Blood pressure measuring method, electronic device and system

Abstract

The invention provides a blood pressure measuring method, electronic equipment and a blood pressure measuring system. The method comprises the following steps: acquiring physiological data of a target user acquired by a plurality of data acquisition devices; determining whether the target user meets a blood pressure measurement condition according to the physiological data acquired by the plurality of data acquisition devices; and under the condition that the target user meets the blood pressure measurement condition, measuring the blood pressure of the target user.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of blood pressure measurement, and more specifically, to a blood pressure measurement method, electronic device, and system. Background Art

[0002] Hypertension is the most common cardiovascular disease. This disease is receiving more and more attention and concern from people. Blood pressure measurement has become an indispensable means to effectively monitor hypertension.

[0003] In the prior art, the user is usually required to measure the blood pressure of the user when the blood pressure measurement conditions are met. If the part where the user measures the blood pressure moves during the measurement process, or the muscles at the part where the user measures the blood pressure contract, or the temperature of the environment where the user is located is too high or too low, the blood pressure will be affected, which will lead to inaccurate blood pressure measurement results. Summary of the invention

[0004] An objective of the embodiments of the present disclosure is to provide a blood pressure measurement method, electronic device, and system.

[0005] According to a first aspect of an embodiment of the present disclosure, there is provided a blood pressure measurement method, comprising:

[0006] Acquire physiological data of a target user collected by multiple data collection devices;

[0007] Determining whether the target user meets the blood pressure measurement condition according to the physiological data collected by the multiple data collection devices;

[0008] When the target user meets the blood pressure measurement condition, the blood pressure of the target user is measured.

[0009] Optionally, the physiological data collected by at least one of the multiple data collection devices includes electromyographic signals of the hand of the target user;

[0010] The determining, based on the physiological data collected by the multiple data collection devices, whether the hand state of the target user meets the blood pressure measurement condition includes:

[0011] Performing signal analysis and processing on the electromyographic signal to obtain target features of the electromyographic signal;

[0012] According to the target feature, it is determined whether the target user meets the blood pressure measurement condition.

[0013] Optionally, the target feature includes at least one of the following: root mean square, integrated electromyogram, median of power distribution, and average frequency of power spectrum.

[0014] Optionally, before performing signal analysis processing on the electromyographic signal to obtain the target feature of the electromyographic signal, the method further includes:

[0015] The electromyographic signal is preprocessed, and the preprocessing method includes at least one of the following: filtering processing, full-wave rectification processing, linear envelope processing, and amplitude normalization processing.

[0016] Optionally, the physiological data collected by at least one data collection device includes inertial data of the target user's tested part within a second set time period;

[0017] The step of determining whether the target user meets the blood pressure measurement condition based on the physiological data collected by the multiple data collection devices includes:

[0018] Determine a first variance of the acceleration of the target user's measured part within the second set time period according to the inertial data;

[0019] Determine a second variance of the angular velocity variation of the target user's measured part within a plurality of third set time periods according to the inertial data; wherein the second set time period includes the plurality of third set time periods;

[0020] It is determined whether the target user meets the blood pressure measurement condition according to the first variance and the second variance.

[0021] Optionally, the physiological data collected by at least one data collection device includes temperature data of the environment where the target user is located within a fourth set time period;

[0022] The step of determining whether the target user meets the blood pressure measurement condition based on the physiological data collected by the multiple data collection devices includes:

[0023] determining a mean and a standard deviation of the temperature data;

[0024] Whether the target user meets the blood pressure measurement condition is determined according to the mean and the standard deviation.

[0025] Optionally, the method comprises:

[0026] Obtaining the clock deviation of each data acquisition device;

[0027] The physiological data collected by the multiple data collection devices are time-series aligned according to the clock deviation.

[0028] Optionally, obtaining a clock deviation of any data acquisition device includes:

[0029] Sending a target data packet to any of the data acquisition devices, and obtaining a first timestamp of sending the target data packet;

[0030] Receiving a second timestamp and a third timestamp sent by any of the data acquisition devices, wherein the second timestamp is the timestamp when any of the data acquisition devices receives the target data packet, and the third timestamp is the timestamp when any of the data acquisition devices sends the second timestamp;

[0031] Acquire a fourth timestamp after receiving the second timestamp and the third timestamp;

[0032] The clock deviation of any one of the data acquisition devices is determined according to the first timestamp, the second timestamp, the third timestamp and the fourth timestamp.

