JP7283693B2 - Swing analysis device - Google Patents

Description

The present invention relates to a swing analysis device, method, and program for analyzing a swing of a hitting tool such as a golf swing.

A swing of a hitting tool by a player is a complex motion including various behaviors. For example, a golf swing is an action that combines various actions such as arm swing-up and swing-down, hip rotation, and body translation. Non-Patent Documents 1 and 2 disclose a method of decomposing a golf swing into a plurality of behaviors (modes) by singular value decomposition of an observation matrix obtained from measurement data obtained by measuring the golf swing.

Kenta Matsumoto, 5 others, ``Motion Analysis of Golf Swing by Singular Value Decomposition for Club Design'', Design Engineering, Vol. 53, No. 6, pp. 447-462, June 2018 Hiroshi Hatanaka, 4 others, ``Evaluation of impact on club characteristics based on analysis of motion of golf swing'', No. 18-15 The Japan Society of Mechanical Engineers Symposium: Sports Engineering and Human Dynamics 2018 Proceedings, B-10, November 2018

According to the methods of Non-Patent Documents 1 and 2, it is possible to individually evaluate various behaviors that constitute a golf swing, and as a result, it is possible to grasp the characteristics of the golf swing more accurately. However, such attempts have just started, and further improvements in analysis techniques are desired. The same applies to the analysis of swings of hitting tools other than golf clubs, such as tennis rackets and baseball bats.

An object of the present invention is to provide a swing analysis device, method, and program capable of more appropriately evaluating individual behaviors included in a swing of a hitting tool.

A swing analysis device according to a first aspect includes a data acquisition unit that acquires time-series behavior data representing the behavior of a player when swinging a hitting tool in time series, and singular value decomposition of the time-series behavior data, and a mode expansion unit that decomposes the behavior into characteristic behaviors. The data acquisition unit acquires measurement data obtained by measuring the behavior of I ₀ observation points (I ₀ ≥ 3) in time series with a measuring device when the player swings the hitting tool, and obtains the I ₀ observation points. are divided into I ₁ groups (I ₀ >I ₁ ≥ 2), and based on the measurement data, for each group included in the I ₁ groups, one observation point belonging to the group Alternatively, observation point data representing the behavior of a representative point representing a plurality of observation points in time series is derived. The time-series behavior data includes the observation point data for the I ₁ groups.

A swing analysis device according to a second aspect is the swing analysis device according to the first aspect, wherein the I ₁ groups include two or more groups each having a plurality of observation points as elements.

A swing analysis device according to a third aspect is the swing analysis device according to the first aspect or the second aspect, wherein the observation point data representing the behavior of the representative point in time series includes the plurality of points represented by the representative point. It is derived by averaging the behavior of observation points.

A swing analysis device according to a fourth aspect is the swing analysis device according to any one of the first aspect to the third aspect, and includes a screen that displays at least one of the singular value decomposition result and the result of further analysis thereof. A screen creating unit for creating is further provided.

A swing analysis device according to a fifth aspect is the swing analysis device according to the fourth aspect, wherein the data acquisition unit obtains the time-series behavior for a plurality of swings by the same or different players using the same or different hitting tools. Get data. The mode expansion unit performs singular value decomposition on the time-series behavior data for the plurality of swings. The screen displays the result of comparing the singular value decomposition results of the plurality of swings.

A swing analysis device according to a sixth aspect is the swing analysis device according to any one of the first to fifth aspects, wherein the observation point data includes the position, orientation, Data representing at least one of velocity, acceleration, angular velocity, angular acceleration, force, and torque in time series.

A swing analysis device according to a seventh aspect is the swing analysis device according to any one of the first aspect to the sixth aspect, wherein the hitting tool is a golf club.

A swing analysis method according to an eighth aspect includes the following. A swing analysis program according to a ninth aspect causes a computer to execute the following.
・Obtaining time-series behavior data representing the behavior of a player when swinging a hitting tool in time series ・Decomposing the behavior into characteristic behaviors by performing singular value decomposition on the time-series behavior data Here and obtaining the time-series behavior data includes: a.
・_Acquisition of measurement data obtained by measuring the behavior of I ₀ (I ₀ ≧ 3) observation points in chronological order by a measuring device when the player swings the hitting tool. Dividing into I ₁ groups (I ₀ >I ₁ ≧ 2) Based on the measurement data, for each group included in the I ₁ groups, one observation point or a plurality of observation points belonging to the group Also, the time-series behavior data includes the observation point data for the I ₁ groups.

