Sports monitoring method based on intelligent basketball

Abstract

The present invention relates to the field of sports monitoring technology, and specifically to a sports monitoring method based on a smart basketball, comprising the following steps: S1, the smart basketball collects the user's sports data; and processes the sports data into a wireless signal that can be used for wireless transmission; S2, detects whether the smart basketball is connected to a mobile terminal device for communication; if connected, wirelessly transmits the sports data to the mobile terminal device; S3, the mobile terminal device substitutes the above sports data into a preset mathematical model for conversion, and generates a first conversion result. The present invention collects sports data in basketball training through a smart basketball, and transmits it to a mobile terminal device in real time, and then analyzes the sports data on the mobile terminal device through a preset mathematical model, which plays an important role in monitoring the user's basketball training situation and conducting relevant analysis and judgment, and can effectively guide basketball trainers to conduct targeted training and help trainers improve their basketball level.

Description

Technical Field

[0001] The present invention relates to the technical field of sports monitoring, and in particular to a sports monitoring method based on intelligent basketball. Background Art

[0002] Basketball is a core event of the Olympic Games. It is a physical confrontation sport centered on the hands. It has a wide audience and is also a sport that people love to participate in in their daily lives. Whether it is an athlete or an ordinary basketball enthusiast, constantly training and improving their basketball skills is one of the pursuits of people.

[0003] For this reason, professional athletes are generally equipped with professional coaches to assist in their training. The coaches can develop professional training plans for the athletes based on their own technical foundation and the athlete's training situation each time, and can observe and record the athletes' sports data during training for auxiliary analysis, but ordinary basketball enthusiasts generally do not have these conditions.

[0004] At present, with the continuous improvement of technology, various intelligent devices are becoming more and more popular. For example, a data acquisition device is used to collect relevant sports data during exercise and upload the data to relevant terminal devices or servers for viewing and analysis. It is used in the field of sports monitoring, allowing athletes or ordinary users to independently collect their own sports data and record and analyze it. Summary of the invention

[0005] The purpose of the present invention is to provide a motion monitoring method based on smart basketball, which is a motion monitoring method based on smart basketball, enabling users to collect relevant data during exercise in real time and upload it to relevant terminal devices for analysis, which is beneficial for users to fully grasp the basketball training situation and analyze it, so as to achieve the purpose of improving the basketball training effect.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A motion monitoring method based on smart basketball comprises the following steps:

[0008] S1, a data collection device installed on the smart basketball collects motion data of the user during the exercise; and processes the collected motion data into a wireless signal that can be used for wireless transmission;

[0009] S2, detecting whether the communication between the smart basketball and the mobile terminal device is connected, and generating a first determination result; if the first determination result is yes, proceeding to step S21; if the first determination result is no, proceeding to step S22;

[0010] S21, wirelessly transmitting the motion data collected in step S2 to the mobile terminal device;

[0011] S22, storing the motion data collected in step S2 into the storage module of the smart basketball, and returning to step S2;

[0012] S3, after receiving the motion data collected in step S21, the mobile terminal device substitutes the motion data into a mathematical model preset in the mobile terminal device for conversion, and generates a first conversion result.

[0013] Preferably, the method further includes step S4: matching the first conversion result with the motion behavior parameters preset on the mobile terminal device to generate target motion parameters, and displaying the target motion parameters on the mobile terminal device.

[0014] Preferably, the method further includes step S5: the mobile terminal device uploads the first conversion result and the target motion parameter to a server, and the server stores them.

[0015] Preferably, step S6 is also included: a training plan database is preset in the server, and the user sends an operation instruction to the server through the mobile terminal device. The server matches the received target motion parameters with the training plan in the database, and sends the matched training plan to the mobile terminal device, so that the corresponding training plan is output on the mobile terminal device.

[0016] Preferably, the method further includes step S7: the mobile terminal device sends a request to the server to read historical data, and substitutes the read historical data into a preset data analysis model in the mobile terminal device for analysis, and generates a first analysis result.

