Blood pressure measuring method, device, equipment and storage medium

Abstract

The embodiment of the invention discloses a blood pressure measuring method and device, equipment and a storage medium. The blood pressure measuring method can comprise the steps that the tightness degree grade of wearable equipment worn by a user is obtained; wherein the tightness level is used for indicating the fitting degree of the wearable device and the measurement part of the user; determining an initial blood pressure value of the user by using the collected pulse wave signal; and obtaining a target blood pressure value of the user according to the initial blood pressure value, the tightness level and a blood pressure correction model. By implementing the method, the measurement precision of the blood pressure can be improved under the condition that a user does not need to adjust the wrist strap of the wearable equipment.

Description

Technical Field

[0001] The present application relates to the technical field of electronic equipment, and in particular to a blood pressure measurement method, device, equipment and storage medium. Background Art

[0002] Wearable devices (such as blood pressure watches or blood pressure wristbands) can be worn by users for a long time due to their smaller size and lighter weight, meeting the needs of long-term and real-time blood pressure monitoring.

[0003] In practice, it is found that when measuring blood pressure, wearing a wearable device too loosely or too tightly usually has a very obvious impact on the accuracy of blood pressure measurement. Therefore, before measuring blood pressure, most users are required to manually adjust the wristband of the wearable device to the appropriate tightness, which is a cumbersome operation. Summary of the invention

[0004] The embodiments of the present application provide a blood pressure measurement method, apparatus, device and storage medium, which can improve the measurement accuracy of blood pressure without the user having to adjust the wristband of the wearable device.

[0005] A first aspect of an embodiment of the present application provides a blood pressure measurement method, which is applicable to a wearable device, and includes:

[0006] Obtaining a tightness level of the wearable device worn by the user; wherein the tightness level is used to indicate the degree of fit between the wearable device and the measurement part of the user;

[0007] Determining the initial blood pressure value of the user using the collected pulse wave signal;

[0008] The target blood pressure value of the user is obtained according to the initial blood pressure value, the tightness level and the blood pressure correction model; wherein the blood pressure correction model is a machine learning model trained according to multiple sample initial blood pressure values, the sample tightness level corresponding to each sample initial blood pressure value in the multiple sample initial blood pressure values, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

[0009] A second aspect of an embodiment of the present application provides a blood pressure measurement device, the device being on a wearable device, and the device comprising:

[0010] A tightness determination unit, used to obtain a tightness level of the wearable device worn by the user; wherein the tightness level is used to indicate the degree of fit between the wearable device and the measurement part of the user;

[0011] A blood pressure measurement unit, used to determine the initial blood pressure value of the user using the collected pulse wave signal;

[0012] A blood pressure correction unit is used to obtain the target blood pressure value of the user based on the initial blood pressure value, the tightness level and the blood pressure correction model; wherein the blood pressure correction model is a machine learning model trained based on multiple sample initial blood pressure values, the sample tightness level corresponding to each sample initial blood pressure value in the multiple sample initial blood pressure values, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

[0013] A third aspect of an embodiment of the present application provides a wearable device, including:

[0014] A memory storing executable program code;

[0015] and a processor coupled to the memory;

[0016] The processor calls the executable program code stored in the memory, and when the executable program code is executed by the processor, the processor implements the method described in the first aspect of the embodiment of the present application.

[0017] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having executable program code stored thereon. When the executable program code is executed by a processor, the method described in the first aspect of the embodiment of the present application is implemented.

[0018] A fifth aspect of an embodiment of the present application discloses a computer program product. When the computer program product runs on a computer, the computer executes the method described in the first aspect of the embodiment of the present application.

[0019] A sixth aspect of an embodiment of the present application discloses an application publishing platform, which is used to publish a computer program product. When the computer program product runs on a computer, the computer executes the method described in the first aspect of the embodiment of the present application.

[0020] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0021] In an embodiment of the present application, the tightness level of the wearable device worn by the user is obtained; wherein the tightness level is used to indicate the degree of fit between the wearable device and the measuring part of the user; the user's initial blood pressure value is determined using the collected pulse wave signal; the user's target blood pressure value is obtained based on the initial blood pressure value, the tightness level and the blood pressure correction model; wherein the blood pressure correction model is a machine learning model trained based on multiple sample initial blood pressure values, the sample tightness level corresponding to each sample initial blood pressure value in the multiple sample initial blood pressure values, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

[0022] By implementing this method, the initial blood pressure value can be corrected according to the tightness level of the wearable device worn by the user and the blood pressure correction model, so that the user can obtain a high-precision blood pressure value when wearing the wearable device autonomously and comfortably, and the user does not need to manually adjust the wristband of the wearable device before measuring blood pressure, which simplifies the user operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments and the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained based on these drawings.

