Performance prediction method, performance prediction system, and performance prediction program for exercise product

Abstract

A performance prediction method for an exercise product 18 comprises: a process for acquiring product identification information for identifying at least a category to which the exercise product 18, which is used by a user 10, belongs; a process for acquiring use information possibly affecting the performance of the exercise product 18 if the user 10 exercises while using the exercise product 18; a process for predicting a change in the performance of the exercise product 18 on the basis of the product identification information and the use information; a process for determining a notification to be provided to the user 10 on the basis of a prediction result; and a process for outputting the notification on the basis of the determination.

Description

Method, System, and Program for Predicting Performance of Sporting Goods

[0001] The present disclosure relates to a technique for predicting changes in the performance of sporting goods.

[0002] In sports such as athletics, users often use sporting goods such as shoes. Sporting goods are designed according to their purposes and uses. However, these sporting goods generally deteriorate in performance gradually with use. Since the deterioration of performance may lead to a decrease in the performance and safety of sports for users, replacement or maintenance at an appropriate timing is required. Patent Document 1 describes a technique for evaluating the fatigue life of equipment.

[0003] Japanese Patent Application Laid-Open No. 2012-112787

[0004] If it is possible to predict changes in the performance of sporting goods, it is beneficial for users. It is conceivable to judge the performance change from the appearance of sporting goods, but it is difficult to grasp the performance change caused by changes in material properties etc. only from the appearance. Further, the technique of Patent Document 1 targets the fatigue life of equipment and is not a technique for predicting performance changes of sporting goods.

[0005] The present disclosure has been made in view of such problems, and an object thereof is to provide a technique for accurately predicting changes in the performance of sporting goods.

[0006] To solve the above problems, a method for predicting the performance of sporting goods according to an aspect of the present disclosure includes a process of obtaining product identification information for at least identifying the classification to which the sporting goods used by the user belong, a process of obtaining usage information that can affect the performance of the sporting goods when the user exercises using the sporting goods, a process of predicting changes in the performance of the sporting goods based on the product identification information and the usage information, a process of determining a notification to the user based on the result of the prediction, and a process of outputting the notification based on the determination.

[0007] Another aspect of this disclosure is a performance prediction system for sports equipment. This system comprises: a product identification information acquisition unit that acquires product identification information that at least identifies the classification to which the sports equipment used by the user belongs; a usage information acquisition unit that acquires usage information that may affect the performance of the sports equipment when the user uses the sports equipment for exercise; a prediction unit that predicts changes in the performance of the sports equipment based on the product identification information and the usage information; a decision unit that decides to notify the user based on the prediction results from the prediction unit; and an output unit that outputs a notification based on the decision from the decision unit.

[0008] Another aspect of this disclosure is a performance prediction program for sports equipment. This program enables a computer to perform the following functions: acquire product identification information that at least identifies the classification to which the sports equipment used by the user belongs; acquire usage information that may affect the performance of the sports equipment when the user exercises using the sports equipment; predict changes in the performance of the sports equipment based on the product identification information and usage information; determine a notification to the user based on the prediction result; and output a notification based on the determination.

[0009] Furthermore, any combination of the above components, or any substitution of the components or expressions of this disclosure between methods, apparatus, programs, temporary or non-temporary storage media storing programs, systems, etc., are also valid forms of this disclosure.

[0010] According to this disclosure, it is possible to accurately predict changes in the performance of sporting goods.

[0011] Figure 1 is a diagram showing the configuration of a performance prediction system for sporting goods. Figure 2 is a functional block diagram showing each configuration of the performance prediction system according to the first embodiment. Figure 3 is a functional block diagram showing each function of the performance prediction server. Figure 4 is a diagram showing an example of the time change of characteristic indicators of sporting goods predicted by the prediction unit of Figure 3. Figure 5 is a diagram showing another example of the time change of characteristic indicators of sporting goods predicted by the prediction unit of Figure 3. Figure 6 is a flowchart schematically showing an example of the performance prediction processing process in the performance prediction server of Figure 2. Figure 7 is a functional block diagram showing each configuration of the performance prediction system according to the second embodiment. Figure 7 is a functional block diagram showing each function of the performance prediction server. Figure 9 is a schematic side view showing an example of shoes as sporting goods used in the second embodiment. Figure 9 is a schematic diagram showing an example of measuring midsole characteristic information. Figure 9 is a graph showing an example of midsole characteristic information. Figure 10 is a schematic side view showing a first modified example of the measurement device. Figure 10 is a schematic side view showing a second modified example of the measurement device. Figure 9 is a schematic diagram showing a part of the first modified example of the shoes in Figure 9. Figure 9 is a schematic diagram showing a part of the second modified example of the shoes in Figure 9. Figure 8 is a flowchart illustrating an example of the performance prediction process in the performance prediction server.

[0012] The present disclosure will be described below with reference to the drawings, based on preferred embodiments. In embodiments and modifications, the same or equivalent components will be denoted by the same reference numerals, and redundant descriptions will be omitted as appropriate.

[0013] [First Embodiment] Figure 1 shows the configuration of the performance prediction system 100 for sports equipment 18. Sports equipment 18 is an item that the user 10 mainly wears and uses during exercise, such as shoes, functional wear, and other sports equipment. Other sports equipment includes equipment used for specific sports, such as baseball gloves and bats, tennis rackets, table tennis rackets, golf clubs and gloves, and boxing gloves. In the following, running shoes will be used as an example of sports equipment 18. Unless otherwise specified, it will be assumed that the user 10 is using sports equipment 18 when exercising.

[0014] The performance prediction system 100 according to the first embodiment includes, as an example, a wristwatch-type device 12 that can be worn by a user 10 who is an exerciser, such as running or walking, a waist-worn device 14, an information terminal-type device 16, and a performance prediction server 60. The wristwatch-type device 12, the waist-worn device 14, and the information terminal-type device 16 are collectively referred to as the measuring device 20. As will be described later, the performance prediction system 100 does not need to include all of the wristwatch-type device 12, the waist-worn device 14, the information terminal-type device 16, and the performance prediction server 60.

[0015] The wristwatch-type device 12 is a sports watch or smartwatch that acquires operational information and environmental information. The wristwatch-type device 12 includes sensors such as a positioning module, motion sensor, heart rate sensor, and barometer, and acquires information such as date and time, position coordinates, altitude, heart rate, temperature, and pitch. The motion sensor basically consists of an inertial sensor that combines an accelerometer and a gyroscope. The waist-worn device 14 is an electronic device that is worn near the user's waist to acquire operational information and environmental information. The information terminal-type device 16 is a portable information terminal such as a smartphone that is held by the user 10 in their pocket or elsewhere to acquire operational information and environmental information.

[0016] User 10 wears one or more measuring devices 20 and performs exercises such as running during races or training, such as marathons, using exercise equipment 18 to acquire motion information and environmental information. If multiple measuring devices 20 are worn, environmental information may be acquired with a wristwatch-type device 12 and motion information with a waist-mounted device 14, for example, by using different devices depending on the information to be acquired.

[0017] The measuring device 20 is not limited to devices such as a wristwatch-type device 12, a waist-worn device 14, or an information terminal-type device 16, but may also be a device attached to the surface or inside of the sports equipment 18. Alternatively, it may be a belt-type device that can be worn around the user 10's chest, wrist, waist, or arm to acquire environmental information, movement information, and heart rate information.

[0018] Information such as distance traveled, time, heart rate, cadence, and stride measured by the measuring device 20 is displayed on the screens of the wristwatch-type device 12 and the information terminal-type device 16 during exercise. The user 10 can check their exercise status by looking at the screens of the wristwatch-type device 12 and the information terminal-type device 16 during exercise.

[0019] The performance prediction system 100 can be implemented with various hardware and software configurations. For example, the performance prediction system 100 may consist of only one of the following devices: a wristwatch-type device 12, a waist-worn device 14, an information terminal 50, or a performance prediction server 60, or it may consist of a combination of two or more of these devices. For example, the performance prediction system 100 does not need to include measuring devices 20 such as a wristwatch-type device 12, a waist-worn device 14, or an information terminal-type device 16. In this case, for example, the user 10 may use an information terminal 50 such as a personal computer to input the user's physical information, movement information, environmental information, etc., as usage information. Details of the user's physical information, movement information, environmental information, and usage information will be described later. When the performance prediction system 100 consists of only one device, such a device is also called a performance prediction device.

[0020] Furthermore, for example, the performance prediction system 100 may be implemented by an application that runs on any one of the following devices: the wristwatch-type device 12, the waist-worn device 14, the information terminal 50, or the performance prediction server 60; or it may be implemented by an application that runs on a combination of any two or more of these devices; and the application that runs may be one or more. For example, the performance prediction system 100 may be implemented by a combination of an existing exercise management application and an application prepared for this embodiment.

[0021] For example, the performance prediction system 100 may consist of only one of the information terminal 50 and the performance prediction server 60, or a combination of both. Alternatively, it may be implemented as a single device that includes all the software configurations included in the information terminal 50 and the performance prediction server 60 shown in this diagram. Therefore, regardless of its hardware configuration, the performance prediction system 100 only needs to include at least the software configurations of the information terminal 50 and the performance prediction server 60 shown in this diagram.

[0022] If the performance prediction system 100 includes a measuring device 20, the user 10 exercises while wearing at least one or all of the following devices as the measuring device 20: a wristwatch-type device 12, a waist-worn device 14, and an information terminal-type device 16. The measuring device 20 transmits information to the performance prediction server 60 via communication and receives notifications based on prediction results from the performance prediction server 60 via communication. However, since the communication means of the wristwatch-type device 12 and the waist-worn device 14 of the measuring device 20 is short-range wireless communication, they do not communicate directly with the performance prediction server 60, but rather synchronize information with the information terminal-type device 16 (which also functions as an "information terminal 50" as will be detailed later), and the information terminal 50 sends and receives information with the performance prediction server 60. Thus, the wristwatch-type device 12 and the waist-worn device 14 send and receive information with the performance prediction server 60 via synchronization with the information terminal 50, and therefore assume that the user possesses an information terminal 50. However, it is not necessary to wear the information terminal 50 during exercise; synchronization with the information terminal 50 may occur after the exercise is completed. As an alternative, the waist-worn device 14 may first synchronize information with the wristwatch-type device 12 via short-range wireless communication, and the wristwatch-type device 12 may then further synchronize information with the information terminal-type device 16 (information terminal 50) via short-range wireless communication.