[0033] According to a second aspect of the present disclosure, an electronic device is provided, comprising a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the method described in the first aspect of the present disclosure under the control of the computer program.

[0034] According to a third aspect of the present disclosure, a blood pressure measurement system is provided, comprising a plurality of data acquisition devices and the electronic device as described in the second aspect of the present disclosure, wherein the data acquisition devices are used to acquire physiological data of a target user.

[0035] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in the first aspect of the present disclosure is implemented.

[0036] Through the embodiments of the present disclosure, whether the target user meets the blood pressure measurement conditions is determined based on the physiological data collected by multiple data acquisition devices, and the blood pressure of the target user is measured when the target user meets the blood pressure measurement conditions, which can improve the accuracy of the blood pressure measurement results.

[0037] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0039] Figure 1 is a block diagram showing a hardware configuration of an electronic device that can implement an embodiment of the present disclosure;

[0040] Figure 2 is a flow chart of a blood pressure measurement method according to an embodiment of the present disclosure;

[0041] Figure 3is a schematic diagram of measuring clock deviation according to an embodiment of the present disclosure;

[0042] Figure 4 is a block diagram of an electronic device according to an embodiment of the present disclosure;

[0043] Figure 5 is a block diagram of a blood pressure measurement system according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0044] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0045] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0046] Technologies, methods and equipment known to persons of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods and equipment should be considered part of the specification.

[0047] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0048] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0049] <Hardware Configuration>

[0050] Figure 1 is a block diagram showing a hardware configuration of an electronic device that can implement an embodiment of the present disclosure.

[0051] The electronic device 1000 may be a portable computer, a desktop computer, a mobile phone, a tablet computer, etc. Figure 1As shown, the electronic device 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and the like. Among them, the processor 1100 may be a processor CPU, a microprocessor MCU, and the like. The memory 1200 includes, for example, a ROM (read-only memory), a RAM (random access memory), a non-volatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 1400 is, for example, capable of wired or wireless communication, and may specifically include Wifi communication, Bluetooth communication, 2G / 3G / 4G / 5G communication, and the like. The display device 1500 is, for example, a liquid crystal display screen, a touch display screen, and the like. The input device 1600 may include, for example, a touch screen, a keyboard, a somatosensory input, and the like. The user may input / output voice information through the speaker 1700 and the microphone 1800.

[0052] Figure 1 The electronic device shown is merely illustrative and does not in any way imply any limitation on the present disclosure, its application or use. In the embodiments of the present disclosure, the memory 1200 of the electronic device 1000 is used to store instructions, and the instructions are used to control the processor 1100 to operate to perform any one of the methods provided in the embodiments of the present disclosure. It should be understood by those skilled in the art that although Figure 1 In the electronic device 1000, multiple devices are shown, but the present disclosure may only involve some of the devices, for example, the electronic device 1000 only involves the processor 1100 and the memory 1200. A technician can design instructions according to the scheme disclosed in the present disclosure. How instructions control the processor to operate is well known in the art, so it will not be described in detail here.

[0053] <Method Example>

[0054] The present disclosure provides a blood pressure measurement method, which can be implemented by an electronic device. The electronic device can be Figure 1 An electronic device 1000 is shown.

[0055] Figure 2 Flow chart of a blood pressure measurement method according to an embodiment of the present disclosure.

[0056] like Figure 2 As shown, the method includes steps S2100 to S2300 as shown below:

[0057] Step S2100, acquiring physiological data of a target user collected by multiple data collection devices.

[0058] In one embodiment, the electronic device may receive a blood pressure measurement instruction and send data acquisition instructions to multiple data acquisition devices respectively. The data acquisition devices collect physiological data of the target user in response to the data acquisition instructions and send the collected physiological data to the electronic device.

