Blood pressure measuring method and related device

Abstract

The invention discloses a blood pressure measuring method and a related device. When the electronic equipment measures the blood pressure of the first user and detects that the first user does not belong to the first crowd, the first blood pressure model is trained based on the physiological signal segment of the first user, and the second blood pressure model is obtained. The electronic device obtains a blood pressure measurement result of the first user through the second blood pressure model. In this way, the electronic device can obtain a more accurate blood pressure measurement result.

Description

Technical Field

[0001] The present application relates to the field of terminals, and in particular to a blood pressure measurement method and related devices. Background Art

[0002] At present, the traditional blood pressure measurement method requires medical staff to test the user through professional blood pressure measurement equipment, which is inconvenient and cannot monitor the user's blood pressure in real time. In recent years, more and more wearable devices that provide blood pressure measurement functions have been introduced. When users use wearable devices to measure blood pressure, the blood pressure measurement results they obtain are not very accurate. Summary of the invention

[0003] The present application provides a blood pressure measurement method and related devices, which realize that when measuring the blood pressure of a first user, when it is detected that the characteristics of the physiological signal of the first user are less similar to the characteristics of the physiological signals of a first group of people, a first blood pressure model is trained based on the physiological signal segments of the first user to obtain a second blood pressure model. The electronic device uses the second blood pressure model to measure the blood pressure of the first user to obtain a more accurate blood pressure measurement result.

[0004] In a first aspect, the present application provides a blood pressure measurement method, which is applied to an electronic device, wherein the electronic device includes a blood pressure sensor; the method includes: receiving personalized information input by a first user, wherein the personalized information includes one or more of age, height, weight, and gender; collecting a first physiological signal segment of the first user through the blood pressure sensor; determining whether the first user belongs to a first group of people based on the first physiological signal segment through a first classifier; when it is determined that the first user does not belong to the first group of people, receiving a first blood pressure value input by the first user, and collecting a second physiological signal segment of the first user through the blood pressure sensor; determining a first user classification result through a first classifier based on the second physiological signal segment, and determining a first blood pressure measurement result through a first blood pressure model; determining a first loss parameter based on the first user classification result, a preset classification result, the first blood pressure value, and the first blood pressure measurement result; adjusting a parameter of the first classifier based on the first loss parameter to obtain a second classifier, and adjusting a parameter of the first blood pressure model to obtain a second blood pressure model; wherein the first blood pressure model is used to identify the blood pressure measurement result of the first group of people, and the user classification result determined by the second classifier for the second physiological signal segment is a second user classification result, and the second user classification result indicates that the first user belongs to the first group of people; obtaining a second blood pressure measurement result based on the second physiological signal segment through the second blood pressure model.

[0005] In this way, when the electronic device determines that the physiological signal of the first user has a low similarity with the physiological signal of the first group of people, that is, when the first user does not belong to the first group of people, the first blood pressure model is calibrated using the physiological signal of the first user, so that the first blood pressure model can learn the characteristics of the physiological signal of the first user. The electronic device can obtain a more accurate blood pressure measurement result for the first user based on the calibrated first blood pressure model, that is, the second blood pressure model. Moreover, when the electronic device determines that the first user belongs to the first group of people, the electronic device can quickly obtain an accurate blood pressure measurement result, which not only saves the time for the electronic device to output the blood pressure measurement result, but also ensures the accuracy of the measurement results of non-specified people, improves the efficiency of blood pressure measurement, and reduces the cost of blood pressure measurement.

[0006] In a possible implementation, the method further includes: when it is determined that the first user belongs to the first population, obtaining a third blood pressure measurement result based on the first physiological signal segment through the first blood pressure model. In this way, under normal circumstances, the first user usually needs to calibrate the device when measuring the blood pressure for the first time, or when measuring the blood pressure again after not measuring the blood pressure for a long time. Among them, during the device calibration process, the user needs to follow the prompts of the wearable device and maintain standard actions for a long time, such as lying down, sitting upright, etc., in order to obtain accurate blood pressure detection results. The calibration process is complicated and inconvenient for users to use. Through the blood pressure measurement method provided in the embodiment of the present application, the electronic device recognizes that the first user belongs to the first population, and directly determines the user's blood pressure measurement result based on the first blood pressure model. Even if the first user detects the blood pressure for the first time, there is no need to perform a calibration operation to obtain a more accurate blood pressure measurement result.

[0007] In a possible implementation, after obtaining the second classifier and the second blood pressure model, the method further includes: collecting a third physiological signal segment of the first user through the blood pressure sensor; determining that the first user belongs to the first group based on the third physiological signal segment through the second classifier; and obtaining a fourth blood pressure measurement result based on the third physiological signal segment through the second blood pressure model. In this way, after the electronic device obtains the second blood pressure model, when the first user measures blood pressure again, the electronic device directly obtains the blood pressure measurement result of the first user based on the second blood pressure model.

[0008] In a possible implementation, the first blood pressure model includes a first feature extraction layer and a blood pressure prediction layer; the first classifier includes a first feature extraction layer, a second feature extraction layer and a classification layer; based on the second physiological signal segment, the first user classification result is determined by the first classifier, and the first blood pressure measurement result is determined by the first blood pressure model, specifically including: determining the first feature information based on the second physiological signal segment through the first feature extraction layer; determining the first blood pressure measurement result based on the first feature information through the blood pressure prediction layer; determining the second feature information based on the first feature information through the second feature extraction layer, and the second feature information is different from the first feature information; determining the first user classification result based on the second feature information through the classification layer.

[0009] The second feature information is more detailed than the first feature information, which facilitates the first classifier to obtain a more accurate user classification result.

[0010] In one possible implementation, based on the first loss parameter, the parameters of the first classifier are adjusted to obtain the second classifier, and the parameters of the first blood pressure model are adjusted to obtain the second blood pressure model, specifically including: the classification layer adjusts the parameters of the classification layer in the direction of reducing the first loss parameter, and determines the second loss parameter of the second feature extraction layer; the second feature extraction layer adjusts the parameters of the second feature extraction layer in the direction of reducing the second loss parameter, and determines the third loss parameter of the first feature extraction layer; the first feature extraction layer adjusts the parameters of the first feature extraction layer in the direction of increasing the third loss parameter; the blood pressure prediction layer adjusts the parameters of the blood pressure prediction layer in the direction of reducing the first loss parameter, and determines the fourth loss parameter of the first feature extraction layer; the first feature extraction layer adjusts the parameters of the first feature extraction layer in the direction of reducing the fourth loss parameter.

[0011] In this way, adjusting the parameters of the first feature extraction layer in a direction that does not reduce the third loss parameter can cause the first blood pressure model and the first classifier to confuse the physiological signal characteristics of the first user with the physiological signal characteristics of the specified population, that is, to learn the physiological signal characteristics of the first user. Adjusting the parameters of the first feature extraction layer in a direction that reduces the fourth loss parameter can enable the first blood pressure model to determine a more accurate blood pressure measurement result based on the physiological signal of the first user.

[0012] In a possible implementation, before receiving the personalized information input by the first user, the method further includes: displaying a first interface, the first interface including a blood pressure measurement application icon; receiving a first input for the blood pressure measurement application icon; in response to the first input, displaying a second interface, the second interface including a first control, the second interface being used to input personalized information; receiving the personalized information input by the first user, specifically including: receiving a second input for the first control, and receiving personalized information in response to the second input. In this way, the electronic device prompts the first user to input personalized information, which facilitates the electronic device to collect personalized information.

