Blood pressure measurement apparatus

Abstract

A blood pressure measurement apparatus, comprising a camera, which is used for collecting a body surface image of the body surface in a first area on the body of a user to be subjected to measurement; a body surface vibration sensor, which is used for collecting vibration waves of the body surface in a second area on the body of said user; and a processor, which is used for calculating a blood pressure value of said user on the basis of the body surface image collected by the camera within a first time and the vibration waves collected by the body surface vibration sensor within the first time, and outputting the blood pressure value. In this way, a blood pressure value can be calculated by collecting dual-point information by means of a camera and a body surface vibration sensor, such that convenient blood pressure measurement is realized, and the accuracy is higher.

Description

A blood pressure detection device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on June 14, 2023, with application number 202310707580.7 and application name “A Blood Pressure Detection Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of artificial intelligence (AI) technology, and in particular to a blood pressure detection device. Background Art

[0003] In daily life, people are increasingly concerned about their health. Blood pressure, as a key physiological indicator, has gradually gained attention. To determine the current blood pressure value, blood pressure testing is often necessary. Therefore, providing a convenient and highly accurate blood pressure monitoring device is an urgent technical challenge.

[0004] Summary of the Invention

[0005] The present application provides a blood pressure detection device that can conveniently perform blood pressure detection with high accuracy.

[0006] The present application provides a blood pressure detection device, comprising: a camera for capturing a surface image of a body surface within a first area on the body of a user to be tested; a surface vibration sensor for capturing vibration waves of a body surface within a second area on the body of the user to be tested; and a processor for calculating the blood pressure value of the user to be tested based on the surface image captured by the camera within a first time and the vibration waves captured by the surface vibration sensor within the first time, and outputting the blood pressure value.

[0007] In this way, by collecting dual-point information through the camera and the body surface vibration sensor, the time difference related to blood flow velocity can be obtained, and the blood pressure value can be calculated based on this time difference. This makes blood pressure testing convenient and highly accurate.

[0008] In a possible implementation, the first area is the face and / or neck of the user to be measured; and the second area is the wrist of the user to be measured.

[0009] In one possible implementation, the camera, body surface vibration sensor, and processor are all configured in a wearable device. This allows blood pressure monitoring using the wearable device, increasing the flexibility of blood pressure monitoring.

[0010] In one possible implementation, a camera is configured in a terminal device, a body vibration sensor is configured in a wearable device, and a processor is configured in either the terminal device or the wearable device. The terminal device and the wearable device establish a communication connection via a network. This allows blood pressure testing to be performed using both the terminal device and the wearable device, increasing the flexibility of blood pressure testing.

[0011] In a possible implementation, the vibration wave includes a tidal wave.

[0012] In one possible implementation, when calculating the blood pressure value of a user to be measured based on a body surface image captured by a camera within a first period of time and vibration waves captured by a body surface vibration sensor within a first period of time, the processor is specifically configured to: select at least one first pixel from the R channel based on the pixel values ​​of each pixel in the R channel of the body surface image captured by the camera during a first phase of the first period of time, wherein the pixel value of the first pixel exhibits periodic variation within the first phase; and calculate the blood pressure value of the user to be measured based on the pixel value of at least one first pixel in the body surface image captured by the camera during a second phase of the first period of time and vibration waves captured by the body surface vibration sensor during the second phase of the first phase of time, wherein the first phase is earlier than the second phase. For example, taking the first region as the face of the user to be measured, the pixel value of a pixel in the facial image reaches a peak value because blood flows to the face during heart contraction, while the pixel value of the same pixel in the facial image reaches a valley value because blood flows to the heart during heart diastole. When a pixel exhibits periodic variation over a period of time, it indicates that the pixel value variation of the pixel is closely related to the heartbeat, and thus the pixel can be selected for subsequent calculations. In addition, since blood pressure is red, the pixels in the R channel of the image can be used for calculation. Furthermore, after filtering out the pixels in the R channel, the blood pressure of the user can be calculated by analyzing the fluctuations of the filtered pixels within a certain period and the fluctuations of the vibration wave within the same period.

