Healthy exercise step counting system based on fusion sensor data and step counting method thereof

Abstract

The invention relates to a health exercise step counting method and system based on fusion sensor data, and the system comprises a starting and login module which is used for loading a starting page and carrying out the login of a user; the information setting and synchronizing module is used for setting basic information of the user and synchronizing motion data in real time; the exercise step counting module is used for recording the walking step number of the user in real time and uploading the walking step number to the server; the activity management module is used for managing adding, modifying and lottery drawing operations of sports activities; and the ranking and interaction module is used for collecting the step number data of the user and carrying out ranking display. The method has the advantages that multi-sensor data fusion is achieved, the system can more accurately capture the motion state of a user by integrating data of a plurality of sensors (such as an accelerometer and a gyroscope), and the step counting accuracy and reliability are improved.

Description

Technical Field

[0001] The invention relates to a healthy exercise pedometer system and a pedometer method based on fused sensor data, and belongs to the field of pedometer. Background Art

[0002] As people's living standards improve and their health awareness increases, more and more people begin to pay attention to their physical condition and improve their health through exercise. In order to record and analyze personal exercise data, healthy pedometer exercise systems have emerged. However, most existing pedometer exercise systems have single functions, lack of fun and interactivity, and are difficult to meet the diverse needs of users. The disadvantages of existing healthy pedometer exercise systems mainly include the following points:

[0003] Single function and lack of comprehensive solutions: Many existing health pedometer systems only focus on recording the number of steps, but ignore the comprehensiveness of the user's health status. These systems often only provide a single pedometer function and do not integrate other health-related data and services, such as heart rate monitoring, sleep quality analysis, etc.

[0004] Lack of fun and interactivity: Existing systems often lack interesting and interactive features to attract users to continue to participate, which may cause users to lose interest after using it for a period of time, thus affecting long-term health monitoring and the formation of exercise habits.

[0005] Data accuracy issues: Some pedometers, especially software-based systems, may have inaccurate step counts due to algorithm limitations or sensor inaccuracies. For example, simply shaking the device can affect the step count, which shows that existing technology has shortcomings in data accuracy.

[0006] Data silo problem: Existing health pedometer systems often fail to integrate and share data, causing users’ health data to be scattered across different devices and applications, making it impossible to form a comprehensive health management view.

[0007] User privacy and data security issues: As health data is collected and analyzed, user privacy and data security become an important issue. Existing systems may not have adequate measures to protect users' personal health information from being leaked or misused.

[0008] Hardware dependency: Some pedometer systems rely on specific hardware devices, such as smart bracelets or mobile phones, which limits the user's usage scenarios and convenience. For those users who do not have these devices, they may not be able to enjoy the convenience brought by the pedometer system.

[0009] Lack of personalized services: Existing pedometer systems often lack personalized services and are unable to provide customized exercise recommendations and health guidance based on the user's specific circumstances (such as age, gender, health status, etc.). Summary of the invention

[0010] In order to overcome the defects of the prior art, the present invention provides a healthy exercise pedometer system based on fusion sensor data. The technical solution of the present invention is:

[0011] A healthy exercise step counting system based on fusion sensor data, comprising:

[0012] The startup and login module is used to load the startup page and log in the user;

[0013] Information setting and synchronization module, used to set the user's basic information and synchronize sports data in real time;

[0014] The sports pedometer module is used to record the user's walking steps in real time and upload them to the server; the activity management module is used to manage the addition, modification and lottery operations of sports activities;

[0015] The ranking and interaction module is used to collect user step data and display the ranking.

[0016] The startup and login module includes:

[0017] Start the page loading module, which is used to display the system name and LOGO when the user clicks the system icon;

[0018] The login interface display module is used to require users to enter their username and password to log in; the password setting module is used to set passwords for users logging in for the first time.

[0019] The information setting and synchronization module includes:

[0020] The user information setting module is used to guide the user to enter the user name and password to log in; the basic information setting module is used for the user to set the weight and exercise slogan;

[0021] The data synchronization module is used to synchronize the user's motion data to the server in real time.

[0022] The motion step counting module comprises:

[0023] The data acquisition module is used to collect the acceleration and angular velocity data of the user through the sensor; the step calculation module is used to calculate the number of steps of the user based on the step counting algorithm of the acceleration sensor; the data upload and storage module is used to upload the calculated number of steps to the server for storage; the specific counting steps of the step calculation module are as follows:

[0024] 1.1 Collect user's motion data in real time through acceleration sensor, including acceleration value and timestamp;

[0025] 1.2 Preprocess the collected acceleration data to remove noise and outliers;

[0026] 1.3 Adopt dynamic threshold algorithm to adjust the threshold in real time according to the user's motion state. When the acceleration value exceeds the set threshold, it is considered that the user has taken a step;

[0027] 1.4 Count the detected steps to get the number of steps of the user;

[0028] The detection formula is as follows: Assume that the acceleration value at time t is a(t) and the threshold is Th, then the condition for step detection can be expressed as: if a(t)>Th, then record a step.

[0029] The specific collection steps of the data collection module are as follows:

[0030] 2.1 Collect motion data from multiple sensors in real time, including acceleration and angular velocity;

[0031] 2.2 Perform time synchronization and coordinate conversion on the collected data to ensure data consistency; 2.3 Use weighted average or Kalman filter algorithm to fuse the data of several sensors to obtain motion state information; When the Kalman filter algorithm is used for fusion, the specific steps are as follows:

[0032] Let x be the state vector, P be the covariance matrix, F be the state transfer matrix, H be the observation matrix, z be the observation vector, R be the observation noise covariance matrix, Q be the process noise covariance matrix, P′ denotes the covariance matrix of the prediction stage, and F′ is the transpose of the state transfer matrix F; then the update formula of Kalman filter is:

[0033] Prediction stage: x'=F*x, P'=F*P*F'+Q;

[0034] Update phase: K = P'*H'*(H*P'*H'+R)^(-1); x = x'+K*(zH*x'),

[0035] P = (1-K*H)*P'.