[0013] In a possible implementation, collecting the first physiological signal segment of the first user through the blood pressure sensor specifically includes: collecting the first physiological signal segment through the blood pressure sensor in response to the second input. In this way, after determining the personalized information input by the first user, the electronic device collects the first physiological signal segment of the first user.

[0014] In a possible implementation, when it is determined that the first user does not belong to the first group, the method further includes: displaying a third interface, the third interface includes a second control, and the third interface is used to input a first blood pressure value; receiving the first blood pressure value input by the first user, specifically including: receiving a third input for the second control, and receiving the first blood pressure value in response to the third input. In this way, the electronic device determines that the first user does not belong to the first group, prompts the user to input the first blood pressure value, and facilitates the electronic device to adjust the first blood pressure model based on the first blood pressure value.

[0015] In a possible implementation, the third interface includes first prompt information, and the first prompt information is used to prompt the first user to input a first blood pressure value.

[0016] In a possible implementation, collecting the second physiological signal segment of the first user through the blood pressure sensor specifically includes: in response to the third input, collecting the second physiological signal segment through the blood pressure sensor. In this way, after receiving the first blood pressure value input by the first user, the electronic device collects the second physiological signal segment of the first user.

[0017] In a possible implementation, when the first physiological signal segment of the first user is collected by the blood pressure sensor, the method further includes: displaying a second prompt information, the second prompt information is used to prompt the first user to maintain a preset posture, the preset posture includes lying on the back or sitting upright. In this way, the user maintains the preset posture, and the electronic device recognizes a more accurate blood pressure measurement result based on the obtained physiological signal.

[0018] In a possible implementation, the first physiological signal segment includes a photoplethysmography (PPG) signal and / or an electrocardiogram (ECG) signal.

[0019] In a possible implementation, before receiving the personalized information input by the first user, the method further includes: receiving a first classifier and a first blood pressure model sent by the server, the first blood pressure model is trained by the physiological signal segments of the first group of people, the personalized information and the blood pressure measurement results, and the first classifier is trained by the physiological signal segments of the first group of people. In this way, after the server obtains the first blood pressure model and the first classifier based on the training data of the first group of people, it sends them to the electronic device, so that the electronic device can provide a blood pressure measurement function.

[0020] In a possible implementation manner, the duration of the first physiological signal segment is a preset segment duration.

[0021] In a possible implementation, collecting a first physiological signal segment of a first user through a blood pressure sensor specifically includes: collecting the first physiological signal of the first user within a preset collection time through the blood pressure sensor; performing a preprocessing operation on the first physiological signal to obtain a first physiological signal segment; wherein the preprocessing operation includes one or more of filtering, slicing, and alignment operations.

[0022] In a possible implementation, the first blood pressure measurement result includes a first diastolic pressure DPB and a first systolic pressure SBP, and the first blood pressure value includes a second diastolic pressure and a second systolic pressure.

[0023] In a second aspect, the present application provides an electronic device comprising: a blood pressure sensor, one or more processors and one or more memories, wherein the blood pressure sensor, the one or more memories are coupled to multiple processors, and the one or more memories are used to store executable programs. When the one or more processors execute the executable programs, the electronic device executes any possible implementation method in the first aspect.

[0024] In a third aspect, the present application provides a readable storage medium storing a program, which, when executed on a processor of an electronic device, enables the electronic device to execute any possible implementation method in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application;

[0026] Figure 2 A flow chart of a blood pressure measurement method provided in an embodiment of the present application;

[0027] Figure 3 A schematic diagram of the structure of a first classifier provided in an embodiment of the present application;

[0028] Figure 4 A schematic diagram of the structure of a blood pressure training model provided in an embodiment of the present application;

[0029] Figure 5A A schematic diagram of a blood pressure measurement process according to an embodiment of the present application;

[0030] Figure 5B A schematic diagram of a process of measuring blood pressure using a blood pressure model by an electronic device provided in an embodiment of the present application;

[0031] Fig. 6A A schematic diagram of a process for training a first blood pressure model provided in an embodiment of the present application;

[0032] Figure 6B A schematic diagram of another process of training a first blood pressure model provided in an embodiment of the present application;

[0033] Figure 7A-7F A set of interface schematic diagrams provided for embodiments of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in the text is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0035] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0036] The term "user interface (UI)" in the following embodiments of the present application refers to a medium interface for interaction and information exchange between an application or operating system and a user, which realizes the conversion between the internal form of information and the form acceptable to the user. The user interface is a source code written in a specific computer language such as Java and extensible markup language (XML). The interface source code is parsed and rendered on an electronic device, and finally presented as content that the user can recognize. The commonly used form of the user interface is a graphical user interface (GUI), which refers to a user interface related to computer operations displayed in a graphical manner. It can include visual interface elements such as text, icons, buttons, menus, tabs, text boxes, dialog boxes, status bars, navigation bars, widgets, etc. displayed on the display screen of an electronic device. The interface serves as a medium interface for interaction and information exchange between an application and a user. Every time a vertical synchronization signal arrives, the electronic device needs to generate the interface of the application for the foreground application.

[0037] In a possible implementation, the server may use the physiological signals, personalized information and blood pressure measurement results of multiple users as training data to train a first blood pressure model. The first blood pressure model can be used to determine the blood pressure measurement results of the user based on the physiological signals and personalized information of the user. Among them, the physiological signals may include but are not limited to photoplethysmography (PPG) signals and / or electrocardiogram (ECG) signals. Personalized information may include but is not limited to one or more of age, weight, height, gender and other information. Among them, the blood pressure measurement results may include systolic blood pressure (SBP) and diastolic blood pressure (DPB). In an embodiment of the present application, the users who provide training data constitute a specified population.

[0038] Among them, since the training data obtained by the server is limited, the training data only includes data of users in the specified group. When the electronic device measures the user's blood pressure through the first blood pressure model, if the characteristics of the user's physiological signal are highly similar to the characteristics of the physiological signal of the specified group, the electronic device can obtain a more accurate blood pressure measurement result. If the characteristics of the user's physiological signal are less similar to the characteristics of the physiological signal of the specified group, the accuracy of the blood pressure measurement result obtained by the electronic device is lower and the error is larger.

[0039] Therefore, an embodiment of the present application provides a blood pressure measurement method. An electronic device can receive personalized information of a first user and collect a first physiological signal segment of the first user. The electronic device can use a first classifier to determine whether the first user belongs to a specified group of people based on the first physiological signal segment. The first classifier is trained by the physiological signals of the specified group of people. When the electronic device determines that the first user belongs to the specified group of people, it can determine the blood pressure measurement result of the first user based on the first physiological signal segment, personalized information and the first blood pressure model. When the electronic device determines that the first user does not belong to the specified group of people, it can collect a second physiological signal segment of the first user and receive a first blood pressure value input by the first user. The electronic device can construct a blood pressure training model with the first classifier and the first blood pressure model. The electronic device can use the blood pressure training model to adjust the first blood pressure model and the first classifier based on the second physiological signal segment, the preset classification result and the first blood pressure value. When the user classification result output by the blood pressure training model indicates that the first user belongs to the specified group of people, the first blood pressure model in the blood pressure training model is adjusted to obtain a second blood pressure model. The electronic device can determine the blood pressure measurement result of the first user through the second blood pressure model based on the second physiological signal segment and personalized information of the first user.

