TWI770787B - Hand-held motion analysis sysyem and method - Google Patents

Abstract

The present invention discloses a hand-held motion analysis system and method. The system includes a signal sensing module, a server and a display module. The signal sensing module is disposed at the hand-held ball equipment, and senses the hitting action of the hand-held ball equipment and outputs a sensing signal. The server is coupled to the signal sensing module. The server includes one or more processing units and a memory unit. The one or more processing units are coupled to the memory unit. The memory unit stores one or more program instructions. When one or more program instructions are executed by the one or more processing units, the one or more processing units perform: a posture estimation step, a hitting trajectory reconstruction step, a hitting stage detection step, a hitting type identification step and a hitting action consistency evaluation step. The display module is coupled to the server, and the display module presents the analysis result.

Description

Handheld motion analysis system and method

The present invention relates to an analysis system and method, in particular to a handheld motion analysis system and analysis method.

Ball sports, such as badminton, billiards, or tennis, are very popular among ordinary people due to the low entry threshold and relatively easy access to venues. However, it is not easy to become a national player representing the country or become a professional athlete. In addition to considerable effort, it may also require the assistance of a batting action analysis system.

In the traditional action analysis system used to study the batting movement, in order to capture the process of the batting action, the researcher must set up a high-speed camera, transmit the image obtained by the camera to the computer, and then connect a transmission line to keep it at the expected trigger. The state of the motion sensor (sensor), to record the information of the hitting process and transmit it to the computer equipment.

However, the conventional motion analysis system must consider the camera's erection angle to facilitate shooting, and the marker points must be fixed on the subject's joints before the experimental test, so that the body angle analysis can have a clear click target; After shooting the image, it is necessary to take a scale (for example, one meter long) picture to facilitate the conversion of data; and the design of the infrared transmitter and receiver is used to form a very complex and bulky analysis system, which is not only inconvenient to use, but can only be used It is used in a specific place and is expensive. In the process of experimental shooting, data processing and analysis, there will be great errors due to human factors.

Therefore, how to provide a handheld sports analysis system and analysis method, which not only has the advantages of convenience, professionalism, practicality and economy, but also provides relevant hitting indicators in real time and objectively, has become one of the very important topics.

In view of the above-mentioned problems, the purpose of the present invention is to provide a handheld motion analysis system and method. Compared with the conventional batting motion analysis system, the present invention has the advantages of convenience, professionalism, practicality and economy, and can also be used in real time. And objectively provide relevant batting indicators to the players or/and coaches for reference, so as to improve the batting action of the players.

To achieve the above objective, a handheld motion analysis system according to the present invention includes a signal sensing module, a server and a display module. The signal sensing module is arranged on the hand-held ball, and the signal-sensing module senses the hitting action of the hand-held ball and outputs a sensing signal; the server is coupled to the signal-sensing module, and the server includes one or more A processing unit and a memory unit, the one or more processing units are coupled to the memory unit, the memory unit stores one or more program instructions, when the one or more program instructions are executed by the one or more processing units, the One or more processing units perform: an attitude estimation step, which is based on the sensing signal to perform the attitude estimation of the hand-held golf equipment for hitting the ball; a hitting trajectory reconstruction step, which is based on the sensing signal and the result of the attitude estimation step The reconstruction of the batting trajectory signal is performed; a batting stage detection step is based on the sensing signal and the result of the attitude estimation step to distinguish different periods of the batting process; a batting ball identification step is based on the sensing signal. classifying the type of the ball hit; and a step of evaluating the consistency of the hitting action, which calculates and evaluates the consistency between the hitting action and the sample action based on the sensing signal. The display module is coupled to the server, and the display module presents the analysis result.

In order to achieve the above-mentioned purpose, a method for analyzing hand-held motion according to the present invention is applied to a hand-held motion analysis system. The hand-held motion analysis system includes a signal sensing module. Measuring the hitting action of the hand-held ball equipment and outputting a sensing signal, the analysis method includes: an attitude estimation step: performing the posture estimation of the hand-held ball equipment for the hitting action according to the sensing signal; a hitting trajectory reconstruction step: according to the The sensing signal and the result of the attitude estimation step are used to reconstruct the batting trajectory signal; the staged detection step of a stroke: distinguish different periods of the batting process according to the sensing signal and the result of the attitude estimation step; The identification step: classifying the type of the ball hit according to the sensing signal; and the step of evaluating the consistency of a hitting action: calculating and evaluating the consistency between the hitting action and the sample action according to the sensing signal.

In one embodiment, the signal sensing module includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer.

In one embodiment, before performing the attitude estimation step, the one or more processing units further perform: a signal preprocessing step, which is to correct the sensing signal output by the signal sensing module, and filter out the sensing signal. 's noise.

In one embodiment, the attitude estimation step utilizes an extended Kalman filter algorithm to estimate the attitude of the hand-held golf equipment by utilizing the sensing signal, thereby obtaining accurate hand-held golf equipment speed and hand-held golf equipment trajectory; The extended Kalman filter algorithm includes a state prediction step, a gravity state update step, and a magnetic north state update step.

In one embodiment, in the state prediction step, the angular velocity signal in the sensing signal is used to predict the state of the extended Kalman filter; wherein, in the gravity state update step and the magnetic north state update step, the sensor is used to predict the state. The acceleration signal and the magnetic force signal in the signal are used to update the state of the extended Kalman filter, so as to obtain the best estimation state of the posture of the hand-held ball.

In one embodiment, in the step of reconstructing the ball trajectory, a trajectory reconstruction algorithm is used to obtain the posture of the golf equipment, the speed of the golf equipment and the trajectory signals of the golf equipment generated when the hitting action is performed; wherein, the trajectory reconstruction algorithm includes: A motion signal segmentation step, a coordinate conversion and gravity compensation step, a speed estimation and zero speed compensation step, and a trajectory reconstruction step.

In one embodiment, the step of batting stage detection is to obtain each period signal in the batting process through a batting action staging algorithm; wherein, the batting action staging algorithm includes a motion signal segmentation step, coordinate conversion and Gravity compensation step, a movement signal pole detection step, and a movement signal phase detection step; wherein, the movement signal pole detection step finds out the starting point of the preparatory period, the starting point of the acceleration period and the hitting point of the hitting action and the end of the residual period.

In one embodiment, in the step of detecting ball hitting stages, different periods of the hitting process include an initial rest period, a preparatory period, an acceleration period, a residual period, and an end rest period.

