Shuttlecock high and far ball auxiliary training system capable of prompting optimal starting time

Abstract

The invention provides a high-distance badminton training assisting system capable of prompting optimal starting time, and belongs to the technical field of physical training equipment. The technical problem that the learning efficiency is low due to the fact that a badminton beginner difficultly captures the high and far ball hitting starting opportunity is solved. According to the technical scheme, the system is composed of the following four modules: M1, an action recognition module; m2, a badminton tracking module; m3: a time sequence calculation module; and M4: a feedback module. The intelligent badminton racket has the beneficial effects that the intelligent badminton racket combines a wearable sensor, a camera system and an intelligent data analysis technology, accurately monitors key actions of an athlete and a falling track of a badminton, and calculates and prompts an optimal kicking starting opportunity in real time, so that the athlete is helped to realize high synchronization of racket swinging and badminton falling; and the ball hitting accuracy and competitive performance are improved.

Description

Technical Field

[0001] The invention relates to the technical field of sports training equipment, in particular to a badminton high-ball auxiliary training system which prompts an optimal starting time. Background Art

[0002] Badminton, as a high-intensity competitive sport that combines speed, skills and strategy, has been widely popularized and developed around the world in recent years. High-level badminton competitions require not only excellent physical fitness and skills, but also precise judgment and coordination of movements in an instant, especially when executing key shots such as high balls, the correct start time is crucial to the effect of the shot.

[0003] In traditional badminton training, athletes mainly rely on the on-site guidance of coaches and their own experience accumulation to optimize their hitting movements and timing. However, this training method has many limitations. First, the coach's observation and feedback are often limited by subjective factors, making it difficult to fully and objectively capture every subtle movement of athletes in high-speed sports. Second, the dynamic changes and pressure environment faced by athletes in actual competitions make it difficult to fully transform the training effect that relies on subjective judgment into actual combat capabilities.

[0004] In order to make up for the shortcomings of traditional training methods, some auxiliary training equipment and systems have appeared on the market. These devices usually monitor and analyze the athletes' movements through technical means such as cameras and sensors, and provide a certain degree of feedback. For example, some training systems can record the athlete's swing speed, angle and other parameters to help athletes understand the characteristics of their own movements. However, most of these existing auxiliary training devices focus on the recording and analysis of action parameters, lack real-time and intelligent prompt functions, and cannot provide timely guidance on the best start time when athletes perform key hitting actions.

[0005] In addition, the success of a badminton shot depends not only on the player's racket swing technique, but also on its synchronization with the falling trajectory of the badminton. Existing training systems often fail to fully consider the real-time coordination between the dynamic falling process of the badminton and the player's movements, making it difficult for players to achieve a high degree of synchronization between their movements and the ball trajectory in actual competitions, thus affecting the accuracy and effectiveness of the shot.

[0006] In summary, the current badminton training field urgently needs an auxiliary training system that can comprehensively monitor the athlete's movements and the falling trajectory of the badminton, analyze in real time and prompt the best start time. The system should have high-precision motion capture capabilities, real-time data processing and analysis functions, and intelligent feedback mechanisms to help athletes achieve precise synchronization of movements and ball trajectories during training, and improve the effect of hitting the ball and the competitive level. Summary of the invention

[0007] The purpose of the present invention is to provide a badminton high-ball auxiliary training system that prompts the best starting time. The system combines wearable sensors, camera systems and intelligent data analysis technology to accurately monitor the key movements of athletes and the falling trajectory of badminton, calculate and prompt the best kicking start time in real time, thereby helping athletes to achieve a high degree of synchronization between swinging the racket and the falling of the badminton, and improve the accuracy of hitting the ball and competitive performance.

[0008] The inventive idea of ​​the present invention is: the present invention provides a badminton high and long ball auxiliary training system that prompts the best starting time, specifically, an intelligent sports training system that realizes action optimization and synchronous prompts by real-time monitoring and analysis of athlete movements and the trajectory of moving objects (such as badminton); the system aims to improve the start timing accuracy and hitting effect of badminton players in key hitting movements such as high and long balls, and is suitable for multiple application scenarios such as professional training, amateur training and sports scientific research.

[0009] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is specifically: a badminton high-flying ball auxiliary training system that prompts the best starting time, which is composed of the following modules:

[0010] M1. Motion recognition module: used for data customization and calibration to help obtain the athlete's personalized motion parameters.

[0011] M2. Badminton tracking module: real-time tracking of the position information and falling speed of the badminton, providing badminton motion data.

[0012] M3. Timing calculation module: Based on the output of the first two modules, predict the best time to start kicking t start , to ensure that the racket hits the shuttlecock at the highest point. start This is the best time to start kicking predicted by the system.

[0013] M4. Feedback module: Based on the timing calculation results, it prompts the athlete to start the leg kicking action in real time to ensure that the action is synchronized with the falling of the badminton.

[0014] Among them, the M1 motion recognition module includes: M1.1 athlete's foot pressure sensor module and M1.2 badminton racket tracking module.

[0015] The function of the M1.1 athlete's foot pressure sensor module is to detect and output when the athlete makes a kicking motion. Implementation steps:

[0016] S1.1.1 Install pressure sensors in the athlete's shoes to monitor the pressure changes on the soles of the feet in real time. The specific implementation steps are as follows:

[0017] Select a thin-film pressure sensor and install it in the forefoot area of ​​the insole, covering the most common areas of the sole (toes and mid-foot) to monitor thrust and propulsion. Number of sensors: Multiple sensor points (e.g., 4-8 sensors) can be arranged in the forefoot area as needed to provide detailed pressure distribution information.

[0018] The resistance change of the thin film pressure sensor generates an analog voltage signal which is proportional to the pressure.

[0019] The analog voltage signal output by the sensor is converted into a digital signal through ADC. The data of each sensor is converted independently. The ADC sets the sampling frequency to 200Hz to ensure that enough signal changes can be captured during the movement. Basic noise filtering is performed on the sensor signal, and a low-pass filter is used to remove high-frequency noise and smooth the signal. Pressure data normalization: According to the calibration data of the sensor, the value output by the sensor is converted into a standard pressure unit (Newton). Among them, ADC refers to analog-to-digital conversion.

