Sprint training method based on motion sensor

Abstract

The invention discloses a sprint training method based on a motion sensor, which comprises a high-precision motion sensor, a data processing unit, a data reference unit, a training knowing unit and a training process monitoring unit, and is characterized in that the high-precision motion sensor comprises an acceleration sensor and an angular velocity sensor; the data processing unit is used for processing data received by the high-precision motion sensor, the data reference unit is used for carrying out reference comparison on the received data and standard data, the training guidance unit is used for generating personalized training guidance suggestions, and the training process monitoring unit is used for monitoring a training process; according to the sprint training method based on the motion sensor, the stride and stride frequency of the athlete can be measured and analyzed in real time, the athlete is guided to conduct targeted training, and the running speed and the motion performance of the athlete are improved.

Description

Technical Field

[0001] The present invention relates to a speed training method based on a motion sensor, and more particularly to a method for measuring stride length and stride frequency by a motion sensor worn on an athlete, and providing training guidance based on these parameters. The present invention aims to provide a scientific and systematic training program to help sprinters improve their running speed and athletic performance. Background Art

[0002] In modern sports competitions, sprinting events require extremely high speed and explosive power from athletes. The stride and stride frequency of sprinters are two key parameters that affect their running speed. Stride refers to the length of each step of an athlete, while stride frequency refers to the number of steps per unit time. The optimization and coordination of these two parameters are crucial to improving the performance of sprinters. In addition, the impact of stride frequency and stride on athletes is mainly reflected in running efficiency, energy consumption, injury risk, and the choice of training methods. Through reasonable training and adjustment of stride frequency and stride, athletes' running performance can be effectively improved and the risk of injury can be reduced.

[0003] Traditional speed training methods usually rely on the coach's experience and the athlete's subjective feelings, and lack scientific and data-based guidance. This method makes it difficult to obtain and analyze the athlete's stride and frequency data in real time during training, resulting in unsatisfactory training results. Existing monitoring equipment is mostly laboratory-specific, complex and expensive, and cannot be widely used in actual training scenarios. In addition, these devices often cannot provide real-time data feedback, making it difficult for athletes and coaches to adjust training strategies based on the data in a timely manner.

[0004] Therefore, a portable, accurate, and real-time monitoring system is needed that can provide real-time data of stride length and frequency during actual training, and provide scientific training suggestions based on these data to help athletes train scientifically and improve their running speed. Summary of the invention

[0005] The invention discloses a sprint training method based on a motion sensor, which guides athletes to perform targeted training by measuring and analyzing the stride and frequency of athletes in real time, so as to improve their running speed and sports performance.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A sprint training method based on a motion sensor, comprising a high-precision motion sensor, a data processing unit, a data reference unit, a training guidance unit and a training process monitoring unit, wherein the high-precision motion sensor comprises an acceleration sensor and an angular velocity sensor, the data processing unit is used to process data received by the high-precision motion sensor, the data reference unit is used to compare the received data with standard data, the training guidance unit is used to generate personalized training guidance suggestions, and the training process monitoring unit is used to monitor the training process;

[0008] The data processing unit includes a data acquisition module, a data transmission module and a computing device. The data acquisition module is used to collect data such as acceleration, angular velocity and displacement in real time when the athlete is running. The data transmission module transmits these data to the computing device for calculation. On the computing device, a dedicated application processes and analyzes the received data to extract the athlete's stride and frequency parameters.

[0009] The training guidance unit includes a data analysis module and a guidance scheme generation module. The data analysis module analyzes the comparison results between the data reference unit and the standard data, and the guidance scheme generation module generates a corresponding guidance scheme based on the analysis results of the data analysis module.

[0010] The computing device may be a smartphone, a tablet, or a computer.

[0011] The existing high-precision motion sensors on the market are used to collect the motion data of athletes during their running process in real time. Secondly, the sensors collect data such as acceleration, angular velocity and displacement in real time when the athletes are running. These data are transmitted to the computing device via Bluetooth or wireless network. On the computing device, dedicated software or applications process and analyze the received data, extract the athlete's stride and frequency parameters, compare the extracted stride and frequency parameters with the pre-stored standard parameters of excellent athletes, identify specific aspects that need improvement, and based on the analysis results, the system can generate personalized training guidance suggestions. Therefore, the advantage of the present invention lies in its scientificity and systematicness. Through motion sensor technology and data analysis, athletes are provided with objective and accurate training guidance. This method is not only suitable for speed training of sprinters, but can also be promoted and applied to other types of sports, and has broad application prospects.

