JP2022046026A - Analyzer, system, method, and program - Google Patents

Abstract

To estimate finger force applied to a ball.SOLUTION: An analyzer comprises: an acquisition unit 1005; a translation acceleration calculation unit 1020 for calculating translation acceleration from output data; filter creation units 1030, 1035 for creating a filter to be used for translation acceleration data; a finger force calculation unit 1040 for calculating finger force applied to a ball; and an output unit 1045 for outputting information. The acquisition unit 1005 acquires the output data from a sensor apparatus or a camera. The translation acceleration calculation unit 1020 calculates the translation acceleration data on the ball on the basis of the output data. The filter creation units 1030, 1035 uses the filter to extract a high frequency component of the translation acceleration data. The finger force calculation unit 1040 calculates the finger force on the basis of the high frequency component and the ball's weight. The output unit 1045 outputs information on the finger force.SELECTED DRAWING: Figure 10

Description

The present disclosure relates to an estimation of the finger force applied to the ball.

In recent years, there has been known a method of measuring rotation parameters such as the movement trajectory, rotation speed, and direction of a rotation axis of a baseball ball at the time of pitching by using information from a sensor built in the ball. For example, Japanese Patent Application Laid-Open No. 2018-134153 (Patent Document 1) discloses a pitching analysis system.

The pitch analysis system according to Patent Document 1 includes a ball having a built-in sensor unit and a transmission unit, and an analysis device that receives and analyzes the detection value of the sensor unit transmitted from the transmission unit. The sensor unit includes a substrate, an acceleration sensor mounted on the substrate, a geomagnetic sensor, and a gyro sensor. The analyzer identifies and stores the initial direction of the substrate in the global coordinate system, calculates the sequentially changing direction of the substrate in the global coordinate system, calculates the sequentially changing acceleration of the ball in the global coordinate system, and calculates the sequentially changing acceleration of the ball in the global coordinate system. Calculate the movement trajectory of the ball in (see [Summary]).

Japanese Unexamined Patent Publication No. 2018-134153

According to the technique disclosed in Patent Document 1, it is not possible to estimate the finger force applied to the ball. Therefore, there is a need for a technique for estimating the finger force applied to the ball.

The present disclosure has been made in view of the above background, and an object in a certain aspect is to provide a technique for estimating a finger force applied to a ball.

An analysis device according to an embodiment includes a sensor device built in the ball, an acquisition unit for acquiring output data from a camera, a translational acceleration calculation unit for calculating translational acceleration from the output data, and translational acceleration. It includes a filter generation unit that generates a filter used for data, a finger force calculation unit for calculating the finger force applied to the ball, and an output unit for outputting information. The acquisition unit acquires output data from the sensor device or the camera. The translational acceleration calculation unit calculates the translational acceleration data of the ball based on the output data. The filter generation unit extracts the high frequency component of the translational acceleration data by the filter. The finger force calculation unit calculates the finger force based on the high frequency component and the mass of the ball. The output unit outputs information about finger force.

According to other embodiments, a system for estimating the finger force applied to the ball is provided. This system includes a ball with a built-in sensor device and an analysis device. The sensor device includes an acceleration sensor, a communication unit for communicating with the analysis device, and a gyro sensor. The analysis device generates an acquisition unit for receiving acceleration data and angular velocity data transmitted by the sensor device, a translational acceleration calculation unit for calculating translational acceleration data from the acceleration data, and a filter used for translational acceleration data. It includes a filter generation unit, a finger force calculation unit for calculating the finger force applied to the ball, and an output unit for outputting information. The sensor device records measurement data including acceleration data and angular velocity data at the time of throwing the ball, and transmits the measurement data to the analysis device. The analysis device acquires the acceleration data and angular velocity data detected in time series by the sensor device by the acquisition unit, and calculates the translational acceleration data of the ball based on the received acceleration data and angular velocity data by the translational acceleration calculation unit. Then, the high frequency component of the translational acceleration data is extracted by the filter, the finger force is calculated based on the high frequency component and the mass of the ball by the force calculation unit, and the information on the finger force is output by the output unit.

According to other embodiments, another system for estimating the finger force applied to the ball is provided. The system includes a camera that captures the ball and an analyzer. The analysis device includes an acquisition unit for receiving imaging data from the camera, a translational acceleration calculation unit for calculating translational acceleration data based on the imaging data, and a filter generation unit for generating a filter used for translational acceleration data. , A finger force calculation unit for calculating the finger force applied to the ball, and an output unit for outputting information. The camera images the thrown ball and outputs the image data of the ball to the analysis device. The analysis device acquires the imaging data by the acquisition unit, calculates the translational acceleration data of the ball by the differential processing of the position information of the center of the ball obtained by analyzing the imaging data by the translational acceleration calculation unit, and filters it. The high-frequency component of the translational acceleration data is extracted, the finger force calculation unit calculates the finger force based on the high-frequency component and the mass of the ball, and the output unit outputs information on the finger force.

According to other embodiments, a method performed by a computer to analyze the finger force applied to the ball is provided, which method is output from a sensor device built into the ball or a camera that images the ball. A step to acquire data, a step to calculate the translational acceleration data of the ball based on the output data, a step to extract the high frequency component of the translational acceleration data by a filter, and a finger based on the high frequency component and the mass of the ball. It includes a step of calculating force and a step of outputting information about finger force.

According to still another embodiment, a program for causing a computer to perform the above method is provided.

According to certain embodiments, it is possible to estimate the finger force exerted on the ball.
The above and other objectives, features, aspects and advantages of this disclosure will become apparent from the following detailed description of this disclosure as understood in connection with the accompanying drawings.

It is a figure which shows an example of the whole structure of the analysis system 1000 according to a certain embodiment. It is a figure which shows an example of the schematic structure of the ball 2 according to a certain embodiment. It is a figure which shows an example of the hardware composition of the analysis apparatus 10 according to a certain embodiment. It is a figure which shows an example of the hardware composition of the sensor device 20 according to a certain embodiment. It is a figure which shows an example of the force applied to a ball 2. It is a figure which shows an example of the analysis result 600 of the data obtained from the sensor device 20 built in the ball 2. It is a figure which shows an example of the pitching data of two different pitchers. It is a figure which shows the 1st example of the display of the analysis result 600 by the analysis apparatus 10. It is a figure which shows the 2nd example of the display of the analysis result 600 by the analysis apparatus 10. An example of a block diagram showing the configuration of the functions included in the analysis device 10 is shown. It is a figure which shows an example of the internal processing of an analysis apparatus 10.

Hereinafter, embodiments of the technical concept according to the present disclosure will be described with reference to the drawings. In the following description, the same parts are designated by the same reference numerals. Their names and functions are the same. Therefore, the detailed description of them will not be repeated.

<Overall system configuration>
FIG. 1 is a diagram showing an example of the overall configuration of the analysis system 1000 according to the present embodiment. With reference to FIG. 1, the analysis system 1000 analyzes the acceleration and the angular velocity of the baseball ball 2 thrown by the pitcher 5, and calculates the force applied to the ball 2. The analysis system 1000 includes an analysis device 10 and a ball 2 in which a sensor device 20 is built. In FIG. 1, three axes orthogonal to each other in the sensor coordinate system are represented by x-axis, y-axis, and z-axis, and three axes orthogonal to each other in the absolute coordinate system are represented by X-axis, Y-axis, and Z-axis. There is.

