Blood pressure measurement method, apparatus, device, and storage medium

Abstract

Embodiments of the present application disclose a blood pressure measurement method, device, equipment and storage medium. The blood pressure measurement method can comprise: acquiring a tightness level of a wearable device worn by a user; wherein the tightness level is used to indicate 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 a 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 accuracy of blood pressure can be improved without the user adjusting the wristband of the wearable device.

Description

Technical Field

[0001] This application relates to the field of electronic equipment technology, and in particular to a blood pressure measurement method, device, equipment, and storage medium. Background Technology

[0002] Wearable devices (such as blood pressure watches or blood pressure wristbands) are smaller and lighter, allowing users to wear them for extended periods, thus meeting the need for long-term and real-time blood pressure monitoring.

[0003] In practice, it has been found that wearing a wearable device too loosely or too tightly during blood pressure measurement can significantly affect the accuracy of the measurement. Therefore, before measuring blood pressure, users usually need to manually adjust the wristband of the wearable device to a suitable tightness, which is a rather cumbersome process. Summary of the Invention

[0004] This application provides a blood pressure measurement method, apparatus, device, and storage medium, which can improve the measurement accuracy of blood pressure without requiring the user to adjust the wristband of the wearable device.

[0005] The first aspect of this application provides a blood pressure measurement method, which is applicable to wearable devices, and the method includes:

[0006] 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 user's measurement area;

[0007] The user's initial blood pressure value is determined using the collected pulse wave signal;

[0008] 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 initial blood pressure values, the tightness level corresponding to each initial blood pressure value, and the target blood pressure value corresponding to each initial blood pressure value.

[0009] A second aspect of this application provides a blood pressure measuring device, the device being housed in a wearable device, the device comprising:

[0010] A tightness determination unit 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 user's measurement area;

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

[0012] A blood pressure correction unit 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, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

[0013] A third aspect of this application provides a wearable device, including:

[0014] Memory containing executable program code;

[0015] and the 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 as described in the first aspect of the embodiments of this application.

[0017] A fourth aspect of this application provides a computer-readable storage medium having executable program code stored thereon, wherein when the executable program code is executed by a processor, it implements the method described in the first aspect of this application.

[0018] The fifth aspect of this application discloses a computer program product that, when run on a computer, causes the computer to execute the method described in the first aspect of this application.

[0019] The sixth aspect of this application discloses an application publishing platform for publishing computer program products, wherein when the computer program product is run on a computer, the computer executes the method described in the first aspect of this application.

[0020] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0021] In this embodiment, 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 user's measurement site; 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, 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. This allows users to obtain a high-precision blood pressure value while wearing the wearable device comfortably and independently. Furthermore, it eliminates the need for users to manually adjust the wristband of the wearable device before measuring blood pressure, thus simplifying the user operation. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments and the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application, and other drawings can be obtained based on these drawings.

[0024] Figure 1A This is a scene illustration of the blood pressure measurement method disclosed in the embodiments of this application;

[0025] Figure 1B This is a schematic diagram of the physical structure of the wearable device 10 disclosed in the embodiments of this application;

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

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

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

[0029] Figure 2 This is a flowchart illustrating a blood pressure measurement method disclosed in an embodiment of this application;

[0030] Figure 3A This is another flowchart illustrating the blood pressure measurement method disclosed in the embodiments of this application;

[0031] Figure 3B This is a structural illustration of the blood pressure correction model h disclosed in the embodiments of this application;

[0032] Figure 4 This is a structural illustration of a blood pressure measuring device disclosed in an embodiment of this application;

[0033] Figure 5 This is a structural illustration of a wearable device disclosed in an embodiment of this application. Detailed Implementation

[0034] This application provides a blood pressure measurement method, apparatus, device, and storage medium, which can improve the measurement accuracy of blood pressure without requiring the user to adjust the wristband of the wearable device.

[0035] To enable those skilled in the art to better understand the present application, the technical solutions of the embodiments of the present application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. All embodiments based on the present application should fall within the scope of protection of the present application.

[0036] It should be noted that, in this application, the words "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 construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0037] "At least one" means one or more, while "more" means two or more. "At least one" means one or more types, while "more" means two or more types. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A, B, and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer 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, and c can be single or multiple.

