Patent Application
20250135311
This nonprovisional application is based on Japanese Patent Application No. 2023-185761 filed on Oct. 30, 2023 with the Japan Patent Office, the entire contents of which are hereby incorporated by reference.
The present disclosure relates to a hitting tool selection diagnosis using measurement data obtained by a measurement device.
Japanese Patent No. 5352805 describes a bat selection system to select and propose a bat suitable for a hitting person using measurement data obtained when a ball is actually hit with the bat.
In the bat selection system of Japanese Patent No. 5352805, when a ball is actually hit by swinging a bat (sensor bat) having a sensor included therein, a plurality of pieces of kinematic information of the bat swing are calculated from measurement values found from measurement data of the bat. Further, it is described to select and propose one optimal bat that matches a type of goal (long hitter/high-average hitter) from a plurality of swung bats based on evaluation parameters of the hitting person's swings and the type of goal. Each of the evaluation parameters is found by analyzing the calculated pieces of kinematic information, and the type of goal is input by the hitting person.
Specifically, parts of the pieces of kinematic information are extracted in accordance with the input type of goal (long hitter/high-average hitter) from the plurality of pieces of kinematic information calculated to correspond to the plurality of swung bats, and the extracted pieces of kinematic information are sorted among the plurality of bats, thereby selecting one optimum bat.
However, in the bat selection system of Japanese Patent No. 5352805, the plurality of swing measurement data values (kinematic information) are found by analysis, whereas the bat is selected in two steps of separately comparing only the two types of swing measurement data values, which are parts of the plurality of swing measurement data values, in an order corresponding to the rough classifications (long hitter/high-average hitter) of the input type of goal. Therefore, it is understood that there is room for improvement in a degree of utilization of the plurality of swing measurement data values found. It is considered that the same problem also arises when proposing selection of a hitting tool using measurement data values obtained when an object such as a ball is actually hit with a hitting tool (for example, a table tennis racket or a tennis racket) other than a bat.
An object of an aspect of the present disclosure is to propose effective selection of a hitting tool with an improved degree of utilization of a plurality of swing measurement data values found from measurement data obtained when a user actually hits an object such as a ball with a hitting tool.
In an embodiment of the present disclosure, a hitting tool selection diagnosis system is provided. The hitting tool selection diagnosis system includes a measurement device and a data analysis device. The measurement device outputs a first swing measurement data value and a second swing measurement data value with a swing behavior when a hitting person swings a hitting tool to hit an object being employed as an input, the first swing measurement data value being dependent on operability of the hitting tool, the second swing measurement data value being dependent on a momentum that the hitting tool has at the time of the hitting. The data analysis device generates diagnosis information for selection of a hitting tool in response to input of the first swing measurement data value and the second swing measurement data value each obtained when the hitting person swings each of a plurality of test hitting tools, the plurality of test hitting tools being three or more test hitting tools. The data analysis device includes a score calculator and a diagnosis information generator. For each of the plurality of test hitting tools, the score calculator calculates a first score value based on the first swing measurement data value and calculates a second score value based on the second swing measurement data value, and calculates a total score value obtained by integrating the first score value and the second score value based on a weighting parameter designated by the hitting person, the first score value being an indicator value of operability of the hitting tool swung, the second score value being an indicator value of a hit-object speed (initial speed). The diagnosis information generator generates the diagnosis information using the total score value calculated by the score calculator and corresponding to each of the plurality of test hitting tools.
In another embodiment of the present disclosure, a hitting tool selection diagnosis method is provided. The hitting tool selection diagnosis method includes: (1) selecting a plurality of test hitting tools based on an input of a user, the plurality of test hitting tools being three or more test hitting tools; (2) obtaining, using a measurement device to output a first swing measurement data value and a second swing measurement data value with a swing behavior when a hitting person swings a hitting tool to hit an object being employed as an input, the first swing measurement data value and the second swing measurement data value for a swing behavior for each of the plurality of test hitting tools, the first swing measurement data value being dependent on operability of the hitting tool, the second swing measurement data value being dependent on a momentum that the hitting tool has at the time of the hitting; (3) for each of the plurality of test hitting tools, calculating a first score value based on the first swing measurement data value and calculating a second score value based on the second swing measurement data value, and calculating a total score value obtained by integrating the first score value and the second score value based on a weighting parameter designated by the hitting person, the first score value being an indicator value of operability of the hitting tool swung, the second score value being an indicator value of a hit-object speed (initial speed); and (4) generating diagnosis information for selection of a hitting tool using the total score value calculated and corresponding to each of the plurality of test hitting tools.