[0012] In a preferred solution, the data reference unit includes a physical parameter uploading module, a physical comparison module, a parameter matching module, a standard parameter storage library and a difference identification module. The physical parameter uploading module is used to upload the athlete's height, weight, leg length, arm length and other data;

[0013] The physical fitness comparison module is used to generate an overall data set of the athlete's physical fitness, and the parameter matching module is used to match the physical fitness data set with the data in the standard parameter storage library;

[0014] The standard parameter repository is used to store the data of excellent athletes as standard parameters, and the parameters are matched with the physical data of excellent athletes. The difference recognition module is used to compare the acceleration, angular velocity, displacement and other data collected in real time when the athletes are running, and find the difference between the data;

[0015] The standard parameters in the standard parameter repository specifically represent the best combination of stride length and stride frequency, and the difference identification module mainly finds out the difference between the athlete's current stride length and stride frequency and the standard parameters through comparative analysis;

[0016] By setting up a data reference unit, the data reference unit mainly uses standard parameters to compare with the athlete's measured parameters to analyze the athlete's shortcomings. By uploading physical parameters and comparing the physical fitness of excellent athletes and measured athletes in standard parameters through the physical fitness comparison module and parameter matching module, a more suitable stride length and frequency combination can be better selected, thereby ensuring the pertinence and adaptability of training instructions.

[0017] In a preferred solution, the training process monitoring unit includes a regular data collection module, a collection frequency setting module and a program optimization module, and the regular data collection module is used to regularly collect and analyze the athlete's training data;

[0018] The acquisition frequency setting module is used to set the regular acquisition frequency and prompt the user based on the acquisition frequency. The program optimization module is used to automatically adjust and optimize the guidance program based on the latest data feedback.

[0019] A sprint training method based on a motion sensor comprises the following specific steps:

[0020] S1: Athletes wear sensors: Athletes wear high-precision motion sensors on their feet;

[0021] S2: Computing device: The collected data of the high-precision motion sensor is transmitted to the computing device for processing and analysis;

[0022] S3: Guidance Plan: Automatically generate personalized training guidance suggestions based on processing and analysis results;

[0023] S4: Feedback and optimization: Collect data regularly during training and optimize the training plan.

[0024] By setting up a training process monitoring unit, data collection and analysis are carried out regularly during the training process to monitor the progress of athletes. Based on the latest data feedback, the training plan is continuously adjusted and optimized to ensure the maximum training effect. At the same time, through long-term tracking and analysis, athletes are helped to gradually reach or exceed the stride and frequency standards of excellent athletes, thereby improving their running speed and overall athletic performance.

[0025] As can be seen from the above, a sprint training method based on a motion sensor includes a high-precision motion sensor, a data processing unit, a data reference unit, a training knowledge unit and a training process monitoring unit, wherein the high-precision motion sensor includes an acceleration sensor and an angular velocity sensor, the data processing unit is used to process data received by the high-precision motion sensor, the data reference unit is based on the received data and the standard data for reference comparison, the training guidance unit is used to generate personalized training guidance suggestions, and the training process monitoring unit is used to monitor the training process;

[0026] The data processing unit includes a data acquisition module, a data transmission module and a computing device. The data acquisition module is used to collect data such as acceleration, angular velocity and displacement in real time when the athlete is running. The data transmission module transmits these data to the computing device for calculation. On the computing device, a dedicated application processes and analyzes the received data to extract the athlete's stride and frequency parameters.

[0027] The training guidance unit includes a data analysis module and a guidance scheme generation module, wherein the data analysis module analyzes the comparison result between the data reference unit and the standard data, and the guidance scheme generation module generates a corresponding guidance scheme based on the analysis result of the data analysis module. The sprint training method based on the motion sensor provided by the present invention can guide the athlete to perform targeted training by real-time measurement and analysis of the athlete's stride and stride frequency, so as to improve their running speed and sports performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a schematic diagram of the overall structure of a sprint training method based on a motion sensor proposed by the present invention.

