A method and system for joint monitoring of cardiovascular health of family members

Abstract

The present application relates to a kind of family member cardiovascular health joint monitoring method and system, belong to health monitoring technical field, solve the data isolated in the prior art in family health monitoring, early warning partial problem.Method includes: the real-time health data of continuous acquisition multiple family members;Wherein, the real-time health data collected at each time includes the real-time detection value of several health parameters;Based on all the real-time health data of all family members, the personal health longitudinal deviation score, personal health transverse deviation score and personal risk score of each health parameter corresponding to each family member at present time are calculated;According to all the personal health longitudinal deviation score, personal health transverse deviation score and personal risk score of each family member at present time, the corresponding personal health comprehensive deviation score is calculated;According to the personal health comprehensive deviation score of all family members, health risk assessment is carried out.The present application realizes the collaborative monitoring and early warning of family group cardiovascular health.

Description

Technical Field

[0001] This invention relates to the field of health monitoring technology, and in particular to a method and system for joint monitoring of cardiovascular health among family members. Background Technology

[0002] With the widespread adoption of wearable devices and home medical equipment, home health monitoring has become an important means of chronic disease management, especially for the early screening and long-term tracking of cardiovascular diseases (such as arrhythmia and hypertension). Currently, most home smart bracelets, blood pressure monitors, electrocardiogram (ECG) patches, and pulse oximeters on the market are designed primarily to serve a single user.

[0003] However, existing technologies have the following significant drawbacks: 1) Data isolation and lack of correlation: Monitoring data of individual family members are stored and analyzed independently, lacking data correlation from the perspective of overall family health. This makes it impossible to effectively analyze the health correlations between family members. At the same time, single monitoring signals are easily interfered with. For example, nighttime non-intrusive monitoring technologies (such as BCG cardiac impaction) have weak anti-interference capabilities, and relying solely on single monitoring can easily lead to misjudgments. 2) Inconsistent time references: Measurement timestamps from different devices and different members are based on their respective device local clocks, which may have time differences of minutes or even longer. When it is necessary to analyze the physiological responses of the entire family before and after specific events (such as meals, family activities, environmental changes), time asynchrony can lead to distorted correlation analysis and make it impossible to accurately capture synchronous or delayed physiological change patterns. 3) Limitations in early warning and intervention, and inability to identify group signals: When multiple members experience non-fatal but trending fluctuations in physiological parameters (such as synchronous increases in blood pressure) within a similar time period, the system may miss the opportunity to indicate shared environmental risks (such as sudden changes in air quality, shared dietary problems, etc.) or the overall stress state of the family, or even miss the valuable opportunity to discover potential family genetic diseases. 4) Low management efficiency: Family members (especially children managing the health of the elderly) need to view multiple apps or data reports separately, lacking a unified family health dashboard with a clear timeline.

[0004] Therefore, there is an urgent need for a method and system for joint monitoring of cardiovascular health among family members, to break down data silos, conduct joint analysis at the family level, and achieve collaborative monitoring and early warning of cardiovascular health in family groups. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a method and system for joint monitoring of cardiovascular health among family members, in order to solve the problems of isolated data and one-sided early warning in the existing family health monitoring technology.

[0006] On one hand, embodiments of the present invention provide a method for joint monitoring of cardiovascular health among family members, including: Continuously collect real-time health data from multiple family members; wherein, the real-time health data collected at each moment includes real-time detection values ​​of several health parameters; Based on all the real-time health data of all the family members, calculate the individual health longitudinal deviation score, individual health horizontal deviation score, and individual risk score for each of the health parameters corresponding to each family member at the current moment; Calculate the corresponding comprehensive personal health deviation score based on all the personal health longitudinal deviation scores, personal health horizontal deviation scores, and personal risk scores of each family member at the current moment; A health risk assessment is conducted based on the individual health deviation scores of all family members.

[0007] Furthermore, real-time health data of each family member is continuously collected, including: Register the device for collecting the real-time health data at the home center device; The home center device broadcasts a precise reference time to the acquisition device, which performs time alignment based on the precise reference time. After time alignment, the acquisition device responds to the monitoring command and continuously acquires the real-time health data.

[0008] Furthermore, the individual health longitudinal deviation score is obtained through the following steps: For each of the health parameters, a personal health baseline is calculated for each of the family members based on all the real-time detection values ​​of all the family members. For each of the health parameters of each family member, the corresponding personal health longitudinal deviation score is calculated based on the real-time detection value at the current moment and the personal health baseline.

[0009] Furthermore, for each of the health parameters, based on all the real-time detection values ​​of all the family members, a personal health baseline is calculated for each of the family members, including: For each health parameter, a personal baseline is calculated for each family member based on all the real-time detection values ​​for each family member. Based on all the real-time detection values ​​of all the health parameters and the physiological coupling relationship between all the health parameters, calculate the parameter coupling baseline corresponding to each health parameter; For each of the health parameters, the corresponding personal health baseline is calculated based on the personal baseline of each of the family members and the parameter coupling baseline.

[0010] Furthermore, the individual health lateral deviation score is obtained through the following steps: For each of the health parameters, a corresponding family health baseline is calculated based on the real-time detection values ​​of all the family members. The corresponding individual health lateral deviation score is calculated based on the family health baseline and the real-time health data of each family member at the current moment.

[0011] Furthermore, the individual risk score is obtained through the following steps: Based on the real-time health data of each family member at the current moment, a risk stratification mapping function is used to determine the individual risk score for each corresponding health parameter.

[0012] Furthermore, the individual health comprehensive deviation score is calculated using the following formula: In the formula, For the first Family members at the current moment The aforementioned individual health comprehensive deviation score, For the first The parameter weights of the aforementioned health parameters, The number of the health parameters, , , The first The family member The aforementioned health parameters at the current time The corresponding personal health longitudinal deviation score, personal health lateral deviation score, and personal risk score. , , The first The vertical weights, horizontal weights, and individual risk weights of the aforementioned health parameters.

[0013] Furthermore, before calculating the corresponding comprehensive personal health deviation score based on all the individual health longitudinal deviation scores, individual health lateral deviation scores, and individual risk scores of each family member at the current moment, the process also includes: Obtain basic health data for each of the aforementioned family members; For each family member, the corresponding age risk factors, medical history factors, and comorbidity factors are determined based on the basic health data; Based on the age risk factor, the medical history factor, and the comorbidity factor, calculate the individual risk factor for each of the health parameters; For each of the health parameters, the individual health longitudinal deviation score, the individual health lateral deviation score, and the individual risk score are adjusted based on the corresponding individual risk factor.

[0014] Furthermore, a health risk assessment is conducted based on the individual health deviation scores of all said family members, including: Determine whether the individual health deviation score of each family member exceeds the corresponding instantaneous health threshold; If any family member's overall health deviation score exceeds the corresponding instantaneous health threshold, then the number of family members whose scores exceed the corresponding instantaneous health threshold is counted. If the number of members exceeds the collaboration threshold, the health risk assessment result is determined to be an abnormal collaboration assessment.

[0015] On the other hand, embodiments of the present invention provide a joint cardiovascular health monitoring system for family members, comprising: The data acquisition module is used to continuously collect real-time health data of each family member; wherein, the real-time health data collected at each moment includes real-time detection values ​​of several health parameters; The joint analysis module calculates the individual health longitudinal deviation score, individual health horizontal deviation score, and individual risk score for each of the health parameters corresponding to each of the family members at the current moment, based on all the real-time health data of all the family members. The comprehensive scoring module calculates the corresponding comprehensive personal health deviation score based on all the personal health longitudinal deviation scores, personal health horizontal deviation scores, and personal risk scores of each family member at the current moment. The risk assessment module is used to conduct health risk assessments based on the individual health deviation scores of all family members.

