Blood pressure measurement method and electronic device

Abstract

The present disclosure provides a blood pressure measurement method and electronic device, which are applied to the field of terminal technology. The method can reduce the user's usage cost and help promote the user to measure blood pressure in a timely manner. The method includes: in response to the user's blood pressure measurement operation, collecting rPPG signals and obtaining the occurrence position timestamps of multiple first wave peaks in the first R wave from the smart wearable device, the rPPG signal is determined based on the video captured by the electronic device, and the first R wave is determined based on the PPG signal collected by the smart wearable device; based on the rPPG signal, determining the occurrence position timestamps of multiple second wave peaks in the second R wave; determining the pulse wave transmission time (PTT) based on the occurrence position timestamps of the multiple first wave peaks in the first R wave and the occurrence position timestamps of the multiple second wave peaks in the second R wave; inputting the PTT into a blood pressure model to obtain the user's blood pressure value, and the blood pressure model is used to describe the relationship between PTT and blood pressure.

Description

Technical Field

[0001] The present disclosure relates to the field of terminal technology, and in particular to a blood pressure measurement method and electronic device. Background Art

[0002] Blood pressure measurement is a primary means of diagnosing, treating, and assessing the severity of hypertension. With technological advancements, blood pressure measurement devices are becoming increasingly compact and lightweight. For example, these devices can be smart wearables. Users who need to measure their blood pressure can wear these devices permanently and use them to measure their blood pressure at any time.

[0003] Existing smart wearable devices typically collect blood pressure readings by collecting electrocardiogram (ECG) and photoplethysmography (PPG) signals. Currently, there are only a few smart wearable devices that can collect both ECG and PPG signals, and those that do are generally quite expensive. This places a significant burden on users who need to measure their blood pressure. Summary of the Invention

[0004] The present disclosure provides a blood pressure measurement method and electronic device that can reduce the cost of blood pressure measurement for users, enabling more users to monitor their blood pressure in a timely manner and providing timely medical treatment when blood pressure measurement data fluctuates or becomes abnormal.

[0005] To achieve the above objectives, the embodiments of the present disclosure adopt the following technical solutions:

[0006] In a first aspect, the present disclosure provides a blood pressure measurement method, which is applied to an electronic device, the method comprising: in response to a user's blood pressure measurement operation, collecting a remote photoplethysmography (rPPG) signal, and obtaining a timestamp of the occurrence positions of multiple first wave peaks in a first R wave from a smart wearable device, the rPPG signal being determined based on a video captured by the electronic device, and the first R wave being determined based on the photoplethysmography (PPG) signal collected by the smart wearable device; determining a timestamp of the occurrence positions of multiple second wave peaks in a second R wave based on the rPPG signal; determining a pulse wave transmission time (PTT) based on the timestamps of the occurrence positions of the multiple first wave peaks in the first R wave and the timestamps of the occurrence positions of the multiple second wave peaks in the second R wave; inputting the PTT into a blood pressure model to obtain the user's blood pressure value, and displaying the blood pressure value, the blood pressure model being used to describe the relationship between PTT and blood pressure.

[0007] Based on the blood pressure measurement method of the first aspect, it can be seen that the present disclosure uses a blood pressure model to determine the user's blood pressure value. Before using the blood pressure model, it is necessary to first determine PTT. PTT is obtained by collecting rPPG signals through a mobile phone and PPG signals through a smart wearable device. Specifically, the occurrence position timestamps of multiple first wave peaks in the first R wave are obtained based on the PPG signal; the occurrence position timestamps of multiple second wave peaks in the second R wave are obtained based on the rPPG signal. Then, based on the occurrence position timestamps of multiple first wave peaks in the first R wave and the occurrence position timestamps of multiple second wave peaks in the second R wave, PTT is obtained, and then the user's blood pressure value is obtained.

[0008] That is to say, the present disclosure uses some easy-to-collect signals for blood pressure monitoring. For example, the easy-to-collect signals are PPG signals collected by smart wearable devices and rPPG signals collected by mobile phones. In this way, the user's blood pressure monitoring comfort can be greatly improved. And because the devices used to measure blood pressure values in the present disclosure are electronic devices (such as mobile phones) and smart wearable devices (such as smart watches) that most people commonly use, the user's usage cost will also be reduced compared to existing smart wearable devices that support the collection of ECG signals and PPG signals. This will help more users monitor their blood pressure in a timely manner, so that they can carry out corresponding treatments in a targeted manner.

