Basketball shooting training system, data processing method and device and wearable device

Abstract

The invention provides a basketball shooting training system, a data processing method and device and a wearable device. A position detection module in the system is configured to detect whether an athlete is located at a corresponding preset shooting point position or not through sensors arranged at a plurality of preset shooting point positions of a training field; the basketball taking detection module is configured to be arranged corresponding to each preset shooting point position so as to detect whether the basketball is taken out from the basketball rack at the corresponding point position or not; the shooting detection module is configured to be arranged at a basket to detect whether a basketball is put into the basket or not; the wearable device is configured to be capable of being worn by an athlete to collect biomechanical parameters related to shooting actions during shooting; the data processing module is in communication connection with all the modules and used for receiving detection data output by all the modules so as to generate shooting training quantitative analysis results of the athletes at all the preset shooting point positions. The system provides data support for quantitative evaluation of the training effect and scientific optimization of the training method.

Description

Technical Field

[0001] This application relates to the field of basketball training equipment technology, and more specifically, to a basketball shooting training system, a data processing method, and a wearable device. Background Technology

[0002] Basketball shooting is an important part of basketball training. Basketball enthusiasts usually develop muscle memory through a lot of shooting practice at various spots to improve their shooting ability. Professional basketball team coaches manually record the spot shooting training data of key players. This method is time-consuming, labor-intensive, and requires a lot of manpower, resulting in a very limited number of players and recording times. Existing technology can also record shooting data by combining video recording with video analysis technology, but this method requires expensive equipment and professional technicians to operate, which makes it difficult to popularize in ordinary schools, clubs, and other similar venues.

[0003] It is evident that existing technologies cannot achieve automated and intelligent data collection and statistical analysis of basketball players' ball handling, shooting accuracy, and key biomechanical parameters (such as release force) during shooting training at low cost. Consequently, it is difficult to scientifically and efficiently quantify and evaluate training effectiveness and guide the development of personalized training plans. Summary of the Invention

[0004] The purpose of this application is to provide a basketball shooting training system, a data processing method, and a wearable device, which integrates a multi-sensor system for position detection, ball retrieval and goal counting, and shot force acquisition to achieve automated collection, fusion, and analysis of basketball shooting training data. This provides data support for the quantitative evaluation of training effects and the scientific optimization of training methods in a low-cost and high-efficiency manner.

[0005] Firstly, a basketball shooting training system is provided, which may include: The position detection module is configured to detect whether an athlete is at a corresponding predetermined shooting point by using sensors deployed at multiple predetermined shooting points on the training ground. The ball retrieval detection module is configured to correspond to each predetermined shooting point and is used to detect whether the basketball has been taken out of the ball rack at the corresponding point. The shooting detection module is configured to be installed at the basket to detect whether the basketball has been put into the basket. Wearable devices, configured to be worn by athletes, are used to collect biomechanical parameters related to the shooting motion during the shooting release; The data processing module is communicatively connected to the position detection module, ball retrieval detection module, shooting detection module, and wearable device. It is used to receive the detection data output by the above modules to generate quantitative analysis results of the athlete's shooting training at each predetermined shooting point.

[0006] In one possible implementation, the position detection module includes a pressure sensor or a membrane switch, which is arranged on the ground in a one-to-one correspondence with the predetermined shooting points of the training field, and the sensing surface of the pressure sensor or membrane switch is flush with the ground.

[0007] In one possible implementation, the ball retrieval detection module includes an infrared photoelectric sensor, which is installed on the ball rack corresponding to each predetermined shooting point. The path of the infrared beam of the infrared photoelectric sensor corresponds to the storage position of the basketball on the ball rack, so that the basketball is placed on the ball rack and just blocks the infrared beam. This is used to determine whether the basketball has been retrieved from the ball rack at the corresponding predetermined shooting point by detecting whether the infrared beam is not blocked.

[0008] In one possible implementation, the ball detection module further includes a miniature weight sensor disposed at the bottom of the corresponding ball rack; The ball detection module is also used to determine that the basketball has been taken from the rack at the corresponding point when the duration of the infrared beam being blocked reaches a configured blocking duration threshold and the weight sensor detects a decrease in weight.

[0009] In one possible implementation, the shot detection module includes an infrared photoelectric sensor positioned above the net around the basket to determine whether the basketball has hit the target by blocking the beam.

[0010] In one possible implementation, the biomechanical parameters include the release force during the shot; The wearable device is a finger sleeve or glove that fits the index and middle fingers of an athlete. It integrates at least one pressure sensor or capacitive sensor to collect the release force value at the moment of shooting and record the maximum value of the release force value.

[0011] In one possible implementation, at least one pressure sensor or capacitive sensor is configured inside the finger sleeve or glove and located at the first and second joints of the athlete's shooting hand index and middle fingers.

