Bracelet for monitoring and improving exercise sleep quality and use method thereof

Abstract

The invention discloses a bracelet for monitoring and improving exercise sleep quality and a use method thereof, and relates to the technical field of intelligent bracelets, the bracelet comprises a hardware part, the hardware part is an intelligent bracelet, and the intelligent bracelet comprises a sensor module used for collecting exercise data and sleep data of a user in real time; the control module is used for processing the motion data and the sleep data collected by the sensor module and obtaining the motion and sleep conditions of the user through algorithm analysis; and the communication module is used for sending the processed data to a matched mobile phone App and receiving improvement suggestions sent by the mobile phone App. Through a software and hardware combination mode, real-time monitoring and deep analysis of the exercise and sleep data of the user are realized, personalized exercise and sleep improvement suggestion guidance can be provided according to the actual condition of the user, so that the user can carry out effective living habit adjustment according to the bracelet data, and the user experience is improved. And a new solution is provided for improving the exercise and sleep quality of the user.

Description

Technical Field

[0001] The present invention relates to the technical field of smart bracelets, and in particular to a bracelet for monitoring and improving exercise sleep quality and a method for using the bracelet. Background Art

[0002] Smart bracelets are wearable smart devices that combine the decorative functions of traditional bracelets with the smart functions of modern technology. Through the built-in sensor method, smart bracelets can record the user's exercise data, sleep data, heart rate and other health data in real time, and synchronize these data to the user's mobile phone or tablet through Bluetooth and other technologies. The main functions of smart bracelets include: Exercise data recording: It can record the user's steps, distance, calories consumed and other exercise data in detail. Some bracelets can also automatically monitor different types of exercise, such as walking, running, swimming, etc. Sleep monitoring: It can monitor the user's sleep duration, deep sleep, light sleep and other sleep data. Health data monitoring: Through the built-in heart rate sensor, blood oxygen sensor, etc., it can monitor the user's heart rate, blood oxygen saturation and other health data in real time. Reminder function: It has functions such as alarm clock, call reminder, message reminder, etc., so that users will not miss any important events. In addition, some high-end smart bracelets also have additional functions such as payment function, waterproof function, music control, etc., making the use of bracelets more convenient and diversified.

[0003] However, the existing wristband devices on the market have many deficiencies in monitoring and improving exercise sleep quality. Existing wristbands can only provide basic step counting and heart rate monitoring functions, and have limited ability to collect and analyze in-depth data such as exercise intensity and exercise mode. It is difficult to provide users with accurate exercise guidance and cannot conduct detailed analysis of sleep quality, resulting in users being unable to fully understand their sleep conditions, and unable to provide personalized exercise and sleep improvement suggestions based on the user's actual conditions, making it difficult for users to make effective adjustments to their lifestyle habits based on wristband data. Therefore, it is necessary to propose a wristband for monitoring and improving exercise sleep quality and a method of using the same to solve the problems in the prior art. Summary of the invention

[0004] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide a bracelet for monitoring and improving exercise and sleep quality and a method of using the bracelet. It can realize real-time monitoring, in-depth analysis and sending of personalized improvement suggestions for users' exercise and sleep data through a combination of software and hardware, providing a new solution for improving users' exercise and sleep quality.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In the first aspect, the present application provides a bracelet for monitoring and improving the quality of exercise and sleep, the bracelet includes a hardware part, the hardware part is a smart bracelet, and the components of the smart bracelet include: a sensor module, which is used to collect the user's exercise data and sleep data in real time; a control module, which is used to process the exercise data and sleep data collected by the sensor module, and obtain the user's exercise and sleep status through algorithm analysis; a communication module, which is used to send the processed data to a matching mobile phone App, and receive improvement suggestions sent by the mobile phone App; a display module, which is used to display the working status, exercise data and received improvement suggestions of the bracelet; the bracelet also includes a software part, the software part is a mobile phone APP, and the components of the mobile phone APP include: a data receiving module, which is used to receive the exercise and sleep data sent by the smart bracelet; a data processing and analysis module, which is used to perform in-depth analysis of the received exercise and sleep data, and generate a user's exercise and sleep status report; a suggestion generation module, which generates personalized exercise and sleep improvement suggestions based on the user's exercise and sleep status report.

