Method, device and wearable apparatus for measuring blood pressure

Abstract

The application provides a method, device and wearable equipment for measuring blood pressure. The wearable equipment comprises an air bag, and the method comprises: acquiring sleep data at a first measurement time; determining a first measurement speed according to the sleep data at the first measurement time, the first measurement speed comprising an inflation speed of the air bag and / or a deflation speed of the air bag; and measuring blood pressure according to the first measurement speed. The method, device and wearable equipment for measuring blood pressure can dynamically adjust the measurement speed according to the sleep data of a user, and can measure the blood pressure of the user while avoiding interference with the sleep of the user as much as possible, thereby improving the user experience.

Description

Technical Field

[0001] This application relates to the field of electronic devices, and more specifically, to a method, apparatus, and wearable device for measuring blood pressure. Background Technology

[0002] To meet users' needs for constant health monitoring, current electronic devices offer blood pressure measurement capabilities, especially wearable devices with ambulatory blood pressure monitoring (ABPM) functions, which can effectively reflect changes in a user's blood pressure. However, current ABPM measures blood pressure at intervals throughout the 24-hour day. This can disrupt a user's sleep, particularly during sleep.

[0003] Therefore, how to avoid disturbing users' sleep while ensuring dynamic monitoring of users' blood pressure to improve user experience is an urgent problem to be solved. Summary of the Invention

[0004] This application provides a method, apparatus, and wearable device for measuring blood pressure. The method avoids disturbing the user's sleep while ensuring dynamic monitoring of the user's blood pressure, thus improving the user experience.

[0005] In a first aspect, a method for measuring blood pressure is provided, the method being applied to a wearable device including an airbag, the method comprising: acquiring sleep data at a first measurement time; determining a first measurement speed based on the sleep data at the first measurement time, the first measurement speed including an inflation speed of the airbag and / or a deflation speed of the airbag; and measuring blood pressure based on the first measurement speed.

[0006] Based on the above solution, wearable devices can dynamically adjust the measurement speed according to the user's sleep data, measuring the user's blood pressure while minimizing disruption to the user's sleep and improving the user experience.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, determining the first measurement speed based on the sleep data at the first measurement time includes: if it is determined that the user is in a first state based on the sleep data at the first measurement time, then the first measurement speed is determined to be a first rate; if it is determined that the user is in a second state based on the sleep data at the first measurement time, then the first measurement speed is determined to be a second rate, wherein the second state represents a deeper level of sleep than the first state represents a deeper level of sleep, and the second rate is greater than the first rate.

[0008] For example, the first state and the second state can be understood as the user's sleep state, which includes deep sleep, light sleep, REM sleep, wakefulness, etc.

[0009] Based on the above scheme, the deeper the user's sleep, the faster the measurement speed, thus avoiding the disturbance to the user's sleep caused by measuring blood pressure when the user is in a light sleep or REM sleep state.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, the first state is a light sleep state and the second state is a deep sleep state; or the first state is the light sleep state and the second state is the light sleep state; or the first state is the deep sleep state and the second state is the deep sleep state; or the first state is the awake state and the second state is the deep sleep state; or the first state is the deep sleep state and the second state is the awake state.

[0011] Understandably, deep sleep is a more profound state of sleep than light sleep, and the measurement speed of deep sleep is greater than that of light sleep. Within the same sleep state, sleep can be further subdivided into multiple sub-states, each corresponding to a different level of sleep intensity.

[0012] For example, when the first state is light sleep, the measured speed is rate 1, and when the second state is deep sleep, the measured speed is rate 2, where rate 2 is greater than rate 1.

[0013] For example, the first state corresponds to the (25,30] sub-interval of light sleep, and the measured speed is rate 1. The second state corresponds to the (30,35] sub-interval of light sleep, and the measured speed is rate 2. The sleep level corresponding to the (30,35] sub-interval is deeper than that corresponding to the (25,30] sub-interval, and rate 2 is greater than rate 1.

[0014] For example, the first state corresponds to the (50,55) sub-interval of deep sleep, and the measured speed is rate 1. The second state corresponds to the (55,60) sub-interval of light sleep, and the measured speed is rate 2. The sleep level corresponding to the (55,60) sub-interval is deeper than that corresponding to the (50,55) sub-interval, and rate 2 is greater than rate 1.

[0015] For example, in the first state, which is the waking state, the measured speed is rate 1. In the second state, which is the deep sleep state, the measured speed is rate 2. Rate 2 can be greater than, equal to, or less than rate 1.

[0016] For example, the measured speed in the waking state and deep sleep state is rate 1, and the measured speed in the light sleep state is rate 2, where rate 2 is greater than rate 1.

[0017] For example, the speed measured in the waking state is rate 1, the speed measured in the deep sleep state is rate 2, and the speed measured in the light sleep state is rate 3. Among these, rate 3 is less than rate 1 or rate 2, and rate 1 and rate 2 may be the same or different.

[0018] Based on the above approach, wearable devices can dynamically adjust their measurement speed according to the user's sleep data. The deeper the user's sleep, the faster the measurement speed. This allows the wearable device to measure the user's blood pressure while minimizing disruption to their sleep.

[0019] In conjunction with the first aspect, in some implementations of the first aspect, before determining the first measurement speed based on the sleep data at the first measurement time, the method further includes: if the sleep data at the first measurement time meets a first preset condition; then determining the first measurement speed based on the sleep data at the first measurement time; if the sleep data at the first measurement time does not meet a second preset condition; then delaying the measurement of blood pressure.

[0020] It should be noted that the first preset condition and the second preset condition can be the same or different.

[0021] For example, when the first preset condition and the second preset condition are the same, the preset condition can be understood as the sleep level represented by the sleep data being sufficiently deep. That is, if the sleep level represented by the sleep data at the first measurement time is sufficiently deep, the first measurement speed can be determined based on the sleep data at the first measurement time for subsequent measurement of the user's blood pressure. If the sleep level represented by the sleep data at the first measurement time is not deep enough, the measurement of the user's blood pressure is delayed.

[0022] Based on the above scheme, in order to further avoid the impact of blood pressure measurement on the user's rest, the user's blood pressure can be measured at a delayed time when the user is in light sleep.

[0023] In conjunction with the first aspect, in some implementations of the first aspect, determining the first measurement speed based on sleep data at the first measurement time includes: determining the first measurement speed based on sleep data at the first measurement time and user parameters, wherein the user parameters include one or more of the following: physiological parameters, physical parameters, and behavioral parameters.

[0024] Based on the above scheme, user parameters can also be incorporated to determine the measurement speed more accurately.

[0025] In conjunction with the first aspect, in some implementations of the first aspect, the physiological parameter includes one or more of the following: sleep parameter, heart rate parameter, blood pressure parameter, and emotional parameter; the physical parameter includes one or more of the following: insomnia parameter, weight parameter, age parameter, and gender parameter; the behavioral parameter includes one or more of the following: sporadic nap parameter, sleeping posture parameter, sleep onset and sleep-out time parameter, and compression parameter of the wearable device.

[0026] It should be noted that the sleep state influencing factors can be determined using the above user parameters. These factors reflect the user's sleep state, thereby determining the measurement speed.

[0027] In conjunction with the first aspect, in some implementations of the first aspect, determining the first measurement speed based on sleep data and user parameters at the first measurement time includes: determining the first measurement speed based on sleep data at the first measurement time, the physiological parameters, the physical parameters, and the behavioral parameters.

[0028] For example, the physiological parameters, physical parameters, and behavioral parameters mentioned above are user parameters at the first measurement time.

[0029] In conjunction with the first aspect, in some implementations of the first aspect, the first measurement speed satisfies the following formula: the first measurement speed = (first preset measurement speed + first influence factor * (second preset measurement speed - the first preset measurement speed)), wherein the first influence factor satisfies the following formula: the first influence factor = (physiological parameter * first preset reference value) * (physiological parameter * second preset reference value) * (behavioral parameter * third preset reference value).

[0030] For example, the first preset measurement speed is the slowest preset measurement speed, the second preset measurement speed is the preset measurement speed under awake conditions, and the first influencing factor can be understood as the sleep state influencing factor. The first preset reference value, the second preset reference value, and the third preset reference value are preset reference values ​​for physiological parameters, physical parameters, and behavioral parameters, respectively.

[0031] For example, the sleep state influence factor can be calculated from user parameters according to a specific formula, and the measurement speed can be calculated from the sleep state influence factor.

[0032] In conjunction with the first aspect, in some implementations of the first aspect, before acquiring the sleep data at the first measurement moment, the method further includes: determining the first measurement moment according to a preset measurement period and a preset measurement cycle.

[0033] It should be noted that before acquiring sleep data at the first measurement moment, the wearable device can also determine the first measurement moment based on a preset measurement period and a preset measurement cycle.

[0034] For example, users can manually set preset measurement periods and preset measurement cycles on wearable devices.