[0038] Please see Figure 1A , Figure 1A This is a scene illustration of the blood pressure measurement method disclosed in the embodiments of this application. Figure 1A The scene illustration shown includes a wearable device 10 and a user 20; 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 compresses the user 20's wrist or upper arm to obtain the pulse wave signal in the artery, and then obtain the user 20's blood pressure based on the pulse wave signal.

[0039] In practice, it was found that when user 20 wears wearable device 10, if the device is too loose, the pressure in the airbag will be greater than the pressure at the radial artery, resulting in a higher blood pressure reading. Conversely, if the device is too tight, the pressure in the airbag will be less than the pressure at the radial artery, resulting in a lower blood pressure reading. Therefore, wearing wearable device 10 too loosely or too tightly significantly affects the accuracy of blood pressure measurement. To reduce the impact of tightness on measurement accuracy, user 20 usually needs to manually adjust the wristband of wearable device 10 to a suitable tightness before measurement, which is a rather cumbersome process.

[0040] In the technical solution of this application, before obtaining the initial blood pressure value, the wearable device can first obtain the tightness level of the user 20 wearing the wearable device 10, 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. This allows the user 20 to obtain a high-precision blood pressure value while wearing the wearable device 10 comfortably and independently, and eliminates the need for the user 20 to manually adjust the wristband of the wearable device 10 to a suitable tightness before measuring blood pressure, thus simplifying the user operation.

[0041] The tightness rating of the wearable device 10 is used to indicate the degree of fit between the wearable device 10 and the measurement area of ​​the user 20.

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

[0043] Please see Figure 1B , Figure 1B The wearable device 10 shown includes a wristband 101, an airbag 102, a measuring body 103, and a buckle 104. The airbag 102 is located inside one side of the wristband 101. When the wearable device 10 is worn by the user 20, the airbag 102 fits against the user 20's wrist or upper arm. The buckle 104 connects the wristbands 101 on different sides. When measuring the user 20's blood pressure, the measuring body 10 can inflate the airbag 102 to compress the user 20's wrist or upper arm to obtain a pulse wave signal in the artery.

[0044] The measuring body 103 may include an air pump, which can inflate the airbag 102. It should be noted that the number and structure of the airbags can be designed according to product requirements; the attached diagram is for illustrative purposes only and is not intended to limit the design.

[0045] Based on the physical structure of the wearable device 10, when the user 20 wears the wearable device 10 on their wrist, the tightness level of the wearable device 10 can indicate the degree of fit between the wristband 101 of the wearable device 10 and the wrist tissue of the user 20. The following is an illustrative example with reference to the figures:

[0046] Please see Figure 1C , Figure 1C The diagram shows the wearable device 10 at level one tightness. Figure 1C As shown, the user's wrist tissue does not fully conform to the wristband 101 of the wearable device 10.

[0047] Please see Figure 1D , Figure 1D The diagram shows the wearable device 10 at level two in terms of tightness. Figure 1D As shown, the user's wrist tissue fits perfectly against the wristband 101 of the wearable device 10.

[0048] Please see Figure 1E , Figure 1E The diagram shows the wearable device 10 at level three in terms of tightness. Figure 1E As shown, the user's wrist tissue fits perfectly against the wristband 101 of the wearable device 10, and the wristband 101 is in a taut state.

[0049] It should be noted that, in Figures 1C-1E Of the three tightness levels shown, the first level indicates the least degree of fit between the wrist tissue and the wristband 101, the third level indicates the greatest degree of fit between the wrist tissue and the wristband 101, and the second level indicates a moderate degree of fit between the wrist tissue and the wristband 101.

[0050] It should be noted that, Figures 1C-1E The number of tightness levels indicated is for illustrative purposes only and is not a limitation on the total number of tightness levels preset in the wearable device 10. The total number of tightness levels preset in the wearable device 10 can also be 5, 10, 15 or 20, or more. This application embodiment does not limit this number.

[0051] The technical solution of this application will be further described below with reference to specific embodiments:

[0052] Please see Figure 2 , Figure 2 This is a flowchart illustrating a blood pressure measurement method disclosed in an embodiment of this 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] The tightness rating indicates the degree of fit between the wearable device and the user's measurement area.