The analysis device 10 acquires information from the sensor device built in the ball 2 and analyzes the acquired information. In a certain aspect, the analysis device 10 may be a PC (Personal Computer), a tablet terminal, a smartphone, a virtual machine in a cloud environment, or an arbitrary information processing device.

The information analyzed by the analyzer 10 is, for example, the acceleration, angular velocity and / or geomagnetism of the ball 2. The analysis device 10 calculates the finger force applied to the ball 2 by analyzing the acceleration, the angular velocity, and / or the geomagnetism of the ball 2. In one aspect, the analyzer 10 may use only the acceleration and angular velocity of the ball 2. The "finger power" here is the power transmitted from the finger of the pitcher 5 to the ball 2 at the moment of the release of the ball 2. Further, in a certain aspect, the analysis device 10 can output advice on improvement of the pitching method and / or advice on the training method based on the obtained analysis result (value of finger force applied to the ball 2 and the like).

The analysis device 10 communicates with the sensor device 20 by a wireless communication method. In a certain aspect, the wireless communication method used by the analyzer 10 may be BLE (Bluetooth (registered trademark) low energy). In another aspect, the wireless communication method used by the analyzer 10 may be Bluetooth®, a wireless LAN (local area network), or any other wireless communication method.

The sensor device 20 may be used, for example, by being incorporated in a baseball ball 2. The sensor device 20 includes various sensors such as an acceleration sensor, a gyro sensor and / or a geomagnetic sensor. The sensor device 20 may transmit the output values obtained from these various sensors to the analysis device 10. In a certain aspect, the sensor device 20 may have a function of analyzing the output value of each sensor instead of the analysis device 10. In that case, the sensor device 20 may transmit the analysis result (value of finger force applied to the ball 2 and the like) to the analysis device 10.

FIG. 2 is a diagram showing an example of a schematic configuration of a ball 2 according to the present embodiment. The appearance of the ball 2 is the same as that of a general hardball. The ball 2 has a leather outer skin and is configured so that the seams can be visually recognized. The ball 2 has a built-in sensor device 20 for detecting the behavior of the ball 2 in the center thereof. The sensor device 20 is fixed with a polycarbonate capsule 62 and a silicone gel 64, and has excellent impact resistance.

Again, with reference to FIG. 1, the sensor device 20 may detect acceleration, angular velocity, and geomagnetism (magnetic field or magnetic flux density) in the sensor coordinate system (ie, the local coordinate system). More specifically, the sensor device 20 includes two accelerometers for low acceleration and high acceleration, a gyro sensor, and a geomagnetic sensor. The accelerometer detects acceleration data indicating acceleration in three axes (x-axis, y-axis, and z-axis) orthogonal to each other. The gyro sensor detects angular velocity data indicating angular velocities in three axes (x-axis, y-axis, and z-axis) orthogonal to each other. The geomagnetic sensor detects geomagnetic data indicating three axial magnetic fields (magnetic flux densities) orthogonal to each other. As the geomagnetic sensor, for example, an MR (Magnet electrochemical) element, an MI (Magnet impedance) element, a Hall element, or the like is used.

<Hardware configuration>
(Analyzer 10)
FIG. 3 is a diagram showing an example of the hardware configuration of the analysis device 10 according to the present embodiment. With reference to FIG. 3, the analysis device 10 has a CPU (Central Processing Unit) 102, a memory 104, a touch panel 106, a button 108, a display 110, a wireless communication unit 112, and a communication interface as main components. It includes 113, a memory interface (I / F) 114, a speaker 116, a microphone 118, a communication interface (I / F) 120, and a camera 122. Further, the recording medium 115 is an external storage medium.

The CPU 102 controls the operation of each part of the analysis device 10 by reading and executing the program stored in the memory 104. More specifically, the CPU 102 realizes each of the processes (steps) of the analysis device 10 described later by executing the program.

The memory 104 is realized by a RAM (Random Access Memory), a ROM (Read-Only Memory), a flash memory, or the like. The memory 104 stores a program executed by the CPU 102, data used by the CPU 102, and the like.

The touch panel 106 is provided on the display 110 having a function as a display unit, and may be of any type such as a resistance film method and a capacitance method. The button 108 is arranged on the surface of the analysis device 10, receives an instruction from the user, and inputs the instruction to the CPU 102. The display 110 is arranged on the surface of the analysis device 10. In a certain aspect, it may be used by connecting to the analysis device 10. In that case, the analysis device 10 includes an output interface for connecting the display 110. In another aspect, the display 110 may be any output device such as a cathode ray tube display, a liquid crystal display or an organic EL (Electro-Luminescence) display.

The wireless communication unit 112 connects to the mobile communication network via the communication antenna 113 and transmits / receives signals for wireless communication. As a result, the analysis device 10 can communicate with a predetermined external device via a mobile communication network such as LTE (Long Term Evolution).

The memory interface (I / F) 114 reads data from the external recording medium 115. The CPU 102 reads the data stored in the external recording medium 115 via the memory interface 114, and stores the data in the memory 104. The CPU 102 reads data from the memory 104 and stores the data in the external recording medium 115 via the memory interface 114.

The recording medium 115 includes a CD (Compact Disc), a DVD (Digital Versatile Disk), a BD (Blu-ray (registered trademark) Disc), a USB (Universal Serial Bus) memory, a memory card, and an FD (Flexible Disk). Alternatively, a medium such as a hard disk that stores a program non-volatilely can be mentioned.

The speaker 116 outputs voice based on a command from the CPU 102. The microphone 118 receives an utterance to the analysis device 10. The communication interface (I / F) 120 is, for example, a communication interface for transmitting and receiving data between the analysis device 10 and the sensor device 20, and is realized by an adapter, a connector, or the like. The communication method is, for example, wireless communication by BLE, wireless LAN, or the like.

The camera 122 photographs the ball 2. In a certain aspect, the camera 122 may be built in the analysis device 10 or may be connected to the analysis device 10. The CPU 102 may estimate the translational acceleration of the ball 2 by using the image pickup data (image or video) of the camera 122 instead of the output value of each sensor acquired from the sensor device 20. In that case, the ball 2 does not have to include the sensor device 20. As an example, the CPU 102 acquires the data of the position of the ball 2 (position coordinates in the image, etc.) from the image pickup data of the camera 122. Then, the CPU 102 can obtain the translational acceleration of the ball 2 by differentiating the position coordinates of the center of the ball 2 (differentiating twice).

(Sensor device 20)
FIG. 4 is a diagram showing an example of the hardware configuration of the sensor device 20 according to the present embodiment. With reference to FIG. 4, the sensor device 20 has, as main components, a CPU 202 for executing various processes, a memory 204 for storing programs, data, etc. executed by the CPU 202, and an acceleration sensor 220. A geomagnetic sensor 208 that detects magnetic fields in three axial directions that are orthogonal to each other, a communication interface (I / F) 210 for communicating with the analyzer 10, and a storage battery 212 that supplies power to various components of the sensor device 20. Includes a gyro sensor 214 that detects three axial angular velocities that are orthogonal to each other.