[0017] Preferably, step S7 can be executed at any time.

[0018] Preferably, the mathematical model in step S3 includes an M model for calculating the number of dribbles per unit time: the motion data collected by the data acquisition device includes the total duration of a continuous acquisition process of the data acquisition device and the total number of dribbles within the total duration; assuming that the first conversion result is M, the total duration of the continuous acquisition process is Tx, and the total number of dribbles within the total duration is Nx, then: M=Nx / Tx.

[0019] Preferably, the mathematical model in step S3 includes a W model for representing the stability of dribbling during a continuous collection process: assuming that the first conversion result is W, the time interval between the i-1th ball dribbling and the i-th ball dribbling is x i (i=1, 2, ..., n), the average time interval between single ball bounces in a continuous collection process is

[0020] The standard deviation of the time interval between a single ball bounce is:

[0021] Then the discrete distribution (coefficient of variation) value W of the single ball-bouncing time interval in the sample satisfies the following formula:

[0022] Preferably, step S3 also includes step S31: the mathematical model is the following judgment rule, and the total number of dribbles Nx within the total duration is judged as follows: Nx>2000, and a second judgment result is generated. If the second judgment result is yes, 2000 is output, and if the second judgment result is no, the current Nx value is output.

[0023] Preferably, step S3 also includes step S32: the mathematical model is the following judgment rule, and the total duration Tx of the continuous acquisition process is judged as follows: Tx≥1200, and a third judgment result is generated. If the third judgment result is yes, Tx=1200 is output; if the third judgment result is no, the current Tx value is output.

[0024] The present invention collects motion data in the basketball training process through an intelligent basketball, and transmits it to a mobile terminal device in real time, and then analyzes the motion data on the mobile terminal device through a preset mathematical model, which plays an important role in monitoring the basketball training situation of basketball trainers and conducting relevant analysis and judgment, and can effectively guide basketball trainers to conduct targeted training and help trainers improve their basketball level. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of the motion monitoring method based on smart basketball of the present invention;

[0026] Figure 2 This is an example of a set of training data in the application example of the present invention;

[0027] Figure 3 Based on Figure 2 The result after the training data in is converted;

[0028] Figure 4 Based on Figure 3 The pentagonal diagram generated by the conversion results in . DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] like Figure 1 Shown is a flow chart of a motion monitoring method based on smart basketball according to the present invention.

[0031] This embodiment provides a motion monitoring method based on a smart basketball, comprising the following steps:

[0032] S1, a data collection device installed on the smart basketball collects motion data of the user during the exercise; and processes the collected motion data into a wireless signal that can be used for wireless transmission;

[0033] S2, detect whether the communication between the smart basketball and the mobile terminal device is connected, and generate a first determination result; if the first determination result is yes, proceed to step S21; if the first determination result is no, proceed to step S22; the mobile terminal device can be a mobile device such as a mobile phone or a tablet computer.

[0034] S21, wirelessly transmitting the motion data collected in step S2 to the mobile terminal device;

[0035] S22, storing the motion data collected in step S2 into the storage module of the smart basketball, and returning to step S2;

[0036] S3, after receiving the motion data collected in step S21, the mobile terminal device substitutes the motion data into the mathematical model preset in the mobile terminal device for conversion, and generates a first conversion result. Specifically, the mobile terminal is provided with a special APP, in which the mathematical model is preset, and the smart basketball and the mobile terminal APP cooperate to achieve the purpose of monitoring and analyzing the training process of the basketball trainer.

[0037] Preferably, the method further includes step S4: matching the first conversion result with the motion behavior parameters preset on the mobile terminal device to generate target motion parameters, and displaying the target motion parameters on the mobile terminal device.