[0023] The information terminal 50 may be an information terminal such as a smartphone or tablet, or it may be a personal computer. The performance prediction server 60 is a server computer connected to the internet that sends and receives data with multiple user information terminals 50 10.

[0024] The information terminal 50 and the performance prediction server 60 may be composed of a computer consisting of a CPU (Central Processing Unit), GPU (Graphics Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), auxiliary storage device, communication device, etc. The information terminal 50 and the performance prediction server 60 may each be composed of separate computers, or they may be implemented in a single computer or information terminal that combines the functions of both. In this embodiment, an example in which they are implemented in separate computers will be described.

[0025] Figure 2 is a functional block diagram showing the various components of the performance prediction system 100 according to the first embodiment. In Figure 2, functional blocks are depicted for the measuring device 20 and the information terminal 50, each realized through the cooperation of various hardware and software configurations. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various ways using hardware alone, software alone, or a combination thereof. The measuring device 20 is composed of a combination of hardware such as a microprocessor, display device, memory, communication module, positioning module, motion sensor, and optical heart rate monitor. The information terminal 50 is composed of a combination of hardware such as a microprocessor, touch panel, memory, communication module, positioning module, and motion sensor. The description in this paragraph is the same for the measuring device 20 and information terminal 50 of the second embodiment, which will be described later with reference to Figure 7. The functions of the measuring device 20 and information terminal 50 will be described below.

[0026] The measuring device 20 includes a communication unit 21, a time measurement unit 22, a position measurement unit 24, a motion detection unit 26, an environmental measurement unit 27, and a calculation unit 28. For example, a waist-worn device 14 is used as the measuring device 20. The time measurement unit 22 measures the exercise start time, i.e., the exercise time from the measurement start time, by counting a timer. The position measurement unit 24 measures the current position of the user 10, who is the exerciser, by position information received from a satellite positioning system by a positioning module such as a GPS module. The motion detection unit 26 detects motion information such as the user 10's running pitch (number of steps per unit time, also called cadence), pelvic rotation and translational movement, or impact value using a motion sensor. The environmental measurement unit 27 acquires environmental information such as temperature, humidity, and atmospheric pressure when the user 10 exercises using the exercise equipment 18, using a temperature sensor, humidity sensor, atmospheric pressure sensor, etc.

[0027] The calculation unit 28 may calculate new motion information and environmental information based on exercise time, location information, motion information, and environmental information. For example, the calculation unit 28 may calculate new motion information such as running time, running distance, running speed, stride, ground contact pattern, exercise frequency, running form, and balance. For example, the calculation unit 28 may calculate new environmental information such as the road surface type, slope, and altitude during running. Alternatively, the information terminal 50 may calculate the above-mentioned new motion information and environmental information instead of the calculation unit 28.

[0028] A wristwatch-type device 12 or an information terminal-type device 16 can also be used as the measuring device 20. When a wristwatch-type device 12 is used as the measuring device 20, the motion detection unit 26 of the wristwatch-type device 12 detects motion information such as the user 10's pitch using a motion sensor and detects physiological indicator information such as heart rate using an optical heart rate monitor. Physiological indicator information may also be included in the motion information. When an information terminal-type device 16 is used as the measuring device 20, the motion detection unit 26 of the information terminal-type device 16 detects motion information such as the user 10's pitch using a motion sensor. The information terminal 50 may also function as the information terminal-type device 16 used as the measuring device 20; in this case, for example, a single mobile terminal such as a smartphone may have all the functions of both the measuring device 20 and the information terminal 50. When the information terminal 50 is not used as the measuring device 20, the information terminal 50 is not limited to a smartphone; it may also be a tablet terminal or personal computer owned by the user 10.

[0029] The information terminal 50 includes an acquisition unit 30, an imaging unit 40, an input / output unit 51, and a communication unit 52. The acquisition unit 30 receives information such as the user 10's movements and environmental information during exercise from a measuring device 20 worn by the user 10, via the communication unit 52. The acquisition unit 30 may synchronize information with the measuring device 20 and acquire information from the measuring device 20 during the user 10's exercise, or it may acquire all the information from the measuring device 20 during the exercise after the user 10 has finished exercising. The acquisition unit 30 may acquire information similar to that of the acquisition unit 64 of the performance prediction server 60, which will be described later.

[0030] The imaging unit 40 acquires images of the sports equipment 18 after it has been used by the user 10 for at least a certain period of time, based on input operations by the user 10. The imaging unit 40 may be configured in hardware form, for example, as a camera module. The images acquired by the imaging unit 40 are acquired by the acquisition unit 30 and transmitted to the performance prediction server 60 via the communication unit 52.

[0031] The input / output unit 51 transmits the operation information and environmental information acquired by the measuring device 20, along with various information based on user 10's input, to the performance prediction server 60 via the communication unit 52. The input / output unit 51 outputs notification information based on the prediction results received from the performance prediction server 60. The input / output unit 51 accepts input from user 10. Hardware-wise, the input / output unit 51 may consist of a touch panel, speaker, microphone, etc. The measuring device 20 or information terminal 50 may also acquire weather information such as temperature, humidity, weather, wind direction, and wind speed corresponding to the time of user 10's exercise from a predetermined server, and transmit that weather information to the performance prediction server 60 along with the environmental information.

[0032] Figure 3 is a functional block diagram showing the various functions of the performance prediction server 60. It depicts functional blocks that are realized through the coordination of various hardware and software configurations for the performance prediction server 60. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various ways using hardware alone, software alone, or a combination thereof. The performance prediction server 60 is composed of a combination of hardware such as a microprocessor, memory, display, and communication module. The explanation in this paragraph is the same for the performance prediction server 160 of the second embodiment, which will be described later with reference to Figure 8.

[0033] The performance prediction server 60 comprises a communication unit 62, an acquisition unit 64, a prediction unit 80, a determination unit 90, and an output unit 99. The acquisition unit 64 includes a product identification information acquisition unit 66 and a usage information acquisition unit 68.

[0034] The product identification information acquisition unit 66 acquires product identification information from the information terminal 50 via the communication unit 62. The product identification information is information that identifies at least the classification to which the sports equipment 18 used by the user 10 belongs. The product identification information may also include information on the size of the sports equipment 18. Depending on the size of the sports equipment 18, there may be differences in the changes in the performance of the sports equipment 18. For example, if the sports equipment 18 is shoes, even if the user 10's weight is the same, the smaller the shoe size, the more likely the cushioning is to deteriorate.

[0035] Product identification information may include the product name of the sports equipment 18. The product name may also be a model number. Depending on the product name of the sports equipment 18, the materials, shape, arrangement, etc. that make up the sports equipment 18 may differ, which may result in differences in the performance of the sports equipment 18. Product identification information may also include manufacturing information of the sports equipment 18. The manufacturing information of the sports equipment 18 may be, for example, a lot number. The manufacturing information of the sports equipment 18 may include information similar to the product name of the sports equipment 18. The manufacturing information of the sports equipment 18 may further include information such as the manufacturing date and production line of the sports equipment 18, and this information may also affect the performance of the sports equipment 18.

[0036] The product identification information acquisition unit 66 does not necessarily have to acquire product identification information from the information terminal 50. Product identification information may be stored in advance within the performance prediction server 60, or it may be acquired from a server other than the performance prediction server 60. For example, the product identification information acquisition unit 66 may acquire product identification information via the communication unit 62 from a product management server (not shown) that manages the sports equipment 18 owned by the user 10. The product identification information acquisition unit 66 may store the acquired product identification information in association with the user 10's identification information.

[0037] The usage information acquisition unit 68 acquires usage information regarding the sports equipment 18 from the information terminal 50 via the communication unit 62. The usage information is information that may affect the performance of the sports equipment 18 when the user 10 uses the sports equipment 18 for exercise.

[0038] The usage information includes, for example, the physical information of user 10. The physical information of user 10 includes information such as user 10's height, weight, foot shape, gender, age or date of birth, place of residence, and race. For example, weight has a high correlation with changes in cushioning and outsole wear among the characteristics of the shoes as athletic equipment 18. For example, foot shape has a high correlation with changes in fit among the characteristics of the shoes as athletic equipment 18. For example, height, gender, age, and race have a high correlation with running motion information such as pitch, stride, and form. The usage information acquisition unit 68 may acquire user 10's physical information from the information terminal 50, from a predetermined server such as the product management server mentioned above, or it may be stored in advance by the performance prediction server 60.

[0039] The usage information includes, for example, information about the user 10's movements during exercise. As the information about movements is as described above, its explanation is omitted. Among the movement information, for example, running time, running distance, and exercise frequency are highly correlated with changes in the overall characteristics of the shoes as athletic equipment 18. Among the movement information, for example, ground contact pattern, running form, and balance are highly correlated with the front-to-back and left-to-right balance in changes to each of the characteristics of the shoes as athletic equipment 18. The usage information acquisition unit 68 acquires the user 10's movement information during exercise from the information terminal 50.

[0040] The usage information includes environmental information when user 10 exercises using the sports equipment 18. The environmental information is as described above, so its explanation is omitted. Among the environmental information, for example, temperature, humidity, and weather information have a high correlation with the deterioration of the material of the shoes as sports equipment 18, and a high correlation with changes in cushioning, rebound, etc., due to the deterioration of the material. Among the environmental information, for example, the type of road surface and incline during running have a high correlation with the balance between front and back and left and right, the degree of wear of the outsole, etc., in changes to each characteristic of the shoes as sports equipment 18. The usage information acquisition unit 68 acquires the environmental information from the information terminal 50 or a predetermined server.