[0059] Furthermore, the blood pressure measurement instruction can be triggered by the target user inputting a corresponding voice, or by the target user performing a corresponding gesture, or by the target user performing a corresponding operation (such as clicking a blood pressure measurement button, etc.).

[0060] In this embodiment, the multiple data acquisition devices may communicate with the electronic device executing the method of this embodiment through a wireless connection such as Bluetooth or WiFi, or may communicate with the electronic device through a wired connection.

[0061] In this embodiment, the data types of the physiological data collected by the multiple data collection devices may be the same or different, which is not limited here.

[0062] The physiological data of this embodiment may include electrical signals such as electrocardiogram, electromyography, and electroencephalogram, and may also include non-electrical signals such as body temperature, pulse, and heart sounds.

[0063] Specifically, the data acquisition device for collecting electrocardiogram signals may be an electrocardiogram (ECG) sensor realized by the principle of biological electrodes; the data acquisition device for collecting pulse signals may be a pulse signal (PPG) sensor realized by optical characteristics; the data acquisition device for collecting heart sound signals may be a heart sound signal (PCG) sensor realized by detecting mechanical vibrations, etc.; the data acquisition device for collecting electromyogram signals may be an electromyogram (EMG) sensor realized by detecting the propagation of motor potentials of muscle fibers; the data acquisition device for collecting temperature data may be an ambient temperature (TMP) sensor realized by the principle of heat conduction; the data acquisition device for collecting inertial data may be an inertial measurement (IMU) sensor realized by the law of inertia.

[0064] In one embodiment of the present disclosure, the data collection device may be provided on a wearable device worn by the target user, and the wearable device may be, for example, a watch, earphones, a ring, glasses, etc.

[0065] In one embodiment of the present disclosure, when physiological data collected by multiple data acquisition devices are obtained, the method may further include: obtaining the clock deviation of each data acquisition device; and performing time alignment on the physiological data collected by the multiple data acquisition devices according to the clock deviation.

[0066] In this embodiment, the clock deviation of the data acquisition device may be a clock deviation of the data acquisition device relative to the electronic device executing this embodiment.

[0067] In this embodiment, the timing of physiological data collected by multiple data acquisition devices is aligned according to the clock deviation. For each data acquisition device, the timing of the physiological data collected by the data acquisition device may be adjusted according to the corresponding clock deviation, so that the timing of the physiological data collected by multiple data acquisition devices is aligned.

[0068] In one embodiment of the present disclosure, obtaining the clock deviation of any data acquisition device may include the following steps S2101 to S2104:

[0069] Step S2101, sending a target data packet to any data acquisition device, and obtaining a first timestamp of sending the target data packet.

[0070] In this embodiment, the first timestamp may be added to the target data packet and sent to any data acquisition device.

[0071] Step S2102, receiving a second timestamp and a third timestamp sent by any data acquisition device, wherein the second timestamp is the timestamp when any data acquisition device receives the target data packet, and the third timestamp is the timestamp when any data acquisition device sends the second timestamp.

[0072] In this embodiment, when any data acquisition device receives the target data packet, it may obtain a second timestamp when any data acquisition device receives the target data packet, and send the second timestamp to the electronic device.

[0073] Furthermore, any data collection device may also send the third timestamp of sending the second timestamp to the electronic device.

[0074] Furthermore, any data collection device may add the second timestamp and the third timestamp to the target data packet.

[0075] Step S2103: Acquire a fourth timestamp after receiving the second timestamp and the third timestamp.

[0076] Step S2104, determining the clock deviation of any data acquisition device according to the first timestamp, the second timestamp, the third timestamp and the fourth timestamp.

[0077] In the case where the first timestamp is T1, the second timestamp is T2, the third timestamp is T3, and the fourth timestamp is T4, the schematic diagram of the electronic device measuring the clock deviation can be as follows Figure 3 shown.

[0078] In this embodiment, the time taken by any data acquisition device to process data is T3-T2, the time taken for the entire transmission process is T4-T1, the time taken for data transmission in the transmission link is duration = (T4-T1) – (T3 – T2), and the clock deviation of any data acquisition device can be offset = [(T2 – T1) + (T3-T4)] / 2.