In one embodiment, the ball type identification step is to obtain the type of ball type through a ball type identification algorithm; wherein, the ball type identification algorithm includes a motion signal segmentation step and a signal normalization step , a convolutional neural network classification step, and a ball species identification step.

In one embodiment, the types of balls classified through the batting type identification step include forehand hits, backhand hits, forehand hits, backhand hits, and forehand push hits. The ball, the backhand push and the backcourt ball, the forehand short ball in the frontcourt, the backhand short ball in the frontcourt, the midfield forehand draw, the midfield backhand draw, the midfield forehand catch and block the front ball, the midfield backhand catch and block Front of the net, backcourt forehand chip, backcourt forehand high ball, backcourt forehand smash, and midfield forehand raid.

In one embodiment, the step of estimating the consistency of the batting action is to compare the batting action with a consistency estimation algorithm; wherein, the consistency estimation algorithm includes a motion signal segmentation step, a sample A selection step, an area boundary dynamic time warp estimation step, and a consistency evaluation step; wherein, the template selection step includes obtaining a template signal, and the template signal is generated by a user using a hand-held ball to hit the ball. The sensing signal is the resampled signal calculated by the boundary area.

Continuing from the above, the hand-held motion analysis system and method of the present invention includes: an attitude estimation step, which performs the motion attitude estimation of the hand-held ball equipment for hitting the ball according to the sensing signal; and a hitting trajectory reconstruction step, which is based on the The sensing signal and the result of the attitude estimation step perform reconstruction of the batting trajectory signal; the batting stage detection step, which distinguishes different periods of the batting process according to the sensing signal and the result of the attitude estimation step; a step of classifying the types of balls hit according to the sensing signal; and a step of evaluating the consistency of the hitting action, which calculates and evaluates the consistency between the hitting action and the sample action according to the sensing signal. Therefore, compared with the conventional batting action analysis system, the hand-held motion analysis system and analysis method of the present invention not only have the advantages of convenience, professionalism, practicality and economy, but also provide relevant batting action instantly and objectively. Indicators can be used as a reference for players and/or coaches to improve athlete's hitting action.

The following will describe the handheld motion analysis system and analysis method according to the embodiments of the present invention with reference to the related drawings, wherein the same elements will be described with the same reference signs.

1A is a functional block diagram of a handheld motion analysis system according to an embodiment of the present invention, FIG. 1B is a functional block diagram of a server of the handheld motion analysis system of FIG. 1A , and FIG. 2 is a schematic diagram of a handheld motion analysis method of the present invention. Schematic diagram of the process steps.

The hand-held motion analysis system 1 can be applied to analyze the hitting action of a hand-held golf tool. Here, hand-held sports can be, for example, but not limited to, badminton, tennis, billiards, baseball, or golf, or other sports that use hand-held golf equipment to hit the ball. Therefore, the above-mentioned hand-held golf equipment can be badminton rackets, tennis rackets, billiards Rackets, bats, or golf clubs, or other golf equipment. The hand-held golf equipment in the following embodiments takes a badminton racket as an example. Therefore, the "hitting" action in this article is the action of "swinging the badminton racket", or "swinging the racket". Of course, if applied to golf, the action of hitting the ball is the action of swinging the golf club, and so on. In addition, the term "athlete" in this article refers to a person who performs batting training under the guidance of a coach.

Referring to FIG. 1A and FIG. 1B , the handheld motion analysis system 1 of this embodiment includes a signal sensing module 11 , a server 12 and a display module 13 .

The signal sensing module 11 is disposed in the hand-held ball. The signal sensing module 11 can sense the batting action (such as swing action) of the player holding the handball equipment (such as a badminton racket) and output a sensing signal SS. Wherein, the signal sensing module 11 is, for example, but not limited to, disposed in the handle of the hand-held golf equipment. Taking a badminton racket as an example, the signal sensing module 11 can be disposed in the handle of the badminton racket, or in the back cover of the handle, but not limited to this. The group 11 can also be installed in other parts of the hand-held golf equipment, such as other positions of the handle or in the middle tube.

The following embodiment is an example in which the signal sensing module 11 is disposed in the handle of the badminton racket provided by Shengli Sports Co., Ltd. Therefore, when the player takes the badminton racket and performs a swing motion, the signal sensing module 11 can sense the player's hitting (swing) motion and output the sensing signal SS. For the specific structure of the badminton racket provided by Shengli Company, please refer to the Republic of China Invention Patent Certificate No.: TW I673088, which will not be further explained here.

The signal sensing module 11 of this embodiment includes an inertial sensor, such as a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, so as to obtain a more accurate hitting (swing) action. Therefore, the sensing signal SS is an inertial sensing signal, which may include an acceleration signal, an angular velocity signal and a magnetic force signal during the swinging motion. In some embodiments, a six-axis sensor including an accelerometer and a gyroscope (eg, ICM-20649) and a three-axis magnetometer (eg, LIS2MDL) can be used as the nine-axis inertial sensor. Among them, the accelerometer is used to sense the earth's gravity and the motion acceleration generated by the motion; the gyroscope is used to sense the angular velocity generated by the motion; and the magnetometer is used to sense the earth's magnetic field vector, and the azimuth angle can be obtained after calculation News.

In some embodiments, the signal sensing module 11 may further include a microcontroller unit and a power supply unit. The power supply unit can be, for example, a lithium battery, which can provide the power required by the signal sensing module 11; and the micro-control unit can capture and collect the inertial sensors (speedometer, gyroscope and magnetometer) caused by the action of hitting the ball. The generated sensing signal SS is processed (eg, temporarily stored and encoded), and the processed sensing signal SS can be wirelessly transmitted to the servo through a batch method such as a Wi-Fi module or a Bluetooth (bluetooth) module. Device 12 for analysis of hitting action.

The server 12 is coupled to the signal sensing module 11 . In some embodiments, the coupling between the server 12 and the signal sensing module 11 may be wirelessly coupled, for example, through a Wi-Fi module or a Bluetooth module, so as to receive, store and process the signal sensing The sensing signal SS outputted by the measuring module 11 . The server 12 can be a local server, a remote server, or a cloud server. The server 12 in this embodiment is a cloud server as an example.