[0016] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. By utilizing real-time health data from all family members, this system calculates individual longitudinal health deviation scores, individual horizontal health deviation scores, and individual risk scores for different health parameters. This enables longitudinal analysis of each family member, horizontal analysis among all family members, and analysis of individual current status, addressing issues of data isolation and lack of correlation. Furthermore, it calculates a corresponding comprehensive individual health deviation score for each family member, completing a comprehensive analysis of each health parameter. This facilitates understanding the overall health status of each member and conducting health risk assessments for all family members. It accurately identifies group signals, enabling collaborative monitoring and early warning among family groups, improving early warning accuracy and overall family health management. It can provide early warning of family cluster risks (such as familial tendencies in hypertension and coronary heart disease), improving monitoring efficiency and providing precise support for family cardiovascular health management. Using a three-dimensional fusion model of individual longitudinal health deviation scores, individual horizontal health deviation scores, and individual risk scores, the risk assessment combines individual longitudinal comparability, family horizontal comparability, and clinical interpretability.

[0017] 2. By broadcasting a precise reference time to the acquisition devices through the family center device, the time alignment of the acquisition devices for real-time health data is achieved, which solves the problem of measurement timestamp differences between different devices and different members, provides a high-precision and consistent time reference, ensures the time consistency of real-time detection values ​​of various health parameters, improves the credibility of correlation analysis, and further enhances the accuracy of capturing synchronous or delayed health synergistic changes of family members by conducting health risk assessment based on the comprehensive deviation scores of the individual health of all family members.

[0018] 3. Dynamically determine individual and family health baselines, abandoning fixed thresholds and updating them dynamically with time, age, season, and health status (e.g., automatically adjusting blood pressure baselines in winter) to avoid misjudgments of special populations (the elderly, children) based on fixed thresholds. Based on scenario tags such as morning rest, daytime activity, and nighttime sleep, use differentiated baselines for different scenarios (e.g., being more sensitive to blood oxygen decline during nighttime sleep) to ensure that warning rules conform to physiological patterns and improve scenario adaptability.

[0019] 4. Introducing individual health cross-deviation scores and family collaborative assessments of health risks expands the monitoring perspective from the individual to the family as a whole, identifying common risk sources. Through family health baselines and individual health cross-deviation scores, the degree of deviation between the individual and the family is quantitatively characterized, intuitively reflecting the relative health status of each member within the family, and providing data support for health care among family members.

[0020] 5. By dynamically adjusting the vertical weight, horizontal weight, and personal risk weight of different health parameters according to scenario tags, it effectively distinguishes between physiological fluctuations (such as increased heart rate due to exercise) and pathological abnormalities (such as hypoxia compensation caused by disease), reducing the false alarm rate and minimizing the inconvenience caused to users by false alarms.

[0021] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0022] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Figure 1 This is a flowchart illustrating a method for joint monitoring of cardiovascular health among family members, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the main modules of a joint cardiovascular health monitoring system for family members according to an embodiment of the present invention. Detailed Implementation

[0023] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0024] A specific embodiment of the present invention discloses a method for joint monitoring of cardiovascular health among family members, such as... Figure 1 As shown, it includes: Step S1: Continuously collect real-time health data from multiple family members; wherein, the real-time health data collected at each moment includes real-time detection values ​​of several health parameters.

[0025] This system continuously collects real-time health data from multiple family members. Each collected data point includes real-time values ​​of several health parameters. In this embodiment, real-time health data from each family member is continuously collected via built-in or external cardiovascular health sensors. These parameters include heart rate (HR), blood pressure (BP), and blood oxygen saturation (SpO2). For example, one or more of the following devices can be used for continuous data collection: a wristband, smart blood pressure monitor, electrocardiograph, and pulse oximeter. In other embodiments, embedded devices can also be used for real-time health data collection, such as a mattress piezoelectric sensor for non-contact acquisition of BCG cardiac impulse signals and simultaneous Holter ECG verification; or millimeter-wave radar for non-contact respiratory / heart rate monitoring, suitable for monitoring the heart rate of members with limited mobility.

[0026] Furthermore, in order to improve the temporal consistency of each real-time detection value in the real-time health data, step S1 also includes steps S11-S13.

[0027] Step S11: Register the device for collecting the real-time health data in the home center device.

[0028] In this embodiment, a smart router is used as the home hub device to register data collection devices for real-time health data acquisition. The data collection devices are registered on the same smart router. In other embodiments, a home gateway device or a home host can be used to register the data collection devices and broadcast a precise reference time to them. It is understood that different health parameters can use different data collection devices, and all data collection devices are registered on the same home hub device to facilitate subsequent time alignment among all devices.

[0029] Step S12: The home center device broadcasts a precision reference time to the acquisition device, and the acquisition device performs time alignment based on the precision reference time.

[0030] In this embodiment, the home central device obtains a precise reference time from a Network Time Protocol (NTP) server and / or a Global Navigation Satellite System (GNSS), performs a reliability verification, and broadcasts the verified precise reference time to registered data acquisition devices, for example, via the NTP protocol or through the home LAN. The data acquisition devices perform time alignment based on the received precise reference time, ensuring that the time information of all data acquisition devices used for collecting real-time health data is synchronized with the precise reference time at millisecond-level or higher precision. In this embodiment, the time synchronization error of all data acquisition devices is ≤1 millisecond.

[0031] Step S13: After time alignment, the acquisition device responds to the monitoring command and continuously acquires the real-time health data.

[0032] After time alignment, the acquisition device responds to the monitoring command and continuously collects real-time health data, collecting various health parameters of all family members. The timestamps corresponding to all real-time detection values ​​are based on the same precise reference time, ensuring the time consistency of real-time detection values.

[0033] For example, heart rate and blood oxygen saturation typically originate from the PPG signal of the same acquisition device. However, blood oxygen saturation calculation requires complex processing of multiple wavelength optical signals, resulting in a fixed processing delay. Time synchronization based on a precise reference time can calibrate this fixed processing delay, ensuring that the heart rate and blood oxygen saturation values ​​used for health risk assessment in subsequent steps S2-S4 correspond to the same heartbeat cycle, thus guaranteeing the homology between heart rate and blood oxygen saturation. Similarly, blood pressure is continuously acquired periodically, while heart rate and blood oxygen saturation are acquired continuously. Aligning the blood pressure measurement time window with the corresponding heart rate and blood oxygen saturation based on timestamps ensures alignment accuracy. Alternatively, if non-invasive continuous blood pressure monitoring based on pulse wave conduction time (PTT) is used, millisecond-level alignment of the ECG R wave with PPG feature points is required. Furthermore, introducing blood oxygen saturation allows for further analysis of its impact on PTT; time alignment processing ensures more accurate blood pressure correction.

[0034] Furthermore, the real-time health data continuously collected from all family members is uploaded to the family central device along with the corresponding real-time detection values ​​and timestamps. The real-time detection values ​​of different family members and different health parameters are stored in chronological order to form a family-level synchronized dataset. The device can also mark the identities (such as the individual, father, mother, and children) and biological relationships (such as direct blood relatives and spouses) of different family members, and label environmental data (such as daytime activities, morning rest, and nighttime sleep) based on timestamps for subsequent analysis.

[0035] In other embodiments, data acquisition devices can be registered at the home central device. Each data acquisition device transmits the collected real-time detection values ​​to the home central device, which then adds a receiving timestamp to the received real-time detection values ​​to complete the time alignment of all real-time detection values.

