Intelligent leg guard

Abstract

The invention relates to the field of sports and wearable equipment, in particular to an intelligent leg guard which can accurately acquire physiological data of an athlete by integrating a PPG sensor and an electrocardio sensor and can record technical actions and movement tracks of the athlete in real time by integrating an acceleration sensor and a GPS sensor. The built-in main control module processes data in real time, a variational mode algorithm and a self-adaptive algorithm are adopted to process collected signals, information is transmitted to mobile equipment through the Bluetooth module, and personalized training suggestions and motion analysis are provided in cooperation with intelligent application. The leg guard plate main body is made of light-weight and high-strength materials, and is designed in combination with human engineering, so that the wearing comfort is ensured. The intelligent leg guard plate not only improves the health management ability of athletes, but also provides a scientific basis for improving the training effect and the competitive level.

Description

Technical Field

[0001] The invention relates to an intelligent leg guard and belongs to the field of sports and wearable equipment. Background Art

[0002] In football matches, football players face a variety of serious risks if they do not wear shin guards. First, the tibia of the lower leg is exposed and easily hit, resulting in injuries such as abrasions, bruises, and even fractures. Secondly, in fierce competitions, the opponent's studs or knee strikes may directly cause muscle contusions or fractures, seriously affecting athletic ability. Psychologically, the lack of shin guard protection will make players afraid of confrontation and affect their performance in the game. In addition, long-term accumulation of small collisions may cause chronic inflammation or tibial stress fractures. Shin guards are particularly important for athletes.

[0003] Football is a high-intensity sport that requires extremely high physical fitness of athletes. Heart rate and blood oxygen saturation are important physiological indicators for assessing the health status and exercise intensity of athletes. Existing monitoring equipment is mostly concentrated on the wrist and chest, which is difficult to provide accurate data during intense exercise. At the same time, the monitoring of technical movements and movement trajectories in football is of great significance to improving training efficiency and competitive level. Developing a smart shin guard that can comprehensively monitor heart rate, blood oxygen, technical movements and movement trajectories in the calf area will provide all-round support for football players' training and competition. Summary of the invention

[0004] In view of the defects existing in the prior art, the present invention provides a smart shin guard for football players. Based on the sports protection of existing football shin guards, a PPG sensor, an electrocardiogram sensor, an acceleration sensor and a GPS sensor for signal acquisition, as well as a main control module and a Bluetooth module for signal processing and sensing are placed through a reasonable structural design, so that the heart rate, blood oxygen, technical movements and movement trajectory of the football players can be comprehensively monitored in the calf area, providing all-round support for the training and competition of the football players.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A smart shin guard for football players, the smart shin guard having an ergonomically designed shin guard shell and an inner cover fixed to the inner side of the shin guard shell, an electrocardiogram sensor for monitoring heart rate, a PPG sensor for monitoring blood oxygen, an acceleration sensor for monitoring the leg technical movements of football players, and a GPS sensor for monitoring the movement trajectory of football players are installed between the shin guard shell and the inner cover, and also includes a circuit board electrically connected to the electrocardiogram sensor, PPG sensor, acceleration sensor and GPS sensor, a power module for supplying power to the electrocardiogram sensor, PPG sensor, acceleration sensor and GPS sensor, a main control module for processing data for the electrocardiogram sensor, PPG sensor, acceleration sensor and GPS sensor, and a Bluetooth module for transmitting data.

[0007] The intelligent shin guard for football players is worn on the calves of football players.

[0008] Preferably, the shell of the smart leg guard is made of high-strength carbon fiber, using carbon fiber composite material molding technology.

[0009] Preferably, the PPG sensor uses 660nm red light and 940nm infrared light as detection light sources.

[0010] Preferably, the ECG sensor is located 5 cm below the PPG sensor, and the two electrodes of the ECG sensor are arranged symmetrically.

[0011] Preferably, the acceleration sensor is an acceleration sensor, which collects leg movement information of football players, and the sampling frequency of the acceleration sensor and the PPG sensor is 50Hz.

[0012] Using the acceleration signal A x , A y , A z Predict the impact of motion artifacts on the PPG signal, and then extract it from the original PPG signal P raw Subtract it from the original image to get the PPG signal P after artifact reduction. filtered The specific formula is as follows:

[0013] P fltered =P raw -(β1Ax+β2Ay+β3Az)

[0014] Where: P raw : Original PPG signal with artifacts; P filtered : PPG signal after artifacts are eliminated; A x , A y, , A z: three axial components of the acceleration signal; β1, β2, β3: regression coefficients, indicating the contribution of each axis acceleration signal to the motion artifact.

