Intelligent monitoring system for cardiovascular disease nursing

Abstract

An intelligent monitoring system for cardiovascular disease nursing comprises a physiological signal acquisition module, a data processing and analysis module, a user interaction module, a communication module, an early warning and alarm module and a power management module, and performs intelligent analysis and risk assessment by acquiring multiple physiological signals in real time and combining a machine learning algorithm. According to the system, the cardiovascular diseases can be monitored and managed comprehensively, and the system has the advantages that multiple physiological indexes are monitored in real time, risks are analyzed and evaluated intelligently, the personalized health suggestions are provided, and remote monitoring and timely early warning are achieved.

Description

Technical Field

[0001] This application relates to the field of medical monitoring devices, and more particularly, to an intelligent monitoring system for cardiovascular disease care. Background Art

[0002] Cardiovascular diseases are a major global health threat, and early detection and continuous monitoring are crucial for improving patient prognosis. Traditional methods for monitoring cardiovascular diseases have many limitations and are difficult to meet the needs of modern medicine. These methods usually cannot track patients' physiological indicators in real time, resulting in medical staff being unable to detect potential health risks in a timely manner. In addition, traditional methods often rely on patients to actively report symptoms, which may lead to some early or mild symptoms being overlooked.

[0003] Existing monitoring devices usually have a single function and can only monitor a single physiological indicator, such as heart rate or blood pressure, and cannot provide a comprehensive assessment of the health status. At the same time, the data analysis capabilities of these devices are limited, making it difficult to conduct in-depth risk assessment and early warning. Many traditional devices also have problems such as poor portability and complex operation, which are not conducive to long-term use and self-management by patients.

[0004] In addition, there are also deficiencies in data transmission and sharing in the prior art. Many devices cannot achieve real-time data transmission, making it difficult for medical staff to remotely monitor the patient's condition. The storage and analysis of data are usually limited to the device itself, lacking an effective connection with medical institutions or cloud platforms, which restricts the long-term tracking and in-depth analysis of data.

[0005] In terms of user interaction, existing devices often lack a friendly interface and personalized functions, making it difficult to provide users with timely and targeted health advice and early warning information. At the same time, many devices do not fully consider the daily life needs of users, such as the lack of functions like medication reminders and exercise suggestions, which affects the comprehensive care of cardiovascular disease patients.

[0006] In view of the above problems, the prior art urgently needs to be improved. Summary of the Invention

[0007] The purpose of this application is to provide an intelligent monitoring system for cardiovascular disease care, which has the advantages of real-time monitoring of multiple physiological indicators, intelligent analysis and assessment of risks, providing personalized health advice, realizing remote monitoring, and timely early warning.

[0008] This application provides an intelligent monitoring system for cardiovascular disease care, and the technical solution is as follows: It includes the following: a physiological signal acquisition module for real-time acquisition of physiological signals such as the user's heart rate, blood pressure, blood oxygen saturation, electrocardiogram (ECG), etc.; a data processing and analysis module for preprocessing, feature extraction, and anomaly detection of the acquired physiological signals, and evaluating the risk of cardiovascular diseases through machine learning algorithms; a user interaction module including a display screen, voice prompts, and a mobile application for providing real-time health data, warning information, and care suggestions to the user; a communication module for transmitting the acquired physiological signals and risk assessment results to a cloud server or a remote monitoring platform of a medical institution; a warning and alarm module that automatically triggers a warning or alarm when abnormal physiological signals or a high risk of cardiovascular diseases are detected, and notifies the user, their family members, or medical staff via text message, phone call, or application; a power management module for providing a stable power supply to the system and supporting a low-power mode to extend the device usage time; the physiological signal acquisition module transfers data to the data processing and analysis module through an ADC, the processed data of the data processing and analysis module is transferred to the user interaction module for display and transmitted through the communication module, the warning and alarm module decides whether to alarm according to the analysis results, and the power management module provides a stable power supply for all modules.

[0009] Further, this application also proposes that the physiological signal acquisition module includes a wearable device and an electrocardiogram sensor. The wearable device is used to monitor the user's heart rate, blood oxygen saturation, and exercise status in real time, and the electrocardiogram sensor is used to acquire the user's electrocardiogram signal. Both the wearable device and the electrocardiogram sensor transmit data to the data processing and analysis module through wireless transmission technology.