[0020] Implementation of the signal acquisition circuit: 1. Use a resistor divider circuit to read the resistance change of the pressure sensor and convert it into a voltage signal. 2. Collect the outputs of multiple pressure sensors to an analog input through an appropriate analog multiplexer. 3. Convert the analog signal to a digital signal through an analog-to-digital converter and output it to a microcontroller for processing; select ESP32 as the microcontroller. 4. ADC resolution: Select an ADC resolution of at least 10 bits to ensure signal accuracy. Among them, ESP32 is a high-performance, low-power Wi-Fi + Bluetooth dual-mode microcontroller system.

[0021] Data transmission design and implementation: The collected pressure data is transmitted to a mobile device or computer via Bluetooth through the built-in Bluetooth of ESP32, using the standard Bluetooth low energy protocol.

[0022] The steps of Bluetooth data transmission include:

[0023] a) Data packaging: Pack the data of multiple sensors into one data packet, which contains information such as sensor number, timestamp, pressure value, etc.

[0024] b) Data transmission: Data packets are transmitted to designated mobile devices or PCs via Bluetooth to ensure data real-time and stability.

[0025] c) Connection management: Use Bluetooth pairing and connection management mechanisms to ensure a stable connection between the data acquisition device and the receiving device.

[0026] d) Data transmission frequency: Set the data transmission frequency to transmit 20 data packets per second.

[0027] e) Data reception and analysis: Design an application on the PC to receive and analyze the data transmitted by Bluetooth. Analyze the received data packets according to the transmission protocol and extract the pressure data.

[0028] f) Thrust: Estimates the athlete's thrust based on pressure data in the forefoot area.

[0029] S1.1.2 When a sudden increase in thrust is detected, it is determined to be the start time of the kicking action. kick Among them, t kick The moment the kick action begins for the athlete's data customization phase.

[0030] M1.2. Function of the badminton racket tracking module: track the height of the racket head and determine the time and height when the racket reaches the highest point. Implementation steps:

[0031] S1.2.1 Use a camera (with a refresh rate of 120 Hz or more) to capture the movement of the racket.

[0032] S1.2.2 Ground calibration: First calibrate the ground position and establish the field coordinate system, with the origin of the coordinate system set at the athlete's feet.

[0033] S1.2.3 Racket marking: Install infrared marking points on the racket head to improve tracking accuracy.

[0034] S1.2.4 Racket trajectory tracking: The movement trajectory of the racket head is captured in real time through the camera system to obtain the change of its height over time.

[0035] S1.2.4 Data processing: Find the moment t when the racket head reaches the highest point peak and the corresponding height h racket Among them, t peak h is the moment when the racket head reaches the highest point. racket The height at which the racket head reaches its highest point;

[0036] S1.2.5 Module output

[0037] The time interval from the start of the kick to the racquet head reaching the highest point:

[0038] t swing =t peak -t kick

[0039] where t swing It is the time interval from the kicking action to the swinging of the racket head to the highest point. The height of the racket at the highest point is h. racket .

[0040] M2. The function of the badminton tracking module is to obtain the position information, falling speed and height from the ground of the badminton in real time. Module output: Output the physical height (t) and falling speed v of the badminton in each frame y (t).

[0041] Among them, h(t) is the physical height of the badminton, v y (t) is the falling speed of the badminton;

[0042] M2. Badminton tracking module includes: M2.1 target recognition submodule; M2.2 target tracking submodule; M2.3 badminton trajectory estimation and analysis submodule.

[0043] Among them, the implementation steps of the M2.1 target recognition submodule include: S2.1.1 data preparation and training of target recognition model; S2.1.2 target recognition execution.

[0044] Among them, the implementation steps of S2.1.1 data preparation and training target recognition model include:

[0045] d) Data collection:

[0046] Collect images or video data containing badminton, preferably covering different backgrounds, lighting, angles, and various motion states of badminton.

[0047] Annotated data: Annotate the location (bounding box) of the badminton for each image or video frame.

[0048] e) Data preprocessing:

[0049] Perform image standardization, such as resizing and color normalization.

[0050] Data enhancement: Perform operations such as rotation, flipping, and cropping on training data to improve the robustness of the model.

[0051] f) Training target recognition model:

[0052] Use the target recognition algorithm YOLOv7-Tiny to train a model to recognize badminton. YOLOv7-Tiny is a simplified version of the YOLOv7 target detection model.

[0053] Ensure that the model can accurately recognize badminton in different scenes and lighting conditions.

[0054] Among them, the implementation steps of S2.1.2 target identification execution include:

[0055] a) Identify the target:

[0056] In each frame of the video, the trained object recognition model is used to identify the location of the badminton. The model outputs the bounding box coordinates of the badminton and the confidence score.

[0057] b) Post-processing:

[0058] Set a confidence threshold (for example, 0.5) to filter out low-confidence recognition results to avoid false detection.

[0059] M2.2. The implementation steps of the target tracking submodule include:

[0060] S2.2.1 Selecting a target tracking algorithm

[0061] Select the KCF (Kernelized Correlation Filter) tracking algorithm to predict the position of the next frame based on the target position of the previous frame. The KCF tracking algorithm refers to the "kernelized correlation filter tracking algorithm".

[0062] S2.2.2 Initialize tracker

[0063] In the first recognized frame, the tracker is initialized using the badminton position (bounding box) output by the object recognition model.

[0064] S2.2.3 Tracking process

[0065] a) Update the target location:

[0066] In subsequent frames, the target tracker uses Kalman filtering to predict the current position of the badminton based on the target position in the previous frame.

[0067] The tracker matches the image features and updates the target's bounding box to ensure that the target is continuously tracked.

[0068] b) Correction of tracking results:

[0069] If target tracking fails in a certain frame (such as target occlusion, leaving the field of view, etc.), the position of the badminton can be re-identified according to the recognition model and the state of the tracker can be corrected.

[0070] If the target is still visible and moving smoothly in the current frame, the tracker continues to predict the target position.

