Health management system and mobile device

Abstract

The invention provides a health management system and a mobile device, and the system comprises a data collection module which is used for collecting health data; the main control module is in communication connection with the data acquisition module, and the main control module is used for analyzing the data acquired by the data acquisition module, obtaining abnormal data and synchronously predicting the health state of a user; the display module is coupled with the main control module, and the display module displays the abnormal data obtained by the main control module in real time and predicts the health state of the user; wherein the main control module outputs risk early warning based on the abnormal data, displays and / or prompts a user by voice through the display module, and outputs corresponding health management suggestions. By means of the mode, the health state of the user can be monitored in real time, corresponding health management suggestions can be given in time, and the practicability of the health management system is effectively improved.

Description

Technical Field

[0001] This application relates to the field of health monitoring technology, and in particular to a health management system and a mobile device. Background Technology

[0002] Currently, smartphone health monitoring functions are limited to single indicators (such as heart rate and steps), lacking a comprehensive assessment of multi-dimensional health status. Traditional health management relies on user-initiated recording or professional equipment, resulting in fragmented data and delayed feedback. Especially in chronic disease management scenarios, users struggle to obtain timely risk warnings and targeted advice. Summary of the Invention

[0003] This application provides a health management system to solve the problem of frequency deviation instability of mobile devices at high and low temperatures in the prior art.

[0004] To address the aforementioned technical problems, this application provides a health management system, comprising: a data acquisition module for collecting health data; a main control module communicatively connected to the data acquisition module, the main control module analyzing the data collected by the data acquisition module, identifying abnormal data, and synchronously predicting the user's health status; and a display module coupled to the main control module, displaying the abnormal data and predicted user health status from the main control module in real time; wherein, the main control module outputs risk warnings based on the abnormal data, and displays and / or voices these warnings to the user via the display module, while also outputting corresponding health management suggestions.

[0005] The data acquisition module includes: a first acquisition module for acquiring the user's physiological data; a second acquisition module for acquiring environmental data of the user's current environment; and a transmission module coupled to the first acquisition module, the second acquisition module, and the main control module, for transmitting physiological data and environmental data to the main control module.

[0006] The health management system also includes a preprocessing module, which is coupled to the data acquisition module and the main control module. The preprocessing module includes a calibration module and a filtering module. The calibration module is used to calibrate the health data, and the filtering module is used to filter the health data to remove noise.

[0007] The main control module includes a deep learning module, which is coupled to the data acquisition module. The deep learning module adjusts and updates based on health data and long-term data to improve the accuracy of predicting the user's health status.

[0008] The health management system further includes: a big data database; the main control module includes a training module, which is coupled to the big data database and the data acquisition module. The training module is trained based on the data in the big data database and the acquired health data to optimize the health management system.

[0009] The first acquisition module includes a blood oxygen sensor, a heart rate sensor, a blood pressure sensor, and a non-invasive blood glucose detector; the second acquisition module includes a temperature sensor and an ultraviolet sensor.

[0010] The display module includes a feedback module, which is coupled to the main control module. The feedback module provides risk warnings based on the health status and provides voice and / or pop-up prompts.

[0011] The main control module also includes an optimization testing module, which is coupled to the display module. The optimization testing module optimizes the health management suggestions for users based on feedback from different users.

[0012] The main control module classifies the output risk warnings based on abnormal data and generates health management recommendations of different levels based on data from a large database.

[0013] To address the aforementioned issues, this application also provides a mobile device, including a health management system, wherein the health management system is any one of the aforementioned health management systems.

[0014] The beneficial effects of this application are as follows: Unlike existing technologies, this application can collect users' health data by setting up a data acquisition module. After the main control module obtains the health data, it analyzes the abnormal data and predicts the user's health status in a timely manner. It also outputs corresponding risk warnings and health management suggestions, and displays and / or reminds users through the display module. This allows for real-time monitoring of the user's health status and timely provision of corresponding health management suggestions, effectively improving the practicality of the health management system. Attached Figure Description

[0015] Figure 1 This is a structural block diagram of an embodiment of the health management system of this application;

[0016] Figure 2 This is a structural block diagram of an embodiment of the data acquisition module of this application;

[0017] Figure 3 This is a structural block diagram of an embodiment of the preprocessing module of this application;

[0018] Figure 4 This is a structural block diagram of an embodiment of the main control module of this application;

[0019] Figure 5This is a structural block diagram showing the connection between the optimized testing module and the feedback module in this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0021] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0022] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0023] Please see Figure 1 , Figure 1 This is a structural block diagram of an embodiment of the health management system provided in this application.