[0009] In conjunction with the first aspect, in another possible implementation, before determining the timestamps of the locations of the multiple second peaks in the second R wave based on the rPPG signal, the method further includes: performing a signal quality test on the rPPG signal to obtain a signal quality test result; if the signal quality test result does not meet the requirements, re-collecting the rPPG signal until the signal quality test result of the rPPG signal meets the requirements. Based on this solution, by performing signal quality test on the rPPG signal, the signal quality and accuracy of the rPPG signal are improved, thereby facilitating the subsequent determination of a more accurate PTT.

[0010] In conjunction with the first aspect, another possible implementation involves determining the occurrence location timestamps of multiple second peaks in the second R wave based on the rPPG signal, including performing signal filtering and peak detection on the rPPG signal to obtain the second R wave and the occurrence location timestamps of the multiple second peaks in the second R wave. Based on this solution, by performing signal filtering and peak detection on the rPPG signal, a clearer and more accurate physiological signal can be extracted. The second R wave and the occurrence location timestamps of the multiple second peaks in the second R wave can then be used to further analyze and understand the physiological state of the human body, thereby facilitating medical personnel to make timely and accurate medical diagnoses based on the user's physiological condition.

[0011] In conjunction with the first aspect, another possible implementation involves determining the pulse wave transit time (PTT) based on the occurrence position timestamps of multiple first peaks in a first R wave and the occurrence position timestamps of multiple second peaks in a second R wave. The method includes: determining the second peak corresponding to each of the multiple first peaks; determining multiple initial PTTs based on the occurrence position timestamps of each first peak and the occurrence position timestamps of the second peak corresponding to each first peak; and averaging the multiple initial PTTs to obtain the PTT. This approach accurately captures the start and end positions of the pulse by determining the multiple first peaks and their corresponding second peaks, thereby providing a more accurate measurement of the time difference. Averaging the multiple initial PTTs reduces the impact of individual noise or abnormal data on the final result, improving the stability and reliability of the PTT. Since the PTT is used to estimate blood pressure, using the average PTT corresponding to multiple peaks can improve the accuracy of blood pressure estimation.

[0012] In conjunction with the first aspect, in another possible implementation, determining multiple initial PTTs based on the timestamp of the occurrence position of each first wave peak and the timestamp of the occurrence position of the second wave peak corresponding to each first wave peak includes: performing a difference processing on the timestamp of the occurrence position of each first wave peak and the timestamp of the occurrence position of each second wave peak corresponding to each first wave peak to obtain the multiple initial PTTs. A method for determining multiple initial PTTs is provided.

[0013] In conjunction with the first aspect, in another possible implementation, before inputting PTT into the blood pressure model, the method further includes: obtaining the user's gold standard blood pressure; and calibrating the parameters in the initial blood pressure model based on the gold standard blood pressure and PTT to obtain a blood pressure model. Based on this solution, before using the blood pressure model, it is also necessary to calibrate the parameters in the blood pressure model using real user data (i.e., the user's gold standard blood pressure) to ensure the accuracy of the blood pressure model. This allows for subsequent use of the blood pressure model to obtain a more accurate user blood pressure value.

[0014] In conjunction with the first aspect, another possible implementation involves collecting rPPG signals in response to a user's blood pressure measurement operation, including: prompting the user to perform a specified action in response to the user's blood pressure measurement operation; the specified action being the user placing their finger over the electronic device's camera; capturing a fixed-length video upon detecting that the user has performed the specified action; and determining the rPPG signal based on the video. This solution, by capturing the user's finger with a camera to obtain the rPPG signal, enables contactless collection, avoiding interference and discomfort caused by contact between the sensor and the user. Furthermore, this method requires no hardware or sensors to be worn; measurements can be performed solely through the camera, improving user convenience and comfort.