[0036] By aligning real-time detection values ​​through central equipment, the system ensures accurate and consistent real-time data, resolving the industry challenge of interoperability between different brands and types of equipment due to clock inconsistencies. This guarantees the credibility of multi-source data collaborative and coupled assessments, ensuring that real-time detection values ​​of health parameters such as heart rate, blood pressure, and blood oxygen used for joint analysis correspond to the same physiological moment, thus making parameter coupling analysis (such as calculating the heart rate-blood oxygen response delay) scientifically meaningful. It supports the precise definition of subclinical time windows, providing timely warnings for mild symptoms or symptoms potentially triggered by family factors, ensuring the temporal continuity and accuracy of assessments. It can accurately determine the chronological order of events involving multiple members (with second-level precision), providing reliable temporal evidence for inferring causal chains (such as an elderly person's fall and family stress responses).

[0037] Step S2: Based on all the real-time health data of all the family members, calculate the individual health longitudinal deviation score, individual health lateral deviation score, and individual risk score for each of the health parameters corresponding to each family member at the current moment.

[0038] Based on all real-time health data of all family members, calculate the individual health longitudinal deviation score, individual health horizontal deviation score, and individual risk score for each health parameter at the current moment for each family member.

[0039] In this embodiment, the individual health longitudinal deviation score is obtained through steps S21-S22.

[0040] Step S21: For each of the health parameters, calculate the personal health baseline for each of the family members based on all the real-time detection values ​​of all the family members.

[0041] For each health parameter, an individual health baseline is calculated for each family member based on all real-time detection values ​​of all family members. In this embodiment, a sliding time window is used to calculate the individual health baseline for each family member. For example, the time window is 28 days. As the window slides over time, the individual health baseline is dynamically updated to reflect the recent physiological status of family members.

[0042] Step S211: For each health parameter, calculate the individual baseline for each family member based on all the real-time detection values ​​of each family member.

[0043] For each health parameter, a personal baseline is calculated for each family member based on all real-time detection values ​​for each family member.

[0044] In this embodiment, for heart rate, the corresponding personal baseline is calculated based on the heart rate under the condition of morning rest (e.g., a fixed time period each day, or a corresponding time period recorded manually after waking up and marked as morning rest). For example, for each family member, the average heart rate during the morning rest period each day is counted as the daily baseline heart rate value, and the average of all daily baseline heart rate values ​​within the time window is used as the personal baseline heart rate for that family member. In other embodiments, the median or mode can also be selected to determine the daily baseline heart rate value and the personal baseline.

[0045] Furthermore, the heart rate distribution of each family member can be statistically analyzed daily, according to... The principle is to determine a reasonable range for the heart rate of the family member, for example... ( , These represent the lower and upper limits of heart rate, respectively, within the reasonable range for this family member.

[0046] In this embodiment, for blood pressure and blood oxygen, for each family member, daily baseline values ​​for blood pressure and blood oxygen are calculated based on the entire day's blood pressure and blood oxygen values. Then, a sliding time window method is used to determine the individual baseline values ​​for blood pressure and blood oxygen for each family member, improving the dynamic fit between individual baseline values ​​and individual health status. Blood pressure includes systolic and diastolic blood pressure, and corresponding individual baselines are determined for each. In other embodiments, systolic and diastolic blood pressure can be used as two health parameters.

[0047] Furthermore, based on the living habits of each family member, the day can be divided into different time periods, and a personal baseline can be determined for each time period. For example, it can be divided into a resting personal baseline, a daily personal baseline, an exercise personal baseline, and a sleep personal baseline, or into a morning personal baseline, a noon personal baseline, an evening personal baseline, and a nighttime personal baseline.

[0048] Step S212: Based on all the real-time detection values ​​of all the health parameters and the physiological coupling relationship between all the health parameters, calculate the parameter coupling baseline corresponding to each health parameter.

[0049] Based on all real-time measured values ​​of all health parameters and the physiological coupling relationships among them, a parameter coupling baseline is calculated for each health parameter. This baseline is used to correct for the mutual influence between different health parameters, ensuring that the subsequently obtained individual health baseline more accurately reflects the expected value under conditions free of abnormal coupling effects. The physiological coupling relationships among all health parameters can be determined based on medical knowledge.

[0050] Based on the physiological coupling relationship between all health parameters, for each health parameter, the corresponding coupling parameter is determined, that is, another health parameter that has a physiological coupling relationship with the health parameter. Based on the real-time detection values ​​of the health parameter and each coupling parameter, the parameter coupling baseline corresponding to the health parameter is calculated. For example, heart rate, blood pressure, and blood oxygen affect each other and have a physiological coupling relationship, so they are each other's coupling parameters.

[0051] In this embodiment, a linear regression method is used to calculate the parameter coupling baseline. First, for each health parameter, a linear regression model is sequentially established between that health parameter and each coupling parameter. In the formula, Indicates the first The first family member Real-time detection values ​​of several health parameters Indicates the first The health parameter of the first Real-time detection values ​​of each coupling parameter , , To calculate the model parameters, a sliding time window approach is used to acquire all real-time detection values ​​of the health parameter and each coupled parameter within the time window, and then calculate the model parameters corresponding to the current moment. For example, the health parameter—heart rate—corresponds to two coupled parameters—blood pressure and blood oxygen—and linear regression models are established for each. The time window is 28 days. When calculating the parameter coupling baseline corresponding to the current moment, all real-time detection values ​​of heart rate, blood pressure, and blood oxygen over the past 28 days are acquired and substituted into the corresponding linear regression models to calculate the model parameters, such as the linear regression model of heart rate and blood pressure. In the formula, , The respective The real-time heart rate and blood pressure readings of each family member. , , This represents the corresponding model parameters, which are calculated using the least squares method. It's understandable that if the sampling periods for health parameters and coupling parameters are different, the real-time detection values ​​of the health parameters and coupling parameters are numerically aligned. This can be done by using the sampling time of low-frequency sampled health parameters or coupling parameters, and for high-frequency sampled health parameters or coupling parameters, extracting the nearest real-time detection value for each sampling time, and then substituting the extracted data into the linear regression model to calculate the model parameters. Alternatively, based on the sampling period of low-frequency sampled health parameters or coupling parameters, the mean of all real-time detection values ​​corresponding to that sampling period is calculated for high-frequency sampled health parameters or coupling parameters, and the calculated mean data is substituting into the linear regression model to calculate the model parameters. For example, with heart rate and blood pressure, heart rate is continuously detected (high-frequency sampling), while blood pressure is detected once per hour (low-frequency sampling), with a sampling period of 1 hour. Therefore, the hourly mean heart rate is calculated, and the hourly pair of real-time heart rate and blood pressure detection values ​​are used as data for calculating the model parameters.

[0052] Then, the coupling deviation corresponding to each coupling parameter is calculated, that is, the deviation of the real-time detection value of each coupling parameter at the current moment relative to the corresponding personal baseline. For example, In the formula, For the first The first family member The coupling parameters at the current time The corresponding coupling deviation, For the first The first family member The coupling parameters at the current time Real-time detection values, For the first The first family member The coupling parameters at the current time The corresponding personal health baseline.

[0053] Finally, for each health parameter, based on the model parameters corresponding to each coupling parameter and the coupling deviation corresponding to each coupling parameter, the parameter coupling baseline corresponding to that health parameter is calculated. For example, In the formula, Indicates the first The first family member The parameter coupling baseline corresponding to the current time t for each health parameter Indicates the first The first family member The health parameter and the first The coupling coefficients in the model parameters at the current time step correspond to the coupling parameters of each coupling parameter. For the first The number of coupling parameters for each health parameter For the first The first family member The coupling parameters at the current time The corresponding coupling deviation.