The server 12 may include one or more processing units 121 and a memory unit 122 , and the one or more processing units 121 are coupled to the memory unit 122 . FIG. 1B is an example of a processing unit 121 and a memory unit 122 . The processing unit 121 can access the data stored in the memory unit 122, and can include the core control components of the server 12, for example, can include at least one central processing unit (CPU) and a memory, or include other control hardware, software or firmware. In addition, the memory unit 122 may be a non-transitory computer readable storage medium, for example, may include at least a memory, a memory card, an optical disc, a video tape, a computer tape , or any combination thereof. In some embodiments, the aforementioned memory may include a read only memory (ROM), a flash memory (Flash), a Field-Programmable Gate Array (FPGA), or a Solid State Drive (Solid). State Disk, SSD), or other forms of memory, or a combination thereof.

Since the server 12 in this embodiment is an example of a cloud server, the memory unit 122 is a cloud memory, and the processing unit 121 is a cloud processor. When the sensing signal SS is transmitted to the server 12 (the server 12 has a corresponding wireless transmission module), it can be stored in the memory unit 122 for processing and analysis by the processing unit 121 . In addition, the memory unit 122 can also store at least one application software, and the application software can include one or more program instructions 1221 when the one or more program instructions 1221 of the application software are executed by the one or more processing units 121 2, the one or more processing units 121 may at least perform the following steps: an attitude estimation step S2, a hitting trajectory reconstruction step S3, a hitting stage detection step S4, a hitting ball species The identification step S5, and the consistency evaluation step S6 of a hitting action. In addition, in addition to steps S2 to S6, after acquiring the sensing signal SS, the processing unit 121 of this embodiment may further perform a signal preprocessing step S1. Hereinafter, please refer to FIG. 3 to describe the detailed technical contents of the above steps S1 to S6 .

FIG. 3 is a schematic diagram of another process step of the handheld motion analysis method of the present invention. Here, in addition to showing the detailed flow steps (or sub-steps) inside steps S1 to S6 of FIG. 2 , FIG. 3 also shows a result presentation step S7 . First of all, it should be noted that the steps S1 to S7 mentioned in this article and their internal functional blocks (steps) can realize their functions in the form of software programs, or can also realize their functions in the form of hardware or firmware. The present invention not limited.

As shown in FIG. 3 , before performing the attitude estimation step S2 , in order to make the subsequent analysis process and the generated results more accurate, the signal preprocessing step S1 needs to be performed first.

The signal preprocessing step S1 : correcting the sensing signal SS output by the signal sensing module 11 and filtering out the noise in the sensing signal SS. Wherein, the signal preprocessing step S1 may include a signal correction step S11 and a signal filtering step S12. The signal calibration step S11 can correct the sensing signal SS, and the signal filtering step S12 can filter out the noise in the sensing signal SS. The technical contents of step S11 and step S12 are described in detail below.

(1) Signal calibration step S11: Due to the characteristics of the inertial sensor itself and other external environmental factors, the sensing signal SS measured by the accelerometer, gyroscope and magnetometer will often produce measurement errors or signal drift. In some embodiments, the inertial sensor may be calibrated using a scale factor (SF) and an offset (bias, B). The calibration process can be as follows; the accelerometer and gyroscope are placed in 14 different directions on the horizontal rotating motion platform in turn, and the three axes (X axis, Y axis) of the accelerometer are placed in a static state. , Z axis) The resultant force of the sensing value is the gravitational acceleration reading (1g), and the resultant force of the three-axis sensing value of the gyroscope under the constant speed rotation of the rotary motion platform is the physical phenomenon of the equal angular velocity reading (𝜔), to Add the scale factor (𝑆F _𝑥 , 𝑆F _𝑦 , 𝑆F _𝑧 ) and the bias value (𝐵 _𝑥 , 𝐵 _𝑦 , 𝐵 _𝑧 ) of the three-axis inertial sensor. When calibrating the magnetometer, in an environment without strong magnetic field interference, the magnetometer can be rotated uniformly in three-dimensional space at a fixed time, so that the magnetic sensing value can cover all directions in the three-dimensional space, and it can be sensed The net force of the magnetic field is proportionally normalized to a fixed constant of 1 Gauss. Finally, after solving the calibration matrix (𝐐) of each sensor through the least square error method, the inertial sensor can be calibrated through the following equation (1).

(1)

Among them, 𝐒 _𝑖 is the sensing value of the uncalibrated accelerometer, gyroscope or magnetometer, and 𝐒 _𝑐 is the sensing value of the corrected accelerometer, gyroscope or magnetometer.

Signal filtering step S12: When the player uses a badminton racket to hit the ball, the sensed signal SS measured by the player includes motion signal, high-frequency noise and motion noise (such as involuntary body tremors), Therefore, in order to accurately measure the sensing signal SS generated when the athlete is exercising, the inertial sensing signal corrected in the above step S11 still needs to pass through a low-pass filter to reduce high-frequency noise and motion noise, so that the The received sensing signal SS can actually reflect the real signal of the hitting action. In the following, the inertial sensing signal after the calibration and filtering of the signal preprocessing step S1 is still denoted as the sensing signal SS.

Attitude Estimation Step S2 : Estimating the posture of the hand-held golf equipment according to the sensing signal SS to perform the hitting action. The attitude estimation step S2 utilizes the sensing signal SS through an extended Kalman filter algorithm to estimate the attitude of the golf equipment in the hitting action, so as to obtain the accurate speed of the golf equipment and the trajectory of the golf equipment. Here, the extended Kalman filter algorithm may include a state prediction step S21 , a gravity state update step S22 , and a magnetic north state update step S23 . In the state prediction step S21, the angular velocity signal in the sensing signal SS is used to predict the state of the extended Kalman filter; and in the gravity state update step S22 and the magnetic north state update step S23, the sensing signal SS is used in the state prediction step S22. The state of the extended Kalman filter is updated by the acceleration signal and the magnetic signal, so as to obtain the best posture estimation state of the hand-held ball. The technical contents of steps S21 to S23 are described in detail below.

），利用陀螺儀現在時間點(𝑡)所感測之角速度(𝛚 _𝑡)及上一個時間點(𝑡–1)更新後之姿態角(

)建立一狀態轉移方程式，如式(2)所示。

                             (2)   其中，

為現在時間點預測之狀態，

為現在時間點的狀態轉移矩陣，

為上一個時間點之狀態雜訊係數矩陣，

為現在時間點的角速度白雜訊，

是一個

之單位矩陣，

為現在時間點陀螺儀所感測之角速度，而

，

是取樣週期。接著，便可預測現在時間點之狀態誤差共變異數矩陣(

)如下：

                    (3)   State prediction step S21: the attitude that can be represented by a quaternion is defined as the state variable of the state transition equation (

), using the angular velocity (𝛚 _𝑡 ) sensed by the gyroscope at the current time point (𝑡) and the updated attitude angle at the previous time point (𝑡–1) (

) to establish a state transition equation, as shown in equation (2).