[0040] In this way, the electronic device can identify whether the physiological signal of the first user is similar to the physiological signal of the designated population through the first classifier. When the electronic device determines that the physiological signal of the first user is low in similarity to the physiological signal of the designated population, that is, when the first user does not belong to the designated population, the first blood pressure model can be calibrated using the physiological signal of the first user, so that the first blood pressure model can learn the characteristics of the physiological signal of the first user. The electronic device can obtain a more accurate blood pressure measurement result based on the calibrated first blood pressure model, that is, the second blood pressure model. Moreover, when the electronic device determines that the first user belongs to the designated population, the electronic device can quickly obtain an accurate blood pressure measurement result. The blood pressure measurement method provided by the embodiment of the present application can save the time for the electronic device to output the blood pressure measurement result, and can also ensure the accuracy of the measurement results of non-designated populations, improve the efficiency of blood pressure measurement, and reduce the cost of blood pressure measurement.

[0041] Next, a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application is introduced.

[0042] The electronic device is a device having a PPG signal and / or ECG signal acquisition function. Exemplarily, the electronic device may be an electronic device having a blood pressure sensor. The blood pressure sensor is used to acquire PPG signals and / or ECG signals. For example, the electronic device having a blood pressure sensor may be a wearable device having a blood pressure sensor (e.g., a watch, a bracelet, etc.). For example, the blood pressure sensor may be a photoelectric sensor. It should be noted that, not limited to wearable devices, other electronic devices having a blood pressure sensor may also implement the blood pressure measurement method provided in the embodiments of the present application. For example, the electronic device may be a mobile phone, a tablet, etc. having a blood pressure sensor.

[0043] In some examples, the electronic device can establish a communication connection with a device having a PPG signal and / or ECG signal acquisition function. For example, the electronic device can establish a communication connection with a device having a PPG signal and / or ECG signal acquisition function through a wireless communication method or a wired communication method. For example, near field communication (NFC) communication connection, wireless fidelity (Wi-Fi) communication connection, ultra wide band (UWB) communication connection, bluetooth communication connection, zigbee communication connection, etc. The electronic device can obtain the PPG signal and / or ECG signal from the device having the PPG signal and / or ECG signal acquisition function through the communication connection.

[0044] It should be noted that the device for collecting PPG signals and / or ECG signals can be the electronic device itself, or a device that can support the electronic device to implement the function, such as a chip system or a combination device or component that can implement the function of the electronic device, and the device can be installed in the electronic device. The embodiments of the present application do not limit the specific technology and specific device form adopted by the electronic device.

[0045] Next, the hardware structure of the electronic device is described by taking an electronic device including a blood pressure sensor as an example. Figure 1 As shown, the electronic device may include but is not limited to a processor 11, a memory 12, a display screen 13, a blood pressure sensor 14, etc.

[0046] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0047] The processor 11 may include one or more processing units, for example, the processor 11 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0048] DSP is used to process digital signals. In addition to processing digital image signals, it can also process other digital signals. For example, when an electronic device selects a frequency point, a digital signal processor is used to perform Fourier transform on the frequency point energy.

[0049] The controller can generate operation control signals according to the instruction operation code and timing signal to complete the control of instruction fetching and execution.

[0050] The processor 11 may also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor 11 is a cache memory. The memory may store instructions or data that the processor 11 has just used or cyclically used. If the processor 11 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor 11, and thus improves the efficiency of the system.

[0051] In some embodiments, the processor 11 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0052] The MIPI interface can be used to connect the processor 11 with peripheral devices such as the display screen 13. The MIPI interface includes a display serial interface (DSI), etc. In some embodiments, the processor 11 and the display screen 13 communicate through the DSI interface to realize the display function of the electronic device.

[0053] The GPIO interface can be configured by software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface can be used to connect the processor 11 to the blood pressure sensor 14, etc. The GPIO interface can also be configured as an I2C interface, an I2S interface, a UART interface, a MIPI interface, etc.

[0054] It is understandable that the interface connection relationship between the modules illustrated in the embodiment of the present invention is only a schematic illustration and does not constitute a structural limitation of the electronic device. In other embodiments of the present application, the electronic device may also adopt different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.

[0055] The memory 12 may include one or more random access memories (RAM) and one or more non-volatile memories (NVM). The random access memory may include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM, for example, the fifth generation DDR SDRAM is generally referred to as DDR5 SDRAM), etc.; the non-volatile memory may include a disk storage device and a flash memory. Flash memory can be divided into NOR FLASH, NAND FLASH, 3D NAND FLASH, etc. according to the operating principle, single-level cell (SLC), multi-level cell (MLC), triple-level cell (TLC), quad-level cell (QLC), etc. according to the storage cell potential level, and universal flash storage (UFS), embedded multi media card (eMMC), etc. according to the storage specification. The random access memory can be directly read and written by the processor 11, and can be used to store executable programs (such as machine instructions) of the operating system or other running programs, and can also be used to store user and application data. The non-volatile memory can also store executable programs and store user and application data, etc., and can be loaded into the random access memory in advance for direct reading and writing by the processor 11.

[0056] The electronic device realizes the display function through a GPU, a display screen 13, and an application processor. The GPU is a microprocessor for image processing, which connects the display screen 13 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 11 may include one or more GPUs, which execute program instructions to generate or change display information.

[0057] The display screen 13 is used to display images, videos, etc. The display screen 13 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device may include 1 or N display screens 13, where N is a positive integer greater than 1.

[0058] The blood pressure sensor 14 can be used to collect physiological signals of the user, and the physiological signals can include one or more of PPG signals and ECG signals. For example, the blood pressure sensor 14 can be a photoelectric sensor. In some embodiments, the electronic device can include 1 or N blood pressure sensors 14, where N is a positive integer greater than 1.

[0059] In an embodiment of the present application, the electronic device can implement a blood pressure measurement function through a processor 11, a blood pressure sensor 14, a display screen 13, etc. Specifically, the electronic device needs to touch a body part of the user (for example, a wrist, a finger, etc.) through the blood pressure sensor 14. The blood pressure sensor 14 can collect the user's physiological signals when in contact with the user's body parts, and send the collected physiological signals to the processor 11. The processor 11 can determine the user's blood pressure measurement result based on the user's personalized information and the physiological signals collected by the blood pressure sensor 14. The display screen 13 can be used to display the blood pressure measurement result determined by the electronic device.

[0060] Next, a flow chart of a blood pressure measurement method provided in an embodiment of the present application is introduced.

[0061] For example, Figure 2 As shown, the blood pressure measurement method comprises the following steps:

[0062] S201. The electronic device collects a first physiological signal segment and personalized information of a first user.

[0063] Specifically, after receiving input for a blood pressure measurement application, the electronic device can collect a first physiological signal segment and personalized information of the first user in response to the input, wherein the first physiological signal segment may include one or more of a PPG signal and an ECG signal. The personalized information may include, but is not limited to, one or more of information such as gender, age, height, and weight. After receiving input for a blood pressure measurement application, the electronic device can collect the first physiological signal segment within a preset segment duration (e.g., 10s) through a blood pressure sensor.

[0064] In some examples, the first physiological signal segment collected by the electronic device includes a PPG signal collected by the electronic device at one part of the user. For example, the electronic device can collect any part of the user (e.g., finger, wrist, etc.) through a blood pressure sensor to obtain the first physiological signal segment. In other examples, the first physiological signal segment collected by the electronic device includes a PPG signal collected by the electronic device at two different parts of the user. In other examples, the first physiological signal segment collected by the electronic device includes a PPG signal and an ECG signal.