[0012] In one possible implementation, the quantitative analysis results of the shooting training include the total number of shots caught, the total number of shots made, and the shooting percentage for each predetermined shooting spot; The data processing module is specifically used for: Based on the detection data from the position detection module and the ball retrieval detection module, the total number of balls retrieved by the athlete at each predetermined shooting point is counted. Based on the detection data from the position detection module and the shooting detection module, the total number of shots made by the athlete at each predetermined shooting point is counted. The shooting percentage of the athletes at each designated shooting spot is calculated based on the total number of shots retrieved and the total number of shots made at each designated shooting spot.

[0013] In one possible implementation, the data processing module is further configured to: The shooting accuracy at each predetermined shooting point is correlated with the corresponding shooting power value to analyze the distribution relationship between the success or failure of a shot and the magnitude of the shooting power value at each predetermined shooting point.

[0014] In one possible implementation, the data processing module is further configured to: The quantitative analysis results of the shooting training are generated into a visualization report, and a visualization trend report is generated based on the quantitative analysis results of the shooting training of the same athlete within a preset time period to evaluate the training effect of the athlete.

[0015] Secondly, a data processing method is provided for the basketball shooting training system described in the first aspect, the method including: The position detection module detects the athlete's designated shooting point. The action of the basketball being retrieved is detected by the ball retrieval detection module; The shooting detection module detects whether a shot is successful. Biomechanical parameters of athletes at the moment of shooting are collected using wearable devices; The data processing module receives the above data and generates quantitative analysis results of the athlete's shooting training at the predetermined shooting spot.

[0016] Thirdly, a data processing device is provided for use in the basketball shooting training system described in the first aspect, the device comprising: The detection unit is used to detect the athlete's predetermined shooting position through the position detection module; to detect the action of the basketball being taken out through the ball retrieval detection module; and to detect whether the shot is successful through the shooting detection module. The data acquisition unit is used to collect biomechanical parameters of athletes when they shoot a basketball using a wearable device. The receiving unit is used to receive the data output by the above-mentioned units through the data processing module; The generation unit is used to generate quantitative analysis results of the athlete's shooting training at the predetermined shooting point based on the data output by the above units.

[0017] Fourthly, a wearable device is provided for use in the basketball shooting training system described in the first aspect, the device comprising: The wearable body is configured as a flexible substrate in the form of finger cots or gloves; At least one sensor, integrated on the wearable body, is used to detect the force applied by the fingers when shooting; The communication unit is used to send the force values ​​detected by each sensor to an external data processing device.

[0018] In one possible implementation, the sensors are thin-film piezoresistive sensors or flexible capacitive sensors, disposed inside the finger sleeve or glove at the first and second joints corresponding to the index and middle fingers.

[0019] In one possible implementation, at least one sensor employs a dot matrix design, with sensors deployed on the pads and backs of the first and second joints of the index and middle fingers, to collect force data from different parts of the fingers in order to obtain the force distribution ratio of different parts.

[0020] In one possible implementation, the contact surfaces of the wearable body with the index and middle fingers are provided with breathable and non-slip textures.

[0021] In one possible implementation, the wearable body is made of a flexible elastic material and has an adjustable Velcro structure.

[0022] In one possible implementation, the device further includes: a processing unit; the processing unit is connected to at least one sensor and a communication unit, respectively; The processing unit is configured to receive sensor signals from each sensor, extract the maximum value of the hand force from the received sensor signals, and send the extracted maximum value of the hand force to an external data processing device.

[0023] Fifthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0024] This application provides a basketball shooting training system, data processing method, apparatus, and wearable device. The system includes a position detection module configured to detect whether the athlete is at a predetermined shooting point using sensors deployed at multiple pre-defined shooting points on the training court; a ball retrieval detection module configured to detect whether the basketball has been retrieved from the rack at each pre-defined shooting point; a shooting detection module configured to detect whether the basketball has been shot into the basket; a wearable device configured to be worn by the athlete to collect biomechanical parameters related to the shooting motion; and a data processing module communicatively connected to the above modules to receive the detection data output by each module, generating quantitative analysis results of the athlete's shooting training at each pre-defined shooting point. By integrating a multi-sensor system including position detection, ball retrieval and shot counting, and release force acquisition, this system achieves automated collection, fusion, and analysis of basketball shooting training data, thus providing data support for the quantitative evaluation of training effectiveness and the scientific optimization of training methods in a low-cost and high-efficiency manner. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the structure of a basketball shooting training system provided in an embodiment of this application; Figure 2 A schematic diagram showing the location distribution of predetermined shooting points in a training field, provided as an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a wearable device provided in an embodiment of this application; Figure 4 A flowchart illustrating a data processing method provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0028] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0029] Example 1 This application provides a basketball shooting training system for basketball shooting practice. This system aims to automate data acquisition, analysis, and feedback for shooting training through multi-sensor data fusion, thereby addressing the shortcomings of existing technologies that rely on manual recording or expensive vision systems. Figure 1 As shown, the basketball shooting training system may include: a position detection module, a ball retrieval detection module, a shooting detection module, a wearable device, and a data processing module. These modules can be connected via wired or wireless communication methods (such as Wi-Fi, Bluetooth, BLE, Zigbee, UWB, etc.) to form a complete training monitoring network.

