Blood pressure measurement method, wearable device and storage medium

Abstract

The invention discloses a blood pressure measuring method, wearable equipment and a storage medium, and relates to the technical field of wearable equipment, and the blood pressure measuring method comprises the following steps: acquiring a target physiological signal detected by a target user in a current time period based on the wearable equipment; collecting detection environment parameters of the wearable device in the current time period; according to the target physiological signal, blood pressure change factor information which causes the change of the blood pressure value level of the target user is identified, and a target blood pressure calculation model mapped by the blood pressure change factor information is determined from a plurality of preset blood pressure calculation models; and inputting the acquired blood pressure baseline characteristics and blood pressure baseline states, the target physiological signals and the detection environment parameters into a target blood pressure calculation model, and outputting a blood pressure value of the target user. Interference caused by individual differences is effectively reduced, and accuracy and individual adaptability of blood pressure monitoring are improved.

Description

Technical Field

[0001] This application relates to the field of wearable device processing technology, and in particular to blood pressure measurement methods, wearable devices, and storage media. Background Technology

[0002] Blood pressure is a key indicator of human physiological health. Regular blood pressure measurement helps people detect potential cardiovascular disease risks early, take corresponding preventive measures, and reduce the incidence of diseases. To meet users' needs for non-intrusive, high-frequency, and even continuous blood pressure monitoring, cuffless blood pressure measurement technology has been widely researched and applied.

[0003] Currently, common cuffless blood pressure measurement methods include arterial tension method, impedance method, cardiac impact mapping method, and Doppler ultrasound method. These methods acquire physiological signal characteristics such as local arterial wall tension, tissue impedance, cardiac mechanical vibration or blood flow velocity through different sensing mechanisms, and calculate and output blood pressure parameters based on the correlation model between these characteristics and blood pressure values.

[0004] However, besides individual internal changes causing blood pressure fluctuations, there are also differences between individuals. For example, age, body mass index (BMI), and vascular elasticity all directly affect the measured physiological signal characteristics. The models or algorithms relied upon by common cuffless measurement methods often struggle to universally and accurately fit this highly personalized and dynamically changing complex relationship, leading to significant errors or uncertainties in measurement results when dealing with different users.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main objective of this application is to provide a blood pressure measurement method, wearable device, and storage medium, aiming to solve the technical problem of how to effectively reduce interference caused by individual differences and improve the accuracy of cuffless blood pressure measurement.

[0007] To achieve the above objectives, this application proposes a blood pressure measurement method, the method comprising: Acquire target physiological signals of the target user based on wearable devices during the current time period, wherein the target physiological signals include at least two of target electrocardiogram signals, target heart sound signals, and target pulse signals; The wearable device collects detection environment parameters during the current time period, wherein the detection environment parameters include IMU data reflecting the human body's state; Based on the target physiological signal, information on blood pressure variability factors that cause changes in the blood pressure level of the target user is identified, and a target blood pressure calculation model is determined from multiple preset blood pressure calculation models to map the information on blood pressure variability factors. The acquired blood pressure baseline features and blood pressure baseline status, the target physiological signal, and the detection environment parameters are input into the target blood pressure calculation model, and the blood pressure value of the target user is output. Wherein, the blood pressure baseline feature is the reference value corresponding to each physiological feature under standard human condition, the standard human condition is sleep state or resting state, the physiological feature is used to characterize the blood pressure value level, the blood pressure baseline state is blood pressure baseline change or blood pressure baseline no change, wherein, when the difference between the blood pressure baseline feature and the target baseline feature stored in the target blood pressure calculation model is greater than a preset degree threshold, the blood pressure baseline state is blood pressure baseline change.

[0008] In some embodiments, prior to the step of acquiring the target physiological signal detected by the wearable device based on the target user in the current time period, the method further includes: Based on the wearable device, the target user's raw physiological signals are collected synchronously during the current time period, wherein the raw physiological signals include at least two of the following: raw electrocardiogram signals, raw heart sound signals, and raw pulse signals; The original physiological signal is preprocessed by a preset signal preprocessing unit to obtain a preprocessed physiological signal. The preprocessing includes at least one of filtering, baseline correction, normalization and effective signal extraction. The target physiological signal is determined based on the preprocessed physiological signal.

[0009] In some embodiments, the detection environmental parameters further include pressure data applied by the wearable device to the target user's body surface measurement location and temperature data of the target user at a preset body part. The step of collecting the detection environmental parameters of the wearable device in the current time period includes: In response to a triggered measurement command, the wearable device collects environmental parameters for the current time period through a preset human body state recognition unit, wherein the human body state recognition unit includes an inertial sensor, a pressure sensor, and a temperature sensor. The human body state recognition unit determines whether the detection environment parameters meet the preset blood pressure measurement conditions. When the detection environment parameters meet the blood pressure measurement conditions, the step of identifying blood pressure mutagenic factors that cause changes in the blood pressure level of the target user based on the target physiological signal is performed.

[0010] In some embodiments, the blood pressure measurement method further includes: When the target user is detected to be in the standard human body state, a preset blood pressure baseline monitoring unit is used to collect baseline physiological signals, and the blood pressure baseline characteristics are determined based on the baseline physiological signals. The baseline physiological signals include at least two of the following: baseline electrocardiogram signals, baseline heart sound signals, and baseline pulse signals. The blood pressure baseline monitoring unit determines the degree of difference between the blood pressure baseline characteristics and the target baseline characteristics, and determines whether the degree of difference is greater than a preset threshold, thereby obtaining the blood pressure baseline state.

[0011] In some embodiments, the blood pressure mutagenic factors include multiple types of blood pressure mutagenic factors and the corresponding proportions of each blood pressure mutagenic factor. The step of identifying information on blood pressure mutagenic factors that cause changes in the blood pressure level of the target user based on the target physiological signal includes: The target physiological signal is input into a preset blood pressure variability identification model, and the multiple blood pressure variability types and the corresponding proportions of each blood pressure variability are output. The blood pressure variability identification model is a deep learning model based on prior physiological knowledge.

[0012] In some embodiments, the target blood pressure calculation model includes a target blood pressure correction model, and the step of inputting the acquired blood pressure baseline features and blood pressure baseline status, the target physiological signal, and the detection environment parameters into the target blood pressure calculation model and outputting the blood pressure value of the target user includes: Feature extraction is performed on the target physiological signal to determine the target physiological characteristics; When the blood pressure baseline state is a change in blood pressure baseline, the value of the target baseline feature is replaced with the value of the blood pressure baseline feature; The physiological characteristic difference is determined based on the difference between the target physiological characteristic and the target baseline characteristic; The detection environment parameters and the physiological characteristic difference are input into the target blood pressure correction model, and the blood pressure correction value is output. The blood pressure correction value is positively correlated with the absolute value of the physiological characteristic difference. The blood pressure value of the target user is determined based on the blood pressure correction value and the preset gold standard blood pressure value, wherein the gold standard blood pressure value is the blood pressure value measured by the target user under the standard human body condition.

[0013] In some embodiments, the blood pressure measurement method further includes: When the wearable device collects the target user's baseline blood pressure characteristics, the target user's gold standard blood pressure value is simultaneously measured using a preset gold standard blood pressure measuring device, wherein the gold standard blood pressure measuring device refers to a blood pressure measuring device whose accuracy is higher than a preset accuracy threshold.

[0014] In some embodiments, the target physiological features include heart rate, pulse wave transit time, and pre-ejection time. The step of extracting features from the target physiological signals to determine the target physiological features includes: Key features within the same cardiac cycle of the target electrocardiogram signal, the target heart sound signal, and the target pulse signal are located to obtain the location time points corresponding to each key feature. The key features in the target electrocardiogram signal include the Q wave, the key features in the target heart sound signal include the first heart sound and the second heart sound, and the key features in the target pulse signal include the rising limb of the pulse wave. Heart rate is obtained by measuring the time interval between two adjacent key features within different cardiac cycles. Based on the location time point corresponding to the Q wave and the location time point corresponding to the first heart sound, the pre-ejection time within the corresponding cardiac cycle is calculated. Based on the location time point corresponding to the first heart sound and the location time point corresponding to the rising branch of the pulse wave, the pulse wave conduction time within the corresponding cardiac cycle is calculated.

[0015] In some embodiments, the blood pressure measurement method further includes: Acquire training data, which includes sample physiological signals, sample environmental parameters, sample baseline features, sample baseline status, and reference blood pressure values. The training data is collected from users corresponding to the same type of blood pressure variability factor. A blood pressure calculation model corresponding to the aforementioned blood pressure variability factors is established, wherein the blood pressure calculation model includes a deep learning model and a physiological model, and the physiological model is used to provide the theoretical basis for blood pressure calculation for the deep learning model; The training data is used as input to the blood pressure calculation model to train the blood pressure calculation model and obtain the blood pressure calculation model mapped by the type of blood pressure variability.