(2) in,

is the predicted state at the current time point,

is the state transition matrix at the current time point,

is the state noise coefficient matrix of the previous time point,

is the white noise of the angular velocity at the current time point,

Is an

The identity matrix of ,

is the angular velocity sensed by the gyroscope at the current time point, and

,

is the sampling period. Then, the state error covariance matrix at the current time point can be predicted (

)as follows:

(3)

為上一個時間點更新後之狀態誤差共變異數矩陣，

為角速度雜訊共變異數矩陣。 in,

is the state error covariance matrix after the update at the last time point,

is the angular velocity noise covariance matrix.

)對預測後的狀態進行更新，在此，將觀測量定義為加速度與磁力值。另外，再建立一重力或磁北觀測方程式對角速度所預測之姿態

)進行狀態更新，如式(4)所示。

(4) Gravity state update step S22 and magnetic north state update step S23: The error generated by the state prediction due to the angular velocity will accumulate over time, so it must be observed through the measurement (

) updates the predicted state, where the observed quantities are defined as acceleration and magnetic force values. In addition, establish a gravity or magnetic north observation equation to predict the attitude of the angular velocity

) to update the state, as shown in formula (4).

(4)

為現在時間點之重力觀測矩陣或磁北觀測矩陣，

為現在時間點所預測之狀態變數，

為現在時間點的加速度或磁力值之白雜訊，

為現在時間點的預測觀測量。接著，獲得重力或磁北觀測方程式之後，便可計算現在時間點的重力或磁北更新卡爾曼增益

，如式(5)所示。最後，即可利用重力或磁北更新卡爾曼增益對所預測之狀態及其狀態誤差共變異數矩陣進行狀態更新，如式(6)及式(7)所示。

(5)

(6)

(7) in,

is the gravity observation matrix or the magnetic north observation matrix at the present time point,

is the state variable predicted for the current time point,

is the white noise of the acceleration or magnetic value at the current time point,

is the predicted observation amount for the current time point. Then, after obtaining the gravity or magnetic north observation equation, you can calculate the gravity or magnetic north at the current time point to update the Kalman gain

, as shown in formula (5). Finally, the predicted state and its state error covariance matrix can be updated by using gravity or magnetic north to update the Kalman gain, as shown in equations (6) and (7).

(5)

(6)

(7)

為觀測量雜訊共變異數矩陣，當觀測量為加速度時，

為加速度雜訊共變異數矩陣(

)；而若觀測量為磁力值時，

即為磁力雜訊共變異數矩陣(

)，

為現在時間點的重力或磁北實際觀測量。 in,

is the observed quantity noise covariance matrix, when the observed quantity is acceleration,

is the acceleration noise covariance matrix (

); and if the observed quantity is a magnetic value,

is the magnetic noise covariance matrix (

),

It is the actual observed amount of gravity or magnetic north at the current time point.

Step S3 of Reconstructing the Shot Track : Reconstructing the shot track signal according to the sensing signal SS and the result of the attitude estimation step S2. Wherein, the batting trajectory reconstruction step S3 uses a trajectory reconstruction algorithm to obtain the hand-held golf equipment posture, the hand-held golf equipment speed and the hand-held golf equipment trajectory signals generated when the batting action is performed. Here, the trajectory reconstruction algorithm may include a motion signal segmentation step S31 , a coordinate conversion and gravity compensation step S32 , a speed estimation and zero-speed compensation step S33 , and a trajectory reconstruction step S34 . It is reminded that, broadly speaking, the trajectory reconstruction algorithm may also include the above-mentioned state prediction step S21 , gravity state update step S22 , and magnetic north state update step S23 . The technical contents of steps S31 to S34 are described in detail below.

Action signal segmentation step S31 : Since the swing action is in progress, there will be a static period of action before and after the swing action. At this time, because the signal sensing module 11 is stationary, the The resultant force value of the three axes is all 0. Therefore, a dynamic threshold value can be set to detect the swing motion range. Standard scores serve as dynamic thresholds.

)，如式(8)所示，並將感測器座標系( s)上濾波過後的加速度訊號(

)轉換成參考座標系( r)上的加速度訊號(

)，如式(9)所示。此外，由於加速度計所量測的加速度值會同時包含了運動所產生的運動加速度及重力加速度，因此，需將重力加速度(

)移除，進而得到真實的運動加速度(

)。

(8)

.                                                            (9) Step S32 of coordinate conversion and gravity compensation: After obtaining the dynamic posture of the player during the exercise, the posture between the sensor coordinate system ( s ) and the reference coordinate system ( r ) can be obtained through the posture estimation step S2 . Coordinate transformation matrix (

), as shown in equation (8), and the filtered acceleration signal ( s ) on the sensor coordinate system ( s )

) into the acceleration signal ( r ) on the reference coordinate system ( r )

), as shown in formula (9). In addition, since the acceleration value measured by the accelerometer includes both the motion acceleration and the gravitational acceleration generated by the movement, the gravitational acceleration (

) is removed, and then the real motion acceleration (

).

(8)

. (9)

(10)

(11) Speed Estimation and Zero Speed Compensation Step S33: After the motion acceleration is obtained, the motion acceleration can be integrated to estimate the speed signal, as shown in equation (10). Since the acceleration signal is easily disturbed by the unconscious tremor of the human body, noise is generated, and when the integral operation of the velocity estimation is performed, the noise of the acceleration signal is amplified by the integral operation, resulting in the distortion of the velocity signal, so through the formula (11 ) for a zero speed update to compensate for a distorted speed signal.

(10)

(11)

為現在時間點之速度訊號；

為上個時間之速度訊號；

為經過零速度更新之速度訊號；

為訊號起始點之速度值；

為訊號結束點之速度值；

為時間間距；

為取樣週期。 in,

is the speed signal at the current time point;

is the speed signal of the previous time;

is the speed signal after zero speed update;

is the speed value of the starting point of the signal;

is the speed value of the signal end point;

is the time interval;

is the sampling period.