According to the above viewpoints, individual behaviors included in the swing of the hitting tool can be evaluated more appropriately.

1 is a functional block diagram showing the overall configuration of a swing analysis system including a swing analysis device according to one embodiment of the present invention; FIG. FIG. 2 shows the location of markers attached to a golfer's body; 4 is a flowchart showing the flow of a swing analysis method according to one embodiment of the present invention; (A) Stick diagram representing the time average of time-series behavior data. (B) Stick diagram representing temporal averages of pseudo-extension data. FIG. 4 is a conceptual diagram showing the relationship between time-series behavior data and pseudo-extended data; Stick diagram of the first mode of 25 observation points. FIG. 4 is a comparative diagram comparing stick diagrams of each mode at the timing of impact between different golf swings. A stick diagram of the first mode of 53 observation points created from the same measurement data as in FIG. 6 (reference diagram). The conceptual diagram showing the relationship between the time-series behavior data and pseudo extended data which concerns on a modification. FIG. 11 is a conceptual diagram showing the relationship between time-series behavior data and pseudo-extended data when the average behavior according to another modified example is brought closer to the behavior at the time of impact;

Hereinafter, a swing analysis device, method, and program according to an embodiment of the present invention will be described with reference to the drawings.

<1. Overview of Swing Analysis System>
FIG. 1 shows an overall configuration diagram of a swing analysis system 100 including a swing analysis device 1 according to this embodiment. The swing analysis system 100 is a system for analyzing golf swings. A golf swing is a complex action that includes various actions, and is configured by combining various actions such as swinging up and down of the arm, rotation of the hip, and parallel movement of the body. The swing analysis system 100 individually evaluates such characteristic behaviors included in the golf swing so that the characteristics of the golf swing can be captured more accurately. More specifically, the golf swing is decomposed into characteristic behaviors by performing mode expansion using singular value decomposition on time-series behavior data representing the behavior of the golfer when he swings the golf club in time series. Based on the results of singular value decomposition described above, the user can understand the characteristic behavior included in the golf swing, and in turn understand the characteristics of the golfer, the characteristics of the golf club, and the like. The user here is a general term for a person who needs the results of golf swing analysis, such as a golfer himself or his instructor, a fitter who fits a golf club suitable for a golfer, or a golf club developer. .

Time-series behavior data to be subjected to singular value decomposition is acquired based on measurement data measured by the measuring device 2 . The measuring device 2 constitutes a swing analysis system 100 together with the swing analysis device 1 . Hereinafter, after explaining the configuration of each part of the swing analysis system 100, the swing analysis method by the swing analysis system 100 will be explained.

<2. Details of each part>
<2-1. Measuring equipment>
A measuring device 2 according to the present embodiment is a motion capture system including a plurality of cameras 21, 21, . As a motion capture system, for example, a three-dimensional motion analysis system manufactured by VICON can be preferably used. A plurality of cameras 21, 21, . .

In this embodiment, markers are attached to positions of observation points on the golfer's body so that the cameras 21, 21, . . . More specifically, as shown in FIG. 2, I ₀ (I ₀ ≧3, In this embodiment, light-reflective spherical markers 20 _, 20, . . . In this example, the markers 20 , 20 , . . . are attached to the bodysuit worn by the golfer 7 .

The cameras 21, 21, . . . continuously photograph the situation at a predetermined sampling frequency while the golfer 7 makes a golf swing using the golf club. The sampling frequency can be, for example, 500 Hz. In the present embodiment, the golf swing is measured at least during the period from address, back at 9 o'clock, top, down at 9 o'clock, impact, follow-up at 3 o'clock in order until the finish. Here, 9 o'clock back is the timing when the golf club points in the 9 o'clock direction during the backswing when the golfer 7 is viewed from the front, and 9 o'clock down is the timing when the golfer 7 is in the downswing when viewed from the same direction. The timing at which the golf club points in the direction of 9 o'clock is the timing at which the golf club points in the direction of 9 o'clock, and the timing at 3 o'clock is the timing in which the golf club points in the direction of 3 o'clock during the follow swing after the impact, as viewed from the same direction. In this embodiment, as measurement data by the measuring device 2, at least time-series image data from the address to the finish is captured. The photography by the cameras 21, 21, .

