Inflation control method, apparatus, blood pressure measurement device, and readable storage medium
By adaptively determining the airbag inflation cutoff threshold by acquiring pulse wave vibration signals, the problem of inaccurate measurement during the inflation process of traditional airbag wrist blood pressure measurement devices is solved, achieving higher measurement accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP LTD
- Filing Date
- 2024-12-05
- Publication Date
- 2026-06-05
AI Technical Summary
Traditional airbag wrist blood pressure measuring devices cannot accurately fill the gap between the user's wrist and the device during inflation, resulting in inaccurate blood pressure measurements.
By acquiring the user's pulse wave vibration signal, the inflation cutoff threshold of the airbag is adaptively determined, and the airbag inflation is controlled by an air pump to accurately fill the gap between the wrist and the device and compensate for the compression efficiency.
This improves the accuracy of blood pressure measurement devices, ensures that the airbag inflation process conforms to individual user differences, and enhances the precision of blood pressure parameter measurements.
Smart Images
Figure CN122140211A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wearable device technology, and in particular to an inflation control method, apparatus, blood pressure measuring device, and readable storage medium. Background Technology
[0002] Blood pressure is an important indicator of human health and has significant implications for users' lives and well-being. Therefore, blood pressure measurement devices are being used more and more frequently, especially cuff-type wrist blood pressure monitors, which are widely used due to their portability and lightweight design.
[0003] For cuff-type wrist blood pressure measurement devices, the user's blood pressure parameters are obtained by inflating the cuff to a fixed threshold. However, the traditional method of inflating the cuff has the problem of inaccurate blood pressure measurements. Summary of the Invention
[0004] This application provides an inflation control method, apparatus, blood pressure measuring device, and readable storage medium, which can improve the measurement accuracy of blood pressure parameters.
[0005] In a first aspect, embodiments of this application provide an inflation control method, which is applied to a blood pressure measuring device, the method comprising:
[0006] During the inflation of the air bladder of the blood pressure measuring device, the user's pulse wave vibration signal is acquired;
[0007] The inflation cutoff threshold of the airbag is determined based on the pulse wave vibration signal.
[0008] The air pump is controlled to inflate the airbag according to the inflation cutoff threshold.
[0009] Secondly, embodiments of this application provide an inflation control device, which is applied to a blood pressure measuring device, the device comprising:
[0010] The first acquisition module is used to acquire the user's pulse wave vibration signal during the inflation of the air bladder of the blood pressure measuring device.
[0011] The first determining module is used to determine the inflation cutoff threshold of the airbag based on the pulse wave vibration signal.
[0012] The first control module is used to control the air pump to inflate the airbag according to the inflation cutoff threshold.
[0013] Thirdly, embodiments of this application provide a blood pressure measuring device, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the inflation control method as described in the first aspect.
[0014] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the inflation control method as described in the first aspect.
[0015] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the inflation control method as described in the first aspect.
[0016] The above-mentioned inflation control method, device, blood pressure measuring device, and readable storage medium, wherein the inflation control method is applied to the blood pressure measuring device, during the inflation of the blood pressure measuring device's bladder, by acquiring the user's pulse wave vibration signal, can adaptively determine a suitable bladder inflation cutoff threshold based on the user's pulse wave vibration signal, and thereby control the air pump to inflate the blood pressure measuring device's bladder according to the determined suitable bladder inflation cutoff threshold, enabling the bladder to accurately fill the gap between the user's wrist and the blood pressure measuring device, and at the same time, can better compensate for the compression efficiency of the blood pressure measuring device, enabling the blood pressure measuring device to more accurately measure the user's blood pressure parameters, and improving the accuracy of the blood pressure measuring device in measuring the user's blood pressure parameters. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a diagram illustrating the application environment of the inflation control method in one embodiment.
[0019] Figure 2 This is a schematic diagram of the structure of the measuring body of a blood pressure measuring device in one embodiment;
[0020] Figure 3 This is a flowchart of an inflation control method in one embodiment;
[0021] Figure 4 A flowchart of an inflation control method in another embodiment;
[0022] Figure 5 This is a flowchart illustrating the training process of a machine learning model in one embodiment.