[0010] Further, this application also proposes that the data processing and analysis module includes the following: a preprocessing unit for filtering, denoising, and normalizing the acquired physiological signals; a feature extraction unit for extracting cardiovascular disease-related features from the preprocessed signals; a risk assessment unit for real-time assessment of the user's cardiovascular disease risk based on the extracted features and generating a risk assessment report.

[0011] Further, this application also proposes that the user interaction module includes a mobile application for displaying the user's real-time physiological data, historical health records, and risk assessment results, and providing personalized health suggestions and care plans; a voice prompt function: when abnormal physiological signals are detected, the system prompts the user to take corresponding countermeasures through voice; a visualization interface for displaying the trend of the user's health data in the form of charts to help the user better understand their own health status.

[0012] Furthermore, this application also proposes that the communication module supports multiple communication protocols, including Bluetooth for transmitting the data collected by the wearable device to a smartphone or a local data processing device; WiFi for transmitting data to a cloud server or a remote monitoring platform; and 4G / 5G for realizing remote data transmission in a WiFi-free environment.

[0013] Furthermore, this application also proposes that the user interaction module further includes a medication reminder function, which, according to the user's medication plan, regularly reminds the user to take medicine and records the medication situation; a sports monitoring and advice function, which, according to the user's health condition, provides personalized sports advice and real-time monitors the changes in physiological signals during the sports process; and a diet advice function, which, according to the user's cardiovascular disease risk, provides personalized diet advice to help the user improve eating habits.

[0014] As can be seen from the above, an intelligent monitoring system for cardiovascular disease care provided by this application includes a physiological signal acquisition module, a data processing and analysis module, a user interaction module, a communication module, an early warning and alarm module, and a power management module. By collecting multiple physiological signals in real time, combining machine learning algorithms for intelligent analysis and risk assessment, and providing personalized health advice and timely early warning, it realizes the comprehensive monitoring and management of cardiovascular diseases, and has the advantages of real-time monitoring of multiple physiological indicators, intelligent analysis and risk assessment, providing personalized health advice, realizing remote monitoring, and timely early warning. Description of the Drawings

[0015] Figure 1 is the overall architecture of the intelligent monitoring system; Figure 2 is the physiological signal acquisition module; Figure 3 is the data processing and analysis module; Figure 4 is the user interaction module; Figure 5 is the communication module; Figure 6 is the early warning and alarm module; Figure 7 is the power management module; Figure 8 is the psychological monitoring module; Detailed Embodiments

[0016] The technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. The components of this application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents the selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative efforts belong to the scope of protection of this application. It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0017] Cardiovascular diseases are a major global health threat. Early detection and continuous monitoring are crucial for improving patient prognosis. Traditional monitoring methods have limitations, such as being unable to track in real time and relying on patients to report actively. Intelligent monitoring systems provide more accurate and convenient monitoring means by integrating advanced technologies.

[0018] In the care of cardiovascular diseases, real-time monitoring and early warning are important technical issues. To effectively solve this problem, an intelligent monitoring system for cardiovascular disease care is proposed. The system includes a physiological signal acquisition module, a data processing and analysis module, a user interaction module, a communication module, an early warning and alarm module, and a power management module. The physiological signal acquisition module is used to collect physiological signals such as the user's heart rate, blood pressure, blood oxygen saturation, electrocardiogram (ECG), etc. in real time. The data processing and analysis module preprocesses, extracts features, and detects abnormalities of the collected physiological signals, and evaluates the risk of cardiovascular diseases through machine learning algorithms. The user interaction module includes a display screen, voice prompts, and a mobile application, which are used to provide the user with real-time health data, early warning information, and care suggestions. The communication module transmits the collected physiological signals and risk assessment results to the cloud server or the remote monitoring platform of medical institutions. The early warning and alarm module automatically triggers an early warning or alarm when abnormal physiological signals or a high risk of cardiovascular diseases are detected, and notifies the user, their family members, or medical staff through text messages, phone calls, or applications. The power management module provides a stable power supply for the system and supports a low-power mode to extend the device usage time.