[0016] Furthermore, to achieve the above objectives, this application also proposes a blood pressure measuring device, which includes: The physiological signal acquisition module is used to acquire target physiological signals detected by the wearable device of the target user in the current time period, wherein the target physiological signals include at least two of the target electrocardiogram signal, target heart sound signal and target pulse signal; An environmental parameter acquisition module is used to acquire the detection environmental parameters of the wearable device during the current time period, wherein the detection environmental parameters include IMU data that reflects the human body state; The model determination module is used to identify blood pressure variability information that causes changes in the blood pressure level of the target user based on the target physiological signal, and to determine the target blood pressure calculation model mapped by the blood pressure variability information from multiple preset blood pressure calculation models. The blood pressure output module is used to input the acquired blood pressure baseline features and blood pressure baseline status, the target physiological signal, and the detection environment parameters into the target blood pressure calculation model, and output the blood pressure value of the target user. The blood pressure baseline features are reference values ​​corresponding to various physiological characteristics under standard human conditions, where the standard human conditions are sleep or resting states. The physiological characteristics are used to characterize blood pressure levels. The blood pressure baseline status is either a change in the blood pressure baseline or no change in the blood pressure baseline. Specifically, if the difference between the blood pressure baseline features and the target baseline features stored in the target blood pressure calculation model is greater than a preset threshold, the blood pressure baseline status is considered a change in the blood pressure baseline.

[0017] In addition, to achieve the above objectives, this application also proposes a wearable device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the blood pressure measurement method as described above.

[0018] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the blood pressure measurement method described above.

[0019] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the blood pressure measurement method described above.

[0020] The one or more technical solutions proposed in this application have at least the following technical effects: First, simultaneously acquiring at least two target physiological signals from the target user in the current time period, including target electrocardiogram signals, target heart sound signals, and target pulse direction, achieves multi-dimensional synchronous capture of cardiovascular status; simultaneously collecting detection environment parameters such as IMU (Inertial Measurement Unit) data reflecting human body status from the wearable device in the current time period, to accurately identify whether the user is in different states such as movement or rest, more accurately interpret changes in physiological signals, and reduce measurement errors caused by the detection environment; furthermore, based on the target physiological signals, identifying the blood pressure variability factors that cause changes in the target user's blood pressure level, and determining the target blood pressure calculation model mapping the blood pressure variability factor information from multiple preset blood pressure calculation models, realizing the adaptation of the blood pressure calculation model to individual etiologies, enhancing the model's specificity and interpretability, thereby improving the calculation accuracy of blood pressure measurement; simultaneously, acquiring blood pressure baseline characteristics and blood pressure baseline status, wherein the blood pressure baseline characteristics are derived from the physiological characteristic reference values ​​of the human body in standard states such as sleep or rest, effectively characterizing Individual differences caused by factors such as age, BMI, and vascular elasticity are considered to form a personalized calibration benchmark. The blood pressure baseline status reflects the degree of difference between this blood pressure baseline feature and the target baseline feature stored in the target blood pressure calculation model. The combination of the two provides a calibration basis to overcome individual differences and helps reduce errors caused by different user physiological conditions. Then, the acquired blood pressure baseline features and blood pressure baseline status, as well as the aforementioned target physiological signals and detection environment parameters, are input into the target blood pressure calculation model. Based on the personalized baseline, the model comprehensively considers the real-time physiological state and finally outputs a value that accurately reflects the individual's true blood pressure status in the current environment, thereby achieving precise and personalized blood pressure measurement and improving the accuracy of blood pressure measurement.

[0021] This application upgrades blood pressure monitoring from the traditional single-signal and general formula model to a dynamic calculation model that includes multi-signal fusion, causal driving, model matching, and personalized calibration through a progressive analysis process that assesses real-time physical condition, diagnoses the main causes of blood pressure changes, matches blood pressure calculation models, establishes personal benchmarks, and performs fusion calculations. This effectively reduces interference from individual differences and improves the accuracy and individual adaptability of blood pressure monitoring. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating an embodiment of the blood pressure measurement method of this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the blood pressure measurement method of this application; Figure 3 This is a schematic diagram of the framework modules of the blood pressure measurement method provided in Embodiment 2 of this application; Figure 4 This is a schematic diagram of the module structure of the blood pressure measuring device according to an embodiment of this application; Figure 5 This is a schematic diagram of the hardware operating environment involved in the blood pressure measurement method in this application embodiment.

[0025] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0026] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0027] It should be noted that in the description of this application and the appended claims, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0029] Currently, common cuffless blood pressure measurement methods include arterial tension method, impedance method, cardiac impact mapping method, and Doppler ultrasound method. These methods acquire physiological signal characteristics such as local arterial wall tension, tissue impedance, cardiac mechanical vibration or blood flow velocity through different sensing mechanisms, and calculate and output blood pressure parameters based on the correlation model between these characteristics and blood pressure values.

[0030] However, besides individual internal changes causing blood pressure fluctuations, there are also differences between individuals. For example, age, body mass index (BMI), and vascular elasticity all directly affect the measured physiological signal characteristics. The models or algorithms relied upon by common cuffless measurement methods often struggle to universally and accurately fit this highly personalized and dynamically changing complex relationship, leading to significant errors or uncertainties in measurement results when dealing with different users.

[0031] This application provides a solution that, firstly, simultaneously acquires at least two target physiological signals from the target user during the current time period, including target electrocardiogram signals, target heart sound signals, and target pulse direction, achieving multi-dimensional synchronous capture of cardiovascular status; simultaneously, it collects detection environment parameters such as IMU data reflecting the human body state from the wearable device during the current time period to accurately identify whether the user is in different states such as exercise or rest, more accurately interpreting changes in physiological signals and reducing measurement errors caused by the detection environment; furthermore, based on the target physiological signals, it identifies blood pressure variability information that causes changes in the target user's blood pressure level, and determines the target blood pressure calculation model mapping the blood pressure variability information from multiple preset blood pressure calculation models, achieving adaptation of the blood pressure calculation model to individual etiologies, enhancing the model's specificity and interpretability, thereby improving the calculation accuracy of blood pressure measurement; simultaneously, it acquires blood pressure... The baseline features and blood pressure baseline status are used in the blood pressure measurement process. The blood pressure baseline features are derived from reference values ​​of physiological characteristics of the human body under standard conditions such as sleep or rest, effectively representing individual differences caused by age, BMI, vascular elasticity, etc., forming a personalized calibration benchmark. The blood pressure baseline status reflects the degree of difference between the blood pressure baseline features and the target baseline features stored in the target blood pressure calculation model. The combination of the two provides a calibration basis to overcome individual differences and helps reduce errors caused by different physiological conditions of users. Then, the acquired blood pressure baseline features and blood pressure baseline status, as well as the aforementioned target physiological signals and detection environment parameters, are input into the target blood pressure calculation model. Based on the personalized baseline, the model comprehensively considers the real-time physiological state and finally outputs a value that accurately reflects the individual's true blood pressure status in the current environment, thereby achieving precise and personalized blood pressure measurement and improving the accuracy of blood pressure measurement.

[0032] As can be seen from the above embodiments, this application upgrades blood pressure monitoring from the traditional single signal and general formula mode to a dynamic calculation mode that includes multi-signal fusion, cause-driven, model matching, and personalized calibration by progressively analyzing the process of assessing real-time physical status, diagnosing the main causes of blood pressure changes, matching blood pressure calculation models, establishing personal benchmarks, and fusion calculation. This effectively reduces the interference caused by individual differences and improves the accuracy and individual adaptability of blood pressure monitoring.

[0033] It should be noted that the executing entity in this embodiment can be a wearable device with data processing, network communication, and program execution functions, such as a smart bracelet, smartwatch, and head-mounted display device. The head-mounted display device can include, but is not limited to, wearable devices such as smart headphones, Mixed Reality (MR) devices (e.g., MR glasses or MR helmets), Augmented Reality (AR) devices (e.g., AR glasses or AR helmets), Virtual Reality (VR) devices (e.g., VR glasses or VR helmets), Extended Reality (XR) devices, or some combination thereof. The following description uses a wearable device as the executing entity to illustrate this embodiment and the subsequent embodiments.

[0034] Based on this, the embodiments of this application provide a blood pressure measurement method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the blood pressure measurement method of this application.

[0035] In this embodiment, the blood pressure measurement method includes steps S10 to S40: Step S10: Obtain the target physiological signals detected by the wearable device for the target user in the current time period; The target physiological signal includes at least two of the target electrocardiogram signal, target heart sound signal, and target pulse signal; As those skilled in the art will know, a physiological signal is a measurable physical quantity that reflects the physiological state or functional activity of an organism. Common types include electrical signals (such as electrocardiogram and electroencephalogram), acoustic signals (such as heart sounds and breath sounds), and mechanical signals (such as pulse and blood pressure), which are used for non-invasive monitoring of human health status.