[0013] In one possible implementation, there is a time difference between the peaks of the first waveform and the peaks of the second waveform, which is used to reflect the blood flow rate in the body of the user being tested. The first waveform is obtained based on the pixel value of at least one first pixel in the body surface image captured by the camera in the second phase; the second waveform is obtained based on the vibration waves captured by the body surface vibration sensor in the second phase. Thus, the blood pressure value can be calculated based on the time difference between the peaks of the two waveforms.

[0014] In one possible implementation, when calculating the blood pressure value of a user to be measured based on the pixel value of at least one first pixel on the body surface image captured by the camera in the second phase and the vibration waves captured by the body surface vibration sensor in the second phase, the processor is specifically configured to: perform first-order and second-order derivative calculations on the pixel value of the at least one first pixel in the second phase, and combine the calculation results with the pixel value of the at least one first pixel in the second phase to obtain a pixel value set; perform first-order and second-order derivative calculations on the vibration waves captured by the body surface vibration sensor in the second phase, and combine the calculation results with the vibration waves captured by the body surface vibration sensor in the second phase to obtain a vibration wave set; and process the pixel value set and the vibration wave set using a neural network model to obtain a blood pressure value. In this way, the neural network model can calculate the blood pressure value. Furthermore, by performing first-order derivative calculations on the waveform, changes in the waveform's slope or velocity can be detected, and by performing second-order derivative calculations on the waveform, changes in the waveform's concavity or acceleration can be detected. Utilizing this information can further improve the accuracy of blood pressure calculation.

[0015] In a possible implementation, the blood pressure detection device further includes: a display screen for displaying blood pressure values. In this way, the user can intuitively obtain blood pressure information through the display screen.

[0016] In a possible implementation, the blood pressure detection device further includes a reminder for reminding the user to complete the blood pressure detection, so that the user can quickly know whether the blood pressure detection is completed through the reminder. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The following is a brief introduction to the drawings required for describing the embodiments or prior art.

[0018] FIG1 is a schematic structural diagram of a blood pressure detection device provided in an embodiment of the present application;

[0019] FIG2 is a schematic diagram comparing a waveform diagram of a pixel value and a waveform diagram of a vibration wave provided in an embodiment of the present application;

[0020] FIG3 is a schematic diagram of a process for calculating blood pressure values ​​provided in an embodiment of the present application;

[0021] FIG4 is a schematic diagram of another process for calculating blood pressure values ​​provided in an embodiment of the present application;

[0022] FIG5 is a flow chart of a blood pressure detection method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The term "and / or" as used herein describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. The symbol " / " as used herein indicates that the related objects are in an "or" relationship, for example, A / B means either A or B.

[0024] The terms "first" and "second" in this specification and claims are used to distinguish different objects rather than to describe a specific order of objects. For example, "first response message" and "second response message" are used to distinguish different response messages rather than to describe a specific order of response messages.

[0025] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0026] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more, for example, multiple processing units means two or more processing units, etc.; multiple elements means two or more elements, etc.

[0027] For example, Figure 1 shows a schematic diagram of the structure of a blood pressure detection device provided in an embodiment of the present application. As shown in Figure 1 , the blood pressure detection device 100 may include: a camera 110 , a body surface vibration sensor 120 , and a processor 130 .

[0028] Camera 110 is primarily used to capture surface images of a first region of the user's body. For example, the first region may be the user's face and / or neck. Of course, the first region may also be any other region of the user's body. Furthermore, the first region is not obscured by clothing or other objects.