[0071] S2.2.4 Lost Target Recovery

[0072] In some cases (such as occlusion or fast motion), the tracker may lose the target. In this case, the target recognition model needs to be reused to locate the badminton and resume target tracking.

[0073] If the target is lost for a long time, the tracker needs to be reinitialized and a new tracking starts based on the current recognition result.

[0074] M2.3 Badminton trajectory estimation and analysis submodule

[0075] According to the flight characteristics of badminton high-flying balls, the final stage of the badminton flight is a vertical drop. Beginners usually practice high-flying balls by feeding the ball, so the position where the badminton falls is the position of the practitioner. This method only focuses on the vertical drop of the final stage of the badminton high-flying ball flight; and it is assumed that the badminton falls at the same position on the floor.

[0076] The implementation steps of the badminton trajectory estimation and analysis submodule include:

[0077] S2.3.1. Setting the height origin

[0078] Drop the badminton on the same position on the floor and mark that position as the height origin, that is, the physical height is 0.

[0079] S2.3.2. Calibrate the ratio of pixels to physical height

[0080] In the video, get the pixel coordinate y when the badminton touches the ground drop , the corresponding physical height is 0. Among them, y drop are the pixel coordinates of the badminton when it touches the ground.

[0081] Use a simple physical model (a 1-meter-high upright ruler) for calibration, obtain the proportionality factor k, and calculate the ratio between the actual physical height and the pixel coordinate difference;

[0082]

[0083] The coefficient k is the ratio between the actual physical height and the pixel coordinate difference;

[0084] S2.3.3. Extracting target tracking results

[0085] Use the target tracking submodule to track the position of the badminton in real time and obtain the pixel coordinate y of the badminton in each frame pixel (t). Among them, y pixel (t) is the pixel coordinate of the badminton at the current moment;

[0086] S2.3.4. Calculation of the vertical height of the badminton

[0087] For each frame, use the pixel coordinate y of the badminton pixel (t), calculate the physical height h(t) of the badminton:

[0088] h(t)=(y drop -y pixel (t))·k

[0089] Among them, y pixel (t) is the pixel coordinate of the badminton at the current moment. h(t) is the physical height of the badminton.

[0090] S2.3.5. Calculation of falling velocity

[0091] For the falling process, the vertical speed of the badminton should be the rate of change of position over time. Assuming that the time interval between each frame is △t (usually 1 / FPS), the speed of the badminton is calculated by the following method:

[0092] Instant velocity estimation: Use position differences to approximate velocity.

[0093]

[0094] in:

[0095] h(t) and h(t-1) are the heights of the shuttlecock in adjacent frames.

[0096] △t is the time interval between two frames.

[0097] v y (t) is the instantaneous speed of the badminton.

[0098] S2.3.6. Output results

[0099] Output the physical height h(t) and falling speed v of the badminton in each frame y (t).

[0100] M3. The timing calculation module predicts the best time to start kicking based on the output data of the action recognition module and the badminton tracking module. start , make sure you hit the shuttlecock at the highest point of your swing.

[0101] The implementation steps include:

[0102] S3.1. Startup timing calculation

[0103] Calculation of the time required for the badminton to reach the hitting height:

[0104] Among them, Δt shuttle is the time required for the badminton to reach the hitting height; t shuttle =t+Δt shuttle Among them, t is the time, t shuttle It is the moment when the badminton reaches the hitting height;

[0105] Calculation of the best time to start kicking:

[0106] t start =t shuttle -t swing

[0107] S3.2 module output

[0108] The best time to kick start start .

[0109] M4. The feedback module prompts the athlete to start the leg kicking action in real time based on the results of the timing calculation module to ensure that the action is synchronized with the falling of the badminton. Implementation steps:

[0110] S4.1. Feedback method

[0111] Feedback method: tactile feedback is used to provide tactile prompts through the vibration bracelet.

[0112] Feedback trigger conditions:

[0113] When t ≥ t start -Δt prompt When the feedback is triggered

[0114] Among them, Δt prompt To provide cue lead time, consider system processing delays and athlete reaction times.

[0115] S4.2 Error correction and adjustment

[0116] Real-time monitoring: monitor the athlete's actual kicking moment and compare it with the optimal timing.

[0117] Error Feedback: Provides error information to help athletes adjust the timing of their movements.

[0118] System Adaptation: Dynamically adjust the prompt advance amount Δt according to the error situation prompt .

[0119] Steps to use the system:

[0120] The system usage steps are divided into two stages: offline work S5 and online work S6.

[0121] S5 offline work: data customization and preparation, the purpose is to customize and calibrate data, obtain the athlete's personalized movement parameters, including the following steps:

[0122] a) Preparation:

[0123] Athletes wear pressure sensors on the soles of their feet.

[0124] Install infrared markers on the racket.

[0125] Make sure the camera system and sensor equipment are connected properly.

[0126] b) Practice without the ball:

[0127] Have the athlete perform 10 high-flying shots without the ball.

[0128] The action recognition module records the data of each action.

[0129] c) Data processing:

[0130] Calculate the time t from the start of each kick to the time the racket head reaches the highest point swing .

[0131] Record the height h of the racket at its highest point racket .

[0132] d) Personalized parameter determination:

[0133] Statistics are performed on the data of multiple trials, and the average value is calculated to obtain the personalized action parameter t swing and h racket .

[0134] S6 works online to provide real-time training and feedback. In actual training, it tracks the movements of the badminton and athletes in real time, provides timely feedback, and helps athletes master the correct timing of hitting the ball.

[0135] The steps include:

[0136] a) Start training:

[0137] The system enters real-time monitoring mode.

[0138] The athlete begins normal high ball training.

[0139] b) Badminton Tracking:

[0140] The badminton tracking module obtains the badminton's position information, falling speed and height from the ground in real time.

[0141] c) Timing calculation:

[0142] The timing calculation module calculates the best time to start kicking based on real-time data. start .

[0143] d) Feedback Tips:

[0144] The feedback module sends prompts to athletes through the vibrating bracelet at the appropriate time, guiding them to start the leg kick.