[0036] Electrocardiogram (ECG) refers to the cardiac electrical activity signal collected by sensors such as ECG surface electrodes. It reflects the depolarization and repolarization process of the myocardium. Its typical waveforms include the P wave, QRS complex (including Q wave and R wave), and T wave. The R wave is the highest amplitude positive wave in the QRS complex and is often used to mark the onset of cardiac electrical activity. The Q wave is the first negative wave in the QRS complex before the R wave and is often used to mark the onset of ventricular depolarization and the entry into the pre-ejection phase.

[0037] Phonocardiogram (PCG) refers to the acoustic signal collected by sensors such as microphones and accelerometers, which is generated by mechanical vibrations such as the closure of heart valves and blood flow turbulence. It mainly includes the first heart sound (S1, corresponding to the closure of the mitral and tricuspid valves) and the second heart sound (S2, corresponding to the closure of the aortic and pulmonary valves). Its internal multi-peak structure contains information on valvular dynamics and cardiac function status.

[0038] Pulse signal refers to the peripheral arterial pulsation signal collected by photoplethysmography (PPG), pressure sensor, etc., which reflects the changes in blood volume caused by the heart pumping blood. Its periodic trough point usually corresponds to the start of ventricular diastole and can be used to calibrate the end of pulse wave transit time (PTT). PPG signal refers to photoplethysmography pulse wave signal (pulse signal).

[0039] It should be noted that the target user refers to the wearer of the wearable device; the current time period refers to the time window with start and end timestamps that begins after the wearable device triggers blood pressure measurement; the target physiological signal refers to the multimodal physiological signal synchronously collected within the current time period, including at least two of the target electrocardiogram signal, target heart sound signal, and target pulse signal. Each physiological signal is strictly aligned in time to ensure physiological consistency in subsequent joint analysis based on time sequence relationships.

[0040] In one possible implementation, prior to step S10, the method further includes: Step S01: Based on the wearable device, the target user's raw physiological signals in the current time period are collected synchronously. The raw physiological signals include at least two of the following: raw electrocardiogram signal, raw heart sound signal, and raw pulse signal. It should be noted that the raw physiological signal refers to the physiological signal in analog or digital form directly acquired by the sensor without any preprocessing. It retains all time and frequency domain information of the physiological signal, but also includes non-physiological components such as environmental noise, motion artifacts, and power supply interference. In this embodiment, the raw physiological signal includes at least two of the following: raw electrocardiogram signal, raw pulse signal, and raw heart sound signal, and is consistent with the target physiological signal.

[0041] For example, a multimodal sensor array (including ECG surface electrodes, PCG vibration sensors, PPG photoelectric sensors, etc.) on a wearable device can be used to synchronously acquire the raw physiological signals of the target user at the current time period, ensuring that each physiological signal is strictly aligned on the time axis. For instance, in a smart bracelet or smartwatch, ECG electrodes, PCG vibration sensors, and PPG photoelectric sensors can be integrated to achieve hardware-level synchronous acquisition of the three types of raw physiological signals: ECG, PCG, and PPG. The sampling frequency can be set according to the characteristics of each physiological signal, such as acquiring ECG and PCG signals at a sampling frequency of 500Hz and acquiring PPG signals at a sampling frequency of 100Hz. The sampling duration can be set to 3 minutes to ensure that a set of signals includes multiple cardiac cycles.

[0042] For example, after initiating blood pressure measurement, the user places the arm with the wearable device in front of their chest, transmitting the PCG signal through the arm to the device, where it is received by the bone conduction VPU (Video Processing Unit) sensor and IMU sensor inside the wearable device; the other finger presses the ECG electrode on the device to measure the single-lead ECG signal, while the PPG signal is collected by the photoelectric sensor on the side of the wearable device close to the wrist.

[0043] Step S02: The original physiological signal is preprocessed by a preset signal preprocessing unit to obtain a preprocessed physiological signal. The preprocessing includes at least two of the following: filtering, baseline correction, normalization, and effective signal extraction. It should be noted that the signal preprocessing unit refers to the module in a wearable device used to preprocess the acquired raw physiological signals. It typically contains a series of algorithms and hardware circuits to perform operations such as filtering, correction, and normalization.

[0044] Filtering refers to using specific filtering algorithms or filters to suppress or enhance specific frequency components in a signal in order to remove noise and interference and retain useful signal components. Common filtering methods include low-pass filtering, high-pass filtering, and band-pass filtering.

[0045] Baseline correction refers to estimating and subtracting the low-frequency slow-changing trend (baseline) in a signal using algorithms (such as moving average, polynomial fitting, morphological operations, etc.) to restore the zero line or center line of the signal to a normal level.

[0046] Normalization refers to scaling the values ​​of different signals to a specific range to eliminate the influence of differences in dimensions and numerical ranges between different signals.

[0047] Effective signal extraction refers to identifying and separating useful signal components that reflect blood pressure levels from the original signal, and removing signal parts that are irrelevant to changes in blood pressure or interfere with the analysis. For example, effective pulse waves can be identified from pulse waves based on amplitude thresholds.

[0048] For example, the signal preprocessing unit can use a high-pass filter or polynomial fitting to remove baseline drift of the original physiological signal, and use a low-pass filter or adaptive filter to remove muscle noise in the original physiological signal; then, it can perform minimum-maximum normalization on the amplitude of each physiological signal to scale the signal amplitude range to the [0,1] interval; and then, based on the signal-to-noise ratio of each physiological signal or physiological prior knowledge, it can remove invalid physiological signal data and signal data that are unrelated to changes in blood pressure.

[0049] Understandably, by filtering out noise, correcting the baseline to eliminate drift, normalizing to unify the scale, and extracting effective signals to remove interference, the detectability of key feature points (such as R-wave peaks, pulse signal troughs, S1 / S2 main peaks, etc.) is enhanced, which can more accurately reflect the user's physiological state and thus improve the accuracy of blood pressure measurement.

[0050] Step S03: Determine the target physiological signal based on the preprocessed physiological signal.

[0051] For example, the preprocessed physiological signal can be directly identified as the target physiological signal of the target user. Alternatively, it can be combined with the pressure data of the pressure sensor in the wearable device, which is applied to the body surface measurement location. Data that does not meet the requirements can be discarded to avoid interfering with the accuracy of blood pressure calculation. For example, if the pressure value during the physiological signal acquisition process is higher or lower than a certain range, the blood pressure measurement data may not meet the requirements of the model calculation, and such data will be discarded.

[0052] For example, the preprocessed physiological signals can also be divided into cardiac cycles. For instance, the time interval between two R waves in the ECG signal can be defined as a cardiac cycle, and the PCG and PPG signals can be divided simultaneously according to the division of the cardiac cycle of the ECG signal, so as to compare and analyze the changing trends of ECG, PCG and PPG signals in different cardiac cycles.

[0053] In this embodiment, by synchronously acquiring raw physiological signals, the timing consistency of multiple signals such as electrocardiogram signals, heart sound signals, and pulse signals is ensured, so as to accurately calculate key features such as pulse wave propagation time and improve the underlying accuracy of blood pressure measurement. Furthermore, through operations such as filtering, baseline correction, and normalization, the signal-to-noise ratio and quality reliability of multimodal signals are improved, effectively overcoming the interference problem in users' daily use scenarios. This is an indispensable preliminary step for achieving high-precision blood pressure measurement.

[0054] Step S20: Collect the detection environment parameters of the wearable device in the current time period; Among them, the environmental parameters to be detected include IMU data used to reflect the state of the human body; It should be noted that detection environment parameters refer to a set of auxiliary data collected synchronously with the original physiological signals. These parameters describe the external physical conditions and the user's own physical behavior during the acquisition of physiological signals, serving as contextual feature inputs for subsequent signal processing and model calculations. Detection environment parameters typically include, but are not limited to, IMU (Inertial Measurement Unit) data, and may also cover skin temperature, pressure applied by wearable devices to the measurement sites on the body surface, etc.

[0055] IMU data refers to information about the motion state of an object in space collected by inertial sensors, including data such as acceleration and angular velocity, which is used to reflect the state of the human body. For example, the raw IMU data can be processed by algorithms (such as activity recognition and posture estimation) to transform it into a semantic classification of the user's current body movements, such as classification as "stationary", "walking", "running", "upper arm raised" and other states.

[0056] In one feasible implementation, detecting environmental parameters further includes pressure data applied by the wearable device to the target user's body surface measurement location and temperature data of the target user at a preset body part. Step S20 includes: Step S21: In response to the triggered measurement command, the wearable device collects the detection environment parameters of the current time period through the preset human body state recognition unit, wherein the human body state recognition unit includes an inertial sensor, a pressure sensor and a temperature sensor. It should be noted that the human body state recognition unit refers to the module in a wearable device used to collect user state information, typically including inertial sensors, pressure sensors, and temperature sensors. An inertial sensor is a sensor capable of measuring an object's three-axis attitude angles (or angular rates) and acceleration. It can sense the object's motion state, including direction, speed, and acceleration, and is commonly used in motion monitoring and navigation positioning. A pressure sensor is a device or apparatus that can sense pressure signals and convert them into usable electrical signals according to a certain rule. It is mainly used to measure the pressure exerted on an object's surface. A temperature sensor is a sensor that can sense temperature and convert it into a usable output signal. It can measure the temperature of an object or environment and convert the temperature signal into an electrical signal or other forms of signal for transmission and processing.