The acceleration sensor 220 includes a low acceleration sensor 205 and a high acceleration sensor 206. The low acceleration sensor 205 is an acceleration sensor for detecting a low acceleration range (for example, less than 24 G), and detects acceleration in three axial directions orthogonal to each other. The high acceleration sensor 206 is an acceleration sensor for detecting a high acceleration range (for example, 24 G or more) that cannot be detected by the low acceleration sensor, and detects acceleration in three axial directions orthogonal to each other. Although the high acceleration sensor 206 can detect a low acceleration range, the low acceleration sensor 205 has higher detection accuracy than the high acceleration sensor 206 in the low acceleration range.

<Analysis data>
Next, an example of analysis data obtained by analysis by the analysis system 1000 will be described with reference to FIGS. 5 to 9. Hereinafter, the analysis data will be described using a list, a graph, or the like, but in reality, the analysis system 1000 may only calculate and output the numerical data included in the analysis data.

FIG. 5 is a diagram showing an example of the force applied to the ball 2. The force applied to the ball 2 from the pitcher 5's finger mainly includes a first force 510 and a second force 520. The first force 510 is a force for the finger to keep the ball 2 when the pitcher 5 performs a pitching motion. When the pitcher 5 performs a pitching motion, the finger of the pitcher 5 applies a first force 510 to the ball 2 in order to counteract the force 505 received from the ball 2. The finger force that produces the first force 510 is sometimes referred to as passive grip strength.

With respect to the first force 510, the second force 520 is the force that the finger applies to the ball 2 in the direction indicated by the arrow of the second force 520 when the pitcher 5 releases the ball 2. The second force 520 is sometimes referred to as finger force or shear force. There is a positive correlation between the magnitude of the second force 520 and the number of revolutions of the ball 2. The finger force that produces the second force 520 is sometimes referred to as the active grip strength. Also, applying a second force 520 to the ball may be referred to as "cutting the ball" or "crushing the ball".

The first force 510 and the second force 520 can be calculated from the combined acceleration of the three axes of the output value of the acceleration sensor 220 of the ball 2. The three-axis combined acceleration is the combined value of the x, y, and z-axis acceleration components. The combined acceleration of the ball 2 includes translational acceleration, centrifugal acceleration, tangential acceleration and gravitational acceleration. In certain aspects, the acceleration of the ball 2 may include a Coriolis component. The translational acceleration is the acceleration generated by the translational motion of the ball. Centrifugal acceleration and tangential acceleration are accelerations generated by the rotational motion of the ball.

Centrifugal acceleration and tangential acceleration are generated due to the deviation of the sensor device 20 from the center of the ball 2. The sensor device 20 located at a position deviated from the center of the ball 2 detects the acceleration due to the rotational movement by the rotation of the ball 2. The acceleration due to the rotational motion can be centrifugal acceleration and tangential acceleration.

First, the analysis device 10 acquires the output value of each sensor of the ball 2 obtained from the sensor device 20. Next, the analysis device 10 calculates the combined acceleration of the ball 2 from the output value of the acceleration sensor 220. Next, the analysis device 10 can calculate the centrifugal acceleration and the tangential acceleration based on the distance from the center of the sensor device 20 to the center of rotation and the data of the angular velocity of the ball 2.

Next, the analysis device 10 calculates the translational acceleration of the ball 2 by reducing the centrifugal acceleration, the tangential acceleration, and the gravitational acceleration from the combined acceleration of the ball 2. In certain aspects, the analyzer 10 may further reduce the Coriolis component from the synthetic acceleration of the ball 2. Further, in another aspect, the analysis device 10 can calculate the centrifugal acceleration and the tangential acceleration by using a part or all of the output value of the gyro sensor 214 and the output value of the geomagnetic sensor 208 as an example.

Next, the analysis device 10 calculates the first force 510 and the second force 520 from the translational acceleration. In a certain aspect, the analyzer 10 can separate the translational acceleration into a first component and a second component by applying the translational acceleration data to a high-pass filter. The first component is mainly the acceleration generated by the first force 510. The second component is the acceleration mainly generated by the second force 520. Generally, the first component is a low frequency component and the second component is a high frequency component. The analyzer 10 can extract the second component from the translational acceleration, for example, by applying the translational acceleration data to a high-pass filter that passes a high-frequency component of a certain frequency (for example, 40 Hz) or higher. The parameters of the high-pass filter may be set arbitrarily.

The analyzer 10 can calculate the first force 510 and the second force 520 by multiplying each of the first component and the second component extracted from the translational acceleration by the mass of the ball 2.

FIG. 6 is a diagram showing an example of an analysis result 600 of data obtained from the sensor device 20 built in the ball 2. The analysis device 10 can calculate the analysis result 600 based on the output values of various sensors obtained from the sensor device 20.

The analysis result 600 includes a rotation speed 601, a shear force appearance timing 602, an active component 603, a passive component 604, a synthetic value 605, an active component ratio 606, and an SPV (Spin rate Per Velocity) 607. .. In addition to the elements of the above analysis result, the analysis result 600 may include any item that can be calculated from the acceleration sensor 220, the geomagnetic sensor 208, and the gyro sensor 214.

The rotation speed 601 is the rotation speed of the ball 2. In a certain aspect, the rotation speed 601 may be calculated based on the angular velocity data output by the gyro sensor 214. In another aspect, the rotation speed 601 may be calculated based on the geomagnetic data output by the geomagnetic sensor 208. Further, in another aspect, the rotation speed 601 may be calculated based on both the angular velocity data and the geomagnetic data.

The shear force appearance timing 602 is a timing at which the second force 520 is generated. The shear force appearance timing 602 is represented, for example, several milliseconds before the release timing with respect to the moment of the release timing. For example, the release timing "-6" indicates that the shear force was generated 6 milliseconds before the release timing. In a certain aspect, the unit of the shear force appearance timing 602 may be any unit such as microseconds or seconds.

The active component 603 indicates a second force 520 exerted on the ball 2. The passive component 604 indicates a first force 510 exerted on the ball 2. The composite value 605 is a composite value of the active component 603 and the passive component 604 when the peak value of the active component 603 occurs. Each component is a composition of force vectors.

The ratio of the active component 606 indicates the ratio of the active component 603 to the synthetic value 605. SPV607 indicates the number of revolutions per unit speed. In certain aspects, the unit of speed can be set arbitrarily.

For example, the pitcher 5 or the leader of the pitcher 5 knows what kind of correlation the rotation speed 601 of the ball has with each of the active component 603 and the passive component 604 by referring to the analysis result 600. be able to.

FIG. 7 is a diagram showing an example of pitching data of two different pitchers. With reference to FIG. 7, the difference between the data of the pitcher having a low rotation speed of the ball 2 at the time of pitching and the data of the pitcher having a high rotation speed of the ball 2 at the time of pitching will be described. The upper graph of FIG. 7 is a graph of pitcher 5A having a low rotation speed of ball 2. The lower graph of FIG. 7 is a graph of pitcher 5B having a high ball rotation speed. The horizontal axis of each graph shows the time axis, and the vertical axis shows the magnitude of the force applied to the ball 2.