[0029] Figure 2 This is a schematic diagram of the structure of a data processing unit of a sprint training method based on a motion sensor proposed by the present invention.

[0030] Figure 3 This is a schematic diagram of the data reference unit structure of a motion sensor-based sprint training method proposed by the present invention.

[0031] Figure 4This is a schematic diagram of the structure of a training guidance unit of a sprint training method based on a motion sensor proposed by the present invention.

[0032] Figure 5 The present invention provides a schematic diagram of the structure of a training process monitoring unit for a sprint training method based on a motion sensor.

[0033] Figure 6 The present invention provides a flow chart of a sprint training method based on a motion sensor. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0035] The present invention discloses a sprint training method based on a motion sensor, which is mainly applied to the scenario of speed training of athletes.

[0036] Reference Figure 1-Figure 4 A sprint training method based on a motion sensor comprises a high-precision motion sensor, a data processing unit, a data reference unit, a training guidance unit and a training process monitoring unit, wherein the high-precision motion sensor comprises an acceleration sensor and an angular velocity sensor, the data processing unit is used to process data received by the high-precision motion sensor, the data reference unit is used to compare the received data with the standard data, the training guidance unit is used to generate personalized training guidance suggestions, and the training process monitoring unit is used to monitor the training process;

[0037] The data processing unit includes a data acquisition module, a data transmission module and a computing device. The data acquisition module is used to collect data such as acceleration, angular velocity and displacement in real time when the athlete is running. The data transmission module transmits these data to the computing device for calculation. On the computing device, a dedicated application processes and analyzes the received data to extract the athlete's stride and frequency parameters.

[0038] The training guidance unit includes a data analysis module and a guidance scheme generation module. The data analysis module analyzes the comparison results between the data reference unit and the standard data, and the guidance scheme generation module generates a corresponding guidance scheme based on the analysis results of the data analysis module.

[0039] Reference Figure 1 In a preferred embodiment, the computing device may be a smart phone, a tablet computer, or a computer.

[0040] Reference Figure 3In a preferred embodiment, the data reference unit includes a physical parameter uploading module, a physical comparison module, a parameter matching module, a standard parameter storage library and a difference identification module. The physical parameter uploading module is used to upload the athlete's height, weight, leg length, and arm length data.

[0041] Reference Figure 3 In a preferred embodiment, the physical fitness comparison module is used to generate an overall data set of the athlete's physical fitness, and the parameter matching module is used to match the physical fitness data set with the data in the standard parameter repository.

[0042] Reference Figure 3 In a preferred embodiment, the standard parameter repository is used to store the data of excellent athletes as standard parameters, and the parameters are matched with the physical data of excellent athletes. The difference recognition module is used to compare the acceleration, angular velocity, displacement and other data collected in real time when the athletes are running, and find the differences between the data.

[0043] Reference Figure 3 In a preferred embodiment, the standard parameters in the standard parameter repository specifically represent the best combination of stride and frequency, and the difference identification module mainly finds out the difference between the athlete's current stride and frequency and the standard parameters through comparative analysis. The data reference unit mainly uses the standard parameters to compare with the athlete's measured parameters to analyze the athlete's shortcomings. These standard parameters are obtained by statistically analyzing the data of many excellent sprinters and represent the best combination of stride and frequency. The comparative analysis can find out the difference between the athlete's current stride and frequency and the standard parameters. By uploading the physical parameters and comparing the physical fitness of the excellent athletes and the measured athletes in the standard parameters with the physical fitness comparison module and the parameter matching module, a more suitable combination of stride and frequency can be better selected, thereby ensuring the pertinence and adaptability of the training guidance.

[0044] Reference Figure 5 In a preferred embodiment, the training process monitoring unit includes a regular data collection module, a collection frequency setting module and a program optimization module. The regular data collection module is used to regularly collect and analyze the athlete's training data.