[0057] In this embodiment, pressure data refers to the pressure value applied by the wearable device to the target user's body surface measurement location, which is collected by a pressure sensor to reflect the degree of contact between the wearable device and the skin and the pressure distribution; temperature data refers to the temperature value measured by a temperature sensor at a preset body part of the target user (such as behind the ear, the skin of the wrist where the wearable device contacts the target user, etc.).

[0058] Measurement commands are control signals that trigger the wearable device to start the blood pressure measurement process. They may originate from the user's active operation (such as clicking a button on the wearable device), a timing strategy, or be automatically generated by other modules (such as detecting that the user has woken up).

[0059] For example, when a user manually clicks a specific button on the wearable device, or when preset conditions (such as a specific time or event) are met, the internal circuitry of the wearable device generates an electrical signal to form a measurement command to initiate the blood pressure measurement process. After the blood pressure measurement process begins, the environmental parameters are first collected by the human body state recognition unit. Specifically, the IMU data such as the target user's motion acceleration and angular velocity can be detected by an inertial sensor, the pressure value applied by the wearable device at the measurement position on the target user's body surface can be detected by a pressure sensor, and the temperature data of the preset human body parts can be detected by a temperature sensor.

[0060] Optionally, after the environmental parameters are collected, they can also be input to the signal preprocessing unit for preprocessing, such as filtering and calibration, to improve the quality and reliability of the environmental parameters.

[0061] Step S22: The human body state recognition unit determines whether the detection environment parameters meet the preset blood pressure measurement conditions. Step S23: If the environmental parameters meet the conditions for blood pressure measurement, proceed to step S30.

[0062] Blood pressure measurement conditions refer to a set of parameters or standards pre-set in wearable devices to determine whether the user's current state is suitable for blood pressure measurement. These conditions are usually set based on medical standards and the measurement requirements of the device. For example, blood pressure measurement conditions may include the user being at rest (determined by IMU data), the device being worn correctly (determined by pressure data), and body temperature being within the normal range (determined by temperature data).

[0063] For example, after collecting environmental parameters through the human body state recognition unit, the IMU data can be analyzed. Based on changes in velocity and acceleration data, it can be identified whether the human body is in motion (such as walking or running) or at rest, and the duration of each state. If it is detected that the user has not rested for a sufficient period of time and does not meet the standard for blood pressure measurement, the user is prompted to rest again and measure again. Alternatively, if the user's state meets the measurement requirements, but during the measurement, there are multiple instances of body movement or abnormal pressure between the wearable device and the body, affecting signal quality, and if there are too many invalid signals, the user is prompted to measure again. If the valid signals are sufficient for blood pressure calculation, invalid data can be removed during signal preprocessing, combined with the state markers of the state recognition unit, and valid data can be used for blood pressure calculation.

[0064] In this embodiment, by collecting detection environmental parameters such as IMU data, pressure data, and temperature data, the physiological and environmental states of the user during blood pressure measurement are comprehensively captured to determine whether the current detection environment is suitable for blood pressure measurement. By determining whether the detection environmental parameters meet the preset blood pressure measurement conditions, environmental factors and human condition interference that are not conducive to accurate measurement can be eliminated. For example, blood pressure values ​​will fluctuate in high temperature and high humidity environments or when the human body is in a state of exercise or emotional excitement, and the measurement results will be inaccurate at this time. Measurement is only performed when the environmental parameters and human condition meet the conditions to obtain more realistic and accurate blood pressure data.

[0065] Step S30: Based on the target physiological signal, identify the blood pressure variability information that causes the change in the blood pressure level of the target user, and determine the target blood pressure calculation model that maps the blood pressure variability information from multiple preset blood pressure calculation models. It should be noted that information on blood pressure variability refers to the specific factors that cause changes in blood pressure identified by analyzing target physiological signals. These factors may include emotional fluctuations, exercise status, temperature, sleep patterns, etc.

[0066] A blood pressure calculation model refers to a set of pre-trained machine learning models stored in wearable devices or the cloud. Each model focuses on learning blood pressure change patterns from signal features strongly correlated with specific catalytic factors during training. A target blood pressure calculation model, on the other hand, is a blood pressure calculation model selected from multiple pre-set models based on identified blood pressure catalytic factors. This model is suitable for the current user's condition and can more accurately reflect the relationship between the target user's blood pressure and related physiological signals in the current state.

[0067] For example, rich physiological features can be extracted from the target physiological signals, and blood pressure variability analysis can be performed based on the extracted physiological features. For instance, physiological features such as heart rate, PTT (Pulse Transit Time), and pulse wave morphology (which can be determined based on changes in pulse wave amplitude) can be extracted from ECG and PPG signals. If a significantly shortened PTT and moderately elevated heart rate are identified, but the pulse wave morphology is relatively normal, and combined with known IMU data (the user is at rest), it can be determined that the current blood pressure change is mainly caused by an acute increase in vascular tension (possibly due to mental stress or cold stimulation). Therefore, the blood pressure variability information leading to the change in the target user's blood pressure level can be determined to include mental stress and temperature. Furthermore, combining temperature data collected by a temperature sensor (e.g., a body surface temperature of 36 degrees Celsius), the blood pressure variability information is determined to be mental stress. Then, based on a pre-defined mapping relationship between blood pressure variability factors and blood pressure calculation models, a dedicated blood pressure calculation model corresponding to mental stress is found and designated as the target blood pressure calculation model for blood pressure calculation in the current state.

[0068] It is understandable that the reasons for blood pressure changes vary from person to person under different conditions. This implementation method identifies blood pressure-causing factors based on the specific circumstances of each target user and selects a matching target blood pressure calculation model. This allows for a more accurate consideration of various factors affecting blood pressure, avoiding the errors that may result from using a single general model, thereby improving the accuracy of blood pressure measurement.

[0069] In one feasible implementation, the blood pressure mutagenic factors include multiple types of blood pressure mutagenic factors and the corresponding proportions of each blood pressure mutagenic factor. Step S30, which involves identifying the blood pressure mutagenic factor information that causes changes in the blood pressure level of the target user based on the target physiological signal, includes: Step S31: Input the target physiological signal into the preset blood pressure variability identification model, and output multiple blood pressure variability types and the corresponding proportions of each blood pressure variability. The blood pressure variability identification model is a deep learning model based on prior physiological knowledge.

[0070] It should be noted that the blood pressure variability identification model refers to a pre-trained deep learning model (such as a convolutional neural network model, a recurrent neural network model, or a multi-head attention model), whose input is at least two physiological signals, and whose output is the type and proportion of blood pressure variability factors. This model incorporates prior physiological knowledge during training, enabling it to extract and analyze features from the input target physiological signals, and identify the types of blood pressure variability factors and the corresponding proportions of each factor.

[0071] For example, this blood pressure variability identification model can compare the target physiological features corresponding to the target physiological signal with the pre-stored blood pressure baseline features in the model to determine the physiological features with differences and their corresponding deviation values. Then, based on the deviation values ​​of each physiological feature, it can determine the type of blood pressure variability factor causing the deviation and the weight of each blood pressure variability factor. Furthermore, the blood pressure calculation model mapped to the blood pressure variability factor with the largest weight can be determined as the target blood pressure calculation model; alternatively, the target blood pressure calculation model can be determined from multiple preset blood pressure calculation models based on the weights of each blood pressure variability factor and the mapping relationship between the preset weight intervals of each blood pressure variability factor and the blood pressure calculation model. For example, when the weight of emotional stress is 80% and the weight of temperature is 20%, model A can be determined as the target blood pressure calculation model, while when the weight of emotional stress is 60% and the weight of temperature is 40%, model B can be determined as the target blood pressure calculation model.

[0072] In this embodiment, a deep learning model is used to dynamically identify blood pressure variability factors and their proportions based on the target user's physiological signals, thereby achieving personalized health monitoring. By combining prior physiological knowledge, the model can more accurately explain the relationship between physiological signals and blood pressure changes, improve the accuracy of judging blood pressure variability factors, and thus improve the accuracy of blood pressure measurement.