Graphs 710A and 710B are passive components of the force applied to the ball 2, and correspond to the passive component 604. Graphs 720A and 720B are active components of the force applied to the ball 2, and correspond to the active component 603. Graphs 730A and 730B are synthetic values of the forces applied to the ball 2, and correspond to the synthetic value 605. The apex portion of each graph is the timing immediately before the release of the ball 2, and is the timing when the force is most applied to the ball 2.

Comparing the upper and lower graphs, there is a clear difference between the combined value of the force applied to the ball 2 thrown by the pitcher 5A (graph 730A) and the combined value of the force applied to the ball 2 thrown by the pitcher 5B (graph 730B). There is no.

Further, there is no clear difference between the passive component of the force applied to the ball 2 thrown by the pitcher 5A (graph 710A) and the passive component of the force applied to the ball 2 thrown by the pitcher 5B (graph 710B).

However, it can be seen that there is a large difference between the active component of the force applied to the ball 2 thrown by the pitcher 5A (graph 720A) and the active component of the force applied to the ball 2 thrown by the pitcher 5B (graph 720B). From this, it is predicted that the rotation speed of the ball 2 will also increase by improving the active component of the force applied to the ball 2 (second force 520).

FIG. 8 is a diagram showing a first example of displaying the analysis result 600 by the analysis device 10. The upper graph 810 of FIG. 8 shows the correlation between the rotation speed 601 of the ball 2 and the active component 603 (second force 520). The horizontal axis of the graph 810 is the rotation speed 601 of the ball 2, and the vertical axis is the peak value of the active component 603. The peak value of the active component 603 corresponds to, for example, the value of the apex of the graphs 720A and 720B.

Graph 810 is obtained from the results of a plurality of pitches (each plot) by regression analysis or the like. Referring to the graph 810, as the rotation speed 601 of the ball 2 increases, the peak value of the active component 603 also increases. From this, it can be seen that there is a positive correlation between the rotation speed 601 of the ball 2 and the active component 603.

The lower graph 820 of FIG. 8 shows the correlation between the rotation speed 601 of the ball 2 and the passive component 604 (first force 510). The horizontal axis of the graph 820 is the rotation speed 601 of the ball 2, and the vertical axis is the peak value of the passive component 604. The peak value of the passive component 604 corresponds to, for example, the value of the apex of the graphs 710A and 710B.

The graph 820 is obtained from the results of a plurality of pitches (each plot) by regression analysis or the like. Referring to the graph 820, the peak value of the passive component 604 hardly changes even if the rotation speed 601 of the ball 2 increases. From this, it can be seen that there is no correlation between the rotation speed 601 of the ball 2 and the passive component 604.

FIG. 9 is a diagram showing a second example of displaying the analysis result 600 by the analysis device 10. The graph shown in FIG. 9 shows the relationship between the SPV 607 of the ball 2 and the active component 603 (second force 520) or the passive component 604 (first force 510).

The upper graph 910 of FIG. 9 shows the correlation between the SPV 607 of the ball 2 and the active component 603 (second force 520). By comparing the SPV 607 of the ball 2 with the active component 603, a correlation can be obtained between the rotation speed 601 of the ball 2 and the active component 603 excluding the influence of the ball speed. The horizontal axis of the graph 910 is the SPV607 of the ball 2, and the vertical axis is the peak value of the active component 603.

Graph 910 is obtained from the results of a plurality of pitches (each plot) by regression analysis or the like. Referring to the graph 910, as the SPV 607 of the ball 2 increases, the peak value of the active component 603 also increases. From this, it can be seen that there is a positive correlation between the rotation speed of the ball 2 and the active component 603 even if the influence of the ball speed of the ball 2 is eliminated.

The lower graph 920 of FIG. 9 shows the correlation between the SPV 607 of the ball 2 and the passive component 604 (first force 510). By comparing the SPV 607 of the ball 2 with the passive component 604, a correlation can be obtained between the rotation speed 601 of the ball 2 and the passive component 604 excluding the influence of the ball speed. The horizontal axis of the graph 920 is the SPV607 of the ball 2, and the vertical axis is the peak value of the passive component 604.

The graph 920 is obtained from the results of a plurality of pitches (each plot) by regression analysis or the like. Referring to the graph 920, even if the SPV607 of the ball 2 is increased, the peak value of the passive component 604 is hardly changed. From this, it can be seen that there is no correlation between the rotation speed of the ball 2 and the passive component 604 even if the influence of the ball speed of the ball 2 is eliminated.

As described above, there is a correlation between the active component 603 (second force 520) of the force applied to the ball 2 and the rotation speed of the ball 2. Therefore, measuring the active component 603 (second force 520) of the force applied to the ball 2 and outputting the active component 603 is about grasping the current finger force of the pitcher, improving the pitching method, and training. It is expected to be useful for creating advice.

In a certain aspect, the analysis device 10 may output the data acquired from the sensor device 20 as shown in FIGS. 5 to 9 above, or a list and / or a graph of the analysis result of the data. In a certain aspect, the analysis device 10 may output the data acquired from the sensor device 20 or a list and / or a graph of the analysis result of the data to the display 110. In another aspect, the analysis device 10 may transmit the data acquired from the sensor device 20 or the list and / or graph of the analysis result of the data from the communication interface 120 to another device. Further, in another aspect, the analysis device 10 may output the data acquired from the sensor device 20 or the list and / or graph of the analysis result of the data to both the display 110 and the communication interface 120.

Further, in a certain aspect, the analysis device 10 may generate advice on improvement of the pitching method and / or advice on the training method from the analysis result of the data acquired from the sensor device 20, and output the advice. In that case, the analyzer 10 may output the advice to the display 110 and / or the communication interface 120.

<Internal processing of analyzer 10>
Next, the internal processing of the analysis device 10 and the input / output of data will be described with reference to FIGS. 10 and 11. FIG. 10 shows an example of a block diagram showing a configuration of functions included in the analysis device 10. Each function can be realized as hardware or software. In certain aspects, the functional block shown in FIG. 10 may be executed as software on the hardware of the analyzer 10 shown in FIG. In another aspect, some or all of the functional blocks shown in FIG. 10 may be executed as software on the hardware of the sensor device 20 shown in FIG.

The analysis device 10 includes an information input unit 1005, an angular velocity setting unit 1010, a centrifugal / tangential acceleration calculation unit 1015, a translational acceleration calculation unit 1020, a high-pass filter coefficient calculation unit 1025, and a forward high-pass filter calculation unit 1030. A reverse high-pass filter calculation unit 1035, a finger force calculation unit 1040, and an advice output unit 1045 are provided. Hereinafter, the input / output of data of each configuration will be described with reference to FIG.

In step S1005, the information input unit 1005 acquires time-series measurement data (acceleration, angular velocity, and / or geomagnetism) from the sensor device 20. In a certain aspect, the information input unit 1005 may also receive data on the distance from the center of the sensor device 20 to the center of rotation. In another aspect, the information input unit 1005 may hold data of the distance from the center of the sensor device 20 to the center of rotation in advance. Alternatively, the information input unit 1005 may acquire the image pickup data of the ball 2 from the camera 122. In that case, the information input unit 1005 transmits the data of the position of the ball 2 obtained from the image pickup data of the ball 2 to the translational acceleration calculation unit 1020. If the position data of the ball 2 is used, steps S1020, S1025, S1035, S1040 may not be executed.