[0041] The prediction unit 80 predicts changes in the performance of the sports equipment 18 based on product identification information and usage information. Based on product identification information and usage information, the prediction unit 80 predicts, for example, the change over time of a characteristic index for at least one characteristic of the sports equipment 18. If the sports equipment 18 is a shoe, the characteristics of the sports equipment 18 may be, for example, cushioning, fit, rebound, and the degree of wear of the outsole. The balance between front, back, left, and right in the changes of each of these characteristics can also be considered one of the characteristics of the sports equipment 18. The prediction unit 80 may also predict the change over time of characteristic indexes for each part that makes up the sports equipment 18. If the sports equipment 18 is a shoe, the characteristic indexes for each part may be, for example, in addition to the degree of wear of the outsole as described above, the cushioning and rebound of the midsole and the fit of the upper.

[0042] The decision unit 90 decides to notify the user 10 based on the prediction results from the prediction unit 80. The decision unit 90 may, for example, decide to notify when a characteristic index predicted by the prediction unit 80 to change over time falls below a predetermined threshold. As described above, the prediction unit 80 predicts the change over time of a characteristic index for at least one characteristic of the sports equipment 18, so the decision unit 90 may decide to notify when one predetermined characteristic index falls below a predetermined threshold, or when all of a predetermined set of multiple characteristic indexes fall below a predetermined threshold set for each of them. As a result, the decision unit 90 can, for example, decide to notify if a characteristic that particularly affects the performance of the sports equipment 18 falls below a certain level, even if other characteristics have not decreased. Therefore, the decision unit 90 can decide to notify the user of a decrease in the performance of the sports equipment 18 at an appropriate time.

[0043] The decision unit 90 may, for example, calculate a comprehensive index for evaluating the performance of the sports equipment 18 by combining characteristic indices for multiple characteristics, and decide to issue a notification when this comprehensive index falls below a predetermined threshold. The characteristic indices for multiple characteristics included in the comprehensive index may each be weighted differently. This allows for the decision to issue a notification of a decline in the performance of the sports equipment 18 using a comprehensive index that appropriately evaluates the performance of the sports equipment 18.

[0044] Based on the determination by the determination unit 90, the output unit 99 outputs a notification to the information terminal 50 via the communication unit 62. The information terminal 50 that has received the notification notifies the user 10 via the input / output unit 51. Thereby, the performance prediction system 100 can notify the user 10 based on the change in the performance of the sports equipment 18.

[0045] As shown in FIG. 3, the performance prediction system 100 may further include a selection unit 76. The selection unit 76 selects at least one characteristic used by the determination unit 90 for the determination of the notification from a plurality of characteristics of the sports equipment 18 based on at least any one of the product identification information, usage information, and the preference information of the user 10. Here, the preference information of the user 10 may be information on characteristics that the user 10 values. Thereby, since the determination unit 90 can output a notification when a characteristic index that can particularly affect the performance of the sports equipment 18 changes, the change in the performance of the sports equipment 18 can be predicted at an appropriate timing.

[0046] Further, the selection unit 76 may change the threshold value used by the determination unit 90 for the determination of the notification based on at least any one of the product identification information, usage information, and the preference information of the user 10. For example, depending on the type and use of the sports equipment 18, the body type of the user 10, the preference of the user 10, etc., there may be a case where it can be determined that the sports equipment 18 should be replaced as soon as the resilience index of the shoes as the sports equipment 18 decreases even slightly, or conversely, a case where it can be determined that the sports equipment 18 can still be used sufficiently even if the resilience index decreases further. By changing the threshold value used by the determination unit 90 for the determination of the notification as described above, it is possible to flexibly respond to such situations.

[0047] When at least one characteristic index used for the determination of the notification is equal to or less than the first threshold value, and when the at least one characteristic index is equal to or less than a second threshold value smaller than the first threshold value, the determination unit 90 may determine notifications in different manners. Thereby, for example, when a predetermined characteristic index of the sports equipment 18 becomes equal to or less than the first threshold value, the performance prediction system 100 notifies the user 10 that the replacement time of the sports equipment 18 is approaching or that it has become difficult for the sports equipment 18 to exhibit high performance. When the index becomes equal to or less than the second threshold value, the system can notify that the use of the sports equipment 18 is at its limit. For example, when the degree of wear of the outsole is used as the characteristic index of the shoes as the sports equipment 18, the second threshold value can be set at the timing when at least a part of the outsole has disappeared due to wear and the midsole is exposed.

[0048] In this way, since the performance prediction system 100 can output notifications step by step according to the decrease in a specific characteristic index, it can predict the decrease in the performance of the sports equipment 18 at an appropriate timing. Similarly to the above, the selection unit 76 may change the first threshold value and the second threshold value based on at least any one of the product identification information, the usage information, and the preference information of the user 10.

[0049] FIG. 4 is a diagram showing an example of the temporal change of the characteristic index of the sports equipment 18 predicted by the prediction unit 80. In FIG. 4, the horizontal axis represents time, and the vertical axis represents the resilience, which is an example of the characteristic index of the shoes as the sports equipment 18.

[0050] Based on the prediction of the change in the performance of the sports equipment 18 by the prediction unit 80, the determination unit 90 may determine a notification for the future at the current time P. For example, at the current time P, the determination unit 90 may determine notifications that the resilience will be equal to or less than the first threshold value Th1 at the first future time F1 and that the resilience will be equal to or less than the second threshold value Th2 at the second future time F2, respectively. Also, for example, the determination unit 90 has information on a predetermined future time F0 such as the timing when the user 10 participates in a race, and may determine, at the current time P, a notification of information indicating the relationship between the resilience at the future time F0, the first threshold value Th1, and the second threshold value Th2.

[0051] The decision unit 90 may decide to send a notification based on the prediction of changes in the performance of the sporting goods 18 by the prediction unit 80, when the performance falls below a predetermined threshold. For example, the decision unit 90 may decide to send a notification at the timing of a first future time point F1 when the rebound performance falls below a first threshold Th1, and at the timing of a second future time point F2 when the rebound performance falls below a second threshold Th2.

[0052] At least one characteristic used by the decision unit 90 to determine whether to notify may include the suitability of the sports equipment 18 to the user 10's body. The suitability of the sports equipment 18 to the user 10's body is also called the fit of the sports equipment 18. Many characteristics of the sports equipment 18 gradually deteriorate with use. However, the fit of the sports equipment 18 may improve, at least temporarily, as the sports equipment 18 becomes more familiar to the user 10's body. For example, in the case of shoes as sports equipment 18, repeated wear by the user 10 can improve the fit, mainly because the shape of the upper conforms to the shape of the user 10's instep. Therefore, the decision unit 90 may decide to notify when a characteristic index regarding the suitability of the sports equipment 18 to the user 10's body exceeds a predetermined threshold. This allows the performance prediction system 100 to notify the user 10 of an appropriate time to use the sports equipment 18.

[0053] Figure 5 shows another example of the time change of characteristic indicators of the sports equipment 18 predicted by the prediction unit 80. In Figure 5, the horizontal axis represents time, and the vertical axis represents fit, which is an example of a characteristic indicator of shoes as sports equipment 18.

[0054] In the example shown in Figure 5, based on the prediction by the prediction unit 80, the fit temporarily improves and then declines. The decision unit 90 may decide to notify the future at the present time P based on the prediction by the prediction unit 80 of the change in the fit of the sports equipment 18. For example, the decision unit 90 may decide at the present time P to notify that the fit will be above a predetermined threshold Th3 at a third future time F3. Alternatively, for example, the decision unit 90 may decide at the present time P to notify that the fit will peak at a fourth future time F4. Furthermore, for example, the decision unit 90 may have information about a predetermined future time F0 in advance, such as the timing when the user 10 will participate in a race, and may decide at the present time P to notify information showing the relationship between the fit at future time F0 and the threshold Th3.

[0055] The decision unit 90 may decide to send a notification based on the prediction of the change in the fit of the sports equipment 18 by the prediction unit 80, when the fit exceeds a predetermined threshold. For example, the decision unit 90 may decide to send a notification at the third future time point F3 when the fit exceeds the threshold Th3. Alternatively, for example, the decision unit 90 may decide to send a notification at the fourth future time point F4 when the fit reaches its peak.

[0056] Returning to Figure 3, the performance prediction server 60 may further include a product usage acquisition unit 70 and a modification unit 82, both included in the acquisition unit 64. The product usage acquisition unit 70 acquires information about the sports equipment 18 after it has been used by the user 10. The information about the sports equipment 18 after it has been used by the user 10 may be, for example, information obtained by analyzing an image of the sports equipment 18 acquired via the imaging unit 40 of the information terminal 50. The information about the sports equipment 18 after it has been used by the user 10 may also be, for example, information obtained by directly analyzing the sports equipment 18. The information about the sports equipment 18 after it has been used by the user 10 may also be information about the sports equipment 18 after its use by the user 10 has ended, or it may be information about the sports equipment 18 at a predetermined timing while the user 10 is still using it, obtained, for example, by periodically taking images of the sports equipment 18.

[0057] The modification unit 82 modifies at least one of the usage information and the prediction results from the prediction unit 80 based on the information acquired by the usage product acquisition unit 70. When the modification unit 82 modifies the usage information, it mainly modifies the environmental information as usage information. When the modification unit 82 modifies the prediction results, for example, if the characteristic index has not decreased more than predicted by the prediction unit 80 based on the information acquired by the usage product acquisition unit 70, it modifies the characteristic index to improve it, and conversely, if the characteristic index has decreased more than predicted, it modifies the characteristic index to decrease it further. This makes it possible to further improve the accuracy of predicting changes in the performance of the sports equipment 18 based on the information of the sports equipment 18 after use.

[0058] The performance prediction server 60 may further include a storage information acquisition unit 72 included in the acquisition unit 64. The storage information acquisition unit 72 acquires storage information regarding the state in which the sports equipment 18 is stored. The characteristic indicators of the sports equipment 18 can change not only during the period when it is used by the user 10, but also during the period when it is not used by the user 10. In particular, it is thought that the degree of change in the characteristic indicators of the sports equipment 18 will differ depending on the environment in which the sports equipment 18 is stored. Therefore, the prediction unit 80 may predict changes in the performance of the sports equipment 18 based on the storage information. The performance prediction system 100 can further improve the accuracy of predicting changes in the performance of the sports equipment 18 by further incorporating the storage information of the sports equipment 18.

