Intelligent health monitoring equipment

Abstract

The invention relates to the technical field of health monitoring equipment, in particular to intelligent health monitoring equipment, which comprises a data acquisition module used for acquiring multi-dimensional health data of a user, the multi-dimensional health data including heart rate, blood pressure, oxyhemoglobin saturation, body temperature, motion state and sleep quality; transmitting the multi-dimensional health data to a data processing module; the system comprises a data acquisition module, a data processing module which is in communication connection with the data acquisition module, an analysis early warning module which is in communication connection with the data processing module, a user interaction module which is in communication connection with the analysis early warning module, and a personalized suggestion module which is in communication connection with the user interaction module and the analysis early warning module and is used for receiving a health management instruction sent by the user interaction module; a personalized health management suggestion is generated based on the health management instruction and the analysis result of the analysis early warning module; personalized health management suggestions are transmitted to the user interaction module to be displayed, and multi-dimensional health data such as the heart rate, the blood pressure, the blood oxygen saturation degree, the body temperature, the motion state and the sleep quality can be monitored at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring devices, particularly intelligent health monitoring devices. Background Art

[0002] With the continuous improvement of people's health awareness, intelligent health monitoring devices have been rapidly developed and widely used in recent years. Such devices can usually collect basic physiological data of users, such as heart rate, blood pressure, etc., and provide simple health status analysis. However, existing intelligent health monitoring devices still have many deficiencies in terms of functional comprehensiveness, data analysis depth, and personalized services.

[0003] Most intelligent health monitoring devices on the market currently can only collect a limited number of physiological indicators, making it difficult to comprehensively reflect the health status of users. For example, some devices only focus on heart rate and step count monitoring and cannot capture equally important health indicators such as blood pressure and blood oxygen. This single or limited data collection method severely limits the accuracy and comprehensiveness of health assessment.

[0004] In addition, existing devices are also relatively weak in data analysis. Most devices only provide simple statistical analysis, such as daily average heart rate, total steps, etc., lacking in-depth insight and early warning capabilities for users' health status. This shallow analysis is difficult to detect potential health risks in a timely manner and cannot provide valuable health management suggestions for users.

[0005] Personalized service is another aspect that urgently needs improvement. Existing health monitoring devices often adopt a "one-size-fits-all" approach, providing the same health advice and goal setting for all users. This method ignores the differences in each person's physical condition, lifestyle, and health needs, resulting in a significant reduction in the practicality and enforceability of the advice.

[0006] Data security and privacy protection are also major challenges faced by existing devices. Many devices lack sufficient encryption and protection measures during data transmission and storage, posing a risk of user privacy leakage. This not only affects the user experience but may also bring serious legal and ethical issues.

[0007] Finally, existing intelligent health monitoring devices generally lack the ability of continuous learning and optimization. They usually conduct health assessments based on fixed algorithms and models and cannot adjust and optimize their functions according to the long-term usage situation and feedback of users, which limits the long-term practicality and accuracy of the devices. Summary of the Invention

[0008] In view of the above problems, the present invention proposes a new type of intelligent health monitoring device, aiming to provide a comprehensive, accurate and personalized health monitoring and management solution. Through innovative designs such as multi-dimensional health data collection, intelligent data analysis, personalized recommendation generation, strict data security protection and continuous learning and optimization, the present invention effectively solves many problems existing in the prior art.