[0036] When identifying motion status information, specifically:

[0037] 3.1 Use support vector machine or neural network machine learning algorithm to train and learn the user's sports data to obtain a sports type classification model;

[0038] 3.2 Collect the user's exercise data in real time and input it into the exercise type classification model to obtain the user's exercise type;

[0039] 3.3 Adjust the pedometer algorithm and sensor fusion strategy according to the type of exercise; such as increasing or decreasing the weight of the sensor, adjusting the threshold, etc.

[0040] When using a support vector machine, let x be the input feature vector, y be the output label (sports type), w be the weight vector, and b be the bias term. Then the decision function of the support vector machine is: f(x) = sign(w*x+b);

[0041] Among them, w and b are obtained through the training process and are used to map the input feature vector x to the output label y.

[0042] A step counting method of a healthy exercise step counting system based on fusion sensor data, comprising the following steps:

[0043] (1) Startup and login:

[0044] When the user clicks the system icon, the system loads the startup page and displays the system name and LOGO;

[0045] The user is directed to the login interface and enters the user name and password to log in;

[0046] For users logging in for the first time, the system provides a password setting module to set a password;

[0047] (2) Information setting and synchronization:

[0048] The user information setting module guides the user to enter the user name and password to log in;

[0049] The basic information setting module allows users to set weight and exercise slogans;

[0050] The data synchronization module synchronizes the user's sports data to the server in real time;

[0051] (3) Step counting:

[0052] The data acquisition module collects the user's acceleration and angular velocity data from multiple sensors in real time;

[0053] After data collection, time synchronization and coordinate conversion are performed to ensure data consistency;

[0054] Use weighted average or Kalman filter algorithm to fuse sensor data and obtain motion state information;

[0055] The step counting module calculates the user's steps through the step counting algorithm of the acceleration sensor;

[0056] Collect user motion data in real time, including acceleration values ​​and timestamps;

[0057] Preprocess the collected acceleration data to remove noise and outliers;

[0058] A dynamic threshold algorithm is used to adjust the threshold in real time. When the acceleration value exceeds the set threshold, it is considered that the user has taken a step.

[0059] Count the detected steps to get the user's step count;

[0060] The data upload and storage module uploads the calculated number of steps to the server for storage;

[0061] (4) Motion status information recognition:

[0062] Use support vector machine or neural network machine learning algorithm to train and learn the user's sports data to obtain a sports type classification model;

[0063] Collect the user's exercise data in real time and input it into the exercise type classification model to obtain the user's exercise type;

[0064] Adjust the pedometer algorithm and sensor fusion strategy according to the type of exercise; such as increasing or decreasing the weight of the sensor, adjusting the threshold, etc.

[0065] (5) Event management: manage the addition, modification and lottery operations of sports events;

[0066] (6) Ranking and interaction: Collect user step data and display rankings to promote interaction and competition among users.

[0067] The advantages of the present invention are:

[0068] Multi-sensor data fusion: By integrating data from multiple sensors (such as accelerometers and gyroscopes), the system can more accurately capture the user's motion status and improve the accuracy and reliability of step counting.

[0069] Dynamic threshold algorithm: The dynamic threshold algorithm can adjust the threshold in real time according to the user's different motion states, making the step counting more in line with individual differences and improving the adaptability and accuracy of the step counting. Kalman filter technology: Using the Kalman filter algorithm to fuse sensor data can effectively reduce noise and errors and improve the stability and accuracy of the data.

[0070] Application of machine learning algorithms: By training and learning user motion data through machine learning algorithms such as support vector machines (SVM) or neural networks, the system can identify different types of motion and adjust the pedometer algorithm and sensor fusion strategy accordingly, making the system more intelligent and personalized.

[0071] User interaction and competition mechanism: The ranking and interaction module can stimulate interaction and competition among users, increase the fun of the sport, and improve user participation and stickiness.

[0072] Comprehensive user information management: Not only does it record the number of steps, it also allows users to set basic information such as weight and exercise slogans, making health management more comprehensive and personalized.

[0073] Real-time data synchronization: The data synchronization module can synchronize the user's motion data to the server in real time to ensure timely update and backup of data.

[0074] Activity management function: The activity management module supports the addition, modification and lottery operations of sports activities, increasing the functionality of the system and user participation. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a structural block diagram of the startup and login module of the present invention.

[0076] Figure 2 It is a structural block diagram of the information setting and synchronization module of the present invention.

[0077] Figure 3 It is a structural block diagram of the motion pedometer module of the present invention. DETAILED DESCRIPTION

[0078] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description proceeds. However, these embodiments are exemplary only and do not constitute any limitation to the scope of the present invention. It should be understood by those skilled in the art that the details and forms of the technical solution of the present invention may be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the scope of protection of the present invention.

[0079] See also Figures 1 to 3 The present invention relates to a healthy exercise pedometer system integrating sensor data, comprising:

[0080] Startup and login module 1, used to load the startup page and log in the user;

[0081] Information setting and synchronization module 2, used to set the user's basic information and synchronize sports data in real time;

[0082] The sports step counting module 3 is used to record the user's walking steps in real time and upload them to the server; the activity management module 4 is used to manage the addition, modification and lottery operations of sports activities;

[0083] The ranking and interaction module 5 is used to collect user step data and display the ranking.