(12) Trajectory reconstruction step S34: Integrate the velocity signal after zero velocity compensation to reconstruct the trajectory of the athlete when swinging or moving, as shown in equation (12).

(12)

為現在時間點之運動軌跡；

為上個時間之運動軌跡；

為取樣週期。 in,

is the trajectory of the current time point;

is the trajectory of the last time;

is the sampling period.

Step S4 of batting stage detection : Different stages of the batting process are distinguished according to the sensing signal SS and the result of the attitude estimation step S2. Wherein, the step S4 of the batting stage detection is to obtain each period signal in the batting process through a batting action staged algorithm. Here, the batting action staging algorithm may include a motion signal segmentation step S41 , a coordinate conversion and gravity compensation step S42 , a motion signal pole detection step S43 , and a motion signal period detection step S44 . The technical contents of steps S41 to S44 are described in detail below.

The motion signal segmentation step S41 and the coordinate conversion and gravity compensation step S42 are the same as the motion signal segmentation step S31 and the coordinate conversion and gravity compensation step S32 in the above trajectory reconstruction algorithm (that is, the motion signal segmentation step S31 and the coordinate conversion and gravity compensation step S31). The result of S32 can be applied to step S4 of batting stage detection), which will not be further described here.

Action signal pole detection step S43: Through this step, the starting point of the preparatory period (start point), the starting point of the acceleration period (start point of acceleration), the impact point (impact) and the after-potential period of the hitting action can be found through this step end point. Here, the starting point of the preparatory period can be defined as: defining the starting point of the action dynamic interval as the starting point of the preparatory period, and thereby distinguishing the initial stationary period and the preparatory period. The hitting point can be defined as: in the whole badminton swing process, the moment when the racket contacts the ball usually occurs at the moment when the angular velocity of the racket reaches the maximum value. Therefore, this characteristic can be used to generate the maximum value of the angular velocity resultant force signal in the entire badminton swing signal. The time point at 2000 is defined as the hitting point, and thereby distinguishes the acceleration period and the remaining momentum period. The starting point of the acceleration period can be defined as: after finding the time point of hitting the ball, use the angular velocity value at this time point to search back for the trough of the first angular velocity signal, which is the starting point of the acceleration period, and use this to distinguish the preparation period and accelerated period. The end point of the residual period can be defined as: the end point of the action dynamic interval is defined as the end point of the residual period, and thus the residual period and the end stationary period are distinguished. As shown in FIG. 4 , it is a schematic diagram of signal detection by stages of a batting action. Here, FIG. 4 shows the staging of the acceleration and angular velocity signals in the sensed signal.

Action signal stage detection step S44: After completing the action signal pole detection step S43, the following five stages can be defined for different periods of the hitting process: 1) The time interval before the starting point of the preparatory period can be defined as The initial rest period (initial rest); 2) the time interval between the start point of the preparation period and the start point of the acceleration period can be defined as the preparation period (preparation); 3) between the start point of the acceleration period and the hitting point The time interval can be defined as the acceleration period (acceleration); 4) the time interval between the hitting point and the end of the after-potential period can be defined as the after-potential period (follow through); 5) after the end of the after-potential period A time interval can be defined as an ending rest.

The step S5 of identifying the ball type of the shot is to classify the type of the shot ball according to the sensing signal SS. Wherein, the ball type identification step S5 is to obtain the ball type through a ball type identification algorithm. Here, the batting ball identification algorithm may include a motion signal segmentation step S51 , a signal normalization step S52 , a convolutional neural network classification step S53 , and a ball identification step S54 . The technical contents of steps S51 to S54 are described in detail below.

The motion signal segmentation step S51 is the same as the motion signal segmentation step S31 of the above-mentioned trajectory reconstruction algorithm (that is, the result of the motion signal segmentation step S31 can be applied to the hitting ball type identification step S5 ), and will not be described here.

Signal normalization step S52 : After the inertial sensing signal is subjected to the signal calibration step S11 , the signal filtering step S12 and the motion signal segmentation step S51 , the signal normalization step S52 can be performed to normalize the sensing signal SS.

Convolutional Neural Network Classification Step S53 and Ball Type Identification Step S54: In the sensing signal SS after the signal normalization step S52, the triaxial angular velocity signal in each batting swing motion signal can be used as a convolutional neural network (Convolution Neural Network, CNN) the input of the classifier, and then classify the forehand to the backcourt, the backhand to the frontcourt, the forehand to pick the backcourt high ball, the backhand to pick the backcourt high ball, the forehand to push the backcourt ball, the backhand to push the backcourt Ball, Front forehand short ball, Front backhand short ball, Midfield forehand draw, Midfield backhand draw, Midfield forehand catch and block front ball, Midfield backhand catch and block front ball, Back field There are 16 types of shots, such as hand-cut, backcourt forehand high ball, backcourt forehand smash, and midfield forehand raid ball. Here, the architecture of the convolutional neural network classifier may include two convolutional layers, two pooling layers, a fully connected layer and an output layer, as detailed below:

Convolutional layer: Each convolutional layer contains multiple convolution kernels. Through the set convolution kernel size and the convolution principle, the window gradually slides and the values in each area are weighted to calculate the value of the convolutional layer. output, thereby extracting important information from the input signal. Among them, 128 convolutional kernels/filters with a size of 1×5 are set in each convolutional layer to extract image features.

為三軸角速度訊號所組成之輸入向量；

為每個步伐視窗中的資料點索引；

為每個步伐視窗中的資料點數；

為層的索引；

為卷積核大小(kernel/filter size)；

為第

層的第

個特徵映射(feature map)之偏權值；

為輸入

與第

層第

個特徵映射之連結權重；

為線性整流活化函數。 in,

is the input vector composed of three-axis angular velocity signals;

index for the data point in each step window;

is the number of data points in each step window;

is the index of the layer;

is the convolution kernel size (kernel/filter size);

for the first

the first

The partial weights of a feature map;

for input

with the first

layer

The link weight of each feature map;

is the linear rectification activation function.

