Statistical method and system for user training data of shiatsu plate

Abstract

The invention discloses a statistical method and system for user training data of a shiatsu plate. The method comprises the steps that basic training data in the process that a user conducts treading training through the shiatsu plate is collected; transmitting the basic training data to a mobile terminal through a wireless communication network so as to enable the mobile terminal to carry out statistical analysis on the basic training data, and calculating advanced training data for evaluating a training effect; and visually displaying the basic training data and the advanced training data to a user through an interface of the mobile terminal. By utilizing the embodiment of the invention, the deep requirements and efficient evaluation of the user on the training effect and the health condition can be met through efficient data acquisition and analysis means.

Description

Technical Field

[0001] The invention belongs to the technical field of training equipment, and in particular to a statistical method and system for user training data of acupressure boards. Background Art

[0002] With the improvement of health awareness, acupressure boards, as a new type of fitness equipment, have gradually attracted the attention of users. They help promote blood circulation, relieve stress and enhance foot strength. However, existing technologies are often limited to single motion data monitoring, lacking a comprehensive analysis of the user's physiological reactions and pressing movements during acupressure board training, making it difficult for users to understand their own training effects and optimize their training plans in real time. In addition, many systems cannot achieve real-time visualization of multi-dimensional data, and users lack intuitive feedback during training, which affects their enthusiasm and sustainability in training. Summary of the invention

[0003] The purpose of the present invention is to provide a statistical method and system for user training data of acupressure boards, so as to solve the deficiencies in the prior art and meet the user's in-depth needs and efficient evaluation of training effects and health conditions through efficient data collection and analysis methods.

[0004] An embodiment of the present application provides a statistical method for user training data of a finger pressure board, the method comprising:

[0005] Collecting basic training data of the user during the stepping and pressing training using the acupressure board, wherein the basic training data includes: physiological parameter data and stepping and pressing related data, and the stepping and pressing related data includes: position data, pressure data, and time data during the stepping and pressing training;

[0006] Transmitting the basic training data to a mobile terminal via a wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates advanced training data for evaluating training effects;

[0007] The basic training data and the advanced training data are visualized and displayed to the user through the interface of the mobile terminal.

[0008] Optionally, the advanced training data includes: a physiological stability index for evaluating the stability of body functions, and a training intensity index for evaluating training intensity.

[0009] Optionally, the physiological stability index is calculated as follows:

[0010]

[0011] in:

[0012]

[0013] Among them, the PSI is the physiological stability index, the C_j is the composite value of the physiological parameters at the jth time point, the C_baseline is the composite value of the baseline of the physiological parameters of the user in a resting state, the M is the number of time points used in the calculation, the H_ij is the parameter value of the i-th physiological parameter at the j-th time point, the w_i is the weight coefficient of the i-th physiological parameter, the H_baseline_i is the physiological parameter baseline value of the i-th physiological parameter of the user in a resting state, and the n is the number of physiological parameters, wherein, when j=1, C_{j-1}=0.

[0014] Optionally, the calculation formula of the training intensity index is:

[0015]

[0016] Among them, the TII is the training intensity index, the F_i is the pedaling frequency in the t-th time period, the P_i is the total pedaling pressure in the i-th time period, the T_i is the average duration of each pedaling in the i-th time period, the F_ideal is the ideal pedaling frequency, the P_ideal is the ideal total pedaling pressure, the T_ideal is the ideal average duration, the HR_max is the maximum heart rate of the user during training, the HR_avg is the average heart rate of the user during training, the HR_rest is the resting heart rate of the user, and T is the number of time periods divided during the training process.

[0017] Another embodiment of the present application provides a statistical system for user training data of a finger pressure board, the system comprising:

[0018] The acquisition module is used to acquire basic training data of the user during the stepping and pressing training using the acupressure board, wherein the basic training data includes: physiological parameter data and stepping and pressing related data, and the stepping and pressing related data includes: position data, pressure data, and time data during the stepping and pressing training;

[0019] A statistical module, used to transmit the basic training data to a mobile terminal via a wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates advanced training data for evaluating training effects;

[0020] The display module is used to visually display the basic training data and the advanced training data to the user through the interface of the mobile terminal.

