A jump rope training method and system

Abstract

This invention relates to the technical field of rope skipping training, specifically to a rope skipping training method and system, comprising the following steps: collecting the jump offset of the rope skipper to obtain a jump offset model, and using the variation model to predict whether the jumper's leg posture is standard; collecting the pixel difference of the jumper's posture offset to obtain an arm posture change offset model, and using the variation model to predict whether the jumper's arm posture is standard; determining whether the leg posture of any jump is qualified; determining whether the arm posture of any jump is qualified; and obtaining a rope skipping success rate model. The rope skipping training method of this invention estimates the first calculation result of rope skipping by collecting the jumper's jump offset, arm posture change offset, and jump frequency, until the calculation result of each rope skipping training session is obtained. The data collection is simple and has strong universality.

Description

Technical Field

[0001] This invention relates to the technical field of rope skipping training, and specifically to a rope skipping training method and system. Background Technology

[0002] Currently, rope skipping training and assessment are included in physical education classes for primary and secondary school students in my country. Rope skipping is an exercise that benefits overall body coordination and promotes the development of children and adolescents, requiring coordinated hand and foot movements and providing brain exercise. However, rope skipping training for primary and secondary school students or sports enthusiasts often relies on manual measurement of jump data, which results in significant errors in both measurement and training effectiveness. Therefore, there is an urgent need to provide a technical solution to address these issues. Summary of the Invention

[0003] One of the objectives of this invention is to provide a jump rope training method, which estimates the jump rope offset, arm posture offset, and number of jumps based on any jump rope offset, to obtain the first calculation result of any jump rope, until the calculation result of each jump rope training session is obtained.

[0004] The second objective of this invention is to provide a jump rope training system that combines hardware and software to collect and calculate jump rope training data through intelligent training methods. This system can effectively help jump rope users develop scientific and standardized jump rope movements, improve classroom training effectiveness, and provide technical support for accelerating the standardization and intelligent development of jump rope training in primary and secondary schools.

[0005] One of the solutions adopted to achieve the objective of this invention is: a rope skipping training method, comprising the following steps:

[0006] The distance between the pressure matrix and the center of the pressure sensor matrix that the jumper steps on during the jump and landing is collected and calculated to obtain the offset of each jump, thereby obtaining the jumper's bounce offset model. The jumper's leg posture is then predicted to be standard by changing the model.

[0007] The pixel width between the two hands in the original infrared image of the jumper in the standard ready position is compared with the pixel width between the two hands in the infrared image of each jump. The pixel difference of the posture offset is obtained, and thus the arm posture change offset model of the jumper is obtained. The change model is used to predict whether the arm posture of the jumper is standard.

[0008] If the calculated jump offset of any jumper is within the normal range in the jump offset model during jump rope training, then the jump rope training is considered successful; otherwise, the leg posture of any jump is considered unqualified.

[0009] If the calculated result of the arm posture offset during any jump is within the normal range of the jump rope training of the arm posture change offset model, then the jump rope training is considered successful; otherwise, the arm posture of any jump is considered unqualified.

[0010] Collect the total number of jumps by the jumper in one jump rope training session. Subtract the set of the number of times the jumper's leg posture and arm posture were incorrect during the jump rope training from the total number of jumps to obtain the number of times the jumper's bounce offset model and arm posture change offset model were successfully trained. Then divide by the total number of jumps to obtain the jump rope success rate model.

[0011] Preferably, the bounce offset model f1 for:

[0012]

[0013] in, x1 For each jump rope jumper's landing point, a pressure sensor is used. x0 The center point of the pressure sensor matrix, a This is the preset offset value.

[0014] Preferably, the arm posture change offset model f2 for:

[0015]

[0016] in, y1 The pixel width between the hands in the infrared image during any jump rope attempt. y0 denoted as , where is the pixel width between the two hands in the infrared image of the jumper's original hand posture, and k is the width coefficient.