[0023] Figure 6 A flowchart of an inflation control method in another embodiment;
[0024] Figure 7 This is a schematic diagram of a scenario in one embodiment where a user is wearing a blood pressure measuring device;
[0025] Figure 8 This is a schematic diagram of the inflation process of a dual-bag blood pressure measurement device in one embodiment;
[0026] Figure 9 This is a structural block diagram of the inflation control device in one embodiment;
[0027] Figure 10 This is a structural block diagram of the inflation control device in another embodiment;
[0028] Figure 11 This is a structural block diagram of the inflation control device in another embodiment;
[0029] Figure 12 This is a structural block diagram of the inflation control device in another embodiment;
[0030] Figure 13 This is a structural block diagram of the inflation control device in another embodiment;
[0031] Figure 14 This is a structural block diagram of the inflation control device in another embodiment. Detailed Implementation
[0032] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0033] The inflation control method provided in this application embodiment can be applied to a blood pressure measuring device with two airbags, or it can be applied to a blood pressure measuring device with one airbag. The blood pressure measuring device may include a measuring body, a wristband, an airbag, a buckle, etc. Optionally, the airbag included in the blood pressure measuring device can be... Figure 1 The first and second airbags shown are independent of each other and can be inflated and deflated independently. Alternatively, the blood pressure measuring device may include a single airbag or multiple airbags, etc. Figure 1 The airbags shown are merely illustrative, and this embodiment does not limit the number of airbags included in the blood pressure measuring device. As an example, the measuring body includes, for instance, the airbags shown. Figure 2The device shown includes an air pump, air path, air valve, pressure sensor, and storage device. The pressure sensor detects the airbag pressure data during measurement, and the storage device stores the airbag pressure data. Optionally, the blood pressure measuring device in this embodiment can be a blood pressure watch, or a blood pressure monitor, etc.
[0034] First, it should be noted that the function of the air bladder in a blood pressure measuring device is to fill the gap between the wrist and the air bladder, and to compensate for the loss of wrist compression efficiency. However, due to differences in the physiological structure of the human wrist and individual wearing habits, the size of the wrist gap is not uniform. Furthermore, because different users have different blood pressure ranges, the wrist compression efficiency varies from user to user. Inflating the air bladder to a fixed threshold cannot precisely fill the gap in each person's wrist, nor can it adequately compensate for compression efficiency. This difference will significantly affect the accuracy of blood pressure measurement. Therefore, this application provides an inflation control method, device, blood pressure measuring device, and readable storage medium to improve the accuracy of blood pressure parameter measurement.
[0035] In one embodiment, such as Figure 3 As shown, an inflation control method is provided, which is applied to... Figure 1 Taking a blood pressure measuring device as an example, the explanation includes the following steps:
[0036] S201: During the inflation of the air bladder of the blood pressure measuring device, the user's pulse wave vibration signal is acquired.
[0037] Optionally, in this embodiment, upon receiving a measurement command, the blood pressure measuring device can control an air pump to inflate the airbag. During the inflation process, the blood pressure measuring device can acquire the airbag pressure value in real time through a pressure sensor within the device, and read the user's pulse wave vibration signal from the airbag pressure value. For example, the user's pulse wave vibration signal within one second can be acquired. Assuming there are n sampling points within this second, the acquired real-time pulse wave vibration signal of length n can be represented as: S = x1, x2, x3, ..., x n .
[0038] As an optional implementation, in this embodiment, after obtaining the user's pulse wave vibration signal, the obtained pulse wave vibration signal can be preprocessed. For example, abnormal signals in the obtained pulse wave vibration signal can be removed, or the obtained pulse wave signal can be normalized or otherwise preprocessed.
[0039] S202, Determine the inflation cutoff threshold of the airbag based on the pulse wave vibration signal.
[0040] It is understandable that pulse wave vibration signals generally appear slowly at first, then intensify, and then gradually flatten out. Therefore, as an optional implementation method, the inflation cutoff threshold of the airbag can be determined by analyzing the pulse wave vibration signal and based on the pressure value corresponding to the point where the pulse wave vibration signal appears slowly. For example, the sum of the pressure value corresponding to the point where the pulse wave vibration signal appears slowly and a preset adjustment threshold can be determined as the inflation cutoff threshold of the airbag.
[0041] As another optional implementation, in this embodiment, the acquired pulse wave vibration signal can be input into a preset neural network model, and the neural network model can be used to determine the inflation cutoff threshold of the airbag.
[0042] S203, controls the air pump to inflate the airbag according to the inflation cutoff threshold.
[0043] Optionally, in this embodiment, the airbag can be inflated by controlling the air pump, and the air pump can be stopped after the airbag pressure is inflated to a certain inflation cutoff threshold; or, the airbag can be inflated by controlling the air pump, and the airbag pressure can be maintained at the inflation cutoff threshold for a period of time after the airbag pressure is inflated to a certain inflation cutoff threshold, and then the air pump can be stopped after the airbag pressure is maintained at the inflation cutoff threshold.
[0044] In the above-mentioned inflation control method, during the inflation of the blood pressure measuring device's bladder, by acquiring the user's pulse wave vibration signal, the system can adaptively determine a suitable bladder inflation cutoff threshold based on the user's pulse wave vibration signal. This allows the air pump to control the inflation of the blood pressure measuring device's bladder according to the determined bladder inflation cutoff threshold, ensuring the bladder accurately fills the gap between the user's wrist and the blood pressure measuring device. Simultaneously, it better compensates for the compression efficiency of the blood pressure measuring device, enabling it to more accurately measure the user's blood pressure parameters and improving the accuracy of the blood pressure measurement.
[0045] In some scenarios, the characteristic sequence of the pulse wave vibration signal can be extracted, and the inflation cutoff threshold of the airbag can be determined based on the characteristic sequence of the pulse wave vibration signal. In one embodiment, such as... Figure 4 As shown, the above S202 includes:
[0046] S301, acquire the characteristic sequence of the pulse wave vibration signal.