[0045] Reference Figure 5In a preferred embodiment, the collection frequency setting module is used to set a regular collection frequency and prompt the user based on the collection frequency. The program optimization module is used to automatically adjust and optimize the guidance program based on the latest data feedback. During the training process, data collection and analysis are performed regularly to monitor the progress of the athletes. According to the latest data feedback, the training plan is continuously adjusted and optimized to ensure the maximum training effect. At the same time, through long-term tracking and analysis, athletes are helped to gradually reach or exceed the stride and frequency standards of excellent athletes, thereby improving their running speed and overall sports performance.

[0046] Reference Figure 6 In a preferred embodiment, the method comprises the following specific steps:

[0047] S1: Athletes wear sensors: Athletes wear high-precision motion sensors on their feet;

[0048] S2: Computing device: The collected data of the high-precision motion sensor is transmitted to the computing device for processing and analysis;

[0049] S3: Guidance Plan: Automatically generate personalized training guidance suggestions based on processing and analysis results;

[0050] S4: Feedback and optimization: Collect data regularly during training and optimize the training plan.

[0051] Embodiment 1:

[0052] like Figure 6 As shown, the method of the present invention comprises the following steps:

[0053] 1. Wearing the sensor:

[0054] The motion sensor is fixed on the athlete's foot, and a lightweight and comfortable sensor on the market is used to ensure that it does not affect the athlete's normal movement.

[0055] 2. Data collection and transmission:

[0056] The sensor collects the athlete's motion data in real time during the running process, including acceleration, angular velocity and displacement, etc. These data are transmitted to a computing device such as a smartphone, tablet or computer via Bluetooth or wireless network module.

[0057] 3. Data processing and analysis:

[0058] Run dedicated software or applications on the smart device to process and analyze the received sports data. By calculating the changes in acceleration and angular velocity, the athlete's stride length and stride frequency parameters can be extracted.

[0059] 4. Parameter comparison and analysis:

[0060] The extracted stride length and frequency parameters are compared with the pre-stored standard parameters of excellent athletes. These standard parameters are obtained by statistically analyzing the data of many excellent sprinters and represent the best stride length and frequency combination. Through comparative analysis, the difference between the athlete's current stride length and frequency and the standard parameters can be found, and specific aspects that need improvement can be identified.

[0061] 5. Training guidance and planning:

[0062] Based on the analysis results, personalized training guidance suggestions are generated. For example, if the athlete's stride is too short and the cadence is too high, it is recommended that they increase their stride and appropriately reduce their cadence. Then, a detailed training plan is formulated to clarify the goals and methods of each training, including stride adjustment exercises, cadence adjustment exercises, and comprehensive exercises.

[0063] 6. Feedback and optimization:

[0064] During the training process, data collection and analysis are performed regularly to monitor the progress of athletes. Based on the latest data feedback, the training plan is continuously adjusted and optimized to ensure the maximum training effect. Through long-term tracking and analysis, athletes are helped to gradually reach or exceed the stride length and frequency standards of excellent athletes, and improve their running speed and overall sports performance.

[0065] Embodiment 2:

[0066] A sprinter wears a motion sensor for sprint training. The sensor is fixed on the athlete's foot, and the data collection device is an electronic device. The athlete runs a 100-meter sprint, and the sensor collects motion data in real time during the running process, including acceleration and angular velocity. The data is transmitted to the electronic device via Bluetooth, and the data is processed by a specific application to calculate the athlete's stride length and stride frequency.

[0067] Comparing these data with the standard parameters of excellent sprinters, it was found that the athletes' stride length was too small and the stride frequency was too high, or too large and the stride frequency was too high. Based on the analysis results, the coach suggested that the athletes increase / decrease their stride length and formulate a specific training plan. In subsequent training, the athletes gradually approached the standard parameters by adjusting their running posture and stride frequency and stride length, thus improving their running speed.