Further, in a certain aspect, the information input unit 1005 may receive various data from the sensor device 20 via the wireless communication path. In another aspect, the information input unit 1005 may receive various data from the sensor device 20 via the wired communication path.

In step S1010, the information input unit 1005 transmits the acquired time-series measurement data to the release calculation unit 1050. The information input unit 1005 transmits at least time-series acceleration data to the release calculation unit 1050.

In step S1015, the release calculation unit 1050 estimates the release time based on the acquired time-series measurement data. In a certain aspect, the release calculation unit 1050 may estimate the release time based on the amount of change in acceleration. The acceleration data is a time-series discrete value, and the release calculation unit 1050 can calculate the amount of change in acceleration by, for example, obtaining the difference between adjacent discrete values. The release calculation unit 1050 transmits the estimated release time to the angular velocity setting unit 1010 to the finger force calculation unit 1040.

In step S1020, the information input unit 1005 transmits the acquired time-series measurement data (at least time-series angular velocity data) to the angular velocity setting unit 1010. In step S1025, the information input unit 1005 transmits the acquired time-series measurement data and the data of the distance from the center of the sensor device 20 to the center of rotation to the centrifugal / tangential acceleration calculation unit 1015. In step S1030, the information input unit 1005 transmits the acquired time-series measurement data or the time-series three-axis combined acceleration data to the translational acceleration calculation unit 1020.

In step S1035, the angular velocity setting unit 1010 calculates the angular velocity data from the time-series angular velocity data to the release time based on the acquired measurement data and the release time. In a certain aspect, the angular velocity setting unit 1010 may use the output value of the gyro sensor 214 as the angular velocity. In another aspect, the angular velocity setting unit 1010 may estimate an approximate value of the angular velocity of the ball 2 from the output value of the gyro sensor 214 and the rotation speed of the ball 2 calculated from the geomagnetic data. The angular velocity setting unit 1010 transmits the angular velocity data up to the release time to the centrifugal / tangential acceleration calculation unit 1015.

In step S1040, the centrifugal / tangential acceleration calculation unit 1015 performs centrifugal acceleration and tangential acceleration based on the acquired angular velocity data up to the release time, the distance data from the center of the sensor device 20 to the center of rotation, and the release time. Calculate the acceleration. The centrifugal / tangential acceleration calculation unit 1015 transmits the centrifugal acceleration and the tangential acceleration to the translational acceleration calculation unit 1020.

In step S1045, the translational acceleration calculation unit 1020 calculates the translational acceleration from the acquired time-series three-axis combined acceleration data, the centrifugal acceleration, the tangential acceleration, and the release time. The translational acceleration calculation unit 1020 can calculate the translational acceleration of the ball 2 by reducing the centrifugal acceleration, the tangential acceleration, and the gravitational acceleration from the combined acceleration. In a certain aspect, the translational acceleration calculation unit 1020 may calculate the translational acceleration data near the release time from the three-axis synthetic acceleration data included within a certain time near the release time based on the release time. .. Alternatively, the translational acceleration calculation unit 1020 can calculate the translational acceleration of the ball 2 based on the data of the position of the ball 2. In a certain aspect, the translational acceleration calculation unit 1020 may calculate the translational acceleration data near the release time from the data of the position of the ball 2 included within a certain time near the release time based on the release time.

Further, the translational acceleration calculation unit 1020 calculates the sampling frequency and the cutoff frequency of the translational acceleration data. The translational acceleration calculation unit 1020 transmits the translational acceleration data, the sampling frequency, and the cutoff frequency to the high-pass filter coefficient calculation unit 1025. In a certain aspect, the translational acceleration calculation unit 1020 may transmit only the translational acceleration data to the high-pass filter coefficient calculation unit 1025. In that case, the high-pass filter coefficient calculation unit 1025 calculates the sampling frequency and cutoff frequency of the translational acceleration data.

In step S1050, the high-pass filter coefficient calculation unit 1025 calculates the high-pass filter coefficient based on the sampling frequency and the cutoff frequency. The high-pass filter coefficient calculation unit 1025 transmits the translational acceleration data and the high-pass filter to the forward high-pass filter calculation unit 1030.

In step S1055, the forward high-pass filter calculation unit 1030 generates a forward high-pass filter based on the acquired high-pass filter coefficient. Further, the forward high-pass filter calculation unit 1030 applies the translational acceleration data to the forward high-pass filter. After that, the forward high-pass filter calculation unit 1030 transmits the translational acceleration data that has passed through the forward high-pass filter and the high-pass filter coefficient to the reverse high-pass filter calculation unit 1035. In a certain aspect, the forward high-pass filter calculation unit 1030 may apply only the translational acceleration data around the release time to the forward high-pass filter based on the release time.

In step S1060, the reverse high-pass filter calculation unit 1035 generates a reverse high-pass filter based on the acquired high-pass filter coefficient. Further, the reverse high-pass filter calculation unit 1035 applies the translational acceleration data that has passed through the forward high-pass filter to the reverse high-pass filter. By passing the translational acceleration data through both the forward high-pass filter and the reverse high-pass filter, the high-frequency component of the translational acceleration is extracted. The high frequency component of the translational acceleration corresponds to the second component described with reference to FIG. The reverse high-pass filter calculation unit 1035 transmits the high frequency component of the translational acceleration to the finger force calculation unit 1040. In a certain aspect, the reverse high-pass filter calculation unit 1035 may apply only the translational acceleration data around the release time to the reverse high-pass filter based on the release time.

In step S1065, the finger force calculation unit 1040 calculates the peak value of the finger force applied to the ball 2 based on the acquired release time and the high frequency component of the translational acceleration. For example, the finger force calculation unit 1040 can calculate the finger force applied to the ball 2 based on the mass of the ball 2, the high frequency component of the translational acceleration, and the like. The finger force applied to the ball 2 corresponds to the second force 520 described with reference to FIG. The peak value of the finger force applied to the ball 2 is, for example, the value of the apex of the graphs 720A and 720B, or an approximate value thereof.

In step S1070, the advice output unit 1045 generates advice (information for improving pitching) based on the acquired peak value of finger force, and outputs the advice. In a certain aspect, the advice output unit 1045 may output the advice to the display 110. In another aspect, the advice output unit 1045 may transmit the advice to another device via the communication interface 120.

Further, in other aspects, the advice may include advice for changing the force applied to the ball 2 (active component of finger force 603). Also, in other aspects, the advice may include training methods for improving the active component 603 of finger force. Further, in another aspect, the advice output unit 1045 may also output history information of the finger force for each pitch and the rotation speed of the ball 2. The history information may include arbitrary data (for example, information showing a correlation between the number of rotations of the ball 2 and the finger force), for example, a list or a list as shown in FIGS. 6 to 9. It may include data for visually displaying the graph. The pitcher 5 or the leader of the pitcher 5 can confirm whether or not the pitching is improved by viewing these historical information.

FIG. 11 is a diagram showing an example of internal processing of the analysis device 10. In a certain aspect, the CPU 102 may read the program for performing the process of FIG. 11 from the recording medium 115 into the memory 104 and execute the program. In other aspects, some or all of the processing may also be realized as a combination of circuit elements configured to perform the processing.