[0055] In this embodiment of the 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 a user wearing a wearable device can be obtained in ways including but not limited to the following:

[0057] Method 1: Obtain the pressure signal corresponding to the airbag. This pressure signal can refer to the pressure signal during the process from the start of airbag inflation to the pressure value inside the airbag reaching the preset pressure threshold. The tightness level of the wearable device is determined based on the pressure slope of this pressure signal.

[0058] The greater the pressure slope, the better the fit between the wearable device and the user's skin tissue.

[0059] The wearable device can have a pre-set first-level list, which can include multiple tightness levels and a corresponding pressure slope for each tightness level. After obtaining the pressure slope, the wearable device can 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. This pressure signal can refer to the pressure signal during the process from the start of airbag inflation to the pressure value inside the airbag reaching the preset pressure threshold. Based on the pressurization time corresponding to this pressure signal, determine the tightness level of the wearable device.

[0061] The longer the pressure application time, the less the wearable device adheres to the user's skin tissue.

[0062] The wearable device can have a pre-set second-level list, which can include multiple tightness levels and the corresponding pressure time for each tightness level. After obtaining the pressure time, the wearable device can use the tightness level in the second-level list that matches the pressure time as the tightness level of the wearable device.

[0063] Method 3: 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.

[0064] The specified time period refers to the period before the pressure inside the airbag reaches a preset pressure threshold.

[0065] In some embodiments, determining the tightness level of a wearable device worn by a user based on the inflation rate sequence of the airbag over a specified time period may include:

[0066] Calculate the target index value based on the airbag inflation rate sequence within a specified time period; wherein the target index value includes the mean of inflation rates corresponding to all rate measurement cycles, or the median of inflation rates corresponding to all rate measurement cycles.

[0067] Based on the target indicator value, determine the tightness level of the wearable device worn by the user.

[0068] The wearable device can have a pre-set third-level list, which can include multiple tightness levels and a target index value for each tightness level. After obtaining the target index value, the wearable device can 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 user wearing the wearable device when it is detected that the wearable device is being worn, or when a blood pressure measurement operation is detected. This application embodiment does not limit this.

[0070] 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 gesture operation, the blood pressure measurement operation can be two consecutive arm swings or three consecutive arm swings. When the blood pressure measurement operation includes voice operation, the blood pressure measurement operation can be inputting the audio "Measure blood pressure". When the blood pressure measurement operation includes touch operation, the blood pressure measurement operation can be two consecutive screen taps.

[0072] 202. Use the collected pulse wave signal to determine the user's initial blood pressure value.

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

[0074] To be more accurate, in this embodiment of the application, the pulse wave signal can characterize the change of the pressure value inside the airbag of the wearable device over time after the pressure value inside the airbag exceeds a preset pressure threshold.

[0075] It should be noted that the preset pressure threshold refers to the pressure value inside the airbag when the pulse wave signal appears, and the preset pressure threshold can 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: initial mean blood pressure, initial systolic blood pressure, and initial diastolic blood pressure.

[0077] 203. Based on the initial blood pressure value, the above-mentioned tightness level, and the blood pressure correction model, the user's target blood pressure value is obtained.

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

[0079] 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 while wearing the wearable device comfortably and independently, 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] Please see Figure 3A , Figure 3A This is another flowchart illustrating the blood pressure measurement method disclosed in the embodiments of this 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 rating may be characterized in vector form or character form, etc.

[0084] The tightness level in vector form can be obtained through one-hot encoding or dummy encoding, and this application embodiment does not limit this. The tightness level in character form can be text, numbers, or letters, etc.

[0085] For example, the wearable device may have five preset tightness levels: Level 1, Level 2, Level 3, Level 4, and Level 5. From Level 1 to Level 5, the degree of fit to the user's skin gradually decreases; that is, Level 1 indicates the wearable device is too tight, Level 2 indicates it is relatively tight, Level 3 indicates it is moderately tight, Level 4 indicates it is relatively loose, and Level 5 indicates it is too loose.

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

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

[0088] When the tightness level is represented by a unique thermal code, the first level can be represented by 10000, the second level by 01000, the third level by 00100, the fourth level by 00010, and the fifth level by 00001.

[0089] It should be noted that a detailed description of step 301 can be found in the description below step 201 above, and will not be repeated here.

[0090] 302. Use the collected pulse wave signal to determine the user's initial blood pressure value.

[0091] The initial blood pressure value may be corrected based on the user's exercise status or may not be corrected based on the user's exercise status; this application embodiment does not impose any limitations on this.