[0030] The position detection module is configured to detect whether an athlete is at a corresponding predetermined shooting point by using sensors deployed at multiple predetermined shooting points on the training ground. The ball retrieval detection module is configured to correspond to each predetermined shooting point and is used to detect whether the basketball has been taken off the rack at the corresponding predetermined shooting point. The shooting detection module is configured to be installed at the basket to detect whether the basketball has been put into the basket. Wearable devices, configured to be worn by athletes, are used to collect biomechanical parameters related to the shooting motion during the shooting release; The data processing module is communicatively connected to the position detection module, ball retrieval detection module, shooting detection module, and wearable device. It is used to receive the detection data output by the above modules to generate quantitative analysis results of the athlete's shooting training at each predetermined shooting point.

[0031] The following is a detailed description of each of the above modules: A. Position detection module, used to detect whether the athlete is at the designated shooting point on the training ground, such as... Figure 2 As shown, five designated shooting positions (e.g., positions 1-5) are set up on the training ground, each corresponding to a commonly used shooting position in basketball training. A ball rack is placed at each shooting position for athletes to retrieve the ball.

[0032] The position detection module may include a pressure sensor or a membrane switch, which is arranged on the ground in a one-to-one correspondence with the predetermined shooting points of the training field, and the sensing surface of the pressure sensor or membrane switch is flush with the ground to avoid protrusions affecting the athlete's shooting action.

[0033] To facilitate replacement and maintenance, the pressure sensor or membrane switch can be connected to the ground using a magnetic, detachable design. The pressure sensor or membrane switch has a magnetic base at its bottom, and a pre-installed metal patch on the ground. When a single sensor fails, it can be directly removed and replaced without damaging the ground structure, reducing maintenance costs.

[0034] In some embodiments, the position detection module may be equipped with a sensor adaptive calibration unit (connected to the location) to collect environmental parameters of the training site in real time (such as ground humidity and temperature). The sensing threshold is dynamically adjusted based on these environmental parameters. For example, when ground humidity is detected to be higher than 60% or temperature is lower than 5°C or higher than 35°C, the sensor adaptive calibration unit automatically corrects the sensor's sensing threshold. For instance, the sensing threshold at 60% humidity is adjusted from 10kg to 12kg to prevent false triggering of the sensor due to damp ground or decreased sensor sensitivity due to extreme temperatures.

[0035] In one example, the calibration formula for the sensor's sensing threshold can be expressed as:

[0036] The calibrated sensing threshold (kg); Reference temperature and humidity ( =60%, Initial sensing threshold (kg) at 25℃; This is a humidity correction factor (experimental calibration, value range 0.001). 0.003), dimensionless; This is a temperature correction factor (experimental calibration, value range 0.0005). 0.002), dimensionless; H is the real-time relative humidity of the site; T is the real-time temperature of the site; Reference relative humidity; This is the reference temperature.

[0037] B. Ball retrieval detection module, which is set up corresponding to the ball rack at each predetermined shooting point. Each predetermined shooting point is equipped with a ball rack, which holds training basketballs. The module is used to detect whether the basketball has been taken out of the ball rack, i.e., it has been effectively retrieved.

[0038] The ball retrieval detection module may include an infrared photoelectric sensor, which is installed on the basketball hoop corresponding to each predetermined shooting point. The path of the infrared beam from the sensor corresponds to the position of the basketball on the hoop, so that the basketball blocks the infrared beam when placed on the hoop and leaves the beam when retrieved. This determines whether the basketball has been retrieved. In other words, the ball retrieval detection module determines whether the basketball has been retrieved from the hoop at the corresponding predetermined shooting point by detecting whether the infrared beam is unblocked. Specifically, the infrared photoelectric sensor uses a through-beam or reflective design, with the beam covering the retrieval area. When the basketball is not retrieved, the infrared beam is blocked; when the basketball is retrieved, the beam is unblocked, and the sensor outputs a level change signal.

[0039] To further improve accuracy, the ball retrieval detection module may also include a miniature weight sensor; this miniature weight sensor is located at the bottom of the ball holder and is used to detect changes in the weight of the ball holder.

[0040] The ball retrieval detection module incorporates a built-in anti-false-judgment logic. First, a threshold for the duration of infrared beam obstruction is set, such as 0.5 seconds. When the infrared beam is obstructed for 0.5 seconds, and the miniature weight sensor detects a weight reduction matching the weight of a single basketball (a standard basketball weighs 600-650g), it is considered a valid ball retrieval, and the retrieval signal is sent to the data processing module. If only the infrared beam is obstructed or only the weight decreases, it is considered an invalid retrieval and is not counted, avoiding counting errors caused by the athlete touching the basketball but not retrieving it, or the basketball rolling away.