Although not shown, the measurement device 2 receives the image data captured by the cameras 21, 21, . . . A communication device for transmitting to the analysis device 1 is also mounted. The communication device can be of a wireless type so as not to interfere with the swing motion, or can be connected to the swing analysis device 1 in a wired manner via a cable. In this embodiment, the image data captured by the cameras 21, 21, . . . are transmitted to the swing analysis device 1 in real time via the communication device. However, for example, the image data is stored in a storage device in the cameras 21, 21, . good too.

<2-2. Swing analysis device>
The swing analysis device 1 is a general-purpose computer as hardware, and is implemented as, for example, a desktop computer, a notebook computer, a tablet computer, or a smart phone. As shown in FIG. 1, the swing analysis device 1 can be obtained by installing a swing analysis program 6 in a general-purpose computer from a computer-readable recording medium 30 such as a CD-ROM or via a communication line such as the Internet. manufactured. The swing analysis program 6 is software for analyzing a golf swing based on the measurement data sent from the measuring device 2, and causes the swing analysis device 1 to perform operations described later.

The swing analysis device 1 includes a display section 11 , an input section 12 , a storage section 13 , a control section 14 and a communication section 15 . These units 11 to 15 are connected to each other via a bus line 16 and can communicate with each other. The display unit 11 can be configured with a liquid crystal display or the like, and displays the golf swing analysis results and the like to the user. The input unit 12 can be composed of a mouse, a keyboard, a touch panel, or the like, and receives an operation of the swing analysis device 1 from the user.

The storage unit 13 can be configured with a hard disk or the like. The storage unit 13 stores the swing analysis program 6 as well as the measurement data sent from the measuring device 2 . The control unit 14 can be composed of a CPU, a ROM, a RAM, and the like. By reading and executing the swing analysis program 6 in the storage unit 13, the control unit 14 virtually operates as a data acquisition unit 14a, a mode development unit 14b, and a screen creation unit 14c. Details of the operation of each unit 14a to 14c will be described later. The communication unit 15 functions as a communication interface that transmits and receives data to and from an external device such as the measuring instrument 2 .

<3. Swing analysis method>
A swing analysis method by the swing analysis system 100 will be described below. In this method, the golf swing by the golfer 7 is measured, singular value decomposed, and decomposed into various characteristic behaviors. The method is used to understand golf swing characteristics in situations such as golf lessons, golf club fitting to golfers, and golf club development. A detailed description will be given below.

FIG. 3 is a flowchart showing the flow of the swing analysis method according to this embodiment. As shown in the figure, in the first step S1, a golf club is prepared and the golf player 7 swings the golf club. At this time, measurement is performed by the measuring device 2, and measurement data representing how the golfer 7 swings the golf club is collected. The measurement data according to the present embodiment is, as described above, data obtained by measuring the behavior of I ₀ (I ₀ =53) observation points in time series when the golfer 7 swings the golf club. The measurement data according to the present embodiment is time-series image data captured by the cameras 21, 21, . . . at least during the period from address to finish. Note that the measurement data here includes time-series image data of a plurality of systems corresponding to the imaging directions of the plurality of cameras 21, 21, . . . Measurement data measured by the measuring device 2 is transmitted from the communication device to the swing analysis device 1 . On the swing analysis device 1 side, the data acquisition unit 14 a acquires this measurement data via the communication unit 15 and stores it in the storage unit 13 .

In subsequent steps S2 to S4, based on the measurement data from the measuring device 2, the data acquisition unit 14a derives time-series behavior data representing the behavior of the golfer 7 when swinging the golf club in time series. First, in step S2, the data acquisition unit 14a acquires I ₀ (I ₀ =53) observation points, which is I ₁ less than I ₀ (I ₀ >I ₁ ≧2). , I ₁ =25). In this embodiment, the grouping method is set in advance, and observation points that are physically close to each other are grouped into the same group. In this embodiment, the same observation point is not divided into multiple groups. Also, the group here includes at least one multi-element group having a plurality of observation points as elements. In this embodiment, the I ₁ groups are all multi-element groups and contain two or more multi-element groups.

In subsequent step S3, the data acquisition unit 14a performs image processing on the image data included in the measurement data to detect images of the markers 20, 20, . Images of the markers 20, 20, . . . are detected from a plurality of photographing directions, and their positions are specified as three-dimensional coordinates. The three-dimensional coordinates of the markers 20 _, 20 _, . It is derived for each time interval step.