[0019] The physiological signal acquisition module is used to collect various physiological signals of the user in real time to ensure the timeliness and accuracy of the data. The data processing and analysis module preprocesses and analyzes the collected signals, and evaluates the cardiovascular disease risk through machine learning algorithms to ensure the effectiveness and reliability of the data. The user interaction module provides real-time health data, warning information and nursing suggestions to the user through the display screen, voice prompt and mobile application to ensure that the user can timely understand their own health status and take corresponding measures. The communication module transmits the data to the cloud server or the remote monitoring platform of the medical institution to ensure the remote transmission and monitoring of the data. The warning and alarm module automatically triggers a warning or alarm when abnormal physiological signals or a high risk of cardiovascular disease are detected to ensure timely notification of the user, their family members or medical staff. The power management module provides a stable power supply for the system and supports the low-power mode to extend the device usage time to ensure the stable operation of the system. Through the above technical means, the system can realize the real-time monitoring and warning of cardiovascular diseases and effectively solve the problems of real-time monitoring and warning in cardiovascular disease care.

[0020] The physiological signal acquisition module transfers the data to the data processing and analysis module through the ADC. The data processed by the data processing and analysis module is transferred to the user interaction module for display and transmitted through the communication module. The warning and alarm module decides whether to alarm according to the analysis result. The power management module provides a stable power supply for all modules. The physiological signal acquisition module includes a wearable device and an electrocardiogram sensor. The wearable device is used to monitor the user's heart rate, blood oxygen saturation and exercise status in real time, and the electrocardiogram sensor is used to collect the user's electrocardiogram signal. Both the wearable device and the electrocardiogram sensor transmit the data to the data processing and analysis module through wireless transmission technology.

[0021] The data processing and analysis module includes a preprocessing unit, a feature extraction unit and a risk assessment unit. The preprocessing unit is used to filter, denoise and normalize the collected physiological signals. The feature extraction unit is used to extract the features related to cardiovascular diseases from the preprocessed signals. The risk assessment unit, based on the extracted features, real-time evaluates the user's cardiovascular disease risk through a machine learning model and generates a risk assessment report. The user interaction module includes a mobile application, a voice prompt function and a visualization interface. The mobile application is used to display the user's real-time physiological data, historical health records and risk assessment results, and provide personalized health suggestions and nursing plans. The voice prompt function, when detecting abnormal physiological signals, prompts the user to take corresponding countermeasures through voice. The visualization interface is used to display the trend of the user's health data in the form of charts to help the user better understand their own health status.

[0022] The communication module supports multiple communication protocols, including Bluetooth, WiFi, and 4G / 5G. Bluetooth is used to transmit the data collected by the wearable device to a smartphone or a local data processing device. WiFi is used to transmit data to a cloud server or a remote monitoring platform. 4G / 5G is used to achieve remote data transmission in a WiFi-free environment. The user interaction module also includes a medication reminder function, a motion monitoring and advice function, and a diet advice function. The medication reminder function, according to the user's medication plan, the system regularly reminds the user to take medicine and records the medication situation. The motion monitoring and advice function, according to the user's health condition, the system provides personalized exercise advice and real-time monitors the changes in physiological signals during exercise. The diet advice function, according to the user's cardiovascular disease risk, the system provides personalized diet advice to help the user improve eating habits.

[0023] This intelligent monitoring system, by integrating a variety of advanced technologies, realizes the real-time collection, processing, and analysis of users' physiological signals, and provides health data and warning information to users through various interaction methods. Compared with traditional monitoring methods, this system has higher real-time performance and accuracy, and can better meet the needs of cardiovascular disease care.

[0024] Furthermore, this application also proposes that the physiological signal acquisition module includes a wearable device and an electrocardiogram sensor. The wearable device is used to monitor the user's heart rate, blood oxygen saturation, and motion state in real time, and the electrocardiogram sensor is used to acquire the user's electrocardiogram signal. Both the wearable device and the electrocardiogram sensor transmit data to the data processing and analysis module through wireless transmission technology.

[0025] The physiological signal acquisition module realizes the real-time monitoring and acquisition of the user's heart rate, blood oxygen saturation, motion state, and electrocardiogram signal through the wearable device and the electrocardiogram sensor. The wearable device and the electrocardiogram sensor transmit the collected data to the data processing and analysis module through wireless transmission technology. In this way, by using the wearable device and the electrocardiogram sensor, the system can monitor various physiological signals of the user in real time and transmit the data to the data processing and analysis module through wireless transmission technology for further analysis and processing, realizing the intelligent monitoring and risk assessment of cardiovascular diseases.