[0021] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0022] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0023] Compared with the prior art, the present invention provides a statistical method for user training data of acupressure boards, which collects basic training data of users during the stepping and pressing training using the acupressure boards; transmits the basic training data to a mobile terminal via a wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates advanced training data for evaluating the training effect; and visualizes the basic training data and the advanced training data to the user through the interface of the mobile terminal, thereby being able to meet the user's deep-level needs and efficient evaluation of training effects and health conditions through efficient data collection and analysis means. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A hardware structure block diagram of a computer terminal for a statistical method of user training data of acupressure pads provided by an embodiment of the present invention;

[0025] Figure 2 A schematic flow chart of a statistical method for user training data of acupressure pads provided by an embodiment of the present invention;

[0026] Figure 3 A schematic diagram of the structure of a statistical system for user training data of an acupressure board provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0028] The embodiment of the present invention firstly provides a statistical method for user training data of acupressure pads. The method can be applied to electronic devices, such as computer terminals, specifically ordinary computers, etc.

[0029] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal for a statistical method of user training data of acupressure board provided by an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0030] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any statistical method for user training data of the acupressure board.

[0031] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0032] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any statistical method for the user training data of the acupressure board.

[0033] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0034] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0035] See also Figure 2 The embodiment of the present invention provides a statistical method for user training data of acupressure boards, which may include the following steps:

[0036] S201, collecting basic training data of a user during a stepping and pressing training using a finger pressure board, wherein the basic training data includes: physiological parameter data and stepping and pressing related data, and the stepping and pressing related data includes: position data, pressure data, and time data during the stepping and pressing training;

[0037] In the present invention, the step of collecting basic training data is crucial when the user uses the acupressure board for stepping training. This process aims to collect the user's physiological parameter data and stepping-related data during training. Specifically, the physiological parameter data may include heart rate, blood pressure, respiratory rate, etc., which can reflect the user's health status and body response. Stepping-related data includes the user's position changes during stepping training, the pressure value applied each time, and the duration of each action. These data not only lay the foundation for subsequent statistical analysis, but also provide multi-dimensional information support for evaluating the training effect. By fully understanding the user's physiological condition and training habits, it can help to better formulate personalized training plans.

[0038] The basic training data collection step of the present invention has important significance and value. First, through the collection of detailed physiological parameters and stepping-related data, a scientific evaluation of the user's training effect can be achieved, thereby optimizing the training plan and improving the user's exercise effect. Secondly, this method can effectively capture the real-time dynamic changes of the user during the training process, helping the user to understand their own physical condition in a timely manner and avoid overtraining or sports injuries. In addition, the multi-dimensional integration of data also lays the foundation for subsequent data analysis and visual display, allowing users to understand and grasp their own health status in a more intuitive way. This real-time feedback mechanism helps to enhance the user's training enthusiasm and sense of participation, thereby prompting them to adhere to a healthy lifestyle.

[0039] In the specific implementation process, the method first needs to configure a user-friendly interface to allow users to connect to the acupressure board through a smartphone or tablet. When the user uses the acupressure board for stepping training, embedded sensors (such as pressure sensors, accelerometers, and heart rate monitors) will collect various data in real time during the training. For example, the position sensor can track the different stepping positions of the user on the acupressure board and record the changes of each step; the pressure sensor monitors the pressure applied by the user on the acupressure board in real time to ensure the accuracy of the data; and the heart rate monitor can record the changes in the user's heart rate during training. The data from these sensors will be integrated and stored in the built-in memory of the acupressure board.

[0040] After the training, the system will transmit these basic training data to the mobile terminal through a wireless communication network (such as Bluetooth or Wi-Fi). At this time, the application on the mobile terminal will analyze and process the received data to extract physiological parameters and pedaling related data. Data processing may include steps such as data cleaning and normalization to ensure data quality. On this basis, the application calculates the user's physiological stability index and training intensity index, and further generates an evaluation report on the user's training effect. These evaluation results will be displayed to the user in a visual form through the graphical interface of the mobile terminal, using charts, curves and other methods to intuitively present, so that users can easily understand their own training status and health indicators. For example, users can see a curve chart reflecting the changes in heart rate and a comparison with the ideal training intensity, thereby providing a basis for subsequent training adjustments. This process helps to form a closed-loop feedback mechanism, where users can understand their own training effects through visualization, and then optimize future training plans and improve the overall exercise effect.

[0041] S202, transmitting the basic training data to a mobile terminal via a wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates advanced training data for evaluating training effects;

[0042] Among them, advanced training data may include but are not limited to: a physiological stability index for evaluating the stability of body functions and a training intensity index for evaluating training intensity.