In other words, in step S3, for n=1, 2, . . . , N (N is an integer equal to or greater than 2) and i= ₁ , ₂ , . , the three-dimensional coordinates r _i (n) of the i-th observation point at the n-th time are derived. The first time is the address and the Nth time is the finish.

x _i (n), y _i (n) and z _i (n) in the above equations are the X, Y and Z direction components of r _i (n) respectively. The X, Y and Z directions are defined as shown in FIG. That is, the X direction is the direction from the back to the abdomen of the golfer 7, the Y direction is the direction of the line of flight, and the Z axis is the direction from the bottom to the top. r _i (n) is the position vector of the i-th observation point at the n-th time.

Now define the following matrix [r _i ] for the i-th observation point. The matrix [r _i ] is a matrix of N rows and 3 columns, and the three-dimensional coordinates r _i (1), r _i (2 ), . . . , r _i (N). In step S3, data [r _i ] representing the three-dimensional position in time series is derived for each of the I ₀ observation points.

In subsequent step S4, the data acquisition unit 14a derives observation point data for each group included in I ₁ groups (I ₁ =25). The observation point data for the i-th group is data representing the behavior of the i-th representative point, which is a representative point representing a plurality of observation points belonging to the i-th group (i = 1, 2, · . . , I ₁ ). That is, in step S4, for i=1, 2, . . . , I ₁ and n=1, 2, . s _i (n) representing the behavior of the representative point is derived. In this embodiment, s _i (n) is a 1-by-3 matrix representing the three-dimensional coordinates of the i-th representative point at the n-th time. The behavior (three-dimensional coordinates) of the i-th representative point at the n-th time is derived by averaging the behaviors (three-dimensional coordinates) of the plurality of observation points represented by the i-th representative point at the n-th time. In this embodiment, the behavior of representative points is calculated by simple averaging. However, without being limited to this, the behavior of the representative points may be calculated by geometric mean, weighted mean, or the like.

Observation point data for the i-th group is represented by the following matrix [s _i ]. The matrix [s _i ] is a matrix with N rows and 3 columns, and the three-dimensional coordinates s _i (1), s _i (2 ), . . . , s _i (N) are arranged. In step S4, data [s _i ] representing the three-dimensional position in time series is derived for each of the I ₁ representative points.

_Furthermore , a matrix in which I ₁ (I ₁ =25) matrices [s ₁ ], [s ₂ ], . Define [R]. The matrix [R] is a matrix of N rows and 75 (=3×25) columns.

The matrix [R] is time-series behavior data representing the behavior (position) of 25 representative points from the first time to the N-th time in time series. The matrix [R] contains observation point data [s ₁ ], [s ₂ ], . . . , [s ₂₅ ] for I ₁ groups. At step S4, a matrix [R], which is such time-series behavior data, is derived. Note that the n-th row of [R] includes three-dimensional coordinates of 25 representative points on the body of the golfer 7 at the n-th time. It shows 7 poses.

In subsequent steps S5 and S6, singular value decomposition is performed on the time-series behavior data [R] of step S4 to decompose the golf swing represented by the time-series behavior data [R] into characteristic behaviors. More precisely, pseudo-extended data [R _a ] is created by pseudo-extending the time-series behavior data [R], and is subjected to singular value decomposition. A golf swing is an aperiodic exercise. Therefore, in the present embodiment, the golf swing is regarded as a pseudo-periodic motion and analyzed by singular value decomposition of the pseudo-extended data [R _a ] obtained by duplicating and synthesizing the golf swing.

In step S5, the data acquisition unit 14a creates pseudo extended data [R _a ] based on the time-series behavior data [R] so that the time average approaches the reference behavior. Pseudo-extended data [R _a ] are created to bring the singular value decomposition reference point closer to the reference behavior. In this embodiment, the standard behavior is the behavior at the time of address. FIG. 4(A) is a stick line formed by connecting the time averages of the positions of 25 representative points included in the time-series behavior data [R] when an actual golfer makes a golf swing. FIG. 4B is a stick diagram obtained by connecting the time averages of the positions of 25 representative points included in the pseudo extended data [R _a ] based on the same golf swing. As can be seen from the figure, the former time average is not in an address posture, but in a posture in which the arm is slightly taken back. Therefore, if the time-series behavior data [R] is subjected to singular value decomposition as it is, there is a possibility that the meaning of each mode including the first mode cannot be understood based on the address. Therefore, in the present embodiment, pseudo extension data [R _a ] is created.