[0007] On the second aspect, the present application provides a method for using a bracelet for monitoring and improving exercise and sleep quality, the method comprising: wearing a smart bracelet on the wrist and ensuring that the smart bracelet establishes a communication connection with a mobile phone App; performing daily exercise and sleep activities, the smart bracelet collects exercise and sleep data in real time, and sends it to the mobile phone App; the mobile phone App receives the data, performs in-depth analysis, generates personalized exercise and sleep improvement suggestions, and sends them to the smart bracelet; the smart bracelet displays improvement suggestions, and the user adjusts exercise and sleep habits according to the suggestions.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The embodiments of the present application provide a wristband for monitoring and improving exercise and sleep quality and a method of using the wristband. By being equipped with advanced sensor modules and control modules, the wristband can realize real-time monitoring and in-depth analysis of the user's exercise and sleep data, and provide the user with accurate exercise and sleep status reports. Secondly, the present invention can generate personalized exercise and sleep improvement suggestions based on the user's actual condition through the deep learning algorithm of the mobile phone App, so as to help the user better adjust his or her living habits and improve the quality of life. The present invention realizes real-time monitoring and in-depth analysis of the user's exercise and sleep data through a combination of software and hardware, and can provide personalized exercise and sleep improvement suggestion guidance based on the user's actual condition, so that the user can make effective adjustments to his or her living habits based on the wristband data, providing a new solution for improving the user's exercise and sleep quality.

[0010] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A system structure diagram of a wristband for monitoring and improving exercise sleep quality is provided for this application;

[0013] Figure 2 A schematic diagram of the use structure of a wristband that combines software and hardware to monitor and improve exercise sleep quality is provided for this application;

[0014] Figure 3 A flowchart of a method for using a wristband for monitoring and improving exercise sleep quality is provided for this application. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example 1

[0017] A wristband for monitoring and improving the quality of exercise and sleep, the hardware part of the wristband includes a smart wristband, and the components of the smart wristband include:

[0018] Sensor module, used to collect user's motion data and sleep data in real time;

[0019] The sensor module includes an accelerometer, a heart rate sensor and a blood oxygen sensor, which can comprehensively and accurately capture the user's movement status, heart rate changes and blood oxygen level during sleep, and provide users with detailed movement data and sleep data. The specific execution steps of the sensor module are: after the smart bracelet is worn on the wrist and a stable communication connection is established between the bracelet and the mobile phone App, the accelerometer begins to record the user's movement trajectory and steps, the heart rate sensor monitors the user's heart rate changes in real time, and the blood oxygen sensor evaluates the user's breathing condition during sleep.

[0020] The control module is used to process the motion data and sleep data collected by the sensor module and obtain the user's motion and sleep status through algorithm analysis;

[0021] The specific execution steps of the control module are as follows: the control module receives the suggestion output generated by the collection APP, that is, personalized exercise and sleep improvement suggestions. These suggestion instructions include adjusting the reminder settings of the smart bracelet and suggesting that the user do a certain exercise. For different types of suggestion instructions, the control module will perform corresponding analysis and execution. For example, if the suggestion instruction is to adjust the reminder time of the smart bracelet, the control module will send a corresponding control signal to the bracelet to make it work according to the new reminder time; for exercise suggestions, the control module may be integrated with sports applications or fitness equipment to automatically set training plans for users or adjust equipment parameters. In the process of executing the suggestion instructions, the control module may also need to monitor the status of the device and user feedback. For example, it can check whether the reminder function of the smart bracelet is working properly, or understand the effect of sleep or exercise improvement through user feedback. According to the monitoring and feedback results, the control module can further adjust the execution strategy or provide feedback to the suggestion generation module to optimize subsequent suggestions;