[0015] The regression coefficients β1, β2, and β3 are obtained by the minimum error method, and the specific formula is as follows:

[0016]

[0017] Where P baseline It is the reference signal of the artifact-free PPG signal in the resting state. i represents the index of a data point in the time series. N is the total number of data points, that is, the length of the collected signal. Loss is an error function used to measure the difference between the predicted value and the true value of the regression model. When the Loss value is the smallest, the corresponding regression coefficients β1, β2, and β3 are obtained.

[0018] Preferably, the GPS sensor and the acceleration sensor are both connected to the main control chip, the signal collected by the GPS sensor and the signal of the acceleration sensor are combined with each other in data processing, and the acceleration sensor records the technical movements of the players on the court based on the changes in the leg acceleration signal and the GPS signal positioning of the smart shin guards.

[0019] Preferably, the main control chip will adopt STM32f103 chip.

[0020] Preferably, the signal collected by the PPG sensor adopts a bandpass filter, the cut-off frequency of the bandpass filter is 0.5-5Hz, the signal in the heart rate and blood oxygen related frequency band is retained, and the high-frequency noise and low-frequency baseline drift are filtered out.

[0021] The filtered signal is further smoothed by applying a sliding average window algorithm, and the number of windows of the sliding average window algorithm used is set to 8.

[0022] The preprocessed PPG signal is decomposed into several intrinsic mode functions (IMFs) using a variational mode algorithm, each of which corresponds to a frequency component.

[0023] Alpha is the penalty factor, tau is the time delay, K is the number of decomposed modes, and the relevant parameters of the variational mode algorithm are set according to the characteristics of the PPG signal as follows:

[0024] alpha=2000, tau=0, K=4.

[0025] According to the frequency band of the heart rate, the intrinsic mode function (IMF) with a center frequency of 0.8-2.5 Hz was selected, and the fast Fourier transform (FFT) was applied to the selected IMF to calculate the main frequency and obtain the real-time heart rate. The effective heart rate range (48-180 bpm) was screened and abnormal values ​​were eliminated.

[0026] The heart rate value obtained after the above PPG signal is processed by the algorithm is compared with the heart rate value extracted from the electrocardiogram (ECG) sensor signal, and the mean error (MAE) and standard deviation (STD) are calculated. The values ​​are discarded and output according to the results, and the heart rate and blood oxygen saturation are output in real time.

[0027] Preferably, the Bluetooth module will upload the football player's heart rate data, blood oxygen data, technical action data and motion trajectory data to the smart mobile device, and each player's data will be stored in the cloud.

[0028] Preferably, the power module will be attached to the leg guard shell through battery packaging glue.

[0029] The circuit board is positioned by the positioning pins on the leg guard shell.

[0030] The inner cover plate of the leg guard is connected to the outer shell of the leg guard by screw connection.

[0031] The power module adopts a lithium battery module with high energy density.

[0032] The beneficial effects of the present invention based on its technical solution are:

[0033] (1) The present invention provides a PPG sensor and an ECG sensor built into a smart shin guard that can continuously track the player's heart rate and blood oxygen changes, and provide heart rate interval analysis to determine whether the exercise intensity is appropriate. At the same time, the ECG sensor records heart rate data in real time to help identify potential heart problems. This real-time monitoring allows coaches to adjust tactical strategies in time during training and competitions to ensure that players are in the best condition and reduce health risks;

[0034] (2) The present invention provides a smart shin guard with a built-in acceleration sensor that can accurately capture every action of a player on the field, including running, kicking, and changing direction. These data can be used to evaluate whether the athlete's technical movements are standard, help coaches identify and correct technical errors, and improve the athlete's overall performance and competitive level.

[0035] (3) The present invention provides a smart shin guard with a built-in GPS sensor that can provide accurate location information, including running trajectory, speed changes, and distance calculations. This information is combined with physiological data (such as heart rate and blood oxygen level) to form a comprehensive sports data set. By analyzing this data, coaches can understand the physical condition and game performance of players, develop scientific training plans, and achieve targeted training.

[0036] In summary, the intelligent football shin guards of the present invention not only improve the safety and health management of athletes through technical monitoring, but also enhance the scientificity and pertinence of training through data analysis, thereby comprehensively improving training efficiency and competitive level. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the structure of the leg guard provided in Example 1 of the present invention.

[0038] Figure 2 It is a schematic diagram of the module connection of the body data and motion data acquisition device.