為池化大小；

為池化的跨度

Pooling layer: It mainly uses the output of the convolutional layer as its input and performs downsampling. Here, the max pooling operation is used to reduce the dimension of the feature map (network training parameters), while only retaining the input image important features in . The pooling size is 1×2 and the stride is 2. in,

is the pooling size;

is the pooling span

作為該層的輸入，其中

為最後一層池化層的神經元個數，並進行以下運算：

) Fully connected layer: Flatten the features obtained after multi-layer convolutional layers and pooling layers into a feature vector

as the input to this layer, where

is the number of neurons in the last pooling layer, and performs the following operations:

)

為全連結層中第

層的第

個神經元與第

層的第

個神經元連結權重值；

為全連結層中第

層的第

個神經元的偏權值；

為線性整流活化函數。最後，

即為經過卷積神經網路運算所得之深度特徵。 in,

is the first in the fully connected layer

the first

neuron and

the first

neuron connection weight value;

is the first in the fully connected layer

the first

The partial weights of each neuron;

is the linear rectification activation function. at last,

It is the depth feature obtained by the operation of the convolutional neural network.

Output layer: It is usually performed by a classifier. Here, the Softmax classifier is used. The Softmax classifier is based on the log-sigmoid function. The X-axis ranges from positive infinity to negative infinity, and the Y-axis ranges from 0 to 1. Map the output of the fully connected layer to the [0,1] interval, convert the obtained value into the corresponding probability, and take the maximum value as the classification result.

Step S6 of evaluating the consistency of the hitting action : calculating and evaluating the consistency between the hitting action and the template action according to the sensing signal SS. Wherein, in the step S6 of evaluating the consistency of the batting action, a consistency estimating algorithm is used to compare the consistency of the batting action. Here, the consistency estimation algorithm includes a motion signal segmentation step S61 , a sample selection step S62 , an area boundary dynamic time warp estimation step S63 , and a consistency evaluation step S64 . The aforesaid template selection step S62 includes obtaining a template signal, which is a motion signal resampled by the boundary area calculation of the sensing signal SS generated by the user using the hand-held ball to hit the ball. The technical contents of steps S61 to S64 are described in detail below.

The motion signal segmentation step S61 is the same as the motion signal segmentation step S31 of the above-mentioned trajectory reconstruction algorithm (that is, the result of the motion signal segmentation step S31 can be applied to the batting action consistency evaluation step S6), and will not be further described here.

)，以供後續的比對。在此，該使用者可為具有較佳球技的人員，例如但不限於教練、或職業運動員。 Sample selection step S62: The angular velocity signal generated by the same hitting action of the user and the player is regarded as a sample signal (

) for subsequent comparisons. Here, the user may be a person with better golf skills, such as, but not limited to, a coach, or a professional athlete.

)中找出區域最大值及最小值，即為正負波峰。過零點偵測為：偵測動作訊號通過零值之取樣點(zero-crossing points, ZC points)，藉此獲得過零點之取樣訊號(

)。另外，邊界面積計算為：依據過零點的數量(

)，將原始長度為

的動作時序訊號分割為(

)個片段，並針對每一個片段進行訊號積分計算出每個片段的面積，如下所示；則積分後的面積即可代表重新取樣後的時間序列(

)。

Area boundary dynamic time warp estimation step S63 : including positive and negative wave peak detection, zero-crossing point detection, boundary area calculation, and consistency score calculation, etc. Among them, the detection of positive and negative peaks is: setting a threshold value to detect the action timing signal (

) to find the maximum and minimum values in the region, which are positive and negative peaks. The zero-crossing point detection is: to detect that the motion signal passes through the zero-crossing points (zero-crossing points, ZC points), thereby obtaining the zero-crossing point sampling signal (

). In addition, the boundary area is calculated as: according to the number of zero crossings (

), converting the original length to

The action timing signal of is divided into (

) segments, and perform signal integration for each segment to calculate the area of each segment, as shown below; then the integrated area can represent the resampled time series (

).

)，並與樣板訊號(

)進行訊號時序扭曲比對，藉由以下式子計算出運動動作訊號與樣板訊號之間的一致性分數(

)，藉此達成運動訓練動作比對的目的。

In addition, the consistency score is calculated as: the angular velocity signal generated when the athlete performs the sports training action is regarded as the sports action signal after the calculation of the boundary area (

), combined with the template signal (

) to compare the signal timing distortion, and calculate the consistency score between the motion signal and the template signal by the following formula (

), so as to achieve the purpose of sports training action comparison.

In addition, the dynamic time warping is the accumulation of the Euclidean distance from the starting timing of the two timing signals to the ending timing. The ABDTW score can be calculated as follows:

。 in,

.

Consistency evaluation step S64: The swing consistency is the degree of similarity between motion signals when the athlete performs a swing each time. The higher the consistency of the swing action, the more stable the swing technique, and the better the overall performance; on the contrary, the lower the consistency of the swing action, the less stable the swing technique. Here, the athlete's alignment with, for example, a coach's movements is assessed by the conformance score calculated by the Area Boundary Dynamic Time Warping Algorithm (AB-DTW). Among them, the lower the score, the higher the consistency of the motion signals between the two comparisons; conversely, the higher the score, the lower the consistency of the motion signals between the two comparisons.

After analyzing the movement movements through the above steps, the athlete-related batting indicators can be obtained, for example, including swing trajectory, bat type identification, swing stage, number of batting, batting speed, average batting speed, and maximum batting speed. Speed, average smashing speed, maximum smashing speed, hitting force, swing arc, swing consistency, etc.

Result presenting step S7 : presenting the analysis result through the display module 13 coupled to the server 12 for reference by the player or/and the coach, thereby improving the hitting action of the player. In some embodiments, the display module 13 may be a fixed display device (eg, a computer), a mobile device (eg, a notebook computer, a mobile phone, a tablet computer), or other types of display devices. In some embodiments, the display module 13 can display, for example, real-time signal presentation, hitting speed, force data presentation, individual comprehensive performance evaluation radar chart, or/and presenting the result of hitting ball identification and hitting action analysis (such as swinging). Special indicators for badminton such as racket stage, number of shots, hitting speed, maximum hitting speed, average smashing speed, maximum smashing speed, hitting strength, swing arc, swing trajectory and swing movement consistency), etc. Athletes and/or coaches can choose which signals or indicators to watch on the display module 13 by themselves.

The present invention also provides an analysis method for hand-held motion, which can be applied to the above-mentioned hand-held motion analysis system 1 . The components and functions of the handheld motion analysis system 1 have been described in detail above, and will not be further described here.