[0024] This application provides a health management system, such as Figure 1 As shown, the system includes: a data acquisition module 10, a main control module 20, and a display module 30. The data acquisition module 10 is used to collect health data. The main control module 20 is communicatively connected to the data acquisition module 10. The main control module 20 analyzes the data collected by the data acquisition module 10, identifies abnormal data, and simultaneously predicts the user's health status. The display module 30 is coupled to the main control module 20 and displays the abnormal data and predicted user health status from the main control module 20 in real time. Specifically, the main control module 20 outputs risk warnings based on abnormal data, which are displayed and / or voice-activated to the user via the display module 30, along with corresponding health management suggestions.

[0025] It should be noted that this health management system is primarily used on mobile devices carried by users. This means that the health management system, applied within the mobile device, can monitor the user's environment and various current bodily data in real time. In other words, this health management system is used in the user's portable device to monitor the user's health data in real time.

[0026] In an optional embodiment, the health management system includes a data acquisition module 10, which can collect users' health data in real time. The data acquisition module 10 is communicatively connected to the main control module 20, meaning that after collecting health data, the data acquisition module can transmit the health data to the main control module 20. After the main control module 20 obtains the health data, it analyzes the health data to identify corresponding abnormal data and makes predictions based on these abnormal data to predict the user's health status.

[0027] In this embodiment, the main control module 20 is coupled to a display module 30. The display module 30 can be used to receive abnormal data and predicted health status analyzed by the main control module 20. Specifically, after the main control module 20 obtains the health data collected by the data acquisition module 10, it analyzes the health data to obtain corresponding abnormal data, and then generates corresponding risk warnings based on the abnormal data. That is to say, after the main control module 20 analyzes and obtains abnormal data, it can determine the user's physical condition based on the abnormal data. For example, if there is data showing elevated blood sugar in the collected health data, the main control module 20 will analyze the blood sugar data as abnormal data, output corresponding risk warnings based on the abnormal data, and transmit them to the display module 30 for display.

[0028] In an optional embodiment, after the health data collected by the data acquisition module 10 is transmitted to the main control module 20 for analysis, the main control module 20 outputs a risk warning based on the abnormal data, which is then displayed via the display module 30. When the main control module 20 outputs a risk warning, it can also output corresponding health management suggestions via the display module 30, and can remind the user through display and / or voice on the display module 30.

[0029] In a specific application scenario, after the data acquisition module 10 collects health data and transmits it to the main control module 20, the main control module 20 analyzes the collected health data to identify corresponding abnormal data. For example, if there is abnormal blood pressure data in the collected health data, a risk warning will be output based on the abnormal blood pressure data. In other words, a risk warning for abnormal blood pressure will be output. Furthermore, the main control module 20 will output corresponding health management settings based on the abnormal blood pressure data, such as rechecking blood pressure within two hours, and will provide user prompts through the display module 30 and / or voice prompts.

[0030] In the above implementation, the data acquisition module 10 can collect the user's health data. After the main control module 20 obtains the health data, it analyzes the abnormal data and predicts the user's health status in a timely manner. It also outputs corresponding risk warnings and health management suggestions, and displays and / or reminds the user through the display module 30. This allows for real-time monitoring of the user's health status and timely provision of corresponding health management suggestions, effectively improving the practicality of the health management system.

[0031] In an optional embodiment, such as Figure 2 As shown, the data acquisition module 10 includes: a first acquisition module 11, used to collect the user's physiological data; a second acquisition module 12, used to collect environmental data of the user's current environment; and a transmission module 13, coupled to the first acquisition module 11, the second acquisition module 12, and the main control module 20, used to transmit the physiological data and environmental data to the main control module 20. That is, the first acquisition module 11 can collect the user's physiological data, and the second acquisition module 12 can collect environmental data of the user's current environment. For example, the first acquisition module 11 can collect data such as the user's blood glucose and blood pressure, while the second acquisition module 12 can collect data such as ultraviolet radiation and temperature of the user's current environment.