[0073] Step S40: Input the acquired blood pressure baseline features and blood pressure baseline status, target physiological signals, and detection environment parameters into the target blood pressure calculation model, and output the target user's blood pressure value. Among them, the blood pressure baseline features are the reference values ​​corresponding to each physiological feature under standard human conditions. The standard human conditions are sleep or rest. The physiological features are used to characterize the blood pressure level. The blood pressure baseline status is either a change in the blood pressure baseline or no change in the blood pressure baseline. In the case where the difference between the blood pressure baseline features and the target baseline features stored in the target blood pressure calculation model is greater than a preset threshold, the blood pressure baseline status is a change in the blood pressure baseline.

[0074] It should be noted that baseline blood pressure characteristics refer to the values ​​of various physiological characteristics detected in a target user under standard human conditions (such as sleep or rest), used to characterize the normal level of blood pressure in a target user under standard human conditions. Physiological characteristics refer to various measurable signals or indicators that can reflect the physiological state and blood pressure level of the human body, such as heart rate, the position and amplitude of S1 and S2 in the PCG signal, pulse wave, pre-ejection time, blood oxygen saturation, etc.

[0075] Standard human condition refers to predefined specific human condition conditions used to obtain baseline blood pressure characteristics. In baseline characteristic acquisition, sleep or resting state is used as the standard because the physiological state of the human body is relatively stable in these two states, allowing for more accurate acquisition of physiological characteristic reference values ​​reflecting blood pressure levels. Sleep refers to a natural physiological cycle state in which the body's conscious activity is relatively reduced, and the ability to respond to external stimuli is decreased. Due to the excitation of the parasympathetic nervous system and the inhibition of the sympathetic nervous system during sleep, blood pressure during sleep is 10%-20% lower than blood pressure when awake. The model needs to incorporate this difference when calculating blood pressure. Resting state refers to a state of quiet, relaxed state when awake, without strenuous exercise or significant mental stress. At this time, the body's activity level is low, energy consumption is low, and the functions of various organ systems are in a relatively stable state. Blood pressure values ​​measured at this time are usually close to normal baseline blood pressure levels.

[0076] Blood pressure baseline status refers to a status indicator determined based on the degree of difference between blood pressure baseline characteristics and target baseline characteristics. It is used to indicate whether the blood pressure baseline has changed and provides additional reference information for the model to calculate blood pressure values.

[0077] The target baseline features refer to the blood pressure baseline features that were calculated and saved in the previous blood pressure measurement process under standard human conditions and stored in the target blood pressure calculation model.

[0078] For example, blood pressure baseline features, blood pressure baseline status, target physiological signals, and detection environment parameters are input into the target blood pressure calculation model; if the blood pressure baseline status is a change in the blood pressure baseline, the target baseline features stored in the model are adjusted to the blood pressure baseline features, and the target user's blood pressure value is calculated based on the adjusted target baseline features, target physiological signals, and detection environment parameters.

[0079] Understandably, by continuously updating and introducing blood pressure baseline features, the blood pressure calculation model can track the slow physiological changes caused by age, training, medication, etc., and use the latest personal benchmark during calculation. This is equivalent to providing a dynamically updated "zero calibration point" for general or scenario-based models, thereby solving the problem of model accuracy decreasing due to user physiological changes (i.e., model drift) in long-term use, effectively reducing interference from individual differences, and improving the accuracy of blood pressure measurement.

[0080] In one feasible implementation, the blood pressure measurement method includes: Step A10: When the target user is detected to be in a standard human body state, the baseline physiological signal is collected through the preset blood pressure baseline monitoring unit, and the blood pressure baseline characteristics are determined based on the baseline physiological signal. The baseline physiological signals include at least two of the following: baseline electrocardiogram signals, baseline heart sound signals, and baseline pulse signals; It should be noted that the blood pressure baseline monitoring unit refers to the module of the wearable device used to collect baseline physiological signals of the target user under standard human conditions. The wearable device activates the blood pressure baseline monitoring unit when it detects that the target user is in a standard human condition (resting or sleeping state), collects and saves the blood pressure baseline characteristics and status for use in subsequent blood pressure measurements. Baseline physiological signals refer to physiological signals collected under standard human conditions. These signals reflect the user's physiological characteristics in a stable state and are used to determine the blood pressure baseline characteristics. In this embodiment, the baseline physiological signals include at least two of the following: baseline electrocardiogram signals, baseline heart sound signals, and baseline pulse signals, and are consistent with the original physiological signals and the target physiological signals.

[0081] For example, when the target user is watching TV or sleeping in the evening, their body may be in a standard human state. The blood pressure baseline monitoring unit is activated, and the ECG sensor, heart sound sensor, and pulse sensor begin to collect baseline physiological signals. Based on the collected baseline ECG signal, baseline PCG signal, baseline PPG signal, etc., the blood pressure baseline characteristics, such as heart rate reference value, pulse wave conduction velocity reference value, etc., are determined.

[0082] Step A20: Using the blood pressure baseline monitoring unit, determine the degree of difference between the blood pressure baseline characteristics and the target baseline characteristics, and determine whether the degree of difference is greater than a preset threshold to obtain the blood pressure baseline status.

[0083] The degree of difference refers to the magnitude of the difference between the currently measured blood pressure baseline characteristics and the target baseline characteristics. It can be represented by calculating the difference between the two, the relative rate of change, the Euclidean distance, or the weighted comprehensive score for the difference in each feature classification. The larger the value, the greater the difference between the newly measured blood pressure baseline characteristics and the previously stored target baseline characteristics.

[0084] The preset threshold is a pre-set critical value used to determine whether the difference is significant.

[0085] For example, the currently measured blood pressure baseline features are compared with the target baseline features stored in the target blood pressure calculation model. The degree of difference between the two is calculated by means of Euclidean distance or weighted summation, and it is determined whether the degree of difference is greater than a preset degree threshold. If the degree of difference is greater than the preset degree threshold, the blood pressure baseline status is determined to be a change in blood pressure baseline. If the degree of difference is less than or equal to the preset threshold, the blood pressure baseline status is determined to be no change in blood pressure baseline.

[0086] In this embodiment, a baseline is determined based on the physiological characteristics of each user under standard conditions, which can accommodate the differences between different individuals. Furthermore, by calculating the degree of difference between the blood pressure baseline characteristics and the target baseline characteristics stored in the model, and comparing it with a preset threshold, the system can accurately identify real changes in the physiological fundamentals that have clinical or health management significance, thereby achieving baseline drift self-calibration for individual users and improving the accuracy of blood pressure measurement.

[0087] This embodiment provides a blood pressure measurement method. Through the identification of blood pressure variability factors and model matching mechanism, the blood pressure calculation model is adapted to the individual etiology, enhancing the model's specificity and interpretability, thereby improving the accuracy of blood pressure measurement. At the same time, by automatically collecting high-quality baseline physiological signals under standard human conditions such as sleep or rest, the blood pressure baseline characteristics representing the user's individual physiological essence can be determined and updated imperceptibly. By continuously comparing the degree of difference between the new and old baseline characteristics, the blood pressure baseline characteristics are self-calibrated, thus solving the problems of model drift and performance degradation in long-term use.

[0088] Based on the first embodiment of this application, a blood pressure measurement method according to the second embodiment of this application is proposed.

[0089] In the second embodiment of this application, the same or similar content as in the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0090] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the blood pressure measurement method of this application. In this embodiment, the target blood pressure calculation model includes a target blood pressure correction model, and step S40 includes: Step S41: Extract features from the target physiological signal to determine the target physiological characteristics; It should be noted that target physiological characteristics refer to physiological characteristics extracted from target physiological signals, used to characterize the target user's physiological state and blood pressure level in the current time period.

[0091] For example, a single cardiac cycle (heartbeat) can be segmented by detecting key points in the target physiological signal (such as the R wave in ECG, the pulse wave initiation and peak value in PPG); within each cycle or a window spanning multiple cycles, feature values ​​such as RR interval (heart rate), PPG peak interval, pulse wave conduction time (delay from S1 to PPG initiation), and pulse wave rise time can be detected, and the raw feature values ​​calculated from multiple cycles can be aggregated (such as taking the median or mean) to obtain the target physiological features.

[0092] In one feasible implementation, the target physiological characteristics include heart rate, pulse wave conduction time, and pre-ejection time. Step S41 includes: Step S411: Locate the key features within the same cardiac cycle in the target electrocardiogram signal, target heart sound signal, and target pulse signal to obtain the location time points corresponding to each key feature. The key features in the target electrocardiogram signal include the Q wave, the key features in the target heart sound signal include the first heart sound and the second heart sound, and the key features in the target pulse signal include the rising limb of the pulse wave. It should be noted that the cardiac cycle refers to the time interval corresponding to one contraction and relaxation of the heart. Various cardiac-related events (such as ventricular depolarization, valve opening and closing, blood flow, etc.) occur sequentially according to physiological laws within this time interval. The key features of multimodal signals belonging to this time interval collectively constitute the key features within the same cardiac cycle.

[0093] The location time of key features refers to the specific moment corresponding to the key features on their respective biosignal timelines, represented by time coordinates.