[0039] Figure 3 It is a schematic diagram of the connection and function between the signal processing of each sensor.

[0040] Figure 4 It is a schematic diagram of the sensor positions on the inner cover of the smart shin guard.

[0041] Figure 5 It is a flow chart of the heart rate and blood oxygen algorithm according to an embodiment of the present invention.

[0042] Figure 6 It is a schematic diagram of the real-time pulse wave obtained after the intelligent shin guard collects and processes it.

[0043] In the figure: 1-shin guard inner cover, 2-circuit board, 3-PPG sensor, 4-ECG sensor, 5-Bluetooth module, 6-GPS sensor, 7-acceleration sensor, 8-power module, 9-shin guard shell. DETAILED DESCRIPTION

[0044] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0045] It should be noted that, in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0046] like Figures 1 to 4 As shown, the present invention provides an intelligent shin guard for football players, including an ergonomically designed shin guard shell 9 and an inner cover plate 1 fixed to the inner side of the shin guard shell, wherein an electrocardiogram sensor 4 for monitoring heart rate, a PPG sensor 3 for monitoring blood oxygen, an acceleration sensor 7 for monitoring the leg technical movements of football players, and a GPS sensor 6 for monitoring the movement trajectory of football players are installed between the shin guard shell and the inner cover plate, and also includes a circuit board 2 electrically connected to the electrocardiogram sensor 4, the PPG sensor 3, the acceleration sensor 7 and the GPS sensor 6, a power supply module 8 for supplying power to the electrocardiogram sensor 4, the PPG sensor 3, the acceleration sensor 7 and the GPS sensor 6, a main control module for processing data for the electrocardiogram sensor 4, the PPG sensor 3, the acceleration sensor and the GPS sensor 6, and a Bluetooth module 5 for transmitting data.

[0047] Furthermore, the smart shin guards for football players are worn on the calves of football players.

[0048] Preferably, the shell of the smart leg guard is made of high-strength carbon fiber, using carbon fiber composite material molding technology.

[0049] Preferably, the PPG sensor uses 660nm red light and 940nm infrared light as detection light sources.

[0050] Preferably, the ECG sensor is located 5 cm below the PPG sensor, and the two electrodes of the ECG sensor are arranged symmetrically.

[0051] Preferably, the acceleration sensor is a three-axis acceleration sensor for collecting leg movement information of football players. The sampling frequency of the acceleration sensor and the PPG sensor is 50 Hz.

[0052] Preferably, the GPS sensor and the acceleration sensor are both connected to the main control chip, and the signal collected by the GPS sensor and the signal of the acceleration sensor are combined with each other in data processing. The acceleration sensor locates the player's technical movements on the court based on the change of the leg acceleration signal and the GPS signal collected by the GPS sensor.

[0053] Preferably, the main control chip will adopt STM32f103 chip.

[0054] Preferably, the signal collected by the PPG sensor adopts a bandpass filter, the cut-off frequency of the bandpass filter is 0.5-5Hz, the signal in the heart rate and blood oxygen related frequency band is retained, and the high-frequency noise and low-frequency baseline drift are filtered out.

[0055] The filtered signal is further smoothed by applying a sliding average window algorithm, and the number of windows of the sliding average window algorithm used is set to 8.

[0056] The preprocessed PPG signal is decomposed into several intrinsic mode functions (IMFs) using a variational mode algorithm, each of which corresponds to a frequency component.

[0057] Alpha is the penalty factor, tau is the time delay, K is the number of decomposed modes, and the relevant parameters of the variational mode algorithm are set according to the characteristics of the PPG signal as follows:

[0058] alpha=2000, tau=0, K=4.

[0059] According to the frequency band of the heart rate, the intrinsic mode function (IMF) with a center frequency of 0.8-2.5 Hz was selected, and the fast Fourier transform (FFT) was applied to the selected IMF to calculate the main frequency and obtain the real-time heart rate. The effective heart rate range (48-180 bpm) was screened and abnormal values ​​were eliminated.

[0060] The heart rate value obtained after the algorithm processing of the above PPG signal is compared with the heart rate value extracted from the ECG signal, the mean error (MAE) and standard deviation (STD) are calculated, and the numerical value is discarded and output according to the results.

[0061] After the heart rate value obtained by PPG signal processing meets the heart rate value error requirement with the ECG signal, the AC component (signal amplitude) and DC component (signal average value) in the red light signal and infrared signal are extracted.

[0062] AC R is the signal amplitude in the red light signal, DC R is the average signal value in the red light signal, AC IR is the signal amplitude in the infrared signal, DC IRis the average value of the infrared signal. The light intensity ratio R is calculated according to the formula: R = (AC R / DC R ) / (AC IR / DC IR ).