More specifically, the data acquisition unit 14a creates the following additional data [R(1)] and [R _t ] based on the time-series behavior data [R]. Now, a 2N-row, 3-column matrix [ _{s i} (1 )]. Then [R(1)] replaces the 25 matrices [s ₁ (1)], [s ₂ (1)], ..., [s ₂₅ (1)] for all representative points with this It is derived as a matrix of 2N rows and 75 (=3×25) columns arranged in order in the column direction (horizontal direction).

On the other hand, the additional data [R _t ] is derived according to the following formula. That is, the additional data [R _t ] is a matrix obtained by inverting the time-series behavior data [R] in the time-series direction.

Subsequently, the data acquisition unit 14a creates pseudo extended data [R a ] by combining additional data [R(1)] and [R _t ] with the time-series behavior data [ _R ] according to the following formula. .

FIG. 5 is a conceptual diagram showing the relationship between the time series behavior data [R] and the pseudo extended data [R _a ] according to this embodiment. That is, the additional data [R(1)] obtained by repeatedly replicating the behavior at the time of address for the time length 2N is linked before the address time n=1 of the time-series behavior data [R]. Furthermore, additional data [R _t ] of time length N obtained by reversing the time-series behavior data [R] in the time-series direction is combined after the finish time n=N of the time-series behavior data [R], and then , add additional data [R(1)]. In this way, the additional data [R(1)] and [R _t ] are continuously combined with the time-series behavior data [R], and the thus-created pseudo-extended data [R _a ] are continuously Data whose value changes. The pseudo extended data [R _a ] is a matrix of 6N rows and 75 (=3×25) columns. The pseudo extended data [R _a ] created as described above is also time-series behavior data representing the behavior of the golfer 7 when swinging the golf club in time series.

In subsequent step S6, the mode expansion unit 14b performs singular value decomposition on the pseudo extended data [R _a ] according to the following equation.

[R ₀ ] in the above formula is the time average of [R _a ]. [R ₀ ] is a matrix of 6N rows and 75 (=3×25) columns in which an average matrix is created by averaging [R _a ] in the time direction (row direction), and 6N average matrices are arranged in the row direction. be. That is, the l-th column of [R ₀ ] is a value string in which 6N average values of the 6N components of the l-th column of [R _a ] are arranged in the row direction. By the way, as described above, the pseudo extension data [R _a ] is added with data representing the behavior (in this embodiment, the posture of the golfer 7) at address for a time length of 4N. Therefore, the behavior represented by the time average [R ₀ ] of the pseudo extended data [R _a ] is closer to the behavior at address than the behavior represented by the similar time average of the time series behavior data [R]. there is In the present embodiment, the average matrix is calculated by simple averaging, but the average matrix may be calculated by geometric averaging, weighted averaging, or the like. In addition, the initial behavior [R ₀ ] is not the time average of the entire [R _a ], but the average matrix obtained by averaging the matrix of the period before and after the address included in [R _a ] in the time direction (row direction). 6N average matrices may be arranged in the row direction. Also, the initial behavior [R ₀ ] may be obtained by arranging 6N rows corresponding to the times of the addresses included in [R _a ] in the row direction.

As described above, in the present embodiment, [R _a ]−[R ₀ ], which is the difference between the pseudo-extended data [R _a ] and its time average [R ₀ ], is singular value decomposed, and j=1, 2, . . , J (J is an integer less than or equal to 75), λ _j , z _j and v _j are derived. That is, the pseudo-extended data [R _a ] is subjected to singular value decomposition with reference to its time average [R ₀ ]. λ _j is the singular value of the j-th mode and represents the ratio of the j-th mode to [R _a ]-[R ₀ ]. v _j is the left singular vector of [R _a ]-[R ₀ ] and is N-dimensional. z _j is the right singular vector of [R _a ]-[R ₀ ] and has 75 dimensions. z _j represents the excitation pattern of each representative point on the body of the golfer 7, and v _j represents the excitation pattern of z _j with respect to time.

Further, the mode expansion unit 14b, for _j =1 _, 2, _. derived according to

By the above singular value decomposition, from the pseudo extended data [R _a ] that represents the behavior of the golf swing in time series as well as the time series behavior data [R], each mode from the first mode to the J mode included in the golf swing Corresponding characteristic behaviors are extracted. [R _j ] representing the behavior of the j-th mode is expressed by the following equation.