[0017] Preferably, the jump rope success rate model f3 for:

[0018]

[0019] in, xn This refers to the total number of jumps performed by the jumper in one jump rope training session. f1 =0 represents the set of instances where the jumper's leg posture was incorrect during jump rope training. f2 =0 represents the set of non-compliant hand postures of the jumper during jump rope training.

[0020] The solution adopted to achieve the second objective of this invention is: a system for implementing the aforementioned rope skipping training method, comprising: a training terminal, a control terminal, and a client terminal;

[0021] The training terminal is used to collect the original training data of the jumper when the jumper is performing jump rope training, and send the original training data to the control terminal.

[0022] The control terminal is used to forward the original jump rope training data to the client.

[0023] The client is used to obtain the final training data of the jumper based on the original jump rope training data, the preset bounce offset model, the arm posture change offset model, and the jump rope success rate model.

[0024] Preferably, the training terminal is used to collect the number of jumps, jump offset, and arm posture offset of the jumper.

[0025] Preferably, the training end includes a gravity sensing device and an infrared array sensor.

[0026] Preferably, the gravity sensing device includes: a plurality of pressure sensors arranged in a matrix; the pressure sensors are placed on the trampoline in a square matrix to detect the landing point of each jump by the jumper and the distance from the center of the sensor matrix.

[0027] Preferably, the control terminal is further configured to receive digital signals generated by the client according to the jumper's training plan, and control the training terminal to operate according to the corresponding operating mode based on the digital signals.

[0028] Preferably, the client is specifically used to: estimate the first calculation result of any jump rope based on the jumper's jump offset, arm posture offset, and number of jumps, until the calculation result of each jump rope training session is obtained.

[0029] The present invention has the following advantages and beneficial effects:

[0030] The rope skipping training method of the present invention estimates the first measurement result of rope skipping by collecting the rope deviation, arm posture change deviation and rope skipping frequency of any rope skipper, until the measurement result of each rope skipping training is obtained. The data collection is simple and has strong universality.

[0031] The rope skipping training system of this invention combines hardware and software, and through intelligent rope skipping training, it realizes the collection and calculation of rope skipping training data. It can effectively help rope skippers form scientific and standardized rope skipping movements, improve classroom training effects, and provide technical support for accelerating the standardization and intelligent construction of rope skipping training in primary and secondary schools. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the structure of a jump rope training system according to an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of a pressure sensor matrix in a jump rope training system according to an embodiment of the present invention;

[0034] Figure 3 This is a flowchart illustrating a jump rope training system according to an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram of the overall structure of a jump rope training system according to an embodiment of the present invention. Detailed Implementation

[0036] To better understand the present invention, the following embodiments are further illustrations of the present invention, but the content of the present invention is not limited to the following embodiments.

[0037] Example 1

[0038] like Figure 1 As shown,

[0039] A jump rope training system includes: a training terminal, a control terminal, and a client terminal;

[0040] The training terminal is used to collect the original training data of the jumper when the jumper is performing jump rope training, and send the original training data to the control terminal.

[0041] The control terminal is used to forward the original jump rope training data to the client.

[0042] The client is used to obtain the final training data of the jumper based on the original jump rope training data, the bounce offset model, the arm posture change offset model, and the jump rope success rate model.

[0043] The training end includes a pressure sensor matrix and an infrared array sensor;

[0044] The infrared array sensor is used to: collect infrared images of the jumper's standard hand posture before training and send the infrared images to the control terminal; and collect infrared images of the jumper's hand posture during training and send the infrared images to the control terminal.

[0045] The pressure sensor matrix is ​​used to: collect the coordinates of the jumper in the matrix for each jump during training, and send the coordinate data to the control terminal.

[0046] The control terminal is used to forward the original jump rope training data to the client.

[0047] The client is used to: calculate the original standard hand posture infrared image of the jumper and the real-time hand infrared image of the jumper according to the arm posture change offset model; calculate the real-time data of the jumper's pressure sensor matrix according to the bounce offset model, and input the jump rope success rate model to obtain the jumper's final training data.