[0035] For example, the preset measurement time period is 9:00 and 22:00, and the preset measurement cycle is 30 minutes. The measurement time determined according to the preset measurement time period and the preset measurement cycle includes 9:00, 9:30, 10:00, etc.

[0036] In a second aspect, a device for measuring blood pressure is provided, the device including an airbag, the device comprising: a transceiver module for acquiring sleep data at a first measurement time; a processing module for determining a first measurement speed based on the sleep data at the first measurement time, the first measurement speed including the inflation speed of the airbag and / or the deflation speed of the airbag; and further for measuring blood pressure based on the first measurement speed.

[0037] In conjunction with the second aspect, in some implementations of the second aspect, the processing module is specifically configured to determine the first measurement speed as a first rate if it is determined that the user is in a first state based on the sleep data at the first measurement time; and to determine the first measurement speed as a second rate if it is determined that the user is in a second state based on the sleep data at the first measurement time, wherein the second state represents a deeper level of sleep than the first state represents a deeper level of sleep, and the second rate is greater than the first rate.

[0038] In conjunction with the second aspect, in some implementations of the second aspect, the first state is a light sleep state and the second state is a deep sleep state; or the first state is the light sleep state and the second state is the light sleep state; or the first state is the deep sleep state and the second state is the deep sleep state; or the first state is the awake state and the second state is the deep sleep state; or the first state is the deep sleep state and the second state is the awake state.

[0039] In conjunction with the second aspect, in some implementations of the second aspect, the processing module is further configured to: determine the first measurement speed based on the sleep data at the first measurement time if the sleep data at the first measurement time meets the first preset condition; and delay the measurement of blood pressure if the sleep data at the first measurement time does not meet the second preset condition.

[0040] In conjunction with the second aspect, in some implementations of the second aspect, the processing module is specifically used to determine the first measurement speed based on the sleep data and user parameters at the first measurement time, wherein the user parameters include one or more of the following: physiological parameters, physical parameters, and behavioral parameters.

[0041] In conjunction with the second aspect, in some implementations of the second aspect, the physiological parameter includes one or more of the following: sleep parameter, heart rate parameter, blood pressure parameter, and emotional parameter; the physical parameter includes one or more of the following: insomnia parameter, weight parameter, age parameter, and gender parameter; the behavioral parameter includes one or more of the following: sporadic nap parameter, sleeping posture parameter, sleep onset and sleep-out time parameter, and the compression parameter of the device.

[0042] In conjunction with the second aspect, in some implementations of the second aspect, the processing module is specifically used to determine the first measurement speed based on the sleep data at the first measurement time, the physiological parameter, the physical parameter, and the behavioral parameter.

[0043] In conjunction with the second aspect, in some implementations of the second aspect, the first measurement speed satisfies the following formula: the first measurement speed = (first preset measurement speed + first influence factor * (second preset measurement speed - the first preset measurement speed)), wherein the first influence factor satisfies the following formula: the first influence factor = (physiological parameter * first preset reference value) * (physiological parameter * second preset reference value) * (behavioral parameter * third preset reference value).

[0044] In conjunction with the second aspect, in some implementations of the second aspect, the processing module is also used to determine the first measurement moment based on a preset measurement time period and a preset measurement cycle.

[0045] Thirdly, a wearable device is provided, comprising: one or more processors; one or more memories; the one or more memories storing one or more computer programs, the one or more computer programs including instructions that, when executed by the one or more processors, cause the wearable device to perform the methods described in the first aspect and any possible implementation thereof.

[0046] Fourthly, a blood pressure measuring device is provided, comprising: a processor coupled to a memory for storing a computer program, the processor for running the computer program such that the blood pressure measuring device performs the methods described in the first aspect and any possible implementation thereof.

[0047] Fifthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a computer, causes the computer to implement the methods described in the first aspect and any possible implementation thereof.

[0048] In a sixth aspect, a computer program product containing instructions is provided, which, when run on a computer, causes the computer to perform the methods described in the first aspect and any possible implementation thereof.

[0049] In a seventh aspect, a chip is provided, the chip including a processor and a data interface, the processor reading instructions stored in a memory through the data interface to execute the methods described in the first aspect and any possible implementation thereof. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the structure of a wearable device.

[0051] Figure 2 This is a software structure block diagram of a wearable device provided in an embodiment of this application.

[0052] Figure 3 This is a schematic diagram of a system architecture provided in an embodiment of this application.

[0053] Figure 4 This is a schematic flowchart of a method for measuring blood pressure provided in an embodiment of this application.

[0054] Figure 5 This is a schematic diagram illustrating the setting of measurement parameters according to an embodiment of this application.

[0055] Figure 6 This is a schematic diagram of an electronic device display interface provided in an embodiment of this application.

[0056] Figure 7 This is a schematic flowchart of a method for measuring blood pressure provided in an embodiment of this application.

[0057] Figure 8 This is a schematic flowchart of a method for measuring blood pressure provided in an embodiment of this application.

[0058] Figure 9 This is a schematic flowchart of a method for measuring blood pressure provided in an embodiment of this application.

[0059] Figure 10 This is a schematic flowchart of a method for measuring blood pressure provided in an embodiment of this application.

[0060] Figure 11 This is a structural example diagram of a blood pressure measuring device provided in an embodiment of this application.

[0061] Figure 12 This is a block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0062] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0063] The terminology used in the following embodiments is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, “at least one” and “one or more” refer to one, two, or more than two. The term “and / or” is used to describe the relationship between related objects, indicating that three relationships may exist; for example, A and / or B can indicate: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship.

[0064] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0065] The electronic device to which the blood pressure measurement method provided in this application is applicable can be a wearable device, or a wrist-worn wearable device. For simplicity, it will be referred to as a wearable device below. For example, a wearable device can be a smartwatch, a blood pressure bracelet, etc. This application does not specifically limit the type of wearable device.

[0066] Figure 1 This is a schematic diagram of the structure of a wearable device 100 provided in an embodiment of this application. In some embodiments, the wearable device 100 may be a smartwatch or bracelet that can be worn around a user's wrist.

[0067] like Figure 1 As shown, the wearable device 100 may include a main body 110, a wristband 120, and a blood pressure detection component 130. The wristband 120 can surround and conform to a user's body part, such as the wrist, upper arm, ankle, or other body part, to ensure the main body 110 is worn securely on the area to be monitored. The wearable device 100 can perform blood pressure measurement via the blood pressure detection component 130.

[0068] The blood pressure monitoring component 130 may include an airbag 131, an air pump 132, an air valve (not shown in the figure), and a pressure sensor (not shown in the figure). The airbag 131 may be disposed on the inner surface of the wristband 120. The air pump 132 may be disposed inside the main body 110 and communicates with the airbag 131 via an air tube 133 for inflating or deflating the airbag 131. The pressure sensor and air valve may also be disposed inside the main body 110 and connected to the airbag 131 for detecting changes in the air pressure of the airbag 131.

[0069] It is understandable that the inner surface of the wristband 120 can be the side of the wristband 20 that comes into contact with the user's body.

[0070] For example, when a user measures blood pressure using a wearable device 100 worn on their wrist, the wearable device 100 can control an air pump 132 to inflate. The air pump 132 inflates an air bladder 131 through a tubing 133, causing the air bladder 131 to expand and compress the radial artery at the wrist. In this situation, the air pressure inside the air bladder 131 can generate pressure fluctuations. The wearable device 100 can acquire the pressure wave signal inside the air bladder 131 through a pressure sensor and calculate the user's diastolic and systolic blood pressure based on this pressure wave signal to achieve the blood pressure measurement function.

[0071] After the blood pressure measurement process is completed, the wearable device 100 can control the air pump 132 to stop the inflation action, and the gas in the air bag 131 can be discharged through the air pump 132 and the air valve, so that the blood pressure can be measured again.

[0072] In some embodiments, the wearable device 100 may also include a photoplethysmography (PPG) sensor for acquiring the user's heart rate.

[0073] In some embodiments, the wearable device 100 may further include a processor, an external memory interface, internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, an antenna, a mobile communication module 150, a wireless communication module, an audio module, a speaker, a receiver, a microphone, a headphone jack, a sensor module, buttons, a motor, an indicator, a camera, a display screen, and a subscriber identification module (SIM) card interface, etc. The sensor module may include the barometric pressure sensor mentioned above, and may also include pressure sensors, gyroscope sensors, magnetic sensors, accelerometers, proximity sensors, proximity sensors, fingerprint sensors, temperature sensors, touch sensors, ambient light sensors, bone conduction sensors, etc.

[0074] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the wearable device 100. In other embodiments of this application, the wearable device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0075] A processor may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0076] The controller can serve as the neural center and command center of the wearable device 100. The controller can generate operation control signals based on instruction opcodes and timing signals to control the fetching and execution of instructions.