[0145] e) Action execution:

[0146] The athlete follows the prompts, starts kicking and swinging the racket to complete the shot.

[0147] f) Error correction:

[0148] The system records the actual kicking moment, compares it with the optimal timing, and provides feedback to help athletes adjust their movements.

[0149] Compared with the prior art, the present invention has the following beneficial effects:

[0150] 1) Optimize the timing of hitting the ball and improve the training effect. The system accurately calculates the falling trajectory of the badminton and the timing of hitting the ball, and reminds athletes in real time the best time to kick their legs. Through scientific and precise timing calculation, it can help athletes master the best time to hit the high ball during training, thereby improving the accuracy and effect of hitting the ball.

[0151] 2) Real-time feedback and personalized training: The system provides instant tactile feedback (such as a vibrating bracelet) during the athlete's training process, guiding the athlete to start the leg kick at the right time, avoiding starting too early or too late, and ensuring synchronization with the fall of the badminton. This real-time feedback provides athletes with personalized training plans and improves training efficiency.

[0152] 3) Reduce athlete errors and improve technical level. This system can accurately predict the time when the badminton reaches the hitting point through motion recognition and timing calculation, avoiding athletes from making mistakes due to inaccurate timing judgment. Through repeated training, athletes can better control the timing of hitting in actual competitions and improve their badminton skills.

[0153] 4) Data customization and personalized calibration. During the initial training process, the system customizes and calibrates data through the action recognition module, and tailors a suitable training plan based on the batting action characteristics of each athlete to ensure that each athlete can start the leg kick at the most appropriate time and optimize personal technology.

[0154] 5) Intelligent and automated training assistance: The system combines motion capture technology, machine learning and real-time data analysis to provide athletes with highly intelligent training assistance. Athletes can use the system to conduct efficient training independently without relying too much on coaches for on-site guidance, greatly improving the level of automation in training.

[0155] 6) Suitable for beginner athletes. This system is especially suitable for beginners. It can help beginners master the timing of hitting the ball as early as possible and avoid action errors caused by wrong timing. BRIEF DESCRIPTION OF THE DRAWINGS

[0156] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0157] Figure 1 Schematic diagram of the module structure and system architecture of an embodiment of the present invention. DETAILED DESCRIPTION

[0158] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Of course, the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0159] Example 1

[0160] See also Figure 1 The technical solution provided by this embodiment is a badminton high-ball assisting training system that prompts the best start time, and a badminton high-ball assisting training system that prompts the best start time, which is composed of the following modules:

[0161] M1. Motion recognition module: used for data customization and calibration to help obtain the athlete's personalized motion parameters.

[0162] M2. Badminton tracking module: real-time tracking of the position information and falling speed of the badminton, providing badminton motion data.

[0163] M3. Timing calculation module: Based on the output of the first two modules, predict the best time to start kicking t start , make sure you hit the shuttlecock at the highest point of your swing.

[0164] M4. Feedback module: Based on the timing calculation results, it prompts the athlete to start the leg kicking action in real time to ensure that the action is synchronized with the falling of the badminton.

[0165] Among them, the M1 motion recognition module includes: M1.1 athlete's foot pressure sensor module and M1.2 badminton racket tracking module.

[0166] The function of the M1.1 athlete's foot pressure sensor module is to detect and output when the athlete makes a kicking motion. Implementation steps:

[0167] S1.1.1 Install pressure sensors in the athlete's shoes to monitor the pressure changes on the soles of the feet in real time. The specific implementation steps are as follows:

[0168] Select a thin-film pressure sensor and install it in the forefoot area of ​​the insole, covering the most common areas of the sole (toes and mid-foot) to monitor thrust and propulsion. Number of sensors: Multiple sensor points (e.g., 4-8 sensors) can be arranged in the forefoot area as needed to provide detailed pressure distribution information.

[0169] The resistance change of the thin film pressure sensor generates an analog voltage signal which is proportional to the pressure.

[0170] The analog voltage signal output by the sensor is converted into a digital signal through the ADC. The data of each sensor is converted independently. The ADC sets the sampling frequency to 200Hz to ensure that enough signal changes can be captured during the movement. Basic noise filtering is performed on the sensor signal, and a low-pass filter is used to remove high-frequency noise and smooth the signal. Pressure data normalization: According to the calibration data of the sensor, the value output by the sensor is converted into a standard pressure unit (Newton).

[0171] Implementation of the signal acquisition circuit: 1. Use a resistor divider circuit to read the resistance change of the pressure sensor and convert it into a voltage signal. 2. Collect the outputs of multiple pressure sensors to an analog input terminal through an appropriate analog multiplexer (MUX). 3. Convert the analog signal to a digital signal through an analog-to-digital converter (ADC) and output it to a microcontroller (MCU) for processing; select ESP32 as the microcontroller. 4. ADC resolution: Select an ADC resolution of at least 10 bits to ensure signal accuracy.

[0172] Data transmission design and implementation: The collected pressure data is transmitted to a mobile device or computer via Bluetooth through the built-in Bluetooth of ESP32, using the standard Bluetooth Low Energy (BLE) protocol.

[0173] The steps of Bluetooth data transmission include:

[0174] g) Data packaging: Pack the data of multiple sensors into one data packet, which contains information such as sensor number, timestamp, pressure value, etc.

[0175] h) Data transmission: Data packets are transmitted to designated mobile devices or PCs via Bluetooth to ensure data real-time and stability.

[0176] i) Connection management: Use Bluetooth pairing and connection management mechanisms to ensure a stable connection between the data acquisition device and the receiving device.

[0177] j) Data transmission frequency: Set the data transmission frequency to transmit 20 data packets per second.

[0178] k) Data reception and analysis: Design an application on the PC to receive and analyze the data transmitted by Bluetooth. Analyze the received data packets according to the transmission protocol and extract the pressure data.

[0179] l) Thrust: Based on the pressure data of the forefoot area, the athlete's thrust is estimated.

[0180] S1.1.2 When a sudden increase in thrust is detected, it is determined to be the start time of the kicking action. kick .