[0048] The client device can be a mobile phone, tablet, or computer. In this embodiment, the client can generate training plans (such as the jumper's hand spread, jump height, number of jumps, and jump frequency) according to the needs of the school or training unit. This training plan is displayed on the screen in the form of digital signals. In addition, the client can also use a loudspeaker to remind the jumper of incorrect postures so that the jumper can correct the incorrect movements in real time.

[0049] In this embodiment, a 5×5 pressure sensor matrix is ​​used. The jumper stands at the center of the matrix and starts jumping rope. When the jumper's foot landing point deviates and deviates from the adjacent pressure sensor at the center of the matrix, the client will broadcast the deviation to the jumper. When the jumper's foot landing point deviates to the outer circle of the matrix, the client will alarm the jumper and prompt the jumper to correct the landing point of the foot.

[0050] In this embodiment, a 320×240 or 640×480 infrared sensor is used to acquire images of the jumper's posture. See Figure 4 First, the standard posture of the jump rope user is captured, and the image is transmitted to the client as the raw data of the jump rope user's hand posture. The specific calculation method is as follows: using a temperature value higher than a certain value (hand temperature), the pixel value between the edges of the two hands is calculated, and this pixel value is multiplied by a certain coefficient (the coefficient is between 0.9 and 1.2). When the pixel value between the edges of the two hands in the real-time infrared image is greater than the result of multiplying the raw data by the coefficient, the client will remind the jump rope user through a loudspeaker that the arms are spread too far.

[0051] The client and the control terminal communicate wirelessly via Bluetooth.

[0052] The client is specifically used to: estimate the first calculation result of any jump rope based on the jumper's jump rope offset, arm posture width, and jump rope frequency, until the calculation result of each jump rope training session is obtained.

[0053] Example 2

[0054] A jump rope training method includes the following steps:

[0055] The distance between the pressure matrix and the center of the pressure sensor matrix that the jumper steps on during the jump and landing is collected and calculated to obtain the offset of each jump, thereby obtaining the jumper's bounce offset model. The jumper's leg posture is then predicted to be standard by changing the model.

[0056] The pixel width between the two hands in the original infrared image of the jumper in the standard ready position is compared with the pixel width between the two hands in the infrared image of each jump. The pixel difference of the posture offset is obtained, and thus the arm posture change offset model of the jumper is obtained. The change model is used to predict whether the arm posture of the jumper is standard.

[0057] If the calculated jump offset of any jumper is within the normal range in the jump offset model during jump rope training, then the jump rope training is considered successful; otherwise, the leg posture of any jump is considered unqualified.

[0058] If the calculated result of the arm posture offset during any jump is within the normal range of the jump rope training of the arm posture change offset model, then the jump rope training is considered successful; otherwise, the arm posture of any jump is considered unqualified.

[0059] Collect the total number of jumps by the jumper in one jump rope training session. Subtract the set of the number of times the jumper's leg posture and arm posture were incorrect during the jump rope training from the total number of jumps to obtain the number of times the jumper's bounce offset model and arm posture change offset model were successfully trained. Then divide by the total number of jumps to obtain the jump rope success rate model.

[0060] The bounce offset model f1 for:

[0061]

[0062] in, x1 For each jump rope jumper's landing point, a pressure sensor is used. x0 The center point of the pressure sensor matrix, a This is the preset offset value.

[0063] The arm posture change offset model f2 for:

[0064]

[0065] in, y1 The pixel width between the hands in the infrared image during any jump rope attempt. y0 denoted as , where is the pixel width between the two hands in the infrared image of the jumper's original hand posture, and k is the width coefficient.

[0066] The jump rope success rate modelf3 for:

[0067]

[0068] in, xn This refers to the total number of jumps performed by the jumper in one jump rope training session. f1 =0 represents the set of instances where the jumper's leg posture was incorrect during jump rope training. f2 =0 represents the set of non-compliant hand postures of the jumper during jump rope training.

[0069] The technical solution of this embodiment adopts a combination of hardware and software. Through intelligent rope skipping training, it realizes the collection and calculation of training data. While meeting the needs of teaching and training projects, it also improves the accuracy of the training effect of rope skippers, making their footwork more precise and their hand placement more appropriate, thereby increasing rope skipping speed.