[0077] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces processor waiting time, and thus improves system efficiency.

[0078] In some embodiments, the processor may include one or more interfaces. Interfaces may include inter-integrated circuit (I2C) interfaces, inter-integrated circuit sound (I2S) interfaces, pulse code modulation (PCM) interfaces, universal asynchronous receiver / transmitter (UART) interfaces, mobile industry processor interfaces (MIPI), general-purpose input / output (GPIO) interfaces, subscriber identity module (SIM) interfaces, and / or universal serial bus (USB) interfaces, etc.

[0079] The wireless communication function of the wearable device 100 can be achieved through an antenna, a mobile communication module, a wireless communication module, a modem processor, and a baseband processor.

[0080] The mobile communication module can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to wearable devices 100. The mobile communication module may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc.

[0081] A modem processor may include a modulator and a demodulator. The modulator modulates a low-frequency baseband signal to be transmitted into a mid-to-high frequency signal. The demodulator demodulates a received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to a baseband processor for processing. After processing by the baseband processor, the low-frequency baseband signal is transmitted to an application processor. In some embodiments, the modem processor may be a separate device. In other embodiments, the modem processor may be independent of the processor and may be integrated within the same device as the mobile communication module or other functional modules.

[0082] The wireless communication module can provide solutions for wireless communication applications on wearable devices 100, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0083] In some embodiments, the antenna of the wearable device 100 is coupled to the mobile communication module and the wireless communication module, enabling the wearable device 100 to communicate with networks and other devices via wireless communication technologies. The wireless communication technologies may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-CDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies. The GNSS may include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou Navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS), and / or Satellite Based Augmentation Systems (SBAS).

[0084] Wearable device 100 implements display functionality through a GPU, display screen, and application processor. The GPU is a microprocessor for image processing, connected to the display screen and application processor. The GPU performs mathematical and geometric calculations for graphics rendering. The processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0085] The display screen is used to display images, videos, etc. The display screen 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 (AMOLED), a flexible light-emitting diode (FLED), a MiniLED, a MicroLED, a Micro-OLED, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the wearable device 100 may include one or N displays, where N is a positive integer greater than 1.

[0086] Wearable device 100 can achieve shooting functions through ISP, camera, video codec, GPU, display and application processor.

[0087] The external storage interface can be used to connect external storage cards, such as Micro SD cards, to expand the storage capacity of the wearable device 100. The external storage card communicates with the processor through the external storage interface to perform data storage functions. For example, music, video, and other files can be saved on the external storage card.

[0088] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of the wearable device 100 by running the instructions stored in the internal memory. The internal memory may include a program storage area and a data storage area.

[0089] Wearable device 100 can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors, such as music playback and recording.

[0090] Figure 2This is a software structure block diagram of an electronic device according to an embodiment of this application. The layered architecture divides the software into several layers, each with a clear role and function. Layers communicate with each other through software interfaces. In some embodiments, the system is divided into four layers, from top to bottom: the application layer, the application framework layer, the system library, and the kernel layer. The application layer may include a series of application packages. The system in this embodiment includes, but is not limited to, […]. wait.

[0091] It should be noted that wearable device 100 can also be used. Figure 2 The software structure diagram is shown.

[0092] like Figure 2 As shown, the application layer can include camera, settings, third-party applications, etc. Third-party applications can include gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, SMS, etc.

[0093] The application framework layer provides application programming interfaces (APIs) and programming frameworks for applications in the application layer. The application framework layer may include some predefined functions.

[0094] like Figure 2 As shown, the application framework layer may include a window manager, content provider, view system, phone manager, resource manager, notification manager, etc.

[0095] The window manager is used to manage windowed applications. It can obtain the screen size, determine if a status bar is present, lock the screen, and capture screenshots. The content provider stores and retrieves data, making this data accessible to applications. This data may include videos, images, audio, made and received phone calls, browsing history and bookmarks, phone books, etc.

[0096] The view system includes visual controls, such as controls for displaying text, controls for displaying images, and such as the indicator information for displaying the virtual shutter button in the embodiments of this application. The view system can be used to build applications. The display interface can consist of one or more views. For example, a display interface including a text message notification icon can include a view for displaying text and a view for displaying images.

[0097] The phone manager is used to provide communication functions for the wearable device 100. For example, it manages call status (including connection, hang-up, etc.).

[0098] The file explorer provides applications with various resources, such as localized strings, icons, images, layout files, video files, and more.

[0099] The notification manager allows applications to display notifications in the status bar. These notifications can be used to deliver informational messages and can disappear automatically after a short pause, requiring no user interaction. For example, the notification manager can be used to notify users of download completion or message alerts. The notification manager can also display notifications as icons or scrolling text in the top status bar, such as notifications from background applications, or as dialog boxes on the screen. Examples include displaying text messages in the status bar, emitting sounds, vibrating wearable devices, and flashing indicator lights.

[0100] The application layer and application framework layer run in a virtual machine. The virtual machine executes the Java files of the application layer and application framework layer as binary files. The virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.

[0101] System libraries can include multiple functional modules. For example: surface manager, media libraries, 3D graphics processing libraries (e.g., OpenGL ES), 2D graphics engines (e.g., SGL), etc.

[0102] The Surface Manager is used to manage the display subsystem and provides the blending of 2D and 3D layers for multiple applications.

[0103] The media library supports playback and recording of various common audio and video formats, as well as still image files. It supports multiple audio and video encoding formats, such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG.

[0104] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0105] A 2D graphics engine is a graphics engine for 2D drawing.

[0106] In addition, the system library may also include a status monitoring service module, such as a physical status recognition module for analyzing and recognizing user gestures; and a sensor service module for monitoring sensor data uploaded by various sensors at the hardware layer to determine the physical status of the wearable device 100.

[0107] The kernel layer is the layer between hardware and software. The kernel layer contains at least the display driver, camera driver, audio driver, and sensor driver.

[0108] The hardware layer can include various types of sensors, such as those described above.

[0109] To meet users' needs for constant health monitoring, wearable devices now support blood pressure measurement, especially ambulatory blood pressure monitoring (ABPM). ABPM is a method of measuring blood pressure at preset intervals over 24 hours, during which the user can continue their daily activities. When blood pressure circadian rhythms are abnormal, ABPM can be used to comprehensively understand one's blood pressure health level.

[0110] As can be seen, ambulatory blood pressure monitoring can measure a user's blood pressure during sleep. However, when measuring blood pressure, the wearable device pressurizes the airbag, which may interfere with the user's sleep. Therefore, this application provides a method, apparatus, and wearable device for measuring blood pressure. These devices can adjust the measurement speed (including the inflation and / or deflation speed of the airbag) according to the user's sleep state (e.g., sleep intensity), and can also determine an appropriate time for blood pressure measurement based on the user's sleep state. This ensures dynamic monitoring of the user's blood pressure while avoiding interference with sleep, thereby improving the user experience.

[0111] like Figure 3 The diagram illustrates a system architecture provided in an embodiment of this application. The wearable device described above can be used as follows: Figure 3 The diagram shows the dynamic blood pressure measurement settings module, sleep data detection module, measurement trigger module, and blood pressure measurement module.

[0112] For example, the ambulatory blood pressure monitoring (ABPM) settings module provides users with the ability to set monitoring plans, specifically configuring measurement parameters such as measurement time period, measurement cycle, and measurement method. The measurement time period represents the start time for measuring the user's blood pressure; the measurement cycle represents the time interval between each measurement; and the measurement method includes automatic measurement (a reminder is issued during the measurement time period, and blood pressure measurement automatically begins after a certain period) and manual measurement (a reminder is issued during the measurement time period, and blood pressure measurement begins after manual confirmation by the user). The sleep data detection module detects the user's sleep data, reflecting their sleep state, such as deep sleep, light sleep, REM sleep, and wakefulness, and can send this data to the measurement trigger module. The measurement trigger module can obtain the user's sleep data from the sleep data detection module and adjust the measurement speed and / or measurement time period based on this data. The blood pressure measurement module measures the user's blood pressure according to the measurement parameters set by the user and the measurement speed or measurement time period adjusted by the measurement trigger module.

[0113] It should be noted that, Figure 3 The division of the various functional modules shown is only an exemplary division in terms of logical function. In actual implementation, there may be other division schemes, and this application embodiment does not limit this.

[0114] The method for measuring blood pressure provided in the embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0115] like Figure 4 The diagram illustrates a schematic flowchart of a method for measuring blood pressure according to an embodiment of this application. This method can be applied to, for example... Figure 3 In the system architecture shown, the method will be described in detail below.

[0116] S401, Dynamic Blood Pressure Monitoring Setting Module: Set measurement parameters.

[0117] It should be noted that the measurement parameters include the measurement period, measurement cycle, and measurement method. These parameters can be manually set by the user.