[0022] The control module is responsible for receiving the suggestions output from the supporting mobile phone App. These suggestions are personalized improvement suggestions generated based on the user's exercise and sleep status reports. The control module will parse these suggestions and perform corresponding operations according to the suggestion type. For suggestions to adjust the reminder settings of the smart bracelet, the control module will immediately send a control signal to the bracelet to make it work according to the new reminder time or reminder method. For example, if the user needs to get up earlier, the control module will adjust the alarm time of the bracelet to ensure that the user gets up on time. For exercise suggestions, the control module will be integrated with the sports application or fitness equipment in the user's mobile phone. It will develop a suitable training plan for the user based on the user's exercise habits and physical condition, or adjust the parameters of the equipment to optimize the exercise effect. In this way, the user can get personalized exercise guidance through the bracelet and improve the exercise effect.

[0023] In the process of executing the recommended instructions, the control module is also responsible for monitoring the status of the device and user feedback. It will regularly check the reminder function of the bracelet and whether the sensor is working normally to ensure the normal operation of the bracelet. At the same time, it will also understand the effect of sleep or exercise improvement through user feedback in order to further optimize the suggestions. According to the monitoring and feedback results, the control module will continuously adjust the execution strategy and provide feedback to the suggestion generation module. In this way, the suggestion generation module can continuously optimize the subsequent suggestions according to the user's actual situation and feedback, so that the suggestions are more in line with the user's personalized needs.

[0024] The communication module is used to send the processed data to the matching mobile app and receive improvement suggestions sent by the mobile app;

[0025] The specific execution steps of the communication module are as follows: the communication module of the smart bracelet establishes a connection with the mobile phone through Bluetooth technology, which is realized through Bluetooth technology because Bluetooth technology is suitable for short-range wireless communication and has low power consumption, which is very suitable for wearable devices such as smart bracelets; before establishing a connection, a pairing and authentication process is required, which usually involves key exchange and verification between devices to ensure the security of communication; once the connection is successfully established, the sensor module of the smart bracelet will begin to send the collected exercise and sleep data to the communication module, and the communication module will package this data into a specific format and send it to the mobile phone App via Bluetooth connection; the communication module will also listen to instructions from the mobile phone App, which include requesting more data, adjusting the data collection frequency, and sending improvement suggestions and guidance. When receiving the instructions, the communication module will parse them and pass them to the control module for processing

[0026] The display module is used to display the working status, exercise data and improvement suggestions received from the bracelet.

[0027] The display module is an important interface for the smart bracelet to interact with the user. It provides an intuitive and concise way to display information, so that users can understand the working status of the bracelet and their own exercise and sleep conditions at any time. Specifically, the display module can display the working status of the bracelet in real time, including the battery level and Bluetooth connection status, so that users can understand the operation status of the bracelet at any time. At the same time, it can also display the user's exercise data, such as the number of steps, exercise duration, and exercise intensity. These data are presented in the form of charts or numbers, which is convenient for users to understand their exercise habits and exercise effects. More importantly, the display module can receive and display improvement suggestions from the mobile phone App. These suggestions are generated based on the user's exercise and sleep status report, aiming to help users improve their exercise habits and sleep quality. The display module can display these suggestions in the form of text or icons, reminding users to pay attention to adjusting exercise time and intensity and optimizing the sleeping environment, so that users can better utilize the functions of the smart bracelet and improve their health level.

[0028] Example 2

[0029] A wristband for monitoring and improving the quality of exercise sleep, the software part of the wristband includes a mobile phone APP, and the composition of the mobile phone APP includes:

[0030] A data receiving module is used to receive exercise and sleep data sent by a smart bracelet;

[0031] The specific execution steps of the data receiving module are: the user starts the mobile phone App and ensures that the Bluetooth connection between the mobile phone and the smart bracelet has been established; the mobile phone App receives the exercise and sleep data from the smart bracelet through the communication module, and the received data is parsed and converted into a data format that can be processed within the App.

[0032] The data receiving module, as one of the core functions of the mobile phone APP for monitoring and improving the quality of exercise and sleep, is mainly responsible for accurately receiving the exercise and sleep data sent by the smart bracelet. The design of this module fully considers the real-time, accuracy and completeness of the data to ensure that users can obtain the most accurate and comprehensive exercise and sleep information.