[0038] Preferably, the mathematical model in step S3 includes an M model for calculating the number of dribbles per unit time: the motion data collected by the data acquisition device includes the total duration of a continuous acquisition process of the data acquisition device and the total number of dribbles within the total duration; assuming that the first conversion result is M, the total duration of the continuous acquisition process is Tx, and the total number of dribbles within the total duration is Nx, then: M = Nx / Tx. This mathematical model is used to evaluate the agility of the basketball trainer during this training process, which is obtained by calculating the number of dribbles per unit time of the basketball trainer during this training process, where Tx is in seconds, Nx is in pieces, and M is in pieces / second. The higher the number of dribbles per unit time, the higher the agility of the basketball trainer.

[0039] The sports behavior parameter corresponding to the first conversion result M is "agility". According to certain evaluation criteria, the M value is divided into several intervals, and the corresponding agility judgment word is matched for each interval. When the M value falls within the interval, the corresponding agility judgment word is output. The evaluation criteria can be the general standard for the number of dribbling times per unit time of basketball technical level in the relevant field.

[0040] Preferably, the mathematical model in step S3 includes a W model for representing the stability of dribbling during a continuous collection process: assuming that the first conversion result is W, the time interval between the i-1th ball dribbling and the i-th ball dribbling is x i (i=1, 2, ..., n), the average time interval between single ball bounces in a continuous collection process is

[0041] The standard deviation of the time interval between a single ball bounce is:

[0042]

[0043] Then the discrete distribution (coefficient of variation) value W of the single ball-bouncing time interval value in the sample (all ball-bouncing time intervals in a continuous collection process) satisfies the following formula: This mathematical model is used to evaluate the dribbling stability of a basketball trainer during this training session. It calculates the time interval x between any two dribblings of the basketball trainer during this training session. i The standard deviation of The ratio between σ and W is obtained. The larger the σ, the larger the W value, which means that the discrete degree of the single ball-bouncing time interval value in the basketball training sample data is greater, and its stability is worse. That is, the frequency of basketball trainers' ball-bouncing during the training is uneven. For example, many people have better physical strength at the beginning of training, the ball-bouncing interval is shorter, and the ball-bouncing frequency is higher; but in the later stage of training, the physical strength decreases, and the ball-bouncing interval becomes longer and longer. On the contrary, the smaller the W value, the better the stability of the basketball trainer during the training. This stability assessment is based on the stability of the ball-bouncing frequency, which reflects the stability of the basketball trainer's endurance, physical strength, or more accurately, the muscle strength of the target muscle group. The higher the muscle stability, the more stable the ball-bouncing technique of the basketball trainer during the basketball movement, and the more uniform the horizontal output, which is more conducive to the stable performance of basketball skills.

[0044] The sports behavior parameter corresponding to the first conversion result W is "stable". According to certain evaluation criteria, the W value is divided into several intervals, and the corresponding stability judgment word is matched for each interval. When the W value falls into the interval, the corresponding stability judgment word is output. The evaluation criteria can be the general standard for basketball technical level in the relevant field.

[0045] Preferably, the mathematical model in step S3 also includes an F model for evaluating the strength training of the basketball trainer during the basketball training: the motion data collected by the data acquisition device includes the maximum velocity v of the ball during a complete dribbling process. x , v x is the combined velocity of the ball in the X, Y, and Z directions. Specifically, a complete dribbling process refers to the process in which the ball touches the hand of the basketball trainer, leaves the hand, falls to the ground, and then bounces back to the hand. Let the mass of the basketball be m (the mass of the basketball needs to be measured and input into the mobile phone APP), and the momentum be f x , then the momentum f during a single dribble is x =m×v x Suppose the total number of dribbles during a continuous data collection process of a certain data collection device is N j times, a 1 is the momentum of the first dribble in this acquisition process 1 The number of repetitions, a 2 is the momentum of the second dribble during this acquisition process 2 The number of repetitions, and so on; and the following relationship exists:

[0046] a 1 +a 2 +…+a x =N j ,

[0047] Then the weighted average F of the momentum of a single dribble in a continuous collection process is:

[0048]

[0049] This model is used to evaluate the dribbling strength level of basketball trainees during the training. The larger F is, the more effective the strength training is. The sports behavior parameter corresponding to the first conversion result F is "strength". According to certain evaluation criteria, the F value is divided into several intervals, and the corresponding strength value judgment words are matched for each interval. When the F value falls within the interval, the corresponding strength value judgment word is output. The evaluation criteria can be the general standard for basketball technical level in the relevant field.