[0047] Optionally, in this embodiment, the waveform features of the pulse wave vibration signal can be extracted and determined as the feature sequence of the pulse wave vibration signal.
[0048] Furthermore, since the pulse wave vibration signal is determined based on the real-time airbag pressure value acquired by the pressure sensor, in this embodiment, as another optional implementation, the pressure feature sequence of the airbag pressure value can be extracted in real-time during the process of controlling the air pump to inflate the airbag, and the extracted pressure feature sequence can be determined as the feature sequence of the pulse wave vibration signal. For example, assuming the acquired real-time pulse wave vibration signal of length n is represented as: S = x1, x2, x3, ..., x n Then, the feature sequence of length m extracted from the pulse wave vibration signal can be represented as:
[0049] S302, Determine the inflation cutoff threshold based on the feature sequence.
[0050] Optionally, in this embodiment, the feature sequence of the pulse wave vibration signal can be input into a preset neural network model, and the inflation cutoff threshold of the airbag can be determined through the neural network model; or, as another optional implementation, in this embodiment, the feature sequence of the pulse wave vibration signal can be feature-fitted, and the inflation cutoff threshold of the airbag can be determined based on the fitting result.
[0051] In this embodiment, since the characteristic sequence of the pulse wave vibration signal can accurately reflect the characteristic information of the pulse wave vibration signal, the inflation cutoff threshold of the airbag can be accurately determined based on the characteristic sequence of the pulse wave vibration signal, ensuring the accuracy of the determined inflation cutoff threshold. In addition, since the characteristic sequence of the pulse wave vibration signal is different for different users, the inflation cutoff threshold of the airbag can be adaptively determined based on the characteristic sequence of the pulse wave vibration signal of different users, so that different users can have an inflation cutoff threshold suitable for themselves.
[0052] The following section will explain the specific implementation method of determining the inflation cutoff threshold of the airbag based on the characteristic sequence of the pulse wave vibration signal.
[0053] Method 1, in one embodiment, the above-mentioned S302 includes:
[0054] Step A: Input the feature sequence into the preset machine learning model to obtain the inflation cutoff threshold; the machine learning model is obtained by training the initial machine learning model based on the sample pulse wave vibration signal of the sample user.
[0055] The preset machine learning model is obtained by training an initial machine learning model based on the sample pulse wave vibration signal of the sample user. In this embodiment, the feature sequence of the obtained user's pulse wave vibration signal can be input into the preset machine learning model, and the output of the machine learning model can be used as the inflation cutoff threshold.
[0056] As an optional implementation method, such as Figure 5 As shown, the training process of the above machine learning model may include:
[0057] S401, mark the target threshold points of the sample pulse wave vibration signal to obtain the marked pulse wave vibration signal.
[0058] As described in the above embodiments, the pulse wave vibration signal generally appears slowly, then intensifies, and then gradually flattens out. In this embodiment, as an optional implementation, the point where the pulse wave vibration signal just begins to appear slowly can be determined as the target threshold point. Optionally, in this embodiment, the appearance time of the target threshold point of the sample pulse wave vibration signal can be marked to obtain a marked pulse wave vibration signal.
[0059] S402, feature extraction is performed on the labeled pulse wave vibration signal to obtain the sample feature sequence.
[0060] Optionally, in this embodiment, the waveform features of the labeled pulse wave vibration signal can be extracted and determined as the sample feature sequence of the labeled pulse wave vibration signal.
[0061] In addition, since the sample pulse wave vibration signal is determined based on the pressure value of the airbag sample obtained in real time by the pressure sensor, in this embodiment, as another optional implementation method, the sample pressure feature sequence of the airbag sample pressure value can also be extracted in real time during the process of controlling the air pump to inflate the airbag, and the extracted sample pressure feature sequence can be determined as the sample feature sequence of the sample pulse wave vibration signal.
[0062] S403, input the sample feature sequence into the initial machine learning model to obtain the predicted inflation cutoff threshold.
[0063] Optionally, in this embodiment, the sample feature sequence can be input into the initial machine learning model, and the output of the initial machine learning model can be determined as the predicted inflation cutoff threshold.
[0064] S404. Based on the predicted inflation cutoff threshold and the target threshold point, the initial machine learning model is learned to obtain the machine learning model.
[0065] Optionally, in this embodiment, the value of the loss function of the initial machine learning model can be determined based on the predicted inflation cutoff threshold output by the initial machine learning model and the target threshold point in the labeled pulse wave vibration signal. The parameters in the initial machine learning model can be adjusted according to the value of the loss function, and the initial machine learning model can be trained to obtain the above-mentioned machine learning model.