[0065] In some examples, after obtaining the first physiological signal segment, the electronic device may detect whether the quality of the first physiological signal segment meets the signal quality requirements. When the electronic device detects that the quality of the first physiological signal segment meets the signal quality requirements, the first physiological signal segment may be sent to the first classifier. When the electronic device detects that the quality of the first physiological signal segment does not meet the signal quality requirements, the electronic device may re-collect the first physiological signal. Until the electronic device detects that the quality of the acquired first physiological signal segment meets the signal quality requirements. For example, the electronic device may detect whether the quality of the first physiological signal segment meets the signal quality requirements based on the quality index characteristics of the signal (for example, amplitude, peak, trough, signal-to-noise ratio, etc.). For another example, the electronic device may also detect whether the quality of the first physiological signal segment meets the quality requirements by detecting whether the first physiological signal segment has a signal loss or other methods. In this way, the electronic device can ensure that the first physiological signal segment is sufficiently reliable and accurate by evaluating the quality of the first physiological signal segment after preprocessing.

[0066] In some examples, the electronic device may output collection prompt information before collecting the first physiological signal segment. The collection prompt information may be used to prompt the user to maintain a preset posture during the blood pressure measurement process. The preset posture may include but is not limited to lying on your back, sitting upright, etc. In this way, when the user maintains the preset posture, the electronic device can collect more accurate physiological signals.

[0067] In some examples, the electronic device stores the personalized information of the first user, and the electronic device can use the stored personalized information. In this way, the electronic device does not need the first user to input the personalized information, which reduces the operation of the first user.

[0068] S202. The electronic device determines whether the first user belongs to a designated group of people based on the first physiological signal segment by using a first classifier.

[0069] Among them, the electronic device can use the first physiological signal segment as input data of the first classifier to determine whether the first user belongs to the user classification result of the specified population. Among them, the first physiological signal segment can be used to represent the physiological characteristics of the user. In this way, when the electronic device determines through the first classifier that the first user belongs to the specified population, it means that the physiological signal of the first user has a high similarity with the physiological signal of the specified population, and the electronic device can execute step S203 to use the first blood pressure model trained by the physiological signal of the specified population. When the electronic device determines through the first classifier that the first user does not belong to the specified population, it means that the physiological signal of the first user has a low similarity with the physiological signal of the specified population, the electronic device can execute step S204, and the first blood pressure model trained with the physiological signal of the specified population cannot be directly used. The electronic device needs to calibrate the first blood pressure model based on the physiological signal of the first user, so that the first blood pressure model can learn the characteristics of the physiological signal of the first user, and the calibrated first blood pressure model can obtain a more accurate blood pressure measurement result of the first user.

[0070] The first blood pressure model may include but is not limited to a first feature extraction layer and a blood pressure prediction layer. The first feature extraction layer may be used to extract feature information of physiological signal segments and personalized information. The blood pressure prediction layer may be used to predict blood pressure measurement results based on the feature information. The first feature extraction layer may include but is not limited to a convolution (CONV) layer, a batch normalization (BN) layer, and a rectified linear unit (ReLu).

[0071] It is understandable that the server can train the first blood pressure model based on the physiological signals of the specified population, the personalized information of the specified population, and the blood pressure measurement results of the specified population. After obtaining the trained first blood pressure model, the server can send the first blood pressure model to the electronic device.

[0072] Among them, the first classifier may include but is not limited to one or more feature extraction layers and classification layers. Among them, one or more feature extraction layers can be used to extract features of the first physiological signal segment, and the one or more feature extraction layers may include but are not limited to a first feature extraction layer and a second feature extraction layer. It should be noted that the first feature extraction layer of the first classifier is the same as the first feature extraction layer of the first blood pressure model. The classification layer can be used to determine the user classification result based on the feature information extracted by one or more feature extraction layers.

[0073] like Figure 3 As shown, the second feature extraction layer may include but is not limited to a convolution layer, a batch normalization layer, and a linear rectifier unit. The classification layer may include but is not limited to a fully connected (FC) layer and a normalized exponential function (softmax) layer. Specifically, the electronic device may input the first physiological signal segment as an input signal into the first feature extraction layer. The first feature extraction layer may obtain the first feature information and input the first feature information into the convolution layer of the second feature extraction layer. The electronic device may perform a convolution operation on the first feature information through the convolution layer to extract the local features of the first feature information. The electronic device may perform batch normalization processing on the local features output by the convolution layer through the batch normalization layer to obtain batch normalized data. The electronic device may process the batch normalized data output by the batch normalization layer through the linear rectifier unit to obtain the second feature information. Among them, the linear rectifier unit is also called an excitation layer, which can prevent data overfitting. The electronic device may process the second feature information output by the linear rectifier unit through the fully connected layer and the normalized exponential function layer to obtain the user classification result. Among them, the second feature information is more detailed than the first feature information, which facilitates the classification layer to determine the user classification result.

[0074] In some examples, the electronic device may use binary to represent the user classification result. For example, when the value of the user classification result obtained by the electronic device is "0", it indicates that the first user who provides the first physiological signal segment belongs to the specified group of people. When the value of the user classification result obtained by the electronic device is "1", it indicates that the first user who provides the first physiological signal segment does not belong to the specified group of people. It should be noted that the value of the user classification result is only an example and should not be construed as a limitation.

[0075] It is understandable that the server can obtain the first classifier based on the physiological signal training of the specified population. After obtaining the first classifier, the server can send the first classifier to the electronic device. Here, the electronic device can use the first physiological signal segment as input data and use the first classifier to determine whether the first user who provides the first physiological signal segment belongs to the specified population.

[0076] In some examples, when the amount of data of the physiological data of a specified population is large, the server can set N second feature extraction layers in the first classifier, so that the server can extract feature information in the output result of the first feature extraction layer N times through the second feature extraction layer, where N is a positive integer, to obtain deeper feature information. In this way, the server can increase the number of feature extraction layers of the first classifier so that the first classifier can process a large amount of input data.

[0077] In some examples, the electronic device can continuously collect the first physiological signal within a preset collection time (for example, 60s). Then, a preprocessing operation is performed based on the first physiological signal to obtain a first physiological signal segment. Specifically, the electronic device can filter the first physiological signal and slice the filtered first physiological signal to obtain one or more first physiological signal segments, where the first physiological signal segment is a signal segment of a preset segment length in the first physiological signal. Exemplarily, the electronic device can use a bandpass filter, a notch filter or other filters to filter the first physiological signal to remove noise from the PPG signal. The electronic device can use a notch filter, a morphological filter or other filters to filter the first physiological signal to remove noise from the ECG signal. It should be noted that if the first physiological signal includes multiple signals, the electronic device can align different signals according to the time point when the electronic device collects the signal, and then obtain the first physiological signal segment based on the aligned signal. In this way, the electronic device can obtain a first physiological signal segment with better signal quality.

[0078] In some examples, the electronic device may collect a first physiological signal and obtain multiple first physiological signal segments based on the first physiological signal, and then input the multiple first physiological signal segments into the first classifier to obtain multiple user classification results. The electronic device may determine whether the first user belongs to a specified group of people based on the multiple user classification results. For example, if the result values ​​of the multiple user classification results obtained by the electronic device are the same, the electronic device may determine whether the first user belongs to the specified group of people according to any user classification result. If the result values ​​of the multiple user classification results obtained by the electronic device are different, the electronic device may determine the number of user classification results corresponding to different result values, and determine whether the first user belongs to the specified group of people based on the user classification result with the largest number. Alternatively, the electronic device may re-collect the physiological signal segments of the first user, and use the re-collected physiological signal segments and the first classifier to determine whether the first user belongs to the specified group of people.