[0009] The present invention proposes an intelligent clinical nursing skill training and evaluation system, including:

[0010] A data collection module, configured to:

[0011] Collect multi-dimensional health data of a user, where the multi-dimensional health data includes heart rate, blood pressure, blood oxygen saturation, body temperature, exercise status and sleep quality;

[0012] Transmit the multi-dimensional health data to a data processing module;

[0013] A data processing module, communicatively connected to the data collection module, configured to:

[0014] Receive the multi-dimensional health data sent by the data collection module;

[0015] Based on the multi-dimensional health data, perform data cleaning, feature extraction and data standardization;

[0016] Transmit the processed health data to an analysis and warning module;

[0017] An analysis and warning module, communicatively connected to the data processing module, configured to:

[0018] Receive the processed health data sent by the data processing module;

[0019] Based on the processed health data, perform health status analysis using a preset machine learning model;

[0020] Generate a health assessment report and risk warning information according to the analysis results;

[0021] Transmit the health assessment report and risk warning information to a user interaction module;

[0022] A user interaction module, communicatively connected to the analysis and warning module, configured to:

[0023] Receive the health assessment report and risk warning information sent by the analysis and warning module;

[0024] Display the health assessment report and risk warning information to the user through a user interface;

[0025] Receive health management instructions input by the user;

[0026] Transmit the health management instruction to the personalized recommendation module;

[0027] The personalized recommendation module, which is communicatively connected to the user interaction module and the analysis and warning module, is used for:

[0028] Receive the health management instruction sent by the user interaction module;

[0029] Generate personalized health management recommendations based on the health management instruction and the analysis result of the analysis and warning module;

[0030] Transmit the personalized health management recommendations to the user interaction module for display.

[0031] Preferably, the data acquisition module includes:

[0032] A multi-sensor unit for collecting physiological parameters of the user, including an electrocardiogram sensor, a photoplethysmogram sensor, an acceleration sensor, and a temperature sensor;

[0033] An environmental monitoring unit for collecting parameters of the environment where the user is located, including temperature, humidity, and air quality;

[0034] A data fusion unit, connected to the multi-sensor unit and the environmental monitoring unit, for preliminarily fusing the collected physiological parameters and environmental parameters to form a comprehensive health data set.

[0035] Preferably, the data processing module includes:

[0036] A data cleaning unit for detecting and processing outliers in the received multi-dimensional health data;

[0037] A feature extraction unit for extracting key health features from the cleaned data;

[0038] A data standardization unit for converting the extracted health features into a standard format for subsequent analysis.

[0039] Preferably, the analysis and warning module includes:

[0040] A health model library storing multiple pre-trained health assessment models;

[0041] A model selection unit for selecting a suitable assessment model from the health model library according to the personal characteristics of the user and the characteristics of the health data;

[0042] A risk assessment unit for analyzing the health status of the user using the selected assessment model to generate a health risk score;

[0043] An early warning generation unit for generating early warning information at corresponding levels according to the health risk score.

[0044] Preferably, the user interaction module includes:

[0045] A visualization display unit for converting the health assessment report and risk early warning information into intuitive charts and text descriptions;

[0046] A voice interaction unit for providing the function of voice broadcasting health information and receiving voice instructions;

[0047] A touch operation unit for receiving various instructions input by the user through the touch screen and providing feedback.

[0048] Preferably, it further includes a data security module, which is communicatively connected to the data collection module, the data processing module, and the analysis and early warning module. The data security module is used for:

[0049] Performing encryption processing on the collected health data;

[0050] Verifying the user's identity information;

[0051] Controlling the access rights of data to ensure that only authorized modules and users can access the relevant health data.

[0052] Preferably, it further includes a remote medical module, which is communicatively connected to the analysis and early warning module and the user interaction module. The remote medical module is used for:

[0053] Automatically connecting to the remote medical service when detecting an emergency health risk;

[0054] Supporting the user to conduct real-time video consultations with remote doctors;

[0055] Receiving the diagnostic opinions of remote doctors and presenting them to the user through the user interaction module.

[0056] Preferably, the personalized recommendation module further includes:

[0057] A behavior analysis unit for analyzing the user's daily behavior patterns;

[0058] A goal setting unit for setting reasonable health improvement goals according to the user's health status and personal preferences;

[0059] A plan generation unit for generating personalized diet, exercise, and work and rest recommendations based on the behavior analysis and goal setting.