[0084] Based on the configuration of the above modules, the present invention achieves the following advantages:

[0085] Modular design: Modular design is adopted, and the functions of each module are clear and independent of each other, which is easy to maintain and upgrade.

[0086] User-friendly startup and login process: Through the startup and login module 1, users can quickly load the startup page and log in, which improves the user experience.

[0087] Personalized information setting: Information setting and synchronization module 2 allows users to set basic personal information, such as weight and exercise slogans, making the system more personalized and in line with user needs. Real-time data synchronization: It can synchronize the user's exercise data to the server in real time to ensure timely update and accuracy of the data.

[0088] Accurate pedometer: The pedometer module 3 can record the user's walking steps in real time and upload them to the server to provide the user with accurate exercise data.

[0089] Activity management to enhance user participation: Activity management module 4 supports the management of sports activities, including adding, modifying and drawing prizes, which increases user participation and system interactivity. Social interaction and competition incentives: Ranking and interaction module 5 collects user step data and displays rankings to promote social interaction and competition among users, motivating users to participate in sports more actively.

[0090] The startup and login module 1 includes:

[0091] The startup page loading module 11 is used to display the system name and LOGO when the user clicks the system icon;

[0092] The login interface display module 12 is used to require the user to enter a user name and password to log in; the password setting module 13 is used for the user who logs in for the first time to set a password.

[0093] The structural design of the startup and login module 1 has the following advantages:

[0094] Intuitive user guidance: The startup page loading module 11 provides users with intuitive system identification and enhances brand impression by displaying the system name and LOGO.

[0095] Simplified login process: The login interface display module 12 requires the user to enter a user name and password. The process is simple and clear, and the user can operate it easily.

[0096] Security enhancement: The password setting module 13 sets a password for the user logging in for the first time, ensuring the security of the account and preventing unauthorized access.

[0097] The information setting and synchronization module 2 includes:

[0098] The user information setting module 21 is used to guide the user to enter the user name and password to log in; the basic information setting module 22 is used for the user to set the weight and exercise slogan;

[0099] The data synchronization module 23 is used to synchronize the user's sports data to the server in real time.

[0100] The structural design of the information setting and synchronization module 2 has the following advantages:

[0101] Personalized settings: The user information setting module 21 allows the user to input personalized information, such as user name and password, which enhances the user's personalized experience.

[0102] Health data customization: The basic information setting module 22 allows users to set weight and exercise slogans, making health data more tailored to personal circumstances and helping to develop more effective health plans. Data real-time: The data synchronization module 23 ensures that the user's exercise data can be synchronized to the server in real time, ensuring the timeliness and accuracy of the data.

[0103] Improve data accuracy: The real-time synchronization function reduces data delays and losses during transmission and improves data accuracy.

[0104] The motion step counting module 3 comprises:

[0105] The data acquisition module 31 is used to collect the acceleration and angular velocity data of the user through the sensor; the step counting module 32 is used to calculate the number of steps of the user based on the step counting algorithm of the acceleration sensor;

[0106] The data uploading and storage module 33 is used to upload the calculated number of steps to the server for storage;

[0107] The structural design of the motion pedometer module 3 has the following advantages:

[0108] Accurate data collection: The data collection module 31 collects the user's acceleration and angular velocity data through sensors, which can provide accurate motion information and provide accurate input data for subsequent step count calculation.

[0109] Scientific step counting: The step counting module 32 calculates the step count based on the step counting algorithm of the acceleration sensor. This method is usually more scientific and accurate than simple step counting and can better reflect the actual exercise situation of the user.

[0110] Persistent storage of data: The data upload and storage module 33 uploads the calculated number of steps to the server for storage, ensuring the persistent storage of the data. Users can access their exercise history data at any time.

[0111] The specific counting steps of the step counting module 32 are as follows:

[0112] 1.1 Collect user's motion data in real time through acceleration sensor, including acceleration value and timestamp;

[0113] 1.2 Preprocess the collected acceleration data to remove noise and outliers;

[0114] 1.3 Adopt dynamic threshold algorithm to adjust the threshold in real time according to the user's motion state. When the acceleration value exceeds the set threshold, it is considered that the user has taken a step;

[0115] 1.4 Count the detected steps to get the number of steps of the user;

[0116] The detection formula is as follows: Assume that the acceleration value at time t is a(t) and the threshold is Th, then the condition for step detection can be expressed as: if a(t)>Th, then record a step.

[0117] The specific collection steps of the data collection module are as follows:

[0118] 2.1 Collect motion data from multiple sensors in real time, including acceleration and angular velocity;

[0119] 2.2 Perform time synchronization and coordinate conversion on the collected data to ensure data consistency; 2.3 Use weighted average or Kalman filter algorithm to fuse the data of several sensors to obtain motion state information; When the Kalman filter algorithm is used for fusion, the specific steps are as follows:

[0120] Let x be the state vector, P be the covariance matrix, F be the state transfer matrix, H be the observation matrix, z be the observation vector, R be the observation noise covariance matrix, Q be the process noise covariance matrix, P′ denotes the covariance matrix of the prediction stage, and F′ is the transpose of the state transfer matrix F; then the update formula of Kalman filter is:

[0121] Prediction stage: x'=F*x, P'=F*P*F'+Q;

[0122] Update phase: K = P'*H'*(H*P'*H'+R)^(-1); x = x'+K*(zH*x'),

[0123] P = (1-K*H)*P'.