[0092] If the initial blood pressure value is corrected based on the user's exercise status, the initial blood pressure value of the user can be determined using the collected pulse wave signal, which may include:

[0093] The user's initial blood pressure value is determined using the collected pulse wave signal;

[0094] Obtain the user's movement status;

[0095] The initial blood pressure value is obtained by correcting the first blood pressure value based on the exercise status.

[0096] The state of motion can include either a state of motion or a state of rest.

[0097] The process of correcting the first blood pressure value based on the user's exercise state to obtain the initial blood pressure value can include: when the user is in an exercise state, correcting the first blood pressure value using preset compensation parameters to obtain the initial blood pressure value.

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

[0099] In some embodiments, determining a user's first blood pressure value using the acquired pulse wave signal may include:

[0100] The acquired pulse wave signals are preprocessed;

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

[0102] The user's first blood pressure value is calculated based on the envelope.

[0103] In some embodiments, preprocessing the acquired pulse wave signal may include: performing digital filtering and / or wavelet decomposition and reconstruction on the acquired 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 the initial blood pressure value, which helps to reduce the impact of the user's exercise state on the accuracy of blood pressure measurement.

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

[0106] Specifically, when the tightness of the wearable device worn by the user is at a specified level, the deviation between the initial blood pressure value and the target blood pressure value is less than a deviation threshold. The specified level indicates that the tightness of the wearable device is moderate, which has little impact on the accuracy of blood pressure measurement and can be ignored in this application.

[0107] In this embodiment of the application, when the 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 characterized by unique thermal coding, the tightness level corresponding to a wearable device can be expressed as:

[0109] Assuming the trained blood pressure correction model is h, and the initial blood pressure value is P0, then the corrected target blood pressure value 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 found in [reference needed]. Figure 3B ,like Figure 3B As shown, the blood pressure correction model h can 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 weight P0 and Perform a linear combination to obtain the first combination result;

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

[0113] The second combination result is processed using an identity function in the output layer to obtain y.

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

[0115] The blood pressure values ​​for the specified blood pressure range are considered normal blood pressure values.

[0116] It should be noted that when the target blood pressure value includes the target mean blood pressure, target systolic blood pressure, and target diastolic blood pressure, the corresponding specified blood pressure interval can include the mean blood pressure interval, systolic blood pressure interval, and diastolic blood pressure interval. When the target blood pressure value includes both target systolic and target diastolic blood pressure, the corresponding specified blood pressure interval can include both systolic and diastolic blood pressure intervals. When the target blood pressure value includes the target systolic blood pressure, the corresponding specified blood pressure interval can include the systolic blood pressure interval. When the target blood pressure value includes the target diastolic blood pressure, the corresponding specified blood pressure interval can include the diastolic blood pressure interval.

[0117] Furthermore, when the target blood pressure value is not within the specified blood pressure range, an alarm message can be sent to the terminal device bound to the wearable device. This alarm message is used to indicate that the wearable device user's blood pressure 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 alerted by outputting a prompt message; alternatively, other users can be notified 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. This 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 status (i.e., it is obtained directly from the pulse wave signal), the target blood pressure value can continue to be corrected according to the user's exercise status after step 303.

[0121] By implementing the above method, when the tightness of the wearable device is not appropriate, the initial blood pressure value is corrected according to the tightness level and combined with the blood pressure correction model. When the tightness of the wearable device is appropriate, 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 helps to improve the efficiency of blood pressure detection.

[0122] Please see Figure 4 , Figure 4 This is a structural illustration of a blood pressure measuring device disclosed in an embodiment of this application. Figure 4The blood pressure measuring device shown may include a tightness determination unit 401, a blood pressure measuring unit 402, and a blood pressure correction unit; wherein:

[0123] 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 user's measurement area;

[0124] The blood pressure measurement unit 402 is used to determine the user's initial blood pressure value 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. The blood pressure correction model is a machine learning model trained based on multiple initial blood pressure values, the tightness level corresponding to each initial blood pressure value, and the target blood pressure value corresponding to each initial blood pressure value.

[0126] In some embodiments, the method by which the blood pressure correction unit 403 obtains the user's target blood pressure value based on the initial blood pressure value, the tightness level, and the blood pressure correction model 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 of the wearable device 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 rating is represented in vector or character form.