[0181] M1.2. Function of the badminton racket tracking module: track the height of the racket head and determine the time and height when the racket reaches the highest point. Implementation steps:

[0182] S1.2.1 Use a camera (with a refresh rate of 120 Hz or more) to capture the movement of the racket.

[0183] S1.2.2 Ground calibration: First calibrate the ground position and establish the field coordinate system, with the origin of the coordinate system set at the athlete's feet.

[0184] S1.2.3 Racket marking: Install infrared marking points on the racket head to improve tracking accuracy.

[0185] S1.2.4 Racket trajectory tracking: The movement trajectory of the racket head is captured in real time through the camera system to obtain the change of its height over time.

[0186] S1.2.4 Data processing: Find the moment t when the racket head reaches the highest point peak and the corresponding height h racket .

[0187] S1.2.5 Module output

[0188] The time interval t from the start of the kicking action to the racket head reaching the highest point swing :

[0189] t swing =t peak -t kick , where t swing It is the time interval from the start of the kicking action to the time when the racket head reaches the highest point;

[0190] The height of the racket at its highest point h racket .

[0191] M2. The function of the badminton tracking module is to obtain the position information, falling speed and height from the ground of the badminton in real time. Module output

[0192] Output the physical height (t) and falling speed v of the badminton in each frame y (t).

[0193] M2. Badminton tracking module includes: M2.1 target recognition submodule; M2.2 target tracking submodule; M2.3 badminton trajectory estimation and analysis submodule.

[0194] Among them, the implementation steps of the M2.1 target recognition submodule include: S2.1.1 data preparation and training of target recognition model; S2.1.2 target recognition execution.

[0195] Among them, the implementation steps of S2.1.1 data preparation and training target recognition model include:

[0196] g) Data collection:

[0197] Collect images or video data containing badminton, preferably covering different backgrounds, lighting, angles, and various motion states of badminton.

[0198] Annotated data: Annotate the location (bounding box) of the badminton for each image or video frame.

[0199] h) Data preprocessing:

[0200] Perform image standardization, such as resizing and color normalization.

[0201] Data enhancement: Perform operations such as rotation, flipping, and cropping on training data to improve the robustness of the model.

[0202] i) Training target recognition model:

[0203] Use the target recognition algorithm YOLOv7-Tiny to train a model to recognize badminton.

[0204] Ensure that the model can accurately recognize badminton in different scenes and lighting conditions.

[0205] Among them, the implementation steps of S2.1.2 target identification execution include:

[0206] c) Identify the target:

[0207] In each frame of the video, the trained object recognition model is used to identify the location of the badminton. The model outputs the bounding box coordinates of the badminton and the confidence score.

[0208] d) Post-processing:

[0209] Set a confidence threshold, such as 0.5, to filter out low-confidence recognition results to avoid false detection.

[0210] M2.2. The implementation steps of the target tracking submodule include:

[0211] S2.2.1 Selecting a target tracking algorithm

[0212] Choose to use the KCF (Kernelized Correlation Filter) tracking algorithm to predict the position of the next frame based on the target position of the previous frame.

[0213] S2.2.2 Initialize tracker

[0214] In the first recognized frame, the tracker is initialized using the badminton position (bounding box) output by the object recognition model.

[0215] S2.2.3 Tracking process

[0216] c) Update the target location:

[0217] In subsequent frames, the target tracker uses Kalman filtering to predict the current position of the badminton based on the target position in the previous frame.

[0218] The tracker matches the image features and updates the target's bounding box to ensure that the target is continuously tracked.

[0219] d) Correction tracking results:

[0220] If target tracking fails in a certain frame, such as the target is blocked or leaves the field of view, the position of the badminton can be re-identified based on the recognition model and the state of the tracker can be corrected.

[0221] If the target is still visible and moving smoothly in the current frame, the tracker continues to predict the target position.

[0222] S2.2.4 Lost Target Recovery

[0223] In some cases, such as occlusion or fast motion, the tracker may lose the target. In this case, the target recognition model needs to be reused to locate the badminton and resume target tracking.

[0224] If the target is lost for a long time, the tracker needs to be reinitialized and a new tracking starts based on the current recognition result.

[0225] M2.3 Badminton trajectory estimation and analysis submodule

[0226] According to the flight characteristics of badminton high-flying balls, the final stage of the badminton flight is a vertical drop. Beginners usually practice high-flying balls by feeding the ball, so the position where the badminton falls is the position of the practitioner. This method only focuses on the vertical drop of the final stage of the badminton high-flying ball flight; and it is assumed that the badminton falls at the same position on the floor.

[0227] The implementation steps of the badminton trajectory estimation and analysis submodule include:

[0228] S2.3.1. Setting the height origin

[0229] Drop the badminton on the same position on the floor and mark that position as the height origin, that is, the physical height is 0. S2.3.2. Calibrating the ratio of pixels to physical height

[0230] In the video, get the pixel coordinate y when the badminton touches the ground drop , the corresponding physical height is 0.

[0231] Use a simple physical model and a 1-meter-high vertical ruler to calibrate and obtain the proportional coefficient k to calculate the ratio between the pixel coordinates and the physical height:

[0232]

[0233] S2.3.3. Extracting target tracking results

[0234] Use the target tracking submodule to track the position of the badminton in real time and obtain the pixel coordinate y of the badminton in each frame pixel (t).

[0235] S2.3.4. Calculation of the vertical height of the badminton

[0236] For each frame, use the pixel coordinate y of the badminton pixel (t), calculate the physical height h(t) of the badminton:

[0237] h(t)=(y drop -y pixel (t))·k

[0238] Among them, y drop is the pixel coordinate when the badminton touches the ground, y pixel (t) is the pixel coordinate of the badminton at the current moment.

[0239] S2.3.5. Calculation of falling velocity

[0240] For the falling process, the vertical speed of the badminton should be the rate of change of position over time. Assuming that the time interval between each frame is △t (usually 1 / FPS), the speed of the badminton is calculated by the following method:

[0241] Instant velocity estimation: Use position differences to approximate velocity.