[0079] Through this embodiment, the physiological data collected by multiple data acquisition devices are time-series aligned, which can ensure the accuracy of the subsequent determination result of whether the target user meets the blood pressure measurement conditions, provide a data basis for subsequent blood pressure measurement and calculation, and ensure the accuracy of the blood pressure measurement results.

[0080] Step S2200: determining whether the target user meets the blood pressure measurement conditions based on the physiological data collected by multiple data collection devices.

[0081] In this embodiment, the blood pressure measurement conditions may include at least one of a relaxed hand posture, a stable posture of the part to be measured, and a suitable ambient temperature.

[0082] In one embodiment of the present disclosure, the physiological data collected by at least one of the multiple data collection devices includes electromyographic signals of the target user's hands.

[0083] In one example, the electromyographic signal may be an electromyographic signal within a first set period of time, wherein the duration of the first set period of time may be pre-set according to an application scenario or specific requirements, for example, the duration of the first set period of time may be 5 seconds.

[0084] In this embodiment, determining whether the target user meets the blood pressure measurement conditions based on the physiological data collected by multiple data collection devices includes the following steps S2211 to S2212:

[0085] Step S2211, performing signal analysis and processing on the electromyographic signal to obtain the target features of the electromyographic signal.

[0086] In one embodiment of the present disclosure, the target feature includes at least one of the following: root mean square, integrated electromyogram, median value of power distribution, and average frequency of power spectrum.

[0087] In an embodiment where the target feature includes a root mean square, the root mean square may be obtained by the following formula:

[0088]

[0089] Wherein, RMS represents root mean square, N represents the number of first sampling moments in the first set period, and x i Represents the amplitude of the electromyographic signal at the i-th first sampling moment within the first set time period.

[0090] In an embodiment where the target feature includes an integrated electromyogram, the integrated electromyogram may be obtained by the following formula:

[0091]

[0092] Wherein, iEMG represents the integrated electromyogram, x(t) represents the amplitude of the electromyographic signal at the first sampling time t within the first set period, t1 represents the start time of the first set period, and t2 represents the end time of the first set period.

[0093] In the embodiment where the target feature includes the median of the power distribution, the electromyographic signal may be first subjected to Fourier transform processing to obtain the power spectrum density; and then based on the power spectrum density, the median MidF of the power distribution may be obtained.

[0094] In the embodiment where the target feature includes the average frequency of the power spectrum, the electromyographic signal may be first subjected to Fourier transform processing to obtain the power spectrum density; and then the average frequency MeanF of the power spectrum may be calculated.

[0095] In one embodiment of the present disclosure, before executing step S2211 to perform signal analysis processing on the electromyographic signal to obtain the target features of the electromyographic signal, the method also includes: preprocessing the electromyographic signal, and the preprocessing method includes at least one of the following: filtering processing, full-wave rectification processing, linear envelope processing, and amplitude normalization processing.

[0096] In the embodiment where the preprocessing method includes filtering, it may include using a Gaussian filter to eliminate the DC component in the signal, remove the offset of the signal in the overall horizontal direction, and may also include using a bandpass filter to remove noise and non-electromyographic signal components that are not within the range.

[0097] In an embodiment where the preprocessing method includes full-wave rectification processing, the negative part of the electromyographic signal may be converted into a positive part to facilitate analysis of the amplitude of the overall electromyographic signal.

[0098] In an embodiment where the preprocessing method includes linear envelope processing, a low-pass filter may be used to obtain the linear envelope of the electromyographic signal to further smooth the electromyographic signal.

[0099] In an embodiment where the preprocessing method includes amplitude normalization processing, the amplitude of the electromyographic signal may be normalized according to the maximum amplitude of the electromyographic signal within the first set time period.

[0100] In this embodiment, by preprocessing the electromyographic signal, it is convenient to perform signal analysis processing on the electromyographic signal.

[0101] Step S2212: Determine whether the target user meets the blood pressure measurement conditions based on the target characteristics.