[0079] S203. The electronic device obtains a first blood pressure measurement result through a first blood pressure model, based on the first physiological signal segment and the personalized information.

[0080] Specifically, the electronic device determines that the first user belongs to a designated group, and the electronic device can use the first physiological signal segment and the personalized information as inputs to a first blood pressure model to obtain a first blood pressure measurement result, wherein the first blood pressure measurement result can include systolic pressure and diastolic pressure.

[0081] Optionally, when the electronic device collects multiple first physiological signal segments, multiple blood pressure measurement results can be obtained based on the multiple first physiological signal segments. The electronic device can obtain the first blood pressure measurement result based on the multiple blood pressure measurement results. For example, the electronic device can weighted average the values ​​of the multiple blood pressure measurement results to obtain the value of the first blood pressure measurement result.

[0082] S204. The electronic device collects a second physiological signal segment of the first user and receives a first blood pressure value input by the first user.

[0083] When the electronic device determines that the first user does not belong to the designated group, the electronic device can collect the second physiological signal segment of the first user. Specifically, the description of the electronic device collecting the second physiological signal segment can refer to the embodiment shown in step S201, which is not repeated here.

[0084] The electronic device may also receive a first blood pressure value input by the first user. The first blood pressure value includes diastolic pressure and systolic pressure. Optionally, the electronic device stores the first blood pressure value of the first user, or the electronic device may obtain the first blood pressure value of the first user from other electronic devices. In this way, the electronic device may obtain the first blood pressure value most recently measured by the first user, and adjust the first blood pressure model with reference to the first blood pressure value.

[0085] S205. The electronic device determines a user classification result based on the second physiological signal segment by using the first classifier, and determines a blood pressure measurement result by using the first blood pressure model.

[0086] S206. The electronic device determines a first loss parameter based on the user classification result, the preset classification result, the first blood pressure value and the blood pressure measurement result, and adjusts the parameters of the first classifier based on the first loss parameter to obtain the second classifier, and adjusts the parameters of the first blood pressure model to obtain the second blood pressure model. The preset classification result indicates that the first user belongs to a specified group of people.

[0087] The electronic device can determine the first user classification result through the first classifier based on the second physiological signal segment, and determine the first blood pressure measurement result through the first blood pressure model. The electronic device can determine the first loss parameter based on the first user classification result, the preset classification result, the first blood pressure value, and the first blood pressure measurement result. The electronic device can adjust the parameters of the first classifier based on the first loss parameter to obtain the second classifier, and adjust the parameters of the first blood pressure model to obtain the second blood pressure model. The first blood pressure model is used to identify the blood pressure measurement results of a specified group of people, and the user classification result determined by the second classifier for the second physiological signal segment is the second user classification result, and the second user classification result indicates that the first user belongs to the specified group of people.

[0088] In some examples, the electronic device may construct the first classifier and the first blood pressure model to obtain a blood pressure training model. The electronic device may use the blood pressure training model to adjust the first blood pressure model and the first classifier based on the second physiological signal segment, the preset classification result, and the first blood pressure value. When the user classification result output by the blood pressure training model indicates that the first user belongs to a specified group of people, the first blood pressure model in the blood pressure training model is adjusted to obtain a second blood pressure model, and the first classifier in the blood pressure training model is adjusted to obtain the second classifier.

[0089] Specifically, during the forward propagation process, the electronic device may use the second physiological signal segment as input data of a blood pressure training model including a first classifier and a first blood pressure model to obtain a user classification result and a blood pressure measurement result.

[0090] When the user classification result indicates that the first user does not belong to the specified group, the electronic device can obtain a first loss parameter based on the user classification result, the preset classification result, the blood pressure measurement result and the first blood pressure value. The electronic device can back-propagate the first loss parameter to adjust the parameters of the blood pressure training model. That is, the electronic device can adjust the parameters of the first classifier and the first blood pressure model based on the first loss parameter during the back-propagation process.

[0091] The electronic device may repeat the forward propagation process and the reverse propagation process until the electronic device obtains a user classification result indicating that the first user belongs to a specified group through the blood pressure training model. At this point, the electronic device determines that the first blood pressure model is calibrated successfully, and the calibrated first blood pressure model may be referred to as the second blood pressure model. The electronic device determines that the first classifier is calibrated successfully, and the calibrated first classifier may be referred to as the second classifier.

[0092] In some examples, the electronic device may obtain a classification loss parameter based on the user classification result and the preset classification result, and obtain a predicted loss parameter based on the blood pressure measurement result and the first blood pressure value. The electronic device may obtain the first loss parameter based on the classification loss parameter and the predicted loss parameter.

[0093] In some examples, the blood pressure training model further includes a gradient reversal layer, which can be used to reduce the difference between the characteristics of the second physiological signal segment and the physiological signal characteristics of the specified population, so as to confuse the input data with the physiological signals of the specified population. It should be noted that the electronic device can reduce the difference between the physiological signal characteristics of the first user and the physiological signal characteristics of the specified population through other neural network structures, which is not limited to the embodiments of the present application.

[0094] For example, Figure 4 As shown, the blood pressure training model may include a first feature extraction layer, a blood pressure prediction layer, a gradient reversal layer, a second feature extraction layer and a classification layer. Among them, the first feature extraction layer and the blood pressure prediction layer may constitute a first blood pressure model, and the first feature extraction layer, the second feature extraction layer and the classification layer may constitute a first classifier. The electronic device may input the second physiological signal segment into the first feature extraction layer as input data of the blood pressure training model. The first feature extraction layer may output the first feature information. The electronic device may use the first feature information as input data of the second feature extraction layer and the blood pressure prediction layer. The second feature extraction layer may obtain the second feature information based on the first feature information. The classification layer may determine the user classification result based on the second feature information. The blood pressure measurement layer may determine the blood pressure measurement result based on the first feature information. In this way, since the features of the second feature information are more detailed than the features of the first feature information, the user classification result identified by the classification layer based on the second feature information is more accurate than the user classification result identified by the classification layer based on the first feature information.

[0095] Wherein, when the user classification result indicates that the first user does not belong to the specified group, the electronic device can calculate the first loss parameter based on the user classification result and the preset classification result, the blood pressure measurement result and the first blood pressure value. Afterwards, the electronic device can adjust the parameters of the blood pressure training model based on the first loss parameter through the back propagation technology.

[0096] Specifically, the electronic device may adjust the parameters of the classification layer in the direction of reducing the first loss parameter, and determine the second loss parameter of the second feature extraction layer. The electronic device may adjust the parameters of the second feature extraction layer in the direction of reducing the second loss parameter, and determine the third loss parameter of the first feature extraction layer. The electronic device may adjust the parameters of the first feature extraction layer in the direction of increasing the third loss parameter. In this way, adjusting the parameters of the first feature extraction layer in the direction of not reducing the third loss parameter can cause the first blood pressure model and the first classifier to confuse the physiological signal characteristics of the first user with the physiological signal characteristics of the specified population, that is, to learn the physiological signal characteristics of the first user.