[0054] In other embodiments, the parameter coupling baseline is calculated using a multivariate parameter coupling method. For each health parameter, a multivariate parameter model is established for that health parameter and all coupled parameters. A sliding time window approach is used to acquire all real-time detection values ​​of the health parameter and each coupled parameter within the time window. The coupling coefficient between the health parameter and each coupling coefficient is determined. Based on each coupling coefficient and the corresponding coupling deviation of each coupled parameter, the parameter coupling baseline corresponding to that health parameter is calculated. For example, a linear model is used for the multivariate parameter model. In the formula, Indicates the first The first family member Real-time detection values ​​of several health parameters Indicates the first The first family member The health parameter of the first Real-time detection values ​​of each coupling parameter , , For the corresponding model parameters, For the first The first family member The health parameter and the first The coupling coefficients corresponding to each coupling parameter For the first The number of coupling parameters for each health parameter is determined by using a sliding time window approach. All real-time detection values ​​of the health parameter and each coupling parameter within the time window are obtained, and the coupling coefficient corresponding to the current moment is calculated. The corresponding parameter coupling baseline ( For the first The first family member The coupling parameters at the current time (Corresponding coupling bias); multivariate parameter models can also use nonlinear models or physical models, based on the quantitative relationships between various health parameters known in the medical field, to determine the coupling coefficient.

[0055] Step S213: For each of the health parameters, calculate the corresponding personal health baseline based on the personal baseline of each family member and the parameter coupling baseline.

[0056] For each health parameter, a corresponding personal health baseline is calculated based on the individual baseline and parameter-coupled baseline of each family member. For example, In the formula, For the first The first family member Health parameters at current time The corresponding personal health baseline, For the first The first family member Health parameters at current time The corresponding individual baseline, For the first The first family member Health parameters at current time The corresponding parameter coupling baseline. It can be understood that if the individual baseline is divided into time periods, then the corresponding individual health baseline is calculated separately for each time period.

[0057] For example, if the coupling parameter for blood pressure is heart rate, and a family member's baseline blood pressure is 120 mmHg, their baseline heart rate is 70 bpm, and the coupling coefficient is 0.5 (meaning that for every 1 bpm increase in heart rate, blood pressure increases by 0.5 mmHg), and the current heart rate is 80 bpm, then the parameter coupling baseline for this family member's blood pressure at the current moment is... This indicates that the heart rate is high at the current moment, and the expected blood pressure is 5 mmHg higher than the corresponding personal baseline. Therefore, the personal baseline is adjusted to obtain the personal health baseline at the current moment. This helps avoid misinterpreting a normal increase in blood pressure caused by a rise in heart rate as an abnormality.

[0058] Step S22: For each health parameter of each family member, calculate the corresponding personal health longitudinal deviation score based on the real-time detection value at the current moment and the personal health baseline.

[0059] For each health parameter of each family member, calculate the corresponding longitudinal deviation score based on the real-time detection value and the individual's health baseline. For example, use a percentage system. In the formula, For the first The first family member A health parameter at the current moment Personal health longitudinal deviation score, For the first The first family member A health parameter at the current moment Real-time detection values, For the first The first family member A health parameter at the current moment The corresponding personal health baseline.

[0060] The longitudinal deviation score of an individual's health reflects the dynamic changes in their health status and can be used to track the progression of chronic diseases or the effectiveness of rehabilitation.

[0061] In this embodiment, the individual health lateral deviation score is obtained through steps S23-S24.

[0062] Step S23: For each of the health parameters, calculate the corresponding family health baseline based on the real-time detection values ​​of all the family members.

[0063] For each health parameter, a corresponding family health baseline is calculated based on the real-time detection values ​​of all family members. In this embodiment, for each health parameter, firstly, the individual baseline for each family member at the current moment is determined according to step S211; secondly, the median of the individual baselines of all family members is selected as the family health baseline at the current moment. In other embodiments, the mean or mode can also be calculated as the family health baseline.

[0064] In another embodiment, for each health parameter, a corresponding family health baseline is determined based on the real-time detection values ​​of all family members at the current moment. For example, for each health parameter, the median of the real-time detection values ​​of all family members at the current moment is taken as the family health baseline at the current moment.

[0065] Furthermore, given the physiological differences between minors and adults, an adult was selected from all family members, and a family health baseline was determined based on the individual baseline of all adults.

[0066] Step S24: Calculate the corresponding individual health horizontal deviation score based on the family health baseline and the real-time health data of each family member at the current moment.

[0067] Based on the family health baseline and the real-time health data of each family member at the current moment, the corresponding individual health horizontal deviation score is calculated. Specifically, based on the family health baseline corresponding to each health parameter and the real-time detection values ​​of each family member's health parameters at the current moment, the individual health horizontal deviation score for each family member's health parameter is calculated. For example, In the formula, For the first The first family member A health parameter at the current moment Personal health horizontal deviation score For the first The first family member A health parameter at the current moment Real-time detection values, For the first A health parameter at the current moment The corresponding family health baseline.

[0068] Furthermore, correlation analysis can be performed based on all real-time detection values ​​of all health parameters to correct for individual health deviation scores, thus accurately determining the degree to which different family members are affected by other family members. In this embodiment, the correlation is used to determine the family correction coefficient for each family member. By adjusting the individual's horizontal deviation score using a family adjustment factor, the individual's horizontal deviation score becomes... ,in For the first The first family member A health parameter at the current moment The corresponding family health baseline.

[0069] For example, real-time health data of all family members (such as heart rate, blood pressure, pulse oximetry, and ECG monitoring signals) are integrated into a standardized time-series data sequence with environmental labels (such as "morning rest" and "nighttime sleep"). For each health parameter, correlation analysis is performed based on the time-series data sequences of all family members. For example, for the first family member... There are several health parameters, and the time series data sequence HA corresponding to family member A is: The time series data sequence HB corresponding to family member B is The time series data sequence HC corresponding to family member C is Where n is the number of real-time detected values ​​in the time series data sequence, the Python functions np.corrcoef(HA,HB)[0,1] and np.corrcoef(HA,HC)[0,1] are used to calculate the degree of influence of family member A on the other two family members, and the two correlation mean values ​​are used as the family correction coefficient corresponding to family member A. Family member A's corrected horizontal deviation score for personal health Furthermore, correlation analysis can be performed using Excel, MATLAB built-in functions, neural networks, random forests, or Pearson coefficients; there are no restrictions here.

[0070] In other embodiments, the family health baseline can be corrected using correlation, and the corresponding individual health lateral deviation score can be calculated using the corrected family health baseline, for example, for the first... The health parameter, the first The correlation between individual family members and different family members is: Then the first Family health baseline for each family member In the formula, Indicates the first The number of family members who are related to each other. No. The first family member Health parameters at current time The corresponding personal health baseline, No. The correlation of the existence of family members The first family member Health parameters at current time The corresponding personal health baseline, the Individual health horizontal deviation score for each family member .

[0071] By revealing an individual's relative risk within the family through horizontal deviation scores of personal health, a reference can be provided for comparing the health of family members.

[0072] In this embodiment, the individual risk score is obtained through step S25.

[0073] Step S25: Based on the real-time health data of each family member at the current moment, determine the individual risk score for each corresponding health parameter using a risk stratification mapping function.