[0032] The first acquisition module 11 includes a blood oxygen sensor, a heart rate sensor, a blood pressure sensor, and a non-invasive blood glucose detector. The second acquisition module 12 includes a temperature sensor and an ultraviolet sensor. Specifically, the first acquisition module 11 collects data such as the user's blood oxygen, heart rate, blood glucose, and blood pressure, which is then transmitted to the main control module 20. The main control module 20 analyzes this data to identify abnormal physiological data, such as abnormal blood pressure, and provides corresponding health management suggestions and risk warnings based on this abnormal blood pressure data. The second acquisition module 12 collects environmental data such as the user's current environment temperature and ultraviolet radiation. For example, if the temperature is too high, the main control module 20 and display module 30 can transmit abnormal temperature data and provide health management suggestions, such as alerting the user that they may be suffering from heatstroke and need to move to a cooler environment.

[0033] In this embodiment, the transmission module 13 is coupled to the first acquisition module 11, the second acquisition module 12, and the main control module 20. That is, after the first acquisition module 11 acquires the user's physiological data and the second acquisition module 12 acquires the environmental data of the user's environment, the first and second acquisition modules 12 can respectively transmit the physiological data and environmental data to the main control module 20 via the transmission module 13. The transmission module 13 can be Bluetooth, meaning that the first and second acquisition modules 11 and 12 can transmit data to the main control module 20 via Bluetooth. In other embodiments, the first and second acquisition modules 11 and 12 can use other signal transmission methods to transmit data to the main control module 20; this application does not impose specific limitations on these methods.

[0034] In an optional embodiment, such as Figure 3 As shown, the health management system also includes a preprocessing module 40, which is coupled to the data acquisition module 10 and the main control module 20. The preprocessing module 40 includes a calibration module 41 and a filtering module 42. The preprocessing module 40 is coupled to the data acquisition module 10 and the main control module 20, respectively. That is, the preprocessing module 40 can be coupled to the first acquisition module 11, the second acquisition module 12, and the main control module 20. The input of the preprocessing module 40 can be connected to the first acquisition module 11 and the second acquisition module 12 via the transmission module 13, thereby receiving physiological data collected by the first acquisition module 11 and environmental data collected by the second acquisition module 12. The output of the preprocessing module 40 is coupled to the main control module 20. In other words, the user's health data is collected by the first acquisition module 11 and the second acquisition module 12 and transmitted to the preprocessing module 40 via the transmission module 13.

[0035] The preprocessing module 40 may include a filtering module 42 and a calibration module 41. The calibration module 41 can calibrate the collected health data. For example, if a certain data point is too high or too low in the physiological data collected by the first acquisition module 11 (e.g., blood oxygen data is generally normal, but a certain data point is too high or too low), the calibration module 41 can calibrate that data. The filtering module 42 is used to filter the health data to remove noise. That is, data noise is unavoidable during data transmission. The filtering module 42 can filter the noise in the collected health data, thereby reducing the pressure on the main control module 20 to analyze the data and the probability of analysis errors after transmission to the main control module 20.

[0036] In an optional embodiment, such as Figure 4As shown, the main control module 20 includes a deep learning module 21, which is coupled to the data acquisition module 10. The deep learning module 21 adjusts and updates based on health data and long-term data to improve the prediction accuracy of the user's health status. The main control module 20 includes a deep learning module 21, which is coupled to the data acquisition module 10. The deep learning module 21 can build a multi-task learning model, thereby learning from the currently acquired health data and previously acquired health data, enabling the main control module 20 to propose optimal health management suggestions based on the user.

[0037] In this system, when a user uses the health management system for an extended period, the deep learning module 21 can update and adjust the system based on the user's long-term health data and the current health data collected by the data acquisition module 10. For example, the long-term data includes the approximate time of any abnormal blood pressure readings and corresponding strategies, allowing the main control module 20 to optimize the health management suggestions provided to the user, enabling the user to implement appropriate strategies. Simultaneously, the deep learning module 21 can determine the user's outdoor time and time spent in high temperatures based on the long-term data, providing corresponding suggestions based on the currently collected health data. For instance, outdoor workers need to work in high temperatures for extended periods; therefore, the deep learning module 21 can obtain data on the user's long-term exposure to high-temperature outdoor environments. Thus, if the current temperature data is within the long-term temperature average, it can only suggest that the user drink more water; if it exceeds the long-term temperature average, it can output a risk warning, such as indicating potential heatstroke. In other words, the health management system can adjust accordingly based on the user's long-term environment. This means that the health management recommendations given to different users are different, which is due to the differences in the physical condition of different users. The health management system can be updated and adjusted according to the user, so as to be suitable for the user and improve the accuracy of the prediction of the user's health status.