[0097] The electronic device may adjust the parameters of the blood pressure prediction layer toward a smaller first loss parameter, and determine the fourth loss parameter of the first feature extraction layer. The electronic device may adjust the parameters of the first feature extraction layer toward a direction that reduces the fourth loss parameter. In this way, adjusting the parameters of the first feature extraction layer toward a direction that reduces the fourth loss parameter can enable the first blood pressure model to determine a more accurate blood pressure measurement result based on the physiological signal of the first user.

[0098] It is understood that the gradient reversal layer reverses the gradient value only during back propagation. The gradient reversal layer does not reverse the gradient value during forward propagation. For example, the gradient reversal layer can be used to multiply the gradient by a negative factor (e.g., -1) during back propagation to reverse the gradient value, thereby achieving gradient subtraction.

[0099] After adjusting the parameters of the first classifier and the first blood pressure model, the electronic device can use the blood pressure training model again to determine the user classification result and the blood pressure measurement result based on the second physiological signal segment. When the user classification result indicates that the first user does not belong to the specified population, the first loss parameter is obtained based on the user classification result and the blood pressure measurement result, and the first classifier and the first blood pressure model are adjusted based on the first loss parameter, and so on. When the user classification result obtained by the electronic device through the blood pressure training model indicates that the first user belongs to the specified population, it is determined that the calibration of the first classifier and the first blood pressure model is completed, and the electronic device obtains the second classifier and the second blood pressure model.

[0100] Optionally, the electronic device can use the second physiological signal segment and the user's personalized information as input data for the blood pressure training model. In this way, the electronic device can learn more characteristic information of the user, so that the electronic device can obtain a more accurate blood pressure measurement result.

[0101] In some examples, the electronic device can collect multiple (for example, 6) second physiological signal segments of the first user, and use the multiple second physiological signal segments to train a second classifier and a second blood pressure model. In this way, the electronic device can learn more feature information of the first user during the training process by training the second classifier and the second blood pressure model through multiple physiological signal segments.

[0102] S207. The electronic device obtains a second blood pressure measurement result through a second blood pressure model based on the second physiological signal segment and the personalized information.

[0103] After obtaining the second blood pressure model, the electronic device can input the second physiological signal segment and the personalized information into the second blood pressure model and output a second blood pressure measurement result, which includes systolic pressure and diastolic pressure. After obtaining the second blood pressure measurement result, the electronic device can display the second blood pressure measurement result.

[0104] In some examples, the electronic device may not collect the second physiological signal segment, but directly use one or more segments of the first physiological signal segment to train the second classifier and the second blood pressure model. The electronic device may use the second blood pressure model to obtain the second blood pressure measurement result based on the first physiological signal segment and the personalized information. In this way, the time spent on collecting physiological signals can be reduced.

[0105] In some examples, the electronic device may collect the third physiological signal segment of the first user after obtaining the second blood pressure model. The electronic device may use the second blood pressure model to obtain the second blood pressure measurement result based on the third physiological signal segment and the personalized information. In this way, the electronic device can obtain a more real-time blood pressure measurement result.

[0106] It is understandable that after the electronic device obtains the second blood pressure model, after the electronic device receives the input of the first user measuring the blood pressure, it can collect the fourth physiological signal of the first user in response to the input. The electronic device can use the second classifier to determine that the first user belongs to the specified population based on the fourth physiological signal. Afterwards, the electronic device can use the second blood pressure model to obtain a third blood pressure measurement result based on the fourth physiological signal segment and the personalized information of the first user. In this way, since the second classifier and the second blood pressure model in the electronic device have learned the characteristics of the first user's physiological signal, an accurate blood pressure measurement result of the first user can be quickly obtained.

[0107] Next, the blood pressure measurement method provided in the embodiment of the present application is explained with reference to examples.

[0108] For example, Figure 5A As shown, the electronic device collects the first physiological signal segment 1 of the subject 1. Based on the first physiological signal segment 1, the electronic device determines the user classification result 1 through the first classifier. Here, the user classification result 1 determined by the electronic device indicates that the subject 1 belongs to the specified population, and the electronic device does not calibrate the first blood pressure model. The electronic device can obtain the blood pressure measurement result of the subject 1 based on the first physiological signal segment 1, the personalized information of the subject 1 and the first blood pressure model.

[0109] The electronic device collects a first physiological signal segment 2 of the subject 2. Based on the first physiological signal segment 2, the electronic device determines a user classification result 2 through a first classifier. Here, the user classification result 2 determined by the electronic device indicates that the subject 2 does not belong to the specified population, and the electronic device calibrates the first blood pressure model. The electronic device can collect a second physiological signal segment of the subject 2. The electronic device can obtain a second blood pressure model based on the second physiological signal segment, the first classifier and the first blood pressure model. For details, see Figure 2 The electronic device can obtain the blood pressure measurement result of the subject 2 based on the second physiological signal segment and the personalized information using the second blood pressure model.

[0110] Exemplarily, the process of an electronic device using a blood pressure model to measure a user's blood pressure is as follows: Figure 5B As shown, the electronic device collects the physiological signals of the user and preprocesses the physiological signals to obtain physiological signal segments. The electronic device can also receive personalized information input by the user. The electronic device can input the physiological signal segments and personalized information into the blood pressure model to obtain blood pressure measurement results, and the blood pressure measurement results include SBP and DBP. Among them, when the electronic device determines that the user belongs to a designated group of people based on the physiological signals, the blood pressure model is the first blood pressure model. When the electronic device determines that the user does not belong to the designated group of people based on the physiological signals, the blood pressure model is the second blood pressure model.

[0111] In this way, since the electronic device can perform characteristic judgment on the user's physiological signals, obtain the judgment results, and determine whether the electronic device for blood pressure measurement needs to be calibrated based on the judgment results, it can save time in outputting results and ensure the accuracy of measurement results for non-specified populations, so as to improve the processing efficiency of blood pressure measurement and reduce the cost of blood pressure measurement.

[0112] Next, a schematic diagram of the training process of a first blood pressure model provided in an embodiment of the present application is introduced.

[0113] For example, Fig. 6A As shown, the server can obtain training data, which includes the user's physiological signals, personalized information, and blood pressure measurement results. The server can use a deep learning algorithm based on the training data to train a first blood pressure model. After obtaining the first blood pressure model, the server can send the first blood pressure model to the electronic device.

[0114] Specifically, Figure 6BAs shown, training data is stored in the database of the server, and the training data includes the correspondence between physiological signals, personalized information and blood pressure detection results. The electronic device can obtain physiological signals and personalized information from the database. The server can preprocess the physiological signals to obtain physiological signal segments. The server can use the physiological signal segments and personalized information as input data of the first blood pressure model to obtain output data, and the output data includes systolic pressure and diastolic pressure. The server can obtain the blood pressure measurement results corresponding to the physiological signals and personalized information from the database, and determine the first loss parameter based on the blood pressure measurement results and the output data. The server can adjust the parameters of the first blood pressure model based on the first loss parameter until the server obtains the first blood pressure model with an accuracy rate reaching a preset accuracy rate (for example, 90%). Among them, the description of the first blood pressure model can be found in Figure 2 The illustrated embodiments will not be described in detail here.

[0115] It is understandable that the users who provide training data in the embodiments of the present application belong to a designated group of people. In some examples, the server may receive input training data, or the server may use crowdsourcing technology to obtain training data. Among them, crowdsourcing technology is a method of obtaining resources through a large number of electronic devices after obtaining user permission. After obtaining user permission, the server can use crowdsourcing technology to collect training data through the user's electronic device without the user's perception.