[0074] Based on the real-time health data of each family member, a risk stratification mapping function is used to determine the individual risk score for each health parameter. In this embodiment, a risk stratification mapping function is preset for each health parameter according to clinical guidelines, mapping the real-time detection value of each health parameter to an individual risk score. In the formula, For the first The first family member A health parameter at the current moment Personal risk score, For the first Risk stratification mapping function corresponding to each health parameter For the first The first family member A health parameter at the current moment The real-time detection value. For example, In the formula, Indicates the first Family members at the current moment The corresponding individual blood pressure risk score, Indicates the first Family members at the current moment The corresponding real-time blood pressure reading.

[0075] Individual risk scores are determined based on medical guidelines (such as hypertension classification) to ensure that the assessment results have clinical reference value and are easy for doctors to interpret and intervene.

[0076] Step S3: Calculate the corresponding comprehensive personal health deviation score based on all the personal health longitudinal deviation scores, personal health lateral deviation scores, and personal risk scores of each family member at the current moment.

[0077] Calculate the corresponding comprehensive personal health deviation score based on each family member's current personal health longitudinal deviation score, personal health horizontal deviation score, and personal risk score.

[0078] In this embodiment, the individual health comprehensive deviation score is calculated using the formula... Calculate, where, For the first Family members at the current moment Personal health comprehensive deviation score, For the first The parameter weights of each health parameter. For the number of health parameters, , , The first Individual health longitudinal deviation score, individual health lateral deviation score, and individual risk score are the three health parameters. , , The first The vertical weights, horizontal weights, and individual risk weights of the aforementioned health parameters.

[0079] In this embodiment, different health parameters have different vertical weights, horizontal weights, and personal risk weights at different time periods, which more accurately reflects the different weights of different dimensions of scores for different health parameters under different states. For example, for heart rate, blood pressure (systolic and diastolic), and blood oxygen, the corresponding parameter weights, vertical weights, horizontal weights, and personal risk weights are different as follows:

[0080] Furthermore, the vertical weights, horizontal weights, and personal risk weights of different health parameters are dynamically adjusted according to the scenario labels. For example, if a family member is identified as being in an active state through time or other operational status monitoring devices, the vertical weight (e.g., 0.6) and the personal risk weight (e.g., 0.2) are increased based on the vertical weights, horizontal weights, and personal risk weights corresponding to morning rest, to avoid the physiological increase due to exercise being misjudged as abnormal.

[0081] Furthermore, to improve the accuracy of the individual health comprehensive deviation score, before calculating the corresponding individual health comprehensive deviation score based on all individual health longitudinal deviation scores, individual health horizontal deviation scores, and individual risk scores at the current moment for each family member, steps S31-S34 are also included.

[0082] Step S31: Obtain basic health data for each family member.

[0083] In this embodiment, basic health data for each family member is obtained. This basic health data includes age, diagnosed diseases, and family history markers of cardiovascular disease.

[0084] Step S32: For each family member, calculate the corresponding age risk factor, medical history factor, and comorbidity factor based on the basic health data.

[0085] For each family member, age-related risk factors, past medical history factors, and comorbidity factors are calculated based on basic health data. In this embodiment, the age-related risk factors for each family member are determined according to the high-incidence age groups of different diseases. For example, based on the distribution of high-incidence age groups for hypertension, people of different ages have different probabilities of developing hypertension. For instance, the age-related risk factors for family members aged 18-30 are lower than those for family members aged 30-50. In other embodiments, age-related risk factors can also be set for each family member based on their age, with different health parameters corresponding to the same age-related risk factor.

[0086] For different health parameters, the medical history factor and comorbidity factor for each family member are determined based on the basic health data. For example, if the diagnosed diseases in the basic health data include hypertension, the family history of cardiovascular disease is marked as having a history of hypertension but not a history of respiratory disease, then the medical history factor corresponding to heart rate and blood pressure is set to 1.2, the medical history factor corresponding to blood oxygen is set to 1.0, the comorbidity factor corresponding to heart rate is set to 1.0, the comorbidity factor corresponding to blood pressure is set to 1.2, and the comorbidity factor corresponding to blood oxygen is set to 1.0.

[0087] Step S33: Calculate the individual risk factor for each of the health parameters based on the age risk factor, the medical history factor, and the comorbidity factor.

[0088] For each health parameter, corresponding individual risk factors are calculated based on age, medical history, and comorbidities, reflecting the individual's baseline risk across each physiological dimension. For example, In the formula, Indicates the first The first family member Individual risk factors for each health parameter , , They represent the first The first family member Each health parameter corresponds to an age risk factor, a medical history factor, and a comorbidity factor.

[0089] Step S34: For each of the health parameters, based on the corresponding personal risk factor, correct the personal health longitudinal deviation score, the personal health lateral deviation score, and the personal risk score.

[0090] For each health parameter, based on the corresponding personal risk factor, the personal health longitudinal deviation score, personal health horizontal deviation score, and personal risk score are adjusted. The adjusted personal health longitudinal deviation score, personal health horizontal deviation score, and personal risk score are as follows: , , .

[0091] Furthermore, based on the corrected longitudinal deviation score, horizontal deviation score, and personal risk score, the corresponding comprehensive deviation score for personal health is calculated. For example, .

[0092] It is understood that in this embodiment, real-time health data of multiple family members are continuously collected, and the individual's comprehensive health deviation score is calculated in real time. The detailed calculation process of the individual's comprehensive health deviation score and the subsequent health risk assessment process are only used as an example at the current moment.

[0093] Step S4: Conduct a health risk assessment based on the individual health deviation scores of all family members.

[0094] A health risk assessment is conducted based on the individual health deviation scores of all family members. In this embodiment, the health risk assessment includes instantaneous assessment and trend assessment.

[0095] The instantaneous assessment includes determining whether each family member's individual comprehensive health deviation score exceeds the corresponding instantaneous health threshold. If a family member's current individual comprehensive health deviation score exceeds the corresponding instantaneous health threshold (e.g., If the threshold for health risk assessment is not met, an immediate warning will be triggered, and the assessment result will be an instantaneous abnormality. In this embodiment, the instantaneous health threshold for each family member can be the same or different.

[0096] Trend assessment involves determining the trend value of each family member within the warning period. If the trend value continuously exceeds the trend warning threshold, an increased trend risk warning is triggered. The health risk assessment result is deemed an abnormal trend assessment. The trend warning threshold can be the same as or different from the instantaneous health threshold, for example, 18. In this embodiment, the warning period is 7 days, and the trend value is the average daily personal health comprehensive deviation score of each family member. In other embodiments, the warning period and trend value can be adjusted; for example, the warning period could be 10 days, and the trend value could be the maximum daily personal health comprehensive deviation score of each family member. In this embodiment, the trend warning threshold for each family member can be the same or different.

[0097] Furthermore, the health risk assessment also includes collaborative assessment, and step S4 further includes steps S41-S43.

[0098] Step S41: Determine whether the individual health deviation score of each family member exceeds the corresponding instantaneous health threshold.

[0099] To determine whether the health deviation score of each family member exceeds the corresponding instantaneous health threshold, the instantaneous assessment method can be used as a reference, which will not be elaborated here.

[0100] Step S42: If a family member's overall personal health deviation score exceeds the corresponding instantaneous health threshold, then count the number of family members whose scores exceed the corresponding instantaneous health threshold.

[0101] If any family member's overall health deviation score exceeds the corresponding instantaneous health threshold, the number of family members exceeding the corresponding instantaneous health threshold is counted. Furthermore, in this embodiment, a statistical time window is set to determine the duration of each statistical analysis, avoiding misjudgments over long periods.