[0038] In an optional embodiment, such as Figure 4As shown, the health management system further includes: a large database 50; and a main control module 20 including a training module 22, which is coupled to the large database 50 and the data acquisition module 10. The training module 22 is trained based on the data in the large database 50 and the acquired health data to optimize the health management system. The training module 22 is coupled to the large database 50 and the data acquisition module 10. If abnormal data exists in the health data acquired by the data acquisition module 10, the training module 22 can respond according to the appropriate actions in the large database 50 for that abnormal data. For example, if blood oxygen data is abnormal, the training module 22 can directly obtain the appropriate responses from the large database 50 for that abnormal blood oxygen data, and provide corresponding health management suggestions for user decision-making. Furthermore, based on the data in the large database 50, the response plans of the health management system can be improved. That is, if there is no corresponding response plan in the health management system, but a corresponding response plan exists in the large database 50, corresponding health management suggestions can be output based on that plan for user decision-making. This plan can also be stored to improve the response plans of the health management system.

[0039] In an optional embodiment, such as Figure 5 As shown, the display module 30 includes a feedback module 31, which is coupled to the main control module 20. The feedback module 31 provides risk warnings based on health status and provides voice and / or pop-up prompts. Specifically, when the main control module 20 outputs corresponding risk warnings and health management suggestions, it can output to the feedback module 31. The main control module 20 can output corresponding risk warnings and health management suggestions based on analyzed abnormal data. After being transmitted to the feedback module 31 of the display module 30, these suggestions can be prompted to the user via voice and / or pop-ups, enabling the user to make appropriate decisions based on the risk warnings and health management suggestions.

[0040] In this embodiment, the main control module 20 classifies the output risk warnings based on abnormal data and generates health management suggestions of different levels based on the data in the big data database 50. It is understood that after the data acquisition module 10 collects health data, the main control module 20 analyzes the collected data to determine the user's risk based on the abnormal data and classify it. For example, the risk level can be divided into three levels based on the abnormal data: Level 1 may only provide a pop-up notification to the user and offer corresponding health management suggestions; Level 2 may provide voice and pop-up notifications to the user and offer corresponding health management suggestions; Level 3 may provide voice and pop-up notifications to the user, issue an alarm, transmit data to a medical institution or call emergency services, and offer corresponding health management suggestions. In other embodiments, the risk level can also be reclassified based on the user's health data and the health management system updated by the deep learning module 21, for example, level five, level six, etc. The specific level can be adjusted by the deep learning module 21, and this application does not impose specific limitations on this.

[0041] In an optional embodiment, such as Figure 5 As shown, the main control module 20 also includes an optimization testing module 23, which is coupled to the display module 30. The optimization testing module 23 optimizes the health management suggestions for users based on feedback from different users. The health management system includes the optimization testing module 23, which is coupled to the display module 30. Since multiple users, i.e., different groups of people using the health management system, have different usage requirements and habits, different users can provide optimization suggestions through the optimization testing module 23 to optimize the health management system and make it suitable for different users' habits. Furthermore, through the optimization testing module 23, users can customize the health management system, thus achieving personalization and real-time feedback.

[0042] In this embodiment, when the user provides feedback, since the optimization testing module 23 and the display module 30 are coupled, the optimization suggestions can be fed back through the display module 30. For example, the display module 30 is a touch screen, and the feedback suggestions can be input through the touch screen. After the optimization testing module 23 obtains the feedback suggestions, it can optimize the health management system through the deep learning module 21 and the training module 22 so that the health management system can be applied to different groups of people.

[0043] In a specific application scenario, after the first acquisition module 11 and the second acquisition module 12 acquire the user's physiological data and the environmental data of the user's environment, respectively, they can be transmitted to the main control module 20 through the transmission module 13. The main control module 20 analyzes the acquired data to identify abnormal data and outputs corresponding risk warnings and health management suggestions, which are then transmitted to the display module 30. The display module 30 prompts the user through voice and / or pop-up windows, enabling the user to make corresponding decisions based on the given health management suggestions. For example, the user may be prompted to have their blood sugar tested within two hours. After receiving the suggestion, the user can make their own decision.