[0070] The algorithms or displays provided herein for the various parameters and steps in the jump rope training system and method described in this embodiment to achieve their respective functions are not inherently related to any particular computer, virtual system, or other device. Furthermore, this embodiment of the invention is not targeted at any particular programming language.

[0071] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. Similarly, for the sake of brevity and to aid in understanding one or more aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0072] The above description is merely a preferred embodiment of the present invention, and should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A jump rope training method, characterized in that, Includes the following steps: The distance between the pressure matrix and the center of the pressure sensor matrix that the jumper steps on during the jump and landing is collected and calculated to obtain the offset of each jump, thereby obtaining the jumper's bounce offset model. The jumper's leg posture is then predicted to be standard by changing the model. The pixel width between the two hands in the original infrared image of the jumper in the standard ready position is compared with the pixel width between the two hands in the infrared image of each jump. The pixel difference of the posture offset is obtained, and thus the arm posture change offset model of the jumper is obtained. The change model is used to predict whether the arm posture of the jumper is standard. If the calculated jump offset of any jumper is within the normal range in the jump offset model during jump rope training, then the jump rope training is considered successful; otherwise, the leg posture of any jump is considered unqualified. If the calculated result of the arm posture offset during any jump is within the normal range of the jump rope training of the arm posture change offset model, then the jump rope training is considered successful; otherwise, the arm posture of any jump is considered unqualified. Collect the total number of jumps by the jumper in one jump rope training session. Subtract the set of the number of times the jumper's leg posture and arm posture were incorrect during the jump rope training from the total number of jumps to obtain the number of times the jumper's bounce offset model and arm posture change offset model were successfully trained. Then divide by the total number of jumps to obtain the jump rope success rate model.

2. The jump rope training method according to claim 1, characterized in that: The bounce offset model for: ; in, For each jump rope jumper's landing point, a pressure sensor is used. The center point of the pressure sensor matrix, This is the preset offset value.

3. The jump rope training method according to claim 1, characterized in that: The arm posture change offset model for: ; in, The pixel width between the hands in the infrared image during any jump rope attempt. The pixel width between the two hands in the infrared image of the jump rope person's original hand posture. This is the width coefficient.

4. The jump rope training method according to claim 1, characterized in that: The jump rope success rate model for: ; in, This refers to the total number of jumps performed by the jumper in one jump rope training session. This refers to the set of individuals whose leg posture was incorrect during rope skipping training. This refers to the set of individuals whose hand postures were incorrect during rope skipping training.

5. A system for implementing the jump rope training method according to any one of claims 1 to 4, characterized in that, include: Training terminal, control terminal, and client terminal; The training terminal is used to collect the original training data of the jumper when the jumper is performing jump rope training, and send the original training data to the control terminal. The control terminal is used to forward the original training data to the client. The client is used to obtain the final training data of the jumper based on the original jump rope training data, the preset bounce offset model, the arm posture change offset model, and the jump rope success rate model.

6. The system for implementing the jump rope training method according to claim 5, characterized in that: The training device is used to collect the number of jumps, jump offset, and arm posture offset of the jumper.

7. The system for implementing the jump rope training method according to claim 5, characterized in that: The training device includes a gravity sensor and an infrared array sensor.

8. The system for implementing the jump rope training method according to claim 7, characterized in that: The gravity sensing device includes: multiple pressure sensors arranged in a matrix; the pressure sensors are placed on the trampoline in a square matrix to detect the landing point of each jump by the jumper and the distance from the center of the sensor matrix.

9. The system for implementing the jump rope training method according to claim 5, characterized in that: The control terminal is also used to receive digital signals generated by the client according to the jumper's training plan, and to control the training terminal to operate according to the corresponding operating mode according to the digital signals.

10. The system for implementing the jump rope training method according to claim 5, characterized in that: The client is specifically used to: estimate the jump offset, arm posture offset and number of jumps based on any jump rope training exercise of the jumper, and obtain the first calculation result of any jump rope training exercise, until the calculation result of each jump rope training exercise is obtained.

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