[0043] In the present invention, the step of transmitting basic training data to the mobile terminal through a wireless communication network is an important step in realizing real-time data analysis and feedback. Specifically, the basic training data includes the user's physiological parameter data and stepping-related data during the acupressure board training process. The transmission of these data is completed through wireless communication technologies such as Bluetooth and Wi-Fi to ensure that users can easily and quickly obtain training analysis results. After receiving the data, the application on the mobile terminal will automatically perform statistical analysis and processing, calculate advanced training data, and help users clearly understand their training effects and physical conditions.

[0044] This data transmission step is of great significance. First, real-time data transmission enables users to get feedback immediately after training, which improves the interactivity and participation of training and promotes users' attention to and understanding of their own health conditions. Secondly, by generating advanced training data through statistical analysis of basic data, users can obtain more scientific training guidance and thus improve training efficiency. This mechanism not only helps users identify their own shortcomings in the training process, but also provides data support for the formulation of personalized training plans, ultimately making the training effect more significant and helping users to continue to improve and develop.

[0045] In the specific implementation process, the system must first ensure that the acupressure board has built-in high-performance sensors and wireless modules. When the user uses the acupressure board, the sensor will collect all basic training data in real time, including physiological parameters, stepping position, pressure data, and time data. After that, the microprocessor built into the acupressure board will perform preliminary processing on this data, and then transmit the data to the user's mobile terminal through a wireless module such as Bluetooth or Wi-Fi.

[0046] When the user completes the training, the connection between the acupressure board and the mobile terminal can be completed through simple pairing or connection steps. Once the connection is successful, the basic training data will be sent to the mobile terminal quickly and stably in the form of data packets. In order to ensure the integrity and security of the data during transmission, the system can use encryption algorithms to protect the data.

[0047] On the mobile terminal, the application will automatically receive the transmitted data and analyze and process it. The application will first clean the data and remove any invalid or abnormal data points. Then, the algorithm will be used to integrate the physiological parameter data and the pedaling-related data to calculate various statistical indicators, such as average pressure, pedaling frequency, etc. After that, the system will calculate advanced training data based on these basic data, such as the physiological stability index (PSI) and the training intensity index (TII). These data will be displayed on the user interface in a visual way such as charts and curves, which is convenient for users to understand intuitively.

[0048] For example, the app may generate a chart showing the user's heart rate curve throughout the training process, and also mark the expected heart rate range for training. Users can easily view their performance at each training stage and compare it with the ideal state in order to make corresponding adjustments. Through this data analysis and feedback mechanism, users can monitor their training effects in real time, develop subsequent training plans, and achieve better fitness results and health management.

[0049] Specifically, a method for calculating the physiological stability index may be:

[0050]

[0051] in:

[0052]

[0053] The formula of the Physiological Stability Index (PSI) is designed to evaluate the physiological adaptability and stability of users during acupressure board training. This formula reflects the physiological load and adaptive response of the user's body during training by quantifying the changes in physiological parameters at different time points. The use of the two parts "1 / (1+|C_j-C_baseline|)" and "1 / (1+|C_j-C_{j-1}|)" can effectively suppress the negative impact of physiological parameter fluctuations on overall stability, ensuring that the index can accurately describe the user's physiological state during training. In addition, this design makes it easier for users and coaches to adjust their training plans in a timely manner when the PSI value is higher, and vice versa.

[0054] The PSI is the physiological stability index, which reflects the stability of the user's physiological state during the entire training process. A higher PSI value means that the user can adapt to the training better and the body function is stable, while a lower PSI value indicates that there may be a risk of excessive fatigue or excessive physiological load.

[0055] The C_j is the composite value of physiological parameters at the jth time point, which is a weighted comprehensive value of the user's physiological parameters (such as heart rate, blood pressure, etc.) at the jth time point. Through the integration of these parameters, C_j can more comprehensively reflect the user's health status at that time point and provide basic data for subsequent physiological stability assessment.

[0056] The C_baseline is the composite value of the user's physiological parameters at rest, which is the weighted sum of the user's physiological parameters when not training, and is used as a reference standard. It provides a physical state that is not affected by training and can be used to judge the impact of training on the user's physiological stability.

[0057] M is the number of time points used in the calculation, H_ij is the parameter value of the i-th physiological parameter at the j-th time point, w_i is the weight coefficient of the i-th physiological parameter, H_baseline_i is the physiological parameter baseline value of the i-th physiological parameter of the user in a resting state, n is the number of physiological parameters, wherein when j=1, C_{j-1}=0.