The foregoing and other objects, features, aspects and advantages of the present disclosure will become more apparent from the following detailed description of the present disclosure when taken in conjunction with the accompanying drawings.
Hereinafter, the present embodiment will be described with reference to figures. In the description below, the same components are denoted by the same reference characters. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated.
As shown in
Measurement device 100 includes: a sensor 110 attached to bat 3; and a computing device 120 to generate swing measurement data values from measurement values of sensor 110. Sensor 110 is constituted of, for example, an inertial sensor attached to a grip end portion of bat 3. In this case, acceleration data, angular velocity data, and geomagnetic data are output as the measurement values from sensor 110.
Sensor 110 and computing device 120 included in measurement device 100 are connected to each other via a wireless communication line such as Bluetooth (registered trademark). Thus, each of the measurement values output from sensor 110 is transmitted to computing device 120 by the wireless communication.
Computing device 120 is constituted of a computer device (for example, a smartphone) in which an application program for performing a calculation process for calculating a predetermined swing measurement data value using a measurement value is installed. Further, data (for example, the length and weight of bat 3) about an inertia characteristic of bat 3 used at the time of measurement is separately input to computing device 120, and this data is used for the calculation of the swing measurement data values.
As an example, “BLAST BASEBALL” commercially available from Mizuno Corporation in Japan can be applied to measurement device 100, for example. In this case, the measurement values of sensor (inertial sensor) 110 are used to generate, as the “swing measurement data values”, a bat speed at the time of impact, an upper swing degree, a bat angle, a swing time, a hand maximum speed, power, and the like.
The swing time is defined by a time taken from detection of start of the swing of bat 3 until the ball is hit by the bat, and can be used as a factor of bat operability, i.e., a swing measurement data value dependent on the bat operability. Alternatively, the acceleration (initial acceleration) at the time of the start of the swing of bat 3 can be obtained as a swing measurement data value, and can be used as an indicator of the bat operability.
Further, the power is found as a product of the swing speed at the time of impact, the average acceleration of the swing until the impact, and the weight of bat 3, and can be used as a factor of a bat momentum, i.e., a swing measurement data value dependent on a magnitude of a bat momentum. Alternatively, the swing speed at the time of impact can be used as a swing measurement data value serving as the factor of the bat momentum. Alternatively, the momentum at the time of impact may be directly obtained as a swing measurement data value from the above-described swing speed and the inertia characteristic of bat 3.
It should be noted that any device and system can be applied to measurement device 100 as long as the swing measurement data value dependent on the swing time or equivalent bat operability and the swing measurement data value dependent on the magnitude of the bat momentum at the time of impact such as the momentum or the power of bat 3 at the time of impact can be generated through the analysis of the swing behavior.
In response to input of the swing measurement data values generated by measurement device 100, data analysis device 200 generates diagnosis information (hereinafter, also referred to as “selection diagnosis information”) for selection of a bat 3 using the swing measurement data values. Data analysis device 200 can be constituted of a computer device (for example, a tablet terminal) in which an application program for performing a control process for a bat selection diagnosis method according to the present embodiment is installed.
The swing measurement data values can be input to data analysis device 200 by the user manually inputting the data values displayed on a screen of measurement device 100. Alternatively, the swing measurement data values generated by measurement device 100 may be automatically transmitted to data analysis device 200 by connecting measurement device 100 (computing device 120) and data analysis device 200 via the wireless communication line such as Bluetooth (registered trademark). Further, computing device 120 of measurement device 100 and data analysis device 200 may be constituted of the same terminal.
In the present embodiment, data analysis device 200 generates the selection diagnosis information for a bat in response to input of the swing measurement data values each obtained when performing the tee-batting using each of M bats 3 (M is an integer of 3 or more).