[0041] C. Shooting detection module, located at the basket, is used to detect whether the basketball has entered the basket.

[0042] The shot detection module includes an infrared photoelectric sensor, which is arranged around the top of the basketball hoop net to form a ring-shaped infrared beam grid. When the basketball hits the hoop, the ring-shaped beam is blocked, triggering the infrared photoelectric sensor to generate an infrared detection signal.

[0043] Furthermore, the shooting detection module can integrate other detection methods, such as sound recognition sensors (for capturing the sound of the ball hitting the net) or vibration sensors (for detecting rim vibration), to improve the reliability of hit judgment through multi-sensor fusion. For example, the sound sensor can be configured with a filter to distinguish specific frequencies of shot hits; the vibration sensor can be set with a threshold to filter out interference such as wind noise.

[0044] In this specific embodiment, the shooting detection module may include an infrared photoelectric sensor, a sound recognition sensor, and a vibration sensor.

[0045] Infrared photoelectric sensors are arranged around the top of the basketball hoop, forming a ring-shaped infrared beam barrier. When the basketball passes through the hoop, it will block the ring beam and trigger the infrared photoelectric sensors to generate an infrared detection signal. The sound recognition sensor is installed on the basketball hoop bracket and presets the characteristic sound wave frequency of the basketball brushing the net. When a matching sound wave is detected, the sound recognition sensor is triggered to generate a sound detection signal. Vibration sensors are attached to the edge of the basketball hoop to collect the vibration frequency and amplitude of the hoop. When the basketball hits the hoop as it enters the net, the vibration sensors are triggered to generate characteristic vibration signals.

[0046] At this time, the shooting detection module is used to determine a hit (i.e., detect the basketball being put into the basket) when two or more of the three detection data from the infrared photoelectric sensor, the sound recognition sensor, and the vibration sensor match, and generates a hit signal.

[0047] D. A wearable device, worn by athletes, is a finger sleeve or glove adapted to the athlete's index and middle fingers, integrating at least one pressure sensor or capacitive sensor for collecting biomechanical parameters of the athlete during shooting, and communicating with a data processing module via Bluetooth Low Energy. The biomechanical parameters may include the release force at the moment of release, therefore this application can record the maximum value of this release force. Furthermore, the biomechanical parameters may also include the distribution of finger force during shooting.

[0048] Specific embodiments of the wearable device will be described in detail in later embodiments, but are briefly described here as a system component: the wearable device is in the form of a finger sleeve or glove, worn by the athlete on the index and middle fingers of the shooting hand. At least one pressure sensor or capacitive sensor is configured inside the finger sleeve or glove, located at the first and second joints of the athlete's index and middle fingers.

[0049] E. The data processing module, the core control unit of the system, is typically deployed on a local server or cloud platform. It communicates with the position detection module, ball retrieval detection module, shooting detection module, and wearable device, using methods such as wired, wireless Bluetooth, and WiFi. The data processing module may include a microprocessor (such as an ARM Cortex series), memory (for data storage), and a communication interface (such as Ethernet or a 4G / 5G module). The specific functions of this data processing module are as follows: (1) Data reception and synchronization: The module receives real-time position signals from the position detection module, ball retrieval signals from the ball retrieval detection module, and hit signals from the shooting detection module, as well as biomechanical parameters (such as release force) from the wearable device. A timestamp mechanism is used to ensure data synchronization and correlation.

[0050] (2) Data processing and analysis: Based on the position signal received from the position detection module and the valid ball retrieval signal from the ball retrieval detection module, the total number of balls retrieved by the athlete at each predetermined shooting point is counted. Based on the position signal from the receiving position detection module and the effective hit signal from the shooting detection module, the total number of shots made by the athlete at each predetermined shooting point is counted; where, the hit rate = total number of hits / total number of ball retrieves × 100%, and the shooting rate of each predetermined shooting point is calculated.

[0051] The basic statistical data table includes information such as the number of groups, athlete names, training dates, training times, number of shots retrieved, number of successful shots, and shooting percentage at each designated shooting spot. For example, in the training session of Group 1, from 9:00 to 12:00 on December 27, 2025, athlete Zhang San retrieved the ball 30 times at spot 1 and made 15 shots, for a shooting percentage of 50%.

[0052] In other words, the quantitative analysis results of shooting training can include the total number of shots caught, the total number of shots made, and the shooting percentage at each predetermined shooting spot.