[0118] like Figure 5 As shown, taking a smartwatch as an example of a wearable device, a schematic diagram of setting measurement parameters is presented.

[0119] like Figure 5 As shown in (a), the measurement plan details interface is displayed. This interface displays measurement plan details, including daytime measurement option 501 and nighttime measurement option 502. The daytime measurement option displays daytime measurement parameters, including the measurement period from 07:00 to 22:00, the measurement cycle every 30 minutes, and the measurement method as manual (e.g., vibration alert, user manually selects measurement). The nighttime measurement option displays nighttime measurement parameters, including the measurement period from 23:00 to 07:00, the measurement cycle every 30 minutes, and the measurement method as automatic (e.g., no alert, automatic inflation measurement). The daytime and nighttime measurement parameters displayed on this interface are temporary settings by the user. The interface also displays a confirm start control 503 and a change settings control 504. The user can use the confirm start control to confirm that the smartwatch will use the set measurement parameters to measure blood pressure, and the user can also use the change settings control to adjust the measurement parameters. In response to the user's operation on the change settings control 504, the wearable device can display as follows: Figure 5 The graphical user interface (GUI) shown in (b) is shown in the image.

[0120] like Figure 5As shown in (b), in response to the user's operation on the setting control 504, a setting interface for the measurement parameters is displayed. Taking nighttime measurement parameters as an example, this interface displays options for nighttime measurement period, nighttime measurement cycle, and nighttime measurement method. The user can adjust these measurement parameters in the setting interface. This interface also displays a start control 505 and a stop control. The user can use the start control to confirm that the smartwatch uses the set measurement parameters to measure blood pressure, and the user can also use the stop control to cancel the use of the set measurement parameters to measure blood pressure. In response to the user's operation on the start control 505, the wearable device can display as follows: Figure 5 The GUI shown in (c) is shown in the image.

[0121] like Figure 5 As shown in (c), in response to the user's operation of the start control 505, the start measurement interface is displayed. This interface can display prompts, providing information that the measurement period and measurement cycle cannot be modified after the measurement plan is started. The interface also displays a continue start control and a change settings control; the user can start dynamic blood pressure monitoring through the continue start control and adjust measurement parameters through the change settings control.

[0122] It should be noted that, in Figure 5 The method for setting measurement parameters, as shown in the example, is used to illustrate how users are awake during the day and asleep at night. Figure 5 In the measurement plan details interface shown in (a), the daytime measurement option displays the measurement method as manual measurement (e.g., vibration alert, user manually selects measurement). Of course, in practical applications, users may sleep during the day, and manual measurement might disturb them. The blood pressure measurement method provided in this application is not limited to any particular time; it can be used during the day or at night.

[0123] S402, the dynamic blood pressure monitoring setting module sends measurement parameters, and correspondingly, the measurement trigger module receives the measurement parameters.

[0124] For example, after receiving measurement parameters including the measurement period, measurement cycle, and measurement method, the measurement trigger module can trigger the measurement of the user's blood pressure at an appropriate time.

[0125] S403, the measurement trigger module has determined that blood pressure needs to be measured.

[0126] For example, the measurement trigger module can determine that there is a need to measure blood pressure by identifying the current time as the measurement time. The measurement time can be determined by the measurement period and measurement cycle set in step S401 above.

[0127] For example, such as Figure 5The measurement parameter setting interface shown in (b) specifies the nighttime measurement period as 23:00-7:00, with a nighttime measurement cycle of 30 minutes. Measurement times include 23:00, 23:30, 24:00, etc. If the current time is 5:00, it indicates that the current time is a measurement time, and the measurement trigger module determines that blood pressure needs to be measured.

[0128] It is understood that in the blood pressure measurement method provided in this application embodiment, the wearable device does not absolutely start measuring the user's blood pressure at the exact moment of measurement. This is because a suitable measurement speed can be determined based on the user's sleep data before measuring the user's blood pressure.

[0129] S404, the measurement trigger module sends message 1, and correspondingly, the sleep data detection module receives message 1.

[0130] For example, the measurement trigger module can retrieve the user's sleep data via message 1. The sleep data detection module is used to detect the user's sleep data.

[0131] S405, the sleep data detection module acquires sleep data 1.

[0132] It should be noted that the user's sleep data 1 can be used to represent the user's sleep state. The user's sleep state can include deep sleep, light sleep, REM sleep, wakefulness, etc. To provide a better user experience, the user's deep sleep state and light sleep state can be further refined.

[0133] For example, sleep data 1 can be used to indicate whether the user is asleep. If the user is awake, blood pressure can be measured manually at a normal measurement speed without considering the user's rest; if the user is asleep, the blood pressure measurement speed can be adjusted automatically to avoid disturbing the user's sleep.

[0134] For example, sleep data 1 can be used to represent a user's sleep state after falling asleep. For instance, sleep data 1 can be used to determine whether the user is in deep sleep, light sleep, or REM sleep.

[0135] In one implementation, sleep data 1 can be a specific sleep value, with different sleep states corresponding to different sleep value ranges. The user's sleep state is determined based on the sleep value range to which the specific sleep value in sleep data 1 belongs.

[0136] For example, sleep value range 1 (0,25) represents REM sleep, sleep value range 2 (25,50) represents light sleep, and sleep value range 3 (50,75) represents deep sleep.

[0137] In one implementation, the user's sleep state can be further refined.

[0138] For example, by further refining the sleep value interval 3 (50,75], we can obtain sleep value sub-intervals such as (50,55], (55,60], (60,65], etc. The larger the value of the sleep value sub-interval, the deeper the sleep in the deep sleep state; the smaller the value of the sleep value sub-interval, the shallower the sleep in the deep sleep state.

[0139] For example, by further refining the sleep value interval 2 (25,50], we can obtain sleep value sub-intervals such as (25,30], (30,35], (35,40], etc. The larger the value of the sleep value sub-interval, the deeper the sleep in the light sleep state; the smaller the value of the sleep value sub-interval, the shallower the sleep in the light sleep state.

[0140] S406, the sleep data detection module sends sleep data 1, and correspondingly, the measurement trigger module receives sleep data 1.

[0141] S407, the measurement trigger module determines the measurement speed based on sleep data 1.

[0142] It should be noted that the measured speed includes the inflation speed of the wearable device's airbag and / or the deflation speed of the airbag.

[0143] For example, the measurement trigger module determines the user's sleep state based on sleep data 1, and determines the measurement speed based on the user's sleep state.

[0144] For ease of understanding, such as Figure 6 The diagram shows a schematic of an electronic device display interface, which can display the user's sleep status.

[0145] For example, taking a mobile terminal as an example, the sleep data of the mobile terminal and the wearable device are synchronized. The sleep data acquired by the sleep detection module of the wearable device can be synchronized to the associated mobile terminal. The mobile terminal can display, for example... Figure 6 The interface shown displays the analysis results of sleep data, such as sleep state and the duration of different sleep states. Specifically, the duration of deep sleep is 2 hours and 24 minutes, the duration of light sleep is 3 hours and 32 minutes, and the duration of REM sleep is 2 hours and 51 minutes.

[0146] It should be noted that sleep data 1 can be used to determine the user's sleep state, with different sleep states corresponding to different sleep levels; it can also be used to determine the measurement speed. The timing of determining the sleep level does not affect the timing of determining the measurement speed.

[0147] For example, while a user is sleeping at night, the wearable device acquires the user's sleep data 1, determines the user is in deep sleep based on sleep data 1, and determines the measurement speed based on the deep sleep state. When the user finishes sleeping at night, the mobile terminal can display something like... Figure 6 The interface shown displays the changes in the user's sleep level throughout the night. Clearly, in the example above, the sleep level can be determined after the sleep process has ended, while the measurement speed is determined during the sleep process; of course, both the sleep level and measurement speed can also be determined during the sleep process, and this embodiment does not limit this.

[0148] It should be noted that sleep state can represent the user's sleep level, and different sleep levels can correspond to different measurement speeds. Generally speaking, the deeper the sleep, the higher the measurement speed.

[0149] For example, when the user is awake based on sleep data 1, the measurement speed is determined as rate 1; when the user is asleep based on sleep data 1, the measurement speed is determined as rate 2. Here, rate 1 and rate 2 are the same, or rate 1 is greater than rate 2.

[0150] For example, when sleep data 1 indicates the user is in deep sleep, the measurement speed is determined as rate 1; when sleep data 1 indicates the user is in light sleep, the measurement speed is determined as rate 2; and when sleep data 1 indicates the user is in REM sleep, the measurement speed is determined as rate 3. Rate 1 is greater than rate 2, and rate 2 is greater than rate 3. Therefore, to avoid disturbing the user's sleep, the lighter the sleep state, the lower the measurement speed.