[0124] When identifying motion status information, specifically:

[0125] 3.1 Use support vector machine or neural network machine learning algorithm to train and learn the user's sports data to obtain a sports type classification model;

[0126] 3.2 Collect the user's exercise data in real time and input it into the exercise type classification model to obtain the user's exercise type;

[0127] 3.3 Adjust the pedometer algorithm and sensor fusion strategy according to the type of exercise; such as increasing or decreasing the weight of the sensor, adjusting the threshold, etc.

[0128] When using a support vector machine, let x be the input feature vector, y be the output label (sports type), w be the weight vector, and b be the bias term. Then the decision function of the support vector machine is: f(x) = sign(w*x+b);

[0129] Among them, w and b are obtained through the training process and are used to map the input feature vector x to the output label y.

[0130] The present invention also relates to a step counting method of a healthy exercise step counting system based on fusion sensor data, comprising the following steps:

[0131] (1) Startup and login:

[0132] When the user clicks the system icon, the system loads the startup page and displays the system name and LOGO;

[0133] The user is directed to the login interface and enters the user name and password to log in;

[0134] For users logging in for the first time, the system provides a password setting module to set a password;

[0135] (2) Information setting and synchronization:

[0136] The user information setting module guides the user to enter the user name and password to log in;

[0137] The basic information setting module allows users to set weight and exercise slogans;

[0138] The data synchronization module synchronizes the user's sports data to the server in real time;

[0139] (3) Step counting:

[0140] The data acquisition module collects the user's acceleration and angular velocity data from multiple sensors in real time;

[0141] After data collection, time synchronization and coordinate conversion are performed to ensure data consistency;

[0142] Use weighted average or Kalman filter algorithm to fuse sensor data and obtain motion state information;

[0143] The step counting module calculates the user's steps through the step counting algorithm of the acceleration sensor;

[0144] Collect user motion data in real time, including acceleration values ​​and timestamps;

[0145] Preprocess the collected acceleration data to remove noise and outliers;

[0146] A dynamic threshold algorithm is used to adjust the threshold in real time. When the acceleration value exceeds the set threshold, it is considered that the user has taken a step.

[0147] Count the detected steps to get the user's step count;

[0148] The data upload and storage module uploads the calculated number of steps to the server for storage;

[0149] (4) Motion status information recognition:

[0150] Use support vector machine or neural network machine learning algorithm to train and learn the user's sports data to obtain a sports type classification model;

[0151] Collect the user's exercise data in real time and input it into the exercise type classification model to obtain the user's exercise type;

[0152] Adjust the pedometer algorithm and sensor fusion strategy according to the type of exercise; such as increasing or decreasing the weight of the sensor, adjusting the threshold, etc.

[0153] (5) Event management: manage the addition, modification and lottery operations of sports events;

[0154] (6) Ranking and interaction: Collect user step data and display rankings to promote interaction and competition among users.

[0155] The specific process of the present invention is as follows:

[0156] Chapter 1: Startup and Login Module

[0157] 1 Start the process

[0158] Startup page loading: When the user clicks the system icon, the startup page will be loaded first. This page displays the system name, LOGO or other background patterns to increase brand recognition and user experience. In the background, the system will initialize related business processing to ensure that it can quickly respond to user operations when logging in.

[0159] Initialization completed: When the background initialization processing (such as network connection, database check, etc.) is completed, the startup page will automatically close and the system will enter the login interface.

[0160] The system is restarted from background operation: If the user has switched the system to background operation and restarts the system through a shortcut or icon, in order to save user time, the system will directly skip the startup page and enter the login interface.

[0161] 2. Login Process

[0162] Login interface display: The login interface requires the user to enter a username and password. The username is the employee ID or other unique identifier recognized by the system. The password box provides a password input function and provides security protection for the password, such as fuzzy display and password input security prompts.

[0163] Password setting: For users logging in for the first time, the system will prompt them to set a password. The initial password will not be synchronized with the password of any other system (such as the human resources system) to ensure security. The password length must be set to 6-12 characters and support a combination of letters (not case-sensitive) and numbers. To improve security, the system may recommend that users change their passwords regularly.

[0164] Login verification: After the user enters the username and password, the system will verify them. If the verification is successful, the user will enter the system main interface; if the verification fails, the system will display an error message and allow the user to re-enter. In order to improve the user experience, the system may issue a security warning or limit the number of login attempts for users who have failed to verify multiple times.

[0165] 3 Declaration and weight initialization settings

[0166] First login prompt: When the user successfully logs in for the first time, the system will prompt him to perform initial settings, including weight and exercise declaration. These settings help the system to more accurately record and analyze the user's exercise data.

[0167] Weight setting: The system will provide an input box for users to enter their current weight information. In order to improve the accuracy of the data, the system may verify the input weight, such as limiting the number of decimal places (XXX.XX). After the verification is passed, the user's weight information will be saved to the server for subsequent use.

[0168] Movement Declaration Settings: The movement declaration is a brief description of the user's movement goals and commitments. The system will provide a text input box for the user to enter the declaration content. In order to increase interactivity and fun, the system may impose certain restrictions or suggestions on the declaration content, such as word limit, specific theme, etc. After the user submits the declaration, the system will save it.

[0169] Save settings: After completing the settings of weight and exercise declaration, the user clicks the Save button and the system will save this information to the server. Later, when the user logs in again, this information will be displayed in the relevant position of the system as the user's personalized information.

[0170] Chapter 2: Information Setting and Synchronization Module

[0171] Information settings module

[0172] (1) User login

[0173] When a user opens the app for the first time, the system attempts to obtain user information from the client.