[0058] Specifically, a calculation formula for the training intensity index may be:

[0059]

[0060] The training intensity index (TII) formula is designed to comprehensively evaluate the intensity and effect of users' training on the acupressure board. By combining data from multiple dimensions such as pressing frequency, total pressure, duration, and heart rate, the formula can dynamically reflect the degree of training load and its adaptability relative to the user's physiological state. This design allows users to deeply understand their training effects and adjust their training strategies in a timely manner, thereby improving the scientificity and safety of training.

[0061] Among them, the TII is the training intensity index, which is calculated through a series of comprehensive factors and reflects the overall intensity and effect of the user during the training process. A higher TII value indicates that the training intensity is moderate and is conducive to physical adaptation, while a lower TII value may mean that the training intensity is insufficient or there is a risk of excessive fatigue. The F_i is the pedaling frequency in the t-th time period, that is, the number of pedaling times per unit time. This parameter directly affects the dynamics and stimulation intensity of the training. A high frequency means more action repetitions, which helps to improve the body's endurance and adaptability.

[0062] The P_i is the total pressure of the pedaling in the i-th time period, usually measured as the total pressure per unit area. The magnitude of the pressure reflects the load of the training. Appropriate pressure helps promote blood circulation and enhance muscle strength, while excessive pressure may cause physical discomfort or injury. The T_i is the average duration of each pedaling in the i-th time period, indicating the average duration of each pedaling action of the user in the t-th time period. The duration directly affects the degree of muscle fatigue and physiological adaptability. A moderate duration helps to increase the effectiveness of training, while too long or too short a duration may affect the training effect.

[0063] The F_ideal is the ideal pedaling frequency, which is set based on sports science and the user's physical condition. This value is used as a standard to help users judge whether their pedaling frequency is reasonable, thereby optimizing the training effect. The P_ideal is the ideal total pedaling pressure, which is set according to the physiological characteristics and training goals of different users. By comparing with the actual pressure data, the user can adjust the training intensity to achieve the best training effect. The T_ideal is the ideal average duration, which is the ideal pedaling duration determined by scientific research and practical experience, and aims to maximize muscle adaptability and training effects. Users can adjust the duration of each pedaling according to this standard.

[0064] The HR_max is the maximum heart rate of the user during training, which is the maximum heart rate measured by the user during training and is usually used to assess the load on the cardiovascular system. Knowing one's maximum heart rate helps users control the intensity of training and avoid overloading the heart. The HR_avg is the average heart rate of the user during training, reflecting the overall intensity and physiological load of the training. A higher average heart rate usually indicates a higher training intensity, while a too low average heart rate may mean insufficient training intensity. The HR_rest is the user's resting heart rate, which is the heart rate of the user at rest, and serves as a basic physiological indicator. Understanding the resting heart rate helps assess the user's cardiovascular health and provides a reference benchmark for other heart rate indicators (such as HR_max and HR_avg). The T is the number of time periods divided during the training process.

[0065] S203: Visually display the basic training data and the advanced training data to the user through the interface of the mobile terminal.

[0066] The key to this method is to visualize the basic training data of users using the acupressure pad and the calculated advanced training data through the interface of the mobile terminal. This process first collects basic data from the user's pressing training, including physiological parameters and specific data related to pressing, such as position, pressure and time information. Through the wireless network, these data will be transmitted to the mobile terminal in real time, and then the terminal will use the built-in algorithm for statistical analysis to calculate advanced training data such as physiological stability index and training intensity index. These data will be presented in an intuitive form through a user-friendly graphical interface, such as using a bar chart to show the changes in the training intensity index in different time periods, or using a line chart to show the trend of the physiological stability index, so that users can quickly understand the effect of their own training and physical condition, and make appropriate adjustments.

[0067] Through this visual display, users can more intuitively understand and perceive their training effects and physical condition. Real-time feedback of basic training data helps users identify problems in the training process, while advanced training data provides users with effective tools to evaluate physical function and training intensity. This feedback mechanism not only enhances the user's sense of participation, but also motivates them to set more scientific and personalized goals. In addition, through intuitive data presentation, users can effectively monitor their own training progress, adjust training plans in time to achieve the best results, and reduce the risk of injury caused by improper training.