[0151] For example, sleep states can be further subdivided into multiple sub-states. For instance, deep sleep can be further subdivided into multiple sub-states, each with varying levels of sleep intensity. The sleep value range corresponding to deep sleep is (50, 75]. Multiple sub-states can correspond to different sleep value ranges, including (50, 55], (55, 60], (60, 65], etc. A larger value in a sleep value range indicates a deeper level of sleep. Based on sleep data 1 corresponding to the sleep value range (50, 55], the measurement speed is determined to be rate 1; based on sleep data 1 corresponding to the sleep value range (60, 65], the measurement speed is determined to be rate 2, where rate 2 is greater than rate 1. It is evident that even within the same deep sleep state, measurement speeds can differ. Similarly, light sleep and REM sleep can also be subdivided into multiple sub-states, which will not be elaborated upon here.

[0152] S408, the measurement trigger module sends message 2, and correspondingly, the blood pressure measurement module receives message 2.

[0153] S409, the blood pressure measurement module measures blood pressure according to message 2.

[0154] For example, message 2 may include the measurement speed determined in step S407 above, and the measurement triggering module instructs the blood pressure measurement module to measure the user's blood pressure at the above measurement speed through message 2.

[0155] Based on the above scheme, the measurement speed can be adjusted according to the user's sleep state. When the user is in a deep sleep, a higher measurement speed can be used to measure the user's blood pressure; when the user is in a light sleep, a lower measurement speed can be used to prevent the blood pressure measurement process from disturbing the user's sleep and improve the user experience.

[0156] like Figure 7 The diagram illustrates a schematic flowchart of a method for measuring blood pressure according to an embodiment of this application. This method can be applied to, for example... Figure 3 In the system architecture shown, the method will be described in detail below.

[0157] S701, Dynamic Blood Pressure Monitoring Setting Module: Sets measurement parameters.

[0158] It should be noted that the measurement parameters include the measurement period, measurement cycle, and measurement method. These parameters can be manually set by the user.

[0159] S702, the dynamic blood pressure monitoring setting module sends measurement parameters, and correspondingly, the measurement trigger module receives the measurement parameters.

[0160] For example, after receiving measurement parameters including the measurement period, measurement cycle, and measurement method, the measurement trigger module can trigger the measurement of the user's blood pressure at an appropriate time.

[0161] S703, the measurement trigger module determines that blood pressure needs to be measured.

[0162] For example, the measurement trigger module can determine that there is a need to measure blood pressure by identifying the current time as the measurement time. The measurement time can be determined by the measurement period and the measurement cycle.

[0163] It is understood that determining the need to measure blood pressure based on the current time is merely an illustrative example. The need to measure blood pressure can also be determined by the user manually activating the wearable device's blood pressure measurement function. This application does not limit the specific method by which the measurement trigger module determines the need to measure blood pressure.

[0164] S704, the measurement trigger module sends message 1, and correspondingly, the sleep data detection module receives message 1.

[0165] For example, the measurement trigger module can call the user's sleep data through message 1, and the sleep data detection module is used to detect the user's sleep data.

[0166] S705, the sleep data detection module acquires sleep data 2.

[0167] It should be noted that the user's sleep data2 can be used to represent the user's sleep state. The user's sleep state can include deep sleep, light sleep, REM sleep, wakefulness, etc.

[0168] For example, sleep data 2 can be used to indicate whether the user is asleep. If the user is awake, blood pressure can be measured manually at a normal measurement speed without disturbing the user's rest; if the user is asleep, the blood pressure measurement speed can be adjusted automatically to avoid disturbing the user's sleep.

[0169] For example, sleep data 2 can be used to represent the user's sleep state after falling asleep. For instance, sleep data 2 can be used to determine whether the user is in deep sleep, light sleep, or REM sleep.

[0170] In addition, sleep data 2 may also include user parameters, which are related to the sleep state influencing factor F.

[0171] This can be understood as sleep data 2 including user parameters, or sleep data 2 including sleep data 1 as described in step S405 and user parameters. User parameters may include physiological parameters, physical parameters, behavioral parameters, etc. Combining user parameters can more accurately determine the user's sleep state.

[0172] For example, physiological parameters may include sleep parameters, heart rate parameters, blood pressure parameters, mood parameters, etc.; physical parameters may include insomnia parameters, weight parameters, age parameters, gender parameters, etc. Behavioral parameters include nap parameters, sleeping posture parameters, sleep onset and wake-up time parameters, and wearable device compression parameters (e.g., used to reflect the compression between the smartwatch and the user's wrist).

[0173] It should be noted that the above user parameters affect the sleep state influence factor F, which is used to reflect the user's sleep state.

[0174] For example, 0 < F < 1, where 0 represents the user being awake and 1 represents the user being in deep sleep. The deeper the user's sleep, the greater the sleep state influence factor F. The relationship between the sleep state influence factor F and user parameters can be expressed by the following formula (1):

[0175]

[0176] Where f represents the preset reference value, The parameter F is used to represent physiological parameters, μ to represent physical parameters, and k to represent behavioral parameters. It is evident that the sleep state influencing factor F is correlated with the user's physiological, physical, and behavioral parameters.

[0177] Specifically, expanding the above formula (1) yields the following formula (2).

[0178]

[0179] Among them, physiological parameters Including sleep parameters Heart rate parameters Blood pressure parameters Emotional parameters Physical constitution parameter μ includes insomnia parameter μ obstacle Weight parameter μ weight Age parameter μ age Gender parameter μ sex The behavioral parameter k includes the parameter k for sporadic naps. naps Sleeping posture parameter k position Sleep onset and wake-up time parameter k time The compression parameter k of wearable devices squeezed .

[0180] As can be seen, the sleep state influencing factor can be calculated according to the above formula (2).

[0181] S706, the sleep data detection module sends sleep data 2, and correspondingly, the measurement trigger module receives sleep data 2.

[0182] S707, the measurement trigger module determines the measurement speed based on sleep data 2.

[0183] It should be noted that the measured speed may include the inflation speed of the wearable device's airbag and / or the deflation speed of the airbag.

[0184] For example, the measurement trigger module determines the sleep state influence factor F based on the user parameters in sleep data 2, and calculates the measurement speed based on the sleep state influence factor F. The relationship between the sleep state influence factor F and the measurement speed can be expressed by the following formula (3):

[0185] V = V min +F(V0-V min ) Formula (3)

[0186] Where F is the sleep state influencing factor, V min The slowest preset measurement speed is set, and V0 is the preset measurement speed under conscious conditions.

[0187] For example, when the user is in a light sleep state, V0 is set to 4.5 mmgh / s, and the measured velocity V is set to 4 mmgh / s.

[0188] It can be understood that in the method for measuring blood pressure provided in the embodiments of this application, the measurement speed can be dynamically adjusted according to the sleep state influencing factors.

[0189] S708, the measurement trigger module sends message 2, and correspondingly, the blood pressure measurement module receives message 2.

[0190] S709, the blood pressure measurement module measures blood pressure according to message 2.

[0191] For example, message 2 may include the measurement speed determined in step S707 above, and the measurement triggering module instructs the blood pressure measurement module to measure the user's blood pressure at the above measurement speed through message 2.

[0192] Based on the above scheme, the user's sleep state or sleep level can be determined according to various user parameters, thereby more accurately determining the measurement speed. When the user is in a deep sleep, a higher measurement speed can be used to measure the user's blood pressure; when the user is in a light sleep, a lower measurement speed can be used to prevent the blood pressure measurement process from disturbing the user's sleep and improve the user experience.

[0193] It should be noted that the above mainly introduced how the measurement speed can be determined based on the user's sleep level. The deeper the sleep, the faster the measurement speed. However, a faster measurement speed may pose a risk of disturbing the user's sleep. Therefore, to further avoid the blood pressure measurement process affecting the user's rest, the blood pressure measurement can be paused if the user's sleep is too light.

[0194] like Figure 8 The diagram illustrates a schematic flowchart of a blood pressure measurement method provided in an embodiment of this application. This method can be applied to, for example... Figure 3 In the system architecture shown, the method will be described in detail below.

[0195] S801, Dynamic Blood Pressure Monitoring Setting Module.

[0196] It should be noted that the measurement parameters include the measurement period, measurement cycle, and measurement method. These parameters can be manually set by the user.

[0197] S802, the dynamic blood pressure monitoring setting module sends measurement parameters, and correspondingly, the measurement trigger module receives the measurement parameters.

[0198] For example, after receiving measurement parameters including the measurement period, measurement cycle, and measurement method, the measurement trigger module can trigger the measurement of the user's blood pressure at an appropriate time.

[0199] S803, the measurement trigger module determines that blood pressure needs to be measured.

[0200] For example, the measurement trigger module can determine that there is a need to measure blood pressure by identifying the current time as the measurement time. The measurement time can be determined by the measurement period and measurement cycle.

[0201] It is understood that determining the need to measure blood pressure based on the current time is merely an illustrative example. The need to measure blood pressure can also be determined by the user manually activating the wearable device's blood pressure measurement function. This application does not limit the specific method by which the measurement trigger module determines the need to measure blood pressure.