As shown in FIG. 2 or FIG. 3 , the method for analyzing the hand-held motion may include an attitude estimation step S2 , a hitting trajectory reconstruction step S3 , a hitting stage detection step S4 , a hitting ball type identification step S5 , and a consistent hitting action Sexuality evaluation step S6. In addition, before the attitude estimation step S2, the analysis method may further include a signal preprocessing step S1. In addition, after the above step S3, step S4, step S5, and step S6, the analysis method may further include a result presentation step S7 to present the analysis result.

The detailed technical contents of each step (including step S1 to step S7 ) and its internal (sub) steps of the hand-held motion analysis method have been described in detail above, and will not be repeated here. It should be reminded that, in the above steps S1 to S7, the attitude estimation step S2, the hitting ball type identification step S5 and the hitting action consistency evaluation step S6 can be performed sequentially or simultaneously, but the hitting trajectory reconstruction step S3 The batting stage detection step S4 needs to be performed after the attitude estimation step S2 , and the batting trajectory reconstruction step S3 and the batting stage detection step S4 can be performed sequentially or simultaneously.

The handheld motion analysis system 1 and its analysis method of this embodiment are actually applied to a badminton court to analyze a player's hitting action. Wherein, the signal sensing module 11 is installed in the back cover of the racket handle, for example, to sense the movement of the player when he swings and hits the ball. As shown in the table below, it is the statistical value of the swing stage, hitting speed, hitting arc and consistency of the 9 badminton players when they swing the long ball. batting action indicator Preliminary period(s) Acceleration period(s) Remaining Potential Period(s) Batting speed (kph) Hit Strength (N) Swing arc (deg) consistency(%) 9 athletes 1.26±0.16 0.10±0.01 0.90±0.09 65.21±5.64 1.07±0.09 248.80±11.95 87.02

In addition, FIGS. 5A and 5B are schematic diagrams of the swing motion signals of two athletes in stages when the handheld exercise system of the present invention is applied for analysis, respectively, and FIGS. 6A and 6B are respectively corresponding to the motion signals of FIGS. 5A and 5B . Schematic diagram of swing trajectory. Here, the swing signal is divided into an initial stationary period, a preparatory period, an acceleration period, an after-potential period and an end stationary period.

As shown in Figure 5A, when the first athlete swings the racket with a long ball, the preparatory period, the acceleration period and the resting period are 1.28 seconds, 0.10 seconds and 0.83 seconds, respectively; as shown in Figure 5B, the second athlete In the case of the long ball swing, the preparatory period, the acceleration period and the resting period time were 1.28 seconds, 0.09 seconds and 0.96 seconds, respectively. In addition, the first player's long-ball swing was 65.48kph, the hitting force was 1.03N, the swing arc was 253.24°, and the swing consistency was 93.10%; while the second player's long-ball swing was 93.10%. The hitting speed of the racket is 70.26kph, the hitting force is 1.03N, the swing arc is 244.76°, and the swing consistency is 94.33%, as shown in the table below. batting action indicator Preliminary period(s) Acceleration period(s) Remaining Potential Period(s) Batting speed (kph) Hit Strength (N) Swing arc (deg) consistency(%) The first one 1.28 0.10 0.83 65.48 1.03 253.24 93.10 second 1.28 0.09 0.96 70.26 1.03 244.76 94.33

In addition, the long-ball swing trajectories of the above two players may correspond to those shown in FIG. 6A and FIG. 6B . It should be noted that, since the signal sensing module 11 of this embodiment is disposed in the back cover of the handle of the badminton racket, each linear trajectory displayed at each time point in FIG. 6B represents the badminton racket itself (to the badminton racket). the top of the beat frame).

Continuing from the above, it can be seen from the above disclosure that the hand-held motion analysis system and analysis method of the present invention can automatically capture the motion trajectory signal (ie, the sensing signal) of the athlete's hitting action through the signal sensing module, and pass the server through the server. After signal analysis, a variety of relevant hitting indicators can be obtained, which saves the trouble of the camera shooting process, increases the convenience and practicability of movement trajectory research, and reduces research costs. The influence of human error can be minimized. Furthermore, the player or/and the coach can improve the batting action by viewing the relevant batting indicators generated by the display module of the analysis system at any time.

To sum up, the hand-held motion analysis system and method of the present invention includes: an attitude estimation step, which performs attitude estimation of the hand-held golf equipment for hitting the ball according to the sensing signal; and a hitting trajectory reconstruction step, which is based on the sensing signal. The measurement signal and the result of the attitude estimation step perform reconstruction of the batting trajectory signal; the batting stage detection step, which distinguishes different periods of the batting process according to the sensing signal and the result of the attitude estimation step; the batting type identification step , which classifies the types of balls hit according to the sensing signal; and a batting action consistency evaluation step, which calculates and evaluates the consistency between the batting action and the sample action according to the sensing signal. Therefore, compared with the conventional batting action analysis system, the hand-held motion analysis system and analysis method of the present invention not only have the advantages of convenience, professionalism, practicality and economy, but also provide relevant batting action instantly and objectively. Indicators can be used as a reference for players and/or coaches to improve athlete's hitting action.

The above description is exemplary only, not limiting. Any equivalent modifications or changes that do not depart from the spirit and scope of the present invention shall be included in the appended patent application scope.

1: Handheld motion analysis system 11: Signal sensing module 12: Server 121: Processing unit 122: Memory Unit 1221: Program command 13: Display module S1, S11, S12, S2, S21, S22, S23, S3, S31, S32, S33, S34, S4, S41, S42, S43, S44, S5, S51, S52, S53, S54, S6, S61, S62, S63, S64, S7: Steps SS: sensing signal

FIG. 1A is a functional block diagram of a handheld motion analysis system according to an embodiment of the present invention. FIG. 1B is a functional block diagram of a server of the handheld motion analysis system of FIG. 1A . FIG. 2 is a schematic diagram of a process step of the handheld motion analysis method of the present invention. FIG. 3 is a schematic diagram of another process step of the handheld motion analysis method of the present invention. FIG. 4 is a schematic diagram of signal stage detection of a batting action. FIG. 5A and FIG. 5B are schematic diagrams of the swing motion signals of two athletes in stages when the hand-held exercise system of the present invention is applied for analysis, respectively. 6A and 6B are schematic diagrams of swing trajectories corresponding to the motion signals of FIGS. 5A and 5B, respectively.