[0044] Furthermore, while the main control module 20 analyzes the collected data, the deep learning module 21 can simultaneously acquire this data and combine it with previously collected data to construct a multi-task learning model for adjusting and updating the health management system. The training module 22 can also acquire the collected data and use data from the large database 50 for training, thereby further optimizing the health management system. Furthermore, after users use the health management system, since different user groups have different usage methods and habits, users can transmit feedback suggestions to the optimization testing module 23 through the display module 30. The optimization testing module 23 can further optimize the health management system based on the feedback suggestions, achieving personalization and applicability of the health management system.

[0045] Through the above methods, this application can collect user health data by setting up a data acquisition module. After the main control module 20 acquires the health data, it analyzes the abnormal data and can simultaneously predict the user's health status, output corresponding risk warnings and health management suggestions, and display and / or voice reminders to the user through the display module 30. It can monitor the user's health status in real time and provide corresponding health management suggestions in a timely manner, effectively improving the practicality of the health management system. By setting up a first acquisition module 11 and a second acquisition module 12, the system can collect user physiological data and environmental data of the surrounding environment, thereby improving the accuracy of the analysis by the main control module 20. The first acquisition module 11 includes a blood oxygen sensor, a blood pressure sensor, etc., and the second acquisition module 12 includes a temperature sensor, an ultraviolet sensor, etc., which can integrate multiple sensors to provide real-time feedback of relevant data. By setting up a preprocessing module 40, the collected data can be preprocessed to reduce the interference of external factors on the data and effectively improve the accuracy of data analysis. By setting up a deep learning module 21 and a training module 22, the health management system can be optimized and updated. By setting up feedback module 31, users can be prompted via voice and / or pop-up windows to make appropriate decisions based on risk warnings and health management suggestions. By setting up optimization testing module 23, users can customize the health management system, thereby achieving personalization and real-time feedback.

[0046] This application also provides a mobile device, which includes a health management system, wherein the health management system is set within the mobile device and is any of the health management systems described above.

[0047] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A health management system, characterized by, The health management system comprises: a data acquisition module for acquiring health data; a main control module in communication connection with the data acquisition module, the main control module being configured to analyze the data acquired by the data acquisition module, derive abnormal data, and synchronize prediction of the health status of a user; a display module coupled to the main control module, the display module being configured to display the abnormal data derived by the main control module and the predicted health status of the user in real time; wherein the main control module outputs a risk warning based on the abnormal data, displays and / or reminds the user via the display module, and outputs corresponding health management suggestions.

2. The health management system of claim 1, wherein, The data acquisition module comprises: a first acquisition module for acquiring physiological data of a user; a second acquisition module for acquiring environmental data of an environment in which the user is currently located; a transmission module coupled to the first acquisition module, the second acquisition module, and the main control module, the transmission module being configured to transmit the physiological data and the environmental data to the main control module.

3. The health management system of claim 1, wherein, The health management system further comprises: a preprocessing module coupled to the data acquisition module and the main control module, the preprocessing module comprising a calibration module and a filtering module; the calibration module being configured to calibrate the health data; the filtering module being configured to filter the health data to remove noise in the health data.

4. The health management system of claim 1, wherein, The main control module comprises a deep learning module coupled to the data acquisition module, the deep learning module being configured to adjust and update based on the health data and long-term data to improve the prediction accuracy of the health status of the user.

5. The health management system of claim 1, wherein, The health management system further comprises: a large database; the main control module comprises a training module coupled to the large database and the data acquisition module, the training module being configured to train based on the data in the large database and the acquired health data to optimize the health management system.

6. The health management system of claim 2, wherein, The first acquisition module comprises an oxygen saturation sensor, a heart rate sensor, a blood pressure sensor, and a non-invasive blood glucose detector; The second acquisition module comprises a temperature sensor and an ultraviolet sensor.

7. The health management system of claim 1, wherein, The display module comprises a feedback module coupled to the main control module, the feedback module being configured to issue a risk warning based on the health status and provide voice and / or pop-up prompts via the feedback module.

8. The health management system of claim 1, wherein, The main control module further comprises an optimization test module coupled to the display module, the optimization test module being configured to optimize the scheme of health management suggestions for a user based on feedback from different users.

9. The health management system of claim 5, wherein, The main control module grades the output risk warning based on the abnormal data and generates health management suggestions of different levels based on the data in the large database.

10. A mobile device comprising the health management system according to any one of claims 1-9.

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