[0094] In this context, the Q wave is the first negative wave in the QRS complex of the ECG signal, indicating the start of ventricular depolarization. Its corresponding positioning time point can be the time point corresponding to the peak of the Q wave or the time point corresponding to the start of the Q wave, which can be selected as needed in specific implementations. The R wave is the first positive wave in the QRS complex. The starting point of S1 is about 50-150 milliseconds after the R wave, which can be used to calibrate S1 in the PCG signal to achieve alignment between the ECG and PCG signals. S1 is generated by the closure of the mitral and tricuspid valves (closure of the atrioventricular valves), and its corresponding positioning time point can also be the time point corresponding to the peak or the start point of the S1 complex. S2 is generated by the closure of the aortic and pulmonary valves, and its corresponding positioning time point can be the time point corresponding to the peak or the start point of the S2 complex. The pulse wave rising limb point refers to the inflection point where the photoplethysmogram waveform rises rapidly upward from the end-diastolic baseline. Its positioning time point can be determined by the tangent method, the maximum first derivative point, or the specific proportional threshold method.

[0095] Step S412: Obtain the heart rate based on the time interval between two adjacent key features within different cardiac cycles; Heart rate refers to the number of heartbeats per minute, usually expressed in units of one minute. Heart rate directly affects cardiac output (the amount of blood pumped per minute). When stroke volume is relatively stable, an increase in heart rate will directly increase cardiac output, leading to an increase in blood volume filling in the arterial system, thereby pushing up systolic blood pressure and mean arterial pressure. This is a direct physiological response of blood pressure to the demands of exercise, stress, etc.

[0096] For example, the duration of a cardiac cycle can be determined based on the time interval between two adjacent key features within different cardiac cycles (e.g., the time interval between two R waves), and the instantaneous heart rate corresponding to the cardiac cycle can be obtained by dividing 60 seconds by the duration. Then, the instantaneous heart rate calculated from multiple consecutive cardiac cycles can be smoothed (e.g., by moving average) to obtain a more stable heart rate value.

[0097] Step S413: Calculate the pre-ejection time within the corresponding cardiac cycle based on the location time point corresponding to the Q wave and the location time point corresponding to the first heart sound. The pre-ejection period (PEP) is the time interval between the onset of ventricular electrical activity and the start of ventricular ejection. It can be determined by the time difference between the location point of the Q wave in the ECG signal and the location point of S1 in the PCG signal within the same cardiac cycle, corresponding to the time from the start of ventricular depolarization to the opening of the aortic valve. A prolonged PEP means the heart needs more time to generate sufficient pressure to open the aortic valve and begin ejection, potentially affecting the effectiveness of stroke volume and the time allocation of systolic ejection, thus impacting peak systolic blood pressure and pulse pressure (the difference between systolic and diastolic blood pressure). Systolic and diastolic blood pressure are two key values ​​in blood pressure.

[0098] Step S414: Calculate the pulse wave conduction time within the corresponding cardiac cycle based on the location time point corresponding to the first heart sound and the location time point corresponding to the rising branch of the pulse wave.

[0099] Pulse wave conduction time (PTT) refers to the time it takes for blood to travel from the heart to the peripheral arteries. It can be determined by the time difference between the location time point corresponding to S1 in the PCG signal and the location time point corresponding to the starting point of the pulse wave's rising limb in the PPG signal within the same cardiac cycle. PTT mainly changes dynamically with blood pressure levels and is the most direct and classic non-invasive signal parameter for estimating blood pressure (especially systolic blood pressure). It primarily reflects the resistance encountered by blood flow from the heart to the periphery, which is closely related to peripheral vascular resistance and the elasticity of large arteries.

[0100] For example, if the user does not keep their arms close to their chest during sleep and the PCG signal cannot be detected, the PTT can be calculated based on the time interval between the positioning time point corresponding to the R wave in the ECG signal and the positioning time point corresponding to the rising limb of the pulse wave. The resulting millisecond-level error is acceptable in clinical medicine.

[0101] In this embodiment, by performing time localization on key features such as the Q wave, S1, and the rising limb of the pulse wave in ECG, PCG, and PPG signals, target physiological features that can reflect changes in blood pressure, such as heart rate, PEP, and PTT, are extracted. Then, by combining the blood pressure baseline features, a comprehensive analysis is performed to determine whether the heart rate is increasing, whether the PEP is shortening or lengthening, and how the PTT is changing, which can accurately determine the changes in blood pressure and improve the accuracy of blood pressure measurement.

[0102] Step S42: When the blood pressure baseline status is changed, replace the value of the target baseline feature with the value of the blood pressure baseline feature. For example, when the blood pressure baseline status is changed, the target blood pressure calculation model replaces the value of the target baseline feature stored therein with the value of the blood pressure baseline feature newly acquired by the blood pressure baseline monitoring unit to achieve calibration of the baseline features in the model.

[0103] Step S43: Determine the physiological characteristic difference based on the difference between the target physiological characteristic and the target baseline characteristic; Physiological feature difference refers to the numerical difference between the target physiological feature and the target baseline feature. Each element is the difference between a certain feature component in the target physiological feature vector and the corresponding component in the target baseline feature vector, reflecting the degree of deviation between the target user's current physiological state and the baseline state.

[0104] Understandably, when the blood pressure baseline changes, the value of the target baseline feature should be replaced in a timely manner so that the target baseline feature in the blood pressure calculation model can better reflect the user's current baseline physiological state and avoid measurement errors caused by outdated baseline data. On this basis, the difference between the target physiological feature and the target baseline feature can be calculated to more sensitively detect abnormal changes in physiological state and provide a more accurate data basis for blood pressure calculation.

[0105] Step S44: Input the difference between the detection environment parameters and physiological characteristics into the target blood pressure correction model and output the blood pressure correction value; Among them, there is a positive correlation between the absolute value of the blood pressure correction value and the difference in physiological characteristics; It should be noted that the target blood pressure correction model is a submodule of the target blood pressure calculation model. It specifically learns and models the expected change in blood pressure relative to an individual's resting baseline (gold standard blood pressure) under the combined effects of environmental parameters (such as pressure applied by wearable sensors and skin temperature) and physiological characteristic differences. The blood pressure correction value refers to the numerical value calculated by this target blood pressure correction model used to adjust the gold standard blood pressure value.

[0106] Step S45: Determine the target user's blood pressure value based on the blood pressure correction value and the preset gold standard blood pressure value; Among them, the gold standard blood pressure value is the blood pressure value measured by the target user under standard human conditions.

[0107] The gold standard blood pressure value refers to the blood pressure value obtained by the target user under standard human conditions (such as sleep or rest) through a gold standard blood pressure measurement device, and is usually used as a reference standard.

[0108] For example, the difference between the detection environment parameters and physiological characteristics of the target user in the current time period is concatenated into a comprehensive feature vector and input into the target blood pressure correction model. The model performs a forward calculation and outputs a blood pressure correction value. Then, the blood pressure correction value is added to the gold standard blood pressure value pre-stored in the wearable device to obtain the blood pressure value of the target user in the current time period.

[0109] Understandably, by taking into account the impact of environmental parameters on blood pressure and inputting them along with physiological characteristic differences into the target blood pressure correction model, the influence of environmental factors on the user's normal blood pressure data can be reduced (such as reducing the impact of pressure applied by wearable devices to the body surface measurement location on blood pressure measurement values), thereby obtaining blood pressure values ​​that more accurately reflect the user's physiological state.

[0110] In one feasible implementation, the blood pressure measurement method further includes: Step S451: When collecting the target user's blood pressure baseline characteristics through a wearable device, the target user's gold standard blood pressure value is measured simultaneously through a preset gold standard blood pressure measuring device. Here, the gold standard blood pressure measuring device refers to a blood pressure measuring device whose accuracy is higher than a preset accuracy threshold.

[0111] Gold standard blood pressure measurement devices refer to a type of independent hardware device that is pre-designated or recognized by the medical community to provide reference values ​​for blood pressure measurement. The accuracy of blood pressure measurement is higher than the preset accuracy threshold, and it is used to provide high-precision blood pressure reference values. Examples include ambulatory blood pressure monitors (ABPM) and upper arm oscillometric electronic blood pressure monitors. The blood pressure values ​​measured by these devices can be input into wearable devices as gold standard blood pressure values ​​through wireless connection or manual input.

[0112] For example, when the target user is in a preset standard human body state, he / she can wear the wearable device involved in the embodiments of this application and a pre-specified gold standard blood pressure measuring device at the same time, and measure the gold standard blood pressure value of the user in a standard human body state while collecting the blood pressure baseline characteristics.

[0113] In this embodiment, while collecting blood pressure baseline characteristics, a gold standard blood pressure measurement device with an accuracy higher than a preset accuracy threshold is used to measure the gold standard blood pressure value. This sets a personalized baseline and provides an accurate reference standard for the blood pressure calculation model of the wearable device, avoiding model baseline drift and improving the accuracy of blood pressure measurement.