[0033] In the actual working process, the data receiving module will first start and establish a connection with the smart bracelet. This connection is usually based on Bluetooth technology to ensure stable and fast data transmission within the effective range. After the connection is established, the data receiving module will enter the waiting state and prepare to receive data packets from the smart bracelet. When the sensor module of the smart bracelet collects the user's exercise and sleep data, these data will be processed and packaged into a specific format, and then sent to the data receiving module of the mobile phone APP through the communication module. After receiving the data packet, the data receiving module will perform a series of data parsing and processing. The data receiving module will perform integrity checks on the received data packets to ensure that the data is not lost or damaged during the transmission process. Then, the module will parse the data and restore it to the original exercise and sleep data. These data may include the user's steps, exercise trajectory, heart rate changes, sleep duration, and sleep depth.

[0034] The design of the data receiving module fully considers the security and privacy of the data. During the data transmission and reception process, the module will use encryption technology to ensure the security of the data and prevent the data from being illegally obtained or tampered with. At the same time, the module will also comply with relevant privacy policies and regulations to ensure that the user's personal information is properly protected.

[0035] The data processing and analysis module is used to conduct in-depth analysis of the received exercise and sleep data, generate a user's exercise and sleep status report, use a deep learning algorithm neural network model to learn user data, and predict exercise and sleep improvement suggestions suitable for the user;

[0036] The specific execution steps of the data processing and analysis module are as follows: collect the user's exercise and sleep data from the smart bracelet; remove outliers and noise to ensure the accuracy and consistency of the data; annotate the collected data, for example, mark different exercise types or sleep stages; convert the original data into a format that CNN can process, such as image or time series data; determine the number of CNN layers, convolution kernel size, and step size parameters, and randomly initialize the weights (W) and biases (b) of each layer in the CNN; for the convolution layer, assume that the input feature map of the lth layer is X (l) (Here X (l) It can be regarded as the encoded representation of the number of steps, heart rate changes, sleep duration, and sleep depth features), and the weight is W (l) , with a bias of b (l) , then the output Z of the convolution operation (l) for: Next, Z is activated by an activation function (such as ReLU). (l) Perform nonlinear transformation to obtain the output A of this layer (l) : A (l) =ReLU(Z (l) ), for the pooling layer (such as maximum pooling), it will (l) Downsampling is performed to reduce the spatial size of the data. The extracted features are analyzed using a pre-trained CNN-based deep learning model. The model outputs the user's exercise and sleep status classification or prediction results based on the input features. The prediction results output by the model can be obtained through the softmax function: Output Y = softmax (W fc A flat +b fc ), where W fc and b fc are the weights and biases of the fully connected layer, A flat It is the vector obtained by flattening the feature map after convolution and pooling. Softmax is the activation function used for multi-classification problems. Based on the results of the model output, a report on the user's exercise and sleep status is generated. The report includes the user's exercise type, intensity, duration, as well as sleep stage and quality information.

[0037] In the process of data processing and analysis, the raw data will be preprocessed first. This step mainly includes removing outliers and noise to ensure the accuracy and consistency of the data. At the same time, the collected data will be annotated and different types of exercise or sleep stages will be marked so that the subsequent model can better understand and analyze the data. The preprocessed data is converted into a format that can be processed by CNN, such as image or time series data. Then, the number of layers, convolution kernel size, step size parameter key parameters of CNN are determined, and the weights and biases of each layer in CNN are randomly initialized. During the training process of CNN, the input feature map is convolved through the convolution layer, and the convolution result is nonlinearly transformed through the activation function. At the same time, the pooling layer downsamples the feature map to reduce the spatial size of the data and improve the generalization ability of the model. After training, a CNN-based deep learning model is obtained, which can output the user's exercise and sleep status classification or prediction results based on the input features. For example, it can identify the user's exercise type, intensity, duration, and sleep stage and quality information. According to the results output by the model, a detailed user exercise and sleep status report will be generated. This report not only includes the user's exercise and sleep data, but also provides targeted improvement suggestions to help users better adjust their exercise and sleep habits.