[0066] In this embodiment, by inputting the feature sequence of the pulse wave vibration signal into a preset machine learning model, the inflation cutoff threshold can be obtained quickly using the machine learning model, ensuring the efficiency of obtaining the inflation cutoff threshold. Furthermore, for different users, after obtaining the user's pulse wave vibration signal, the process of extracting the feature sequence of the pulse wave vibration signal and then using the preset machine learning model to obtain the inflation cutoff threshold is relatively simple, ensuring the efficiency of obtaining the inflation cutoff threshold corresponding to different users.
[0067] Method 2, in one embodiment, the above-mentioned S302 includes:
[0068] Step B involves fitting the feature sequence using a preset fitting method to obtain the inflation cutoff threshold.
[0069] The preset fitting method can include any one of linear fitting, polynomial fitting, or exponential fitting. Optionally, in this embodiment, the inflation cutoff threshold of the airbag can be obtained by performing feature fitting on the pulse wave vibration signal and using the result of feature fitting.
[0070] In this embodiment, the process of fitting the characteristic sequence of the pulse wave vibration signal using a preset fitting method is relatively simple and requires less computation. Therefore, the characteristic sequence of the pulse wave vibration signal can be quickly fitted using a preset fitting method, thereby enabling the timely acquisition of the airbag inflation cutoff threshold and ensuring the timeliness of acquiring the airbag inflation cutoff threshold.
[0071] In some scenarios, to obtain more accurate blood pressure parameter measurements, multiple factors can be considered when determining the airbag inflation cutoff threshold to ensure its accuracy. As an optional implementation, in one embodiment, such as... Figure 6 As shown, the above method also includes:
[0072] S501, obtains the user's physiological characteristics.
[0073] The user's physiological characteristics can include height, age, weight, wrist circumference, etc. Optionally, in this embodiment, when the user sends a start command to the blood pressure measuring device and starts the device, the device can issue a voice or text prompt reminding the user to input their physiological characteristics.
[0074] S502, determine the inflation cutoff threshold based on the feature sequence and the user's physiological characteristics.
[0075] Optionally, in this embodiment, the feature sequence of the pulse wave vibration signal and the user's physiological characteristics can be input into a preset machine learning model, and the output of the machine learning model can be determined as the inflation cutoff threshold of the airbag; or, the aforementioned preset fitting method can be used to fit the feature sequence of the pulse wave vibration signal and the user's physiological characteristics to determine the inflation cutoff threshold of the airbag.
[0076] Optionally, in this embodiment, as an optional implementation method, the inflation cutoff threshold can also be determined based on the user's physiological characteristics. That is, when determining the airbag inflation cutoff threshold, only the influence of the user's physiological characteristics on the airbag inflation cutoff threshold is considered. Optionally, in this scenario, the user's physiological characteristics can be input into a preset machine learning model, and the output of the machine learning model can be determined as the airbag inflation cutoff threshold; or, the aforementioned preset fitting method can be used to fit the user's physiological characteristics to determine the airbag inflation cutoff threshold.
[0077] In this embodiment, by acquiring the user's physiological characteristics, the inflation cutoff threshold of the airbag can be determined based on the characteristic sequence of the pulse wave vibration signal and the user's physiological characteristics. This allows for consideration from multiple perspectives during the determination of the airbag inflation cutoff threshold, fully taking into account all influencing factors that affect the airbag inflation cutoff threshold, thereby ensuring the accuracy of the determined airbag inflation cutoff threshold.
[0078] This embodiment will explain the specific process of acquiring the characteristic sequence of the pulse wave vibration signal. In one embodiment, S301 includes:
[0079] Step C: Extract features from at least one feature dimension of the pulse wave vibration signal to obtain a feature sequence.
[0080] In this embodiment, the feature dimensions may include various different feature dimensions such as the morphological feature dimension, time domain feature dimension, and frequency domain feature dimension of the pulse wave vibration signal.
[0081] Optionally, in this embodiment, features can be extracted from the pulse wave vibration signal from at least one feature dimension to obtain a feature sequence of the pulse wave vibration signal. For example, features can be extracted from the morphological feature dimension to obtain a feature sequence of the pulse wave vibration signal; or, features can be extracted from the time domain feature dimension to obtain a feature sequence of the pulse wave vibration signal; or, features can be extracted from the frequency domain feature dimension to obtain a feature sequence of the pulse wave vibration signal; or, features can be extracted from both the morphological feature dimension and the time domain feature dimension to obtain a feature sequence of the pulse wave vibration signal. This embodiment will not explain each feature extraction method of the pulse wave vibration signal from at least one feature dimension. Various feasible feature extraction methods that can extract features from the pulse wave vibration signal from at least one feature dimension to obtain a feature sequence of the pulse wave vibration signal can be applied in this solution.
[0082] In this embodiment, feature extraction is performed on the pulse wave vibration signal in at least one feature dimension, which makes the extracted feature sequence information richer and ensures the accuracy of the feature sequence of the obtained pulse wave vibration signal, thereby ensuring the accuracy of the airbag inflation cutoff threshold determined by the feature sequence of the pulse wave vibration signal.