[0114] In one feasible implementation, the blood pressure measurement method further includes: Step E10: Obtain training data, which includes sample physiological signals, sample environmental parameters, sample baseline features, sample baseline status, and reference blood pressure values. The training data is collected from users corresponding to the same type of blood pressure mutagenic factor. It should be noted that training data refers to the dataset used to train the blood pressure calculation model, which includes input features and corresponding output labels. The input features include sample physiological signals, sample environmental parameters, sample baseline features, and sample baseline states, which correspond to the target physiological signals, detection environmental parameters, blood pressure baseline features, and blood pressure baseline states, respectively. The output labels include reference blood pressure values, which are obtained through high-precision equipment (such as gold standard blood pressure measurement equipment).

[0115] For example, users affected by a specific type of blood pressure-inducing factor are selected as data collection subjects. Baseline blood pressure characteristics and states of the users in standard human conditions are collected as sample baseline characteristics and states. Physiological signals, environmental parameters, and blood pressure values ​​measured by a gold standard blood pressure measuring device in other daily states are also collected as sample physiological signals, environmental parameters, and reference blood pressure values, thus obtaining training data. Simultaneously, blood pressure values ​​measured by a gold standard blood pressure measuring device in standard human conditions can also be used as preset gold standard blood pressure values ​​in the subsequently constructed blood pressure calculation model.

[0116] Step E20: Establish a blood pressure calculation model corresponding to the type of blood pressure mutating factor. The blood pressure calculation model includes a deep learning model and a physiological model. The physiological model is used to provide the theoretical basis for blood pressure calculation for the deep learning model. It should be noted that physiological models refer to mathematical models built upon the working mechanisms of human physiological systems (such as the cardiovascular system) and the relationships between physiological parameters, providing a theoretical framework and computational methods for blood pressure calculation based on human physiological mechanisms. Deep learning models can employ neural network structures (such as fully connected networks, convolutional neural networks, recurrent neural networks, etc.) or other model architectures to automatically learn the complex, nonlinear mapping relationship between a user's physiological characteristics and blood pressure from large amounts of training data.

[0117] Step E30: Use the training data as input to train the blood pressure calculation model and obtain a blood pressure calculation model that maps the types of blood pressure variability factors.

[0118] For example, for the type of target mutagenic factor (such as temperature or mental state), the physiological pathways of blood pressure changes dominated by it (such as increased cardiac output and dynamic regulation of peripheral resistance) are analyzed. A suitable core physiological equation (such as a blood pressure calculation formula based on stroke volume, heart rate, and total peripheral resistance) is selected as the physiological model to provide the rationale for blood pressure calculation. Based on this, a neural network is designed, whose input layer receives input features from the training data mentioned above. Some nodes in the network are designed to correspond to key parameters in the physiological model (such as peripheral resistance). The output layer combines these intermediate physiological parameters and the physiological model to output the predicted blood pressure value. During model training, its loss function, in addition to minimizing the blood pressure prediction error, adds a physiological rationality constraint to penalize situations where the intermediate physiological parameters or final blood pressure value predicted by the network deviate too much from the theoretical prediction value of the physiological model.

[0119] In this embodiment, training data based on the same type of variable factor is acquired, enabling the model to focus on learning the most direct and stable causal relationship between physiological characteristics and blood pressure under specific variable factors. At the same time, by establishing a blood pressure calculation model that integrates deep learning and physiological models, the model combines the powerful nonlinear fitting capability of deep learning with the interpretable theoretical framework and safety boundary provided by physiological models. Based on this, training is performed to obtain a dedicated blood pressure calculation model that accurately maps to each type of blood pressure variable factor, which is beneficial for high-precision blood pressure calculation.

[0120] For example, to help understand the implementation flow of the blood pressure measurement method obtained by combining this embodiment with the above embodiment one, please refer to... Figure 3 , Figure 3A schematic diagram of a blood pressure measurement method framework module is provided. The wearable device for implementing the above-mentioned blood pressure measurement method includes a signal preprocessing unit, a human body state recognition unit, a blood pressure variability factor recognition unit, a blood pressure baseline monitoring unit, and a blood pressure model base unit. The human body state recognition unit collects environmental parameters (such as IMU data, pressure data applied to the target user's body surface measurement location, and temperature data) from the wearable device during the current time period to determine whether the current detection environment meets the preset blood pressure measurement conditions. If it does, the signal preprocessing unit further preprocesses the raw physiological signals of the target user during the current time period (such as raw electrocardiogram signals, raw heart sound signals, and raw pulse signals) to remove high-frequency noise and baseline drift, and determines the target physiological signal based on the preprocessed physiological signal. Then, the target physiological signal is input to the blood pressure variability factor recognition unit, which uses a blood pressure variability factor recognition model to identify the blood pressure variability factors that cause changes in the target user's blood pressure level. Furthermore, when the human body state recognition unit detects that the target user is in a standard human body state, the blood pressure baseline is used to... The monitoring unit acquires blood pressure baseline characteristics, specifically by collecting baseline physiological signals through sensors such as electrocardiogram (ECG), heart sound sensors, and pulse sensors. These signals are then input into a preprocessing unit for preprocessing before feature extraction to determine the blood pressure baseline characteristics. Based on the degree of difference between these blood pressure baseline characteristics and the target baseline characteristics uniformly stored in various preset blood pressure calculation models, the blood pressure baseline state is confirmed. Subsequently, the aforementioned target physiological signals, blood pressure variability information, blood pressure baseline characteristics, blood pressure baseline state, and detection environment parameters are input into the blood pressure model base unit. This blood pressure model base unit then determines the target blood pressure calculation model mapped by the blood pressure variability information from multiple preset blood pressure calculation models. The target physiological signals, blood pressure baseline characteristics, blood pressure baseline state, and detection environment parameters are input into the target blood pressure calculation model, which performs forward calculations and outputs the target user's blood pressure value.

[0121] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the blood pressure measurement method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0122] This application also provides a blood pressure measuring device; please refer to... Figure 4 The blood pressure measuring device includes: The physiological signal acquisition module 10 is used to acquire the target physiological signals detected by the wearable device of the target user in the current time period, wherein the target physiological signals include at least two of the target electrocardiogram signals, target heart sound signals and target pulse signals; The environmental parameter acquisition module 20 is used to acquire the detection environmental parameters of the wearable device in the current time period, including IMU data that reflects the human body's state. The model determination module 30 is used to identify the blood pressure variability information that causes the blood pressure level of the target user to change based on the target physiological signal, and to determine the target blood pressure calculation model that maps the blood pressure variability information from multiple preset blood pressure calculation models. The blood pressure output module 40 is used to input the acquired blood pressure baseline features and blood pressure baseline status, target physiological signals, and detection environment parameters into the target blood pressure calculation model and output the blood pressure value of the target user. Among them, the blood pressure baseline features are the reference values ​​corresponding to each physiological feature under standard human conditions, which are sleep or resting states. The physiological features are used to characterize the blood pressure level. The blood pressure baseline status is either a change in the blood pressure baseline or no change in the blood pressure baseline. In the case where the difference between the blood pressure baseline features and the target baseline features stored in the target blood pressure calculation model is greater than a preset threshold, the blood pressure baseline status is a change in the blood pressure baseline.

[0123] The blood pressure measuring device provided in this application, employing the blood pressure measuring method described in the above embodiments, can solve the technical problem of effectively reducing interference caused by individual differences and improving the accuracy of cuffless blood pressure measurement. Compared with the prior art, the beneficial effects of the blood pressure measuring device provided in this application are the same as those of the blood pressure measuring method provided in the above embodiments, and other technical features in the blood pressure measuring device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0124] This application provides a wearable device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the blood pressure measurement method in the first embodiment described above.

[0125] The following is for reference. Figure 5This document illustrates a structural schematic diagram suitable for implementing wearable devices according to embodiments of this application. Wearable devices in these embodiments may include, but are not limited to, smart bracelets, smartwatches, and head-mounted displays. Specifically, head-mounted displays may include, but are not limited to, wearable devices such as smart headphones, mixed reality (MR) devices (e.g., MR glasses or MR helmets), augmented reality (AR) devices (e.g., AR glasses or AR helmets), virtual reality (VR) devices (e.g., VR glasses or VR helmets), extended reality (XR) devices, or some combination thereof. Figure 5 The wearable device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0126] like Figure 5 As shown, the wearable device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the wearable device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the wearable device to communicate wirelessly or wiredly with other devices to exchange data. While wearable devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0127] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0128] The wearable device provided in this application, employing the blood pressure measurement method described in the above embodiments, can solve the technical problem of effectively reducing interference caused by individual differences and improving the accuracy of cuffless blood pressure measurement. Compared with the prior art, the beneficial effects of the wearable device provided in this application are the same as those of the blood pressure measurement method provided in the above embodiments, and other technical features of the wearable device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0129] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0131] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the blood pressure measurement method in the above embodiments.