[0242]

[0243] in:

[0244] h(t) and h(t-1) are the heights of the shuttlecock in adjacent frames.

[0245] △t is the time interval between two frames.

[0246] v v (t) is the instantaneous speed of the badminton.

[0247] S2.3.6. Output results

[0248] Output the physical height h(t) and falling speed v of the badminton in each frame y (t).

[0249] M3. The timing calculation module predicts the best time to start kicking based on the output data of the action recognition module and the badminton tracking module. start , make sure you hit the shuttlecock at the highest point of your swing.

[0250] The implementation steps include:

[0251] S3.1. Startup timing calculation

[0252] Calculation of time for badminton to reach hitting height:

[0253]

[0254] t shuttle =t+Δt shuttle Among them, t is the time

[0255] Calculation of the best time to start kicking:

[0256] t start =t shuttle -t swing

[0257] S3.2 module output

[0258] The best time to kick start start .

[0259] M4. The feedback module prompts the athlete to start the leg kicking action in real time based on the results of the timing calculation module to ensure that the action is synchronized with the falling of the badminton. Implementation steps:

[0260] S4.1. Feedback method

[0261] Feedback method: tactile feedback is used to provide tactile prompts through the vibration bracelet.

[0262] Feedback trigger conditions:

[0263] When t ≥ t start -Δt prompt When the feedback is triggered

[0264] Among them, Δt prompt To provide cue lead time, consider system processing delays and athlete reaction times.

[0265] S4.2 Error correction and adjustment

[0266] Real-time monitoring: monitor the athlete's actual kicking moment and compare it with the optimal timing.

[0267] Error Feedback: Provides error information to help athletes adjust the timing of their movements.

[0268] System Adaptation: Dynamically adjust the prompt advance amount Δt according to the error situation prompt .

[0269] Steps to use the system:

[0270] The system usage steps are divided into two stages: offline work S5 and online work S6.

[0271] S5 offline work: data customization and preparation, the purpose is to customize and calibrate data, obtain the athlete's personalized movement parameters, including the following steps:

[0272] e) Preparation:

[0273] Athletes wear pressure sensors on the soles of their feet.

[0274] Install infrared markers on the racket.

[0275] Make sure the camera system and sensor equipment are connected properly.

[0276] f) Practice without the ball:

[0277] Have the athlete perform 10 high-flying shots without the ball.

[0278] The action recognition module records the data of each action.

[0279] g) Data processing:

[0280] Calculate the time t from the start of each kick to the time the racket head reaches the highest point swing .

[0281] Record the height h of the racket at its highest point racket .

[0282] h) Personalized parameter determination:

[0283] Statistics are performed on the data of multiple trials, and the average value is calculated to obtain the personalized action parameter t swing and h racket .

[0284] S6 works online to provide real-time training and feedback. In actual training, it tracks the movements of the badminton and athletes in real time, provides timely feedback, and helps athletes master the correct timing of hitting the ball.

[0285] The steps include:

[0286] g) Start training:

[0287] The system enters real-time monitoring mode.

[0288] The athlete begins normal high ball training.

[0289] h) Badminton Tracking:

[0290] The badminton tracking module obtains the badminton's position information, falling speed and height from the ground in real time.

[0291] i) Timing calculation:

[0292] The timing calculation module calculates the best time to start kicking based on real-time data. start .

[0293] j) Feedback Tips:

[0294] The feedback module sends prompts to athletes through the vibrating bracelet at the appropriate time, guiding them to start the leg kick.

[0295] k) Action execution:

[0296] The athlete follows the prompts, starts kicking and swinging the racket to complete the shot.

[0297] 1) Error correction:

[0298] The system records the actual kicking moment, compares it with the optimal timing, and provides feedback to help athletes adjust their movements.

[0299] Example 2

[0300] Based on Example 1, a submodule is added to the M1. Action Recognition Module to quickly read and store the athlete's personalized action parameters using face recognition technology. With the support of face recognition technology, a unique identity can be created for each athlete to quickly and accurately read and store personalized action parameters. It mainly includes the following steps: 1. System architecture

[0301] Face recognition module: Uses the FaceNet face recognition algorithm for identity authentication.

[0302] Database: Stores each athlete’s identity information and personalized movement parameters.

[0303] Personalized motion capture module: Capture the athlete’s key motion data through wearable sensors and cameras.

[0304] Data calibration and customization: Bind the captured motion data to the facial identity and establish a personalized parameter library.

[0305] 2. Facial data collection and modeling

[0306] Step 1: Use a camera to capture facial images of each athlete, making sure to include facial features from different angles (front, side, smiling, etc.).

[0307] Step 2: Use the FaceNet face recognition algorithm to extract a 128-dimensional facial feature vector to ensure uniqueness and distinguishability. Step 3: Bind each athlete's facial feature vector to its unique ID and store it in the facial feature database.

[0308] 3. Identification

[0309] Step 1: The athlete stands in front of the training equipment and the system scans his face through the camera.

[0310] Step 2: Extract the real-time facial feature vector and compare it with the feature vector in the database. Set the similarity threshold to 0.7.

[0311] Step 3: Once the match is successful, the system returns a unique ID to confirm the identity.

[0312] 4. Bind personalized action parameters

[0313] Step 1: After successful face recognition, the system calls the personalized parameter database.

[0314] Step 2: The system retrieves the athlete's action parameters, t swing and h racket .

[0315] By combining facial recognition technology with motion parameter recognition, the M1 module not only realizes rapid verification of the identity of each athlete, but also effectively manages and optimizes personalized motion parameters.

[0316] Example 3

[0317] On the basis of Example 1, the effectiveness of the badminton high-flying ball auxiliary training system of the present invention, which prompts the optimal starting time, was verified through experiments.

[0318] 1. Purpose of the experiment

[0319] 1. Verify the accuracy of the system: Confirm that the system can accurately identify the athlete's key movements and the falling trajectory of the badminton, and accurately prompt the best start time.

[0320] 2. Evaluate training results: measure the improvement in athletes’ hitting accuracy and competitive performance after using the system.