[0059] Storage information may be generated, for example, using a predetermined sensor that acquires information about the sports equipment 18 in storage. For example, a sensor may be installed in a shoe dryer or the like, which is the sports equipment 18, and the information from that sensor may be used. Storage information may also be information based on information that the user 10 inputs to the information terminal 50 via the input / output unit 51.

[0060] The prediction unit 80 may predict temporary changes in the performance of the sports equipment 18 based on at least one of the frequency of use, intensity of use, duration of use, and storage information of the sports equipment 18 over a predetermined period. A temporary change in the performance of the sports equipment 18 may be, for example, a decrease in cushioning due to temporary wear and tear of the shoes, and can be restored by proper storage. The performance prediction system 100 can predict temporary changes in the performance of the sports equipment 18 by providing notifications based on such predictions of positional changes, thereby giving the user 10 an opportunity to review the frequency of use of the sports equipment 18, etc.

[0061] Figure 6 is a flowchart illustrating an example of the performance prediction process in the performance prediction server 60. The product identification information acquisition unit 66 acquires product identification information (S10). The usage information acquisition unit 68 acquires usage information regarding the sports equipment 18 (S12). The used product acquisition unit 70 acquires information about the sports equipment 18 after it has been used by the user 10 (S14). The storage information acquisition unit 72 acquires storage information regarding the state in which the sports equipment 18 is stored (S16). The modification unit 82 modifies the usage information based on the information acquired by the used product acquisition unit 70 (S18). The prediction unit 80 predicts changes in the performance of the sports equipment 18 (S20). The modification unit 82 modifies the prediction result by the prediction unit 80 based on the information acquired by the used product acquisition unit 70 (S22). The selection unit 76 selects at least one characteristic from a plurality of characteristics of the sports equipment 18 that the decision unit 90 will use to decide whether to notify, based on at least one of product identification information, usage information, and user 10 preference information (S24). Based on the prediction result by the prediction unit 80, the decision is made to notify the user 10 (S26). Based on the decision by the decision unit 90, the output unit 99 outputs the notification to the information terminal 50 via the communication unit 62 (S28).

[0062] In step S20, if the prediction unit 80 has made predictions in the past, it may predict only the difference from that past point in time. Also, the performance prediction server 60 does not necessarily have to execute steps S14 to S18, S22, and S24. Note that the processing order shown in Figure 6 is just one example, and the order of information acquisition, information modification, etc., may be in other orders.

[0063] [Second Embodiment] Figure 7 is a functional block diagram showing the configurations of the performance prediction system 200 according to the second embodiment. The performance prediction system 200 comprises a measuring device 20, an information terminal 50, and a performance prediction server 160. The measuring device 20 and the information terminal 50 in this embodiment are identical or equivalent to the measuring device 20 and the information terminal 50 in the first embodiment, so their description is omitted.

[0064] Figure 8 is a functional block diagram showing the configurations of the performance prediction server 160. The performance prediction server 160 comprises a communication unit 62, an acquisition unit 164, a selection unit 76, a prediction unit 180, a modification unit 182, a determination unit 90, and an output unit 99. The acquisition unit 164 includes a product identification information acquisition unit 66, a usage information acquisition unit 68, a usage product acquisition unit 70, a storage information acquisition unit 72, and a characteristic information acquisition unit 74. The communication unit 62, selection unit 76, determination unit 90, output unit 99, product identification information acquisition unit 66, usage information acquisition unit 68, usage product acquisition unit 70, and storage information acquisition unit 72 in this embodiment are the same or equivalent components as those described with the same reference numerals in the first embodiment, so their description is omitted. The characteristic information acquisition unit 74, prediction unit 180, and modification unit 182 will be described below.

[0065] The characteristic information acquisition unit 74 acquires midsole characteristic information. Midsole characteristic information is information about the amount of deformation of a part of the shoe, which is a sports item 18, in response to a load on that part, including the midsole. The midsole characteristic information is at least information related to the cushioning of the midsole. The midsole characteristic information can be measured using, for example, a measuring device described later. The measured midsole characteristic information may be input by the user 10 via the input / output unit 51 of the information terminal 50. If the measuring device has a communication function, the information terminal 50 may receive the measured midsole characteristic information from the measuring device via the communication unit 52. The characteristic information acquisition unit 74 may acquire midsole characteristic information from the information terminal 50 via the communication unit 62.

[0066] Figure 9 is a schematic side view showing an example of a shoe 110 used as athletic equipment 18 in this embodiment. The shoe 110 is, for example, a running shoe. In Figure 9, the arrows extending in the left-right direction indicate the foot length direction L of the shoe 110, and the arrows extending in the up-down direction indicate the height direction H of the shoe 110. The explanation of the foot length direction L and the height direction H will be the same in Figures 10, 13, and 14, which will be described later.

[0067] The shoe 110 comprises, as its main components, an outsole 112, a midsole 114, an insole 116, and an upper 118. The outsole 112, midsole 114, and insole 116 are integrated by bonding, such as by adhesive, with the midsole 114 and insole 116 stacked on top of the outsole 112, which is the contact area. The midsole 114 and upper 118 are integrated by bonding, such as by adhesive, around the insole 116. In Figure 9, the insole 116, which is not visible in the side view, is shown with a dashed line. When the user 10 wears the shoe 110, an insole (not shown) placed on top of the insole 116 through a foot insertion opening 119 provided in the upper 118 may be used. In this example, the shoe 110 has a hole 120 formed in a part of the insole 116, and a part of the midsole 114 is exposed to the outside so that it can be seen through the foot insertion opening 119.

[0068] Figure 10 is a schematic diagram illustrating an example of measuring the midsole characteristic information of a shoe 110. In the example shown in Figure 10, the midsole characteristic information of the shoe 110 is measured using a measuring device 150. The measuring device 150 may be, for example, a known constant load compression test device. A part of the measuring device 150 is inserted into the inside of the shoe 110 from, for example, the foot insertion part 119, and presses the upper surface of the midsole 114 downward along the height direction H. The measuring device 150 detects the amount of displacement when the pressing load applied to the upper surface of the midsole 114 becomes a predetermined target load. By sequentially changing the target load using the measuring device 150 and detecting the amount of displacement corresponding to each target load, information on the amount of displacement in response to the pressing load can be obtained. The information on the amount of displacement in response to the pressing load obtained using the measuring device 150 corresponds to information on the amount of deformation of the part of the shoe 110 including the midsole 114 in response to the load on that part, i.e., midsole characteristic information.

[0069] Figure 11 is a graph showing an example of midsole characteristic information. The example shown in Figure 11 is an example of measurement results from the measuring device 150, with the horizontal axis representing displacement [mm] and the vertical axis representing load [N]. In Figure 11, the first graph 136 is the load-displacement curve for the shoe 110 before use, and the second graph 138 is the load-displacement curve for the shoe 110 at a predetermined point in time after use. The relationship between the deformation amount of the part of the shoe 110 including the midsole 114 and the load can be seen from the first graph 136 and the second graph 138, so that information such as the height of the load relative to the deformation amount, i.e., the resistance to deformation, can be obtained from the way each curve changes. In the second graph 138, the degree of increase in load relative to the increase in displacement is greater than in the first graph 136. This is thought to be because the cushioning performance has decreased due to continuous use, so-called wear and tear, has occurred in the midsole 114 of the shoe 110. In this way, the degree of wear and tear of the shoe 110 can be evaluated using the midsole characteristic information.

[0070] As shown in Figure 10, the measuring device 150 applies a load to the outsole 112 in addition to the midsole 114 and detects the total displacement including the midsole 114 and outsole 112. Therefore, in this example, the midsole characteristic information may also include the characteristic information of the outsole 112. However, since the midsole 114 is softer than the outsole 112, the displacement of the outsole 112 can be ignored. Also, the insole 116 of the shoe 110 does not necessarily have a hole 120 formed therein, and the measuring device 150 may acquire the midsole characteristic information by pressing the upper surface of the insole 116 downwards.

[0071] The method for obtaining midsole characteristic information is not limited to the examples described above. For example, the direction in which a pressing load is applied to the part including the midsole 114 is not limited to the height direction H, but may be any direction. However, by applying the pressing load along the height direction H, it is possible to apply a load in a direction that is approximately equal to the direction of the load applied in the usage state by the user 10.

[0072] Returning to Figure 8, the characteristic information acquisition unit 74 may acquire time information, such as the date and time the midsole characteristic information was measured, in association with the midsole characteristic information. The timing at which the midsole characteristic information is measured is not particularly limited. The characteristic information acquisition unit 74 may acquire midsole characteristic information measured before the shoes 110 are put into use. For example, the midsole characteristic information may be measured at a store or the like when the user 10 purchases the shoes 110. Alternatively, the user 10 may measure the midsole characteristic information at home or the like after purchasing the shoes 110 but before putting them into use.

[0073] The characteristic information acquisition unit 74 may acquire midsole characteristic information measured at any point after the start of use of the shoes 110. For example, at any point after the start of use of the shoes 110, the user 10 may measure the midsole characteristic information at home or at a store. The timing of measurement of the midsole characteristic information is not particularly limited, but may be, for example, every time the distance traveled with the shoes 110 reaches a predetermined distance, for example, every 100 km. In this case, for example, the information terminal 50 may notify the user 10 of the measurement timing each time the distance traveled reaches a predetermined distance, based on the distance traveled calculated by the calculation unit 28 of the measuring device 20.

[0074] The measurement location for midsole characteristic information may be one location or multiple locations. Figure 12 is a schematic diagram showing an example of a measurement location for midsole characteristic information. Figure 12 shows a top view of the midsole 114 in a shoe 110 for the right foot. In Figure 12, the arrows extending in the left-right direction indicate the foot width direction W of the midsole 114, and the arrows extending in the up-down direction indicate the foot length direction L of the midsole 114. The explanation of the foot width direction W and the foot length direction L will be the same in Figures 15 and 16, which will be described later.