[0029] The body surface vibration sensor 120 is primarily used to collect vibration waves on the surface of the user's body within a second region. For example, the second region may be the user's wrist. Of course, the second region may also be other regions on the user's body. Furthermore, the second region may or may not be covered by clothing or other objects, depending on the specific circumstances. For example, the body surface vibration sensor 120 may be a radar sensor, an ultrasonic sensor, or a mechanical sensor. In some embodiments, the body surface vibration sensor 120 may be a low-frequency resonant radar, which can be a micro-vibration sensor on the human body surface operating in a low-frequency band (e.g., 2.4 GHz to 2.49 GHz). Because low-frequency resonant radar detects physical vibrations, it is less affected by noise and can therefore capture more detailed information related to blood pressure. This enables it to detect micro-vibrations on the skin surface caused by blood flowing through blood vessels, and thus to detect tidal waves, such as the return wave after blood reaches the tip of a finger, as shown in regions 23 and 24 in Figure 2. Of course, the body surface vibration sensor 120 may also be a radar such as photoplethysmography (PPG).

[0030] The processor 130 is mainly used to calculate the blood pressure value of the user to be measured based on the body surface image collected by the camera 110 within the first time and the vibration wave collected by the body surface vibration sensor 120 within the first time, and output the blood pressure value. Exemplarily, the body surface image collected by the camera 110 within the first time can constitute a video, and each frame of the video can be a body surface image. In this embodiment, the processor 130 can input the acquired body surface image and vibration wave into a pre-trained neural network model, so that the neural network model outputs the blood pressure value of the user to be measured. Exemplarily, the processor 130 can be a general-purpose processor or a dedicated processor. For example, the processor 130 can include a central processing unit (CPU) and / or a neural network processing unit (NPU). In some embodiments, the processor 130 can be an NPU, wherein the NPU can quickly process input information by drawing on the structure of a biological neural network, such as drawing on the transmission mode between neurons in the human brain, and can also continuously self-learn.

[0031] In some embodiments, before calculating the blood pressure value, the processor 130 may first filter out at least one first pixel from the red (red, R) channel based on the pixel values ​​of each pixel in the red channel of the body surface image captured by the camera within the second time. The pixel value of the first pixel changes periodically within the second time. Since the color of blood pressure is red, the first pixel selected in the embodiment of the present application is the pixel on the R channel. Of course, the processor 130 may also filter out the at least one first pixel through the body surface image captured by the camera 110 in the first stage within the first time; and then, calculate the blood pressure value of the user to be measured by using the body surface image captured by the camera 110 in the second stage within the first time and the vibration wave captured by the body surface vibration sensor 120 in the second stage. The end point of the first stage is the same as the starting point of the second stage, or the end point of the first stage is earlier than the starting point of the second stage, that is, the first stage is earlier than the second stage. For example, when the first time is 9:15am-9:18am, the first stage may be 9:15am-9:16am, the second stage may be 9:16am-9:18am, or the second stage may be 9:17am-9:18am.

[0032] The following describes the principles of selecting the first pixel point and calculating the blood pressure value by the processor 130.

[0033] In the embodiment of the present application, when using the blood pressure detection device 100 to perform blood pressure detection, the camera 110 may first continuously capture multiple body surface images (also referred to as body surface images) of a first area on the body of the user to be detected over a period of time, and input the captured multiple images into the processor 130. The processor 130 may then perform statistical analysis on the pixel values ​​of each pixel in the R channel of these images to select pixel points whose pixel values ​​show periodic changes during the time period when the camera 110 captures these multiple images.

[0034] After screening out the first pixel point, the camera 110 can continue to collect multiple images of the body surface in the first area on the body of the user to be tested within a period of time (of course, the camera 110 can also continue to collect images during the process of screening out the first pixel point, which is not limited here), and the collected images are input into the processor 130. At the same time, the body surface vibration sensor 120 is used to collect the vibration waves of the body surface in the second area on the body of the user to be tested during this period of time, and the collected vibration waves are input into the processor 130. After obtaining the images collected by the camera 110, the processor 130 can extract the pixel value of the first pixel point in each image. Through the extracted pixel value of the first pixel point, the processor 130 can obtain a waveform diagram obtained by the pixel value of the first pixel point, such as the waveform diagram of the pixel value shown in Figure 2. And through the obtained vibration wave, the processor 130 can obtain a waveform diagram of the vibration wave, such as the waveform diagram of the vibration wave shown in Figure 2. Each contraction of the heart causes blood to flow from the heart to the various tissue extremities of the body (such as the fingers). Due to the different distances from the heart, the time it takes for blood to reach the extremities of each tissue will also be different, so there is a time difference between the times it arrives at different extremities. Furthermore, since the time difference is caused by the different distances the blood travels, the time difference aspect contains important information such as blood flow rate. Continuing to refer to FIG2 , in FIG2 , the time difference between peak 21 in the waveform of the pixel value and peak 22 in the waveform of the vibration wave can be considered to be the time difference between the time it takes for blood to reach different extremities on the body of the user to be tested.