[0068] In order to visualize the basic training data and advanced training data through the mobile terminal, first, the mobile terminal will receive the real-time collected data from the acupressure board. The data includes the user's physiological parameters (such as heart rate, blood pressure, etc.) and stepping-related data (such as stepping time, position and pressure). Once the data is transmitted to the mobile terminal, the built-in data processing software will analyze and organize the data. To ensure that users obtain intuitive and detailed information, the system will integrate basic data with calculated advanced data (such as physiological stability index and training intensity index) on the same interface.

[0069] In the display interface, users will first see a dashboard that displays different data dimensions in various forms such as charts, pie charts, and line charts. For example, a line chart can clearly show the changes in the user's physiological stability index during a training session, marking key time points and data fluctuations. Pie charts can be used to show the time proportions of different training phases, helping users understand the training intensity of each phase. Next to these charts, the system will also list the values ​​of the training intensity index so that users can quickly obtain an overall evaluation of the training effect.

[0070] In order to enable users to intuitively compare the training effects of different time periods, the system can also provide a historical data comparison function. Users can choose to view the physiological stability index and training intensity index of previous training sessions to understand their own training progress. For example, after completing a week of training, users can select "Historical Training Comparison" through the interface to view the changing trends of the physiological stability index and training intensity index between last week and this week. This way, users can not only clearly see their own progress, but also make further adjustments based on the data.

[0071] In addition, the interface design of mobile terminals should also take into account user experience. For example, users can interact through the touch screen, select different charts to view detailed data, or click on a specific time point to obtain the specific physiological parameter value at that moment. This interactive design is intended to increase users' attention to and understanding of data, so that users can more specifically adjust their plans and goals for the next training while viewing the training effects. Ultimately, this visual display will effectively promote users to continuously optimize their practice methods during training and improve the scientificity and effectiveness of training.

[0072] It can be seen that the basic training data of the user in the process of stepping on the acupressure board for training is collected; the basic training data is transmitted to the mobile terminal through the wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates the advanced training data for evaluating the training effect; the basic training data and the advanced training data are visualized to the user through the interface of the mobile terminal, so that the user's deep-level needs and efficient evaluation of training effects and health conditions can be met through efficient data collection and analysis methods.

[0073] Another embodiment of the present invention provides a statistical system for user training data of acupressure boards, see Figure 3 , the system may include:

[0074] The collection module 301 is used to collect basic training data of the user during the stepping and pressing training using the acupressure board, wherein the basic training data includes: physiological parameter data and stepping and pressing related data, and the stepping and pressing related data includes: position data, pressure data, and time data during the stepping and pressing training;

[0075] A statistical module 302, configured to transmit the basic training data to a mobile terminal via a wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates advanced training data for evaluating training effects;

[0076] The display module 303 is used to visually display the basic training data and the advanced training data to the user through the interface of the mobile terminal.

[0077] It can be seen that the basic training data of the user in the process of stepping on the acupressure board for training is collected; the basic training data is transmitted to the mobile terminal through the wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates the advanced training data for evaluating the training effect; the basic training data and the advanced training data are visualized to the user through the interface of the mobile terminal, so that the user's deep-level needs and efficient evaluation of training effects and health conditions can be met through efficient data collection and analysis methods.

[0078] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0079] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

[0080] S201, collecting basic training data of a user during a stepping and pressing training using a finger pressure board, wherein the basic training data includes: physiological parameter data and stepping and pressing related data, and the stepping and pressing related data includes: position data, pressure data, and time data during the stepping and pressing training;

[0081] S202, transmitting the basic training data to a mobile terminal via a wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates advanced training data for evaluating training effects;

[0082] S203: Visually display the basic training data and the advanced training data to the user through the interface of the mobile terminal.

[0083] It can be seen that the basic training data of the user in the process of stepping on the acupressure board for training is collected; the basic training data is transmitted to the mobile terminal through the wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates the advanced training data for evaluating the training effect; the basic training data and the advanced training data are visualized to the user through the interface of the mobile terminal, so that the user's deep-level needs and efficient evaluation of training effects and health conditions can be met through efficient data collection and analysis methods.

[0084] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0085] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0086] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0087] S201, collecting basic training data of a user during a stepping and pressing training using a finger pressure board, wherein the basic training data includes: physiological parameter data and stepping and pressing related data, and the stepping and pressing related data includes: position data, pressure data, and time data during the stepping and pressing training;

[0088] S202, transmitting the basic training data to a mobile terminal via a wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates advanced training data for evaluating training effects;

[0089] S203: Visually display the basic training data and the advanced training data to the user through the interface of the mobile terminal.