[0202] S804, the measurement trigger module sends message 1, and correspondingly, the sleep data detection module receives message 1.

[0203] For example, the measurement trigger module can call the user's sleep data through message 1, and the sleep data detection module is used to detect the user's sleep data.

[0204] S805, the sleep data detection module acquires sleep data 1.

[0205] It should be noted that the user's sleep data 1 can be used to represent the user's sleep state. The user's sleep state can include deep sleep, light sleep, REM sleep, wakefulness, etc.

[0206] For example, if the user determines that blood pressure needs to be measured in step S803 based on the current time as the measurement time, then in step S805, the sleep data 1 obtained by the sleep data detection module is the user's sleep data at the current time.

[0207] It is understandable that the moment when sleep data 1 is acquired in step S805 is not necessarily the exact moment of measurement in step S803.

[0208] For example, sleep data 1 can be used to indicate whether the user is asleep at the current moment. If the user is awake at the current moment, blood pressure can be measured manually at a normal measurement speed without considering the user's rest; if the user is asleep at the current moment, blood pressure measurement can be automatically adjusted to avoid disturbing the user's sleep.

[0209] For example, sleep data 1 can be used to represent the user's sleep state at the current moment after falling asleep. For instance, sleep data 1 can be used to determine whether the user is in deep sleep, light sleep, or REM sleep at the current moment.

[0210] For example, the current user's sleep state can be further refined. For instance, it can be further divided into deep sleep and light sleep states; even within the same deep or light sleep state, the intensity of sleep will differ. Sleep data 1 can be used to represent the refined sleep state of the user at the current moment.

[0211] S806, the sleep data detection module sends sleep data 1, and correspondingly, the measurement trigger module receives sleep data 1.

[0212] S807, the measurement trigger module determines the sleep level based on sleep data 1.

[0213] For example, the measurement trigger module determines the user's sleep state at the current moment based on sleep data 1. If the user is in deep sleep at the current moment, it means that the user's sleep level is deep enough, and the following step S808 can be executed; if the user is in light sleep or REM sleep at the current moment, it means that the user's sleep level is not deep enough, and the following step S811 can be executed.

[0214] For example, the measurement trigger module determines the user's sleep state at the current moment after refinement based on sleep data 1. If the sleep level is deep enough within the same sleep state, the following step S808 can be executed; if the sleep level is not deep enough, the following step S811 can be executed.

[0215] Scenario 1:

[0216] S808: When the sleep level meets the preset conditions, the measurement speed is determined based on sleep data 1.

[0217] It should be noted that meeting the preset conditions for sleep level can be understood as sleep being deep enough. For example, deep sleep being deep enough.

[0218] For example, the measurement trigger module determines the user's sleep state based on sleep data 1, and determines the measurement speed based on the user's sleep state.

[0219] It should be noted that sleep state can represent the user's sleep level, and different sleep levels can correspond to different measurement speeds. Generally speaking, the deeper the sleep, the higher the measurement speed.

[0220] For example, when the sleep level meets the preset conditions, it indicates that the user is in deep sleep, and the measurement speed is determined to be rate 1.

[0221] For example, when the sleep level meets the preset conditions, it means that the user is in a deep sleep state. The deep sleep state is further subdivided into multiple sub-states, and the sleep level of each sub-state is different. The measurement speed of each sub-state is also different.

[0222] S809, the measurement trigger module sends message 2, and correspondingly, the blood pressure measurement module receives message 2.

[0223] S810, the blood pressure measurement module measures blood pressure according to message 2.

[0224] For example, message 2 may include the measurement speed determined in step S808, and the measurement triggering module instructs the blood pressure measurement module to measure the user's blood pressure at the aforementioned measurement speed via message 2.

[0225] Scenario 2:

[0226] S811, when the sleep level does not meet the preset conditions, determine the extension time t.

[0227] It should be noted that if the sleep level does not meet the preset conditions, it indicates that the sleep is not deep enough. For example, the sleep level in the light sleep state or REM sleep state is not deep enough. When the user's sleep level is not deep enough, blood pressure measurement can be delayed.

[0228] For example, the extension time t is related to the measurement time.

[0229] For example, the measurement times are 9:00, 9:30, and 10:00. If the current time is 9:00, and this is the measurement time, the measurement trigger module acquires the sleep data 1 for the current time. If the sleep level does not meet the preset conditions based on the sleep data 1, the extension time is set to 30 minutes. After the 30-minute extension, which is 9:30, the measurement trigger module can continue to acquire the sleep data 1 for 9:30.

[0230] S812, after an extended time t, the measurement trigger module sends message 1 again, and the sleep data detection module receives message 1 accordingly.

[0231] For example, after an extended time t, the measurement trigger module can retrieve the user's sleep data again via message 1. The sleep data retrieved this time is the sleep data at the measurement time after the extended time t.

[0232] For example, after extending the time by 30 minutes, the measurement trigger module determines that blood pressure needs to be measured and sends message 1 to the sleep data detection module. The sleep data detection module obtains the current sleep data 1 based on message 1. The measurement trigger module determines the current sleep level based on the current sleep data 1 and judges whether the current sleep level meets the preset conditions. If it does, the measurement trigger module determines the measurement speed based on the current sleep data 1. If it does not meet the conditions, the measurement trigger module determines the extension time again, and after the extension time, sends message 1 to the sleep data detection module.

[0233] Based on the above scheme, when the user is in a deep enough sleep, the measurement speed is adjusted according to the user's sleep data; when the user is not in a deep enough sleep, the blood pressure measurement can be paused to prevent disturbing the user's sleep.

[0234] like Figure 9 The diagram illustrates a schematic flowchart of a method for measuring blood pressure according to an embodiment of this application. This method is applicable to, for example... Figure 3 In the system architecture shown, the method will be described in detail below.

[0235] S901, Dynamic Blood Pressure Monitoring Setting Module: Sets measurement parameters.

[0236] It should be noted that the measurement parameters include the measurement period, measurement cycle, and measurement method. These parameters can be manually set by the user.

[0237] S902, the dynamic blood pressure monitoring setting module sends measurement parameters, and correspondingly, the measurement trigger module receives the measurement parameters.

[0238] For example, after receiving measurement parameters including the measurement period, measurement cycle, and measurement method, the measurement trigger module can trigger the measurement of the user's blood pressure at an appropriate time.

[0239] S903, the measurement trigger module determines the blood pressure measurement.

[0240] For example, the measurement trigger module can determine that there is a need to measure blood pressure by identifying the current time as the measurement time. The measurement time can be determined by the measurement period and measurement cycle.

[0241] It is understood that determining the need to measure blood pressure based on the current time is merely an illustrative example. The need to measure blood pressure can also be determined by the user manually activating the wearable device's blood pressure measurement function. This application does not limit the specific method by which the measurement trigger module determines the need to measure blood pressure.

[0242] S904, the measurement trigger module sends message 1, and correspondingly, the sleep data detection module receives message 1.

[0243] For example, the measurement trigger module can call the user's sleep data through message 1, and the sleep data detection module is used to detect the user's sleep data.

[0244] S905, the sleep data detection module acquires sleep data 2.

[0245] It should be noted that the user's sleep data2 can be used to represent the user's sleep state. The user's sleep state can include deep sleep, light sleep, REM sleep, wakefulness, etc.

[0246] For example, if the user determines that blood pressure needs to be measured in step S903 based on the current time as the measurement time, then in step S905, the sleep data 2 obtained by the sleep data detection module is the user's sleep data at the current time.

[0247] It is understandable that the time when sleep data 2 is acquired in step S905 is not necessarily the exact measurement time summarized in step S903.

[0248] For example, sleep data 2 can be used to indicate whether the user is asleep at the current moment. If the user is awake at the current moment, blood pressure can be measured manually at a normal measurement speed without considering the user's rest; if the user is asleep at the current moment, the blood pressure measurement speed can be adjusted automatically to avoid disturbing the user's sleep.

[0249] For example, sleep data 2 can be used to represent the user's sleep state at the current moment after falling asleep. For instance, sleep data 2 can be used to determine whether the user is in deep sleep, light sleep, or REM sleep at the current moment.

[0250] In addition, sleep data 2 may also include user parameters at the current moment, which are related to the sleep state influencing factor F.

[0251] This can be understood as sleep data 2 including user parameters at the current moment, or sleep data 2 including sleep data 1 at the current moment from step S805 and user parameters at the current moment. The user parameters at the current moment can include physiological parameters, physical parameters, behavioral parameters, etc. Combining the user parameters at the current moment allows for a more accurate determination of the user's sleep state.

[0252] S906, the sleep data detection module sends sleep data 2, and correspondingly, the measurement trigger module receives sleep data 2.