[0026] The wearable device and the electrocardiogram sensor can take various forms. For example, the wearable device can be a smart watch, a wristband, or other portable devices, and the electrocardiogram sensor can be embedded in a chest strap or a patch. The wireless transmission technology can use Bluetooth, WiFi, or other low-power wireless communication technologies to ensure the stability and real-time performance of data transmission.

[0027] By introducing wearable devices and electrocardiogram sensors, this application realizes the real-time monitoring and acquisition of users' heart rate, blood oxygen saturation, motion state, and electrocardiogram signals. Compared with the prior art, this application can provide more comprehensive and accurate physiological signal monitoring, especially having significant advantages in the early detection and continuous monitoring of cardiovascular diseases. Through wireless transmission technology, data can be transmitted to the data processing and analysis module in real time, further improving the intelligence and convenience of the system.

[0028] Furthermore, this application also proposes that the data processing and analysis module includes the following: The preprocessing unit is used to filter, denoise, and normalize the collected physiological signals; the feature extraction unit is used to extract features related to cardiovascular diseases from the preprocessed signals; the risk assessment unit, based on the extracted features, uses a machine learning model to perform real-time assessment of the user's cardiovascular disease risk and generate a risk assessment report.

[0029] The data processing and analysis module includes a preprocessing unit, a feature extraction unit, and a risk assessment unit. The preprocessing unit filters, denoises, and normalizes the collected physiological signals to ensure the quality and consistency of the signals. The feature extraction unit extracts features related to cardiovascular diseases from the preprocessed signals, and these features are the key data for disease risk assessment. The risk assessment unit then, based on the extracted features, uses a machine learning model to perform real-time assessment of the user's cardiovascular disease risk and generate a risk assessment report. In this way, the data processing and analysis module can effectively process and analyze the collected physiological signals, thereby assessing the user's cardiovascular disease risk and providing timely health warnings and suggestions.

[0030] The implementation method of the preprocessing unit can include various filtering techniques, such as low-pass filtering, high-pass filtering, and band-pass filtering, for removing noise and interference in the signals. The denoising process can be achieved through algorithms such as wavelet transform or adaptive filtering. The normalization process can use standardization or min-max scaling methods. The feature extraction unit can adopt time-domain, frequency-domain, and time-frequency domain analysis methods to extract features from the preprocessed signals, such as heart rate variability, QT interval, QRS complex, etc. The risk assessment unit can, based on machine learning models such as support vector machines, random forests, or neural networks, classify and predict the extracted features to assess the cardiovascular disease risk.

[0031] Through the data processing and analysis module, this application can efficiently process and analyze the collected physiological signals, solving the technical problem of how to effectively process and analyze the collected physiological signals to evaluate the risk of cardiovascular diseases. Compared with the prior art, this application provides a more real-time, accurate, and comprehensive method for evaluating the risk of cardiovascular diseases, capable of providing timely health warnings and personalized care suggestions for users, significantly improving the early detection and intervention effects of cardiovascular diseases.

[0032] Furthermore, this application also proposes that the user interaction module includes a mobile application, a voice prompt function, and a visual interface. The mobile application displays the user's real-time physiological data, historical health records, and risk assessment results, and provides personalized health suggestions and care plans. The voice prompt function, when detecting abnormal physiological signals, prompts the user to take corresponding countermeasures through voice. The visual interface displays the trend of the user's health data in the form of charts to help the user better understand their own health status.

[0033] The user interaction module includes a mobile application for displaying the user's real-time physiological data, historical health records, and risk assessment results, and providing personalized health suggestions and care plans. The voice prompt function, when detecting abnormal physiological signals, prompts the user to take corresponding countermeasures through voice. The visual interface displays the trend of the user's health data in the form of charts to help the user better understand their own health status. These technical features cooperate with each other to solve the technical problems of the user obtaining real-time health data, historical health records, and risk assessment results during the monitoring process of cardiovascular diseases, and providing personalized health suggestions and care plans.

[0034] The mobile application can be installed and used through a smartphone or tablet, and the user can view their health data anytime and anywhere. The voice prompt function can perform voice broadcasts through the built-in speaker of the device or the connected earphone to ensure that the user obtains important health prompt information in the first time. The visual interface can adopt various chart forms such as line charts and bar charts to intuitively display the change trend of the user's health data, and the user can view the detailed data through touch screen operations.