[0075] Figure 12 shows an example of multiple measurement positions, including a first measurement position 130, a second measurement position 132, and a third measurement position 134. The first measurement position 130 is a position corresponding to the heel of the user 10, for example, approximately in the center in the foot width direction W and about 80% from the toe side in the foot length direction L. The second measurement position 132 is a position corresponding to the ball of the big toe of the user 10, for example, about 20% from the medial side in the foot width direction W and about 20% from the toe side in the foot length direction L. The third measurement position 134 is a position corresponding to the ball of the little toe of the user 10, for example, about 80% from the medial side in the foot width direction W and about 20% from the toe side in the foot length direction L.

[0076] By measuring midsole characteristic information at multiple measurement positions, information regarding the balance of characteristics such as cushioning of the part of the shoe 110 including the midsole 114 can be obtained. For example, by comparing the midsole characteristic information at the first measurement position 130 with the midsole characteristic information at at least one of the second measurement position 132 and the third measurement position 134, information regarding the front-to-back balance of the part including the midsole 114 can be obtained. Also, for example, by comparing the midsole characteristic information at the second measurement position 132 with the midsole characteristic information at the third measurement position 134, information regarding the left-to-right balance of the part including the midsole 114 can be obtained.

[0077] The timing at which midsole characteristic information is measured at each measurement location may be simultaneous or at different times. By acquiring time-series data of midsole characteristic information for each measurement location and obtaining an approximation curve (e.g., a regression curve or interpolation curve) for this time-series data, the midsole characteristic information of that measurement location at any given time can be estimated. Therefore, even if the data is measured at different times, the midsole characteristic information of each measurement location at the same time can be compared with each other. As a result, the distribution of changes in cushioning across the entire midsole 114 can be evaluated under conditions where usage conditions are standardized.

[0078] Returning to Figure 8, the prediction unit 180 has the same functions as the prediction unit 80 in the first embodiment. Furthermore, the prediction unit 180 predicts changes in the performance of the shoe 110 based on the midsole characteristic information acquired by the characteristic information acquisition unit 74. As a result, the actual characteristic information of the midsole 114 can be used to predict changes in the performance of the shoe 110, thereby improving the prediction accuracy.

[0079] As described above, the characteristic information acquisition unit 74 may acquire midsole characteristic information measured before the shoes 110 are put into use. In this case, the prediction unit 180 may use the performance of the shoes 110 based on the midsole characteristic information of the shoes 110 measured before use as an initial value and predict changes in the performance of the shoes 110. For example, the prediction unit 180 determines an initial value for the cushioning characteristic of the midsole 114 among the performance of the shoes 110 based on the midsole characteristic information measured before use, and predicts the change in the cushioning of the midsole 114 after use as a change from that initial value. This makes it possible to predict changes in the performance of the shoes 110 using the actual characteristic information of the midsole 114 at the time before the shoes 110 are put into use as an initial value, thereby improving the prediction accuracy from immediately after the shoes 110 are put into use.

[0080] As described above, the characteristic information acquisition unit 74 may acquire midsole characteristic information measured at any point after the start of use of the shoes 110. In this case, the modification unit 182 may modify the prediction of changes in the performance of the shoes 110 based on the midsole characteristic information of the shoes 110 measured after the start of use. For example, each time midsole characteristic information is measured after the start of use, the modification unit 182 may modify the predicted value of the cushioning characteristic of the midsole 114 to match the value corresponding to the said midsole characteristic information.

[0081] This allows the prediction of changes in the performance of the shoes 110 to be corrected based on the actual characteristic information of the midsole 114 at any point after the shoes 110 have been put into use, thereby suppressing the occurrence of deviations in prediction accuracy as time passes after the shoes 110 have been put into use. Furthermore, by combining this with using the actual characteristic information of the midsole 114 at the time before the shoes 110 have been put into use as the initial value, the prediction accuracy can be further improved.

[0082] The performance prediction server 160 may output the prediction results to an external device at any time via the output unit 99 and the communication unit 62. The destination of the output of the prediction results may be an information terminal 50 or any other device such as a computer. For example, if the performance prediction server 160 outputs the prediction results to the information terminal 50, the status of the shoes 110 can be notified to the user through the information terminal 50. For example, if the performance prediction server 160 outputs the prediction results to another device, the prediction results can be used, for example, in the development of shoes through that other device.

[0083] In Figure 10, the measuring device 150 was described as having a shape that extends in one direction. However, the shape of the measuring device 150 is not particularly limited. Figure 13 is a schematic side view showing a measuring device 150A, which is a first modified example of the measuring device 150. The measuring device 150A comprises a first part 152A, a second part 152B, and a third part 152C. In the measurement state shown in Figure 13, the first part 152A is positioned so that a portion of it protrudes outward from the foot insertion part 119, extends downward along the height direction H, and its lower end is located inside the shoe 110. The second part 152B extends from the lower end of the first part 152A toward the toe side along the foot length direction L. The third part 152C extends downward along the height direction H from the toe-side end of the second part 152B and can press against the upper surface of the midsole 114 through the hole 120 of the insole 116. The position pressed by the measuring device 150A may be the second measuring position 132 or the third measuring position 134 shown in Figure 12. In this way, the measuring device 150A can easily press a position on the midsole 114 that is toe-side to the foot insertion portion 119 while inserted through the foot insertion portion 119.

[0084] Figure 14 is a schematic side view showing a measuring device 150B, which is a second modified example of the measuring device 150. The measuring device 150B comprises a first part 154A, a second part 154B, a third part 154C, a fourth part 154D, and a fifth part 154E. In the measurement state shown in Figure 14, the first part 154A is positioned so that a portion of it protrudes outward from the foot insertion part 119, extends downward along the height direction H, and its lower end is located inside the shoe 110. The second part 154B extends from the lower end of the first part 154A toward the toe along the foot length direction L. The third part 154C extends from the lower end of the first part 154A toward the heel along the foot length direction L. The fourth portion 154D extends downward along the height direction H from the toe end of the second portion 154B and can press against the upper surface of the midsole 114 through the hole 120 of the insole 116. The fifth portion 154E extends downward along the height direction H from the heel end of the third portion 154C and can press against the upper surface of the midsole 114 through the hole 120 of the insole 116. The position pressed by the fourth portion 154D of the measuring device 150B may be the second measuring position 132 or the third measuring position 134 shown in Figure 12. The position pressed by the fifth portion 154E of the measuring device 150B may be the first measuring position 130 shown in Figure 12. In this way, since the measuring device 150B has a bifurcated shape, it can simultaneously press two locations on the midsole 114, including a position on the toe side of the foot insertion portion 119, when inserted through the foot insertion portion 119.

[0085] Figure 15 is a schematic diagram showing a part of a shoe 110A according to a first modified example of the shoe 110. Figure 15 shows a top view of the midsole 114A provided in the right shoe 110A. The midsole 114A has measuring pieces 122 at multiple locations. Each of the multiple measuring pieces 122 is, for example, cylindrical and is a member that connects from the top surface to the bottom surface of the midsole 114A, and is configured to be removable from other parts of the midsole 114A. As a result, each measuring piece 122 can be removed from the outside of the shoe 110A, so that midsole characteristic information at multiple locations on the midsole 114A can be easily measured.

[0086] Figure 16 is a schematic diagram showing a part of a shoe 110B, which is a second modified example of the shoe 110. Figure 16 shows a top view of the midsole 114B provided in the right shoe 110B. The midsole 114B comprises a midsole body 124 and a midsole support portion 126. The midsole support portion 126 is a frame-shaped member that surrounds the midsole body 124 and is harder than the midsole body 124. The midsole body 124 has a plurality of measuring pieces 128. The plurality of measuring pieces 128 are arranged adjacent to each other along the foot length direction L and are members that connect from the top surface to the bottom surface of the midsole 114B, and are configured to be removable. As a result, each measuring piece 128 can be removed from the outside of the shoe 110A, so that midsole characteristic information at multiple positions on the midsole 114A can be easily measured.

[0087] In Figure 16, the midsole body 124 may be integrally constructed without a measuring piece 128. Alternatively, the midsole body 124 may be connected to the upper 118, and the midsole support portion 126 may be connected to the outsole 112. This allows the midsole body 124 and upper 118, which are connected to each other, to be removed from the other components of the shoe 110B. Because the removed component is more flexible overall than the other components, it is easier to measure the midsole characteristic information at any position on the midsole body 124. For example, by turning the upper 118 portion of the component consisting of the midsole body 124 and upper 118 inside out, the surface of the midsole body 124 can be easily exposed and the midsole characteristic information can be measured.

[0088] Figure 17 is a flowchart illustrating an example of the performance prediction process in the performance prediction server 160. Since the performance prediction server 160 can execute the processes shown in the flowchart in Figure 6, only the differences from Figure 6 will be explained in Figure 17. The processes shown in Figure 6 and Figure 17 can be combined as appropriate.

[0089] The characteristic information acquisition unit 74 acquires midsole characteristic information measured before the shoes 110 are put into use (S40). The prediction unit 180 sets the performance of the shoes 110 to an initial value based on the midsole characteristic information before the shoes 110 are put into use (S42), and predicts changes in the performance of the shoes 110 using the set initial value (S44). The characteristic information acquisition unit 74 acquires midsole characteristic information measured at any point after the shoes 110 are put into use (S46). The correction unit 182 corrects the prediction made by the prediction unit 180 based on the midsole characteristic information after the shoes 110 are put into use (S48).

[0090] The present disclosure has been described above based on embodiments. The embodiments are illustrative, and it will be understood by those skilled in the art that various modifications are possible in the combination of their components and processing processes, and that such modifications are also within the scope of the present disclosure. Furthermore, the above-described embodiments can be generalized to obtain the following embodiments.

[0091] [Aspect 1] A method for predicting the performance of sports equipment, comprising: a step of acquiring product identification information that at least identifies the classification to which sports equipment used by a user belongs; a step of acquiring usage information that may affect the performance of the sports equipment when the user uses the sports equipment for exercise; a step of predicting changes in the performance of the sports equipment based on the product identification information and the usage information; a step of deciding to notify the user based on the result of the prediction; and a step of outputting a notification based on the decision.