[0024] Figure 1A It is a scenario diagram of the blood pressure measurement method disclosed in the embodiment of the present application;

[0025] Figure 1B is a diagram of the physical structure of the wearable device 10 disclosed in the embodiment of the present application;

[0026] Figure 1C is a schematic diagram of the wearable device 10 disclosed in the embodiment of the present application when the tightness level is the first level;

[0027] Figure 1D is a schematic diagram of the wearable device 10 disclosed in the embodiment of the present application when the tightness level is the second level;

[0028] Figure 1E is a schematic diagram of the wearable device 10 disclosed in the embodiment of the present application when the tightness level is the third level;

[0029] Figure 2 It is a flowchart of the blood pressure measurement method disclosed in the embodiment of the present application;

[0030] Figure 3A is another flowchart of the blood pressure measurement method disclosed in the embodiment of the present application;

[0031] Figure 3B It is a structural diagram of the blood pressure correction model h disclosed in the embodiment of the present application;

[0032] Figure 4 It is a structural diagram of a blood pressure measurement device disclosed in an embodiment of the present application;

[0033] Figure 5 It is a structural diagram of a wearable device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The embodiments of the present application provide a blood pressure measurement method, apparatus, device and storage medium, which can improve the measurement accuracy of blood pressure without the user having to adjust the wristband of the wearable device.

[0035] In order to make the technical personnel in the technical field better understand the scheme of the present application, the technical scheme in the embodiment of the present application will be described below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, not all of the embodiments. Based on the embodiments in the present application, they should all fall within the scope of protection of the present application.

[0036] It should be noted that, in this application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplarily" or "for example" in this application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

[0037] "At least one" means one or more, and "more" means two or more. "At least one" means one or more, and "more" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A, B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can mean: a, or b, or c, or a and b, or a and c, or b and c, or a, b and c, where a, b, c can be single or plural.

[0038] See also Figure 1A , Figure 1A This is a scenario diagram of the blood pressure measurement method disclosed in the embodiment of this application. Figure 1A The scene diagram shown includes a wearable device 10 and a user 20; wherein, when the user 20 wears the wearable device 10, the measuring body of the wearable device 10 can inflate the airbag of the wearable device 10, so that the airbag presses the wrist or upper arm of the user 20 to obtain a pulse wave signal in the artery, and then obtain the blood pressure of the user 20 based on the pulse wave signal.

[0039] In practice, it is found that when the user 20 wears the wearable device 10, if it is worn too loosely, the pressure value in the airbag will be greater than the pressure value at the radial artery, and the measured blood pressure value will be too high. If it is worn too tightly, the pressure value in the airbag will be less than the pressure value at the radial artery, and the measured blood pressure value will be too low. It can be seen that wearing the wearable device 10 too loosely / too tightly will have a very obvious impact on the accuracy of blood pressure measurement. In order to reduce the impact of the tightness of wearing on the accuracy of blood pressure measurement, before blood pressure measurement, the user 20 is mostly required to manually adjust the wristband of the wearable device 10 to the appropriate tightness, which is a cumbersome operation.

[0040] In the technical solution of the present application, before obtaining the initial blood pressure value, the wearable device can first obtain the tightness level of the wearable device 10 worn by the user 20, and then correct the initial blood pressure value according to the tightness level and the blood pressure correction model to obtain the target blood pressure value, so that the user 20 can obtain a high-precision blood pressure value when wearing the wearable device 10 independently and comfortably, and the user 20 does not need to manually adjust the wristband of the wearable device 10 to the appropriate tightness before measuring the blood pressure, thereby simplifying the user operation.

[0041] The tightness level of the wearable device 10 is used to indicate the fit between the wearable device 10 and the measurement part of the user 20 .

[0042] The physical structure of the wearable device 10 in the technical solution of this application is described below:

[0043] See also Figure 1B , Figure 1B The wearable device 10 shown includes a wristband 101, an airbag 102, a measuring body 103, and a clasp 104; wherein the airbag 102 is located inside one side of the wristband 101, and when the wearable device 10 is worn by the user 20, the airbag 102 fits against the wrist or upper arm of the user 20, and the clasp 104 is used to connect the wristbands 101 on different sides. When measuring the blood pressure of the user 20, the measuring body 10 can inflate the airbag 102 so that the airbag 102 presses the wrist or upper arm of the user 20 to obtain a pulse wave signal in the artery.

[0044] The measuring body 103 may include an air pump, and the measuring body 103 may inflate the airbag 102 through the air pump. It should be noted that the number and structure of the airbags may be designed according to product requirements, and the drawings are only examples and are not limited here.