[0035] This application combines a mobile application, a voice prompt function, and a visual interface to provide a comprehensive and convenient user interaction method. Compared with the prior art, this application can not only monitor the user's physiological data in real time, but also help the user timely understand their own health status through voice prompts and visual charts, and provide personalized health suggestions and care plans, greatly enhancing the user experience and the effect of health management. Thus, this application has significant technical advantages in the field of cardiovascular disease monitoring.

[0036] Furthermore, this application also proposes that the communication module supports multiple communication protocols including Bluetooth for transmitting the data collected by the wearable device to a smart phone or a local data processing device; WiFi for transmitting the data to a cloud server or a remote monitoring platform; and 4G / 5G for realizing remote data transmission in a WiFi-free environment.

[0037] The communication module supports multiple communication protocols such as Bluetooth, WiFi, and 4G / 5G. It realizes data transmission between the wearable device and a smart phone or a local data processing device through Bluetooth, transmits data to a cloud server or a remote monitoring platform through WiFi, and realizes remote data transmission in a WiFi-free environment through 4G / 5G. These technical features cooperate with each other to ensure the diversity and stability of data transmission, and solve the problems of diversity and stability of data transmission in the intelligent monitoring system for cardiovascular disease care.

[0038] The Bluetooth protocol can adopt low-power Bluetooth technology to reduce energy consumption and extend the device usage time. The WiFi protocol can support dual-band WiFi to improve the stability and speed of data transmission. The 4G / 5G protocol can be compatible with multiple network modes by integrating a multi-mode modem. The communication module can automatically select the optimal communication protocol according to the current network environment through an automatic switching mechanism to ensure the continuity and reliability of data transmission.

[0039] By supporting multiple communication protocols, the technical solution of this application can effectively solve the problems of diversity and stability of data transmission in the intelligent monitoring system for cardiovascular disease care. Compared with the prior art, the solution of this application can provide stable and reliable data transmission in different network environments, ensure that the user's physiological signals and risk assessment results can be transmitted to the cloud server or the remote monitoring platform in real time, and improve the practicability and reliability of the system.

[0040] Furthermore, this application also proposes that the user interaction module further includes a drug reminder function. According to the user's medication plan, the system regularly reminds the user to take medicine and records the medication situation; a sports monitoring and suggestion function. According to the user's health condition, the system provides personalized sports suggestions and real-time monitors the changes in physiological signals during the sports process; and a diet suggestion function. According to the user's cardiovascular disease risk, the system provides personalized diet suggestions to help the user improve eating habits.

[0041] The medication reminder function of this application ensures that users take their medications on time by providing timed reminders and recording medication usage, thus avoiding missed or incorrect doses and improving the treatment effect. The exercise monitoring and advice function provides personalized exercise advice based on the user's health condition and monitors real-time changes in physiological signals during exercise to help users exercise scientifically and reasonably, preventing over-exercise or under-exercise. The diet advice function provides personalized diet advice based on the user's cardiovascular disease risk to help users improve their eating habits and reduce the risk of cardiovascular diseases. Through the integration of these functions, the system can provide comprehensive management and monitoring in terms of medication, exercise, and diet, helping users better manage their health and prevent and control cardiovascular diseases.

[0042] The implementation of the medication reminder function can include setting up a medication plan in a mobile application and sending reminder notifications through the application. The system can also record the time and dose of each medication taken to generate a medication record for users and medical staff to refer to. The exercise monitoring and advice function can use wearable devices to monitor the user's heart rate, blood oxygen saturation, and exercise status in real time. The system provides personalized exercise advice based on this data and monitors real-time changes in physiological signals during exercise to remind users to adjust their exercise intensity. The diet advice function can generate personalized diet advice by analyzing the user's health data and cardiovascular disease risk to help users choose a healthy diet plan.

[0043] This application realizes comprehensive management and monitoring of users in terms of medication, exercise, and diet by integrating the medication reminder, exercise monitoring and advice, and diet advice functions. Compared with the prior art, this application provides a more comprehensive and personalized health management solution, helping users better prevent and control cardiovascular diseases and improve their quality of life.