[0053] (3) Analysis of the completeness and rhythm of the shooting motion: Combining the stability duration of the position detection module (e.g., the athlete needs to stand stably for more than 3 seconds) and the force value change curve of the wearable device (e.g., the force value needs to show a complete parabolic feature), the integrity of the shooting action is judged: the stability duration threshold is set to 1 second. When the athlete's stability duration at the predetermined shooting point is ≥1 second, and the force value change curve collected by the wearable device shows a complete force exertion process of rising-peak-falling, it is judged as a valid shot; if the stability duration is <1 second or the force value curve does not have a complete force exertion peak, it is judged as an invalid shot, and it is not included in the total number of balls retrieved and the total number of shots made.

[0054] Furthermore, it can calculate the interval between consecutive shots by an athlete at the same predetermined shooting spot (e.g., based on the time difference of the ball-retrieval signal) and analyze the correlation between shooting rhythm and accuracy. For example, statistics show that when the interval between shots at spot 1 is 8 seconds, the accuracy can reach 60%, while when the interval is less than 5 seconds, the accuracy drops to 30%.

[0055] (4) Multidimensional parameter correlation analysis: The system correlates the release force and finger force distribution collected by the wearable device with shooting accuracy and shot type to output the optimal force combination for each predetermined shooting point. For example, the analysis shows that the optimal force combination for point 1 is: release force of 10-12N, with the middle finger contributing 40% of the force, achieving a shooting accuracy of 65%.

[0056] In addition, this module can also support horizontal comparative analysis, enabling the comparison of force exertion data of the same athlete at different training periods, and the comparison of force exertion habits of different athletes at the same predetermined shooting point, providing data reference for team training.

[0057] (5) Visualized reports and feedback The data processing module can generate a visualization report based on the quantitative analysis results of shooting training, and generate a visualization trend report to evaluate the training effect of the same athlete based on the quantitative analysis results of shooting training within a preset time period.

[0058] Specifically, the data processing module generates a visual training report from the statistical basic data and correlation analysis results. The report includes a bar chart of the shooting percentage at each predetermined shooting point, a line chart of the change in shooting force, and a radar chart of the optimal force combination. At the same time, based on the training data of the same athlete within a preset time period (such as one week or one month), a visual trend report of training effect is generated, which intuitively shows the changes in the athlete's shooting percentage and the changes in the stability of force.

[0059] The data processing module also automatically generates personalized training program recommendations based on trend reports and optimal force combinations. For example, for point 1, it is necessary to increase shooting training with a force value in the range of 10-12N, with a daily training volume of 50 times. It is also recommended to adjust the force ratio of the middle finger to 40% for point 2.

[0060] In addition, the data processing module can have a built-in sports injury early warning function. When it detects that the athlete's force suddenly increases abnormally (such as exceeding the daily average by 50%), or that the force distribution of the fingers is seriously unbalanced (such as the force of a single finger accounting for more than 70%), it will automatically issue an early warning and push the warning information to the coach's mobile device.

[0061] The data processing module supports real-time synchronization with mobile devices, and can push visual reports, training plans, and early warning information to the mobile devices of athletes and coaches, enabling real-time viewing of training data and timely adjustment of training plans.

[0062] In one example, the athlete stands at point 1 (triggered by the position detection module), retrieves the ball from the basket (confirmed by the ball retrieval detection module), completes the shooting motion (the wearable device records the maximum release force, e.g., 10.5N), and the basketball hits the rim (triggered by the shooting detection module). The data processing module synchronizes these events, updates the total number of ball retrievals and successful shots at point 1, calculates the shooting percentage, and correlates the force data. After multiple training sessions, the module generates a personalized report to guide the athlete in adjusting their force application.

[0063] Example 2 This application provides a wearable device, which may include: The wearable body is configured as a flexible substrate in the form of finger cots or gloves; At least one sensor, integrated on the wearable body, is used to detect the force applied by the fingers when shooting; The communication unit is used to send the force values ​​detected by each sensor to an external data processing device.

[0064] (1) For the wearer itself: The main body of the garment is a finger sleeve that fits the athlete's index and middle fingers, or a glove that covers the entire hand. It is made of a flexible and elastic material (such as silicone or flexible polymer), which gives it good extensibility and fit to fingers of different sizes without affecting the athlete's finger movements when shooting.

[0065] Furthermore, to ensure a secure fit, the wearable device can have an adjustable Velcro structure. This Velcro structure, located at the finger opening or wrist of the glove, can be adjusted to fit the athlete's finger size and hand size, adapting to athletes of different body types.

[0066] The surface of the wearer that comes into contact with the athlete's fingers features a breathable and non-slip texture, such as a diamond mesh design. This increases the friction between the fingers and the basketball, improves wearing comfort, and prevents the fingers from slipping due to sweat during long training sessions.

[0067] (2) For at least one sensor: At least one sensor constitutes a sensor assembly. Each sensor is a thin-film piezoresistive sensor or a flexible capacitive sensor. The sensor is flexible and bendable and fits inside the wearable body, i.e., inside the finger sleeve or glove.