[0068] Working principle: The high-precision motion sensors available on the market are used to collect the motion data of athletes during their running process in real time. They are lightweight and easy to wear, and can be fixed on the athlete's feet to ensure the accuracy and reliability of data collection. Secondly, the sensors collect data such as acceleration, angular velocity and displacement in real time when the athlete is running. These data are transmitted to the computing device via Bluetooth or wireless network. On the computing device, dedicated software or applications process and analyze the received data, extract the athlete's stride and stride frequency parameters, and compare the extracted stride and stride frequency parameters with the pre-stored standard parameters of excellent athletes to identify specific aspects that need improvement. Based on the analysis results, the system can generate personalized training guidance suggestions. For example, if the athlete's stride is too small and the stride frequency is too high, it is recommended that the athlete increase the stride and stride frequency. Appropriately reduce the stride frequency, formulate a detailed training plan, and clarify the goals and methods of each training, including stride adjustment exercises, stride frequency adjustment exercises and comprehensive exercises. The advantages of the present invention lie in its scientificity and systematicness. Through motion sensor technology and data analysis, objective and accurate training guidance is provided to athletes. This method is not only suitable for speed training of sprinters, but can also be promoted and applied to other types of sports. It has broad application prospects. During the training process, data collection and analysis are carried out regularly to monitor the progress of athletes. According to the latest data feedback, the training plan is continuously adjusted and optimized to ensure the maximum training effect. At the same time, through long-term tracking and analysis, athletes are helped to gradually reach or exceed the stride and stride frequency standards of excellent athletes, thereby improving their running speed and overall sports performance.

[0069] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A sprint training method based on a motion sensor, comprising a high-precision motion sensor, a data processing unit, a data reference unit, a training unit and a training process monitoring unit, characterized in that: The high-precision motion sensor includes an acceleration sensor and an angular velocity sensor, the data processing unit is used to process data received by the high-precision motion sensor, the data reference unit is used to compare the received data with standard data, the training guidance unit is used to generate personalized training guidance suggestions, and the training process monitoring unit is used to monitor the training process; The data processing unit includes a data acquisition module, a data transmission module and a computing device. The data acquisition module is used to collect data such as acceleration, angular velocity and displacement in real time when the athlete is running. The data transmission module transmits these data to the computing device for calculation. On the computing device, a dedicated application processes and analyzes the received data to extract the athlete's stride and frequency parameters. The training guidance unit includes a data analysis module and a guidance scheme generation module. The data analysis module analyzes the comparison results between the data reference unit and the standard data, and the guidance scheme generation module generates a corresponding guidance scheme based on the analysis results of the data analysis module.

2. A sprint training method based on a motion sensor according to claim 1, characterized in that: The computing device may be a smartphone, a tablet, or a computer.

3. A sprint training method based on a motion sensor according to claim 1, characterized in that: The data reference unit includes a physical parameter uploading module, a physical comparison module, a parameter matching module, a standard parameter storage library and a difference identification module. The physical parameter uploading module is used to upload the athlete's height, weight, leg length, and arm length data.

4. A sprint training method based on a motion sensor according to claim 3, characterized in that: The physical fitness comparison module is used to generate an overall data set of the athlete's physical fitness, and the parameter matching module is used to match the physical fitness data set with the data in the standard parameter storage library.

5. A sprint training method based on a motion sensor according to claim 4, characterized in that: The standard parameter repository is used to store the data of excellent athletes as standard parameters, and the parameters are matched with the physical data of excellent athletes. The difference recognition module is used to compare the acceleration, angular velocity, displacement and other data collected in real time when the athletes are running, and find the differences between the data.

6. A sprint training method based on motion sensor according to claim 5, characterized in that: The standard parameters in the standard parameter repository specifically represent the best combination of stride length and stride frequency, and the difference identification module mainly finds out the difference between the athlete's current stride length and stride frequency and the standard parameters through comparative analysis.

7. A sprint training method based on a motion sensor according to claim 1, characterized in that: The training process monitoring unit includes a regular data collection module, a collection frequency setting module and a program optimization module. The regular data collection module is used to regularly collect and analyze the training data of the athletes.

8. A sprint training method based on motion sensors according to claim 7, characterized in that: The acquisition frequency setting module is used to set the regular acquisition frequency and prompt the user based on the acquisition frequency. The program optimization module is used to automatically adjust and optimize the guidance program based on the latest data feedback.

9. A sprint training method based on motion sensors according to claim 7, characterized in that: The specific steps include: S1: Athletes wear sensors: Athletes wear high-precision motion sensors on their feet; S2: Computing device: The collected data of the high-precision motion sensor is transmitted to the computing device for processing and analysis; S3: Guidance Plan: Automatically generate personalized training guidance suggestions based on processing and analysis results; S4: Feedback and optimization: Collect data regularly during training and optimize the training plan.

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