[0174] If there is no user information, the user is guided to the login page to enter the user name and password to log in.

[0175] It is recommended that the username use a unique identifier, such as a mobile phone number or email address, to ensure the uniqueness of the user's identity.

[0176] Password settings must meet security requirements, including length limits (such as 6-12 characters), character types (support letters and numbers, not case sensitive), etc.

[0177] During the login process, password transmission must be in ciphertext to ensure user information security.

[0178] (2) Basic information settings

[0179] After successfully logging in, users need to set basic information, including weight and exercise slogan.

[0180] Weight data is used for subsequent exercise effect analysis and personalized recommendations.

[0181] Sports slogans can enhance the user's motivation for exercise and serve as part of the user's personal information. This information will be saved to the server for subsequent use.

[0182] Data synchronization module

[0183] (1) Real-time synchronization

[0184] After the application is started, the background will automatically record the user's exercise data, including number of steps, running distance, etc.

[0185] These data will be synchronized to the server in real time, ensuring that users can view and analyze their exercise status at any time.

[0186] (2) Secure synchronization

[0187] During the data synchronization process, encrypted transmission is used to ensure the security of data during transmission.

[0188] The server will verify and store the received data to ensure the accuracy and completeness of the data.

[0189] (3) Exception handling

[0190] If an exception occurs during the synchronization process (such as network failure, server failure, etc.), the system will record the error information and try to resynchronize.

[0191] If synchronization fails after multiple attempts, the system will prompt the user to check the network connection or try again later.

[0192] Chapter 3: Sports Pedometer Module

[0193] 1Module Overview

[0194] The motion pedometer module is one of the core modules of the system. It is responsible for recording the user's walking steps in real time and uploading the data to the server for storage and analysis. The module uses advanced sensor technology and algorithms to accurately and quickly identify the user's walking movements and calculate the number of steps.

[0195] 2. Workflow

[0196] The workflow of the motion pedometer module mainly includes the following steps:

[0197] 2.1 Initialization settings

[0198] When a user uses the system for the first time, initialization settings are required. In this step, the system will prompt the user to enter some basic information, such as height, weight, etc., so as to conduct more accurate motion data analysis later. At the same time, the system will also activate the sensor and start real-time monitoring of the user's walking movements.

[0199] 2.2 Data Collection

[0200] The sensor will continuously collect the user's walking data, including acceleration, angular velocity, etc. These data will be transmitted to the motion pedometer module for processing.

[0201] 2.3 Step Calculation

[0202] The step counting module is one of the core functions of this system. In order to accurately count the number of steps of the user, we use a step counting algorithm based on an acceleration sensor. This algorithm determines the user's pace by monitoring the acceleration changes generated by the user during walking or running.

[0203] The specific steps are as follows:

[0204] (1) The user's motion data is collected in real time through the acceleration sensor, including acceleration value and timestamp.

[0205] (2) Preprocess the collected acceleration data to remove noise and outliers.

[0206] (3) A dynamic threshold algorithm is used to adjust the threshold in real time according to the user's motion state. When the acceleration value exceeds the set threshold, it is considered that the user has taken a step.

[0207] (4) Count the detected steps to obtain the user's step count.

[0208] Assume that the acceleration value at time t is a(t) and the threshold is Th, then the condition for step detection can be expressed as: if a(t)>Th, then one step is recorded.

[0209] 2.4 Data upload and storage

[0210] The calculated number of steps will be uploaded to the server for storage in a timely manner. The server will further process and analyze the data to provide users with a more detailed and accurate exercise report.

[0211] 3 Innovations

[0212] 3.1 Sensor Fusion Technology

[0213] In order to improve the accuracy and stability of step counting, this system uses sensor fusion technology. By fusing sensor data from different parts (such as wrist, waist, ankle, etc.), the user's movement state can be comprehensively judged to reduce misjudgment and missed judgment.

[0214] The specific steps are as follows:

[0215] (1) Collect motion data from multiple sensors in real time, including acceleration, angular velocity, etc.

[0216] (2) Perform time synchronization and coordinate conversion on the collected data to ensure data consistency. (3) Use algorithms such as weighted average or Kalman filtering to fuse the data from multiple sensors to obtain more accurate motion status information.

[0217] Formula representation (taking Kalman filtering as an example):

[0218] Let x be the state vector, P be the covariance matrix, F be the state transfer matrix, H be the observation matrix, z be the observation vector, R be the observation noise covariance matrix, Q be the process noise covariance matrix, then the update formula of Kalman filter is:

[0219] Prediction stage: x'=F*x; P'=F*P*F'+Q

[0220] Update phase: K = P'*H'*(H*P'*H'+R)^(-1);

[0221] x=x'+K*(zH*x'); P=(1-K*H)*P'.

[0222] 3.2 Intelligent Recognition Algorithm

[0223] To further improve the intelligence level of the system, we introduced an intelligent recognition algorithm that can identify the user's exercise type (such as walking, running, cycling, etc.) and adjust the step counting algorithm and sensor fusion strategy according to the exercise type, thereby improving the accuracy and adaptability of step counting.

[0224] The specific steps are as follows:

[0225] (1) Use machine learning algorithms (such as support vector machines, neural networks, etc.) to train and learn the user's motion data to obtain a motion type classification model.

[0226] (2) Collect the user's motion data in real time and input it into the classification model to obtain the user's motion type.

[0227] (3) Adjust the pedometer algorithm and sensor fusion strategy according to the type of exercise, such as increasing or decreasing the sensor weight, adjusting the threshold, etc.