[0102] In this embodiment, a corresponding first threshold may be set in advance for each target feature according to the application scenario or specific needs, and then each target feature may be compared with the corresponding first threshold. For example, the first threshold corresponding to the root mean square may be set to 5, the first threshold corresponding to the integrated electromyogram may be set to 3, the first threshold corresponding to the median of the power distribution may be set to 120, and the first threshold corresponding to the average frequency of the power spectrum may be set to 110.

[0103] In this embodiment, when all target features are less than or equal to the corresponding first threshold, it can be determined that the target user's hand posture is relaxed; when any one or more target features are greater than the corresponding first threshold, it can be determined that the target user's hand posture is not relaxed.

[0104] Muscle contraction can cause changes in blood vessels, resulting in changes in blood flow, which in turn affects the measurement of blood pressure. The electromyographic signal can reflect the amplitude and strength of muscle contraction. The denser the electromyographic signal and the higher the amplitude, the stronger the muscle contraction. Therefore, the electromyographic signal can be used to determine whether the hand muscles are in a relaxed state.

[0105] In one embodiment of the present disclosure, the physiological data collected by at least one data collection device includes inertial data of the target user's test site within a second set period of time. The duration of the second set period of time may be pre-set according to an application scenario or specific needs, for example, the duration of the second set period of time may be 3 seconds.

[0106] Furthermore, the part to be measured may be an arm, a wrist, or other part of the target user.

[0107] In this embodiment, determining whether the target user meets the blood pressure measurement conditions based on the physiological data collected by multiple data collection devices includes the following steps S2221 to S2223:

[0108] Step S2221, determining a first variance of acceleration of a part to be measured of the target user within a second set time period according to the inertial data.

[0109] In this embodiment, the inertial data may include three-axis acceleration data. Then, the variance of the acceleration at all second sampling moments in the second set time period may be determined in advance for each axis, which are ax, ay, and az respectively, and then the first variance a=ax+ay+az is obtained based on the variance of the acceleration corresponding to each axis.

[0110] Step S2222, determining a second variance of the angular velocity variation of the target user's measured part within a plurality of third set time periods according to the inertial data; wherein the second set time period includes a plurality of third set time periods.

[0111] In this embodiment, the second set period may be pre-divided into multiple third set periods on an even basis. Specifically, the second set period may be divided according to the duration of the third set period, or the second set period may be divided according to the number of third set periods. The duration of the third set period or the number of third set periods may be pre-set according to the application scenario or specific requirements. For example, the duration of the third set period may be 100 milliseconds, and the number of the third set period may be 100.

[0112] In this embodiment, the inertial data may include three-axis angular velocity data, and the angular velocity change amounts rx, ry, and rz within each third set period may be determined in advance for each axis.

[0113] Specifically, the angular velocity variation within a third set period of time for the x-axis may be determined according to the following formula:

[0114]

[0115] Wherein, rx represents the angular velocity change within a third setting period for the x-axis, Δt represents the time interval between two adjacent second sampling moments, ωx(n) represents the angular velocity of the x-axis corresponding to the j-th second sampling moment within the third setting period, and m represents the number of second sampling moments within a third setting period.

[0116] Correspondingly, the angular velocity variation within a third set period of time for the y-axis may be determined according to the following formula:

[0117]

[0118] Among them, ry represents the angular velocity change of the y-axis within a third setting period, Δt represents the time interval between two adjacent second sampling moments, ωy(n) represents the angular velocity of the y-axis corresponding to the j-th second sampling moment in the third setting period, and m represents the number of second sampling moments in a third setting period.

[0119] Correspondingly, the angular velocity variation within a third set period of time for the z-axis may be determined according to the following formula:

[0120]

[0121] Among them, rz represents the change in angular velocity of the z-axis within a third set time period, Δt represents the time interval between two adjacent second sampling moments, ωz(n) represents the angular velocity of the z-axis corresponding to the j-th second sampling moment in the third set time period, and m represents the number of second sampling moments in a third set time period.

[0122] Furthermore, the variance rdx of the angular velocity change amount within all third set periods for the x-axis, the variance rdy of the angular velocity change amount within all third set periods for the y-axis, and the variance rdz of the angular velocity change amount within all third set periods for the z-axis may be determined.