[0045] Based on the physical structure of the wearable device 10, when the user 20 wears the wearable device 10 on the wrist, the tightness level of the wearable device 10 may indicate the degree of fit between the wristband 101 of the wearable device 10 and the wrist tissue of the user 20, which is exemplified below with reference to the figure:

[0046] See also Figure 1C , Figure 1C The figure shows the wearable device 10 when the tightness level is the first level. Figure 1C As shown, the user's wrist tissue does not completely fit the wristband 101 of the wearable device 10.

[0047] See also Figure 1D , Figure 1D The figure shows the wearable device 10 when the tightness level is the second level. Figure 1D As shown, the user's wrist tissue completely fits the wristband 101 of the wearable device 10.

[0048] See also Figure 1E , Figure 1E The figure shows the wearable device 10 when the tightness level is the third level. Figure 1E As shown, the wrist tissue of the user completely fits the wristband 101 of the wearable device 10, and the wristband 101 is in a stretched state.

[0049] It should be noted that in Figure 1C-1E Among the three tightness levels shown, the first level represents the smallest fit between the wrist tissue and the wristband 101 , the third level represents the largest fit between the wrist tissue and the wristband 101 , and the second level represents a moderate fit between the wrist tissue and the wristband 101 .

[0050] It should be noted that Figure 1C-1E The number of indicated tightness levels is for illustrative purposes only and is not intended to limit the total number of tightness levels preset for the wearable device 10. The total number of tightness levels preset for the wearable device 10 may also be 5, 10, 15, 20, or a greater number, which is not limited in the embodiments of the present application.

[0051] The technical solution of this application is further described below in conjunction with specific embodiments:

[0052] See also Figure 2 , Figure 2 FIG. 1 is a flow chart of the blood pressure measurement method disclosed in the embodiment of the present application. Figure 2 The blood pressure measurement method shown may include the following steps:

[0053] 201. Obtain the tightness level of the wearable device worn by the user.

[0054] Among them, the tightness level is used to indicate the degree of fit between the wearable device and the user's measuring part.

[0055] In an embodiment of the present application, the tightness level of the wearable device worn by the user is determined by inflating the airbag.

[0056] Based on this, the tightness level of the wearable device worn by the user can be obtained in the following ways, but not limited to:

[0057] Method 1: Obtain a pressure signal corresponding to the airbag, which may refer to a pressure signal from the beginning of inflation of the airbag to the pressure value in the airbag reaching a preset pressure threshold, and determine the tightness level of the wearable device according to the pressurization slope of the pressure signal.

[0058] Among them, the larger the pressure slope, the greater the fit between the wearable device and the user's skin tissue.

[0059] The wearable device may be pre-set with a first level list, which may include multiple tightness levels and a pressure slope corresponding to each tightness level. After obtaining the pressure slope, the wearable device may use the tightness level in the first level list that matches the pressure slope as the tightness level of the wearable device.

[0060] Method 2: Obtain the pressure signal corresponding to the airbag, which may refer to the pressure signal during the process from the start of inflation of the airbag to the pressure value inside the airbag reaching a preset pressure threshold, and determine the tightness level of the wearable device based on the pressurization time corresponding to the pressure signal.

[0061] Among them, the longer the pressurization time, the less fit the wearable device is to the user's skin tissue.

[0062] The wearable device may be pre-set with a second level list, which may include multiple tightness levels and the pressurization time corresponding to each tightness level. After obtaining the pressurization time, the wearable device may use the tightness level in the second level list that matches the pressurization time as the tightness level of the wearable device.

[0063] Method 3: determining the tightness level of the wearable device worn by the user according to the inflation speed sequence of the airbag within a specified time period; wherein the specified time period includes multiple speed measurement cycles, and the inflation speed sequence includes the inflation speed corresponding to each speed measurement cycle in the multiple speed measurement cycles.

[0064] The specified time period may refer to a specified time period before the pressure value in the airbag reaches a preset pressure threshold.

[0065] In some embodiments, determining the tightness level of the wearable device worn by the user according to the inflation speed sequence of the airbag within a specified time period may include:

[0066] Calculating a target index value according to the inflation speed sequence of the airbag within a specified time period; wherein the target index value includes the mean of the inflation speeds corresponding to all speed measurement cycles, or the median of the inflation speeds corresponding to all speed measurement cycles;

[0067] According to the target indicator value, the tightness level of the wearable device worn by the user is determined.

[0068] The wearable device may be pre-set with a third level list, which may include multiple tightness levels and target index values ​​corresponding to each tightness level. After obtaining the target index value, the wearable device may use the tightness level in the third level list that matches the target index value as the tightness level of the wearable device.