Note that J is 75 at maximum, but up to what mode the behavior is to be derived can be appropriately selected according to the purpose of the analysis and the like. According to the studies of the present inventors, generally, the contribution rate of the first mode alone is about 90%, and the cumulative contribution rate from the first mode to the second mode is about 97%. The cumulative contribution rate up to the third mode exceeds 99%, and the cumulative contribution rate from the first mode to the fifth mode reaches about 99.8%. Therefore, by deriving the behavior up to the fifth mode, the golf swing can be roughly evaluated.

In addition, the mode expansion unit 14b derives [R ₁ to _k ] representing the accumulated behavior of the first to k-th modes for k=1, 2, . . . , J according to the following equation .

[R ₁ to _k ] includes, as components, behavior (three-dimensional coordinates) obtained by accumulating behaviors from the first mode to the k-th mode at each time of each representative point. Therefore, deriving [R ₁ to _k ] for k=1, 2, . . . , J means deriving the golfer's posture at each time in each mode.

Steps S1 to S6 described above can be repeatedly performed for multiple golf swings performed by the same or different golf players using the same or different golf clubs.

In the subsequent step S7, the screen creation unit 14c creates a screen (hereinafter referred to as a result screen) for displaying the singular value decomposition result and the result of further analysis thereof, and displays it on the display unit 11. FIG. For example, the result screen can display a stick diagram as shown in FIG. FIG. 6 shows the calculation of [R ₁ ] (=[R ₁ ~ ₁ ]) representing the behavior of the first mode when a certain golfer actually makes a certain golf swing, and 25 representative points included in this. is a stick diagram formed by connecting the three-dimensional coordinates of . The figure shows stick diagrams at seven times from address to finish. Similarly, for k≧2, [R ₁ to _k ] may be calculated, and based on this, stick diagrams at various times in the k-th mode may be displayed on the result screen. A user viewing such a stick diagram can appropriately evaluate individual behaviors included in the golf swing and understand the characteristics of the golf swing.

As shown in Non-Patent Documents 1 and 2, it is known that the behaviors of the first to fifth modes are as follows. Together with such knowledge, it is also possible to deepen the understanding of golf swing characteristics that appear in stick diagrams.

The result screen can also display the singular value λ _j and the contribution C _j for j=1, 2, . . . , J. Also, for k=1, 2, . . . , J, the cumulative contribution rate C ₁ +C _{2 +} .

Further, when steps S1 to S6 are repeatedly executed for a plurality of golf swings, the mode development unit 14b can compare the behaviors of these golf swings by mode. _For example, for j=1, 2 _, . . . , J, k=1, ₂ , . ₁ - _k ] or [R ₁ - _k ]-[R ₀ ] can be compared. In this case, differences in characteristics between golf swings can be quantitatively or qualitatively evaluated at the level of individual behaviors included in the golf swing (ie, by mode). For example, in a specific mode, when the hips rotate well during the downswing in a certain golf swing, but the hips do not rotate much during the downswing in another golf swing, the mode expansion unit 14b Such rotation differences can be evaluated qualitatively or quantitatively. Further, the mode expansion unit 14b may detect a portion of the golf swings to be compared where there is a significant difference in behavior. The screen creation unit 14c can also display the result of such comparison on the result screen, and the user who sees this can appropriately understand the difference in characteristics between golf swings.

The result screen can also display a comparison of stick diagrams when the same or different golfers swing the same or different golf clubs. A comparative diagram is, for example, a diagram in which a plurality of stick diagrams are displayed overlaid or side by side. As an example, FIG. 7 is an overlay of stick diagrams of various modes at the timing of impact when the same golfer performs a draw swing and a fade swing with the same golf club. A user viewing such a diagram can compare different golf swings at the level of individual behaviors involved in the golf swings, and can properly understand the differences in characteristics between the golf swings.

When the above comparison is made between golf swings using different golf clubs, the difference in golf club characteristics can be clarified. For example, it is possible to know at what timing during a golf swing the action of what part changes due to the difference in golf clubs. According to this method, golf clubs with different specs such as shaft hardness, head weight and center of gravity position, golf club length, etc. are tested by the same or different golfers, so that the characteristics of the specs of the golf clubs can be known. be able to. Such information can be preferably used in golf club development, golf club fitting, and the like. In addition, together with the findings shown in Table 1, these understandings can be further deepened.