[0035] Since the time difference between the peak in the waveform obtained by the pixel value of the first pixel point and the peak in the waveform of the vibration wave can reflect the blood flow rate in the body of the user to be tested, and the blood flow rate is strongly correlated with the blood pressure. Therefore, the blood pressure value of the user to be tested can be calculated by obtaining the time difference between the peak in the waveform obtained by the pixel value of the first pixel point and the peak in the waveform of the vibration wave. Therefore, the processor 130 can calculate the blood pressure value of the user to be tested based on the pixel value of the first pixel point on the body surface image captured by the camera 110 within the first time and the vibration wave captured by the body surface vibration sensor 120 within the first time.

[0036] In some embodiments, when multiple first pixels are selected, the average or variance of the pixel values ​​of these multiple first pixels at a certain moment can be calculated. Blood pressure can then be calculated using the vibration wave and the pixel values ​​calculated at each moment. The calculation process is detailed in the calculation process using a single first pixel and will not be further described here.

[0037] Next, the process of calculating the blood pressure value by the processor 130 is introduced.

[0038] Exemplarily, FIG3 shows a schematic diagram of a process for calculating blood pressure values ​​by a processor provided in an embodiment of the present application. As shown in FIG3 , after the processor 130 obtains the pixel value of the first pixel point on the body surface image captured by the camera 110 within the first time, the first-order derivative and the second-order derivative can be performed on the waveform composed of the pixel value of the first pixel point within the first time, and the processed result can be merged with the waveform composed of the pixel value of the first pixel point within the first time to obtain a waveform set (also referred to as a pixel value set). Among them, by performing a first-order derivative calculation on the waveform, changes in the slope or speed of the waveform can be detected, and by performing a second-order derivative calculation on the waveform, changes in the convexity or acceleration of the waveform can be detected. Using this information can better improve the accuracy of blood pressure calculation. Then, the processor 130 can filter the waveform obtained after merging through a filter, perform regularization processing on the filtered data, and input the processed waveform data into the neural network model. In some embodiments, the processor 130 may directly input the pixel value of the first pixel point within the first time into the neural network model, or may only perform filtering processing before inputting it into the neural network model, or may perform both filtering processing and regularization processing before inputting it into the neural network model. The specific method may depend on the actual situation and is not limited here.

[0039] After the processor 130 obtains the vibration wave collected by the body surface vibration sensor 120 within the first time, it can also perform the first-order derivative and the second-order derivative on the waveform of the vibration wave respectively, and merge the processed results with the vibration wave to obtain a waveform set (also referred to as a vibration wave set). Then, the processor 130 can filter the waveform obtained after the merger through a filter, remove the baseline drift in the waveform, and perform regularization processing on the data after removing the baseline drift, and input the processed waveform data into the neural network model. In some embodiments, the processor 130 can also directly input the vibration wave into the neural network model, or only perform filtering processing and then input it into the neural network model, or perform both filtering processing and regularization processing and then input it into the neural network model. The specific method can be determined according to the actual situation and is not limited here.

[0040] After the neural network model obtains the data related to the body surface image, it can extract features from these data through the convolution layer therein. In addition, after the neural network model obtains the data related to the vibration wave, it can also extract features from these data through the convolution layer therein. Finally, the neural network module can process the extracted features through the fully connected layer therein to obtain the blood pressure value of the user to be measured. In some embodiments, after the neural network model obtains the data related to the body surface image and the data related to the vibration wave, it can also directly process these data through the fully connected layer therein to obtain the blood pressure value of the user to be measured. The specific details can be determined according to the actual situation and are not limited here.