[0069] In some embodiments, the wearable device may obtain the tightness level of the wearable device worn by the user when it detects that the wearable device is in a worn state, or obtain the tightness level of the wearable device worn by the user when a blood pressure measurement operation is detected, which is not limited in the embodiments of the present application.

[0070] Among them, the blood pressure measurement operation may include but is not limited to gesture operation, voice operation and touch operation.

[0071] For example, when the blood pressure measurement operation includes a gesture operation, the blood pressure measurement operation may be swinging the arm twice or three times in a row. When the blood pressure measurement operation includes a voice operation, the blood pressure measurement operation may be inputting an audio "measure blood pressure". When the blood pressure measurement operation includes a touch operation, the blood pressure measurement operation may be clicking the screen twice in a row.

[0072] 202. Determine the user's initial blood pressure value using the collected pulse wave signal.

[0073] Among them, the pulse wave signal can represent the change of the pressure value in the airbag of the wearable device over time, and the user's initial blood pressure value can be determined based on the change.

[0074] For greater accuracy, in an embodiment of the present application, the pulse wave signal can represent the change in the pressure value in the airbag of the wearable device over time after the pressure value in the airbag is greater than a preset pressure threshold.

[0075] It should be noted that the preset pressure threshold may refer to the pressure value in the airbag when the pulse wave signal appears, and the preset pressure threshold may be determined based on a large amount of sample data.

[0076] The user's initial blood pressure value may include at least one of the following: an initial mean pressure value, an initial systolic pressure value, and an initial diastolic pressure value.

[0077] 203. Obtain the user's target blood pressure value based on the initial blood pressure value, the above tightness level and the blood pressure correction model.

[0078] Among them, the blood pressure correction model is a machine learning model trained based on multiple sample initial blood pressure values, the sample tightness level corresponding to each sample initial blood pressure value, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

[0079] Among them, the blood pressure correction model includes at least one of the following models: vector machine model, neural network model, decision tree model, random forest model and gradient boosting tree model.

[0080] By implementing the above method, the initial blood pressure value can be corrected according to the tightness level of the wearable device worn by the user and the blood pressure correction model, so that the user can obtain a high-precision blood pressure value when wearing the wearable device autonomously and comfortably, and the user does not need to manually adjust the wristband of the wearable device before measuring blood pressure, which simplifies the user operation.

[0081] See also Figure 3A , Figure 3A FIG. 1 is another flow chart of the blood pressure measurement method disclosed in the embodiment of the present application. Figure 3A The blood pressure measurement method shown may include the following steps:

[0082] 301. Obtain the tightness level of the wearable device worn by the user.

[0083] In some embodiments, the tightness level may be represented in a vector form or a character form.

[0084] The tightness level in vector form may be obtained by one-hot encoding or dummy encoding, which is not limited in the present embodiment. The tightness level in character form may be text, numbers or letters.

[0085] Exemplarily, the preset tightness level of the wearable device may include 5 levels, namely, the first level, the second level, the third level, the fourth level and the fifth level. Among them, from the first level to the fifth level, the corresponding degree of fit with the user's skin gradually decreases, that is, the first level indicates that the wearable device is worn too tightly, the second level indicates that the wearable device is worn tightly, the third level indicates that the wearable device is worn moderately, the fourth level indicates that the wearable device is worn loosely, and the fifth level indicates that the wearable device is worn too loosely.

[0086] When the tightness level is represented by numbers, the first level can be represented by 1, the second level can be represented by 2, the third level can be represented by 3, the fourth level can be represented by 4, and the fifth level can be represented by 5.

[0087] When the tightness levels are represented by letters, the first level can be represented by a, the second level can be represented by b, the third level can be represented by c, the fourth level can be represented by d, and the fifth level can be represented by e.

[0088] When the tightness levels are represented by one-hot encoding, the first level can be represented by 10000, the second level can be represented by 01000, the third level can be represented by 00100, the fourth level can be represented by 00010, and the fifth level can be represented by 00001.

[0089] It should be noted that, for a detailed description of step 301, reference can be made to the introduction below step 201, which will not be repeated here.

[0090] 302. Determine the user's initial blood pressure value using the collected pulse wave signal.

[0091] Among them, the initial blood pressure value may be corrected based on the user's exercise state, or may not be corrected based on the user's exercise state, which is not limited in the embodiment of the present application.

[0092] In the case where the initial blood pressure value is corrected based on the user's exercise state, determining the initial blood pressure value of the user using the collected pulse wave signal may include:

[0093] Determining a first blood pressure value of the user using the collected pulse wave signal;

[0094] Get the user's exercise status;

[0095] The first blood pressure value is corrected according to the exercise state to obtain an initial blood pressure value.