[0044] Furthermore, this application also proposes a psychological monitoring module for detecting the user's mental health status. The psychological monitoring module includes a psychological stress sensor for detecting the user's psychological stress level and a mental health assessment unit for evaluating the user's mental health status. The mental health assessment unit evaluates the user's mental health status based on the psychological stress level detected by the psychological stress sensor and generates a mental health report. The psychological monitoring module transmits the data to the data processing and analysis module through wireless transmission technology.

[0045] The psychological monitoring module is used to detect the mental health status of users. The psychological stress sensor detects the psychological stress level of users. The mental health assessment unit evaluates the mental health status of users based on the data from the psychological stress sensor and generates a mental health report. The psychological monitoring module transmits the data to the data processing and analysis module through wireless transmission technology. Through the psychological monitoring module, the system can detect and evaluate the mental health status of users in real time, generate a mental health report, and transmit the data to the data processing and analysis module, thus solving the mental health problems of users during the care process of cardiovascular diseases.

[0046] Specifically, the psychological stress sensor can be a skin conductance sensor based on resistance change, which can monitor the changes in the psychological stress of users in real time. The mental health assessment unit can adopt an algorithm based on machine learning to analyze the psychological stress data and evaluate the mental health status of users. The mental health report can be presented to users through a mobile application and corresponding mental health suggestions can be provided. The wireless transmission technology can adopt Bluetooth or WiFi to achieve real-time data transmission.

[0047] Thus, by adding the psychological monitoring module, this application realizes the real-time monitoring and evaluation of the mental health status of users, generates a mental health report, and transmits the data to the data processing and analysis module through wireless transmission technology, providing a more comprehensive solution for the care of cardiovascular diseases. Compared with the prior art, this application not only pays attention to the physical health of users, but also takes into account the mental health, and can improve the overall health status of users more comprehensively.

[0048] Furthermore, this application also proposes that the user interaction module further includes a diet advice function. According to the cardiovascular disease risk of users, the system provides personalized diet advice to help users improve their eating habits.

[0049] The user interaction module adds a diet advice function, which provides personalized diet advice according to the cardiovascular disease risk of users. By providing these suggestions, the system helps users improve their eating habits, thus better managing and preventing cardiovascular diseases. This technical feature directly solves the problem of how to provide personalized diet advice in the intelligent monitoring system for the care of cardiovascular diseases through personalized diet advice.

[0050] The diet advice function can analyze based on the user's real-time health data and historical health records. For example, the system can generate personalized diet plans by analyzing physiological signals such as the user's heart rate, blood pressure, and blood oxygen saturation, combined with the user's eating habits and nutritional needs. Further, the system can use machine learning algorithms to continuously optimize and adjust diet advice according to the user's health status and diet feedback. In addition, the diet advice function can be combined with a mobile application, and users can view diet advice, record their diet, and obtain real-time feedback through the application.

[0051] By adding the diet advice function, this application can provide a more comprehensive health management solution. Compared with the prior art, this application can not only monitor the user's physiological signals in real time, but also provide personalized diet advice according to the monitoring results to help users improve their eating habits, thereby effectively managing the risk of cardiovascular diseases. Thus, this application has significant advantages in improving the user's health management effect.

[0052] Further, this application also proposes that the user interaction module further includes a medication reminder function. According to the user's medication plan, the system regularly reminds the user to take medicine and records the medication situation; a sports monitoring and advice function. According to the user's health status, the system provides personalized sports advice and real-time monitors the changes in physiological signals during the sports process; a diet advice function. According to the user's cardiovascular disease risk, the system provides personalized diet advice to help users improve their eating habits.

[0053] The medication reminder function in the user interaction module ensures that the user takes medicine on time and avoids missing or mis-taking doses by regularly reminding the user to take medicine and recording the medication situation; the sports monitoring and advice function helps users choose appropriate exercise intensity and type to promote cardiovascular health by providing personalized sports advice according to the user's health status and real-time monitoring the changes in physiological signals during the sports process; the diet advice function helps users improve their eating habits and reduce the risk of cardiovascular diseases by providing personalized diet advice according to the user's cardiovascular disease risk. These functions cooperate with each other, and by providing comprehensive health management services, they solve the personalized needs of users in terms of medication, exercise, and diet, and improve the effect of cardiovascular disease care and the user's compliance.