[0102] For example, the statistical time window is 24 hours. That is, if a family member's personal health comprehensive deviation score exceeds the corresponding instantaneous health threshold at the current moment, we check if any other family members' personal health comprehensive deviation scores exceed the corresponding instantaneous health threshold (i.e., trigger an immediate warning) in the previous 24 hours. If so, we count the number of family members who triggered the immediate warning in these 24 hours and proceed to step S43. If not, we start from the current moment and continuously monitor whether any other family members trigger the immediate warning. If so, we record the number of family members who triggered the immediate warning in this period and proceed to step S43. If no other family members trigger the immediate warning in the 24 hours starting from the starting point, we record the number of members as 1 and proceed to step S43, or we directly determine that the health risk assessment result is no collaborative assessment abnormality.

[0103] Step S43: If the number of members exceeds the collaboration threshold, the assessment result of the health risk assessment is determined to be an abnormal collaboration assessment.

[0104] If the number of members exceeds the collaboration threshold, the health risk assessment result is determined to be an abnormal collaboration assessment. In this embodiment, the collaboration threshold is 2. If two family members trigger an immediate warning within the statistical time window, the health risk assessment result is determined to be an abnormal collaboration assessment, triggering a family collaboration warning. Risk prompts can also be issued, such as "Multiple family members have been detected to have elevated heart rate risk. Please pay attention to shared environmental or emotional factors."

[0105] For example, family members include the user, the user's father, and the user's mother. The instantaneous health threshold is set to 20, the collaborative quantity threshold is set to 2, and the statistical time window is set to 24 hours. If the individual health comprehensive deviation scores of the three family members are all less than 20 today, no warning will be triggered. If the individual health comprehensive deviation scores of the user's father reach 25, the individual health comprehensive deviation scores of the user's mother reach 22, and the individual health comprehensive deviation scores of the user are 15 when the user wakes up this morning, then a family collaborative warning will be triggered, prompting "Multiple family members have cardiopulmonary dysfunction. Please pay attention to common environmental or emotional factors," and an abnormality pattern analysis will be attached (such as all showing borderline high blood pressure).

[0106] Furthermore, for collaborative assessment anomalies, the time interval between each family member triggering the instant reminder can be calculated. If the time interval is less than the time threshold, the collaborative assessment anomaly is determined to be a precise collaborative anomaly, thus achieving further precision in the simultaneity of anomalies.

[0107] Furthermore, shared family events can be acquired, and the causes of collaborative assessment anomalies can be determined based on the occurrence time of these events and the timing of each family member's triggering of immediate alerts. In this embodiment, shared family events include meals, exercise, arguments, and sudden changes in environmental temperature and humidity. These events can be tagged by a central device to facilitate analysis of the delay and response patterns of cardiovascular parameters of each member before and after the event.

[0108] In this embodiment, a collaborative assessment is conducted based on an individual's comprehensive health deviation score, focusing on common triggers that may lead to immediate alerts (environmental toxins, emotional stress, and sources of infection). Furthermore, in the fields of medicine and health monitoring, the time window for the effects of common triggers is often calculated in minutes or even seconds. In this embodiment, the individual's comprehensive health deviation score is calculated based on real-time detection values ​​with temporal consistency, using millisecond-level time-aligned real-time detection values ​​to improve the reliability, temporal continuity, and medical significance of the collaborative assessment of anomalies.

[0109] Furthermore, the health risk assessment also includes a coupling assessment, and step S4 further includes step S44.

[0110] Step S44: Perform a coupled assessment based on the overall deviation scores of each parameter corresponding to the overall deviation scores of the individual health scores of all family members.

[0111] A coupled assessment is performed based on the overall deviation scores of each parameter corresponding to the overall deviation scores of the individual health scores of all family members. For example, the overall deviation scores of each health parameter are... , Indicates the first The first family member The combined deviation score of each health parameter.

[0112] Coupled assessment includes conducting single-member coupled assessments for each family member based on the changing direction of the overall deviation scores of each parameter corresponding to their individual health overall deviation score. For example, health parameters include heart rate, blood pressure, and blood oxygen saturation. If, for three consecutive time points, the overall deviation score of heart rate increases, the overall deviation score of blood pressure remains stable or increases, and the overall deviation score of blood oxygen saturation decreases, an abnormal coupled assessment is triggered, leading to a single-member coupled warning. This is identified as a hypoxia compensation pattern, typically caused by respiratory problems (such as COPD, asthma) or sleep apnea. By performing coupled analysis based on the physiological coupling relationships between various health parameters, different abnormal patterns can be identified, determining the possible pathological states of each family member, such as distinguishing between "cardiac" and "pulmonary" abnormalities.

[0113] For example:

[0114] In this process, a corresponding parameter stability threshold is set for each health parameter. If the difference between the comprehensive deviation scores of the parameters at two adjacent time points does not exceed the corresponding parameter stability threshold, then the comprehensive deviation score of the health parameter is considered to be stable.

[0115] Furthermore, the coupling assessment also includes synergistic coupling assessment based on the changing direction of the comprehensive deviation scores of each parameter corresponding to the individual health comprehensive deviation scores of all family members. For example, if two family members simultaneously trigger hypoxia compensation mode, or if one family member's blood pressure parameter comprehensive deviation score reaches the blood oxygen deviation threshold and another family member's heart rate parameter comprehensive deviation score increases, a family coupling warning is triggered. Similarly, when multiple family members simultaneously exhibit similar abnormal patterns (such as simultaneous hypoxia compensation), it can effectively indicate the possible existence of common environmental risk factors (such as carbon monoxide leakage, deteriorating indoor air quality) or family emotional events (such as collective anxiety). Through synergistic coupling assessment, early signals can be captured where a single parameter has not yet exceeded the standard but the coupling relationship of multiple parameters has already become abnormal (such as a slight decrease in blood oxygen but a compensatory increase in heart rate), achieving ultra-early warning of diseases.

[0116] Furthermore, the system generates a family health time-series dashboard based on the results of each health risk assessment. This dashboard visualizes the assessment using parallel timelines, overlaid curves, or heatmaps, and pushes analysis reports and early warning information to designated family members or family doctors. For example, it generates a heart rate-risk trend graph, displaying individual health baselines, family health baselines, actual detection values, and early warning event points in chart form, clearly presenting the risk evolution process and supporting remote medical intervention.

[0117] The cardiovascular health joint monitoring method provided in this embodiment breaks through the limitations of traditional single-parameter threshold judgment. By leveraging the temporal correlation characteristics of various health parameters such as heart rate, blood pressure, and blood oxygen, it can accurately identify complex pathological patterns (such as the "hypoxia compensation pattern" where heart rate increases accompanied by blood oxygen decreases, and the "circulatory failure pattern" where heart rate increases accompanied by blood pressure decreases). In this embodiment, health risk assessment can be performed on a central device without relying on the cloud, achieving low-latency and high-privacy health monitoring. Abnormal events are analyzed and triggered locally in milliseconds, and sensitive health data is processed within the home, with only anonymized summary information uploaded to the cloud, meeting the high privacy requirements of home users for medical data.

[0118] This invention provides a joint cardiovascular health monitoring system for family members, such as... Figure 2 As shown, it includes: The data acquisition module is used to continuously collect real-time health data of each family member; wherein, the real-time health data collected at each moment includes real-time detection values ​​of several health parameters; The joint analysis module calculates the individual health longitudinal deviation score, individual health horizontal deviation score, and individual risk score for each of the health parameters corresponding to each of the family members at the current moment, based on all the real-time health data of all the family members. The comprehensive scoring module calculates the corresponding comprehensive personal health deviation score based on all the personal health longitudinal deviation scores, personal health horizontal deviation scores, and personal risk scores of each family member at the current moment. The risk assessment module is used to conduct a health risk assessment based on the individual health deviation scores of all the family members.