[0041] In addition, when the vibration wave collected by the surface vibration sensor 120 includes a tidal wave, the neural network model can also capture the relationship between the peak of the tidal wave and the peak of the vibration wave in the vibration wave, and consider the influence of this relationship when subsequently calculating the blood pressure value. The blood pressure calculation process at this time can be shown in Figure 4. The main difference between Figure 4 and Figure 3 is that: in Figure 4, the neural network model can use the self-attention layer therein to capture the relationship between the peak of the tidal wave and the peak of the vibration wave in the vibration wave, and transmit the captured information to the fully connected layer. Finally, the fully connected layer can comprehensively process the features extracted by the convolution layer and the features extracted by the self-attention layer to obtain the blood pressure value of the user to be measured.

[0042] In the embodiment of the present application, the camera 110, the body surface vibration sensor 120 and the processor 130 can all be configured in a wearable device, such as a smart watch or a smart bracelet. In this way, blood pressure measurement can be achieved through the wearable device.

[0043] In addition, the camera 110 can also be configured in a terminal device, such as a mobile phone or tablet computer, the body surface vibration sensor 120 can be configured in a wearable device, and the processor 130 can be configured in both the terminal device and the wearable device. At this time, a communication connection can be established between the terminal device and the wearable device through a network such as Bluetooth to exchange data. In this way, blood pressure measurement can be achieved by combining the terminal device and the wearable device. Of course, the camera 110, the body surface vibration sensor 120 and the processor 130 can also be configured in other devices, which can be determined according to actual conditions.

[0044] In addition, the blood pressure detection device 100 may also include a display screen and / or a reminder. The display screen may be used to display the blood pressure value calculated by the processor 130, so that the user can intuitively obtain blood pressure information. The reminder may be used to remind the user to complete the blood pressure test after the processor 130 completes the blood pressure measurement. Exemplary reminders may include a vibration motor, a buzzer, and the like.

[0045] The above is an introduction to the blood pressure detection device provided in the embodiment of the present application. The following describes the effect of using the blood pressure detection device to perform blood pressure detection.

[0046] For example, in this embodiment, the detection effect of using pulse wave (also known as vibration wave) alone and facial image alone is compared with the detection effect of the blood pressure detection device provided in the embodiment of the present application. During the detection process, all are based on 124 sets of self-collected data, among which the diastolic blood pressure (DBP) range is 56-78 mmHg, and the systolic blood pressure (SBP) range is 92-118 mmHg. The performance of the deep learning model is shown in Table 1, where the performance of the test is statistically calculated in the form of mean absolute error ± standard deviation, that is, the smaller the value, the higher the measurement accuracy.

[0047] Table 1

[0048] As shown in Table 1, when blood pressure is measured using only the pulse wave, the mean absolute error of SBP is 5.5, while when using only facial images, the mean absolute error of SBP is 6.1. However, when both the pulse wave and facial images are used, the mean absolute error of SBP decreases by 4.9, improving blood pressure measurement accuracy by at least 10.9%, which is consistent with the expected measurement principle. This shows that the blood pressure detection device 100 provided in this embodiment of the present application can achieve highly accurate blood pressure detection.

[0049] Next, based on the blood pressure detection device described in the above embodiment, a blood pressure detection method provided in an embodiment of the present application is introduced.

[0050] For example, FIG5 shows a flow chart of a blood pressure detection method provided in an embodiment of the present application. As shown in FIG5 , the blood pressure detection method may include the following steps:

[0051] S501: Use a camera to capture a body surface image of a first area on the body of a user to be tested.

[0052] S502: Using a body surface vibration sensor to collect vibration waves on the body surface of a second area of ​​the user to be tested.

[0053] S503: Calculate the blood pressure value of the user to be measured based on the body surface image collected by the camera within the first time and the vibration wave collected by the body surface vibration sensor within the first time.