[0060] Preferably, it further includes a data synchronization module, which is communicatively connected to the data processing module. The data synchronization module is used for:

[0061] Synchronize data with other smart devices of the user;

[0062] Obtain the user's historical health data from an external health management platform;

[0063] Upload the health data collected and processed by this device to cloud storage to achieve data consistency among multiple devices.

[0064] Preferably, it further includes an artificial intelligence learning module, which is communicatively connected to the analysis and warning module and the personalized recommendation module. The artificial intelligence learning module is used for:

[0065] Continuously learn the user's health data patterns and feedback;

[0066] Optimize and update the health assessment model;

[0067] According to the learning results, adjust the generation strategy of personalized recommendations to improve the pertinence and effectiveness of the recommendations.

[0068] Specifically, the intelligent health monitoring device of the present invention has achieved significant technological breakthroughs and improvements in the following aspects:

[0069] First of all, by integrating a variety of sensor technologies, the present invention realizes all-round health data collection. Different from existing devices that only focus on one or a few indicators, the present invention can simultaneously monitor multi-dimensional health data such as heart rate, blood pressure, blood oxygen saturation, body temperature, exercise status, and sleep quality. This comprehensive data collection provides a rich and reliable data basis for subsequent health analysis, greatly improving the accuracy and comprehensiveness of health assessment.

[0070] Secondly, the present invention adopts advanced machine learning algorithms, such as long short-term memory network (LSTM), to deeply analyze the collected health data. This intelligent analysis method can not only timely detect the user's current health problems, but also predict potential health risks. For example, by analyzing the long-term change trends of the user's blood pressure, heart rate, etc., the system can early identify the risks of cardiovascular diseases, so as to achieve preventive intervention for diseases.

[0071] In terms of personalized services, the innovation of the present invention lies in its dynamic and adaptive health management plan generation mechanism. The system will comprehensively consider the user's health data, living habits, personal preferences, and previous feedback to generate highly personalized health recommendations. This "tailor-made" health management plan greatly improves the executability and effectiveness of the recommendations, and helps users continuously improve their health status.

[0072] In terms of data security, the present invention adopts multi-level security protection measures. Advanced encryption algorithms are used throughout the entire process from data collection, transmission to storage. At the same time, multi-factor authentication and role-based access control are introduced, effectively ensuring the security and privacy of users' health data. This not only enhances users' trust in the device but also makes it possible to use and share health data in a compliant manner.

[0073] Finally, a major innovation of the present invention lies in its continuous learning and optimization ability. Through the artificial intelligence learning module, the system can continuously learn the health data patterns and feedback of users, and dynamically adjust the health assessment model and recommendation generation strategy. This adaptive learning mechanism ensures that the device can maintain its accuracy and practicality in the long term, providing users with up-to-date health management services.

[0074] In summary, the intelligent health monitoring device of the present invention effectively solves many problems existing in the prior art through its comprehensive data collection, in-depth intelligent analysis, personalized recommendation generation, strict data protection, and continuous self-optimization. This innovation not only improves the accuracy and comprehensiveness of health monitoring but also greatly enhances the personalization and effectiveness of health management. Through this intelligent and personalized health management method, the present invention is expected to significantly improve users' health levels and quality of life, bringing a revolutionary change to the fields of personal health management and preventive medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 is the overall logical block diagram of the system of the present invention.

[0076] Figure 2 is the logical block diagram of the data collection module of the present invention.

[0077] Figure 3 is the logical block diagram of the data processing module of the present invention.

[0078] Figure 4 is the logical block diagram of the analysis and warning module of the present invention.

[0079] Figure 5 is the logical block diagram of the user interaction module of the present invention.

[0080] Figure 6 is the logical block diagram of the personalized recommendation module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0081] Referring to Figure 1-6 , the present invention provides an intelligent health monitoring device, which is characterized in that it includes a data collection module 1, a data processing module 2, an analysis and warning module 3, a user interaction module 4, and a personalized recommendation module 5.