In step S1105, the CPU 102 acquires data on the acceleration and the angular velocity generated in the ball 2 during the pitching operation of the pitcher 5. Alternatively, the CPU 102 acquires the imaging data of the ball 2 from the camera 122, and acquires the data of the position of the ball 2 from the imaging data. The CPU 102 may execute the process of acquiring the data of the position of the ball 2 from the image pickup data in step S1150. In a certain aspect, the angular velocity data may be the output value of the gyro sensor 214. In another aspect, the angular velocity data may be calculated based on the output value of the geomagnetic sensor 208 and the output value of the gyro sensor 214.

Further, in a certain aspect, the CPU 102 may acquire acceleration data and angular velocity data from the ball 2 via the wireless communication unit 112 or the communication interface 120. Further, in another aspect, the CPU 102 may also acquire geomagnetic data from the ball 2.

In step S1110, the CPU 102 compares each of the time-series accelerations included in the acceleration data at the time of pitching, and sets a time when the difference value of the front and rear accelerations exceeds a predetermined threshold value as the release time. Acceleration data includes time series discrete values. The CPU 102 may compare adjacent discrete values and set a time at which the difference value between the adjacent discrete values exceeds a predetermined threshold value as the release time. Alternatively, the CPU 102 may set the time when the speed of the ball 2 becomes maximum as the release time based on the position information of the ball 2.

In step S1115, the CPU 102 determines whether or not the acquired data is acceleration / angular velocity data. When the CPU 102 determines that the acquired data is the acceleration / acceleration data of the ball 2 (YES in step S1115), the CPU 102 shifts control to step S1120. If this is not the case, that is, if the acquired data is the data at the position of the ball 2 (NO in step S1115), the CPU 102 shifts control to step S1150.

In step S1120, the CPU 102 sets 200 milliseconds before the release time as the start time of the pitching operation. In a certain aspect, the CPU 102 may set a time retroactive by an arbitrary number of seconds from the release time as the start time of the pitching operation. In another aspect, the CPU 102 may estimate the start time of the pitching operation based on the amount of change in the acceleration data.

In step S1125, the CPU 102 obtains the difference between adjacent data in the time-series geomagnetic data. Geomagnetic data contains discrete values in time series. The CPU 102 can calculate the difference between adjacent discrete values. For example, when the geomagnetic data includes time-series data A to C, the CPU 102 can calculate the difference between the data A and the data B and the difference between the data B and the data C.

Further, the CPU 102 selects geomagnetic data having a time stamp that is after the release time and is closest to the release time. For example, when the time stamp of the data B in the data A to C is after the release time and is closest to the release time, the CPU 102 selects the data B.

Further, the CPU 102 calculates the rotation speed (rpm (rotations per minute)) of the ball 2 based on the difference between the selected data and the data before and after the selected data. The CPU 102 counts points where the difference value intersects 0 of the output value of the geomagnetic data (zero cross point), and calculates the predetermined count (for example, 3 counts) as one rotation. The CPU 102 can calculate the rotation speed of the ball 2 from the count number of the zero cross points in the unit period.

In step S1130, the CPU 102 determines whether or not the angular velocity at each time is less than the threshold value. In certain aspects, the threshold may be a predetermined value. When the CPU 102 determines that the angular velocity at each time is less than the threshold value (YES in step S1130), the CPU 102 shifts control to step S1130. If not (NO in step S1130), the CPU 102 shifts control to step S1135.

In step S1135, the CPU 102 sets the output value of the gyro sensor 214 to the value of the angular velocity. In step S1140, the CPU 102 sets a linear approximation value estimated from the output value of the gyro sensor 214 immediately before the threshold value or more and the rotation speed immediately after release calculated from the geomagnetic data as the value of the angular velocity.

In step S1145, the CPU 102 calculates the centrifugal acceleration and the tangential acceleration of the ball 2 with the angular velocity and the distance from the center of the ball 2 to the center of the sensor device 20 as input values.

In step S1150, the CPU 102 calculates the combined value (combined acceleration) of the three axes (x, y, z axes) of the acceleration sensor, and reduces the centrifugal acceleration, the tangential acceleration, and the gravitational acceleration from the combined acceleration, so that the ball 2 Calculate the translational acceleration of. In certain aspects, the CPU 102 may further reduce the Coriolis component from the combined acceleration. Alternatively, the CPU 102 analyzes the image pickup data of the ball 2, acquires the data of the position of the ball 2, and differentiates the position coordinates of the center of the ball 2 in the data of the position, thereby performing the ball 2 differentiation process (twice differentiation). Calculate the translational acceleration of 2.

In step S1155, the CPU 102 extracts a high frequency component from the time-series translational acceleration data of the ball 2 by a high-pass filter. The high-pass filter may pass frequency components above a certain frequency (eg, 40 Hz) set by the parameter.

In step S1160, the CPU 102 multiplies the high-frequency component of the translational acceleration of the ball 2 by the mass of the ball 2, and the finger force acting on the ball 2 from the pitcher's finger (second force described with reference to FIG. 5). (Equivalent to 520) is calculated.

In step S1165, the CPU 102 calculates the peak value of the active component of the finger force (second force 520) in the section from the start of the pitching operation to the release of the ball 2. For example, the active component of the finger force (second force 520) is time-series discrete value data, and the CPU 102 can select the maximum value (peak value) from the discrete value data.

In step S1170, the CPU 102 has a peak value of the calculated active component of the finger force (second force 520) and a peak of the active component of the finger force (second force 520) with respect to the rotation speed after the release of the ball 2. Compares with the average value of the values, and outputs advice (information for improving pitching) to the pitcher 5 and / or the pitcher 5 leader.

For example, by comparing the value of the active component of finger power in this pitch with the average value of the active component of finger power in the past several pitches, does the pitcher 5 improve his / her finger power? Alternatively, it can be confirmed whether the pitching method has been improved. Further, the pitcher 5 can perform training for improving his / her finger strength or training for improving the pitching method based on the obtained advice. In some aspects, the CPU 102 may output these advices to the display 110 and / or the communication interface 120.

<Additional Notes>
The present embodiment as described above includes the following technical ideas.

[Structure 1] An analysis device according to a certain embodiment includes a sensor device built in the ball or an acquisition unit for acquiring output data from a camera, and a translational acceleration calculation for calculating translational acceleration from the output data. It includes a unit, a filter generation unit that generates a filter used for translational acceleration data, a finger force calculation unit for calculating the finger force applied to the ball, and an output unit for outputting information. The acquisition unit acquires the output data from the sensor device or the camera. The translational acceleration calculation unit calculates the translational acceleration data of the ball based on the output data. The filter generation unit extracts the high frequency component of the translational acceleration data by the filter. The finger force calculation unit calculates the finger force based on the high frequency component and the mass of the ball. The output unit outputs information regarding the finger force.

[Structure 2] In a certain aspect, the output data includes acceleration data and angular velocity data output by the sensor device, or imaging data output by the camera.

[Structure 3] In a certain aspect, the filter generation unit generates a high-pass filter and applies the translational acceleration data to the high-pass filter to extract the high-frequency component.

[Structure 4] In a certain aspect, the translational acceleration calculation unit uses the acceleration data, the centrifugal acceleration data, the tangential acceleration data, and the gravitational acceleration data of the ball based on the output data including the acceleration data and the angular velocity data. The translational acceleration data is calculated by subtracting.