[0119] The above-described method and system embodiments are based on the same principles, and their related aspects can be referenced from each other to achieve the same technical effects. For specific implementation processes, please refer to the foregoing embodiments, which will not be repeated here.

[0120] This invention provides another family member cardiovascular health joint monitoring system. Taking health parameters including heart rate, blood pressure (systolic / diastolic), and blood oxygen as an example, and family members including father (62 years old), mother (58 years old), and the user (25 years old), the specific execution process is explained.

[0121] (1) Data acquisition module The data acquisition module includes a central device (such as a customized Raspberry Pi or a smart router with edge computing capabilities) and acquisition devices (such as smartwatches). The central device runs an NTP server and communicates with each acquisition device via Wi-Fi, providing a millisecond-level time synchronization benchmark for all acquisition devices. The acquisition devices continuously collect real-time health data of each family member.

[0122] Furthermore, at 3:00 AM daily, the central device broadcasts a time synchronization signal to all acquisition devices via the NTP protocol, ensuring that the time error between each acquisition device and the central device is ≤1 millisecond. When a new acquisition device is connected, the time synchronization process is automatically triggered to ensure that multi-source data is accurately aligned on a unified timeline.

[0123] Real-time health data collected at each moment includes device ID and member identifier, original timestamp (built into the device), calibrated system timestamp (attached to the master control node), parameter type (HR / SBP / DBP / SpO2), real-time detection value, scene label (identified by accelerometer and time information), and signal quality index (SQI).

[0124] Among them, heart rate and blood oxygen are continuously monitored (once per second), while blood pressure (SBP / DBP) is measured discretely (once each morning and before bedtime, or by the user).

[0125] (2) Joint Analysis Module The execution of the joint analysis module includes the startup phase, the convergence phase, and the running phase.

[0126] The initial phase typically lasts 10 days, starting from the first collection of real-time health data by the system. During this phase, only real-time health data is collected, and no alerts are issued. Scene tags (such as "morning rest" or "after exercise") can be added to the central device or the collection devices, or the scene can be automatically identified through the device's built-in accelerometer to establish a preliminary "family member-scene tag-real-time detection value" triplet record.

[0127] The convergence phase typically lasts 20 days, starting from the end of the initiation phase. During this phase, the co-analysis module calculates individual and family health baselines based on the collected real-time health data. Taking the father as an example, after 30 days of real-time health data across the initiation and convergence phases, the following baselines are determined: heart rate - individual health baseline, systolic blood pressure - individual health baseline, diastolic blood pressure - individual health baseline, and blood oxygen saturation - individual health baseline:

[0128] Based on the real-time health data of three family members, the corresponding family health baseline is determined. Taking the morning resting scenario as an example, the family health baseline is heart rate 70 beats / min, systolic blood pressure 122 mmHg, diastolic blood pressure 78 mmHg, and blood oxygen 97%.

[0129] Based on the "Guidelines for the Prevention and Treatment of Hypertension in China (2024 Edition)" and the "Guidelines for the Diagnosis and Treatment of Respiratory Diseases in China," the risk stratification mapping function was determined and is presented in tabular form as follows:

[0130] During the operation phase, the sliding window mean method is used to dynamically update the individual health baseline and family health baseline for each family member and each health parameter. Based on real-time health data, the individual health longitudinal deviation score, individual health horizontal deviation score, and individual risk score for each family member and each health parameter at the current moment are calculated.

[0131] At the current time (morning rest period 6:30-7:00), the real-time health parameters of the three family members are as follows:

[0132] The current family health baselines are: heart rate (family health baseline: 70 bpm), systolic blood pressure (family health baseline: 122 mmHg), diastolic blood pressure (family health baseline: 78 mmHg), and blood oxygen saturation (family health baseline: 97%). Taking the father as an example, his individual baselines are 68 bpm, 125 mmHg, 80 mmHg, and 97%, respectively. The parameter coupling baselines are all 0, and the family correction factor is 1.2. The calculated individual health horizontal deviation scores are 39.71, 16.0, 15.0, and 0, respectively; the individual health horizontal deviation scores are 42.85, 22.62, 21.54, and 0, respectively; and the individual risk scores are 25, 25, 50, and 0, respectively.

[0133] (3) Comprehensive scoring module The individual health comprehensive deviation score is calculated based on the current personal health longitudinal deviation score, personal health horizontal deviation score, and personal risk score of each family member.

[0134] First, calculate the individual risk score for each family member. The father is 62 years old. The probability of developing various diseases increases after age 60, and the corresponding age risk factor is uniformly set as follows: History of hypertension, no other complications, heart rate-past medical history factor (Characterizing the effect of hypertension history on heart rate), Blood pressure-medical history factor (This characterizes the impact of a history of hypertension on blood pressure, with a greater impact than on heart rate), blood oxygen saturation - past medical history factor. (Characteristics: no history of respiratory system disease), comorbidity factors are uniformly defined as follows: (Indicating no comorbidities), calculate the corresponding individual risk score, heart rate - individual risk factor. The diastolic blood pressure-individual risk factor is the same as the systolic blood pressure-individual risk factor. Blood oxygen - personal risk factor Mother, 58 years old, no medical history, no comorbidities, age risk factors are uniformly [not specified]. Each factor for past medical history and comorbidities is 1.0, and the calculated risk factor for each individual is 1.07. The user is 25 years old, has no medical history, and no comorbidities; the age risk factor is uniformly set to... Each factor for past medical history and comorbidity was 1.0, and the risk factor for each individual was calculated to be 1.0.

[0135] Next, the individual health comprehensive deviation score for each family member is calculated. For the father in this example, the deviation scores are calculated based on his current longitudinal and lateral health deviation scores, personal risk score, and personal risk factors. The longitudinal, lateral, and personal risk weights for each health parameter are set to 0.3, 0.2, and 0.5 respectively; the weights for heart rate, systolic blood pressure, diastolic blood pressure, and blood oxygen saturation are set to 0.25, 0.3, 0.3, and 0.15 respectively. The calculated individual health comprehensive deviation score is 31.33. Similarly, the mother's individual health comprehensive deviation score is calculated to be 4.91, and the user's own individual health comprehensive deviation score is 1.25.

[0136] (4) Risk assessment module The instantaneous health threshold for each family member is set to 20, the statistical time window is set to 24 hours, and the collaboration quantity threshold is set to 2. For the father in this embodiment, the individual health comprehensive deviation score of 31.33 is greater than 20, triggering an immediate warning and recording the abnormal pattern: "mixed abnormality" (significantly increased heart rate and moderate blood pressure abnormality). Neither the mother nor the user has reached the instantaneous health threshold. Within the 24-hour window, only one person triggers the warning, and the collaboration quantity threshold (2) is not reached, so the family collaboration warning is not triggered.

[0137] If the mother also exhibits abnormalities the following morning (assuming a heart rate of 88 bpm, systolic blood pressure of 142 mmHg, and blood oxygen saturation of 95%), and her overall risk score reaches 22.5, then two individuals within the 24-hour window will trigger an immediate alert, with similar abnormal patterns (both showing elevated heart rate and blood pressure). This will trigger a family-coordinated alert, outputting a warning message such as, "Multiple family members (father and mother) have been detected to have abnormally elevated heart rate and blood pressure. Note: Please investigate common environmental factors (such as indoor air quality, diet, and emotional stress). It is recommended to ensure a resting state before measurement and contact your family doctor if necessary."