S1, S2, S3, S4, S5, S6: Steps

SS: sensing signal

Claims (12)

A hand-held motion analysis system is applied to analyze the hitting action of a hand-held ball. The analysis system includes: a signal sensing module disposed on the hand-held ball, and the signal sensing module senses the movement of the hand-held ball. hitting the ball and outputting a sensing signal; a server, coupled to the signal sensing module, the server includes one or more processing units and a memory unit, the one or more processing units and the memory unit coupled, the memory unit stores one or more program instructions, when the one or more program instructions are executed by the one or more processing units, the one or more processing units perform: an attitude estimation step based on The sensing signal is used to estimate the posture of the hand-held golf equipment for the hitting action; a hitting trajectory reconstruction step is based on the sensing signal and the result of the posture estimating step to perform the reconstruction of the hitting trajectory signal; a hitting stage detection The detecting step is to distinguish different periods of the hitting process according to the sensing signal and the result of the attitude estimation step; the identifying step of a hitting ball is to classify the hitting ball type according to the sensing signal; wherein The batting type identification step uses a convolutional neural network classification step that uses the angular velocity signal in the sensing signal after signal normalization as an input to a convolutional neural network classifier , and then classify a variety of batting ball types; and a batting action consistency evaluation step, which calculates and evaluates the consistency between the batting action and the sample action based on the sensing signal; and a display module, and The server is coupled, and the display module presents analysis results. The analysis system according to claim 1, wherein the step of batting stage detection is to obtain each period signal in the batting process through a batting action staging algorithm; wherein the batting action staging algorithm includes a motion signal A segmentation step, a coordinate conversion and gravity compensation step, a movement signal pole detection step, and a movement signal phase detection step; wherein, the movement signal pole detection step finds out the starting point of the preparatory period, the acceleration of the hitting action The starting point of the period, the hitting point and the ending point of the remaining period. The analysis system as claimed in claim 1, wherein, in the step of detecting the hitting stages, different periods of the hitting process include an initial rest period, a preparatory period, an acceleration period, a residual period, and a End the resting period. The analysis system according to claim 1, wherein the ball type identification step is to obtain the ball type through a ball type identification algorithm; wherein, the ball type identification algorithm includes a motion signal segmentation steps, a signal normalization step, the convolutional neural network classification step, and a ball species identification step. The analysis system according to claim 1, wherein the types of balls classified through the step of identifying the types of batting balls include forehand serving in the backcourt, backhand serving in the front court, forehand hitting the backcourt high ball, and backhand hitting the backcourt high ball The ball, the forehand push to pick the backcourt ball, the backhand push to pick the backcourt ball, the frontcourt forehand short ball, the frontcourt backhand short ball, the midfield forehand draw ball, the midfield backhand draw ball, the midfield forehand catch and block the front ball , The midfield backhand catches the ball in front of the net, the backcourt forehand cuts the ball, the backcourt forehand high ball, the backcourt forehand smash, and the midfield forehand raid ball. The analysis system according to claim 1, wherein the step of evaluating the consistency of the batting action is to compare the consistency of the batting action through a consistency estimation algorithm; wherein, the consistency estimation algorithm includes a A motion signal segmentation step, a sample selection step, an area boundary dynamic time warp estimation step, and a consistency evaluation step; wherein, the sample selection step includes obtaining a sample signal, the sample signal is a user using the The sensing signal generated by the hand-held ball-batting action is a resampled signal through the calculation of the boundary area. A method for analyzing hand-held motion, applied to a hand-held motion analysis system, the hand-held motion analysis system includes a signal sensing module, the signal sensing module is arranged on a hand-held ball tool, and senses the stroke of the hand-held ball tool The ball moves and outputs a sensing signal. The analysis method includes: an attitude estimation step: according to the sensing signal to perform attitude estimation of the hand-held golf equipment for hitting the ball; a hitting trajectory reconstruction step: according to the sensing signal and The result of the attitude estimation step executes the reconstruction of the batting trajectory signal; a stroke stage detection step: distinguishes different periods of the batting process according to the sensing signal and the result of the attitude estimation step; A bat type identification step: classifying the type of the bat according to the sensing signal; wherein the bat type identification step uses a convolutional neural network classification step, and the convolutional neural network classification step will The angular velocity signal in the sensing signal after signal normalization is used as the input of the convolutional neural network classifier, and then a variety of hitting ball types are classified; and a hitting action consistency evaluation step: according to the sensing The signal calculates and evaluates the consistency between the hitting action and the template action. The analysis method according to claim 7, wherein, in the step of detecting the batting action by stages, a signal of each period in the batting process is obtained through a batting action staging algorithm; wherein, the batting action staging algorithm Including a motion signal segmentation step, a coordinate conversion and gravity compensation step, a motion signal pole detection step, and a motion signal phase detection step; wherein, the motion signal pole detection step finds out the preparatory period of the hitting action. The starting point, the starting point of the acceleration period, the hitting point and the ending point of the remaining momentum period. The analysis method as claimed in claim 7, wherein, in the step of detecting ball hitting stages, different periods of the hitting process include an initial rest period, a preparatory period, an acceleration period, a residual period, and a End the resting period. The analysis method according to claim 7, wherein, in the bat type identification step, a bat type identification algorithm is used to obtain the bat type; wherein the bat type identification algorithm comprises: A motion signal segmentation step, a signal normalization step, the convolutional neural network classification step, and a ball species identification step. The analysis method according to claim 7, wherein the types of balls classified through the step of identifying the types of batting balls include forehand serving in the backcourt, backhand serving in the front court, forehand hitting the backcourt lofty ball, and backhand hitting the backcourt lofty ball The ball, the forehand push to pick the backcourt ball, the backhand push to pick the backcourt ball, the frontcourt forehand short ball, the frontcourt backhand short ball, the midfield forehand draw ball, the midfield backhand draw ball, the midfield forehand catch and block the front ball , The midfield backhand catches the ball in front of the net, the backcourt forehand cuts the ball, the backcourt forehand high ball, the backcourt forehand smash, and the midfield forehand raid ball. The analysis method according to claim 7, wherein, in the step of evaluating the consistency of the batting action, a consistency estimating algorithm is used to compare the batting action for consistency; wherein, the consistency estimation The algorithm includes a motion signal segmentation step, a pattern selection step, an area boundary dynamic time distortion estimation step, and a consistency evaluation step; Wherein, the template selection step includes obtaining a template signal, the template signal is a signal that is resampled by the boundary area calculation of the sensing signal generated by the user using the hand-held golf equipment to hit the ball.

Images