[0116] In some examples, the server may send the updated first blood pressure model to the electronic device every preset period (e.g., one month). In this way, the server can continuously obtain training data and update the first blood pressure model so that the electronic device can obtain blood pressure measurement results more accurately.

[0117] Next, the blood pressure measurement method provided in the embodiment of the present application is introduced in combination with application scenarios.

[0118] In one possible implementation, after receiving input for a blood pressure measurement application icon, the electronic device may, in response to the input, collect a first physiological signal segment of the first user and obtain personalized information of the first user. When the electronic device uses the first classifier to determine that the first user belongs to a designated population based on the first physiological signal segment, the electronic device may use the first blood pressure model to determine the blood pressure measurement result of the first user based on the first user's physiological signal and personalized data. When the electronic device uses the first classifier to determine that the first user does not belong to the designated population based on the first physiological signal segment, the electronic device may prompt the first user to input a first blood pressure value and collect a second physiological signal segment. The electronic device may calibrate the first blood pressure model based on the first blood pressure value, the preset classification result, the second physiological signal segment, etc. to obtain a second blood pressure model. For details, please refer to Figure 2The illustrated embodiment will not be described in detail here. The electronic device can use the second blood pressure model to determine the blood pressure measurement result of the first user based on the second physiological signal segment and the personalized data. The electronic device can display the blood pressure measurement result of the first user. In this way, the electronic device can display the corresponding user interface when performing blood pressure measurement, prompting the user to perform corresponding operations, so that the electronic device can quickly obtain accurate blood pressure measurement results.

[0119] In some examples, the electronic device may display collection prompt information when collecting the user's physiological signals. The collection prompt information can be used to prompt the user to remain still or maintain a preset posture. In this way, the electronic device can collect physiological signals more efficiently with the cooperation of the user. Optionally, the electronic device may display collection prompt information only when collecting physiological signals for calibrating the first blood pressure model. In this way, the electronic device can prompt the user to remain still or maintain a preset posture when calibrating the first blood pressure model, so that the electronic device can calibrate the first blood pressure model faster.

[0120] Next, taking an electronic device such as a watch or a bracelet as an example, the blood pressure measurement method provided in an embodiment of the present application is introduced.

[0121] For example, Fig. 7A As shown. The electronic device may display an interface 800. The interface 800 may include one or more application icons. The one or more application icons may include a blood pressure measurement application icon 801. The blood pressure measurement application icon 801 may be used to trigger the electronic device to display an interface of a blood pressure measurement application.

[0122] The electronic device may receive a request from the first user Fig. 7A After input (e.g., single-clicking) of the blood pressure measurement application icon 801 shown in FIG. 8 , in response to the input, a display such as Figure 7B The interface 810 shown. Figure 7B As shown, the interface 810 includes a user information bar 812 and a confirmation control 813. The user information bar 812 is used to input the user's personalized information, for example, one or more of the information such as gender, height, weight, age, etc.

[0123] For example, the user information bar 812 includes weight information and age information. The electronic device can receive input from the first user on the touch screen of the electronic device and type in personalized information. Alternatively, the electronic device can receive personalized information input by voice from the first user. Alternatively, the electronic device stores the personalized information of the first user, and the electronic device can display the stored personalized information in the user information bar 812. In some examples, the electronic device can obtain the weight information of the first user through a weight measurement device. Here, the user information bar 812 displays the weight information typed in by the first user: "52kg" and age information: "23 years old". Among them, the confirmation control 813 can be used to trigger the electronic device to determine the personalized information.

[0124] Optionally, the interface 810 may further include prompt information 811, which may be used to prompt the user to enter personalized information. The prompt information 811 may be one or more of text information, image information, voice information, etc. For example, the prompt information 811 may be a text prompt information: "Please enter personal information."

[0125] The electronic device can receive the user's Figure 7B After the input of the confirmation control 813 is entered, in response to the input, the following is displayed: Figure 7C The interface 820 shown. Figure 7C As shown, the interface 820 may include but is not limited to prompt information 821. Prompt information 821 may be used to prompt the user that the electronic device is measuring blood pressure. Prompt information 821 may include but is not limited to text prompt information, image prompt information, animation prompt information, and voice prompt information, etc. For example, the prompt information 821 may be a text prompt information: "Measuring...". Optionally, the prompt information 821 can also be used to prompt the user to maintain a preset posture. In this way, since the user maintains a preset posture, it is convenient for the electronic device to collect more accurate physiological signals.

[0126] The electronic device can receive the user's Figure 7B After the input of the confirmation control 813 is shown, the first physiological signal segment of the first user is collected. When the electronic device uses the first classifier to determine that the first user belongs to the specified group based on the first physiological signal segment, the electronic device can use the first blood pressure model to determine the blood pressure measurement result based on the first physiological signal segment of the first user. For details, the description of the electronic device determining the blood pressure measurement result can be found in Figure 2 The embodiment shown is not described in detail here. Here, the diastolic pressure in the blood pressure measurement result obtained by the electronic device is 110mHg, and the systolic pressure is 75mHg.

[0127] After determining the blood pressure measurement result of the user, the electronic device can display the following information: Fig.7D The interface 830 shown. Fig.7D As shown, the interface 830 includes result information 831, and the result information 831 is used to indicate the blood pressure measurement result of the user. The result information 831 can be one or more of text information, picture information, voice information, etc. For example, the result information 831 can include text information "diastolic pressure: 110mHg" and text information "systolic pressure: 75mHg".

[0128] The electronic device can receive the user's Figure 7B After the input of the confirmation control 813 shown in FIG. 8 is entered, the first physiological signal segment of the first user is collected. When the electronic device uses the first classifier to determine that the first user does not belong to the specified group based on the first physiological signal segment, the electronic device may display the following information: Fig. 7E Interface 850 is shown.

[0129] like Fig. 7E As shown, interface 850 can be used for a first user to input a first blood pressure value. Interface 850 may include, but is not limited to, a diastolic pressure input box 851, a systolic pressure input box 852, and a confirmation control 853. Among them, the diastolic pressure input box 851 can be used to input the diastolic pressure in the first blood pressure value. The systolic pressure input box 852 can be used to input the systolic pressure in the first blood pressure value. The confirmation control 853 can be used to trigger the electronic device to determine the first blood pressure value. Optionally, interface 850 may also include prompt information for prompting the user to input the first blood pressure value. For example, the prompt information may be a text prompt information: "Please enter the result of your most recent blood pressure measurement."

[0130] After receiving the input of the first user to the confirmation control 853, the electronic device can collect the second physiological signal segment of the first user in response to the input. After collecting the second physiological signal segment, the electronic device can calibrate the first blood pressure model based on the second physiological signal segment, the first blood pressure value and the preset classification result to obtain the second blood pressure model. The electronic device can use the second blood pressure model to determine the blood pressure measurement result of the first user based on the second physiological signal segment. For details, please refer to Figure 2 The electronic device can display the blood pressure measurement result after determining the blood pressure measurement result. Fig.7D Interface 830 is shown.

[0131] Optionally, the electronic device may display the following after receiving the input for the confirmation control 853: Figure 7F The interface 860 shown, or, as shown Figure 7C The interface 820 shown. For example, Figure 7FAs shown, interface 860 may include but is not limited to prompt information 861. Prompt information 861 may be used to prompt the user that the electronic device is testing blood pressure. Prompt information 861 may include but is not limited to text prompt information, image prompt information, animation prompt information, and voice prompt information, etc. For example, the prompt information 861 may be a text prompt information: "It is detected that your blood pressure is tested for the first time. You need to measure it again. Please wait patiently..." The electronic device displays as shown. Figure 7F In the interface 860 shown in FIG. 1 , the electronic device may display the following information when determining the blood pressure measurement result: Fig.7D In this way, the electronic device needs to spend a longer time to re-collect the user's physiological signals, and the electronic device displays the following Figure 7F The interface 860 shown prompts the user that it will take a longer time to detect blood pressure.