As shown in
Programs, which include a program for causing CPU 202 to perform the bat selection diagnosis method according to the present embodiment, are stored in advance in a part of region of memory 203, and CPU 202 can execute the program to perform the bat selection diagnosis using the swing measurement data values obtained by measurement device 100.
I/O circuit 204 can receive and output a signal and data from and to another device, such as computing device 120 of measurement device 100 or another equipment, via a communication device (not shown). I/O circuit 204 can communicate with the other device via a communication network such as the Internet.
Further, I/O circuit 204 is configured to receive an input value for an input key (not shown). The input key may be provided by dedicated hardware, or may be provided in a part of region of display unit 206 when display unit 206 is constituted of a touch panel.
As shown in
User interface unit 210 performs a data input guidance for a user of the bat selection diagnosis system, a swing guidance at the time of the tee-batting, and a process of outputting the selection diagnosis information.
In the data input guidance, a guidance is provided to input data of the user who is hitting person 2 in the tee-batting, such as an identification ID and a user attribute (classification of type of sport from baseball/rubber-ball baseball/softball, and classifications of age group and level such as a sport level). In accordance with the user attribute having been input, M test bats are selected from a bat group lined up in advance for each predetermined user classification. The user classification is defined in advance by a combination of the above-described classification of type of sport and the classification of level (for example, elementary school student/junior high school student/high school student/common person/professional).
Further, in the present embodiment, the user is guided to input, as a BS value, whether the user is oriented toward a high-average hitter or a power hitter, rather than choosing either one of the high-average hitter and the power hitter. The high-average hitter is a person who puts an importance on certainty, and the power hitter is a person who puts an importance on long-hitting power. The BS value is a weighting coefficient that can be continuously set. The BS value is input in a range of 0≤BS≤1.0, and it is defined that as the BS value is larger, the user is more oriented toward the high-average hitter who puts an importance on certainty in contact, whereas as the BS value is smaller, the user is more oriented toward the power hitter who puts an importance on a distance of hit. The BS value input by the user is received by input processing unit 220.
In the swing guidance, the selected M test bats are presented to guide the user to perform tee-batting by swinging the M bats one after another. Data (model name, moment of inertia, and the like) of the selected M test bats are input to input processing unit 220. It is assumed that M=3 in the below description of the present embodiment. The M test bats correspond to one example of a “plurality of test hitting tools”.
For example, a first test bat having a maximum moment of inertia, a second test bat having a minimum moment of inertia, and a third test bat having a moment of inertia smaller than that of the first test bat and larger than that of the second test bat are selected from the bat group corresponding to the user classification.
Alternatively, the user can directly designate M test bats as selection candidates from a bat line-up corresponding to the user classification.
Measurement device 100 outputs the swing measurement data values in conjunction with the swing guidance with the swing behavior for each of the M bats being employed as an input. Thus, swing time SWT and power PWR described above can be obtained from measurement device 100 for each of the M bats.
It should be noted that swing time SWT corresponds to one example of a “first swing measurement data value” dependent on the bat operability, and power PWR corresponds to one example of a “second swing measurement data value” dependent on the bat momentum at the time of impact.
In response to input of the swing measurement data values output from measurement device 100, input processing unit 220 generates a data file shown in
As shown in
Referring to
Next, the control process for the bat selection diagnosis by data analysis device 200 will be described with reference to a flowchart of
In a step (hereinafter simply described as “S”) 110, CPU 202 guides the user to input user data, and in S120, CPU 202 receives the user data input by the user.
The process of S110 corresponds to the data input guidance for the user data. Thus, in S120, input processing unit 220 receives, as the user data, the identification ID and the sport attribute (distinguishment among the hardball baseball/rubber-ball baseball/softball, age group/sport level, and the like).
In S130, CPU 202 selects M (here, M=3) test bats from a bat group corresponding to the user classification based on the user's attribute information received in S120. Further, in S130, the selected M test bats are presented to the user by user interface unit 210. For example, information for notifying the user of the M test bats can be presented using display unit 206.
It should be noted that in S130, the user may be requested to directly input M candidate bats among which swing behaviors are to be directly compared. In this case, the M test bats are selected in accordance with the user input.
In S135, a count value i for distinguishing the M test bats is initialized (i=1).