[0138] In summary, the method and system for joint monitoring of cardiovascular health among family members according to embodiments of the present invention have at least one of the following beneficial effects: 1. By utilizing real-time health data from all family members, this system calculates individual longitudinal health deviation scores, individual horizontal health deviation scores, and individual risk scores for different health parameters. This enables longitudinal analysis of each family member, horizontal analysis among all family members, and analysis of individual current status, addressing issues of data isolation and lack of correlation. Furthermore, it calculates a corresponding comprehensive individual health deviation score for each family member, completing a comprehensive analysis of each health parameter. This facilitates understanding the overall health status of each member and conducting health risk assessments for all family members. It accurately identifies group signals, enabling collaborative monitoring and early warning among family groups, improving early warning accuracy and overall family health management. It can provide early warning of family cluster risks (such as familial tendencies in hypertension and coronary heart disease), improving monitoring efficiency and providing precise support for family cardiovascular health management. Using a three-dimensional fusion model of individual longitudinal health deviation scores, individual horizontal health deviation scores, and individual risk scores, the risk assessment combines individual longitudinal comparability, family horizontal comparability, and clinical interpretability.

[0139] 2. By broadcasting a precise reference time to the acquisition devices through the family center device, the time alignment of the acquisition devices for real-time health data is achieved, which solves the problem of measurement timestamp differences between different devices and different members, provides a high-precision and consistent time reference, ensures the time consistency of real-time detection values ​​of various health parameters, improves the credibility of correlation analysis, and further enhances the accuracy of capturing synchronous or delayed health synergistic changes of family members by conducting health risk assessment based on the comprehensive deviation scores of the individual health of all family members.

[0140] 3. Dynamically determine individual and family health baselines, abandoning fixed thresholds and updating them dynamically with time, age, season, and health status (e.g., automatically adjusting blood pressure baselines in winter) to avoid misjudgments of special populations (the elderly, children) based on fixed thresholds. Based on scenario tags such as morning rest, daytime activity, and nighttime sleep, use differentiated baselines for different scenarios (e.g., being more sensitive to blood oxygen decline during nighttime sleep) to ensure that warning rules conform to physiological patterns and improve scenario adaptability.

[0141] 4. Introducing individual health cross-deviation scores and family collaborative assessments of health risks expands the monitoring perspective from the individual to the family as a whole, identifying common risk sources. Through family health baselines and individual health cross-deviation scores, the degree of deviation between the individual and the family is quantitatively characterized, intuitively reflecting the relative health status of each member within the family, and providing data support for health care among family members.

[0142] 5. By dynamically adjusting the vertical weight, horizontal weight, and personal risk weight of different health parameters according to scenario tags, it effectively distinguishes between physiological fluctuations (such as increased heart rate due to exercise) and pathological abnormalities (such as hypoxia compensation caused by disease), reducing the false alarm rate and minimizing the inconvenience caused to users by false alarms.

[0143] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0144] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for joint monitoring of cardiovascular health among family members, characterized in that, include: Continuously collect real-time health data from multiple family members; wherein, the real-time health data collected at each moment includes real-time detection values ​​of several health parameters; Based on all the real-time health data of all the family members, calculate the individual health longitudinal deviation score, individual health horizontal deviation score, and individual risk score for each of the health parameters corresponding to each family member at the current moment; Calculate the corresponding comprehensive personal health deviation score based on all the personal health longitudinal deviation scores, personal health horizontal deviation scores, and personal risk scores of each family member at the current moment; A health risk assessment is conducted based on the individual health deviation scores of all family members.

2. The method according to claim 1, characterized in that, Continuously collect real-time health data for each family member, including: Register the device for collecting the real-time health data at the home center device; The home center device broadcasts a precise reference time to the acquisition device, which performs time alignment based on the precise reference time. After time alignment, the acquisition device responds to the monitoring command and continuously acquires the real-time health data.

3. The method according to claim 1, characterized in that, The individual health longitudinal deviation score is obtained through the following steps: For each of the health parameters, a personal health baseline is calculated for each of the family members based on all the real-time detection values ​​of all the family members. For each of the health parameters of each family member, the corresponding personal health longitudinal deviation score is calculated based on the real-time detection value at the current moment and the personal health baseline.

4. The method according to claim 3, characterized in that, For each of the health parameters, based on all the real-time detection values ​​of all the family members, calculate the personal health baseline for each family member, including: For each health parameter, a personal baseline is calculated for each family member based on all the real-time detection values ​​for each family member. Based on all the real-time detection values ​​of all the health parameters and the physiological coupling relationship between all the health parameters, calculate the parameter coupling baseline corresponding to each health parameter; For each of the health parameters, the corresponding personal health baseline is calculated based on the personal baseline of each of the family members and the parameter coupling baseline.

5. The method according to claim 1, characterized in that, The individual health lateral deviation score is obtained through the following steps: For each of the health parameters, a corresponding family health baseline is calculated based on the real-time detection values ​​of all the family members. The corresponding individual health lateral deviation score is calculated based on the family health baseline and the real-time health data of each family member at the current moment.

6. The method according to claim 1, characterized in that, The individual risk score is obtained through the following steps: Based on the real-time health data of each family member at the current moment, a risk stratification mapping function is used to determine the individual risk score for each corresponding health parameter.

7. The method according to claim 1, characterized in that, The individual health deviation score is calculated using the following formula: In the formula, For the first Family members at the current moment The aforementioned individual health comprehensive deviation score, For the first The parameter weights of the aforementioned health parameters, The number of the health parameters, , , The first The family member The aforementioned health parameters at the current time The corresponding personal health longitudinal deviation score, personal health lateral deviation score, and personal risk score. , , The first The vertical weights, horizontal weights, and individual risk weights of the aforementioned health parameters.

8. The method according to claim 1, characterized in that, Before calculating the corresponding comprehensive personal health deviation score based on all the individual health longitudinal deviation scores, individual health lateral deviation scores, and individual risk scores of each family member at the current moment, the calculation also includes: Obtain basic health data for each of the aforementioned family members; For each family member, the corresponding age risk factors, medical history factors, and comorbidity factors are determined based on the basic health data; Based on the age risk factor, the medical history factor, and the comorbidity factor, calculate the individual risk factor for each of the health parameters; For each of the health parameters, the individual health longitudinal deviation score, the individual health lateral deviation score, and the individual risk score are adjusted based on the corresponding individual risk factor.

9. The method according to claim 1, characterized in that, A health risk assessment is conducted based on the individual health deviation scores of all family members, including: Determine whether the individual health deviation score of each family member exceeds the corresponding instantaneous health threshold; If any family member's overall health deviation score exceeds the corresponding instantaneous health threshold, then the number of family members whose scores exceed the corresponding instantaneous health threshold is counted. If the number of members exceeds the collaboration threshold, the assessment result of the health risk assessment is determined to be an abnormal collaboration assessment.

10. A joint cardiovascular health monitoring system for family members, characterized in that, include: The data acquisition module is used to continuously collect real-time health data of each family member; wherein, the real-time health data collected at each moment includes real-time detection values ​​of several health parameters; The joint analysis module calculates the individual health longitudinal deviation score, individual health horizontal deviation score, and individual risk score for each of the health parameters corresponding to each of the family members at the current moment, based on all the real-time health data of all the family members. The comprehensive scoring module calculates the corresponding comprehensive personal health deviation score based on all the personal health longitudinal deviation scores, personal health horizontal deviation scores, and personal risk scores of each family member at the current moment. The risk assessment module is used to conduct a health risk assessment based on the individual health deviation scores of all the family members.

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