[0083] In some scenarios, blood pressure measuring devices may include dual airbags, namely a first airbag and a second airbag. This embodiment will explain in detail the inflation control method for a dual-airbag blood pressure measuring device. In one embodiment, if the blood pressure measuring device includes a first airbag and a second airbag, the above-mentioned S203 includes:
[0084] Step D: Control the air pump to inflate the first airbag, and when the air pressure of the first airbag reaches the inflation cutoff threshold, control the air pump to stop inflating the first airbag, and control the air pump to inflate the second airbag.
[0085] First, it's important to clarify that in dual-bag blood pressure monitors, the first bag inflates for two purposes: one is to fill the gap between the monitor and the wrist when the user wears it, and the other is to compensate for any loss of wrist compression efficiency. Regarding the first purpose, when a user wears the monitor, the physiological structure of the wrist and the wristband cannot perfectly fit together, resulting in a physical gap between them. Figure 7As shown. Regarding the second function, compared to upper arm compression, wrist compression, because the wristband is narrower than the upper arm airbag, results in lower compression efficiency under the same inflation pressure, making it less likely to rupture the wrist artery. A wider airbag can more easily rupture the blood vessel, while a shorter airbag requires greater pressure. The second function of the first airbag inflation is to compensate for the loss of wrist compression efficiency. Optionally, in this embodiment, please continue to refer to... Figure 1 The second airbag can be located on the side near the wristband, and the first airbag can be located outside the second airbag. Alternatively, the first airbag can be located on the side near the wristband, and the second airbag can be located outside the first airbag. The first airbag, the second airbag, the wristband, and the measuring body of the blood pressure measuring device are connected.
[0086] In this embodiment, the inflation process of the dual-bag blood pressure measurement device can be found in [reference needed]. Figure 8 The flowchart shown in this example illustrates that, during the inflation of the first airbag of the blood pressure measuring device, the user's pulse wave vibration signal is acquired. Based on the user's pulse wave vibration signal, the inflation cutoff threshold of the first airbag is determined. The air pump is then controlled to inflate the first airbag, and when the air pressure of the first airbag reaches the inflation cutoff threshold, the air pump stops inflating the first airbag and begins inflating the second airbag. The determined inflation cutoff threshold is tailored to the specific circumstances of each user, ensuring that the first airbag accurately fills the wrist gap while better compensating for compression efficiency, thus guaranteeing the accuracy of the user's blood pressure parameter measurements.
[0087] Furthermore, the air pump can be controlled to inflate the second airbag until a preset cutoff condition is reached. This cutoff condition can be determined based on the peak value of the user's pulse wave vibration signal. For example, the cutoff condition could be that the acquired pulse wave vibration signal is two, three, or four times the peak value of the pulse wave vibration signal, etc. The peak value of the pulse wave vibration signal is determined during the inflation of the second airbag; that is, the first peak point of the pulse wave vibration signal during inflation can be defined as the peak value. Alternatively, as an optional implementation, the user's target pulse wave vibration signal when the aforementioned cutoff condition is reached can be acquired, and the user's blood pressure value can be determined based on the target pulse wave vibration signal.
[0088] In this embodiment, if the blood pressure measuring device includes a first airbag and a second airbag, after determining the inflation cutoff threshold based on the user's pulse wave vibration signal, the first airbag is inflated by controlling the air pump, and the second airbag is inflated when the air pressure of the first airbag reaches the inflation cutoff threshold. Since the determined inflation cutoff threshold is adaptively determined based on the user's pulse wave vibration signal, the first airbag can accurately fill the gap in the wrist while better compensating for the compression efficiency, thus ensuring the accuracy of the measurement of the user's blood pressure parameters.
[0089] To facilitate understanding by those skilled in the art, the inflation control method provided in this disclosure will be described in detail below using a dual-bag blood pressure measuring device as an example. This method may include:
[0090] S1, during the inflation of the first air bladder of the blood pressure measuring device, acquires the user's pulse wave vibration signal.
[0091] S2, extract features from at least one feature dimension of the pulse wave vibration signal to obtain the feature sequence of the pulse wave vibration signal.
[0092] S3, obtains the user's physiological characteristics.
[0093] S4, input the feature sequence of the pulse wave vibration signal and / or the user's physiological characteristics into a preset machine learning model to obtain the inflation cutoff threshold of the first airbag; or, use a preset fitting method to fit the feature sequence of the pulse wave vibration signal and / or the user's physiological characteristics to obtain the inflation cutoff threshold of the first airbag.
[0094] S5, control the air pump to inflate the first airbag, and when the air pressure of the first airbag reaches the inflation cutoff threshold, control the air pump to stop inflating the first airbag, and control the air pump to inflate the second airbag.
[0095] S6, control the air pump to inflate the second airbag until a preset cutoff condition is reached; the cutoff condition is determined based on the peak value of the user's pulse wave vibration signal; the peak value of the pulse wave vibration signal is determined during the inflation of the second airbag.
[0096] S7: Obtain the user's target pulse wave vibration signal when the cutoff condition is met, and determine the user's blood pressure value based on the target pulse wave signal.