In S140, CPU 202 guides the user to input swing measurement data values (for example, swing time SWT and power PWR) obtained when the i-th test bat of the M test bats is swung. In S145, CPU 202 determines whether or not the swing measurement data values (for example, swing time SWT and power PWR), which are received in response to S140, is normal.
For example, the determination in S145 can be made by setting threshold values for swing time SWT and power PWR so as to distinguish their normal values from their abnormal values.
Further, when swing time SWT is too long because, for example, the test bat is too heavy, it is concerned that an appropriate bat selection diagnosis cannot be performed. To address this, when swing time SWT is larger than a first threshold value determined in advance, NO is determined in S145 to return the process to S140, thereby prohibiting to calculate a below-described total score value using such a swing time. Similarly, also when swing time SWT is larger than a second threshold value (shorter than the first threshold value) determined in advance in order to eliminate an abnormal value, NO can be determined in S145. It should be noted that when returning the process to S140, it is also possible to guide the user to change the test bat to another model having a smaller moment of inertia.
It should be noted that in the case where NO is determined in S145, the process may proceed to S150, in favor of reduction of time required to obtain the swing measurement data value with information being added to indicate that the swing measurement data value is an abnormal value.
In S150, CPU 202 stores the swing measurement data value (SWT, PWR) obtained when the i-th test bat is swung. In S150, the swing measurement data value determined as being the normal value by determining YES in S145 is stored. Alternatively, the swing measurement data value deviated from the threshold value may be stored with the information being added as described above to identify that it is an abnormal value.
In S155, CPU 202 compares count value i with M, which is the number of test bats. When i<M (NO in S155), count value i is incremented by one in S157, and the process returns to S140. Thus, the processes of S140 to S150 are repeatedly performed from i=1 to i=M.
When i≥M (YES is determined in S155), the swings of the M test bats are ended and CPU 202 therefore proceeds the process to S160. In S160, the data file shown in
It should be noted that in the processes of S140 to S150, it is also possible to guide the user to collectively input pieces of data for the M test bats.
Further, in the case of the configuration in which the swing measurement data values from measurement device 100 are automatically transmitted to data analysis device 200 through the wireless communication line, the user can be guided to perform the tee-batting using the i-th test bat in S140, and the swing measurement data values transmitted from measurement device 100 can be received before S145 with the swing behavior of the test bat by the user being employed as an input.
In S170, CPU 202 calculates a total score value TS for each test bat using the data stored in the data file.
Total score value TS is calculated in accordance with the following formula (1) using a score value STx for the bat operability, a score value SMy for the magnitude of the bat momentum at the time of impact, and the BS value input by the user.
In the formula (1), score value STx is calculated in accordance with a predetermined function formula having swing time SWT as a variable. The function formula is set such that the score value becomes higher as swing time SWT is smaller. For example, by dividing a predetermined constant by swing time SWT, score value STx can be calculated so as to be inversely proportional to swing time SWT.
Score value SMy can be calculated from power PWR, which is a swing measurement data value, in accordance with a function formula (linear function) represented by a formula (2). In accordance with the formula (2), score value SMy represents the momentum of the bat at the impact position.
In the formula (2), m represents a bat weight. r represents a distance between the impact position and the center of gravity, and the position of the center of percussion of the bat is used as the impact position. Further, I represents a moment of inertia of the bat. Each of m, r, and I in the formula (2) can be obtained as a constant for each test bat by input processing unit 220 when the M test bats are selected in S130.
Score value STx in the formula (1) corresponds to a “first score value”, which is an indicator value of the bat operability, and score value SMy corresponds to a “second score value”, which is an indicator value of a hit-ball speed (initial speed) that affects the distance of the hit ball. The “hit-ball speed” corresponds to a “hit-object speed”, when a ball 3 is an example of an “object” hit by a “hitting tool”.
It should be noted that when the formula (1) or (2) is calculated using the swing measurement data value to which the information for identifying that it is an abnormal value is added, the value of score value STx or SMy is set to zero, with the result that the calculation of total score value TS based on the abnormal value can be prohibited.
It should be noted that when finding score values STx, SMy, the physical quantities (measurement values) may not be used without modification, and statistical processing values may be employed as the swing measurement data values.