[0082] The data acquisition module 1 is used to collect the multi-dimensional health data of users and transmit this data to the data processing module 2. Preferably, the multi-dimensional health data includes but is not limited to heart rate, blood pressure, blood oxygen saturation, body temperature, exercise status, and sleep quality. In an embodiment of the present invention, the data acquisition module 1 can collect the physiological parameters of users in real time through a variety of sensors. For example, the heart rate can be collected through a photoplethysmogram sensor, the blood pressure can be measured through a pressure sensor, and the body temperature can be obtained through an infrared temperature sensor. This multi-dimensional data acquisition method can comprehensively reflect the health status of users and provide a rich data basis for subsequent analysis.

[0083] The data processing module 2 is communicatively connected to the data acquisition module 1 and is used to receive the multi-dimensional health data sent by the data acquisition module 1. Based on the received multi-dimensional health data, the data processing module 2 performs operations such as data cleaning, feature extraction, and data standardization. Preferably, in the data cleaning process, a sliding window median filtering algorithm can be used to remove outliers, and wavelet transform methods can be adopted for feature extraction to extract the time-frequency features of the signal. These processing steps can effectively improve the data quality and provide reliable data input for subsequent analysis. The processed health data is then transmitted to the analysis and warning module 3.

[0084] The analysis and warning module 3 is communicatively connected to the data processing module 2 and is used to receive the processed health data sent by the data processing module 2. Based on these processed health data, the analysis and warning module 3 uses a preset machine learning model for health status analysis. In a preferred embodiment of the present invention, the machine learning model can be a deep learning model based on a long short-term memory network (LSTM), which can effectively capture the temporal features of health data. The input of the model is the multi-dimensional health data sequence of the user, and the output is the health risk score. Specifically, the model can be expressed as:

[0085] h t = LSTM(x t , h t-1 ),

[0086] y t = softmax(W·h t + b),

[0087] The input of the model is the health data vector x t of the user at different time points, and the output is the predicted health risk score y t . Among them, h t represents the hidden state of the LSTM layer at time t, W and b are the weight matrix and bias vector respectively, and the softmax function is used to convert the output into a probability distribution form, representing the possibilities of different health states.

[0088] Based on the analysis results, the analysis and warning module 3 generates a health assessment report and risk warning information. For example, when it is detected that the user's blood pressure continuously exceeds 140 / 90 mmHg, the system generates a hypertension risk warning. This information is then transmitted to the user interaction module 4.

[0089] The user interaction module 4 is communicatively connected to the analysis and warning module 3 and is used to receive the health assessment report and risk warning information sent by the analysis and warning module 3. The user interaction module 4 presents this information to the user through the user interface, which can be presented in an intuitive way such as charts, text descriptions, etc. At the same time, the user interaction module 4 also receives health management instructions input by the user, such as setting health goals, adjusting the monitoring frequency, etc. These health management instructions are then transmitted to the personalized advice module 5.

[0090] The personalized advice module 5 is communicatively connected to the user interaction module 4 and the analysis and warning module 3. It receives the health management instructions sent by the user interaction module 4 and generates personalized health management advice based on these instructions and the analysis results of the analysis and warning module 3. For example, if the user sets a weight loss goal and the analysis results show that the user has mild obesity, the personalized advice module 5 may generate suggestions including a low-calorie diet plan and 30 minutes of aerobic exercise per day. These personalized health management suggestions are then transmitted to the user interaction module 4 for display.