[0092] According to the performance prediction method of Embodiment 1, changes in the performance of sporting goods can be accurately predicted based on product identification information and usage information related to sporting goods.

[0093] [Aspect 2] The performance prediction method according to aspect 1, wherein the product identification information may include at least one of the size of the sports equipment, the product name of the sports equipment, and the manufacturing information of the sports equipment.

[0094] According to the performance prediction method of embodiment 2, changes in the performance of sporting goods can be predicted using information that is easily obtainable as information about sporting goods.

[0095] [Aspect 3] The performance prediction method according to aspect 1 or 2, wherein the usage information may include the user's physical information.

[0096] According to the performance prediction method of embodiment 3, changes in the performance of sports equipment can be predicted using user physical information that is easily obtained from existing applications, etc.

[0097] [Aspect 4] The performance prediction method according to any one of aspects 1 to 3, wherein the usage information may include the user's movement information during exercise.

[0098] According to the performance prediction method of embodiment 4, changes in the performance of sports equipment can be predicted using motion information during exercise that can be obtained from a measuring device worn by the user.

[0099] [Aspect 5] The performance prediction method according to any one of aspects 1 to 4, wherein the usage information may include environmental information when the user exercises using the sports equipment.

[0100] According to the performance prediction method of embodiment 5, changes in the performance of sports equipment can be predicted using environmental information obtainable from a measuring device worn by the user.

[0101] [Aspect 6] The performance prediction method according to any one of aspects 1 to 5, wherein in the prediction process, the change over time of a characteristic index for at least one characteristic of the sporting goods may be predicted based on the product identification information and the usage information, and in the determination process, when the at least one characteristic index falls below a predetermined threshold, the notification may be determined.

[0102] According to the performance prediction method of embodiment 6, a notification can be output when a specific characteristic index declines, so the decline in the performance of sporting goods can be predicted at an appropriate time.

[0103] [Aspect 7] The performance prediction method according to aspect 6, which may further include a step of selecting at least one characteristic from a plurality of characteristics of the sports equipment based on at least one of the product identification information, the usage information, and the user preference information.

[0104] According to the performance prediction method of embodiment 7, a notification can be output when a characteristic index that particularly affects the performance of sports equipment changes, so that changes in the performance of sports equipment can be predicted at an appropriate time.

[0105] [Aspect 8] The performance prediction method according to aspect 6 or 7, wherein in the process of determining, the notification may be determined in a different manner when the at least one characteristic index falls below a first threshold and when the at least one characteristic index falls below a second threshold that is smaller than the first threshold.

[0106] According to the performance prediction method of embodiment 8, notifications can be output in stages in response to a decline in a specific characteristic index, making it possible to predict the decline in the performance of sporting goods at an appropriate time.

[0107] [Aspect 9] The performance prediction method according to any one of aspects 6 to 8, wherein the at least one characteristic may include the suitability of the sports equipment to the user's body, and in the determination process, the notification may be decided when the characteristic index for the suitability exceeds a predetermined threshold.

[0108] According to the performance prediction method of embodiment 9, it is possible to notify the user of the appropriate timing for using the sports equipment.

[0109] [Aspect 10] A performance prediction method according to any one of aspects 1 to 9, which may further include: a step of acquiring information on the sports equipment after it has been used by the user; and a step of modifying at least one of the usage information and the prediction result based on the acquired information on the sports equipment after it has been used.

[0110] According to the performance prediction method of embodiment 10, the accuracy of predicting changes in the performance of sports equipment can be further improved based on information about sports equipment after use.

[0111] [Aspect 11] A performance prediction method according to any one of aspects 1 to 10, which further includes a step of acquiring storage information relating to the state in which the sports equipment is stored, and in the prediction step, a change in the performance of the sports equipment may be predicted based on the storage information.

[0112] According to the performance prediction method of embodiment 11, the accuracy of predicting changes in the performance of sporting goods can be further improved by incorporating storage information of sporting goods.

[0113] [Aspect 12] The performance prediction method according to aspect 11, wherein in the prediction process, a temporary change in the performance of the sports equipment may be predicted based on at least one of the frequency of use, intensity of use, duration of use, and storage information of the sports equipment during a predetermined period.

[0114] According to the performance prediction method of embodiment 12, it is possible to predict temporary changes in the performance of sports equipment, thus giving users an opportunity to reconsider their frequency of use of sports equipment.

[0115] [Aspect 13] The performance prediction method according to any one of aspects 1 to 12, further comprising a step of acquiring information on the amount of deformation of a part of a shoe, which is an athletic product, in response to a load on the part thereof as midsole characteristic information, and in the prediction step, a change in the performance of the shoe may be predicted based on the midsole characteristic information.

[0116] According to the performance prediction method of embodiment 13, the actual characteristic information of the shoe's midsole can be used to predict changes in the shoe's performance, thereby improving prediction accuracy.

[0117] [Aspect 14] The performance prediction method according to aspect 13, wherein in the process of acquiring the midsole characteristic information, the midsole characteristic information measured before the start of use of the shoes may be acquired, and in the prediction process, the performance of the shoes based on the midsole characteristic information may be used as an initial value to predict changes in the performance of the shoes.

[0118] According to the performance prediction method of embodiment 14, since the actual characteristic information of the midsole before the start of shoe use is used as initial values, changes in the performance of the shoe can be predicted, thereby improving the prediction accuracy from immediately after the start of shoe use.

[0119] [Aspect 15] The performance prediction method according to aspect 13 or 14, wherein the process of acquiring the midsole characteristic information may include acquiring the midsole characteristic information measured at any point after the start of use of the shoes, and further includes a process of correcting the prediction of changes in the performance of the shoes based on the midsole characteristic information.

[0120] According to the performance prediction method of embodiment 15, the prediction of changes in shoe performance can be corrected based on the actual characteristic information of the midsole at any point after the start of shoe use, thereby suppressing the occurrence of deviations in prediction accuracy due to the passage of time after the start of shoe use. Furthermore, by combining this with using the actual characteristic information of the midsole at a point before the start of shoe use as the initial value, the prediction accuracy can be further improved.

[0121] [Aspect 16] A sports equipment performance prediction system comprising: a product identification information acquisition unit that acquires product identification information that at least identifies the classification to which the sports equipment used by the user belongs; a usage information acquisition unit that acquires usage information that may affect the performance of the sports equipment when the user uses the sports equipment for exercise; a prediction unit that predicts changes in the performance of the sports equipment based on the product identification information and the usage information; a decision unit that decides to notify the user based on the prediction result by the prediction unit; and an output unit that outputs a notification based on the decision by the decision unit.

[0122] According to the performance prediction system of embodiment 16, changes in the performance of sporting goods can be predicted based on product identification information and usage information related to sporting goods.

[0123] [Aspect 17] The performance prediction system according to aspect 16, wherein the product identification information may include at least one of the size of the sports equipment, the product name of the sports equipment, and the manufacturing information of the sports equipment.

[0124] According to the performance prediction system of embodiment 17, changes in the performance of sporting goods can be predicted using information that is easily obtainable as information about sporting goods.

[0125] [Aspect 18] The performance prediction system according to aspect 16 or 17, wherein the usage information may include the user's physical information.

[0126] According to the performance prediction system of embodiment 18, changes in the performance of sports equipment can be predicted using user physical information that is easily obtained from existing applications, etc.

[0127] [Aspect 19] The performance prediction system according to any one of aspects 16 to 18, wherein the usage information may include the user's movement information during exercise.

[0128] According to the performance prediction system of embodiment 19, changes in the performance of sports equipment can be predicted using motion information during exercise that can be obtained from a measuring device worn by the user.

[0129] [Aspect 20] The performance prediction system according to any one of aspects 16 to 19, wherein the usage information may include environmental information when the user exercises using the sports equipment.

[0130] According to the performance prediction system of embodiment 20, changes in the performance of sports equipment can be predicted using environmental information obtainable from a measuring device worn by the user.

[0131] [Aspect 21] The performance prediction system according to any one of aspects 16 to 20, wherein the prediction unit may predict the change over time of a characteristic index for at least one characteristic of the sporting goods based on the product identification information and the usage information, and the determination unit may decide to issue the notification when the at least one characteristic index falls below a predetermined threshold.

[0132] According to the performance prediction system of embodiment 21, a notification can be output when a specific characteristic index declines, so the decline in the performance of sporting goods can be predicted at an appropriate time.

[0133] [Aspect 22] The performance prediction system according to aspect 21, further comprising a selection unit that selects at least one characteristic from a plurality of characteristics of the sports equipment based on at least one of the product identification information, the usage information, and the user preference information.

[0134] According to the performance prediction system of embodiment 22, a notification can be output when a characteristic index that particularly affects the performance of sporting goods changes, so that changes in the performance of sporting goods can be predicted at an appropriate time.

[0135] [Aspect 23] The performance prediction system according to aspect 21 or 22, wherein the determination unit may determine the notification in a different manner when the at least one characteristic index falls below a first threshold and when the at least one characteristic index falls below a second threshold that is smaller than the first threshold.

[0136] According to the performance prediction system of embodiment 23, notifications can be output in stages in response to a decline in a specific characteristic index, making it possible to predict the decline in the performance of sporting goods at an appropriate time.

[0137] [Aspect 24] The performance prediction system according to any one of aspects 21 to 23, wherein the at least one characteristic may include the suitability of the sports equipment to the user's body, and the determination unit may decide to give the notification when the characteristic index for the suitability exceeds a predetermined threshold.

[0138] According to the performance prediction system of embodiment 24, the system can notify the user of the appropriate timing for using sports equipment.

[0139] [Aspect 25] The performance prediction system according to any one of aspects 16 to 24, further comprising: a product usage acquisition unit that acquires information on the sports equipment after it has been used by the user; and a modification unit that modifies at least one of the usage information and the prediction result based on the information acquired by the product usage acquisition unit.

[0140] According to the performance prediction system of embodiment 25, the accuracy of predicting changes in the performance of sports equipment can be further improved based on information about sports equipment after use.