[0038] The suggestion generation module generates personalized exercise and sleep improvement suggestions based on the user's exercise and sleep status reports.

[0039] The suggestion generation module generates personalized exercise and sleep improvement suggestions for users based on the user's exercise and sleep status report, combined with advanced algorithms and professional knowledge. The suggestion generation module generates personalized exercise and sleep improvement suggestions for users by deeply analyzing user data, combining professional knowledge, and optimizing multiple aspects of the algorithm. These suggestions are designed to help users improve their exercise and sleep quality and improve their quality of life. With the continuous advancement of technology and the continuous accumulation of data, it is believed that the function and performance of the suggestion generation module will be further improved to provide users with more accurate and personalized services.

[0040] The specific execution steps of the suggestion generation module are as follows: the suggestion generation module first receives the user's exercise and sleep status report transmitted from the data processing and analysis module; conducts in-depth analysis of the pre-processed data, which may include evaluation of key indicators of the user's exercise habits, sleep duration, sleep quality, heart rate changes, and blood oxygen level, and uses deep learning algorithms to build models based on a large amount of user data to conduct a more accurate evaluation of the user's exercise and sleep status; based on the results of the analysis and evaluation, combined with medical and kinesiology expertise, generates personalized exercise and sleep improvement suggestions, which include adjusting exercise time, increasing or decreasing exercise volume, improving sleep environment, and adjusting sleep habits; based on the user's historical data and feedback, continuously optimizes and adjusts the suggestion generation algorithm to ensure the accuracy and effectiveness of the suggestions, taking into account the user's personal preferences and physical condition, and generating personalized suggestions that better meet the user's needs; the generated personalized suggestions are sent to the user's mobile phone App through the communication module, and displayed on the display module of the smart bracelet. The user can provide feedback on the suggestions through the mobile phone App or the bracelet, such as whether they are adopted and whether they are effective, so as to further optimize the suggestion generation algorithm in the future.

[0041] The suggestion generation module first receives the user's exercise and sleep status report from the data processing and analysis module. This report records in detail the user's exercise habits, sleep duration, sleep quality, heart rate changes, and blood oxygen level key indicators, providing a solid data foundation for suggestion generation. In order to generate more accurate and personalized suggestions, the suggestion generation module will conduct an in-depth analysis of the pre-processed data. It uses deep learning algorithms to build models based on a large amount of user data to conduct a more accurate assessment of the user's exercise and sleep status. Through this step, the module can more accurately grasp the user's physical condition and exercise needs. Based on the analysis and evaluation, the suggestion generation module combines the professional knowledge of medicine and kinesiology to generate personalized exercise and sleep improvement suggestions for users. These suggestions include but are not limited to adjusting exercise time, increasing or decreasing exercise volume, improving sleep environment, and adjusting sleep habits. At the same time, the module will also consider the user's personal preferences and physical condition to ensure that the generated suggestions not only meet the user's actual needs, but also can effectively improve their exercise and sleep quality. The generated personalized suggestions will be sent to the user's mobile phone App through the communication module and displayed on the display module of the smart bracelet. Users can view these suggestions anytime and anywhere, and adjust and implement them according to their actual situation.