[0228] Formula representation (taking support vector machine as an example):

[0229] Let x be the input feature vector, y be the output label (motion type), w be the weight vector, and b be the bias term. The decision function of the support vector machine is:

[0230] f(x)=sign(w*x+b)

[0231] Among them, w and b are obtained through the training process and are used to map the input feature vector x to the output label y.

[0232] 3.3 Real-time feedback and reminders

[0233] The sports pedometer module also has real-time feedback and reminder functions. When the user walks a certain number of steps, the system will automatically pop up a prompt box to inform the user of the current number of steps and calories consumed. At the same time, the system can also issue a reminder when the preset number of steps is reached according to the user's settings, helping users better control the amount of exercise.

[0234] Chapter 4: Activity Management Module

[0235] 1. New Activities

[0236] step:

[0237] The administrator first logs in to the system and ensures that he has the relevant permissions.

[0238] Go to the event management page and select the "Add event" option.

[0239] On the new event page, fill in the basic information of the event, including the event name, event type (such as brisk walking, fun run, etc.), event introduction, total number of prizes, start and end time, and whether to display in a carousel.

[0240] The administrator needs to configure the event prizes, including prize level, prize picture, prize quantity, etc. After completing the form, submit the event information, and the system will add the event.

[0241] logic:

[0242] The system will check the administrator's login status and permissions to ensure that the administrator has the authority to add new activities.

[0243] The system will verify the submitted activity information to ensure its integrity and validity. If the information is verified, the system will save the activity information into the database and generate an activity ID as a unique identifier.

[0244] The system will update the event list to display the newly added event information.

[0245] 2. Activity Modification

[0246] step:

[0247] The administrator logs in to the system and ensures that he has relevant permissions.

[0248] Go to the event management page, find the event you want to modify, and select the "Modify" option. On the event modification page, modify the basic information or prize configuration of the event.

[0249] After the modification is completed, submit the modification information and the system will perform the activity modification operation.

[0250] logic:

[0251] The system will check the administrator's login status and permissions to ensure that the administrator has the authority to modify the activity.

[0252] The system will verify whether the activity being modified exists and load the current information of the activity. The changes made by the administrator on the modification page will be submitted to the system, and the system will perform information verification.

[0253] If the verification is successful, the system will update the activity information in the database and generate a modification record. The system will update the activity list to display the modified activity information.

[0254] 3. Management Desk Activity List

[0255] step:

[0256] The administrator logs in to the system and enters the activity management page.

[0257] The system will display a list of activities, including information such as activity ID, activity name, participating departments, activity type, activity rules and activity status.

[0258] Administrators can filter and categorize queries by activity status and activity type.

[0259] Click an activity to enter the detail page of that activity.

[0260] logic:

[0261] The system will load a list of activities that meet the administrator's permissions based on the administrator's login status and permissions. The system supports filtering and classification queries by activity status and activity type, so that administrators can quickly find the activities they need.

[0262] Click on an activity in the activity list, and the system will jump to the detail page of the activity to display more detailed activity information.

[0263] IV. Activity Details Query

[0264] step:

[0265] The administrator enters the detail page of an activity through the activity list of the management console.

[0266] On the details page, administrators can view detailed information about the event, including the event name, event type, event description, total number of prizes, start and end time, and whether to display in a carousel. Logic:

[0267] The system will load and display detailed information of the specified activity based on the administrator's request.

[0268] Administrators can perform further operations on the details page, such as viewing the list of winners (if the event has ended and the draw has been held), downloading event data, etc.

[0269] 5. Lottery Operation

[0270] Steps (performed by backend administrator user):

[0271] After the event ends, the backend administrator logs into the system and enters the event management page.

[0272] Find the ended event and select the Sweepstakes option.

[0273] The system will display the lucky draw page, showing the event prize settings and a list of qualified users.

[0274] When the administrator clicks the "Lottery" button, the system will conduct a lottery based on the set number of prizes and the range of qualifying standards.

[0275] After the lottery is completed, the system generates a list of winners and displays it to the administrator.

[0276] logic:

[0277] The system will check the status of the event to ensure that it has ended and is in a drawable state.

[0278] The system will load the event's prize settings and a list of qualified users.

[0279] After the administrator clicks the lucky draw button, the system will conduct the lucky draw according to the set lucky draw rules. During the lucky draw process, the system will select users who meet the qualifying criteria and have the same number of prizes as the set ones as the winning users.

[0280] After the draw is completed, the system will generate a list of winners and save it in the database. At the same time, the system will update the activity status and mark it as drawn.

[0281] Chapter 5: Ranking and Interaction Module

[0282] 1. Ranking function

[0283] The ranking data collection system collects data through the user's step counting data (such as daily steps, weekly steps, monthly steps, etc.).

[0284] After the data is collected, the system will classify and store it according to time periods (such as "today", "this week", "this month").

[0285] Ranking calculation

[0286] The system will rank users according to the number of steps based on the collected data.

[0287] When calculating the ranking, data from different time periods will be taken into account to show the user's exercise performance in different time periods.

[0288] Ranking display

[0289] The ranking results will be displayed above the statistical chart and below the statistical list on the "Healthy Walking" page.

[0290] Users can see their own ranking and information about other users who are ranked adjacent to them.

[0291] When the ranking is displayed, it will be arranged in order from high to low (or low to high, according to the requirements).

[0292] Department Ranking

[0293] The system supports statistical ranking by department, and the department is only accurate to the third level.

[0294] The department ranking will show the average or total number of steps taken by users in each department, as well as the ranking of each department.

[0295] Ranking Updates

[0296] The system will update the ranking in real time based on the user's new steps.