[0015] In conjunction with the first aspect, in another possible implementation, before determining that the signal quality test result does not meet the requirements, the method further includes: determining whether the signal quality test result exceeds a preset threshold; and if the signal quality test result does not meet the requirements, determining that the signal quality test result does not meet the requirements. Based on this solution, by setting a preset threshold for signal quality testing, low-quality or noisy signals can be excluded, ensuring that only high-quality rPPG signals are used. This helps reduce data interference and improves signal quality and reliability. It also reduces the workload of subsequent processing and analysis, thereby improving signal quality measurement efficiency and saving time and costs.

[0016] In a second aspect, embodiments of the present disclosure provide a blood pressure measurement device that can be used in electronic devices to implement the method described in the first aspect. The functions of the blood pressure measurement device can be implemented using hardware or by executing corresponding software on the hardware. The hardware or software includes one or more modules corresponding to the aforementioned functions, such as an acquisition module, a determination module, and a display module.

[0017] The acquisition module is configured to, in response to a user's blood pressure measurement operation, acquire a remote photoplethysmography (rPPG) signal and obtain the occurrence location timestamps of multiple first wave peaks in the first R wave from the smart wearable device. The rPPG signal is determined based on a video captured by the electronic device, and the first R wave is determined based on the photoplethysmography (PPG) signal acquired by the smart wearable device. The determination module is configured to determine the occurrence location timestamps of multiple second wave peaks in the second R wave based on the rPPG signal; and to determine the pulse wave transit time (PTT) based on the occurrence location timestamps of the multiple first wave peaks in the first R wave and the occurrence location timestamps of the multiple second wave peaks in the second R wave. The display module is configured to input the PTT into a blood pressure model, derive the user's blood pressure value, and display the blood pressure value. The blood pressure model is used to describe the relationship between PTT and blood pressure.

[0018] In conjunction with the second aspect, in one possible implementation, the determination module is further configured to perform a signal quality test on the rPPG signal to obtain a signal quality test result. If the signal quality test result does not meet the requirements, the acquisition module is further configured to reacquire the rPPG signal until the signal quality test result meets the requirements.

[0019] In combination with the second aspect, in a possible implementation, the determination module is further configured to perform signal filtering and peak detection on the rPPG signal to obtain the timestamps of the occurrence positions of the second R wave and multiple second wave peaks in the second R wave.

[0020] In combination with the second aspect, in one possible implementation, the determination module is further configured to determine a second peak corresponding to each first peak among multiple first peaks; determine multiple initial PTTs based on the occurrence position timestamp of each first peak and the occurrence position timestamp of the second peak corresponding to each first peak; and average the multiple initial PTTs to obtain PTT.

[0021] In combination with the second aspect, in a possible implementation, the determination module is further configured to perform difference processing on the occurrence position timestamp of each first wave peak and the occurrence position timestamp of the second wave peak corresponding to each first wave peak to obtain multiple initial PTTs.

[0022] In combination with the second aspect, in a possible implementation, the acquisition module is further configured to obtain the user's gold standard blood pressure; the determination module is further configured to calibrate the parameters in the initial blood pressure model based on the gold standard blood pressure and PTT to obtain the blood pressure model.

[0023] In combination with the second aspect, in one possible implementation, the determination module is further configured to prompt the user to perform a specified action in response to the user's blood pressure measurement operation; the specified action is the user covering the camera of the electronic device with his finger; the acquisition module is further configured to capture a video of a fixed length when it is detected that the user has performed the specified action; and the rPPG signal is determined based on the video.

[0024] In combination with the second aspect, in a possible implementation, the determination module is further configured to determine whether the signal quality detection result exceeds a preset threshold; if it does not exceed the preset threshold, determine that the signal quality detection result does not meet the requirements.

[0025] In a third aspect, the present disclosure provides an electronic device comprising: a memory, a display, and one or more processors; the memory, display, and processors are coupled. The memory is configured to store computer program code, which includes computer instructions; when the electronic device is in operation, the processor is configured to execute the one or more computer instructions stored in the memory, causing the electronic device to perform any of the data processing methods described in the first aspect.

[0026] In a fourth aspect, the present disclosure provides a computer storage medium comprising computer instructions, which, when executed on an electronic device, enables the electronic device to execute any one of the data processing methods in the first aspect.