[0063] According to experimental calibration, calculate blood oxygen saturation SpO2 = AR 2 +BR+C (A, B, C are constants obtained from experimental calibration).

[0064] The main control chip calculates the heart rate value and blood oxygen value and outputs the result.

[0065] Preferably, the main control module uses the acceleration signal collected by the acceleration sensor to eliminate the motion artifacts of the pulse wave signal:

[0066] Using the acceleration signal A x , A y , A z Predict the impact of motion artifacts on the PPG signal, and then extract it from the original PPG signal P raw Subtract it from the original image to get the PPG signal P after artifact reduction. filtered , the specific formula is as follows:

[0067] P fltered =P raw -(β1A x +β2A y +β3A z )

[0068] Where: P raw represents the original PPG signal containing artifacts; P filtered A represents the PPG signal after the artifact is eliminated; x , A y , A z represents the three axial components of the acceleration signal; β1, β2, and β3 are regression coefficients, indicating the contribution of the acceleration signal of each axis to the motion artifact.

[0069] The regression coefficients β1, β2, and β3 are obtained by the minimum error method, and the specific formula is as follows:

[0070]

[0071] Where P baseline It is the reference signal of the artifact-free PPG signal in the resting state. i represents the index of a data point in the time series. N is the total number of data points, that is, the length of the collected signal. Loss is an error function used to measure the difference between the predicted value and the true value of the regression model. When the Loss value is the smallest, the corresponding regression coefficients β1, β2, and β3 are obtained.

[0072] like Figure 6 The smart leg guard shown obtains a pulse wave image after collection and processing.

[0073] Preferably, the Bluetooth module will upload the football player's heart rate data, blood oxygen data, technical action data and motion trajectory data to the smart mobile device, and each player's data will be stored in the cloud.

[0074] Preferably, the power module will be attached to the leg guard shell through battery packaging glue.

[0075] The circuit board is positioned by the positioning pins on the leg guard shell.

[0076] The inner cover plate of the leg guard is connected to the outer shell of the leg guard by screw connection.

[0077] The power module adopts a lithium battery module with high energy density.

[0078] The present invention provides a PPG sensor and an ECG sensor built into a smart shin guard that continuously tracks the player's heart rate and blood oxygen changes, and provides heart rate interval analysis to determine whether the exercise intensity is appropriate. At the same time, the ECG sensor records heart rate data in real time to help identify potential heart problems. This real-time monitoring allows coaches to adjust tactical strategies in time during training and competitions to ensure that players are in the best condition and reduce health risks.

[0079] The present invention provides a smart shin guard with a built-in acceleration sensor that can accurately capture every action of a player on the field, including running, kicking and changing direction. These data can be used to evaluate whether the athlete's technical movements are standard, help coaches identify and correct incorrect techniques, and improve the overall performance and competitive level of athletes.

[0080] The present invention provides a smart shin guard with a built-in GPS sensor that can provide accurate location information, including running trajectory, speed change and distance calculation. This information is combined with physiological data (such as heart rate and blood oxygen level) to form a comprehensive sports data set. By analyzing this data, coaches can understand the physical condition and game performance of players, formulate scientific training plans, and achieve targeted training.

[0081] In summary, the intelligent football shin guards of the present invention not only improve the safety and health management of athletes through technical monitoring, but also enhance the scientificity and pertinence of training through data analysis, thereby comprehensively improving training efficiency and competitive level.

[0082] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover exclusive inclusion, so that the process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "include..." or "comprise..." do not exclude the existence of other elements in the process, method, article or terminal device including the elements. In addition, in this article, "greater than", "less than", "exceed", etc. are understood to exclude the number itself; "above", "below", "within", etc. are understood to include the number itself.

[0083] Although the above embodiments have been described, once those skilled in the art know the basic creative concepts, they can make additional changes and modifications to these embodiments. Therefore, the above description is only an embodiment of the present invention and does not limit the patent protection scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the specification and drawings of the present invention, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A smart leg guard, characterized in that: The intelligent shin guard comprises: The main protective gear module comprises a leg guard shell and an inner cover plate fixed to the inner side of the leg guard shell; A circuit module, which is disposed between the outer shell of the leg guard and the inner cover plate and is used to collect athlete data in real time, wherein the athlete data includes body data, motion state data and motion posture data; The circuit module includes: an ECG sensor for monitoring heart rate, a PPG sensor for monitoring blood oxygen, an acceleration sensor for monitoring the athlete's leg technical movements, and a GPS sensor for monitoring the athlete's movement trajectory. It also includes a circuit board electrically connecting the ECG sensor, PPG sensor, acceleration sensor and GPS sensor, a power module for powering the ECG sensor, PPG sensor, acceleration sensor and GPS sensor, a main control module for processing data of the ECG sensor, PPG sensor, acceleration sensor and GPS sensor, and a Bluetooth module for transmitting data.