As shown in
Therefore, in order to express a quantitative difference when the same user swings the M test bats, the swing measurement data values can be converted into statistical processing values for which a relative relation is quantified among the M swing measurement data values.
As understood from the formula (1), total score value TS is changed according to the BS value under such a condition that score values STx, SMy are unchanged.
In
Generally, in the case of a bat having a small moment of inertia, swing time SWT becomes short whereas power PWR becomes small. Therefore, when the BS value is large, i.e., when the user is oriented toward the high-average hitter, total score value TS tends to be large in the case of the bat having a small moment of inertia.
In
It is understood that as the BS value is larger (the user is more oriented toward the high-average hitter), total score value TS of the bat having a small moment of inertia (IS1) with swing time SWT being shorter and power PWR being smaller becomes larger. In the example of
With the weighting coefficient (BS value) thus introduced, total score value TS reflecting a quantitative degree of the type of the user's orientation (toward the high-average hitter/power hitter) can be found for each of the M test bats. Thus, the bat selection diagnosis reflecting the type of the user's orientation can be performed using the indicator value (total score value) reflecting both the swing measurement data value for the bat operability and the swing measurement data value for the bat momentum at the time of impact.
Further, as shown in
With the function approximation formulas thus introduced, a total score value corresponding to a bat having a moment of inertia different from moments of inertia I1 to I3 of the test bats can also be found as a value on a graph.
In the example of
Referring to
It should be noted that in the present example in which M=3, the approximation to a quadratic function is employed as the function approximation; however, when the number of test bats is increased to M≥4, it is also possible to perform the function approximation using a higher order function. However, it can be said that M=3 is preferable in terms of actual operations in consideration of an increased required time resulting from the increased number of bats to be swung.
In S190, CPU 202 causes user interface unit 210 to output and present, to the user, the diagnosis information generated in S180.
In the example of
As in the example of
Further, in the example of
In a display area 360, the horizontal axis represents the moment of inertia, the vertical axis represents the total score value, the total score values in the swing behaviors when the tee-batting is performed with the three test bats (candidate bats 1 to 3) are plotted at 300A to 300C. Further, a function graph 310 according to a function approximation formula found from these plot points is presented.
By using function graph 310, a total score value of a bat other than the bats (candidate bats 1 to 3) actually swung can be evaluated using the moment of inertia of the bat.
Therefore, the total score values of the bats of the bat group corresponding to the user classification of the user who is a target for selection diagnosis can be calculated from the respective moments of inertia of the bats so as to generate pieces of selection diagnosis information (S180), and the pieces of selection diagnosis information can be presented together in display area 360.
In order to improve the effect of such a function approximation, the bat having a minimum moment of inertia, the bat having a maximum moment of inertia, and the bat having an intermediate moment of inertia therebetween among the bat group are preferably used as the test bats as described above.
As described above, with the bat selection diagnosis system according to the present embodiment, the diagnosis information for selection of a bat can be generated using the total score value obtained by integrating the indicator value (first score value) of the operability of the bat and the indicator value (second score value) of the hit-ball speed (initial speed) by the weighting coefficient (BS value) designated by the user. Therefore, both the swing measurement data value dependent on the operability and the swing measurement data value dependent on the momentum of the hitting tool (bat) at the time of impact can be utilized to propose an effective bat selection with an improved degree of utilization of the swing measurement data values through the input of the BS value that quantitatively represents the type of the user's orientation (toward the high-average hitter/power hitter). Further, with the BS value thus introduced, the selection of the type of orientation is not limited to selecting one from options and a selection diagnosis with increased flexibility can be attained.
Further, since the total score value calculated by the swing of each of the M test bats is indicated as a function with respect to the moment of inertia of the test bat, the total score value of a bat not actually swung can be found from the moment of inertia of the bat. Thus, more effective bat selection can be proposed.