[0297] Every time users open the "Healthy Walking" page, they will see the latest ranking results.

[0298] 2. Interactive Function

[0299] Activity Settings

[0300] Backstage administrators can set up various sports activities, including activity name, activity time, activity introduction, etc.

[0301] When setting up an event, administrators can set the range of achievement criteria, as well as the types and quantities of prizes.

[0302] Participants can participate in the activity through the "Walking" page, and the system will record the user's participation. After participating in the activity, the user will compare his or her step count with the standard to determine whether he or she has met the standard.

[0303] Lottery function

[0304] After the event is over, the backend administrator can click the "Draw" button to conduct the draw. During the draw, the system will draw the winning users from the qualified users according to the set number of prizes and the range of qualified standards.

[0305] The lottery results will generate a list of winners and display it to the backend administrator.

[0306] View and export the list of winners

[0307] Backstage administrators can view the winning user information through the winner list.

[0308] Administrators can click the "Export Winners List" button to export the winners list information as a file (such as Excel, CSV, etc.).

[0309] User Interaction

[0310] Users can interact with other users through the chat function within the system and share sports insights and experiences.

[0311] Users can also like and comment on other users’ sports achievements to increase interactivity and participation among users.

[0312] 3. Logical process User login: When a user enters the App for the first time, the system will check whether the client has user information. If there is no user information, the system will enter the login page to prompt the user to log in; if there is user information, the system will perform background verification. After successful login, the user will automatically jump to the "Healthy Walking" page. Data collection and ranking calculation:

[0313] The system will collect the user's step data in real time and store it by category according to time periods. The system will calculate the ranking based on the collected data and display it on the "Healthy Walking" page.

[0314] Users can participate in the activity through the "Healthy Walking" page and judge whether they have met the requirements based on their step count.

[0315] After the event is over, the backend administrator will conduct a lottery, generate a list of winners and display it to the administrator.

[0316] View and export the list of winners:

[0317] Administrators can view the winning user information through the winner list and export the winning list as a file.

[0318] User interaction;

[0319] Users can interact with other users through the chat function within the system and share sports insights and experiences.

[0320] Chapter VI: Specific Implementation Methods

[0321] 1. System initialization and user login

[0322] Startup page settings: When the user opens the APP for the first time, the startup page will be displayed, and then the login process will be automatically entered.

[0323] User login:

[0324] First login (use case ID: 002):

[0325] After the user enters the APP, the system checks whether the client already has user information.

[0326] If there is no user information, it will jump to the login page and prompt the user to enter the username and password. After the user enters the username and password, the system will perform background verification and the password will be transmitted in ciphertext.

[0327] After successful verification, a Toast prompt is given and the page is redirected to the home page. The username and password are saved on the client.

[0328] Not the first login (use case ID: 003):

[0329] After the user enters the APP, the system checks the existing user information on the client.

[0330] Pass the username and password to the server for verification. The password is also transmitted in ciphertext. After successful verification, you will be redirected to the homepage.

[0331] 2. Basic Information Settings

[0332] Basic information settings after first login:

[0333] After the user logs in for the first time, he / she needs to set his / her weight and exercise declaration.

[0334] After the settings are completed, the information is saved on the server and you do not need to set it again the next time you enter the APP.

[0335] 3. Implementation of Healthy Walking Function

[0336] Step recording and display:

[0337] After the APP is started and logged in successfully, it will automatically enter the "Healthy Walking" page.

[0338] The background automatically records the user's walking steps and displays the number of walking and running steps each day of the week in a chart.

[0339] Ranking function implementation:

[0340] View ranking (use case ID: 006,007):

[0341] On the “Healthy Walking” page, users can check their walking and running rankings.

[0342] Click on the ranking to enter the corresponding ranking list page.

[0343] Ranking logic:

[0344] The system ranks users based on their walking and running steps this week.

[0345] The ranking is displayed by time period, such as "Today", "This Week", and "This Month".

[0346] When ranking by department, only the third-level department is accurate, and the ranking below the third-level department is not displayed. The first column of the ranking list shows the user's personal ranking.

[0347] Event list and lottery function:

[0348] Activity details display (mobile phone segment activity list):

[0349] On the ranking page or related activity page, basic information of the activity is displayed, including the activity name, activity time, activity introduction, etc.

[0350] Implementation of lottery function (use case ID: 018):

[0351] After the event ends, the backend administrator can click the lottery button to draw the prize.

[0352] Lottery rules: Winning users will be selected from users who meet the criteria based on the number of event prizes and the range of eligible standards.

[0353] Each event has multiple prizes, each with different prize numbers and achievement standards. Each event has different prizes for which a draw is held separately, and users who have not won a prize and have achieved the standard will no longer be included in the draw for the next level of prizes.

[0354] After the draw is completed, a list of winners will be generated.

[0355] 4. Data Synchronization and Processing

[0356] Human Resources Data Synchronization (Use Case ID: 028):

[0357] Synchronize human resources data to the intermediate table and connect to the control+M system to ensure data accuracy and consistency.

[0358] Synchronize the intermediate table data to the business table (use case ID: 029):

[0359] The data in the intermediate table is synchronized to the department table every day for subsequent business processing and display.

[0360] V. Other matters needing attention

[0361] Data security: During the entire implementation process, we focus on protecting user data, such as using ciphertext for password transmission to ensure the security of user information.

[0362] User experience: The interface design is concise and clear, and the operation process is smooth, providing a friendly user experience. System stability: Ensure that the system can still run stably under high concurrency and large data volume conditions and provide reliable services.