[0321] 3. User feedback: Collect athletes’ satisfaction with the system experience and suggestions for improvement.

[0322] 2. Experimental Design

[0323] 1. Experimental subjects

[0324] Experimental group: badminton beginners who were trained using the auxiliary training system of the present invention.

[0325] Control group: badminton beginners using traditional training methods (without auxiliary systems).

[0326] Sample size: 20 people in each group.

[0327] 2. Experimental Procedure

[0328] 1. Pre-test:

[0329] All participants were initially evaluated and their current shot accuracy was recorded. When the athlete's timing of starting is accurate, the shuttlecock will fly high and far. If the start is too late, the athlete's arm cannot be fully extended at the moment of hitting the ball, resulting in a weak shot and a low and short shot. If the start is too early, the athlete will miss the shot.

[0330] These situations are counted as inaccurate shots. Shot accuracy is the percentage of accurate shots in all shots.

[0331] 2. Training phase:

[0332] The experimental group used the auxiliary training system of the present invention for 8 weeks of training, 4 times a week, and each training session lasted 20 minutes.

[0333] The control group performed the same training cycle according to the conventional training method.

[0334] 3. Post-test:

[0335] All participants were again assessed for their hitting accuracy.

[0336] 4. Data Collection:

[0337] The accuracy rates of the experimental and control groups were collected.

[0338] 3. Evaluation indicators: Batting accuracy: the proportion of accurate batting.

[0339] 4. Data Analysis

[0340] 1. Data Collection

[0341] Experimental group (using auxiliary training system)

[0342] Testing Phase Average batting accuracy (%) Standard deviation (%) Minimum value (%) Maximum value (%) Pre-test 23 7.8 12 35 Post-test 78 9.5 60 95

[0343] Control group (using traditional training methods)

[0344] Testing Phase Average batting accuracy (%) Standard deviation (%) Minimum value (%) Maximum value (%) Pre-test 22 8.2 10 38 Post-test 52 10.3 30 75

[0345] 2. Data interpretation

[0346] 1) Experimental group (using auxiliary training system)

[0347] Pre-test:

[0348] Average batting accuracy: 23%

[0349] Standard Deviation: 7.8%

[0350] Minimum: 12%

[0351] Maximum: 35%

[0352] Note: In the initial stage, the participants' hitting accuracy was relatively low, and the standard deviation reflects the performance differences among beginners.

[0353] Post-test:

[0354] Average batting accuracy: 78%

[0355] Standard Deviation: 9.5%

[0356] Minimum: 60%

[0357] Maximum value: 95%

[0358] Note: After 4-8 weeks of auxiliary training, the participants' hitting accuracy improved significantly, and the standard deviation increased slightly, indicating that some participants made more significant progress.

[0359] 2) Control group (using traditional training methods)

[0360] Pre-test:

[0361] Average batting accuracy: 22%

[0362] Standard Deviation: 8.2%

[0363] Minimum value: 10%

[0364] Maximum: 38%

[0365] Note: In the initial stage, the batting accuracy was similar to that of the experimental group, with a slightly higher standard deviation, reflecting larger individual differences.

[0366] Post-test:

[0367] Average batting accuracy: 52%

[0368] Standard Deviation: 10.3%

[0369] Minimum: 30%

[0370] Maximum: 75%

[0371] Note: Under the traditional training method, the batting accuracy rate has improved, but the increase is smaller than that of the experimental group, and the standard deviation has increased significantly, which may reflect the uneven training effect.

[0372] 3. Data analysis expectations

[0373] Based on the above estimated data, the following analysis was conducted:

[0374] 1) Independent samples t-test:

[0375] Comparing the average hitting accuracy of the experimental group and the control group in the post-test, it is expected that the experimental group (78%) is significantly higher than the control group (52%).

[0376] The difference was statistically significant (p<0.05).

[0377] 2) Paired sample t-test:

[0378] Experimental group: The difference between the pre-test (23%) and the post-test (78%) was significant (increase of 55%).

[0379] Control group: The difference between the pre-test (22%) and the post-test (52%) was also significant (increase of 30%), but the increase was smaller than that of the experimental group.

[0380] 3) Effect size analysis:

[0381] Cohen's d: The effect size of the experimental group is expected to be large (e.g., d≈2.0), and the effect size of the control group is moderate (e.g., d≈1.0), indicating that the auxiliary training system has a strong practical significance for improving the batting accuracy.

[0382] Through systematic data analysis, the effectiveness of the badminton high and long ball auxiliary training system of the present invention is comprehensively evaluated, and its actual role in improving hitting accuracy and competitive performance is verified.

[0383] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A badminton high-ball auxiliary training system that prompts the best start time, characterized in that: The system consists of the following four modules: M1: Action recognition module: used for data customization and calibration to help obtain the athlete's personalized action parameters; M2: Badminton tracking module: real-time tracking of the position information and falling speed of the badminton, providing badminton motion data; M3: Timing calculation module: Based on the output of the action recognition module and the badminton tracking module, predict the best time to start the kick t start , to ensure that the racket hits the shuttlecock at the highest point; where t start It is the best time to start kicking predicted by the system; M4: Feedback module: Based on the timing calculation results, it prompts the athlete to start the leg kicking action in real time to ensure that the action is synchronized with the falling of the badminton.

2. The badminton high-ball auxiliary training system for prompting the best start time according to claim 1 is characterized in that: The action recognition module includes: M1.1 athlete's foot pressure sensor module and M1.2 badminton racket tracking module; The M1.1 athlete foot pressure sensor module is used to detect and output when the athlete makes a kicking motion; the implementation steps are as follows: S1.1.1: Install pressure sensors in the athlete’s shoes to monitor the pressure changes on the sole of the foot in real time; S1.1.2: When a sudden increase in pressure is detected, it is determined to be the start time of the kicking action t kick ; Among them, t kick The moment the kick action begins for the athlete's data customization phase.