[0090] It can be seen that the basic training data of the user in the process of stepping on the acupressure board for training is collected; the basic training data is transmitted to the mobile terminal through the wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates the advanced training data for evaluating the training effect; the basic training data and the advanced training data are visualized to the user through the interface of the mobile terminal, so that the user's deep-level needs and efficient evaluation of training effects and health conditions can be met through efficient data collection and analysis methods.

[0091] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A statistical method for user training data of acupressure board, characterized in that: The method comprises: Collecting basic training data of the user during the stepping and pressing training using the acupressure board, wherein the basic training data includes: physiological parameter data and stepping and pressing related data, and the stepping and pressing related data includes: position data, pressure data, and time data during the stepping and pressing training; Transmitting the basic training data to a mobile terminal via a wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates advanced training data for evaluating training effects; The basic training data and the advanced training data are visualized and displayed to the user through the interface of the mobile terminal.

2. The method according to claim 1, characterized in that The advanced training data includes: a physiological stability index for evaluating the stability of body functions, and a training intensity index for evaluating training intensity.

3. The method according to claim 2, characterized in that The physiological stability index is calculated as follows: in: Among them, the PSI is the physiological stability index, the C_j is the composite value of the physiological parameters at the jth time point, the C_baseline is the composite value of the baseline of the physiological parameters of the user in a resting state, the M is the number of time points used in the calculation, the H_ij is the parameter value of the i-th physiological parameter at the j-th time point, the w_i is the weight coefficient of the i-th physiological parameter, the H_baseline_i is the physiological parameter baseline value of the i-th physiological parameter of the user in a resting state, and the n is the number of physiological parameters, wherein, when j=1, C_{j-1}=0.

4. The method according to claim 3, characterized in that The calculation formula of the training intensity index is: Among them, the TII is the training intensity index, the F_i is the pedaling frequency in the t-th time period, the P_i is the total pedaling pressure in the i-th time period, the T_i is the average duration of each pedaling in the i-th time period, the F_ideal is the ideal pedaling frequency, the P_ideal is the ideal total pedaling pressure, the T_ideal is the ideal average duration, the HR_max is the maximum heart rate of the user during training, the HR_avg is the average heart rate of the user during training, the HR_rest is the resting heart rate of the user, and T is the number of time periods divided during the training process.

5. A statistical system for user training data of acupressure boards, characterized in that: The system comprises: A collection module is used to collect basic training data of the user during the stepping and pressing training using the acupressure board, wherein the basic training data includes: physiological parameter data and stepping and pressing related data, and the stepping and pressing related data includes: position data, pressure data, and time data during the stepping and pressing training; A statistical module, used to transmit the basic training data to a mobile terminal via a wireless communication network, so that the mobile terminal performs statistical analysis on the basic training data and calculates advanced training data for evaluating training effects; The display module is used to visually display the basic training data and the advanced training data to the user through the interface of the mobile terminal.

6. The system according to claim 5, characterized in that The advanced training data includes: a physiological stability index for evaluating the stability of body functions, and a training intensity index for evaluating training intensity.

7. The system according to claim 6, characterized in that The physiological stability index is calculated as follows: in: Among them, the PSI is the physiological stability index, the C_j is the composite value of the physiological parameters at the jth time point, the C_baseline is the composite value of the baseline of the physiological parameters of the user in a resting state, the M is the number of time points used in the calculation, the H_ij is the parameter value of the i-th physiological parameter at the j-th time point, the w_i is the weight coefficient of the i-th physiological parameter, the H_baseline_i is the physiological parameter baseline value of the i-th physiological parameter of the user in a resting state, and the n is the number of physiological parameters, wherein, when j=1, C_{j-1}=0.

8. The system according to claim 7, characterized in that The calculation formula of the training intensity index is: Among them, the TII is the training intensity index, the F_i is the pedaling frequency in the t-th time period, the P_i is the total pedaling pressure in the i-th time period, the T_i is the average duration of each pedaling in the i-th time period, the F_ideal is the ideal pedaling frequency, the P_ideal is the ideal total pedaling pressure, the T_ideal is the ideal average duration, the HR_max is the maximum heart rate of the user during training, the HR_avg is the average heart rate of the user during training, the HR_rest is the resting heart rate of the user, and T is the number of time periods divided during the training process.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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