[0096] The motion state may include a motion state or a static state.

[0097] Correcting the first blood pressure value according to the motion state to obtain the initial blood pressure value may include: when the user's motion state is the motion state, correcting the first blood pressure value using a preset compensation parameter to obtain the initial blood pressure value.

[0098] The compensation parameters may be obtained from a large amount of sample data.

[0099] In some embodiments, determining the first blood pressure value of the user using the collected pulse wave signal may include:

[0100] Preprocessing the collected pulse wave signal;

[0101] extracting the envelope of the preprocessed pulse wave signal;

[0102] A first blood pressure value of the user is calculated according to the envelope curve.

[0103] In some embodiments, preprocessing the collected pulse wave signal may include: performing digital filtering and / or wavelet decomposition and reconstruction on the collected pulse wave signal.

[0104] By implementing this method, after obtaining the first blood pressure value using the pulse wave signal, the first blood pressure value can be corrected according to the user's exercise state to obtain an initial blood pressure value, which is helpful to reduce the impact of the user's exercise state on the measurement accuracy of blood pressure.

[0105] 303. When the tightness level is not at the specified level, the user's target blood pressure value is obtained according to the initial blood pressure value, the tightness level and the blood pressure correction model.

[0106] Among them, when the tightness level of the wearable device worn by the user is a specified level, the offset between the initial blood pressure value and the target blood pressure value is less than the offset threshold. The specified level may represent that the tightness of the wearable device is moderate, which has little effect on the accuracy of blood pressure measurement and can be ignored in this application.

[0107] In an embodiment of the present application, when the above tightness level is at a specified level, the initial blood pressure value can be output as the user's target blood pressure value.

[0108] For example, when the tightness level is represented by one-hot encoding, the tightness level corresponding to the wearable device can be expressed as:

[0109] Assuming that the blood pressure correction model obtained through training is h and the initial blood pressure value is P0, the target blood pressure value after correction can be expressed as:

[0110] In some embodiments, when the blood pressure correction model h is a neural network model, the structure of the blood pressure correction model h can be seen in Figure 3B ,like Figure 3B As shown, the blood pressure correction model h may include an input layer, a hidden layer, and an output layer, and each layer of the blood pressure correction model h is composed of neuron nodes. Correspondingly, the calculation process of the target blood pressure value y can be described as follows:

[0111] In the input layer, the first weight matrix is ​​used to compare P0 and Perform linear combination to obtain the first combination result;

[0112] In the hidden layer, a second weight matrix is ​​used to linearly combine the first combination result to obtain a second combination result;

[0113] In the output layer, the second combination result is processed using the identity function to obtain y.

[0114] In some embodiments, after step 303, when the target blood pressure value is not within the specified blood pressure interval, a reminder message indicating that the user's blood pressure is abnormal may be output.

[0115] Among them, the blood pressure value in the specified blood pressure range is the normal blood pressure value.

[0116] It should be noted that when the target blood pressure value includes a target mean pressure value, a target systolic pressure value, and a target diastolic pressure value, the corresponding designated blood pressure interval may include a mean pressure value interval, a systolic pressure value interval, and a diastolic pressure value interval. When the target blood pressure value includes a target systolic pressure value and a target diastolic pressure value, the corresponding designated blood pressure interval may include a systolic pressure value interval and a diastolic pressure value interval. When the target blood pressure value includes a target systolic pressure value, the corresponding designated blood pressure interval may include a systolic pressure value interval. When the target blood pressure value includes a target diastolic pressure value, the corresponding designated blood pressure interval may include a diastolic pressure value interval.

[0117] Furthermore, when the target blood pressure value is not within the specified blood pressure interval, an alarm message may be sent to a terminal device bound to the wearable device, where the alarm message is used to indicate that the blood pressure of the user of the wearable device is abnormal.

[0118] By implementing this method, when the target blood pressure value is not within the specified blood pressure range, the user of the wearable device can be reminded by outputting a prompt message; or, other users can be informed by sending an alarm message to other devices.

[0119] In some embodiments, after step 303, the wearable device may output the target blood pressure value in a specified manner, wherein the specified manner may include but is not limited to: audio output, text output, or animation output.

[0120] In some embodiments, if the initial blood pressure value has not been corrected according to the user's exercise state (ie, is obtained directly from the pulse wave signal), after step 303, the target blood pressure value may continue to be corrected according to the user's exercise state.

[0121] By implementing the above method, when the tightness of the wearable device is not moderate, the initial blood pressure value is corrected according to the tightness level in combination with the blood pressure correction model. When the tightness of the wearable device is moderate, the initial blood pressure value can be directly used as the target blood pressure value without the need for correction using the blood pressure correction model, which is beneficial to improving the efficiency of blood pressure detection.