It should be noted that the total score value can be calculated according to a modification described below in a formula (3) in addition to the basic calculation with the formula (1). In the formula (3), a total score value TS1 can be calculated by further multiplying with a coefficient of restitution RV of each bat. Coefficient of restitution RV differs depending on the material and structure of each bat; however, when the momentum of the bat is the same, the initial speed of the hit ball is higher as coefficient of restitution RV is higher, with the result that coefficient of restitution RV is considered to favorably affect the distance of hit. Therefore, for score value SMy which is an indicator value for the hit-ball speed (initial speed), the total score value (TS1) is calculated by using a value obtained by multiplying with coefficient of restitution RV, thus resulting in further enhanced selection diagnosis information for a bat.
Further, since a bat having a high coefficient of restitution RV is generally expensive in price, it is also possible to calculate a total score value TS2 by using a below-described formula (4) in which total score value TS1 of the formula (3) is divided by a coefficient CST for the price of the bat. By using total score value TS2, it is possible to provide selection diagnosis information in consideration of cost performance.
It is understood that total score value TS1 or TS2 calculated by the formula (3) or (4) reflects the coefficient of restitution unique to each bat or reflects the coefficient of restitution and the price, and is therefore useful for performance comparison among the designated candidate bats as illustrated in
It should be noted that in
Therefore, measurement device 100 and data analysis device 200 do not necessarily need to be disposed at a short distance, and the bat selection diagnosis based on the calculation of the total score value may be performed in such a manner that the data measured by measurement device 100 is recorded and is then separately input to data analysis device 200, for example.
Alternatively, the system configuration can be such that the swing measurement data values generated by measurement device 100 are input to data analysis device 200 via the Internet or the like. In this case, data analysis device 200 can be constituted of a server. Further, the hitting tool (bat) selection diagnosis according to the present embodiment can be similarly performed in such a manner that the user accesses data analysis device 200 using the Internet or the like and directly inputs the swing measurement data values.
It should be noted that in the above-described embodiment, the selection diagnosis through the calculation of the total score value using the swing measurement data values at the time of hitting of a stationary ball (object) with the bat has been illustratively described; however, the present embodiment is not limited to being applied to the time of hitting a stationary ball (object). For example, even in the case where the swing measurement data values are used with a swing behavior when actually hitting a moving (moving due to flying or the like) object with the hitting tool being employed as an input, the total score value can be calculated in the same principle to perform the selection diagnosis.
Further, the hitting tool, which is a target for the selection diagnosis, is not limited to a bat for baseball or softball as described at the beginning of the description. Specifically, a similar selection diagnosis can be performed for a hitting tool (for example, a table tennis racket or a tennis racket) used in a sport that requires consideration of a balance between operability and speed of the hit ball due to existence of constraint in time until hitting. Likewise, in view of the principle of the present disclosure, it is apparent that the object to be hit with the hitting tool when obtaining the swing measurement data values is not limited to a ball as long as similar swing measurement data values can be obtained.
In the above-described embodiment, it is also possible to provide a program for causing a computer to function to perform the control described in the above-described flowchart. Such a program can also be provided as a program product by recording it on a non-transitory computer-readable recording medium such as a flexible disk, CD-ROM (Compact Disk Read Only Memory), ROM, RAM, or memory card to be attached to the computer. Alternatively, the program can be provided by recording it on a recording medium such as a hard disk included in a computer. Further, the program can also be provided by downloading it via a network.
The program may be a program that invokes a necessary module among program modules, which are provided as a part of an operating system (OS) of the computer, at a predetermined timing so as to perform a process. In this case, the program itself does not include the above-described modules, and the process is performed in cooperation with the OS. Such a program not including the modules can also be included in the program according to the present embodiment.
Further, the program according to the present embodiment may be provided by incorporating it into a part of another program. Also in this case, the program itself does not include modules included in the other program, and a process is performed in cooperation with the other program. Such a program incorporated in the other program can also be included in the program according to the present embodiment.
The configuration illustrated as the above-described embodiment is an exemplary configuration of the present embodiment, and can be combined with another known technique, or can be modified, for example, can be partially omitted without departing from the gist of the present embodiment. Further, the above-described embodiment may be implemented with process and configuration described in another embodiment being appropriately employed.
Although the present disclosure has been described and shown in detail, it is clearly understood that the same is by way of illustration and example only and is not to be taken by way of limitation, the scope of the present disclosure being interpreted by the terms of the appended claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2023-185761 | Oct 2023 | JP | national |