[0123] Furthermore, the second variance rd=rdx+rdy+rdz may be obtained according to the variance rdx, rdy, rdz of the angular velocity change corresponding to each axis.

[0124] Step S2223: Determine whether the target user meets the blood pressure measurement condition based on the first variance and / or the second variance.

[0125] In this embodiment, the second threshold and the third threshold may be set in advance according to the application scenario or specific requirements. For example, the second threshold may be set to 0.1 m / s 2 , set the third threshold to 0.1rad 2 .

[0126] In an embodiment of determining whether the target user meets the blood pressure measurement conditions based on the first variance and the second variance, the first variance may be compared with the second threshold, and the second variance may be compared with the third threshold. When the first variance is less than or equal to the second threshold, and the second variance is less than or equal to the third threshold, it is determined that the posture of the target user's part to be measured is stable; when the first variance is greater than the second threshold and / or the second variance is greater than the third threshold, it is determined that the posture of the target user's part to be measured is unstable.

[0127] In an embodiment of determining whether the target user meets the blood pressure measurement conditions based on the first variance, the first variance may be compared with a second threshold value. When the first variance is less than or equal to the second threshold value, it is determined that the posture of the target user's part to be measured is stable; when the first variance is greater than the second threshold value, it is determined that the posture of the target user's part to be measured is unstable.

[0128] In an embodiment of determining whether the target user meets the blood pressure measurement conditions based on the second variance, the second variance may be compared with a third threshold value. When the second variance is less than or equal to the third threshold value, it is determined that the posture of the target user's part to be measured is stable; when the second variance is greater than the third threshold value, it is determined that the posture of the target user's part to be measured is unstable.

[0129] In this embodiment, if the user's part to be measured moves during the measurement process, it will affect the quality of the blood pressure measurement result. Therefore, it is possible to determine whether the posture of the part to be measured is stable through inertial data.

[0130] In one embodiment of the present disclosure, the physiological data collected by at least one data collection device includes temperature data of the environment in which the target user is located within a fourth set time period. The duration of the fourth set time period may be pre-set according to an application scenario or specific needs, for example, the duration of the fourth set time period may be 5 seconds.

[0131] In this embodiment, determining whether the target user meets the blood pressure measurement conditions based on the physiological data collected by multiple data collection devices includes the following steps S2231-S2232:

[0132] Step S2231, determine the statistics of temperature data.

[0133] The statistics of the temperature data may include a mean and / or a standard deviation of the temperature data.

[0134] Step S2232: Determine whether the target user meets the blood pressure measurement conditions based on the statistics of the temperature data.

[0135] In this embodiment, the normal range corresponding to each statistic can be set in advance according to the application scenario or specific needs. For example, the normal range of the mean can be set to be greater than or equal to 20 and less than or equal to 25, and the normal range of the standard deviation can be set to be less than or equal to 0.5.

[0136] In an embodiment of determining whether the target user meets the blood pressure measurement conditions based on the mean and standard deviation of the temperature data, it can be determined whether the mean and standard deviation of the temperature data are both within the corresponding normal range. When the mean and standard deviation of the temperature data are both within the corresponding normal range, it can be determined that the ambient temperature of the target user is suitable; when the mean and / or standard deviation of the temperature data are outside the corresponding normal range, it can be determined that the ambient temperature of the target user is not suitable.

[0137] In an embodiment of determining whether the target user meets the blood pressure measurement conditions based on the mean value of the temperature data, it can be determined whether the mean value of the temperature data is within the corresponding normal range. When the mean value of the temperature data is within the corresponding normal range, it can be determined that the ambient temperature of the target user is suitable; when the mean value of the temperature data exceeds the corresponding normal range, it can be determined that the ambient temperature of the target user is not suitable.

[0138] In an embodiment of determining whether the target user meets the blood pressure measurement conditions based on the standard deviation of the temperature data, it can be determined whether the standard deviation of the temperature data is within the corresponding normal range. When the standard deviation of the temperature data is within the corresponding normal range, it can be determined that the ambient temperature of the target user is suitable; when the standard deviation of the temperature data exceeds the corresponding normal range, it can be determined that the ambient temperature of the target user is not suitable.