[0042] Example 3

[0043] A method for using the above-mentioned smart bracelet and mobile phone App, the steps of the method include: wearing the smart bracelet on the wrist, and ensuring that the smart bracelet establishes a communication connection with the mobile phone App. The bracelet is made of light and comfortable material, which is convenient and comfortable to wear. The user only needs to wear the bracelet on the wrist and ensure that the bracelet and the mobile phone Bluetooth connection are normal to start using it; performing daily exercise and sleep activities, the smart bracelet collects exercise and sleep data in real time and sends it to the mobile phone App. During the user's daily activities, the bracelet will record the user's exercise steps, heart rate changes, sleep duration, and sleep depth data in real time, and send these data to the mobile phone App via Bluetooth connection; the mobile phone App receives the data, performs in-depth analysis, generates personalized exercise and sleep improvement suggestions, and sends them to the smart bracelet. Through deep learning algorithms and professional medical and kinematics knowledge, the mobile phone App can accurately evaluate the user's exercise and sleep status, and generate personalized exercise and sleep improvement suggestions. These suggestions are designed to help users adjust exercise intensity and improve sleep quality, thereby achieving better health results. These suggestions will be sent back to the smart bracelet via Bluetooth connection; the smart bracelet will display improvement suggestions, and users can adjust their exercise and sleep habits according to the suggestions, such as increasing the amount of exercise and improving the sleeping environment. After viewing the improvement suggestions displayed on the bracelet, users can adjust their exercise plans and sleep habits according to their actual situation and needs to improve their exercise and sleep quality.

[0044] Through the use of this smart bracelet and mobile phone app, users can more conveniently understand their exercise and sleep conditions, get personalized improvement suggestions, and adjust their lifestyle habits accordingly. This not only helps improve the user's exercise and sleep quality, but also helps users better manage their health and enjoy a better life.

[0045] In summary: The present invention realizes real-time monitoring and in-depth analysis of user motion and sleep data through a combination of software and hardware, and can provide personalized motion and sleep improvement suggestions and guidance according to the actual situation of the user, so that the user can make effective adjustments to his or her living habits according to the bracelet data, and provides a new solution for improving the user's motion and sleep quality, which has broad market prospects and application value. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the attached claims rather than the above description, and it is intended to include all changes within the meaning and scope of the same elements of the claims in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

Claims

1. A wristband for monitoring and improving exercise sleep quality, characterized in that: The wristband includes a hardware part, and the hardware part is a smart wristband. The components of the smart wristband include: Sensor module, used to collect user's motion data and sleep data in real time; The control module is used to process the motion data and sleep data collected by the sensor module and obtain the user's motion and sleep status through algorithm analysis; The communication module is used to send the processed data to the matching mobile app and receive improvement suggestions sent by the mobile app; The display module is used to display the working status, exercise data and improvement suggestions received from the bracelet.

2. A wristband for monitoring and improving exercise sleep quality, characterized in that: The wristband also includes a software part, which is a mobile phone APP. The composition of the mobile phone APP includes: A data receiving module is used to receive exercise and sleep data sent by a smart bracelet; The data processing and analysis module is used to conduct in-depth analysis of the received exercise and sleep data and generate a user's exercise and sleep status report; The suggestion generation module generates personalized exercise and sleep improvement suggestions based on the user's exercise and sleep status reports.

3. A wristband for monitoring and improving exercise sleep quality according to claim 1, characterized in that: The sensor module includes an acceleration sensor, a heart rate sensor and a blood oxygen sensor, which can comprehensively and accurately capture the user's exercise status, heart rate changes and blood oxygen level during sleep, and provide the user with detailed exercise data and sleep data.

4. A wristband for monitoring and improving exercise sleep quality according to claim 1, characterized in that: The specific execution steps of the control module are as follows: Receiving suggestion instructions: The control module receives suggestion outputs generated by the collection APP, namely personalized exercise and sleep improvement suggestions. These suggestion instructions include adjusting the reminder settings of the smart bracelet and suggesting that the user do a certain exercise; Parse and execute instructions: For different types of suggested instructions, the control module will parse and execute them accordingly; Monitoring and feedback: During the execution of the recommended instructions, the control module may also need to monitor the status of the device and the user's feedback.

5. A wristband for monitoring and improving exercise sleep quality according to claim 1, characterized in that: The specific execution steps of the communication module are as follows: Establish connection: The communication module of the smart bracelet establishes connection with the mobile phone through Bluetooth technology; Pairing and authentication: Before establishing a connection, a pairing and authentication process is required; Data transmission: Once the connection is established, the sensor module of the smart bracelet will start to send the collected exercise and sleep data to the communication module. The communication module will package this data into a specific format and send it to the mobile phone App via Bluetooth connection.