[Structure 5] In a certain aspect, the centrifugal acceleration data and the tangential acceleration data are calculated based on the distance from the center of the sensor device to the center of the ball and the angular velocity data.

[Structure 6] In a certain aspect, the acquisition unit further receives geomagnetic data detected in time series by the sensor device. The centrifugal acceleration data and the tangential acceleration data are calculated based on the distance from the center of the sensor device to the center of the ball, the angular velocity data, and the geomagnetic data.

[Structure 7] In a certain aspect, the translational acceleration calculation unit performs differential processing of the position information of the center of the ball obtained by analyzing the imaging data based on the output data being the imaging data. , The translational acceleration data is calculated.

[Structure 8] In a certain aspect, the analysis device further includes a release calculation unit for calculating the release time of the ball. The translational acceleration calculation unit calculates the translational acceleration data from the output data within a certain period of time near the release time.

[Structure 9] In a certain aspect, the output unit further outputs information for improving pitching based on the finger force.

[Structure 10] In a certain aspect, the information for improving the pitching includes advice for changing the force applied to the ball by a finger.

[Structure 11] In a certain aspect, the analysis device further includes a rotation speed calculation unit for calculating the rotation speed of the ball. The information for improving the pitching includes the history information of the rotation speed and the finger force.

[Structure 12] In a certain aspect, the history information includes data for visually displaying the correlation between the rotation speed and the finger force.

[Structure 13] In a certain aspect, the history information includes data for visually displaying the correlation between the result of dividing the rotation speed by the speed of the ball and the finger force.

[Structure 14] According to another embodiment, a system for analyzing the finger force of a ball is provided. This system includes a ball with a built-in sensor device and an analysis device. The sensor device includes an acceleration sensor, a communication unit for communicating with the analysis device, and a gyro sensor. The analysis device is used for the acquisition unit for receiving the acceleration data and the angular velocity data transmitted by the sensor device, the translational acceleration calculation unit for calculating the translational acceleration data from the acceleration data, and the translational acceleration data. It includes a filter generation unit for generating a filter, a finger force calculation unit for calculating the finger force applied to the ball, and an output unit for outputting information. The sensor device records measurement data including the acceleration data and the angular velocity data at the time of throwing the ball, and transmits the measurement data to the analysis device. The analysis device acquires the acceleration data and the angular velocity data detected in time series by the sensor device by the acquisition unit, and based on the acceleration data and the angular velocity data received by the translational acceleration calculation unit. , The translational acceleration data of the ball is calculated, the high frequency component of the translational acceleration data is extracted by the filter, and the finger force is calculated by the finger force calculation unit based on the high frequency component and the mass of the ball. It is calculated, and the information regarding the finger force is output by the output unit.

[Structure 15] According to another embodiment, another system for analyzing the finger force of the ball is provided. The system includes a camera that captures the ball and an analyzer. The analysis device generates an acquisition unit for receiving imaging data from the camera, a translational acceleration calculation unit for calculating translational acceleration data based on the imaging data, and a filter used for the translational acceleration data. It includes a filter generation unit, a finger force calculation unit for calculating the finger force applied to the ball, and an output unit for outputting information. The camera takes an image of the thrown ball and outputs the imaged data of the ball to the analysis device. The analysis device acquires the imaging data by the acquisition unit, and the translational acceleration calculation unit analyzes the imaging data to obtain the position information of the center of the ball. The translational acceleration data is calculated, the high frequency component of the translational acceleration data is extracted by the filter, the finger force is calculated by the finger force calculation unit based on the high frequency component and the mass of the ball, and the output is output. The unit outputs information on the above finger force.

[Structure 16] In a certain aspect, the filter generation unit generates a high-pass filter and applies the translational acceleration data to the high-pass filter to extract the high-frequency component.

[Structure 17] In a certain aspect, the system further includes a release calculation unit for calculating the release time of the ball. The translational acceleration calculation unit calculates the translational acceleration data within a certain period of time near the release time.

[Structure 18] In a certain aspect, the output unit further outputs information for improving pitching based on the finger force.

[Structure 19] In a certain aspect, the information for improving the pitching includes advice for changing the force applied to the ball by a finger.

[Structure 20] In a certain aspect, the system further includes a rotation speed calculation unit for calculating the rotation speed of the ball. The information for improving the pitching includes the history information of the rotation speed and the finger force.

[Structure 21] In a certain aspect, the history information includes data for visually displaying the correlation between the rotation speed and the finger force.

[Structure 22] In a certain aspect, the history information includes data for visually displaying the correlation between the result of dividing the rotation speed by the speed of the ball and the finger force.

[Structure 23] According to another embodiment, a method performed by a computer for analyzing the finger force applied to the ball is provided. This method includes a step of acquiring output data from a sensor device built in the ball or a camera that images the ball, a step of calculating translational acceleration data of the ball based on the output data, and a filter. This includes a step of extracting a high frequency component of the translational acceleration data, a step of calculating the finger force based on the high frequency component and the mass of the ball, and a step of outputting information on the finger force.

[Structure 24] In a certain aspect, the output data includes acceleration data and angular velocity data output by the sensor device, or imaging data output by the camera.

[Structure 25] In a certain aspect, the method further includes a step of generating a high-pass filter and a step of extracting the high-frequency component by applying the translational acceleration data to the high-pass filter.

[Structure 26] In a certain aspect, the method reduces the centrifugal acceleration data, the tangential acceleration data, and the gravitational acceleration data of the ball from the acceleration data based on the output data including the acceleration data and the angular acceleration data. Further includes a step of calculating the translational acceleration data.

[Structure 27] In a certain aspect, the centrifugal acceleration data and the tangential acceleration data are calculated based on the distance from the center of the sensor device to the center of the ball and the angular velocity data.

[Structure 28] In an aspect, the method further includes a step of receiving geomagnetic data detected in time series by the sensor device. The centrifugal acceleration data and the tangential acceleration data are calculated based on the distance from the center of the sensor device to the center of the ball, the angular velocity data, and the geomagnetic data.

[Structure 29] In a certain aspect, the method translates by differentiating the position information of the center of the ball obtained by analyzing the imaging data based on the output data being the imaging data. It also includes a step to calculate acceleration data.

[Structure 30] In a certain aspect, the method further includes a step of calculating the translational acceleration data from the output data within a certain period of time near the release time of the ball.

[Structure 31] In a certain aspect, the method further includes a step of outputting information for improving pitching based on the finger force.

[Structure 32] In a certain aspect, the information for improving the pitching includes advice for changing the force applied to the ball by a finger.

[Structure 33] In a certain aspect, the method further includes a step of calculating the number of revolutions of the ball. The information for improving the pitching includes the history information of the rotation speed and the finger force.

[Structure 34] In a certain aspect, the history information includes data for visually displaying the correlation between the rotation speed and the finger force.

[Structure 35] In a certain aspect, the history information includes data for visually displaying the correlation between the result of dividing the rotation speed by the speed of the ball and the finger force.

[Structure 36] According to another embodiment, a program for causing a computer to execute the above method is provided.