Further, when the above comparison is made between golf swings by different golfers, it is possible to clarify the difference in golfer's characteristics. For example, it is possible to know the difference between the golf swings of advanced players and beginners, and to know the golf swing problems of beginners. In addition, together with the findings shown in Table 1, these understandings can be further deepened. Such information can be preferably used, for example, in golf lessons, development of golf clubs, and the like.

<4. Features>
According to the above embodiment, the number of modes (number of dimensions) in subsequent singular value decomposition is reduced by combining I ₀ observation points into a smaller number of I ₁ representative points. That is, if the number of points of interest (observation points or representative points) to be subjected to singular value decomposition is large, the degree of freedom of the swing will be high, but the number of modes will increase accordingly, and the information possessed by one mode will be small. Become. In this case, it may rather be difficult to appropriately evaluate individual behaviors included in the golf swing from the results of singular value decomposition. On the other hand, if the number of observation points is blindly reduced in order to reduce the number of modes, the accuracy of swing analysis may decrease. In this respect, according to the above embodiment, a plurality of observation points are grouped, and the information of the plurality of observation points is compressed into the information of one representative point. Therefore, the amount of information in each mode can be maintained while maintaining the accuracy of swing analysis. Therefore, individual behaviors (behaviors in each mode) included in the golf swing can be evaluated more appropriately.

FIG. 6, already described, is a first mode stick diagram of 25 representative points. On the other hand, FIG. 8 is a stick diagram obtained by connecting three-dimensional coordinates of 53 observation points in the first mode based on the same measurement data as in FIG. 6 without creating representative points. As can be seen by comparing FIG. 6 and FIG. 8, even if the points of interest are reduced, the entire swing behavior is successfully expressed. On the other hand, the expansion and contraction of the upper body can be seen from the stick diagram of FIG. 8, which has many points of interest. In contrast, the stick diagram of FIG. 6, which has fewer points of interest and is simpler, shows rotational movement rather than extension and contraction of the upper body. In this way, new information can be obtained by compressing the points of interest and expressing the behavior more simply.

In addition, when the behavior is expressed simply by reducing the number of modes as described above, it becomes easier to compare different swings. In other words, if there are too many points of interest to be subjected to singular value decomposition, the physique, posture, etc. of the golfer will be reproduced too accurately, making comparison rather difficult. In such a case, the problem can be solved by representing the swing with a simple behavior.

Also, by averaging the behavior of a plurality of observation points to the behavior of one representative point, it is possible to cut the rotation information. For example, rotation of the hips about the direction of the line of flight may be less needed in analyzing a golf swing. In such a case, by combining the observation points near the waist into one representative point, it is possible to reduce unnecessary information and focus on more necessary information.

<5. Variation>
Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and various modifications are possible without departing from the scope of the invention. For example, the following changes are possible. Also, the gist of the following modified examples can be combined as appropriate.

<5-1>
The configuration of the measuring device 2 described above is an example. For example, the measuring device 2 may be an inertial sensor, a distance image sensor, or the like, or may be selected from the group consisting of an inertial sensor, a distance image sensor, and a motion capture system. Any combination of two or more devices may be used.

<5-2>
In the above embodiment, the golf swing was analyzed, but the analysis can also be performed on swings of hitting tools other than golf clubs, such as tennis rackets and baseball bats.

<5-3>
In the above embodiment, the reference behavior is the behavior at the time of address. However, the reference behavior can also be behavior at other times, such as impact or top behavior. For example, if the behavior at the top is used as the reference behavior, pseudo-extended data can be created as shown in FIG. Pseudo extended data can be created.

<5-4>
In the above embodiment, the observation point data to be subjected to singular value decomposition is data representing the position of the representative points in time series. , or a combination of data representing two or more elements selected from the group consisting of these and positions in time series. Note that the orientation here is a parameter having angle information such as a quaternion.

<5-5>
In the above embodiment, the observation point was set only on the golfer's body, but the observation point may be set on the golf club.

<5-6>
The I ₁ groups that divide the I ₀ observation points may include a single element group whose element is one observation point. In this case, the observation point data corresponding to the single element group can be data representing the behavior of one observation point belonging to the single element group in chronological order.