[0141] [Aspect 26] The performance prediction system according to any one of aspects 16 to 25, further comprising a storage information acquisition unit that acquires storage information relating to the state in which the sports equipment is stored, and the prediction unit further predicts changes in the performance of the sports equipment based on the storage information.

[0142] According to the performance prediction system of embodiment 26, the accuracy of predicting changes in the performance of sporting goods can be further improved by incorporating storage information of sporting goods.

[0143] [Aspect 27] The performance prediction system according to aspect 26, wherein the prediction unit may predict a temporary change in the performance of the sports equipment based on at least one of the frequency of use, intensity of use, duration of use, and storage information of the sports equipment during a predetermined period.

[0144] According to the performance prediction system of embodiment 27, temporary changes in the performance of sports equipment can be predicted, which can give users an opportunity to reconsider how often they use the sports equipment.

[0145] [Aspect 28] The performance prediction system according to any one of aspects 16 to 27, further comprising a characteristic information acquisition unit that acquires information on the amount of deformation of a part of the sporting goods shoe, including the midsole, in response to a load on said part, as midsole characteristic information, and the prediction unit may further predict changes in the performance of the shoe based on the midsole characteristic information.

[0146] According to the performance prediction system of embodiment 28, the actual characteristic information of the shoe's midsole can be used to predict changes in the shoe's performance, thereby improving prediction accuracy.

[0147] [Aspect 29] The performance prediction system according to aspect 28, wherein the characteristic information acquisition unit may acquire the midsole characteristic information measured before the start of use of the shoes, and the prediction unit may predict changes in the performance of the shoes using the performance of the shoes based on the midsole characteristic information as an initial value.

[0148] According to the performance prediction system of embodiment 29, changes in the performance of the shoes can be predicted using the actual characteristic information of the midsole before the shoes are put into use as initial values, thereby improving the accuracy of predictions from immediately after the shoes are put into use.

[0149] [Aspect 30] The performance prediction system according to aspect 28 or 29, wherein the characteristic information acquisition unit may acquire the midsole characteristic information measured at any point after the start of use of the shoes, and further comprises a correction unit that corrects the prediction of changes in the performance of the shoes based on the midsole characteristic information.

[0150] According to the performance prediction system of embodiment 30, the prediction of changes in shoe performance can be corrected based on the actual characteristic information of the midsole at any point after the start of shoe use, thereby suppressing the occurrence of deviations in prediction accuracy due to the passage of time after the start of shoe use. Furthermore, by combining this with using the actual characteristic information of the midsole before the start of shoe use as the initial value, the prediction accuracy can be further improved.

[0151] [Aspect 31] A performance prediction program for sports equipment that enables a computer to perform the following functions: a function to acquire product identification information that at least identifies the classification to which sports equipment used by a user belongs; a function to acquire usage information that may affect the performance of the sports equipment when the user uses the sports equipment for exercise; a function to predict changes in the performance of the sports equipment based on the product identification information and the usage information; a function to decide on a notification to the user based on the result of the prediction; and a function to output a notification based on the decision.

[0152] According to the performance prediction program of embodiment 31, changes in the performance of sports equipment can be predicted based on product identification information and usage information related to sports equipment.

[0153] [Aspect 32] The performance prediction program according to aspect 31, wherein the product identification information may include at least one of the size of the sports equipment, the product name of the sports equipment, and the manufacturing information of the sports equipment.

[0154] According to the performance prediction program of embodiment 32, changes in the performance of sporting goods can be predicted using information that is easily obtainable as information about sporting goods.

[0155] [Aspect 33] The performance prediction program according to aspect 31 or 32, wherein the usage information may include the user's physical information.

[0156] According to the performance prediction program of embodiment 33, changes in the performance of sports equipment can be predicted using user physical information that is easily obtained from existing applications, etc.

[0157] [Aspect 34] The performance prediction program according to any one of aspects 31 to 33, wherein the usage information may include the user's movement information during exercise.

[0158] According to the performance prediction program of embodiment 34, changes in the performance of sports equipment can be predicted using motion information during exercise that can be obtained from a measuring device worn by the user.

[0159] [Aspect 35] The performance prediction program according to any one of aspects 31 to 34, wherein the usage information may include environmental information when the user exercises using the sports equipment.

[0160] According to the performance prediction program of embodiment 35, changes in the performance of sports equipment can be predicted using environmental information obtainable from a measuring device worn by the user.

[0161] [Aspect 36] The performance prediction program according to any one of aspects 31 to 35, wherein the predicting function may predict the change over time of a characteristic index for at least one characteristic of the sporting goods based on the product identification information and the usage information, and the determining function may determine to give the notification when the at least one characteristic index falls below a predetermined threshold.

[0162] According to the performance prediction program of embodiment 36, a notification can be output when a specific characteristic index declines, so the decline in the performance of sporting goods can be predicted at an appropriate time.

[0163] [Aspect 37] The performance prediction program according to aspect 36, wherein the computer may further implement a function to select at least one characteristic from a plurality of characteristics of the sports equipment based on at least one of the product identification information, the usage information, and the user preference information.

[0164] According to the performance prediction program of embodiment 37, a notification can be output when a characteristic index that particularly affects the performance of sporting goods changes, so that changes in the performance of sporting goods can be predicted at an appropriate time.

[0165] [Aspect 38] The performance prediction program according to aspect 36 or 37, wherein the function for determining the notification may determine the notification in different ways when the at least one characteristic index falls below a first threshold and when the at least one characteristic index falls below a second threshold that is smaller than the first threshold.

[0166] According to the performance prediction program of embodiment 38, notifications can be output in stages in response to a decline in a specific characteristic index, making it possible to predict the decline in the performance of sporting goods at an appropriate time.

[0167] [Aspect 39] The performance prediction program according to any one of aspects 36 to 38, wherein the at least one characteristic may include the suitability of the sports equipment to the user's body, and the function to determine may determine the notification when the characteristic index for the suitability exceeds a predetermined threshold.

[0168] According to the performance prediction program of embodiment 39, the program can notify the user of the appropriate timing for using the sports equipment.

[0169] [Aspect 40] A performance prediction program according to any one of aspects 31 to 39, further comprising a function to acquire information about the sports equipment after it has been used by the user, and a function to modify at least one of the usage information and the prediction result based on the acquired information about the sports equipment after it has been used, the computer.

[0170] According to the performance prediction program of embodiment 40, the accuracy of predicting changes in the performance of sports equipment can be further improved based on information about sports equipment after use.

[0171] [Aspect 41] The performance prediction program according to any one of aspects 31 to 40, wherein the computer may further implement a function to acquire storage information regarding the state in which the sports equipment is stored, and the prediction function may predict changes in the performance of the sports equipment based on the storage information.

[0172] According to the performance prediction program of embodiment 41, the accuracy of predicting changes in the performance of sporting goods can be further improved by incorporating storage information of sporting goods.

[0173] [Aspect 42] The performance prediction program according to aspect 41, wherein the predictive function may predict a temporary change in the performance of the sports equipment based on at least one of the frequency of use, intensity of use, duration of use, and storage information of the sports equipment during a predetermined period.

[0174] According to the performance prediction program of embodiment 42, temporary changes in the performance of sports equipment can be predicted, which can give users an opportunity to reconsider the frequency of use of sports equipment, etc.

[0175] [Aspect 43] The performance prediction program according to any one of aspects 31 to 32, wherein the computer may further implement a function to acquire information on the amount of deformation of a part of the sporting goods, including the midsole, in response to a load on the part said part, said part, as midsole characteristic information, and the prediction function may further predict changes in the performance of the shoes based on the midsole characteristic information.

[0176] According to the performance prediction program of embodiment 43, the actual characteristic information of the shoe's midsole can be used to predict changes in the shoe's performance, thereby improving prediction accuracy.

[0177] [Aspect 44] The performance prediction program according to aspect 43, wherein the function for acquiring midsole characteristic information may acquire midsole characteristic information measured before the start of use of the shoes, and the prediction function may predict changes in the performance of the shoes using the performance of the shoes based on the midsole characteristic information as an initial value.

[0178] According to the performance prediction program of embodiment 44, changes in shoe performance can be predicted using the actual characteristic information of the midsole before the start of shoe use as initial values, thereby improving the prediction accuracy from immediately after the start of shoe use.

[0179] [Aspect 45] The performance prediction program according to aspect 43 or 44, wherein the function for acquiring the midsole characteristic information may acquire the midsole characteristic information measured at any point after the start of use of the shoes, and the computer may further implement a function for correcting the prediction of changes in the performance of the shoes based on the midsole characteristic information.

[0180] According to the performance prediction program of embodiment 45, the prediction of changes in shoe performance can be corrected based on the actual characteristic information of the midsole at any point after the start of shoe use, thereby suppressing the occurrence of deviations in prediction accuracy due to the passage of time after the start of shoe use. Furthermore, by combining this with using the actual characteristic information of the midsole at a point before the start of shoe use as the initial value, the prediction accuracy can be further improved.

[0181] [Aspect 46] A sports equipment performance prediction device comprising: a product identification information acquisition unit that acquires product identification information that identifies at least the classification to which the sports equipment used by the user belongs; a usage information acquisition unit that acquires usage information that may affect the performance of the sports equipment when the user uses the sports equipment for exercise; a prediction unit that predicts changes in the performance of the sports equipment based on the product identification information and the usage information; a decision unit that decides to notify the user based on the prediction result by the prediction unit; and an output unit that outputs a notification based on the decision by the decision unit.

[0182] According to the performance prediction device of embodiment 46, changes in the performance of sporting goods can be predicted based on product identification information and usage information related to sporting goods.

[0183] [Aspect 47] The performance prediction device according to aspect 46, wherein the product identification information may include at least one of the size of the sports equipment, the product name of the sports equipment, and the manufacturing information of the sports equipment.

[0184] According to the performance prediction device of embodiment 47, changes in the performance of sporting goods can be predicted using information that is easily obtainable as information about sporting goods.

[0185] [Aspect 48] The performance prediction device according to aspect 46 or 47, wherein the usage information may include the user's physical information.