[0068] At least one sensor adopts a dot matrix design and is deployed on the pads and backs of the first and second joints of the index and middle fingers, for example, by covering each joint with a 3x3 dot matrix, to collect force data from different parts of the finger in order to obtain the distribution ratio of different parts (such as the proportion of force exerted by the finger pads).

[0069] At least one sensor is placed at the critical point of the final force exerted when shooting, which can accurately collect the force data at the moment of release and record the maximum value of the release force.

[0070] (3) Communication unit: This communication unit can be a low-power Bluetooth transmission module, integrated into the athlete's wrist or the back of the glove. It employs an intermittent data transmission mode, automatically waking up after the shooting motion to send standardized parameter data to the data processing module, and entering sleep mode the rest of the time. This low-power design can extend the wearable device's battery life to over 8 hours, meeting the needs of athletes during all-day training.

[0071] like Figure 3 As shown, taking the wearable device as an example of a finger sleeve adapted to the athlete's index and middle fingers, the finger sleeve can cover the first and second joints of the athlete's index and middle fingers, or it can only cover the first joint of the athlete's index and middle fingers.

[0072] The inner side of the finger sleeve is equipped with at least one thin-film piezoresistive sensor or flexible capacitive sensor using a dot-matrix design to collect the release force applied by the athlete's fingers when shooting a basketball; the sensor can be positioned at the pads of the first and second joints, specifically at the first and second joints of the index and middle fingers on the inner side of the palm, such as... Figure 3 As shown; alternatively, the sensors can be evenly distributed on the pads and backs of the fingers in the first and second segments (this arrangement is not shown in the figure). At least one thin-film piezoresistive sensor or flexible capacitive sensor is electrically connected to the communication unit, which is integrated into the athlete's wrist and worn on the athlete's wrist via a wristband.

[0073] In some embodiments, the wearable device may further include a processing unit; the processing unit is connected to at least one sensor and a communication unit respectively; each sensor is connected to the processing unit via a flexible circuit, and the processing unit is integrated into the athlete's wrist or the back of the glove.

[0074] The processing unit is configured to receive sensor signals from various sensors, extract the maximum value of the hand force from the received sensor signals, and send the extracted maximum value of the hand force to an external data processing device. Specifically, the processing unit receives force signals and force distribution signals collected by the sensor components, and automatically filters out force spikes caused by involuntary finger tremors using a built-in force anomaly filtering algorithm, retaining only the effective peak force value at the moment of shooting. Simultaneously, the processing unit extracts the maximum value of the hand force from the received sensor signals, standardizes the maximum value, force distribution ratio, and other data, and generates parameter data in a uniform format, which is then sent to the external data processing device.

[0075] Furthermore, the wearable device may also include a power supply unit; this power supply unit can be a button cell battery or a flexible rechargeable battery, wherein the button cell battery is adapted to the finger-cot type wearable body, and the flexible rechargeable battery is adapted to the glove type wearable body. The button cell battery or flexible rechargeable battery is embedded in the wearable body and is charged via a USB-C interface or wireless charging.

[0076] The power supply unit is electrically connected to the processing unit and communication unit, providing power support for the entire wearable device.

[0077] In some embodiments, a nano-waterproof and dustproof coating with a thickness of 2μm is sprayed onto the surface of the wearable body and sensor components. This coating can effectively prevent short circuits of the sensors caused by sweaty fingers during training, while also preventing dust from entering the body and affecting the device's performance, thus meeting the needs of athletes in complex outdoor training scenarios.

[0078] Example 3 Figure 4 This is a flowchart illustrating a data processing method provided in an embodiment of this application. Figure 4 As shown, the method may include: Step S410: Detect the athlete's predetermined shooting position using the position detection module.

[0079] The position detection module detects the athlete's designated shooting spot. Specifically, when the athlete stands at the spot, a pressure sensor or membrane switch generates a signal, which is converted by an ADC and sent to the data processing module, which also records the timestamp of the position.

[0080] Alternatively, the adaptive calibration unit of the position detection module can first correct the sensing threshold of the pressure sensor or membrane switch based on the real-time temperature and humidity data of the training venue; when the athlete stands at a predetermined shooting position, the corresponding sensor detects the pressure signal, converts the position signal through an ADC, sends it to the data processing module, and records the position timestamp.

[0081] Step S420: Detect the action of the basketball being taken out by the ball retrieval detection module.

[0082] When an athlete retrieves a ball from the rack, if the infrared photoelectric sensor beam is blocked, the ball retrieval detection module confirms the ball retrieval event and sends the ball retrieval signal to the data processing module.

[0083] Alternatively, when an athlete retrieves the ball from the rack, the infrared beam is blocked, and at the same time, a miniature weight sensor detects that the weight reduction of the rack placement layer matches the weight of a single basketball. When the duration of the infrared beam blockage reaches a threshold of 0.5 seconds and the weight reduction meets the requirements, it is determined to be a valid ball retrieval, and the ball retrieval signal is sent to the data processing module.