[0091] In another embodiment of the present invention, the data acquisition module 1 further includes a multi-sensor unit 11, an environmental monitoring unit 12, and a data fusion unit 13. The multi-sensor unit 11 is used to collect the user's physiological parameters, including but not limited to an electrocardiogram sensor, a photoplethysmogram sensor, an acceleration sensor, and a temperature sensor. The combined use of these sensors can comprehensively capture the changes in the user's physiological state. The environmental monitoring unit 12 is used to collect the parameters of the user's environment, such as temperature, humidity, and air quality. These environmental factors have an important impact on the user's health status, and taking them into account can improve the accuracy of health assessment. The data fusion unit 13 is connected to the multi-sensor unit 11 and the environmental monitoring unit 12 and is used to preliminarily fuse the collected physiological parameters and environmental parameters to form a comprehensive health data set. Data fusion can use the Kalman filtering algorithm to minimize the noise impact of each sensor's data.

[0092] In addition, the data processing module 2 of the present invention includes a data cleaning unit 21, a feature extraction unit 22, and a data normalization unit 23. The data cleaning unit 21 is used to detect and process outliers in the received multi-dimensional health data. In practical applications, a reasonable threshold range can be set. For example, the normal range of heart rate is 60-100 beats per minute, and data outside this range may need to be further verified or excluded. The feature extraction unit 22 is used to extract key health features from the cleaned data. For example, for electrocardiogram data, features such as R-R interval and QT interval can be extracted. The data normalization unit 23 then converts the extracted health features into a standard format for subsequent analysis. The normalization process can adopt the Z-score method, that is, (x-μ) / σ, where x is the original feature value, μ is the mean value, and σ is the standard deviation.

[0093] Through the above detailed module design and data processing flow, the intelligent health monitoring device of the present invention can comprehensively and accurately collect and analyze the health data of users, provide timely health assessments and personalized suggestions, and effectively improve the health management effect of users. In a preferred embodiment of the present invention, the analysis and warning module 3 includes a health model library 31, a model selection unit 32, a risk assessment unit 33, and a warning generation unit 34. The health model library 31 stores multiple pre-trained health assessment models, which may include but are not limited to cardiovascular disease risk models, diabetes risk models, and sleep quality assessment models, etc. Preferably, these models can adopt different machine learning algorithms, such as support vector machine (SVM), random forest (Random Forest), or deep neural network (DNN), etc., to adapt to different types of health data and assessment tasks.

[0094] The model selection unit 32 selects a suitable assessment model from the health model library 31 according to the personal characteristics of the user and the characteristics of the health data. For example, for a middle-aged user with a family history of hypertension, the system may preferentially select a cardiovascular disease risk model for assessment. This dynamic model selection mechanism can improve the pertinence and accuracy of health assessment.

[0095] The risk assessment unit 33 analyzes the health status of the user using the selected assessment model and generates a health risk score. In an embodiment of the present invention, the health risk score can adopt a standardized scale of 0-100, where 0 represents the lowest risk and 100 represents the highest risk. The calculation of the score can be based on the following formula:

[0096] Risk_Score=∑(w i ·f i (x i )),

[0097] where, w i is the weight of the i-th health indicator, fi is the risk mapping function of the corresponding indicator, x i is the actual measured value. The weight can be set according to the impact of each health indicator on overall health. For example, the weight of blood pressure may be higher than that of body temperature.

[0098] The warning generation unit 34 generates warning information of corresponding levels according to the health risk score. The system of the present invention can divide the risk level into three levels: low, medium and high. For example, when the health risk score is between 0-30, it is low risk, between 31-70 is medium risk, and 71-100 is high risk. Different risk levels will trigger different warning mechanisms. For example, high risk may trigger real-time notification and medical advice.

[0099] In another embodiment of the present invention, the user interaction module 4 includes a visualization display unit 41, a voice interaction unit 42 and a touch operation unit 43. The visualization display unit 41 converts the health assessment report and risk warning information into intuitive charts and text descriptions. For example, a line chart can be used to display the user's recent blood pressure change trend, or a dashboard style can be used to intuitively display the current health risk level. This visualization display method helps users quickly understand their health status.