[0050] Preferably, step S3 also includes step S31: the mathematical model is the following judgment rule, and the total number of dribbles Nx within the total duration is judged as follows: Nx>2000, and a second judgment result is generated. If the second judgment result is yes, 2000 is output, and if the second judgment result is no, the current Nx value is output.

[0051] The sports behavior parameter corresponding to the above step S31 is "total number of dribbles". According to certain evaluation criteria, the Nx value is divided into several intervals, and the corresponding dribbling number determination word is matched for each interval. When the Nx value falls within the interval, the corresponding dribbling number determination word is output. The evaluation criteria can be the general standard for basketball technical level in the relevant field.

[0052] Preferably, step S3 also includes step S32: the mathematical model is the following judgment rule, and the total duration Tx of the continuous acquisition process is judged as follows: Tx≥1200, and a third judgment result is generated. If the third judgment result is yes, Tx=1200 is output; if the third judgment result is no, the current Tx value is output.

[0053] The sports behavior parameter corresponding to the above step S32 is "endurance". According to certain evaluation criteria, the Tx value is divided into several intervals, and the corresponding dribbling quantity judgment word is matched for each interval. When the Tx value falls into the interval, the corresponding endurance value judgment word is output. The evaluation criteria can be the general standard for basketball technical level in the relevant field.

[0054] Preferably, the method further includes step S5: the mobile terminal device uploads the first conversion result and the target motion parameter to a server, and the server stores them.

[0055] Preferably, step S6 is also included: a training plan database is preset in the server, and the user sends an operation instruction to the server through the mobile terminal device. The server matches the received target motion parameters with the training plan in the database, and sends the matched training plan to the mobile terminal device, so that the corresponding training plan is output on the mobile terminal device.

[0056] Preferably, the method further includes step S7: the mobile terminal device sends a request to the server to read historical data, and substitutes the read historical data into a preset data analysis model in the mobile terminal device for analysis, and generates a first analysis result. The step S7 can be executed at any time. The basketball trainer can not only analyze the data of the current basketball training, but also conduct a comprehensive analysis and evaluation of the previous training data stored in the server, so as to achieve the purpose of periodic evaluation of the same trainer.

[0057] Optionally, the data analysis model is a pentagonal graph, and the above-mentioned mathematical models are used as the five evaluation dimensions of the pentagonal graph respectively, so that they can be displayed in the same pentagonal graph, so that the comprehensive ability of basketball trainers can be presented more intuitively, and it is also more conducive to basketball trainers to make vertical comparisons of their past training data, or horizontal comparisons with other users' training data.

[0058] like Figure 2 As shown, it is an example of one set of training data. The training collection time is 42.85 seconds, and the total number of ball bounces is 50 times. The data of ball bounce momentum here has been scaled to a certain extent to adapt to the presentation of the pentagonal diagram.

[0059] Based on the above training data, the final conversion results corresponding to the five mathematical models are as follows: Figure 3 As shown, further, the pentagonal diagram generated according to the training data is as follows Figure 4 shown.

[0060] Depend on Figure 4 It can be seen intuitively that the basketball trainee has better strength and stability during this training, followed by agility, and weaker endurance.

[0061] The present invention collects motion data in the basketball training process through an intelligent basketball and transmits it to a mobile terminal device in real time, and then analyzes the motion data on the mobile terminal device through a preset mathematical model, which plays an important role in monitoring the basketball training situation of basketball trainers and conducting relevant analysis and judgment, and can effectively guide the skills of basketball trainers and help trainers improve their basketball level.