[0363] 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 healthy exercise pedometer system based on fusion sensor data, characterized in that: include: The startup and login module is used to load the startup page and log in the user; Information setting and synchronization module, used to set the user's basic information and synchronize sports data in real time; The pedometer module is used to record the user's walking steps in real time and upload them to the server; Activity management module, used to manage the addition, modification and lottery operations of sports activities; The ranking and interaction module is used to collect user step data and display the ranking.

2. The healthy exercise pedometer system based on fusion sensor data according to claim 1, characterized in that: The startup and login module includes: Start the page loading module, which is used to display the system name and LOGO when the user clicks the system icon; Login interface display module, used to require users to enter username and password to log in; The password setting module is used to set passwords for users logging in for the first time.

3. The healthy exercise pedometer system based on fusion sensor data according to claim 1 or 2, characterized in that: The information setting and synchronization module includes: User information setting module, used to guide users to enter username and password to log in; Basic information setting module, used by users to set weight and exercise slogans; The data synchronization module is used to synchronize the user's motion data to the server in real time.

4. The healthy exercise pedometer system based on fusion sensor data according to claim 3 is characterized in that: It is characterized in that The motion step counting module comprises: A data acquisition module, used to collect acceleration and angular velocity data of the user through sensors; The step counting module is used to calculate the user's step count based on the step counting algorithm of the acceleration sensor; the data uploading and storage module is used to upload the calculated step count to the server for storage; The specific counting steps of the step counting module are as follows: 1.1 Collect user's motion data in real time through acceleration sensor, including acceleration value and timestamp; 1.2 Preprocess the collected acceleration data to remove noise and outliers; 1.3 Adopt dynamic threshold algorithm to adjust the threshold in real time according to the user's motion state. When the acceleration value exceeds the set threshold, it is considered that the user has taken a step; 1.4 Count the detected steps to get the number of steps of the user; The detection formula is as follows: Assume that the acceleration value at time t is a(t) and the threshold is Th, then the condition for step detection can be expressed as: if a(t)>Th, then record a step.

5. The healthy exercise pedometer system based on fusion sensor data according to claim 4 is characterized in that: It is characterized in that The specific collection steps of the data collection module are as follows: 2.1 Collect motion data from multiple sensors in real time, including acceleration and angular velocity; 2.2 Perform time synchronization and coordinate conversion on the collected data to ensure data consistency; 2.3 Use weighted average or Kalman filter algorithm to fuse the data of several sensors to obtain motion state information; when the Kalman filter algorithm is used for fusion, the specific steps are as follows: Let x be the state vector, P be the covariance matrix, F be the state transfer matrix, H be the observation matrix, z be the observation vector, R be the observation noise covariance matrix, Q be the process noise covariance matrix, P′ denotes the covariance matrix of the prediction stage, and F′ is the transpose of the state transfer matrix F; then the update formula of Kalman filter is: Prediction stage: x'=F*x, P'=F*P*F'+Q; Update phase: K = P'*H'*(H*P'*H'+R)^(-1); x = x'+K*(zH*x'), P = (1-K*H)*P'.

6. The healthy exercise pedometer system based on fusion sensor data according to claim 5, characterized in that: When identifying motion status information, specifically: 3.1 Use support vector machine or neural network machine learning algorithm to train and learn the user's sports data to obtain a sports type classification model; 3.2 Collect the user's exercise data in real time and input it into the exercise type classification model to obtain the user's exercise type; 3.3 Adjust the pedometer algorithm and sensor fusion strategy according to the type of exercise; such as increasing or decreasing the weight of the sensor, adjusting the threshold, etc. When using a support vector machine, let x be the input feature vector, y be the output label (sports type), w be the weight vector, and b be the bias term. Then the decision function of the support vector machine is: f(x) = sign(w*x+b); Among them, w and b are obtained through the training process and are used to map the input feature vector x to the output label y.

7. A step counting method of a healthy exercise step counting system based on the fusion sensor data according to any one of claims 1 to 6, characterized in that: The following steps are involved: (1) Startup and login: When the user clicks the system icon, the system loads the startup page and displays the system name and LOGO; The user is directed to the login interface and enters the user name and password to log in; For users logging in for the first time, the system provides a password setting module to set a password; (2) Information setting and synchronization: The user information setting module guides the user to enter the user name and password to log in; The basic information setting module allows users to set weight and exercise slogans; The data synchronization module synchronizes the user's sports data to the server in real time; (3) Step counting: The data acquisition module collects the user's acceleration and angular velocity data from multiple sensors in real time; After data collection, time synchronization and coordinate conversion are performed to ensure data consistency; Use weighted average or Kalman filter algorithm to fuse sensor data and obtain motion state information; The step counting module calculates the user's steps through the step counting algorithm of the acceleration sensor; Collect user motion data in real time, including acceleration values ​​and timestamps; Preprocess the collected acceleration data to remove noise and outliers; A dynamic threshold algorithm is used to adjust the threshold in real time. When the acceleration value exceeds the set threshold, it is considered that the user has taken a step. Count the detected steps to get the user's step count; The data upload and storage module uploads the calculated number of steps to the server for storage; (4) Motion status information recognition: Use support vector machine or neural network machine learning algorithm to train and learn the user's sports data to obtain a sports type classification model; Collect the user's exercise data in real time and input it into the exercise type classification model to obtain the user's exercise type; Adjust the pedometer algorithm and sensor fusion strategy according to the type of exercise; such as increasing or decreasing the weight of the sensor, adjusting the threshold, etc. (5) Event management: manage the addition, modification and lottery operations of sports events; (6) Ranking and interaction: Collect user step data and display rankings to promote interaction and competition among users.

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