[0100] The voice interaction unit 42 provides the functions of voice broadcasting health information and receiving voice commands. Preferably, the voice interaction can adopt natural language processing (NLP) technology to enable the user to interact with the device through natural language. For example, the user can obtain a health status overview of the day through the voice command "broadcast today's health summary". This interaction mode is particularly suitable for elderly users or users with poor vision.

[0101] The touch operation unit 43 is used to receive various instructions and feedback input by the user through the touch screen. The system of the present invention adopts an intuitive touch interface design, such as using large fonts and high-contrast interface elements to improve the convenience of operation. The user can browse health data, set health goals or adjust device parameters by simple sliding and clicking operations.

[0102] In order to further improve the safety and reliability of the intelligent health monitoring device of the present invention, the preferred embodiment of the present invention further includes a data security module 6. The data security module 6 is connected to the data acquisition module 1, the data processing module 2 and the analysis and early warning module 3 for protecting the user's health data and privacy information.

[0103] Specifically, the data security module 6 encrypts the collected health data. The present invention uses the Advanced Encryption Standard (AES) algorithm for data encryption, with a key length of 256 bits to ensure the security of data transmission and storage. The encryption process can be expressed as:

[0104] C=Ek (P),

[0105] where C is the ciphertext, E is the encryption function, k is the key, and P is the plaintext (original health data).

[0106] In addition, the data security module 6 is also responsible for verifying the user's identity information. The system of the present invention adopts a multi-factor authentication mechanism, combining biometric recognition (such as fingerprint or face recognition) and traditional password authentication to ensure that only authorized users can access health data.

[0107] The data security module 6 also controls the access rights of data to ensure that only authorized modules and users can access relevant health data. The present invention adopts a role-based access control (RBAC) model to assign different access rights to different types of users (such as ordinary users, doctors, system administrators). This fine-grained permission control helps to protect user privacy while allowing necessary data sharing and analysis.

[0108] Through the above detailed security mechanism design, the intelligent health monitoring device of the present invention can effectively protect the user's health data and privacy information while providing comprehensive health monitoring and analysis functions, enhancing the user's trust and willingness to use the device. In a preferred embodiment of the present invention, the personalized recommendation module 5 further includes a behavior analysis unit 51, a goal setting unit 52, and a plan generation unit 53. These units work together to provide highly personalized health management recommendations for users.

[0109] The behavior analysis unit 51 is used to analyze the user's daily behavior patterns. This unit adopts a time series pattern mining algorithm, such as sequential pattern mining, to extract meaningful behavior patterns from the user's historical health data and behavior records. For example, the system may find that the user's blood sugar level often rises after lunch on weekdays, or the exercise volume significantly decreases on weekends. This behavior pattern analysis helps to identify the key factors affecting the user's health and lays a foundation for formulating personalized recommendations.

[0110] The goal setting unit 52 sets reasonable health improvement goals according to the user's health status and personal preferences. The system of the present invention adopts the SMART principle (specific, measurable, achievable, relevant, time-bound) to formulate health goals. For example, for a slightly obese user, the system may set a goal of "losing 5% of body weight within the next 3 months through 30 minutes of moderate-intensity exercise per day and controlling diet". Preferably, the goal setting process also considers the user's physiological limitations and living habits to ensure the achievability of the goals.

[0111] The solution generation unit 53 generates personalized diet, exercise, and work and rest suggestions based on behavior analysis and goal setting. The present invention adopts a rule-based expert system combined with machine learning algorithms, comprehensively considering the user's health status, behavior patterns, personal preferences, and set goals to generate the most suitable health management solution. For example, for the above weight loss goal, the system may generate a comprehensive solution including a low-calorie diet plan, a daily step goal, and sleep improvement suggestions. Preferably, these suggestions are dynamically adjusted according to the changes in the user's health status and the completion of the goals to maintain their continuous effectiveness.