[0186] According to the performance prediction device of embodiment 48, changes in the performance of sports equipment can be predicted using user physical information that is easily obtained from existing applications, etc.

[0187] [Aspect 49] The performance prediction device according to any one of aspects 46 to 48, wherein the usage information may include the user's movement information during exercise.

[0188] According to the performance prediction device of embodiment 49, changes in the performance of sports equipment can be predicted using motion information during exercise that can be obtained from a measuring device worn by the user.

[0189] [Aspect 50] The performance prediction device according to any one of aspects 46 to 49, wherein the usage information may include environmental information when the user exercises using the sports equipment.

[0190] According to the performance prediction device of embodiment 50, changes in the performance of sports equipment can be predicted using environmental information obtainable from a measuring device worn by the user.

[0191] [Aspect 51] The performance prediction device according to any one of aspects 46 to 50, wherein the prediction unit may predict the change over time of a characteristic index for at least one characteristic of the sporting goods based on the product identification information and the usage information, and the determination unit may decide to issue the notification when the at least one characteristic index falls below a predetermined threshold.

[0192] According to the performance prediction device of embodiment 51, a notification can be output when a specific characteristic index declines, so the decline in the performance of sporting goods can be predicted at an appropriate time.

[0193] [Aspect 52] The performance prediction device according to aspect 51, further comprising a selection unit that selects at least one characteristic from a plurality of characteristics of the sports equipment based on at least one of the product identification information, the usage information, and the user preference information.

[0194] According to the performance prediction device of embodiment 52, a notification can be output when a characteristic index that particularly affects the performance of sporting goods changes, so that changes in the performance of sporting goods can be predicted at an appropriate time.

[0195] [Aspect 53] The performance prediction device according to aspect 51 or 52, wherein the determination unit may determine the notification in a different manner when the at least one characteristic index falls below a first threshold and when the at least one characteristic index falls below a second threshold that is smaller than the first threshold.

[0196] According to the performance prediction device of embodiment 53, notifications can be output in stages in response to a decrease in a specific characteristic index, making it possible to predict the decline in the performance of sporting goods at an appropriate time.

[0197] [Aspect 54] The performance prediction device according to any one of aspects 51 to 53, wherein the at least one characteristic may include the suitability of the sports equipment to the user's body, and the determination unit may decide to give the notification when the characteristic index for the suitability exceeds a predetermined threshold.

[0198] According to the performance prediction device of embodiment 54, the user can be notified of the appropriate timing for using the sports equipment.

[0199] [Aspect 55] The performance prediction device according to any one of aspects 46 to 54, further comprising: a product usage acquisition unit that acquires information on the sports equipment after it has been used by the user; and a modification unit that modifies at least one of the usage information and the prediction result based on the information acquired by the product usage acquisition unit.

[0200] According to the performance prediction device of embodiment 55, the accuracy of predicting changes in the performance of sports equipment can be further improved based on information about sports equipment after use.

[0201] [Aspect 56] The performance prediction device according to any one of aspects 46 to 55, further comprising a storage information acquisition unit that acquires storage information relating to the state in which the sports equipment is stored, and the prediction unit further predicts changes in the performance of the sports equipment based on the storage information.

[0202] According to the performance prediction device of embodiment 56, the accuracy of predicting changes in the performance of sporting goods can be further improved by incorporating storage information of sporting goods.

[0203] [Aspect 57] The performance prediction device according to aspect 56, wherein the prediction unit may predict a temporary change in the performance of the sports equipment based on at least one of the frequency of use, intensity of use, duration of use, and storage information of the sports equipment during a predetermined period.

[0204] According to the performance prediction device of embodiment 57, temporary changes in the performance of sports equipment can be predicted, which can give users an opportunity to reconsider the frequency of use of sports equipment, etc.

[0205] [Aspect 58] The performance prediction device according to any one of aspects 46 to 57, further comprising a characteristic information acquisition unit that acquires information on the amount of deformation of a part of the shoe, which is an athletic product, in response to a load on the part said part, including the midsole, as midsole characteristic information, and the prediction unit may further predict changes in the performance of the shoe based on the midsole characteristic information.

[0206] According to the performance prediction device of embodiment 58, actual characteristic information of the shoe's midsole can be used to predict changes in the shoe's performance, thereby improving prediction accuracy.

[0207] [Aspect 59] The performance prediction device according to aspect 58, wherein the characteristic information acquisition unit may acquire the midsole characteristic information measured before the start of use of the shoes, and the prediction unit may predict changes in the performance of the shoes using the performance of the shoes based on the midsole characteristic information as an initial value.

[0208] According to the performance prediction device of embodiment 59, changes in shoe performance can be predicted using the actual characteristic information of the midsole before the start of shoe use as initial values, thereby improving the prediction accuracy from immediately after the start of shoe use.

[0209] [Aspect 60] The performance prediction device according to aspect 58 or 59, wherein the characteristic information acquisition unit may acquire the midsole characteristic information measured at any point after the start of use of the shoes, and further comprises a correction unit that corrects the prediction of changes in the performance of the shoes based on the midsole characteristic information.

[0210] According to the performance prediction device of embodiment 60, the prediction of changes in shoe performance can be corrected based on the actual characteristic information of the midsole at any point after the start of shoe use, thereby suppressing the occurrence of deviations in prediction accuracy due to the passage of time after the start of shoe use. Furthermore, by combining this with using the actual characteristic information of the midsole at a point before the start of shoe use as the initial value, the prediction accuracy can be further improved.

[0211] This disclosure relates to a technology for predicting changes in the performance of sporting goods.

[0212] 10 Users, 18 Sporting Goods, 60, 160 Performance Prediction Server, 64, 164 Acquisition Unit, 66 Product Identification Information Acquisition Unit, 68 Usage Information Acquisition Unit, 70 Product Used Acquisition Unit, 72 Storage Information Acquisition Unit, 74 Characteristic Information Acquisition Unit, 76 Selection Unit, 80, 180 Prediction Unit, 82, 182 Correction Unit, 90 Decision Unit, 99 Output Unit, 100, 200 Performance Prediction System, 110 Shoes, 114 Midsole, 150 Measurement Device.

Claims

1. A method for predicting the performance of sports equipment, comprising: a process of acquiring product identification information that at least identifies the classification to which the sports equipment used by the user belongs; a process of acquiring usage information that may affect the performance of the sports equipment when the user uses the sports equipment for exercise; a process of predicting changes in the performance of the sports equipment based on the product identification information and the usage information; a process of deciding to notify the user based on the result of the prediction; and a process of outputting a notification based on the decision.

2. The performance prediction method according to claim 1, wherein the product identification information includes at least one of the size of the sports equipment, the product name of the sports equipment, and the manufacturing information of the sports equipment.

3. The performance prediction method according to claim 1, wherein the usage information includes the user's physical information.

4. The performance prediction method according to claim 1, wherein the usage information includes the user's movement information during exercise.

5. The performance prediction method according to claim 1, wherein the usage information includes environmental information when the user exercises using the sports equipment.

6. The performance prediction method according to claim 1, wherein in the prediction process, the change over time of a characteristic index for at least one characteristic of the sporting goods is predicted based on the product identification information and the usage information, and in the determination process, when the at least one characteristic index falls below a predetermined threshold, the notification is determined.

7. The performance prediction method according to claim 6, further comprising the step of selecting at least one characteristic from a plurality of characteristics of the sports equipment based on at least one of the product identification information, the usage information, and the user preference information.

8. The performance prediction method according to claim 6, wherein in the determination process, a different form of notification is determined when the at least one characteristic index falls below a first threshold and when the at least one characteristic index falls below a second threshold that is smaller than the first threshold.

9. The performance prediction method according to claim 6, wherein the at least one characteristic includes the suitability of the sports equipment to the user's body, and in the determination process, the notification is determined when the characteristic index for the suitability exceeds a predetermined threshold.

10. A performance prediction method according to claim 1, further comprising: a step of acquiring information on the sports equipment after it has been used by the user; and a step of modifying at least one of the usage information and the prediction result based on the acquired information on the sports equipment after it has been used.

11. The performance prediction method according to claim 1, further comprising a step of acquiring storage information relating to the state in which the sports equipment is stored, wherein the prediction step further predicts changes in the performance of the sports equipment based on the storage information.

12. The performance prediction method according to claim 11, wherein the prediction process predicts a temporary change in the performance of the sports equipment based on at least one of the frequency of use, intensity of use, duration of use, and storage information of the sports equipment during a predetermined period.

13. The performance prediction method according to claim 1, further comprising a step of acquiring information on the amount of deformation of a part of a shoe, which is an athletic product, in response to a load on the part thereof, as midsole characteristic information, wherein the prediction step further predicts a change in the performance of the shoe based on the midsole characteristic information.

14. The performance prediction method according to claim 13, wherein in the process of acquiring the midsole characteristic information, the midsole characteristic information measured before the start of use of the shoes is acquired, and in the prediction process, the performance of the shoes based on the midsole characteristic information is used as an initial value to predict changes in the performance of the shoes.

15. The performance prediction method according to claim 13 or 14, further comprising the process of acquiring the midsole characteristic information, which is measured at any point after the start of use of the shoes, and correcting the prediction of changes in the performance of the shoes based on the midsole characteristic information.

16. A sports equipment performance prediction system comprising: a product identification information acquisition unit that acquires product identification information that identifies at least the classification to which the sports equipment used by the user belongs; a usage information acquisition unit that acquires usage information that may affect the performance of the sports equipment when the user uses the sports equipment for exercise; a prediction unit that predicts changes in the performance of the sports equipment based on the product identification information and the usage information; a decision unit that decides to notify the user based on the prediction results by the prediction unit; and an output unit that outputs a notification based on the decision by the decision unit.

17. A program for predicting the performance of sports equipment, which enables a computer to perform the following functions: a function to acquire product identification information that identifies at least the classification to which the sports equipment used by the user belongs; a function to acquire usage information that may affect the performance of the sports equipment when the user uses the sports equipment for exercise; a function to predict changes in the performance of the sports equipment based on the product identification information and the usage information; a function to determine a notification to the user based on the result of the prediction; and a function to output a notification based on the determination.

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