[0139] Through this embodiment, it is possible to determine whether the target user meets the blood pressure measurement conditions based on at least one of the electromyographic signal, inertial data, and temperature data.

[0140] Step S2300: When the target user meets the blood pressure measurement conditions, measure the blood pressure of the target user.

[0141] In the case where the blood pressure measurement conditions include a relaxed hand posture and / or a stable posture of the part to be measured, it may be possible that when the electronic device receives a blood pressure measurement instruction, whether the target user meets the blood pressure measurement conditions is detected according to the electromyographic signal and / or inertial data at a set detection frequency. In the process of measuring the target user's blood pressure, if it is detected that the target user's hand posture is not relaxed and / or the posture of the part to be measured is unstable, a first prompt message for reminding the target user to keep a stable posture and a relaxed hand posture may be output; if it is detected that the target user's hand posture is not relaxed and / or the posture of the part to be measured is unstable for a number of times reaching a set number, the measurement of the target user's blood pressure may be stopped.

[0142] In this embodiment, the first prompt information may be text information and / or voice information, which is not limited here.

[0143] Furthermore, the detection frequency and the set number of times may be respectively set in advance according to application scenarios or specific requirements. For example, the detection frequency may be 10 Hz, and the set number of times may be 3.

[0144] Furthermore, when the blood pressure measurement result of the target user is obtained, the acquisition of the electromyographic signal and / or inertial data of the target user may be stopped to reduce the power consumption of the data acquisition device and the electronic device.

[0145] When the blood pressure measurement condition includes a suitable ambient temperature, it may be that when the electronic device receives a blood pressure measurement instruction, whether the target user meets the blood pressure measurement condition is detected based on the temperature data. If it is detected that the ambient temperature is suitable, the blood pressure of the target user is measured; if it is detected that the ambient temperature is not suitable, a second prompt message may be output to remind the target user that the blood pressure measured in the current environment may be inaccurate. In addition, when it is detected that the ambient temperature is not suitable, the blood pressure of the target user may continue to be measured, or the blood pressure of the target user may not be measured, which is not limited here.

[0146] In this embodiment, the second prompt information may be text information and / or voice information, which is not limited here.

[0147] Through the embodiments of the present disclosure, whether the target user meets the blood pressure measurement conditions is determined based on the physiological data collected by multiple data acquisition devices, and the blood pressure of the target user is measured when the target user meets the blood pressure measurement conditions, which can improve the accuracy of the blood pressure measurement results.

[0148] <Electronic Equipment Embodiment>

[0149] This embodiment provides an electronic device, such as Figure 4 As shown, the electronic device 4000 may include a processor 4100 and a memory 4200, the memory 4200 is used to store a computer program, and the processor 4100 is used to control the electronic device to execute the method of any embodiment of the present disclosure under the control of the computer program.

[0150] <System Embodiment>

[0151] This embodiment provides a blood pressure measurement system. Figure 5 As shown, the blood pressure measurement system 5000 may include a plurality of data acquisition devices 5100 and the electronic device 4000 described in the aforementioned embodiment.

[0152] The data collection device 5100 can be used to collect physiological data of a target user.

[0153] In this embodiment, each data acquisition device 5100 can communicate with the electronic device 4000. Specifically, the data acquisition device 5100 can communicate with the electronic device 4000 via a wireless connection such as Bluetooth or WiFi, or can communicate with the electronic device 4000 via a wired connection.

[0154] In this embodiment, the data types of the physiological data collected by the multiple data collection devices may be the same or different, which is not limited here.

[0155] The physiological data of this embodiment may include electrical signals such as electrocardiogram, electromyography, and electroencephalogram, and may also include non-electrical signals such as body temperature, pulse, and heart sounds.