[0084] Step S430: Detect whether the shot is successful using the shot detection module.

[0085] When an athlete shoots a basket, the infrared sensor beam of the shooting detection module is blocked, generating a hit signal and sending the hit signal to the data processing module.

[0086] Alternatively, after an athlete shoots, the infrared photoelectric sensor, sound recognition sensor, and vibration sensor of the shooting detection module collect detection signals respectively; when two or more of these three signals match, it is determined that the shot has been made, a shot signal is generated, and the shot signal is sent to the data processing module.

[0087] Step S440: Collect biomechanical parameters of the athlete when shooting a basketball using a wearable device.

[0088] Athletes wear wearable devices to shoot hoops. The sensor components of the wearable devices collect biomechanical parameters such as the release force and finger force distribution at the moment of release, and send the standardized biomechanical parameters to the processing unit.

[0089] Step S450: Receive the data from the above steps through the data processing module and generate quantitative analysis results of the athlete's shooting training at the predetermined shooting point.

[0090] The data processing module receives position signals, ball retrieval signals, hit signals, and standardized biomechanical parameters. It counts the total number of ball retrievals and hits at each predetermined shooting point and calculates the hit rate. Combining the position stability duration and force value change curve, it distinguishes between effective and ineffective shots. Through a multi-dimensional parameter correlation model, it analyzes the correlation between the optimal force combination and the hit rate. Finally, it generates a visualized training report and a training effect trend report containing basic data and correlation analysis results.

[0091] Furthermore, the data processing module can automatically match the optimal force combination model based on the generated quantitative analysis results, generate personalized training program recommendations for training weaknesses at each predetermined shooting point, and push the training program and visualization report to the mobile devices of athletes and coaches; at the same time, it monitors force value data fluctuations and issues sports injury warnings when abnormal data is detected.

[0092] Corresponding to the above method, this application also provides a data processing device applied to the aforementioned basketball shooting training system, such as... Figure 5 As shown, the device includes: The detection unit 510 is used to detect the predetermined shooting point of the athlete through the position detection module; to detect the action of the basketball being taken out through the ball retrieval detection module; and to detect whether the shot is successful through the shooting detection module. The acquisition unit 520 is used to acquire biomechanical parameters of an athlete when shooting a basketball via a wearable device; The receiving unit 530 is used to receive the data output by the above-mentioned units through the data processing module; The generation unit 540 is used to generate quantitative analysis results of the athlete's shooting training at the predetermined shooting point based on the data output by the above units.

[0093] The functions of each functional unit of the data processing apparatus provided in the above embodiments of this application can be implemented through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the data processing apparatus provided in the embodiments of this application will not be repeated here.

[0094] This application also provides an electronic device, such as... Figure 6 As shown, it includes a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640.

[0095] Memory 630 is used to store computer programs; When the processor 610 executes the program stored in the memory 630, it performs the following steps: The position detection module detects the athlete's designated shooting point. The action of the basketball being retrieved is detected by the ball retrieval detection module; The shooting detection module detects whether a shot is successful. Biomechanical parameters of athletes at the moment of shooting are collected using wearable devices; The data processing module receives the data from the above steps and generates quantitative analysis results of the athlete's shooting training at the predetermined shooting spot.

[0096] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0097] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0098] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0099] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0100] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 4 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0101] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the data processing methods described in the above embodiments.

[0102] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform any of the data processing methods described in the above embodiments.

[0103] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0108] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A basketball shooting training system, characterized in that, The system includes: The position detection module is configured to detect whether an athlete is at a corresponding predetermined shooting point by using sensors deployed at multiple predetermined shooting points on the training ground. The ball retrieval detection module is configured to correspond to each predetermined shooting point and is used to detect whether the basketball has been taken out of the ball rack at the corresponding point. The shooting detection module is configured to be installed at the basket to detect whether the basketball has been put into the basket. Wearable devices, configured to be worn by athletes, are used to collect biomechanical parameters related to the shooting motion during the shooting release; The data processing module is communicatively connected to the position detection module, ball retrieval detection module, shooting detection module, and wearable device. It is used to receive the detection data output by the above modules to generate quantitative analysis results of the athlete's shooting training at each predetermined shooting point.

2. The system as described in claim 1, characterized in that, The position detection module includes a pressure sensor or a membrane switch. The pressure sensor or membrane switch is installed on the ground in a one-to-one correspondence with the predetermined shooting points of the training field, and the sensing surface of the pressure sensor or membrane switch is flush with the ground.

3. The system as described in claim 1, characterized in that, The ball retrieval detection module includes an infrared photoelectric sensor, which is installed on the ball rack corresponding to each predetermined shooting point. The path of the infrared beam of the infrared photoelectric sensor corresponds to the storage position of the basketball on the ball rack, so that the basketball is placed on the ball rack and just blocks the infrared beam. The module is used to determine whether the basketball has been taken from the ball rack at the corresponding predetermined shooting point by detecting whether the infrared beam is not blocked.