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

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

[0131] The tightness determination unit 401 may specifically 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. 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 method by which the blood pressure measuring unit 402 determines the user's initial blood pressure value using the acquired pulse wave signal may specifically include:

[0133] The blood pressure measurement unit 402 is used to determine a user's first blood pressure value using the acquired pulse wave signal; and to acquire the user's exercise state; and to correct the first blood pressure value based on 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: vector machine model, neural network model, decision tree model, random forest model, and gradient boosting tree model.

[0135] In some embodiments, Figure 4 The blood pressure monitoring device shown may also include an abnormality warning unit. Figure 4 (Not shown), an abnormal warning unit is used by the blood pressure correction unit 403 to 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 after the initial blood pressure value, the tightness level and the blood pressure correction model are obtained.

[0136] Please see Figure 5 , Figure 5 This is a structural illustration of a wearable device disclosed in an embodiment of this application. Figure 5 The wearable device shown includes components such as a processor 510, a memory 520, a display unit 530, an input unit 540, a sensor 550, and an audio circuit 560.

[0137] The processor 510 serves as the control center of the wearable device, connecting various parts of the device via interfaces and lines. It executes software programs and / or modules stored in the memory 520, and accesses data stored in the memory 520, thereby performing various functions and processing data to monitor the wearable device as a whole. Optionally, the processor 510 may include one or more processing units; alternatively, it may integrate an application processor, which primarily handles operating devices, user interfaces, and applications. Other processors may also be included, which are not listed here.

[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. The program storage area may store applications required for operating the device and at least one function (such as sound playback function, image playback function, etc.); the data storage area may store data created based on the use of the wearable device (such as audio data, phone book, etc.). In addition, the memory 520 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0139] The display unit 530 can be used to display information input by the user or information provided to the user, as well as various menus of the wearable device. The display unit 530 may include a display panel, optionally configured as a liquid crystal display (LCD), organic light-emitting diode (OLED), or similar display panel. Furthermore, a touch panel may cover the display panel. When the touch panel detects a touch operation on or near it, it transmits the information to the processor 510 to determine the type of touch event. Subsequently, the processor 510 provides corresponding visual output on the display panel based on the type of touch event. The touch panel and the display panel are not located on the same surface. Figure 5 As shown above, the touch panel and display panel can be used as two separate components to realize the input and output functions of the wearable device, or they can be integrated to realize the input and output functions of the wearable device.

[0140] Input unit 540 can be used to receive input numerical or character information and generate key signal inputs related to user settings and function control of the wearable device. Specifically, input unit 540 may include a touch panel and other input devices. A touch panel, also known as a touchscreen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel) and drive corresponding connected devices according to a pre-set program. Furthermore, touch panels can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel, 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 buttons, power buttons, etc.), trackballs, joysticks, etc.

[0141] Wearable devices may also include at least one sensor 550, such as a magnetometer, gyroscope, motion sensor, and other sensors. Specifically, the magnetometer is used to determine the orientation of the wearable device, and the gyroscope can be used to determine the movement posture of the wearable device, which can be used for image stabilization, navigation, and motion-sensing game scenarios. As a type of motion sensor, the accelerometer can detect the magnitude of acceleration in various directions and, when stationary, can detect the magnitude and direction of gravity, which can be used for applications that identify the posture of the wearable device, such as landscape / portrait switching, related games, and magnetometer posture calibration. Other sensors that wearable devices may also be equipped with, such as pressure gauges, barometers, hygrometers, thermometers, and infrared sensors, will not be elaborated here.

[0142] The audio circuit 560 may include a speaker and a microphone, providing an audio interface between the user and the wearable device. The audio circuit 560 can convert received audio data into electrical signals and transmit them to the speaker, where the speaker converts them into sound signals for output. Conversely, the microphone converts collected sound signals into electrical signals, which are then received by the audio circuit 560, converted back into audio data, and output to the processor 510 for processing. The audio data is then transmitted via video circuitry to, for example, another device, or output to the memory 520 for further processing.

[0143] Although not shown, wearable devices may also include a power source and a camera. Optionally, the camera may be positioned on the front or rear of the wearable device; this embodiment does not limit the location in this application.