3. The badminton high-ball auxiliary training system for prompting the best start time according to claim 2 is characterized in that: The M1.2: badminton racket tracking module is used to track the height of the racket head and determine the time and height when the racket reaches the highest point. The implementation steps are as follows: S1.2.1: Use a camera to record the movement of the racket; S1.2.2: Ground calibration: First, calibrate the ground position and establish the field coordinate system, with the origin set at the athlete's feet; S1.2.3: Racket marking: Install infrared marking points on the head of the racket; S1.2.4: Racket trajectory tracking: The camera system is used to capture the trajectory of the racket head in real time and obtain the change of its height over time; S1.2.5 Data processing: Find the moment t when the racket head reaches the highest point peak and the corresponding height h racket ; Among them, t peak h is the moment when the racket head reaches the highest point. racket The height at which the racket head reaches its highest point; S1.2.6 module output: the time interval from the start of the kicking action to the racket head reaching the highest point: t swing =t peak -t kick , where t swing It is the time interval from the start of the kicking action to the swinging of the racket head to the highest point The height of the racket at its highest point h racket .

4. The badminton high-ball auxiliary training system for prompting the best start time according to claim 1 is characterized in that: The M2 badminton tracking module is used to obtain the position information, falling speed and height from the ground of the badminton in real time; the module outputs the physical height h(t) and falling speed v of the badminton y (t); Among them, h(t) is the physical height of the badminton, v y (t) is the falling speed of the badminton; M2. Badminton tracking module includes: M2.1 target recognition submodule; M2.2 target tracking submodule; M2.3 badminton trajectory estimation and analysis submodule; Among them, the implementation steps of the M2.1 target recognition submodule are as follows: S2.1.1 Data preparation and training of target recognition model; S2.1.2 Target identification execution.

5. The badminton high-ball auxiliary training system for prompting the best start time according to claim 4 is characterized in that: The steps of implementing the S2.1.1 data preparation and training target recognition model include: a) Data collection: a-1) Collect images or video data containing badminton, covering different backgrounds, lighting, angles and various motion states of badminton; a-2) Annotated data: annotate the position of the badminton for each image or video frame; b) Data preprocessing: b-1) Standardize the image, adjust the image size and normalize the color; b-2) Data enhancement: Rotate, flip, and crop the training data to improve the robustness of the model; c) Training target recognition model: c-1) Use the target recognition algorithm YOLOv7-Tiny to train a model to recognize badminton; c-2) Ensure that the model can accurately identify badminton in different scenes and lighting conditions.

6. The badminton high-ball auxiliary training system for prompting the best start time according to claim 4 is characterized in that: The implementation steps of S2.1.2 target identification execution include: 1) Identify the target: 1-1) In each frame of the video, the position of the badminton is identified using the trained object recognition model, which outputs the bounding box coordinates and confidence level of the badminton.

7. The badminton high-ball auxiliary training system for prompting the best start time according to claim 4 is characterized in that: The implementation steps of the M2.

2. target tracking submodule include: S2.2.1 Selecting a target tracking algorithm Select the KCF tracking algorithm to predict the position of the next frame based on the target position of the previous frame; the KCF tracking algorithm refers to the kernel correlation filter tracking algorithm; S2.2.2 Initialize tracker In the first recognized frame, the badminton position output by the object recognition model is used to initialize the tracker; S2.2.3 Tracking process (1-1) Update target location: (1-1-1) In the subsequent frames, the target tracker uses Kalman filtering to predict the current position of the badminton according to the target position in the previous frame; (1-1-2) The tracker matches the image features and updates the target’s bounding box to ensure continuous tracking of the target; (1-2) Correction tracking results: (1-2-1) If target tracking fails in a certain frame, the position of the badminton is re-identified according to the recognition model and the state of the tracker is corrected; (1-1-3) If the target is still visible and moving smoothly in the current frame, the tracker continues to predict the target position; S2.2.4 Lost Target Recovery S2.2.4.1: In case of occlusion or fast motion, the tracker will lose the target and re-use the target recognition model to locate the badminton and resume target tracking; S2.2.4.2: If the target is lost for a long time, the tracker is reinitialized and a new tracking is started based on the current recognition result.

8. The badminton high-ball auxiliary training system for prompting the best start time according to claim 4 is characterized in that: The M2.3 badminton trajectory estimation and analysis submodule, the implementation steps of the badminton trajectory estimation and analysis submodule include: S2.3.

1. Setting the height origin Drop the badminton on the same position on the floor and mark that position as the height origin, that is, the physical height is 0; S2.3.

2. Calibrate the ratio of pixels to physical height In the video, get the pixel coordinates when the badminton touches the ground, corresponding to a physical height of 0; Calibrate with the physical model to obtain the proportional coefficient k, and calculate the ratio between the actual physical height and the pixel coordinate difference; Among them, the coefficient k is the ratio between the actual physical height and the pixel coordinate difference; S2.3.

3. Extracting target tracking results Use the target tracking submodule to track the position of the badminton in real time and obtain the pixel coordinate y of the badminton in each frame pixel (t); S2.3.

4. Calculate the vertical height of the badminton For each frame, use the pixel coordinate y of the badminton pixel (t), calculate the physical height h(t) of the badminton: h(t)=(y drop -y pixel (t))·k Among them, y drop is the pixel coordinate when the badminton touches the ground, y pixel (t) is the pixel coordinate of the badminton at the current moment; S2.3.

5. Calculation of falling velocity For the falling process, the vertical velocity of the badminton is the rate of change of position over time. Assuming that the time interval between each frame is Δt, the velocity of the badminton is calculated by the following method: Instant velocity estimation: Use position difference to approximate velocity; in: h(t) and h(t-1) are the heights of the shuttlecock in adjacent frames; Δt is the time interval between two frames; v y (t) is the instantaneous speed of the badminton; S2.3.

6. Output results Output the physical height h(t) and falling speed v of the badminton in each frame y (t); M3. The timing calculation module predicts the best time to start kicking based on the output data of the action recognition module and the badminton tracking module. start , to ensure that the racket hits the shuttlecock at the highest point; the steps to achieve this include: S3.

1. Startup timing calculation Calculation of the time it takes for the badminton to reach the hitting height: Among them, Δt shutt It is the time required for the badminton to reach the hitting height.

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