[0132] The computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0133] The aforementioned computer-readable storage medium may be included in the wearable device; or it may exist independently and not assembled into the wearable device.

[0134] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a wearable device, cause the wearable device to: acquire target physiological signals detected by the wearable device in the current time period based on the target user's physiological signals, wherein the target physiological signals include at least two of target electrocardiogram signals, target heart sound signals, and target pulse signals; collect detection environment parameters of the wearable device in the current time period, wherein the detection environment parameters include IMU data reflecting the human body's state; identify blood pressure variability information that causes changes in the target user's blood pressure level based on the target physiological signals, and determine the blood pressure from multiple preset blood pressure calculation models. The target blood pressure calculation model maps information on mutagenic factors. It inputs the acquired blood pressure baseline features and blood pressure baseline status, target physiological signals, and detection environment parameters into the target blood pressure calculation model and outputs the blood pressure value of the target user. Among them, the blood pressure baseline features are the reference values ​​corresponding to each physiological feature under standard human conditions, which are sleep or resting states. The physiological features are used to characterize the blood pressure level. The blood pressure baseline status is either a change in the blood pressure baseline or no change in the blood pressure baseline. In the case where the difference between the blood pressure baseline features and the target baseline features stored in the target blood pressure calculation model is greater than a preset threshold, the blood pressure baseline status is a change in the blood pressure baseline.

[0135] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0137] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0138] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described blood pressure measurement method. This addresses the technical problem of effectively reducing interference from individual differences and improving the accuracy of cuffless blood pressure measurement. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the blood pressure measurement method provided in the above embodiments, and will not be elaborated upon here.

[0139] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the blood pressure measurement method described above.

[0140] The computer program product provided in this application can solve the technical problem of how to effectively reduce interference caused by individual differences and improve the accuracy of cuffless blood pressure measurement. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the blood pressure measurement method provided in the above embodiments, and will not be repeated here.

[0141] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for measuring blood pressure, characterized in that, The blood pressure measurement method includes: Acquire target physiological signals of the target user based on wearable devices during the current time period, wherein the target physiological signals include at least two of target electrocardiogram signals, target heart sound signals, and target pulse signals; The wearable device collects detection environment parameters during the current time period, wherein the detection environment parameters include IMU data reflecting the human body's state; Based on the target physiological signal, information on blood pressure mutagenesis factors that cause changes in the blood pressure level of the target user is identified, and a target blood pressure calculation model is determined from multiple preset blood pressure calculation models to map the information on blood pressure mutagenesis factors. The acquired blood pressure baseline features and blood pressure baseline status, the target physiological signal, and the detection environment parameters are input into the target blood pressure calculation model, and the blood pressure value of the target user is output. Wherein, the blood pressure baseline feature is the reference value corresponding to each physiological feature under standard human condition, the standard human condition is sleep state or resting state, the physiological feature is used to characterize the blood pressure value level, the blood pressure baseline state is blood pressure baseline change or blood pressure baseline no change, wherein, when the difference between the blood pressure baseline feature and the target baseline feature stored in the target blood pressure calculation model is greater than a preset degree threshold, the blood pressure baseline state is blood pressure baseline change.

2. The blood pressure measurement method as described in claim 1, characterized in that, Prior to the step of acquiring the target physiological signal detected by the wearable device in the current time period by the target user, the method further includes: Based on the wearable device, the target user's raw physiological signals are synchronously collected during the current time period, wherein the raw physiological signals include at least two of the following: raw electrocardiogram signals, raw heart sound signals, and raw pulse signals; The original physiological signal is preprocessed by a preset signal preprocessing unit to obtain a preprocessed physiological signal. The preprocessing includes at least one of filtering, baseline correction, normalization and effective signal extraction. The target physiological signal is determined based on the preprocessed physiological signal.

3. The blood pressure measurement method as described in claim 1, characterized in that, The detection environment parameters also include pressure data applied by the wearable device to the target user's body surface measurement location and temperature data of the target user at preset body parts. The step of collecting the detection environment parameters of the wearable device in the current time period includes: In response to a triggered measurement command, the wearable device collects environmental parameters for the current time period through a preset human body state recognition unit, wherein the human body state recognition unit includes an inertial sensor, a pressure sensor, and a temperature sensor. The human body state recognition unit determines whether the detection environment parameters meet the preset blood pressure measurement conditions. When the detection environment parameters meet the blood pressure measurement conditions, the step of identifying blood pressure mutagenic factors that cause changes in the blood pressure level of the target user based on the target physiological signal is performed.

4. The blood pressure measurement method as described in claim 1, characterized in that, The blood pressure measurement method also includes: When the target user is detected to be in the standard human body state, a preset blood pressure baseline monitoring unit is used to collect baseline physiological signals, and the blood pressure baseline characteristics are determined based on the baseline physiological signals. The baseline physiological signals include at least two of the following: baseline electrocardiogram signals, baseline heart sound signals, and baseline pulse signals. The blood pressure baseline monitoring unit determines the degree of difference between the blood pressure baseline characteristics and the target baseline characteristics, and determines whether the degree of difference is greater than a preset threshold, thereby obtaining the blood pressure baseline state.

5. The blood pressure measurement method as described in claim 1, characterized in that, The blood pressure mutagenic factors include multiple types of blood pressure mutagenic factors and the corresponding proportions of each blood pressure mutagenic factor. The step of identifying the blood pressure mutagenic factors that cause changes in the blood pressure level of the target user based on the target physiological signal includes: The target physiological signal is input into a preset blood pressure variability identification model, and the multiple blood pressure variability types and the corresponding proportions of each blood pressure variability are output. The blood pressure variability identification model is a deep learning model based on prior physiological knowledge.

6. The blood pressure measurement method as described in claim 1, characterized in that, The target blood pressure calculation model includes a target blood pressure correction model. The step of inputting the acquired blood pressure baseline features and blood pressure baseline status, the target physiological signal, and the detection environment parameters into the target blood pressure calculation model and outputting the target user's blood pressure value includes: Feature extraction is performed on the target physiological signal to determine the target physiological characteristics; When the blood pressure baseline state is a change in blood pressure baseline, the value of the target baseline feature is replaced with the value of the blood pressure baseline feature; The physiological characteristic difference is determined based on the difference between the target physiological characteristic and the target baseline characteristic; The detection environment parameters and the physiological characteristic difference are input into the target blood pressure correction model, and the blood pressure correction value is output. The blood pressure correction value is positively correlated with the absolute value of the physiological characteristic difference. The blood pressure value of the target user is determined based on the blood pressure correction value and the preset gold standard blood pressure value, wherein the gold standard blood pressure value is the blood pressure value measured by the target user under the standard human body condition.

7. The blood pressure measurement method as described in claim 6, characterized in that, The blood pressure measurement method also includes: When the wearable device collects the target user's baseline blood pressure characteristics, the target user's gold standard blood pressure value is simultaneously measured using a preset gold standard blood pressure measuring device, wherein the gold standard blood pressure measuring device refers to a blood pressure measuring device whose accuracy is higher than a preset accuracy threshold.

8. The blood pressure measurement method as described in claim 6, characterized in that, The target physiological features include heart rate, pulse wave transit time, and pre-ejection time. The step of extracting features from the target physiological signals to determine the target physiological features includes: Key features within the same cardiac cycle of the target electrocardiogram signal, the target heart sound signal, and the target pulse signal are located to obtain the location time points corresponding to each key feature. The key features in the target electrocardiogram signal include the Q wave, the key features in the target heart sound signal include the first heart sound and the second heart sound, and the key features in the target pulse signal include the rising limb of the pulse wave. Heart rate is obtained by measuring the time interval between two adjacent key features within different cardiac cycles. Based on the location time point corresponding to the Q wave and the location time point corresponding to the first heart sound, the pre-ejection time within the corresponding cardiac cycle is calculated. Based on the location time point corresponding to the first heart sound and the location time point corresponding to the rising branch of the pulse wave, the pulse wave conduction time within the corresponding cardiac cycle is calculated.

9. The blood pressure measurement method as described in claim 1, characterized in that, The blood pressure measurement method also includes: Acquire training data, which includes sample physiological signals, sample environmental parameters, sample baseline features, sample baseline status, and reference blood pressure values. The training data is collected from users corresponding to the same type of blood pressure variability factor. A blood pressure calculation model corresponding to the aforementioned blood pressure variability factor type is established, wherein the blood pressure calculation model includes a deep learning model and a physiological model, and the physiological model is used to provide the theoretical basis for blood pressure calculation for the deep learning model; The training data is used as input to the blood pressure calculation model to train the blood pressure calculation model and obtain the blood pressure calculation model mapped by the type of blood pressure variability.

10. A wearable device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the blood pressure measurement method as described in any one of claims 1 to 9.

11. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the blood pressure measurement method as described in any one of claims 1 to 9.

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