2. The intelligent leg guard according to claim 1, characterized in that: The electrocardiogram sensor is located at the lower side of the PPG sensor, and the two electrodes of the electrocardiogram sensor are arranged symmetrically.

3. The intelligent leg guard according to claim 1, characterized in that: The main control module uses the acceleration signal collected by the acceleration sensor to eliminate the motion artifacts of the pulse wave signal: Using the acceleration signal A x , A y , A z Predict the impact of motion artifacts on the PPG signal, and then extract it from the original PPG signal P raw Subtract it from the original image to get the PPG signal P after artifact reduction. filtered , the specific formula is as follows: P fltered =P raw -(β1A x +β2A y +β3A z ) Where: P raw represents the original PPG signal containing artifacts; P filtered A represents the PPG signal after the artifact is eliminated; x , A y , A z represents the three axial components of the acceleration signal; β1, β2, and β3 are regression coefficients, indicating the contribution of the acceleration signal of each axis to the motion artifact.

4. The intelligent leg guard according to claim 3, characterized in that: The regression coefficients β1, β2, and β3 are obtained by the minimum error method, and the specific formula is as follows: Where P baseline It is the reference signal of the artifact-free PPG signal in the resting state. i represents the index of a data point in the time series. N is the total number of data points, that is, the length of the collected signal. Loss is an error function used to measure the difference between the predicted value and the true value of the regression model. When the Loss value is the smallest, the corresponding regression coefficients β1, β2, and β3 are obtained.

5. The intelligent leg guard according to claim 1, characterized in that: The main control module compares the heart rate value calculated by the pulse wave signal collected by the ECG sensor signal and the PPG sensor signal.

6. The intelligent leg guard according to claim 1, characterized in that: The GPS sensor and the acceleration sensor are both connected to the main control module. The signals collected by the GPS sensor and the signals of the acceleration sensor are combined with each other in data processing. The acceleration sensor locates the athlete according to the changes in the leg acceleration signal and the GPS signal collected by the GPS sensor, and records the athlete's technical movements.

7. The intelligent leg guard according to any one of claims 1 to 6, characterized in that: The main control module processes the signal collected by the PPG sensor using a bandpass filter, the cutoff frequency of the bandpass filter is 0.5-5Hz, the signals in the heart rate and blood oxygen related frequency bands are retained, high-frequency noise and low-frequency baseline drift are filtered out, and the filtered signal is further smoothed using a sliding average window algorithm, and the number of windows of the sliding average window algorithm used is set to 8.

8. The intelligent leg guard according to claim 7, characterized in that: The main control module uses a variational mode algorithm on the smoothed PPG signal to decompose the PPG signal into several intrinsic mode functions IMFs, each IMF corresponding to a frequency component; according to the frequency band of the heart rate, an intrinsic mode function with a center frequency of 0.8-2.5 Hz is selected, and the intrinsic mode function is applied with a fast Fourier transform to calculate the main frequency and obtain the real-time heart rate, and outliers are eliminated according to the effective heart rate range.

9. The intelligent leg guard according to claim 8, characterized in that: The main control module compares the real-time heart rate obtained after processing the PPG signal with the heart rate value extracted by the electrocardiogram sensor signal, calculates the average error and standard deviation, discards and outputs the numerical value according to the result, calculates the light intensity ratio in the PPG signal, and outputs the heart rate and blood oxygen saturation in real time.

10. The intelligent leg guard according to claim 9, characterized in that: The method of calculating the light intensity ratio in the PPG signal and outputting the heart rate and blood oxygen saturation in real time includes: Extracting signal amplitude and signal average value from red light signal and infrared signal; Set AC R is the signal amplitude in the red light signal, DC R is the average signal value in the red light signal, AC IR is the signal amplitude in the infrared signal, DC IR is the average value of the infrared signal. The light intensity ratio R is calculated according to the following formula: R = (AC R / DC R ) / (AC IR / DC IR ); According to experimental calibration, calculate blood oxygen saturation SpO2 = AR 2 +BR+C, where A, B, and C are constants obtained from experimental calibration.

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