[0132] In some examples, the electronic device stores the personalized information of the first user. Fig. 7A After inputting the blood pressure measurement application icon 801 shown, the display Figure 7C The interface 820 shown in the figure is displayed, and the first physiological signal segment of the first user is collected. Afterwards, the electronic device can determine the blood pressure measurement result of the user through the blood pressure measurement method provided in the embodiment of the present application. For details, please refer to the above embodiment, which will not be repeated here.

[0133] In this way, the electronic device can implement the blood pressure measurement method provided in the embodiment of the present application through the above interface.

[0134] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A blood pressure measurement method, characterized in that: Applied to an electronic device, the electronic device includes a blood pressure sensor; the method includes: Receiving personalized information input by a first user, wherein the personalized information includes one or more of age, height, weight, and gender; collecting a first physiological signal segment of the first user by using the blood pressure sensor; determining, by a first classifier, whether the first user belongs to a first group of people based on the first physiological signal segment; When it is determined that the first user does not belong to the first group, receiving a first blood pressure value input by the first user, and collecting a second physiological signal segment of the first user through the blood pressure sensor; Based on the second physiological signal segment, determining a first user classification result by the first classifier, and determining a first blood pressure measurement result by the first blood pressure model; determining a first loss parameter based on the first user classification result, a preset classification result, the first blood pressure value, and the first blood pressure measurement result; Based on the first loss parameter, adjusting the parameters of the first classifier to obtain a second classifier, and adjusting the parameters of the first blood pressure model to obtain a second blood pressure model; wherein the first blood pressure model is used to identify the blood pressure measurement result of the first population, and the user classification result determined by the second classifier for the second physiological signal segment is a second user classification result, and the second user classification result indicates that the first user belongs to the first population; A second blood pressure measurement result is obtained through the second blood pressure model based on the second physiological signal segment.

2. The method according to claim 1, characterized in that The method further comprises: When it is determined that the first user belongs to the first group, a third blood pressure measurement result is obtained based on the first physiological signal segment using the first blood pressure model.

3. The method according to claim 1, characterized in that After obtaining the second classifier and the second blood pressure model, the method further includes: collecting a third physiological signal segment of the first user through the blood pressure sensor; determining, by the second classifier, based on the third physiological signal segment, that the first user belongs to the first group; A fourth blood pressure measurement result is obtained through the second blood pressure model based on the third physiological signal segment.

4. The method according to claim 1, characterized in that: The first blood pressure model includes a first feature extraction layer and a blood pressure prediction layer; the first classifier includes the first feature extraction layer, the second feature extraction layer and the classification layer; the first user classification result is determined by the first classifier based on the second physiological signal segment, and the first blood pressure measurement result is determined by the first blood pressure model, specifically including: Determining first feature information based on the second physiological signal segment through the first feature extraction layer; Determining, by the blood pressure prediction layer, the first blood pressure measurement result based on the first feature information; determining, by the second feature extraction layer, second feature information based on the first feature information, where the second feature information is different from the first feature information; The first user classification result is determined through the classification layer based on the second feature information.

5. The method according to claim 4, characterized in that The adjusting the parameters of the first classifier based on the first loss parameter to obtain the second classifier, and adjusting the parameters of the first blood pressure model to obtain the second blood pressure model specifically includes: The classification layer adjusts the parameters of the classification layer in a direction of reducing the first loss parameter, and determines a second loss parameter of the second feature extraction layer; The second feature extraction layer adjusts the parameters of the second feature extraction layer in a direction of reducing the second loss parameter, and determines a third loss parameter of the first feature extraction layer; The first feature extraction layer adjusts the parameters of the first feature extraction layer in a direction of increasing the third loss parameter; The blood pressure prediction layer adjusts the parameters of the blood pressure prediction layer in a direction of reducing the first loss parameter, and determines a fourth loss parameter of the first feature extraction layer; The first feature extraction layer adjusts the parameters of the first feature extraction layer in a direction of reducing the fourth loss parameter.

6. The method according to claim 1 or 2, characterized in that: Before receiving the personalized information input by the first user, the method further includes: Displaying a first interface, wherein the first interface includes a blood pressure measurement application icon; receiving a first input for the blood pressure measurement application icon; In response to the first input, displaying a second interface, wherein the second interface includes a first control, and the second interface is used to input the personalized information; The receiving of personalized information input by the first user specifically includes: A second input for the first control is received, and the personalized information is received in response to the second input.

7. The method according to claim 6, characterized in that The collecting of the first physiological signal segment of the first user by the blood pressure sensor specifically includes: In response to the second input, the first physiological signal segment is acquired by the blood pressure sensor.

8. The method according to claim 7, characterized in that When it is determined that the first user does not belong to the first group, the method further includes: displaying a third interface, the third interface including a second control, the third interface being used to input the first blood pressure value; The receiving the first blood pressure value input by the first user specifically includes: A third input to the second control is received, and in response to the third input, the first blood pressure value is received.

9. The method according to claim 8, characterized in that The third interface includes first prompt information, and the first prompt information is used to prompt the first user to input the first blood pressure value.

10. The method according to claim 8, characterized in that The collecting of the second physiological signal segment of the first user by the blood pressure sensor specifically includes: In response to the third input, the second physiological signal segment is acquired by the blood pressure sensor.

11. The method according to claim 7, characterized in that When collecting the first physiological signal segment of the first user by the blood pressure sensor, the method further includes: The second prompt information is displayed, where the second prompt information is used to prompt the first user to maintain a preset posture, where the preset posture includes lying on your back or sitting upright.

12. The method according to claim 1, characterized in that The first physiological signal segment includes a photoplethysmography (PPG) signal and / or an electrocardiogram (ECG) signal.

13. The method according to claim 1, characterized in that Before receiving the personalized information input by the first user, the method further includes: The first classifier and the first blood pressure model sent by the receiving server are obtained by training the first blood pressure model based on the physiological signal segments of the first group of people, personalized information and blood pressure measurement results, and the first classifier is obtained by training the physiological signal segments of the first group of people.

14. The method according to claim 1, characterized in that The duration of the first physiological signal segment is a preset segment duration.

15. The method according to claim 14, characterized in that The collecting of the first physiological signal segment of the first user by the blood pressure sensor specifically includes: Collecting a first physiological signal of the first user within a preset collection time period by the blood pressure sensor; A preprocessing operation is performed on the first physiological signal to obtain the first physiological signal segment; wherein the preprocessing operation includes one or more of filtering, slicing, and alignment operations.

16. The method according to claim 1, characterized in that The first blood pressure measurement result includes a first diastolic pressure DPB and a first systolic pressure SBP, and the first blood pressure value includes a second diastolic pressure and a second systolic pressure.

17. An electronic device, characterized in that: include: a blood pressure sensor, one or more processors, and one or more memories; The blood pressure sensor and the one or more memories are coupled to the one or more processors, and the one or more memories are used to store executable programs. When the one or more processors execute the executable programs, the electronic device executes the method as described in any one of claims 1-16.

18. A readable storage medium storing a program, characterized in that: When the program is executed on an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 16.

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