[0097] It should be noted that the descriptions of the above steps can be found in the relevant descriptions in the above embodiments, and their effects are similar, so they will not be repeated here.
[0098] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0099] Based on the same inventive concept, this application also provides an inflation control device for implementing the inflation control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more inflation control device embodiments provided below can be found in the limitations of the inflation control method described above, and will not be repeated here.
[0100] In one embodiment, such as Figure 9 As shown, an inflation control device is provided for use in blood pressure measuring equipment. The inflation control device includes: a first acquisition module 10, a first determination module 11, and a first control module 12, wherein:
[0101] The first acquisition module 10 is used to acquire the user's pulse wave vibration signal during the inflation of the air bladder of the blood pressure measuring device.
[0102] The first determining module 11 is used to determine the inflation cutoff threshold of the airbag based on the pulse wave vibration signal.
[0103] The first control module 12 is used to control the air pump to inflate the airbag according to the inflation cutoff threshold.
[0104] The inflation control device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0105] Based on the above embodiments, such as Figure 10 As shown, optionally, the first determining module 11 mentioned above includes: an acquisition unit 111 and a determining unit 112, wherein:
[0106] Acquisition unit 111 is used to acquire the characteristic sequence of pulse wave vibration signal.
[0107] The determining unit 112 is used to determine the inflation cutoff threshold based on the feature sequence.
[0108] The inflation control device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0109] Based on the above embodiments, please continue to refer to Figure 10 Optionally, the aforementioned determining unit 112 is specifically used to input the feature sequence into a preset machine learning model to obtain the inflation cutoff threshold; the machine learning model is obtained by training an initial machine learning model based on the sample pulse wave vibration signal of the sample user.
[0110] The inflation control device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0111] Based on the above embodiments, such as Figure 11 As shown, optionally, the above-mentioned device further includes: a labeling module 13, an extraction module 14, a second acquisition module 15, and a training module 16, wherein:
[0112] The annotation module 13 is used to annotate the target threshold points of the sample pulse wave vibration signal to obtain the annotated pulse wave vibration signal.
[0113] The extraction module 14 is used to extract features from the labeled pulse wave vibration signal to obtain the sample feature sequence.
[0114] The second acquisition module 15 is used to input the sample feature sequence into the initial machine learning model to obtain the predicted inflation cutoff threshold.
[0115] Training module 16 is used to train the initial machine learning model based on the predicted inflation cutoff threshold and the target threshold point to obtain the machine learning model.
[0116] The inflation control device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0117] Based on the above embodiments, please continue to refer to Figure 10 Optionally, the aforementioned determining unit 112 is specifically used to fit the feature sequence using a preset fitting method to obtain the inflation cutoff threshold.
[0118] The inflation control device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0119] Based on the above embodiments, such as Figure 12 As shown, optionally, the above-mentioned device further includes: a third acquisition module 17 and a second determination module 18, wherein:
[0120] The third acquisition module 17 is used to acquire the user's physiological characteristics.
[0121] The second determining module 18 is used to determine the inflation cutoff threshold based on the feature sequence and the user's physiological characteristics.
[0122] The inflation control device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0123] Based on the above embodiments, please continue to refer to Figure 10 Optionally, the acquisition unit 111 is specifically used to extract features from at least one feature dimension of the pulse wave vibration signal to obtain a feature sequence.
[0124] The inflation control device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0125] Based on the above embodiments, if the blood pressure measuring device includes a first airbag and a second airbag, such as Figure 13 As shown, optionally, the first control module 12 mentioned above includes: a control unit 121, wherein:
[0126] The control unit 121 is used to control the air pump to inflate the first airbag, and when the air pressure of the first airbag reaches the inflation cutoff threshold, control the air pump to stop inflating the first airbag, and control the air pump to inflate the second airbag.
[0127] The inflation control device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0128] Based on the above embodiments, such as Figure 14 As shown, optionally, the above-mentioned device further includes: a second control module 19 and a third determining module 20, wherein:
[0129] The second control module 19 is used to control the air pump to inflate the second airbag until a preset cutoff condition is reached; the cutoff condition is determined based on the peak value of the user's pulse wave vibration signal; the peak value of the pulse wave vibration signal is determined during the inflation of the second airbag.
[0130] The third determining module 20 is used to acquire the user's target pulse wave vibration signal when the cutoff condition is met, and to determine the user's blood pressure value based on the target pulse wave signal.
[0131] The inflation control device provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.
[0132] Each module in the aforementioned inflation control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.
[0133] In one embodiment, a blood pressure measuring device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0134] During the inflation of the air bladder of the blood pressure measuring device, the user's pulse wave vibration signal is acquired;
[0135] The inflation cutoff threshold of the airbag is determined based on the pulse wave vibration signal.
[0136] The air pump is controlled to inflate the airbag according to the inflation cutoff threshold.
[0137] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0138] Obtain the characteristic sequence of the pulse wave vibration signal;
[0139] The inflation cutoff threshold is determined based on the characteristic sequence.