[0144] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the wearable device. In other embodiments of this application, the wearable device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

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

[0146] Obtain the tightness rating of the wearable device worn by the user; whereby the tightness rating indicates the degree of fit between the wearable device and the user's measurement area.

[0147] The user's initial blood pressure value is determined using the collected pulse wave signal;

[0148] 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. The blood pressure correction model is a machine learning model trained based on multiple initial blood pressure values, the tightness level corresponding to each initial blood pressure value, and the target blood pressure value corresponding to each initial blood pressure value.

[0149] In this embodiment of the 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; where the user wears the wearable device at the specified tightness level, the deviation between the initial blood pressure value and the target blood pressure value is less than the deviation threshold.

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

[0152] In this embodiment, the tightness level in vector form is obtained through one-hot encoding.

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

[0154] Obtain 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 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.

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

[0157] The user's initial blood pressure value is determined using the collected pulse wave signal;

[0158] Obtain the user's movement status;

[0159] The initial blood pressure value is obtained by correcting the first blood pressure value based on the exercise status.

[0160] In this application embodiment, 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 this embodiment of the application, the processor 510 also has the following functions:

[0163] When the target blood pressure value is not within the specified blood pressure range, an alert message indicating abnormal blood pressure is output to the user.

[0164] This application discloses a computer-readable storage medium storing executable program code thereon. When the executable program code is executed by a processor, it implements the method described in the wearable device of this application.

[0165] This application discloses a computer program product that, when run on a computer, enables the computer to implement the method described in the wearable device of this application.

[0166] This application discloses an application publishing platform for publishing computer program products. When the computer program product is run on a computer, the computer enables the wearable device described in this application to implement the method described in the embodiment.

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

[0168] It should be understood that the phrases "one embodiment," "an embodiment," or "some embodiments" mentioned throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment," "in one embodiment," or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; 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 embodiments of this application. The sequence numbers of the above-described embodiments are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The descriptions of the various embodiments above tend to emphasize the differences between the various embodiments; their similarities or commonalities can be referred to mutually, and for the sake of brevity, they will not be repeated here.

[0169] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three kinds of relationships. For example, object A and / or object B can represent three situations: object A exists alone, object A and object B exist simultaneously, and object B exists alone.

[0170] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

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

[0172] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0173] In addition, each functional module in the various embodiments of this application can be integrated into one processing unit, or each module can be a separate unit, or two or more modules can be integrated into one unit; the integrated modules can be implemented in hardware or in the form of hardware plus software functional units.

[0174] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0175] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause the wearable device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

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

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

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

[0179] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for measuring blood pressure, characterized in that, The method is applicable to wearable devices, and the method includes: 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 user's measurement area, and the tightness level is represented in vector form, which is obtained through one-hot encoding; 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 initial blood pressure values, the tightness level corresponding to each initial blood pressure value, and the target blood pressure value corresponding to each initial blood pressure value.

2. The method according to claim 1, characterized in that, The process of obtaining the user's target blood pressure value based on 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 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 user wears the wearable device at the specified tightness 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 pulse wave signal is collected by the airbag of the wearable device during the inflation process; The process of 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 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 of the multiple speed measurement cycles.

4. The method according to any one of claims 1-3, characterized in that, The process of determining the user's initial blood pressure value using the acquired pulse wave signal includes: The user's first blood pressure value is determined using the collected pulse wave signal; Obtain the user's motion state; The first blood pressure value is corrected based on the exercise state to obtain the initial blood pressure value.

5. The method according to any one of claims 1-3, 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.

6. The method according to any one of claims 1-3, characterized in that, After obtaining the user's target blood pressure value based on 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 range, an alert message indicating that the user's blood pressure is abnormal is output.

7. A blood pressure measuring device, characterized in that, The device is located on a wearable device, and the device includes: A tightness determination unit 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 user's measurement area, the tightness level is represented in vector form, and the tightness level in vector form is obtained through one-hot encoding; A blood pressure measurement unit is used to determine the user's initial blood pressure value using the collected pulse wave signal; The blood pressure correction unit 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, and the sample target blood pressure value corresponding to each sample initial blood pressure value.

8. A wearable device, characterized in that, include: Memory containing executable program code; and the 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 as described in any one of claims 1-6.

9. A computer-readable storage medium having executable program code stored thereon, characterized in that, When the executable program code is executed by the processor, it implements the method as described in any one of claims 1-6.

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