[0062] In summary, the present invention is based on the data collection function of smart basketball products, collects various types of sports data, and transmits these data to the mobile phone APP in real time through long-distance Bluetooth communication for analysis, and gives personalized suggestions for different users through the APP interface prompts, acting as a personal basketball coach. The cloud server corresponding to the APP also has some built-in training courses for users to learn and train to improve their skills. It can also match basketball training data with big data, realize the accumulation and analysis of training data, and graphically display the results of analyzing ball control ability; it can also associate corresponding social interaction accounts, realize team communication and confrontational learning between teams through the community; realize social interaction entertainment mode, and accumulate wonderful sports moments together.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A motion monitoring method based on smart basketball, characterized in that: The following steps are involved: S1, a data collection device installed on the smart basketball collects the motion data of the user during the motion; and processing the collected motion data into wireless signals that can be used for wireless transmission; S2, detecting whether the communication between the smart basketball and the mobile terminal device is connected, and generating a first determination result; If the first determination result is yes, then go to step S21; if the first determination result is no, then go to step S22; S21, wirelessly transmitting the motion data collected in step S2 to the mobile terminal device; S22, storing the motion data collected in step S2 into the storage module of the smart basketball, and returning to step S2; S3, after receiving the motion data collected in step S21, the mobile terminal device substitutes the motion data into a mathematical model preset in the mobile terminal device for conversion, and generates a first conversion result; The mathematical model in step S3 includes an M model for calculating the number of dribbles per unit time: the motion data collected by the data acquisition device includes the total duration of a continuous acquisition process of the data acquisition device and the total number of dribbles within the total duration; assuming that the first conversion result is M, the total duration of the continuous acquisition process is Tx, and the total number of dribbles within the total duration is Nx, then: M= Nx / Tx; The mathematical model in step S3 also includes a W model for representing the stability of dribbling during a continuous collection process: assuming that the first conversion result is W, the time interval between the i-1th ball dribbling and the i-th ball dribbling is (i=1, 2, ..., n), the average time interval between single ball bounces in a continuous collection process is ; The standard deviation of the time interval between a single ball bounce is: ; Then the discrete distribution (coefficient of variation) value W of the single ball-bouncing time interval in the sample satisfies the following formula: .

2. The motion monitoring method based on smart basketball according to claim 1, characterized in that: The step S4 is also included: matching the first conversion result with the motion behavior parameters preset on the mobile terminal device to generate target motion parameters, and displaying the target motion parameters on the mobile terminal device.

3. The motion monitoring method based on smart basketball according to claim 2, characterized in that: The method further includes step S5: the mobile terminal device uploads the first conversion result and the target motion parameter to a server, and the server stores the result and the target motion parameter.

4. The motion monitoring method based on smart basketball according to claim 3 is characterized in that: It also includes step S6: a training plan database is preset in the server, and the user sends an operation instruction to the server through the mobile terminal device. The server matches the received target motion parameters with the training plan in the database, and sends the matched training plan to the mobile terminal device, so that the corresponding training plan is output on the mobile terminal device.

5. The motion monitoring method based on smart basketball according to claim 4 is characterized in that: The method further includes step S7: the mobile terminal device sends a request to the server to read historical data, and substitutes the read historical data into a preset data analysis model in the mobile terminal device for analysis, and generates a first analysis result.

6. The motion monitoring method based on smart basketball according to claim 5, characterized in that: The step S7 can be executed at any time.

7. The motion monitoring method based on smart basketball according to claim 1, characterized in that: Step S3 also includes step S31: the mathematical model is the following judgment rule, and the total number of dribbles Nx within the total time is judged as follows: Nx>2000, and a second judgment result is generated. If the second judgment result is yes, 2000 is output, and if the second judgment result is no, the current Nx value is output.

8. The motion monitoring method based on smart basketball according to claim 1, characterized in that: Step S3 also includes step S32: the mathematical model is the following judgment rule, the total duration Tx of the continuous acquisition process is judged as follows: Tx ≥ 1200, and a third judgment result is generated. If the third judgment result is yes, Tx = 1200 is output; If the third determination result is no, the current Tx value is output.

Images