[0122] See also Figure 4 , Figure 4 FIG. 1 is a structural diagram of a blood pressure measuring device disclosed in an embodiment of the present application. Figure 4The blood pressure measurement device shown may include a tightness determination unit 401, a blood pressure measurement unit 402 and a blood pressure correction unit; wherein:

[0123] The tightness determination unit 401 is used to obtain the tightness level of the wearable device worn by the user; wherein the tightness level is used to indicate the degree of fit between the wearable device and the measurement part of the user;

[0124] The blood pressure measurement unit 402 is used to determine the initial blood pressure value of the user using the collected pulse wave signal;

[0125] The blood pressure correction unit 403 is used to obtain the user's target blood pressure value based on the initial blood pressure value, the tightness level and the blood pressure correction model; wherein the blood pressure correction model is a machine learning model trained based on multiple sample initial blood pressure values, the sample tightness level corresponding to each sample initial blood pressure value in the multiple sample initial blood pressure values, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

[0126] In some embodiments, the blood pressure correction unit 403 may be used to obtain the user's target blood pressure value according to the initial blood pressure value, the tightness level and the blood pressure correction model, and the method may specifically include:

[0127] The blood pressure correction unit 403 is used to obtain the user's target blood pressure value based on the initial blood pressure value, the tightness level and the blood pressure correction model when the tightness level is not at the specified level; wherein, when the tightness level of the wearable device worn by the user is at the specified level, the offset between the initial blood pressure value and the target blood pressure value is less than the offset threshold.

[0128] In some embodiments, the tightness level is represented in vector form or character form.

[0129] In some embodiments, the tightness level in vector form is obtained by one-hot encoding.

[0130] In some embodiments, the pulse wave signal is collected by an airbag of the wearable device during inflation;

[0131] The method in which the tightness determination unit 401 is used to obtain the tightness level of the wearable device worn by the user may specifically include: the tightness determination unit 401 is used to determine the tightness level of the wearable device worn by the user based on the inflation speed sequence of the airbag within a specified time period; wherein the specified time period includes multiple speed measurement cycles, and the inflation speed sequence includes the inflation speed corresponding to each speed measurement cycle in the multiple speed measurement cycles.

[0132] In some embodiments, the blood pressure measurement unit 402 may determine the initial blood pressure value of the user by using the collected pulse wave signal, which may specifically include:

[0133] The blood pressure measurement unit 402 is used to determine the first blood pressure value of the user using the collected pulse wave signal; and obtain the user's exercise state; and correct the first blood pressure value according to the exercise state to obtain an initial blood pressure value.

[0134] In some embodiments, the blood pressure correction model includes at least one of the following models: a vector machine model, a neural network model, a decision tree model, a random forest model, and a gradient boosting tree model.

[0135] In some embodiments, Figure 4 The blood pressure detection device shown may also include an abnormal warning unit ( Figure 4 (not shown), an abnormal warning unit is used for the blood pressure correction unit 403 to obtain the user's target blood pressure value based on the initial blood pressure value, tightness level and blood pressure correction model, and then output a reminder message indicating that the user's blood pressure is abnormal when the target blood pressure value is not within the specified blood pressure range.

[0136] See also Figure 5 , Figure 5 FIG. 1 is a structural diagram of a wearable device disclosed in an embodiment of the present application. Figure 5 The wearable device shown includes: a processor 510, a memory 520, a display unit 530, an input unit 540, a sensor 550, an audio circuit 560 and other components.

[0137] The processor 510 is the control center of the wearable device, and uses various interfaces and lines to connect the various parts of the entire wearable device. By running or executing the software programs and / or modules stored in the memory 520, and calling the data stored in the memory 520, the processor 510 performs various functions of the wearable device and processes data, thereby monitoring the wearable device as a whole. Optionally, the processor 510 may include one or more processing units; optionally, the processor 510 may integrate an application processor, which mainly processes operating devices, user interfaces, and application programs, etc. Of course, other processors may also be included, which are not listed here one by one.

[0138] The memory 520 can be used to store software programs and modules. The processor 510 executes various functional applications and data processing of the wearable device by running the software programs and modules stored in the memory 520. The memory 520 may mainly include a program storage area and a data storage area, wherein the program storage area may store operating devices, applications required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the wearable device (such as audio data, phone book, etc.), etc. In addition, the memory 520 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0139] The display unit 530 can be used to display information input by the user or information provided to the user and various menus of the wearable device. The display unit 530 may include a display panel. Optionally, the display panel may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. Further, the touch panel may cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 510 to determine the type of touch event. Subsequently, the processor 510 provides a corresponding visual output on the display panel according to the type of touch event. Among them, the touch panel and the display panel are not in Figure 5 The touch panel and the display panel can be used as two independent components to realize the input and output functions of the wearable device, or the touch panel and the display panel can be integrated to realize the input and output functions of the wearable device.