4. The system as described in claim 3, characterized in that, The ball-retrieving detection module also includes a miniature weight sensor located at the bottom of the corresponding ball rack; The ball detection module is also used to determine that the basketball has been taken from the rack at the corresponding point when the duration of the infrared beam being blocked reaches a configured blocking duration threshold and the weight sensor detects a decrease in weight.

5. The system as described in claim 1, characterized in that, The shooting detection module includes an infrared photoelectric sensor, which is arranged around the top of the basketball net and is used to determine whether the basketball has hit the target by blocking the beam.

6. The system as described in claim 1, characterized in that, The biomechanical parameters include the force applied during a shot. The wearable device is a finger sleeve or glove that fits the index and middle fingers of an athlete. It integrates at least one pressure sensor or capacitive sensor to collect the release force value at the moment of shooting and record the maximum value of the release force value.

7. The system as described in claim 6, characterized in that, At least one pressure sensor or capacitive sensor is configured inside the finger sleeve or glove and located at the first and second joints of the athlete's shooting hand index and middle fingers.

8. The system as described in claim 1, characterized in that, The quantitative analysis results of the shooting training include the total number of shots caught, the total number of shots made, and the shooting percentage at each predetermined shooting point; The data processing module is specifically used for: Based on the detection data from the position detection module and the ball retrieval detection module, the total number of balls retrieved by the athlete at each predetermined shooting point is counted. Based on the detection data from the position detection module and the shooting detection module, the total number of shots made by the athlete at each predetermined shooting point is counted. The shooting percentage of the athletes at each designated shooting spot is calculated based on the total number of shots retrieved and the total number of shots made at each designated shooting spot.

9. The system as described in claim 1, characterized in that, The data processing module is also used for: The shooting accuracy at each predetermined shooting point is correlated with the corresponding shooting power value to analyze the distribution relationship between the success or failure of a shot and the magnitude of the shooting power value at each predetermined shooting point.

10. The system as claimed in claim 1, characterized in that, The data processing module is also used for: The quantitative analysis results of the shooting training are generated into a visualization report, and a visualization trend report is generated based on the quantitative analysis results of the shooting training of the same athlete within a preset time period to evaluate the training effect of the athlete.

11. A data processing method, characterized in that, The method, applied to the basketball shooting training system as described in any one of claims 1-10, comprises: The position detection module detects the athlete's designated shooting point. The action of the basketball being retrieved is detected by the ball retrieval detection module; The shooting detection module detects whether a shot is successful. Biomechanical parameters of athletes at the moment of shooting are collected using wearable devices; The data processing module receives the data from the above steps and generates quantitative analysis results of the athlete's shooting training at the predetermined shooting spot.

12. A data processing apparatus, characterized in that, The device, used in any one of claims 1-10, comprises: The detection unit is used to detect the athlete's predetermined shooting position through the position detection module; to detect the action of the basketball being taken out through the ball retrieval detection module; and to detect whether the shot is successful through the shooting detection module. The data acquisition unit is used to collect biomechanical parameters of athletes when they shoot a basketball using a wearable device. The receiving unit is used to receive the data output by the above-mentioned units through the data processing module; The generation unit is used to generate quantitative analysis results of the athlete's shooting training at the predetermined shooting point.

13. A wearable device, characterized in that, The device used in the basketball shooting training system described in any one of 1-10 includes: The wearable body is configured as a flexible substrate in the form of finger cots or gloves; At least one sensor, integrated on the wearable body, is used to detect the force applied by the fingers when shooting; The communication unit is used to send the force values ​​detected by each sensor to an external data processing device.

14. The apparatus as claimed in claim 13, characterized in that, Each sensor is a thin-film piezoresistive sensor or a flexible capacitive sensor, and is installed inside the finger sleeve or glove at the first and second joints corresponding to the index and middle fingers.

15. The apparatus as claimed in claim 14, characterized in that, At least one sensor employs a dot matrix design, with sensors deployed on the pads and backs of the first and second joints of the index and middle fingers to collect force data from different parts of the fingers, thereby obtaining the force distribution ratio of different parts.

16. The apparatus as claimed in claim 14, characterized in that, The contact surfaces of the wearable body with the index and middle fingers are provided with breathable and non-slip textures.

17. The apparatus as claimed in claim 13, characterized in that, The wearable body is made of flexible elastic material and has an adjustable Velcro structure.

18. The apparatus as claimed in claim 13, characterized in that, The device further includes: a processing unit; the processing unit is connected to at least one sensor and a communication unit respectively; The processing unit is configured to receive sensor signals from each sensor, extract the maximum value of the hand force from the received sensor signals, and send the extracted maximum value of the hand force to an external data processing device.

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of claim 11.

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