[0054] The implementation methods of the medication reminder function can include setting a timer, using the notification function of a mobile application or a wearable device to remind users to take their medications on time, and recording the users' medication-taking situations in the application. The implementation methods of the exercise monitoring and advice function can include using a wearable device to monitor real-time data such as the users' heart rate, steps, and exercise time, and analyzing the users' health conditions through a data processing and analysis module to generate personalized exercise advice. The implementation methods of the diet advice function can include analyzing the users' cardiovascular disease risks by combining the diet records and health data input by the users, and generating personalized diet advice to help users adjust their eating habits.

[0055] Thus, by adding the medication reminder, exercise monitoring and advice, and diet advice functions, the present application further improves the practicality and comprehensiveness of the system in cardiovascular disease care. Compared with the prior art, the present application can not only monitor the users' physiological signals in real time, but also provide comprehensive health management services, meet the personalized needs of users in terms of medication, exercise, and diet, and significantly improve the users' compliance and care effects.

[0056] Furthermore, the present application also proposes a diet advice function. According to the users' cardiovascular disease risks, the system provides personalized diet advice to help users improve their eating habits.

[0057] The diet advice function analyzes the users' cardiovascular disease risks and provides personalized diet advice to help users improve their eating habits. This function plays an important role in solving the problem of how to provide personalized diet advice according to the users' health conditions. In this way, the intelligent monitoring system can not only monitor the users' physiological signals in real time, but also provide targeted diet advice based on the monitoring results, thereby helping users better manage their health and prevent the occurrence or deterioration of cardiovascular diseases.

[0058] To implement personalized diet advice, the system first analyzes physiological signals such as the users' heart rate, blood pressure, blood oxygen saturation, and electrocardiogram through a data processing and analysis module to evaluate the users' cardiovascular disease risks. Based on the risk assessment results, the system generates a diet plan suitable for the users in the diet advice function. For example, for users with a higher risk of hypertension, the system may recommend reducing salt intake; for users with abnormal blood lipids, the system may recommend reducing the intake of high-fat foods and increasing the intake of fiber-rich foods. As a preferred implementation method, the system can also further optimize the diet advice by combining the users' diet preferences and allergy information.

[0059] In this way, the system can provide more accurate and personalized diet suggestions to help users improve their eating habits, thereby reducing the risk of cardiovascular diseases. Compared with the prior art, the diet suggestion function of this application can provide a dynamically adjusted diet plan based on real-time monitored data, with higher accuracy and practicality. Thus, users can better manage their own health in daily life and prevent the occurrence and development of cardiovascular diseases.

[0060] The above are only embodiments of this application and are not intended to limit the protection scope of this application. For those skilled in the art, various changes and modifications can be made to this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. An intelligent monitoring system for cardiovascular disease care, characterized in that: It includes a physiological signal acquisition module, which includes a wearable device and an electrocardiogram sensor. The wearable device is used to monitor the user's heart rate, blood oxygen saturation and exercise status in real time, and the electrocardiogram sensor is used to collect the user's electrocardiogram signal. The wearable device and the electrocardiogram sensor both transmit data to the data processing and analysis module through wireless transmission technology; A data processing and analysis module, the data processing and analysis module includes a preprocessing unit, a feature extraction unit and a risk assessment unit, the preprocessing unit is used to filter, denoise and normalize the collected physiological signals, the feature extraction unit is used to extract features related to cardiovascular disease from the preprocessed signals, and the risk assessment unit performs real-time assessment of the user's cardiovascular disease risk through a machine learning model based on the extracted features and generates a risk assessment report; A user interaction module, including a display screen, voice prompts and a mobile application, for providing real-time health data, early warning information and nursing advice to the user. The mobile application is used to display the user's real-time physiological data, historical health records and risk assessment results, and provide personalized health advice and nursing plans. When the voice prompt function detects abnormal physiological signals, it prompts the user to take corresponding countermeasures through voice. The user interaction module also includes a visualization interface for displaying the user's health data trend in the form of a chart; A communication module, which is used to transmit the collected physiological signals and risk assessment results to a cloud server or a remote monitoring platform of a medical institution. The communication module supports Bluetooth, WiFi and 4G / 5G communication protocols. Bluetooth is used to transmit the data collected by the wearable device to a smartphone or a local data processing device. The WiFi is used to transmit the data to a cloud server or a remote monitoring platform. The 4G / 5G is used to achieve remote data transmission in an environment without WiFi. Early warning and alarm module: when abnormal physiological signals or high risk of cardiovascular disease are detected, the system automatically triggers early warning or alarm, and notifies the user, his / her family or medical staff via SMS, phone or application; Power management module, used to provide a stable power supply for the system and support low power mode to extend the use time of the device; The physiological signal acquisition module transmits data to the data processing and analysis module through ADC. The data processed by the data processing and analysis module is transmitted to the user interaction module for display and transmitted through the communication module. The early warning and alarm module decides whether to alarm according to the analysis results. The power management module provides stable power for all modules.