[0054] S504: Output the blood pressure value of the user to be measured.

[0055] For the detailed process of S501 to S504, please refer to the relevant description of the blood pressure detection device 100 mentioned above, and will not be repeated here.

[0056] It can be understood that the size of the serial numbers of the steps in the above embodiments does not mean the 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.

[0057] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0058] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0059] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0060] It will be understood that the various numerical numbers involved in the embodiments of the present application are merely distinctions for the convenience of description and are not intended to limit the scope of the embodiments of the present application.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the protection scope of the technical solutions of the embodiments of the present application.

Claims

1. A blood pressure detection device, characterized in that: include: A camera, used to collect a body surface image of a first area on the body of the user to be tested; A body surface vibration sensor, used for collecting vibration waves on the body surface of the second area of ​​the body of the user to be tested; The processor is used to calculate the blood pressure value of the user to be measured based on the body surface image collected by the camera within the first time and the vibration wave collected by the body surface vibration sensor within the first time, and output the blood pressure value.

2. The blood pressure detection device according to claim 1, characterized in that: The first area is the face and / or neck of the user to be tested; The second area is the wrist of the user to be tested.

3. The blood pressure detection device according to claim 1 or 2, characterized in that: The camera, the body surface vibration sensor and the processor are all configured in a wearable device.

4. The blood pressure detection device according to claim 1 or 2, characterized in that: The camera is configured in a terminal device, the body surface vibration sensor is configured in a wearable device, and the processor is configured in the terminal device or the wearable device, wherein the terminal device and the wearable device establish a communication connection through a network.

5. The blood pressure detection device according to any one of claims 1 to 4, characterized in that: The vibration waves include tidal waves.

6. The blood pressure detection device according to any one of claims 1 to 5, characterized in that: When the processor calculates the blood pressure value of the user to be measured based on the body surface image collected by the camera within the first time and the vibration wave collected by the body surface vibration sensor within the first time, it is specifically used to: Based on the pixel values ​​of each pixel point in the R channel of the body surface image acquired by the camera in the first stage within the first time, at least one first pixel point is screened out from the R channel, wherein the pixel value of the first pixel point changes periodically within the first stage; The blood pressure value of the user to be measured is calculated based on the pixel value of at least one first pixel point on the body surface image collected by the camera in the second stage within the first time and the vibration wave collected by the body surface vibration sensor in the second stage, wherein the first stage is earlier than the second stage.

7. The blood pressure detection device according to claim 6, characterized in that: There is a time difference between the peak on the first waveform and the peak on the second waveform, and the time difference is used to reflect the blood flow rate in the body of the user to be tested; Wherein, the first waveform is obtained based on the pixel value of the at least one first pixel point on the body surface image acquired by the camera in the second stage; The second waveform diagram is obtained based on the vibration waves collected by the body surface vibration sensor in the second stage.

8. The blood pressure detection device according to claim 6 or 7, characterized in that: When the processor calculates the blood pressure value of the user to be measured based on the pixel value of the at least one first pixel point on the body surface image collected by the camera in the second stage within the first time and the vibration wave collected by the body surface vibration sensor in the second stage, it is specifically used to: Performing first-order derivative calculation and second-order derivative calculation on the pixel value of the at least one first pixel point in the second stage respectively, and combining the calculation result with the pixel value of the at least one first pixel point in the second stage to obtain a pixel value set; Performing first-order derivative calculation and second-order derivative calculation on the vibration waves collected by the body surface vibration sensor in the second stage respectively, and combining the calculation results with the vibration waves collected by the body surface vibration sensor in the second stage to obtain a vibration wave set; The pixel value set and the vibration wave set are processed by a neural network model to obtain the blood pressure value.

9. The blood pressure detection device according to any one of claims 1 to 8, characterized in that: Also includes: A display screen is used to display the blood pressure value.

10. The blood pressure detection device according to any one of claims 1 to 9, characterized in that: Also includes: Reminder to complete blood pressure test.

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