with reference to the above-described Daniels' dataset. The correction unit 116 corrects the prediction result of the running ability of the prediction target user using the difference (prediction error) between the prediction result of the running ability of the reference user predicted by the prediction unit 115 and the actual running ability. At this time, a correction term that minimizes the sum of the prediction errors calculated for a plurality of reference users is calculated and applied to the correction of the prediction result of the prediction target user.

[0052] As described above, according to one aspect of the present invention, data of a reference user who has actually run a predetermined distance and whose actual running ability is known is used to correct the prediction result of the prediction target user. Therefore, it is possible to improve the prediction accuracy even for a prediction target user who has not actually run a predetermined distance.

[0053] Although the present invention has been described based on the drawings and embodiments, it should be noted that those skilled in the art can easily make various modifications and corrections based on the present disclosure. Therefore, it should be noted that these modifications and corrections are included in the scope of the present invention. For example, the functions included in each means, each step, etc. can be rearranged so as not to be logically contradictory, and a plurality of means, steps, etc. can be combined into one or divided. Also, the configurations shown in the above embodiments may be appropriately combined. For example, each component described as being provided in the server 100 may be realized by being distributed among a plurality of servers. Also, the processing described as the function of the server 100 may be performed by the user terminal 200, the sensor devices 301, 302. Conversely, the processing assumed to be performed by the user terminal 200 may be performed by the server 100, the sensor devices 301, 302.

[0054] For example, in the above, the case of predicting the running ability has been described. However, the prediction target is not limited to this, and for example, it may be lap time, AT value, full marathon completion time, etc.

[0055] Each functional unit of the server 100 or the information processing apparatus 100 may be realized by a logic circuit (hardware) formed in an integrated circuit (IC (Integrated Circuit) chip, LSI (Large Scale Integration)), etc. or a dedicated circuit, or may be realized by software using a CPU (Central Processing Unit). Also, each functional unit may be realized by one or a plurality of integrated circuits, and the functions of a plurality of functional units may be realized by one integrated circuit.

[0056] The program of each embodiment of the present disclosure may be provided in a state stored in a storage medium readable by an information processing apparatus. The storage medium can store the program in a "non-transitory tangible medium". The program includes, for example, a software program or an information processing apparatus program. When each functional unit of the information processing apparatus 100 is realized by software, the information processing apparatus 100 functions as an acquisition unit 111, a calculation unit 112, a determination unit 113, a generation unit 114, a prediction unit 115, a correction unit 116, and an output unit 117 by executing the program loaded onto the memory by the processor.

[0057] When appropriate, the storage medium can include one or more semiconductor-based or other integrated circuits (ICs) (e.g., field programmable gate arrays (FPGAs), application specific ICs (ASICs), etc.), hard disk drives (HDDs), hybrid hard drives (HHDs), optical disks, optical disk drives (ODDs), magneto-optical disks, magneto-optical drives, floppy disks, floppy disk drives (FDDs), magnetic tapes, solid state drives (SSDs), RAM drives, secure digital cards or drives, any other suitable storage medium, or any suitable combination of two or more of these. When appropriate, the storage medium can be volatile, non-volatile, or a combination of volatile and non-volatile.

[0058] Also, the program of the present disclosure may be provided to the information processing apparatus 100 via any transmission medium (such as a communication network or a broadcast wave) capable of transmitting the program.

[0059] Also, each embodiment of the present disclosure can also be realized in the form of a data signal embedded in a carrier wave in which the program is embodied by electronic transmission. Note that the program of the present disclosure may be implemented using, for example, script languages such as JavaScript (registered trademark), Python, C language, Go language, Swift, Kotlin, Java (registered trademark), etc.

[0060] It is understood by those skilled in the art that the above-described embodiments are specific examples of the following aspects. [1] The information processing apparatus of the present disclosure includes a first storage unit that stores a correspondence relationship between a running time corresponding to a running distance and a running ability, a second storage unit that stores a lap time of running for each running activity for a plurality of running activities performed by a user to be predicted, a running ability calculation unit that calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence relationship, a determination unit that determines a representative value of the running ability for each running activity based on the plurality of running abilities, a generation unit that generates a regression model having the representative value as an objective variable and a running distance in the running activity when the representative value is determined as an explanatory variable, and a prediction unit that predicts a running ability when the user to be predicted runs a predetermined distance based on the regression model. [2] In the information processing apparatus of the above [1], the second storage unit further stores a lap time of running for each running activity for a plurality of running activities performed by a reference user having running data when the predetermined distance was run in the past, the running ability calculation unit calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity of the reference user based on the lap time of the reference user and the correspondence relationship, the determination unit determines a representative value of the running ability for each running activity of the reference user based on the plurality of running abilities of the reference user, the generation unit generates a regression model having the representative value of the reference user as an objective variable and a running distance in the running activity when the representative value is determined as an explanatory variable, the prediction unit predicts a running ability when the reference user runs a predetermined distance based on the regression model of the reference user, and a correction unit that corrects the prediction result of the running ability of the user to be predicted by the prediction unit using the prediction result of the running ability of the reference user and the actual running ability of the reference user based on the running data when the reference user ran the predetermined distance in the past may be further provided. [3] In the information processing apparatus described in [1] above, the determination unit may determine the maximum value among the plurality of running capabilities calculated by the running capability calculation unit as the representative value. [4] In the information processing apparatus described in [1] above, the determination unit may determine the median value among the plurality of running capabilities calculated by the running capability calculation unit as the representative value. [5] In the information processing apparatus described in [1] above, the determination unit may determine the average value among the plurality of running capabilities calculated by the running capability calculation unit as the representative value. [6] The information processing apparatus described in [1] above may further include an output unit that causes a terminal to display information regarding the time required to travel the predetermined distance, which is converted from the running capability of the prediction target user corrected by the correction unit. [7] The control method of the present disclosure includes steps in which an information processing apparatus stores a correspondence relationship between a running time corresponding to a running distance and a running capability in a first storage unit, stores a lap time of running for each running activity for a plurality of running activities performed by a prediction target user in a second storage unit, calculates a plurality of running capabilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence relationship, determines a representative value of the running capability for each running activity based on the plurality of running capabilities, generates a regression model having the representative value as an objective variable and the running distance in the running activity when the representative value is determined as an explanatory variable, and predicts the running capability when the prediction target user travels a predetermined distance based on the regression model. [8] In an information processing system including the information processing apparatus of the present disclosure and the terminal of the user to be predicted, the information processing apparatus includes: a first storage unit that stores a correspondence relationship between a running time corresponding to a running distance and a running ability; a second storage unit that stores, for a plurality of running activities performed by the user to be predicted, a lap time of running for each running activity acquired from the terminal of the user to be predicted; a running ability calculation unit that calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence relationship; a determination unit that determines a representative value of the running ability for each running activity based on the plurality of running abilities; a generation unit that generates a regression model having the representative value as an objective variable and the running distance in the running activity when the representative value is determined as an explanatory variable; and a prediction unit that predicts the running ability when the user to be predicted runs a predetermined distance based on the regression model.