[0140] The input unit 540 can be used to receive input digital or character information, and to generate key signal input related to user settings and function control of the wearable device. Specifically, the input unit 540 may include a touch panel and other input devices. A touch panel, also known as a touch screen, can collect user touch operations on or near it (such as operations performed by a user using any suitable object or accessory such as a finger, stylus, etc. on or near the touch panel), and drive the corresponding connection device according to a pre-set program. In addition, a variety of types such as resistive, capacitive, infrared, and surface acoustic waves can be used to implement the touch panel. In addition to the touch panel, the input unit 540 may also include other input devices. Specifically, other input devices may include, but are not limited to, one or more of function keys (such as volume control keys, switch keys, etc.), trackballs, joysticks, etc.

[0141] The wearable device may also include at least one sensor 550, such as a magnetometer, a gyroscope sensor, a motion sensor, and other sensors. Specifically, the magnetometer is used to determine the orientation of the wearable device, and the gyroscope sensor can be used to determine the motion posture of the wearable device, which can be used for anti-shake shooting, navigation, and somatosensory game scenes. As a type of motion sensor, the accelerometer can detect the magnitude of acceleration in all directions, and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of wearable devices, such as horizontal and vertical screen switching, related games, magnetometer posture calibration, etc.; As for other sensors that can be configured on wearable devices, such as pressure gauges, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be repeated here.

[0142] The audio circuit 560 may include a speaker and a microphone, and may provide an audio interface between the user and the wearable device. The audio circuit 560 may transmit the electrical signal converted from the received audio data to the speaker, which is converted into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 560 and converted into audio data, and then the audio data is output to the processor 510 for processing, and then sent to another device through the video circuit, or the audio data is output to the memory 520 for further processing.

[0143] Although not shown, the wearable device may further include a power source and a camera. Optionally, the camera may be located at the front or rear of the wearable device, which is not limited in the present embodiment.

[0144] It is understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the wearable device. In other embodiments of the present application, the wearable device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0145] In the embodiment of the present application, the processor 510 also has the following functions:

[0146] Obtaining the tightness level of the wearable device worn by the user; wherein the tightness level is used to indicate the degree of fit between the wearable device and the measurement part of the user;

[0147] Using the collected pulse wave signal, determine the user's initial blood pressure value;

[0148] The user's target blood pressure value is obtained based on the initial blood pressure value, tightness level and blood pressure correction model; wherein the blood pressure correction model is a machine learning model trained based on multiple sample initial blood pressure values, the sample tightness level corresponding to each sample initial blood pressure value in the multiple sample initial blood pressure values, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

[0149] In the embodiment of the present application, the processor 510 also has the following functions:

[0150] When the tightness level is not at the specified level, the user's target blood pressure value is obtained based on the initial blood pressure value, the tightness level and the blood pressure correction model; wherein, when the tightness level of the wearable device worn by the user is at the specified level, the offset between the initial blood pressure value and the target blood pressure value is less than the offset threshold.

[0151] In the embodiment of the present application, the tightness level is represented in vector form or character form.

[0152] In the embodiment of the present application, the tightness level in vector form is obtained by one-hot encoding.

[0153] In the embodiment of the present application, the processor 510 also has the following functions:

[0154] Get the tightness level of the wearable device worn by the user, including:

[0155] The tightness level of the wearable device worn by the user is determined according to the inflation speed sequence of the airbag within a specified time period; wherein the specified time period includes multiple speed measurement cycles, and the inflation speed sequence includes the inflation speed corresponding to each speed measurement cycle in the multiple speed measurement cycles.

[0156] In the embodiment of the present application, the processor 510 also has the following functions:

[0157] Determining a first blood pressure value of the user using the collected pulse wave signal;

[0158] Get the user's exercise status;

[0159] The first blood pressure value is corrected according to the exercise state to obtain an initial blood pressure value.

[0160] In the embodiment of the present application, the blood pressure correction model includes at least one of the following models:

[0161] Vector machine model, neural network model, decision tree model, random forest model and gradient boosting tree model.

[0162] In the embodiment of the present application, the processor 510 also has the following functions:

[0163] When the target blood pressure value is not within the specified blood pressure interval, a reminder message indicating that the user's blood pressure is abnormal is output.

[0164] The embodiment of the present application discloses a computer-readable storage medium having executable program code stored thereon. When the executable program code is executed by a processor, the method described in the wearable device in the embodiment of the present application is implemented.