2. The intelligent monitoring system for cardiovascular disease care according to claim 1, characterized in that: The user interaction module also includes: a medication reminder function, according to the user's medication plan, the system regularly reminds the user to take medication and records the medication situation; The exercise monitoring and suggestion function provides personalized exercise suggestions based on the user's health status and monitors the changes in physiological signals during exercise in real time. The diet suggestion function provides personalized diet suggestions based on the user's cardiovascular disease risk to help users improve their eating habits.

3. The intelligent monitoring system for cardiovascular disease care according to claim 1, characterized in that: It also includes a psychological monitoring module for detecting the user's mental health status, the psychological monitoring module includes a psychological stress sensor for detecting the user's psychological stress level and a mental health assessment unit for assessing the user's mental health status, the mental health assessment unit assesses the user's mental health status based on the psychological stress level detected by the psychological stress sensor, and generates a mental health report, the psychological monitoring module transmits the data to the data processing and analysis module through wireless transmission technology.

4. The intelligent monitoring system for cardiovascular disease care according to claim 1, characterized in that: The wearable device is a smart watch, wristband or other portable device, the electrocardiogram sensor is embedded in a chest strap or patch, and the wireless transmission technology uses Bluetooth, WiFi or other low-power wireless communication technology.

5. The intelligent monitoring system for cardiovascular disease care according to claim 1, characterized in that: The filtering techniques of the preprocessing unit include low-pass filtering, high-pass filtering and band-pass filtering, the denoising process is implemented by wavelet transform or adaptive filtering algorithm, and the normalization process uses standardization or minimum to maximum scaling method.

6. The intelligent monitoring system for cardiovascular disease care according to claim 1, characterized in that: The feature extraction unit uses time domain, frequency domain and time-frequency domain analysis methods to extract features related to cardiovascular diseases such as heart rate variability, QT interval, QRS complex, etc. from the preprocessed signal.

7. The intelligent monitoring system for cardiovascular disease care according to claim 1, characterized in that: The risk assessment unit classifies and predicts the extracted features based on a machine learning model such as a support vector machine, a random forest or a neural network to assess the risk of cardiovascular disease.

8. The intelligent monitoring system for cardiovascular disease care according to claim 2, characterized in that: The medication reminder function is achieved by setting a medication plan in the mobile application and sending reminder notifications through the application. The system also records the time and dosage of each medication and generates a medication record. The exercise monitoring and suggestion function monitors the user's heart rate, blood oxygen saturation and exercise status in real time through wearable devices. The system provides personalized exercise suggestions based on these data, and monitors physiological signal changes in real time during exercise to remind users to adjust exercise intensity. The diet suggestion function generates personalized diet suggestions by analyzing the user's health data and cardiovascular disease risks, combined with the user's dietary preferences and allergy information, to help users choose a healthy diet plan. The diet suggestion function is also combined with the mobile application. Users can view diet suggestions, record diet conditions, and get real-time feedback through the application.

9. The intelligent monitoring system for cardiovascular disease care according to claim 3, characterized in that: The psychological stress sensor is a skin conductance sensor based on resistance change. The mental health assessment unit uses an algorithm based on machine learning to analyze the psychological stress data. The mental health report is displayed to the user through a mobile application and provides corresponding mental health advice. The wireless transmission technology uses Bluetooth or WiFi.

10. The intelligent monitoring system for cardiovascular disease care according to claim 1, characterized in that :The Bluetooth protocol adopts low-power Bluetooth technology, the WiFi protocol supports dual-band WiFi, the 4G / 5G protocol achieves compatibility with multiple network standards by integrating a multi-mode modem, and the communication module automatically selects the optimal communication protocol according to the current network environment through an automatic switching mechanism.

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