[0156] Specifically, the data acquisition device for collecting electrocardiogram signals may be an electrocardiogram (ECG) sensor realized by the principle of biological electrodes; the data acquisition device for collecting pulse signals may be a pulse signal (PPG) sensor realized by optical characteristics; the data acquisition device for collecting heart sound signals may be a heart sound signal (PCG) sensor realized by detecting mechanical vibrations, etc.; the data acquisition device for collecting electromyogram signals may be an electromyogram (EMG) sensor realized by detecting the propagation of motor potentials of muscle fibers; the data acquisition device for collecting temperature data may be an ambient temperature (TMP) sensor realized by the principle of heat conduction; the data acquisition device for collecting inertial data may be an inertial measurement (IMU) sensor realized by the law of inertia.

[0157] In one embodiment of the present disclosure, the data collection device may be provided on a wearable device worn by the target user, and the wearable device may be, for example, a watch, earphones, a ring, glasses, etc.

[0158] <Readable Storage Medium Embodiment>

[0159] This embodiment provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method described in any method embodiment of the present disclosure is executed.

[0160] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0161] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0162] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0163] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0164] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0165] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0166] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0167] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0168] Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A blood pressure measurement method, characterized in that: include: Acquire physiological data of a target user collected by multiple data collection devices; Determining whether the target user meets the blood pressure measurement condition according to the physiological data collected by the multiple data collection devices; When the target user meets the blood pressure measurement condition, the blood pressure of the target user is measured.

2. The method according to claim 1, characterized in that The physiological data collected by at least one of the plurality of data collection devices includes electromyographic signals of the hand of the target user; The determining, based on the physiological data collected by the multiple data collection devices, whether the hand state of the target user meets the blood pressure measurement condition includes: Performing signal analysis and processing on the electromyographic signal to obtain target features of the electromyographic signal; According to the target feature, it is determined whether the target user meets the blood pressure measurement condition.

3. The method according to claim 2, characterized in that The target feature includes at least one of the following: root mean square, integrated electromyogram, median value of power distribution, and average frequency of power spectrum.

4. The method according to claim 2, characterized in that: Before performing signal analysis processing on the electromyographic signal to obtain the target feature of the electromyographic signal, the method further includes: The electromyographic signal is preprocessed, and the preprocessing method includes at least one of the following: filtering processing, full-wave rectification processing, linear envelope processing, and amplitude normalization processing.

5. The method according to claim 2, characterized in that: The physiological data collected by at least one data collection device includes inertial data of the target user's tested part within a second set time period; The step of determining whether the target user meets the blood pressure measurement condition based on the physiological data collected by the multiple data collection devices includes: Determine a first variance of the acceleration of the target user's measured part within the second set time period according to the inertial data; Determine a second variance of the angular velocity variation of the target user's measured part within a plurality of third set time periods according to the inertial data; wherein the second set time period includes the plurality of third set time periods; It is determined whether the target user meets the blood pressure measurement condition according to the first variance and the second variance.

6. The method according to claim 1, characterized in that The physiological data collected by at least one data collection device includes temperature data of the environment where the target user is located within a fourth set time period; The step of determining whether the target user meets the blood pressure measurement condition based on the physiological data collected by the multiple data collection devices includes: determining a mean and a standard deviation of the temperature data; Whether the target user meets the blood pressure measurement condition is determined according to the mean and the standard deviation.

7. The method according to claim 1, characterized in that The method comprises: Obtaining the clock deviation of each data acquisition device; The physiological data collected by the multiple data collection devices are time-series aligned according to the clock deviation.

8. The method according to claim 7, characterized in that Get the clock offset of any data acquisition device, including: Sending a target data packet to any of the data acquisition devices, and obtaining a first timestamp of sending the target data packet; Receiving a second timestamp and a third timestamp sent by any of the data acquisition devices, wherein the second timestamp is the timestamp when any of the data acquisition devices receives the target data packet, and the third timestamp is the timestamp when any of the data acquisition devices sends the second timestamp; Acquire a fourth timestamp after receiving the second timestamp and the third timestamp; The clock deviation of any one of the data acquisition devices is determined according to the first timestamp, the second timestamp, the third timestamp and the fourth timestamp.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to execute the method according to any one of claims 1 to 8 under the control of the computer program.

10. A blood pressure measurement system, characterized in that: The electronic device comprises a plurality of data collection devices and the electronic device as claimed in claim 9, wherein the data collection devices are used to collect physiological data of a target user.

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