<5-7>
In the above embodiment, the observation points are grouped so that the same observation points are not redundantly divided into a plurality of groups. However, it is also possible to divide one or more observation points into multiple groups redundantly.

<5-8>
In the above embodiment, the pseudo-extended data [R _a ] is singular value decomposed, but the time-series behavior data [R] may be singular value decomposed as it is.

<5-9>
In the above embodiment, the behavior of the representative point was derived by averaging the behaviors of the plurality of observation points represented by the representative point, but it may be derived by other methods. For example, as the behavior of the representative point, the largest behavior among the behaviors of the plurality of observation points represented by the representative point may be selected, or the largest behavior and the smallest behavior may be averaged.

1 Swing analysis device (computer)
100 swing analysis system 14a data acquisition unit 14b mode development unit 14c screen creation unit 2 measuring device 6 swing analysis program 7 golfer

Claims (9)

a data acquisition unit that acquires time-series behavior data representing the behavior of the player when swinging the hitting tool in time series;
a mode expansion unit that decomposes the behavior into characteristic behaviors by singular value decomposition of the time-series behavior data,
The data acquisition unit
Obtaining measurement data obtained by measuring the behavior of I ₀ (I ₀ ≥ 3) observation points in time series by a measuring device when the player swings the hitting tool,
dividing the I ₀ observation points into I ₁ groups (I ₀ >I ₁ ≧2);
Based on the measurement data, for each group included in the I ₁ groups, observation point data representing the behavior of one observation point belonging to the group or a representative point representing a plurality of observation points in time series. and derive
The I ₁ groups include at least one group containing a plurality of observation points near a specific site where rotation information is to be cut,
The time-series behavior data includes the observation point data for the _I groups,
Swing analysis device. The _I groups include two or more groups each having a plurality of observation points as elements,
The swing analysis device according to claim 1. The observation point data representing the behavior of the representative point in time series is derived by averaging the behaviors of the plurality of observation points represented by the representative point,
The swing analysis device according to claim 1 or 2. Further comprising a screen creation unit that creates a screen that displays at least one of the result of the singular value decomposition and the result of further analysis thereof,
The swing analysis device according to any one of claims 1 to 3. The data acquisition unit acquires the time-series behavior data for a plurality of swings by the same or different players using the same or different hitting tools,
The mode expansion unit performs singular value decomposition on the time-series behavior data for the plurality of swings,
The screen displays the result of comparing the singular value decomposition results for the plurality of swings,
The swing analysis device according to claim 4. The observation point data is data representing at least one of the position, attitude, velocity, acceleration, angular velocity, angular acceleration, force and torque of the one observation point or the representative point in time series.
The swing analysis device according to any one of claims 1 to 5. The hitting tool is a golf club,
The swing analysis device according to any one of claims 1 to 6. Acquiring time-series behavior data representing the behavior of the player when swinging the hitting tool in time series;
decomposing the behavior into characteristic behaviors by singular value decomposition of the time series behavior data;
Acquiring the time-series behavior data includes:
Acquiring measurement data obtained by measuring the behavior of I ₀ observation points (I ₀ ≥ 3) in time series by a measuring device when the player swings the hitting tool;
dividing the I ₀ observation points into I ₁ groups (I ₀ >I ₁ ≧2);
Based on the measurement data, for each group included in the I ₁ groups, observation point data representing the behavior of one observation point belonging to the group or a representative point representing a plurality of observation points in time series. and deriving
The I ₁ groups include at least one group containing a plurality of observation points near a specific site where rotation information is to be cut,
The time-series behavior data includes the observation point data for the _I groups,
Swing analysis method. Acquiring time-series behavior data representing the behavior of the player when swinging the hitting tool in time series;
causing a computer to decompose the behavior into characteristic behaviors by singular value decomposition of the time-series behavior data;
Acquiring the time-series behavior data includes:
Acquiring measurement data obtained by measuring the behavior of I ₀ observation points (I ₀ ≥ 3) in time series by a measuring device when the player swings the hitting tool;
dividing the I ₀ observation points into I ₁ groups (I ₀ >I ₁ ≧2);
Based on the measurement data, for each group included in the I ₁ groups, observation point data representing the behavior of one observation point belonging to the group or a representative point representing a plurality of observation points in time series. and deriving
The I ₁ groups include at least one group containing a plurality of observation points near a specific site where rotation information is to be cut,
The time-series behavior data includes the observation point data for the _I groups,
Swing analysis program.

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