6. A wristband for monitoring and improving exercise sleep quality according to claim 1, characterized in that: The display module is an important interface for the smart bracelet to interact with the user. It provides an intuitive and concise information display method, allowing the user to understand the working status of the bracelet and their own exercise and sleep conditions at any time.

7. The mobile phone APP for bracelet according to claim 2, characterized in that: The specific execution steps of the data receiving module are as follows: Start and connect: The user starts the mobile app and ensures that the Bluetooth connection between the mobile phone and the smart bracelet is established; Data reception and analysis: The mobile phone App receives the exercise and sleep data from the smart bracelet through the communication module. The received data is analyzed and converted into a data format that can be processed within the App.

8. The mobile phone APP for a bracelet according to claim 2, characterized in that: The specific execution steps of the data processing and analysis module include: Data preprocessing: Collect users’ exercise and sleep data from smart bracelets; remove outliers and noise to ensure data accuracy and consistency; and annotate the collected data; Data conversion: converting raw data into a format that CNN can process, such as images or time series data; Model construction: determine the number of CNN layers, convolution kernel size, and step size parameters, and randomly initialize the weights (W) and biases (b) of each layer in the CNN; Model training: For the convolutional layer, assume that the input feature map of the lth layer is X (l) (Here X (l) It can be regarded as the encoded representation of the number of steps, heart rate changes, sleep duration, and sleep depth features), and the weight is W (l) , with a bias of b (l) , then the output Z of the convolution operation (l) for: Next, Z is activated by an activation function (such as ReLU). (l) Perform nonlinear transformation to obtain the output A of this layer (l) : A (l) =ReLU(Z (l) ), for the pooling layer (such as maximum pooling), it will (l) Perform downsampling operations to reduce the spatial size of the data; Model application: Use the pre-trained CNN-based deep learning model to analyze the extracted features. The model will output the user's exercise and sleep status classification or prediction results based on the input features. The prediction results output by the model can be obtained through the softmax function: Output Y = softmax (W fc A flat +b fc ), where W fc and b fc are the weights and biases of the fully connected layer, A flat It is the vector after flattening the feature map after convolution and pooling, and softmax is the activation function for multi-classification problems; Result generation: Generate a user's exercise and sleep status report based on the results output by the model. The report includes the user's exercise type, intensity, duration, as well as sleep stage and quality information.

9. The mobile phone APP for bracelet according to claim 2, characterized in that: The specific execution steps of the suggestion generation module include: Data reception: The suggestion generation module first receives the user's exercise and sleep status report transmitted from the data processing and analysis module; Analysis and evaluation: Conduct in-depth analysis of the pre-processed data, which may include evaluation of key indicators such as user exercise habits, sleep duration, sleep quality, heart rate changes, and blood oxygen levels. Use deep learning algorithms to build models based on a large amount of user data to more accurately evaluate the user's exercise and sleep conditions. Recommendation generation: Based on the results of analysis and evaluation, combined with medical and kinesiology expertise, personalized exercise and sleep improvement recommendations are generated; Suggestion optimization and adjustment: Based on the user's historical data and feedback, the suggestion generation algorithm is continuously optimized and adjusted to ensure the accuracy and effectiveness of the suggestions, taking into account the user's personal preferences and physical condition, and generating personalized suggestions that better meet the user's needs; Suggestion output and feedback reception: The generated personalized suggestions are sent to the user's mobile phone app through the communication module and displayed on the display module of the smart bracelet.

10. A method of using the above-mentioned smart bracelet and mobile phone App, characterized in that: The method comprises the following steps: Wear the smart bracelet on your wrist and ensure that the smart bracelet is connected to the mobile phone app; During daily exercise and sleep activities, the smart bracelet collects exercise and sleep data in real time and sends it to the mobile phone app; The mobile app receives the data, performs in-depth analysis, generates personalized exercise and sleep improvement suggestions, and sends them to the smart bracelet; The smart bracelet displays improvement suggestions, and users adjust their exercise and sleep habits based on the suggestions.

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