[0253] S907, the measurement trigger module determines the sleep state influencing factor F based on sleep data 2.

[0254] It should be noted that the user parameters in sleep data 2 can be used to determine the sleep state influence factor F, which reflects the user's sleep state.

[0255] For example, the measurement trigger module can determine the sleep state influence factor F at the current moment according to formulas (1) and (2) in step S705 above. If the sleep state influence factor F at the current moment is greater than the preset threshold, the following step S908 can be executed; if the sleep state influence factor F at the current moment is less than or equal to the preset threshold, the following step S911 can be executed.

[0256] Scenario 1:

[0257] S908, when the sleep state influence factor F is greater than the preset threshold F0, the measurement speed is determined based on the sleep data 2.

[0258] For example, 0 < F < 1, where 0 represents that the user is awake and 1 represents that the user is in deep sleep. The deeper the user's sleep, the greater the sleep state influence factor F.

[0259] It should be noted that when the sleep state influence factor F is greater than the preset threshold, it indicates that the user's sleep is deep enough.

[0260] For example, the measurement trigger module determines that the user's sleep state influence factor F is greater than a preset value at the current moment based on the user parameters in sleep data 2, and determines the measurement speed according to formula (3) in step S707 above. The larger the sleep state influence factor F, the faster the measurement speed.

[0261] S909, the measurement trigger module sends message 2, and correspondingly, the blood pressure measurement module receives message 2.

[0262] S910, the blood pressure measurement module measures blood pressure according to message 2.

[0263] For example, message 2 may include the measurement speed determined in step S908, and the measurement triggering module instructs the blood pressure measurement module to measure the user's blood pressure at the aforementioned measurement speed via message 2.

[0264] Scenario 2:

[0265] S911, when the sleep state influence factor F is less than or equal to the preset threshold F0, determine the extension time t.

[0266] It should be noted that if the sleep state influence factor F is less than or equal to the preset threshold, it indicates that the sleep level is not deep enough. When a user's sleep level is not deep enough, blood pressure measurement can be delayed.

[0267] For example, the extension time t is related to the measurement time.

[0268] S912, after an extended time t, the measurement trigger module sends message 1 again, and the sleep data detection module receives message 1 accordingly.

[0269] For example, after an extended time t, the measurement trigger module can retrieve the user's sleep data again via message 1. The sleep data retrieved this time is the sleep data at the measurement time after the extended time t.

[0270] Based on the above scheme, when the user is in a deep enough sleep, the measurement speed is adjusted according to the user's sleep data; when the user is not in a deep enough sleep, the blood pressure measurement can be paused to prevent disturbing the user's sleep.

[0271] like Figure 10 The diagram shows a schematic flowchart of a method for measuring blood pressure according to an embodiment of this application. This method can be executed by a wearable device, which may include an airbag. The method will be described in detail below.

[0272] S1001, acquire sleep data at the first measurement time.

[0273] For example, sleep data can be sleep data 1 as described in step S405 above.

[0274] Understandably, sleep data at the first measurement moment can be used to represent the user's sleep state at the first measurement moment, which can include deep sleep, light sleep, REM sleep, wakefulness, etc.

[0275] It should be noted that before acquiring sleep data at the first measurement moment, the wearable device can also determine the first measurement moment based on a preset measurement period and a preset measurement cycle.

[0276] For example, users can manually set preset measurement periods and preset measurement cycles on wearable devices.

[0277] For example, the preset measurement time period is 9:00 and 22:00, and the preset measurement cycle is 30 minutes. The measurement time determined according to the preset measurement time period and the preset measurement cycle includes 9:00, 9:30, 10:00, etc.

[0278] S1002, determine the first measurement speed based on the sleep data at the first measurement time.

[0279] It should be noted that the first measured speed includes the airbag inflation speed and / or airbag deflation speed.

[0280] For example, if it is determined that the user is in a first state based on the sleep data at the first measurement time, then the first measurement speed is determined to be the first rate; if it is determined that the user is in a second state based on the sleep data at the first time, then the first measurement speed is determined to be the second rate, wherein the user's sleep level represented by the second state is deeper than the user's sleep level represented by the first state, and the second rate is greater than the first rate.

[0281] Understandably, the user's sleep state is determined based on the sleep data at the first measurement moment. Different sleep states correspond to different levels of sleep, and the deeper the sleep, the faster the measurement speed.

[0282] The first and second states described above may exist in the following ways:

[0283] For example, the first state is a light sleep state and the second state is a deep sleep state; or the first state is a light sleep state and the second state is a light sleep state; or the first state is a deep sleep state and the second state is a deep sleep state; or the first state is a waking state and the second state is a deep sleep state; or the first state is a deep sleep state and the second state is a waking state.

[0284] Understandably, deep sleep is a more profound state of sleep than light sleep, and the measurement speed of deep sleep is greater than that of light sleep. Within the same sleep state, sleep can be further subdivided into multiple sub-states, each corresponding to a different level of sleep intensity.

[0285] For example, when the first state is light sleep, the measured speed is rate 1, and when the second state is deep sleep, the measured speed is rate 2, where rate 2 is greater than rate 1.

[0286] For example, the first state corresponds to the (25,30] sub-interval of light sleep, and the measured speed is rate 1. The second state corresponds to the (30,35] sub-interval of light sleep, and the measured speed is rate 2. The sleep level corresponding to the (30,35] sub-interval is deeper than that corresponding to the (25,30] sub-interval, and rate 2 is greater than rate 1.

[0287] For example, the first state corresponds to the (50,55) sub-interval of deep sleep, and the measured speed is rate 1. The second state corresponds to the (55,60) sub-interval of light sleep, and the measured speed is rate 2. The sleep level corresponding to the (55,60) sub-interval is deeper than that corresponding to the (50,55) sub-interval, and rate 2 is greater than rate 1.

[0288] For example, in the first state, which is the waking state, the measured speed is rate 1. In the second state, which is the deep sleep state, the measured speed is rate 2. Rate 2 can be greater than, equal to, or less than rate 1.

[0289] For example, the measured speed in the waking state and deep sleep state is rate 1, and the measured speed in the light sleep state is rate 2, where rate 2 is greater than rate 1.

[0290] For example, the speed measured in the waking state is rate 1, the speed measured in the deep sleep state is rate 2, and the speed measured in the light sleep state is rate 3. Among these, rate 3 is less than rate 1 or rate 2, and rate 1 and rate 2 may be the same or different.

[0291] In one implementation, before step S1002, it can be determined whether the sleep data meets preset conditions. If it does, step S1002 can be executed; if it does not, the blood pressure measurement is delayed.

[0292] For example, before determining the first measurement speed based on the sleep data at the first measurement time; if the sleep data at the first measurement speed meets a first preset condition; then the first measurement speed is determined based on the sleep data at the first measurement time; if the sleep data at the first measurement time does not meet a second preset condition; then the blood pressure measurement is delayed.

[0293] It should be noted that the first preset condition and the second preset condition can be the same or different.

[0294] For example, when the first preset condition and the second preset condition are the same, the preset condition can be understood as the sleep level represented by the sleep data being sufficiently deep. That is, if the sleep level represented by the sleep data at the first measurement time is sufficiently deep, the first measurement speed can be determined based on the sleep data at the first measurement time for subsequent measurement of the user's blood pressure. If the sleep level represented by the sleep data at the first measurement time is not deep enough, the measurement of the user's blood pressure is delayed.

[0295] Furthermore, user parameters can be incorporated to determine the measurement speed more accurately.

[0296] For example, the first measurement speed is determined based on sleep data at the first measurement time and user parameters, which include one or more of the following: physiological parameters, physical parameters, and behavioral parameters.

[0297] Physiological parameters include one or more of the following: sleep parameters, heart rate parameters, blood pressure parameters, and mood parameters; physical parameters include one or more of the following: insomnia parameters, weight parameters, age parameters, and gender parameters; behavioral parameters include one or more of the following: sporadic nap parameters, sleeping posture parameters, sleep onset and sleep-out time parameters, and wearable device compression parameters.

[0298] It should be noted that the sleep state influencing factors can be determined using the above user parameters. These factors reflect the user's sleep state, thereby determining the measurement speed.

[0299] For example, the first measurement speed is determined based on sleep data, physiological parameters, physical parameters, and behavioral parameters at the first measurement time.

[0300] It is understandable that the aforementioned physiological parameters, physical parameters, and behavioral parameters are user parameters at the first measurement time.

[0301] Specifically, the first measured velocity satisfies the following formula:

[0302] First measurement speed = (First preset measurement speed + First influence factor * (Second preset measurement speed - First preset measurement speed))

[0303] The formula can be formula (3), where the first preset measurement speed is the preset slowest measurement speed, the first influencing factor is the sleep state influencing factor, and the second preset measurement speed is the preset measurement speed in the awake state.