[0112] To further enhance the functionality and practicality of the intelligent health monitoring device of the present invention, the present invention further includes a data synchronization module 7. The data synchronization module 7 is communicatively connected to the data processing module 2 and is used to realize data intercommunication and information sharing between devices.

[0113] Specifically, the data synchronization module 7 can synchronize data with other intelligent devices of the user. For example, the user may simultaneously use devices such as a smart watch and a smart weighing scale. The data synchronization module 7 integrates the data collected by these devices into the health monitoring system of the present invention through wireless communication technologies such as Bluetooth or Wi-Fi. This integration of multi-source data helps to construct a more comprehensive user health profile.

[0114] In addition, the data synchronization module 7 can also obtain the user's historical health data from an external health management platform. The system of the present invention supports docking with the APIs of mainstream health management platforms, such as Apple Health and Google Fit. Through a secure data exchange protocol, the system can import the user's historical health records on other platforms, thereby realizing health trend analysis over a longer time span.

[0115] The data synchronization module 7 is also responsible for uploading the health data collected and processed by this device to cloud storage to achieve data consistency between multiple devices. Preferably, the data upload process adopts an incremental synchronization strategy, only transmitting newly added or changed data to improve synchronization efficiency and save network resources. Cloud storage adopts distributed database technology to ensure high availability and fast access of data.

[0116] In another embodiment of the present invention, the intelligent health monitoring device of the present invention further includes an artificial intelligence learning module 8. The artificial intelligence learning module 8 is communicatively connected to the analysis and warning module 3 and the personalized suggestion module 5 and is used to continuously optimize the analysis ability and suggestion quality of the system.

[0117] The artificial intelligence learning module 8 first continuously learns the user's health data patterns and feedback. This module adopts online learning algorithms, such as Online Gradient Descent, which can update the model parameters in real time and adapt to the dynamic changes of the user's health status. For example, for a user who is gradually improving their eating habits, the system can timely adjust the weights of its health risk assessment model.

[0118] Based on the learning results, the artificial intelligence learning module 8 optimizes and updates the health assessment model. The present invention adopts transfer learning technology, which allows for quickly adapting to the health characteristics of specific users while retaining general knowledge. The model update process can be expressed as:

[0119]

[0120] where θ represents the model parameters, η is the learning rate, and L(θ) is the loss function. Through this continuous learning and optimization, the system can continuously improve the accuracy of health assessment.

[0121] In addition, the artificial intelligence learning module 8 also adjusts the generation strategy of personalized suggestions according to the learning results, improving the pertinence and effectiveness of the suggestions. For example, if the system observes that the user responds positively to a certain type of health suggestion, it will increase the probability of generating similar suggestions. This adaptive suggestion generation mechanism helps to improve the user's compliance and the effect of health management.

[0122] Through the above detailed module design and function description, the intelligent health monitoring device of the present invention can not only provide comprehensive and accurate health monitoring and analysis, but also continuously optimize its functions according to the individual characteristics and feedback of the user. This intelligent and personalized health management method is expected to significantly improve the user's health level and quality of life.

[0123] It should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. Intelligent health monitoring equipment, characterized in that, include: Data acquisition module for: Collecting multi-dimensional health data of the user, including heart rate, blood pressure, blood oxygen saturation, body temperature, exercise status and sleep quality; Transmitting the multi-dimensional health data to a data processing module; A data processing module is connected to the data acquisition module for: Receiving the multi-dimensional health data sent by the data acquisition module; Based on the multidimensional health data, perform data cleaning, feature extraction and data standardization; Transmit the processed health data to the analysis and early warning module; The analysis and early warning module is connected to the data processing module for: Receiving the processed health data sent by the data processing module; Based on the processed health data, using a preset machine learning model to perform health status analysis; Generate health assessment reports and risk warning information based on analysis results; Transmitting the health assessment report and risk warning information to a user interaction module; The user interaction module is connected to the analysis and early warning module for: Receive the health assessment report and risk warning information sent by the analysis and warning module; Displaying the health assessment report and risk warning information to the user through a user interface; Receive health management instructions input by users; Transmitting the health management instruction to a personalized suggestion module; The personalized suggestion module is in communication with the user interaction module and the analysis and early warning module, and is used to: Receiving a health management instruction sent by the user interaction module; Generate personalized health management suggestions based on the health management instructions and the analysis results of the analysis and early warning module; The personalized health management suggestions are transmitted to the user interaction module for display.