[0140] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0141] The feature sequence is input into the preset machine learning model to obtain the inflation cutoff threshold; the machine learning model is obtained by training the initial machine learning model based on the sample pulse wave vibration signal of the sample user.
[0142] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0143] The target threshold points of the sample pulse wave vibration signal are marked to obtain the marked pulse wave vibration signal;
[0144] Feature extraction is performed on the labeled pulse wave vibration signal to obtain the sample feature sequence;
[0145] Input the sample feature sequence into the initial machine learning model to obtain the predicted inflation cutoff threshold;
[0146] The initial machine learning model is trained based on the predicted inflation cutoff threshold and the target threshold point to obtain the machine learning model.
[0147] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0148] The feature sequence is fitted using a preset fitting method to obtain the inflation cutoff threshold.
[0149] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0150] Obtain the user's physiological characteristics;
[0151] The inflation cutoff threshold is determined based on the feature sequence and the user's physiological characteristics.
[0152] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0153] Feature extraction is performed on at least one feature dimension of the pulse wave vibration signal to obtain a feature sequence.
[0154] In one embodiment, if the blood pressure measuring device includes a first airbag and a second airbag, the processor, when executing the computer program, further performs the following steps:
[0155] The air pump is controlled to inflate the first airbag, and when the air pressure of the first airbag reaches the inflation cutoff threshold, the air pump is controlled to stop inflating the first airbag, and the air pump is controlled to inflate the second airbag.
[0156] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0157] The air pump is controlled to inflate the second airbag until a preset cutoff condition is reached; the cutoff condition is determined based on the peak value of the user's pulse wave vibration signal; the peak value of the pulse wave vibration signal is determined during the inflation of the second airbag.
[0158] The system acquires the user's target pulse wave vibration signal when the cutoff condition is met, and determines the user's blood pressure value based on the target pulse wave signal.
[0159] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:
[0160] During the inflation of the air bladder of the blood pressure measuring device, the user's pulse wave vibration signal is acquired;
[0161] The inflation cutoff threshold of the airbag is determined based on the pulse wave vibration signal.
[0162] The air pump is controlled to inflate the airbag according to the inflation cutoff threshold.
[0163] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0164] Obtain the characteristic sequence of the pulse wave vibration signal;
[0165] The inflation cutoff threshold is determined based on the characteristic sequence.
[0166] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0167] The feature sequence is input into the preset machine learning model to obtain the inflation cutoff threshold; the machine learning model is obtained by training the initial machine learning model based on the sample pulse wave vibration signal of the sample user.
[0168] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0169] The target threshold points of the sample pulse wave vibration signal are marked to obtain the marked pulse wave vibration signal;
[0170] Feature extraction is performed on the labeled pulse wave vibration signal to obtain the sample feature sequence;
[0171] Input the sample feature sequence into the initial machine learning model to obtain the predicted inflation cutoff threshold;
[0172] The initial machine learning model is trained based on the predicted inflation cutoff threshold and the target threshold point to obtain the machine learning model.
[0173] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0174] The feature sequence is fitted using a preset fitting method to obtain the inflation cutoff threshold.
[0175] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0176] Obtain the user's physiological characteristics;
[0177] The inflation cutoff threshold is determined based on the feature sequence and the user's physiological characteristics.
[0178] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0179] Feature extraction is performed on at least one feature dimension of the pulse wave vibration signal to obtain a feature sequence.
[0180] In one embodiment, if the blood pressure measuring device includes a first airbag and a second airbag, the computer program, when executed by the processor, further performs the following steps:
[0181] The air pump is controlled to inflate the first airbag, and when the air pressure of the first airbag reaches the inflation cutoff threshold, the air pump is controlled to stop inflating the first airbag, and the air pump is controlled to inflate the second airbag.
[0182] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0183] The air pump is controlled to inflate the second airbag until a preset cutoff condition is reached; the cutoff condition is determined based on the peak value of the user's pulse wave vibration signal; the peak value of the pulse wave vibration signal is determined during the inflation of the second airbag.
[0184] The system acquires the user's target pulse wave vibration signal when the cutoff condition is met, and determines the user's blood pressure value based on the target pulse wave signal.
[0185] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:
[0186] During the inflation of the air bladder of the blood pressure measuring device, the user's pulse wave vibration signal is acquired;
[0187] The inflation cutoff threshold of the airbag is determined based on the pulse wave vibration signal.
[0188] The air pump is controlled to inflate the airbag according to the inflation cutoff threshold.
[0189] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0190] Obtain the characteristic sequence of the pulse wave vibration signal;
[0191] The inflation cutoff threshold is determined based on the characteristic sequence.
[0192] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0193] The feature sequence is input into the preset machine learning model to obtain the inflation cutoff threshold; the machine learning model is obtained by training the initial machine learning model based on the sample pulse wave vibration signal of the sample user.