As described above, the analysis system 1000 according to the present embodiment can calculate the active component of the force applied to the ball 2 (second force 520). Further, the analysis system 1000 gives advice to the pitcher 5 and / or advice to the instructor of the pitcher 5 based on the active component of the force applied to the ball 2 (second force 520) and the rotation speed of the ball 2. Is output. With these functions, the pitcher 5 can effectively perform training for improving the pitching motion.

It should be considered that the embodiments disclosed this time are exemplary in all respects and not restrictive. The scope of the present disclosure is shown by the scope of claims rather than the above description, and is intended to include all modifications within the meaning and scope equivalent to the scope of claims. Further, the disclosure contents described in the embodiments and the respective modifications are intended to be carried out alone or in combination as much as possible.

2 balls, 5 pitchers, 10 analyzers, 20 sensor devices, 62 capsules, 64 silicone gels, 102, 202 CPUs, 104, 204 memories, 106 touch panels, 108 buttons, 110 displays, 112 wireless communication units, 113 communication antennas, 114 Memory interface, 115 recording medium, 116 speaker, 118 microphone, 120 communication interface, 122 camera, 205 low acceleration sensor, 206 high acceleration sensor, 208 geomagnetic sensor, 212 storage battery, 213,214 gyro sensor, 220 acceleration sensor, 505 ball 2 Force received from 510 1st force, 520 2nd force, 600 analysis result, 601 rotation speed, 602 shear force appearance timing, 603 active component, 604 passive component, 605 synthetic value, 606 active component ratio, 607 SPV , 710A, 710B, 720A, 720B, 730A, 730B, 810, 820, 910, 920 Graph, 1000 analysis system, 1005 Information input unit, 1010 Angle speed setting unit, 1015 tangential acceleration calculation unit, 1020 Translational acceleration calculation unit, 1025 High pass Filter coefficient calculation unit, 1030 Forward high-pass filter calculation unit, 1035 Reverse high-pass filter calculation unit, 1040 Finger force calculation unit, 1045 Advice output unit, 1050 Release calculation unit.

Claims (17)

A sensor device built into the ball, or an acquisition unit for acquiring output data from the camera,
A translational acceleration calculation unit for calculating translational acceleration from the output data,
A filter generator that generates a filter to be used for translational acceleration data,
A finger force calculation unit for calculating the finger force applied to the ball, and
Equipped with an output unit for outputting information
The acquisition unit acquires the output data from the sensor device or the camera, and obtains the output data.
The translational acceleration calculation unit calculates the translational acceleration data of the ball based on the output data.
The filter generation unit extracts the high frequency component of the translational acceleration data by the filter.
The finger force calculation unit calculates the finger force based on the high frequency component and the mass of the ball.
The output unit is an analysis device that outputs information regarding the finger force. The analysis device according to claim 1, wherein the output data includes acceleration data and angular velocity data output by the sensor device, or imaging data output by the camera. The filter generation unit
Generate a high pass filter and
The analysis device according to claim 1 or 2, wherein the high frequency component is extracted by applying the translational acceleration data to the high-pass filter. The translational acceleration calculation unit reduces the centrifugal acceleration data, the tangential acceleration data, and the gravity acceleration data of the ball from the acceleration data based on the output data including the acceleration data and the angular acceleration data. The analysis device according to claim 2, which calculates data. The analysis device according to claim 4, wherein the centrifugal acceleration data and the tangential acceleration data are calculated based on the distance from the center of the sensor device to the center of the ball and the angular velocity data. The acquisition unit further receives the geomagnetic data detected in time series by the sensor device, and receives the geomagnetic data.
The analysis device according to claim 4, wherein the centrifugal acceleration data and the tangential acceleration data are calculated based on the distance from the center of the sensor device to the center of the ball, the angular velocity data, and the geomagnetic data. .. The translational acceleration calculation unit calculates the translational acceleration data by differential processing of the position information of the center of the ball obtained by analyzing the imaging data based on the output data being the imaging data. , The analysis apparatus according to claim 2. It also has a release calculation unit that calculates the release time of the ball.
The analysis device according to any one of claims 1 to 7, wherein the translational acceleration calculation unit calculates the translational acceleration data from the output data within a certain period of time near the release time. The analysis device according to any one of claims 1 to 8, wherein the output unit further outputs information for improving pitching based on the finger force. The analysis device according to claim 9, wherein the information for improving the pitching includes advice for changing the force applied to the ball by a finger. A rotation speed calculation unit for calculating the rotation speed of the ball is further provided.
The analysis device according to claim 9, wherein the information for improving the pitching includes the history information of the rotation speed and the finger force. The analysis device according to claim 11, wherein the history information includes data for visually displaying the correlation between the rotation speed and the finger force. The analysis device according to claim 11, wherein the history information includes data for visually displaying the correlation between the result of dividing the rotation speed by the speed of the ball and the finger force. A ball with a built-in sensor device and
Equipped with an analyzer
The sensor device is
Accelerometer and
A communication unit for communicating with the analysis device,
Including gyro sensor
The analyzer is
An acquisition unit for receiving acceleration data and angular velocity data transmitted by the sensor device, and
A translational acceleration calculation unit for calculating translational acceleration data from the acceleration data,
A filter generation unit that generates a filter used for the translational acceleration data,
A finger force calculation unit for calculating the finger force applied to the ball, and
Including an output section for outputting information
The sensor device is
The measurement data including the acceleration data and the angular velocity data at the time of throwing the ball are recorded.
The measurement data is transmitted to the analysis device, and the measurement data is transmitted to the analysis device.
The analyzer is
The acquisition unit acquires the acceleration data and the angular velocity data detected in time series by the sensor device.
The translational acceleration calculation unit calculates the translational acceleration data of the ball based on the received acceleration data and the angular velocity data.
The high frequency component of the translational acceleration data is extracted by the filter.
The finger force calculation unit calculates the finger force based on the high frequency component and the mass of the ball.
A system that outputs information about the finger force by the output unit. A camera that captures the ball and
Equipped with an analyzer
The analyzer is
An acquisition unit for receiving imaging data from the camera,
A translational acceleration calculation unit for calculating translational acceleration data based on the imaging data, and a translational acceleration calculation unit.
A filter generation unit that generates a filter used for the translational acceleration data,
A finger force calculation unit for calculating the finger force applied to the ball, and
Including an output section for outputting information
The camera
The thrown ball is imaged and
The imaging data of the ball is output to the analysis device, and the image data is output to the analysis device.
The analyzer is
The image pickup data is acquired by the acquisition unit, and the image pickup data is acquired.
The translational acceleration data of the ball is calculated by the translational acceleration calculation unit by differential processing of the position information of the center of the ball obtained by analyzing the imaging data.
The high frequency component of the translational acceleration data is extracted by the filter.
The finger force calculation unit calculates the finger force based on the high frequency component and the mass of the ball.
A system that outputs information about the finger force by the output unit. A method performed by a computer to analyze the finger force applied to the ball.
A step of acquiring output data from a sensor device built in the ball or a camera that captures the ball.
A step of calculating the translational acceleration data of the ball based on the output data,
The step of extracting the high frequency component of the translational acceleration data by the filter, and
The step of calculating the finger force based on the high frequency component and the mass of the ball, and
A method comprising the step of outputting information about the finger force. A program for causing a computer to execute the method according to claim 16.

Images