[0304] Specifically, the first impact factor satisfies the following formula:

[0305] First Influence Factor = (Physiological Parameter * First Preset Reference Value) * (Physical Parameter * Second Preset Reference Value) * (Behavioral Parameter * Third Preset Reference Value)

[0306] The formula can be formula (1), or formula (2) obtained by expanding formula (1). The first preset reference value, the second preset reference value and the third preset reference value are the preset reference values ​​of physiological parameters, physical parameters and behavioral parameters, respectively.

[0307] In other words, the sleep state influence factor can be calculated from user parameters using a specific formula, and the measurement speed can be calculated from the sleep state influence factor.

[0308] It is understandable that if the sleep state influencing factors reflect the user's sleep state, then it is possible to determine whether the first or second preset condition is met based on the sleep state influencing factors.

[0309] For example, before determining the first measurement speed based on sleep data at the first measurement time, a first influencing factor can be determined. If the first influencing factor meets a first preset condition, the first measurement speed is determined based on the first influencing factor; if the first influencing factor does not meet a second preset condition, the blood pressure measurement is delayed.

[0310] It should be noted that the first preset condition may be the same as or different from the second preset condition.

[0311] For example, when the first preset condition and the second preset condition are the same, the preset condition is greater than the preset threshold. A first influence factor greater than the preset threshold indicates that the user's sleep is sufficiently deep. A first influence factor less than or equal to the preset threshold indicates that the user's sleep is not deep enough. When the first preset condition and the second preset condition are different, the first preset condition is greater than the first preset threshold, and the second preset condition is greater than the second preset threshold. A first influence factor greater than the first preset threshold indicates that the user's sleep is sufficiently deep. A first influence factor less than or equal to the second preset threshold indicates that the user's sleep is not deep enough.

[0312] S1003, blood pressure is measured according to the first measurement speed.

[0313] This application provides a method, apparatus, and electronic device for blood pressure measurement. In a dynamic blood pressure monitoring scenario, the measurement speed is dynamically adjusted based on the user's sleep data. When the user is in deep sleep, a higher measurement speed can be used to measure blood pressure; when the user is in light sleep, a lower measurement speed can be used. Furthermore, if the user's sleep is too light, the blood pressure measurement can be delayed to prevent the measurement process from disturbing the user's sleep and improve the user experience.

[0314] The above describes a method for measuring blood pressure according to embodiments of this application. The following will be combined with... Figure 11 and Figure 12 This application describes apparatus and devices according to embodiments of the present application. It should be understood that the descriptions of the method embodiments correspond to the descriptions of the apparatus embodiments; therefore, any parts not described in detail can be referred to the foregoing method embodiments.

[0315] Figure 11 This diagram illustrates the structure of a blood pressure measuring device 1100 provided in an embodiment of this application. The blood pressure measuring device 1100 may possess the functions of the wearable device described in the above method embodiments and can be used to perform the steps executed by the wearable device in the above method embodiments. This function can be implemented in hardware, or in software, or in software executed by hardware. The hardware or software includes one or more modules corresponding to the above functions.

[0316] In one possible implementation, the blood pressure measuring device 1100 includes a transceiver module 1110 and a processing module 1120. The transceiver module 1110 and the processing module 1120 are coupled to each other.

[0317] Figure 12 A wearable device 1200 is provided as an embodiment of this application. This wearable device may be a smartwatch that supports blood pressure measurement. As shown in the figure, the wearable device 1200 includes at least one processor 1210 and a transceiver 1220. The processor 1210 is coupled to a memory 1230 and is used to execute instructions stored in the memory to control the transceiver 1220 to transmit and / or receive signals.

[0318] Optionally, the wearable device 1200 also includes a memory 1230 for storing instructions.

[0319] In some embodiments, the processor 1210 and the memory 1230 can be combined into a single processing device, with the processor 1210 executing program code stored in the memory 1230 to implement the aforementioned functions. Specifically, the memory 1230 can be integrated into the processor 1210 or independent of it.

[0320] In some embodiments, transceiver 1220 may include a receiver (or receiver unit) and a transmitter (or transmitter unit).

[0321] The transceiver 1220 may further include an antenna, and the number of antennas may be one or more. The transceiver 1220 may be a communication interface or an interface circuit.

[0322] This embodiment also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the aforementioned method steps to implement the method for measuring blood pressure in the above embodiment.

[0323] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the method for measuring blood pressure in the above embodiment.

[0324] Additionally, embodiments of this application also provide an apparatus, which may specifically be a chip, component, or module. In the case of a chip, the chip may include a connected processor and a memory. The memory stores computer-executable instructions, and when the chip is running, the processor executes the computer-executable instructions stored in the memory to cause the chip to perform the blood pressure measurement method in the above-described method embodiments.

[0325] In one possible implementation, the chip implementing the blood pressure measurement method provided in this application includes a transceiver module and a processing module. The transceiver module can be an input / output circuit or a communication interface; the processing module can be a processor, microprocessor, or integrated circuit integrated on the chip.

[0326] In this embodiment, the electronic device, computer-readable storage medium, computer program product or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0327] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0328] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0329] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or split into more modules, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or modules may be electrical, mechanical, or other forms.

[0330] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0331] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0332] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0333] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for measuring blood pressure, characterized in that, The method is applied to a wearable device, the wearable device including an airbag, and the method includes: Acquire sleep data at the first measurement time; A first measurement speed is determined based on the sleep data at the first measurement time, and the first measurement speed includes the inflation speed of the airbag and / or the deflation speed of the airbag. Blood pressure is measured based on the first measurement speed.

2. The method according to claim 1, characterized in that, Determining the first measurement speed based on the sleep data at the first measurement time includes: If the user is determined to be in the first state based on the sleep data at the first measurement time; Then the first measured speed is determined to be the first rate; If the user is determined to be in the second state based on the sleep data at the first measurement time; Then the first measured speed is determined to be the second speed. The second state represents a deeper level of sleep than the first state represents, and the second rate is greater than the first rate.

3. The method according to claim 2, characterized in that, The first state is a light sleep state, and the second state is a deep sleep state; or The first state is the light sleep state, and the second state is the light sleep state; or The first state is the deep sleep state, and the second state is the deep sleep state; or The first state is the waking state, and the second state is the deep sleep state; or The first state is the deep sleep state, and the second state is the awake state.

4. The method according to any one of claims 1 to 3, characterized in that, Before determining the first measurement speed based on the sleep data at the first measurement time, the method further includes: If the sleep data at the first measurement time meets the first preset condition; The first measurement speed is then determined based on the sleep data at the first measurement time. If the sleep data at the first measurement time does not meet the second preset condition; Then delay the measurement of blood pressure.

5. The method according to any one of claims 1 to 4, characterized in that, Determining the first measurement speed based on the sleep data at the first measurement time includes: The first measurement speed is determined based on sleep data at the first measurement time and user parameters, wherein the user parameters include one or more of the following: Physiological parameters, physical parameters, and behavioral parameters.

6. The method according to claim 5, characterized in that, The physiological parameters include one or more of the following: Sleep parameters, heart rate parameters, blood pressure parameters, mood parameters; The physical parameters include one or more of the following: Insomnia parameters, weight parameters, age parameters, gender parameters; The behavioral parameters include one or more of the following: Parameters for sporadic naps, sleeping posture, sleep onset and wake-up times, and compression parameters of the wearable device.

7. The method according to claim 5 or 6, characterized in that, Determining the first measurement speed based on sleep data and user parameters at the first measurement time includes: The first measurement speed is determined based on the sleep data at the first measurement time, the physiological parameters, the physical parameters, and the behavioral parameters.

8. The method according to any one of claims 5 to 7, characterized in that, The first measured velocity satisfies the following formula: The first measurement speed = (first preset measurement speed + first influence factor * (second preset measurement speed - first preset measurement speed)), where the first influence factor satisfies the following formula: The first influencing factor = (the physiological parameter * the first preset reference value) * (the physical condition parameter * the second preset reference value) * (the behavioral parameter * the third preset reference value).

9. The method according to any one of claims 1 to 8, characterized in that, Before acquiring the sleep data at the first measurement time, the method further includes: The first measurement time is determined based on the preset measurement time period and the preset measurement cycle.

10. A wearable device, characterized in that, include: One or more processors; One or more memory units; The one or more memories store one or more computer programs, the one or more computer programs including instructions that, when executed by the one or more processors, cause the wearable device to perform the method as described in any one of claims 1 to 9.

11. A device for measuring blood pressure, characterized in that, include: A processor coupled to a memory for storing a computer program, the processor for running the computer program such that the blood pressure measuring device performs the method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a computer, causes the computer to perform the method as described in any one of claims 1 to 9.

13. A computer program product containing instructions, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1 to 9.

14. A chip, characterized in that, The chip includes a processor and a data interface. The processor reads instructions stored in the memory through the data interface to execute the method as described in any one of claims 1 to 9.

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