[0165] The embodiment of the present application discloses a computer program product. When the computer program product is executed on a computer, the computer implements the method described in the wearable device in the embodiment of the present application.

[0166] An embodiment of the present application discloses an application publishing platform, which is used to publish a computer program product. When the computer program product runs on a computer, the computer implements the method described in the wearable device in the embodiment of the present application.

[0167] It should be noted here that the description of the above storage medium and device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0168] It should be understood that "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in one embodiment" or "in some embodiments" appearing throughout the specification may not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments. The above description of each embodiment tends to emphasize the differences between the various embodiments, and the same or similar aspects can be referenced to each other. For the sake of brevity, this article will not repeat them.

[0169] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist at the same time, and object B exists alone.

[0170] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0171] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical or other forms.

[0172] The modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules; they may be located in one place or distributed on multiple network units; some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0173] In addition, all functional modules in the embodiments of the present application may be integrated into one processing unit, or each module may be a separate unit, or two or more modules may be integrated into one unit; the above-mentioned integrated modules may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0174] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, etc., various media that can store program codes.

[0175] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling the wearable device to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0176] The methods disclosed in several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0177] The features disclosed in several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0178] The features disclosed in several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0179] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A blood pressure measurement method, characterized in that: The method is applicable to a wearable device, and the method comprises: Obtaining a tightness level of the wearable device worn by the user; wherein the tightness level is used to indicate the degree of fit between the wearable device and the measurement part of the user; Determining the initial blood pressure value of the user using the collected pulse wave signal; The target blood pressure value of the user is obtained according to the initial blood pressure value, the tightness level and the blood pressure correction model; wherein the blood pressure correction model is a machine learning model trained according to multiple sample initial blood pressure values, the sample tightness level corresponding to each sample initial blood pressure value in the multiple sample initial blood pressure values, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

2. The method according to claim 1, characterized in that The step of obtaining the target blood pressure value of the user according to the initial blood pressure value, the tightness level and the blood pressure correction model includes: When the tightness level is not at the specified level, the target blood pressure value of the user is obtained according to the initial blood pressure value, the tightness level and the blood pressure correction model; wherein, when the tightness level of the wearable device worn by the user is the specified level, the offset between the initial blood pressure value and the target blood pressure value is less than the offset threshold.

3. The method according to claim 1, characterized in that The tightness level is represented in vector form or character form.

4. The method according to claim 3, characterized in that: The tightness level in vector form is obtained by one-hot encoding.

5. The method according to claim 1, characterized in that The pulse wave signal is collected by the airbag of the wearable device during the inflation process; The obtaining the tightness level of the wearable device worn by the user includes: The tightness level of the wearable device worn by the user is determined according to the inflation speed sequence of the airbag within the specified time period; wherein the specified time period includes multiple speed measurement cycles, and the inflation speed sequence includes the inflation speed corresponding to each speed measurement cycle in the multiple speed measurement cycles.

6. The method according to any one of claims 1 to 5, characterized in that ,, the method of determining the initial blood pressure value of the user by using the collected pulse wave signal includes: Determining a first blood pressure value of the user using the collected pulse wave signal; Acquire the user's motion status; The first blood pressure value is corrected according to the exercise state to obtain an initial blood pressure value.

7. The method according to any one of claims 1 to 5, characterized in that ,, the blood pressure correction model includes at least one of the following models: Vector machine model, neural network model, decision tree model, random forest model and gradient boosting tree model.

8. The method according to any one of claims 1 to 5, characterized in that After obtaining the target blood pressure value of the user according to the initial blood pressure value, the tightness level and the blood pressure correction model, the method further includes: When the target blood pressure value is not within the specified blood pressure interval, a reminder message indicating that the user's blood pressure is abnormal is output.

9. A blood pressure measuring device, characterized in that: The device is on a wearable device, and includes: A tightness determination unit, used to obtain a tightness level of the wearable device worn by the user; wherein the tightness level is used to indicate the degree of fit between the wearable device and the measurement part of the user; A blood pressure measurement unit, used to determine the initial blood pressure value of the user using the collected pulse wave signal; A blood pressure correction unit is used to obtain the target blood pressure value of the user based on the initial blood pressure value, the tightness level and the blood pressure correction model; wherein the blood pressure correction model is a machine learning model trained based on multiple sample initial blood pressure values, the sample tightness level corresponding to each sample initial blood pressure value in the multiple sample initial blood pressure values, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

10. A wearable device, characterized in that: include: A memory storing executable program code; and a processor coupled to the memory; The processor calls the executable program code stored in the memory, and when the executable program code is executed by the processor, the processor implements the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having executable program code stored thereon, characterized in that: When the executable program code is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

Images