Explanation of Signs

[0061] 100 Server (information processing apparatus) 110 Control unit 111 Acquisition unit 112 Calculation unit 113 Determination unit 114 Generation unit 115 Prediction unit 116 Correction unit 117 Output unit 120 Communication unit 130 Display unit 170 Storage unit 101 Database server 200 User terminal (communication terminal) 210 Control unit 211 Position information acquisition unit 212 Conversion unit 220 Communication unit 230 Display unit 240 Input / output unit 270 Storage unit 301 Sensor device 302 Sensor device 500 Network 600 Practice Support System

Claims

1. A first storage unit that stores a correspondence relationship between running time and running ability according to running distance; A second storage unit that stores the lap time of running for each running activity for a plurality of running activities performed by a user to be predicted; A running ability calculation unit that calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence relationship; A determination unit that determines a representative value of the running ability for each running activity based on the plurality of running abilities; A generation unit that generates a regression model having the representative value as an objective variable and the running distance in the running activity when the representative value is determined as an explanatory variable; A prediction unit that predicts the running ability when the user to be predicted runs a predetermined distance based on the regression model; An information processing apparatus comprising:

2. The second storage unit further stores the lap time of running for each running activity for a plurality of running activities performed by a reference user having running data when running the predetermined distance in the past, The running ability calculation unit calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity of the reference user based on the lap time of the reference user and the correspondence relationship, The determination unit determines a representative value of the running ability for each running activity of the reference user based on the plurality of running abilities of the reference user, The generation unit generates a regression model having the representative value of the reference user as an objective variable and the running distance in the running activity when the representative value is determined as an explanatory variable, The prediction unit predicts the running ability when the reference user runs a predetermined distance based on the regression model of the reference user, and uses the prediction result of the running ability of the reference user and the actual running ability of the reference user based on the running data when the reference user ran the predetermined distance in the past. The information processing apparatus according to claim 1, further comprising a correction unit that corrects the prediction result of the running ability of the user to be predicted by the prediction unit. The information processing apparatus according to claim 1.

3. The determination unit determines the maximum value among the plurality of running abilities calculated by the running ability calculation unit as the representative value. The information processing apparatus according to claim 1.

4. The determination unit determines the median value among the plurality of running abilities calculated by the running ability calculation unit as the representative value. The information processing apparatus according to claim 1.

5. The determination unit determines the average value of the plurality of running abilities calculated by the running ability calculation unit as the representative value. The information processing apparatus according to claim 1.

6. The information processing apparatus further includes an output unit that causes a terminal to display information on the time required to travel the predetermined distance, which is converted from the running ability of the prediction target user corrected by the correction unit. The information processing apparatus according to claim 1.

7. An information processing apparatus stores a correspondence relationship between a running time corresponding to a running distance and a running ability in a first storage unit; stores, in a second storage unit, a lap time of running for each running activity for a plurality of running activities performed by a prediction target user; calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence relationship; determines a representative value of the running ability for each running activity based on the plurality of running abilities; generates a regression model having the representative value as an objective variable and the running distance in the running activity when the representative value is determined as an explanatory variable; predicts the running ability when the prediction target user travels a predetermined distance based on the regression model; A control method for an information processing apparatus that executes the above.

8. An information processing system including an information processing apparatus and a terminal of a prediction target user, wherein the information processing apparatus includes a first storage unit that stores a correspondence relationship between a running time corresponding to a running distance and a running ability; a second storage unit that stores, for a plurality of running activities performed by the prediction target user, a lap time of running for each running activity acquired from the terminal of the prediction target user; a running ability calculation unit that calculates a plurality of running abilities corresponding to a predetermined running distance for each running activity based on the lap time and the correspondence relationship; a determination unit that determines a representative value of the running ability for each running activity based on the plurality of running abilities; a generation unit that generates a regression model having the representative value as an objective variable and the running distance in the running activity when the representative value is determined as an explanatory variable; A prediction unit that predicts the running ability of the user to be predicted when running a predetermined distance based on the regression model; An information processing system comprising the same.

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