2. The intelligent health monitoring device according to claim 1, characterized in that: The data acquisition module comprises: A multi-sensor unit for collecting physiological parameters of the user, including an electrocardiogram sensor, a photoplethysmogram sensor, an acceleration sensor, and a temperature sensor; Environmental monitoring unit, used to collect parameters of the user's environment, including temperature, humidity and air quality; The data fusion unit is connected to the multi-sensor unit and the environmental monitoring unit, and is used to initially fuse the collected physiological parameters and environmental parameters to form a comprehensive health data set.

3. The intelligent health monitoring device according to claim 1, characterized in that: The data processing module comprises: A data cleaning unit, used for detecting and processing outliers on the received multi-dimensional health data; A feature extraction unit, used to extract key health features from the cleaned data; Data standardization unit, used to convert the extracted health features into a standard format for subsequent analysis.

4. The intelligent health monitoring device according to claim 1, characterized in that: The analysis and early warning module includes: Health model library, which stores multiple pre-trained health assessment models; A model selection unit, used to select a suitable evaluation model from the health model library according to the user's personal characteristics and health data characteristics; A risk assessment unit, used to analyze the health status of the user using a selected assessment model and generate a health risk score; The warning generation unit is used to generate warning information of corresponding levels according to the health risk score.

5. The intelligent health monitoring device according to claim 1, characterized in that: The user interaction module comprises: Visual display unit, used to convert health assessment reports and risk warning information into intuitive charts and text descriptions; Voice interaction unit, used to provide voice broadcast of health information and receive voice commands; The touch operation unit is used to receive various instructions and feedback input by the user through the touch screen.

6. The intelligent health monitoring device according to claim 1, characterized in that: It also includes a data security module, which is in communication with the data acquisition module, the data processing module and the analysis and early warning module, and the data security module is used to: Encrypt the collected health data; Verify the user's identity information; Control access rights to data to ensure that only authorized modules and users can access relevant health data.

7. The intelligent health monitoring device according to claim 1, characterized in that: It also includes a telemedicine module, which is in communication with the analysis and warning module and the user interaction module, and the telemedicine module is used to: Automatically connect to telemedicine services when an urgent health risk is detected; Support real-time video consultation between users and remote doctors; The diagnosis opinion of the remote doctor is received and displayed to the user through the user interaction module.

8. The intelligent health monitoring device according to claim 1, characterized in that: The personalized suggestion module also includes: Behavior analysis unit, used to analyze users' daily behavior patterns; A goal setting unit, used to set reasonable health improvement goals according to the user's health status and personal preferences; A program generation unit is used to generate personalized diet, exercise and daily routine recommendations based on the behavior analysis and goal setting.

9. The intelligent health monitoring device according to claim 1, characterized in that: It also includes a data synchronization module, which is in communication with the data processing module, and the data synchronization module is used to: Synchronize data with the user's other smart devices; Obtain users’ historical health data from external health management platforms; Upload the health data collected and processed by this device to cloud storage to achieve data consistency among multiple devices.

10. The intelligent health monitoring device according to claim 1, characterized in that: It also includes an artificial intelligence learning module, which is in communication with the analysis and warning module and the personalized suggestion module, and the artificial intelligence learning module is used to: Continuously learn users’ health data patterns and feedback; Optimize and update health assessment models; Based on the learning results, adjust the strategy for generating personalized recommendations to improve the pertinence and effectiveness of the recommendations.

Images