[0194] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0195] The target threshold points of the sample pulse wave vibration signal are marked to obtain the marked pulse wave vibration signal;
[0196] Feature extraction is performed on the labeled pulse wave vibration signal to obtain the sample feature sequence;
[0197] Input the sample feature sequence into the initial machine learning model to obtain the predicted inflation cutoff threshold;
[0198] The initial machine learning model is trained based on the predicted inflation cutoff threshold and the target threshold point to obtain the machine learning model.
[0199] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0200] The feature sequence is fitted using a preset fitting method to obtain the inflation cutoff threshold.
[0201] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0202] Obtain the user's physiological characteristics;
[0203] The inflation cutoff threshold is determined based on the feature sequence and the user's physiological characteristics.
[0204] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0205] Feature extraction is performed on at least one feature dimension of the pulse wave vibration signal to obtain a feature sequence.
[0206] In one embodiment, if the blood pressure measuring device includes a first airbag and a second airbag, the computer program, when executed by the processor, further performs the following steps:
[0207] The air pump is controlled to inflate the first airbag, and when the air pressure of the first airbag reaches the inflation cutoff threshold, the air pump is controlled to stop inflating the first airbag, and the air pump is controlled to inflate the second airbag.
[0208] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0209] The air pump is controlled to inflate the second airbag until a preset cutoff condition is reached; the cutoff condition is determined based on the peak value of the user's pulse wave vibration signal; the peak value of the pulse wave vibration signal is determined during the inflation of the second airbag.
[0210] The system acquires the user's target pulse wave vibration signal when the cutoff condition is met, and determines the user's blood pressure value based on the target pulse wave signal.
[0211] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.
[0212] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0213] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0214] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. An inflation control method, characterized in that, The inflation control method is applied to a blood pressure measuring device, and the method includes: During the inflation of the air bladder of the blood pressure measuring device, the user's pulse wave vibration signal is acquired; The inflation cutoff threshold of the airbag is determined based on the pulse wave vibration signal. The air pump is controlled to inflate the airbag according to the inflation cutoff threshold.
2. The method according to claim 1, characterized in that, Determining the inflation cutoff threshold of the airbag based on the pulse wave vibration signal includes: Obtain the characteristic sequence of the pulse wave vibration signal; The inflation cutoff threshold is determined based on the characteristic sequence.
3. The method according to claim 2, characterized in that, Determining the inflation cutoff threshold based on the feature sequence includes: The feature sequence is input into a preset machine learning model to obtain the inflation cutoff threshold; the machine learning model is obtained by training an initial machine learning model based on the sample pulse wave vibration signal of the sample user.
4. The method according to claim 3, characterized in that, The training process of the machine learning model includes: The target threshold points of the sample pulse wave vibration signal are marked to obtain the marked pulse wave vibration signal. Feature extraction is performed on the labeled pulse wave vibration signal to obtain a sample feature sequence; The sample feature sequence is input into the initial machine learning model to obtain the predicted inflation cutoff threshold; The initial machine learning model is trained based on the predicted inflation cutoff threshold and the target threshold point to obtain the machine learning model.
5. The method according to claim 2, characterized in that, Determining the inflation cutoff threshold based on the feature sequence includes: The feature sequence is fitted using a preset fitting method to obtain the inflation cutoff threshold.
6. The method according to any one of claims 2 to 5, characterized in that, The method further includes: Obtain the user's physiological characteristics; The inflation cutoff threshold is determined based on the feature sequence and the user's physiological characteristics.
7. The method according to any one of claims 2 to 5, characterized in that, The acquisition of the characteristic sequence of the pulse wave vibration signal includes: The feature sequence is obtained by extracting features from at least one feature dimension of the pulse wave vibration signal.
8. The method according to claim 1, characterized in that, If the blood pressure measuring device includes a first airbag and a second airbag, the step of controlling the air pump to inflate the airbag according to the inflation cutoff threshold includes: The air pump is controlled to inflate the first airbag, and when the air pressure of the first airbag reaches the inflation cutoff threshold, the air pump is controlled to stop inflating the first airbag, and the air pump is controlled to inflate the second airbag.
9. The method according to claim 8, characterized in that, The method further includes: The air pump is controlled to inflate the second airbag until a preset cutoff condition is reached; the cutoff condition is determined based on the peak value of the user's pulse wave vibration signal; the peak value of the pulse wave vibration signal is determined during the inflation of the second airbag; The target pulse wave vibration signal of the user is acquired when the cutoff condition is met, and the blood pressure value of the user is determined based on the target pulse wave signal.
10. An inflation control device, characterized in that, The inflation control device is used in a blood pressure measuring device, and the device includes: The first acquisition module is used to acquire the user's pulse wave vibration signal during the inflation of the air bladder of the blood pressure measuring device. The first determining module is used to determine the inflation cutoff threshold of the airbag based on the pulse wave vibration signal. The first control module is used to control the air pump to inflate the airbag according to the inflation cutoff threshold.
11. A blood pressure measuring device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, the processor causes the processor to perform the steps of the inflation control method as described in any one of claims 1 to 9.
12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 9.
13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.