[0027] In a fifth aspect, the present disclosure provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the data processing method as described in any one of the first aspects.

[0028] In a sixth aspect, a device (e.g., a system-on-a-chip) is provided. The device includes a processor configured to support a first terminal device in implementing the functions described in the first aspect. In one possible design, the device also includes a memory configured to store program instructions and data necessary for the first terminal device. When the device is a system-on-a-chip, it may consist of a single chip or may include a chip and other discrete components.

[0029] It should be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is one of the structural schematic diagrams of a blood pressure measurement system provided in an embodiment of the present disclosure.

[0031] Figure 2 This is a second structural diagram of a blood pressure measurement system provided in an embodiment of the present disclosure.

[0032] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure.

[0033] Figure 4 A schematic diagram of the software structure of an electronic device provided in an embodiment of the present disclosure.

[0034] Figure 5 A schematic diagram of the hardware structure of a smart wearable device provided in an embodiment of the present disclosure.

[0035] Figure 6 This is one of the flow charts of a blood pressure measurement method provided in an embodiment of the present disclosure.

[0036] Figure 7 This is one of the scenario diagrams provided in an embodiment of the present disclosure.

[0037] Figure 8 This is a second flow chart of a blood pressure measurement method provided in an embodiment of the present disclosure.

[0038] Figure 9 A schematic diagram of a first R wave provided in an embodiment of the present disclosure.

[0039] Figure 10 A display schematic diagram provided for an embodiment of the present disclosure.

[0040] Figure 11 A schematic diagram of a second R wave provided in an embodiment of the present disclosure.

[0041] Figure 12 The present invention provides a second scenario diagram for an embodiment of the present invention.

[0042] Figure 13 A flow chart illustrating the calibration and application processes of a blood pressure model provided in an embodiment of the present disclosure.

[0043] Figure 14 This is a third flow chart of a blood pressure measurement method provided in an embodiment of the present disclosure.

[0044] Figure 15 A schematic structural diagram of a blood pressure measurement device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present disclosure will be described below in conjunction with the drawings in the embodiments of the present disclosure. In the description of the present disclosure, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B can represent A or B; "and / or" in the present disclosure is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, in the description of the present disclosure, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate a clear description of the technical solutions of the embodiments of the present disclosure, in the embodiments of the present disclosure, words such as "first" and "second" are used to distinguish between identical or similar items with substantially identical functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit differences. At the same time, in the embodiments of the present disclosure, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present disclosure should not be interpreted as being more preferred or more advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0046] In addition, the network architecture and business scenarios described in the embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Ordinary technicians in this field can know that with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0047] Hypertension is a common chronic disease. Also known as high blood pressure, it refers to a condition in which the pressure exerted on the blood vessels by blood flow is consistently higher than normal. Often called a "silent killer," hypertension develops without any symptoms in most patients. Because high blood pressure is constantly exerting pressure on the blood vessels, patients with hypertension are susceptible to cardiovascular and cerebrovascular diseases, such as coronary heart disease and stroke, which seriously impact human health. Currently, the number of people with hypertension in my country exceeds 270 million, a significant number.

[0048] With the continuous improvement of living standards, people are paying more and more attention to their own health. Blood pressure, as a major physiological indicator of the physical condition of hypertensive patients, is also increasingly valued by people. For hypertensive patients, regular blood pressure measurement is very important. This can help hypertensive patients better understand their condition and also facilitate medical staff to adjust the treatment plan of hypertensive patients in a timely manner according to the blood pressure measurement results to avoid the occurrence of cardiovascular and cerebrovascular diseases.

[0049] Traditionally, blood pressure is measured using specialized equipment such as a blood pressure cuff and stethoscope. Medical personnel assist the user in operating the equipment to obtain a blood pressure reading. The medical personnel then interpret the reading to help the user understand their physical condition. This method of blood pressure measurement is not only inconvenient but also expensive.

[0050] In recent years, with the advancement of blood pressure measurement technology, smart wearable devices suitable for blood pressure measurement have emerged in large numbers. Due to their compactness and lightness, these devices offer a more convenient and economical way to measure blood pressure. Typically, these devices simultaneously record the user's ECG and PPG signals when measuring blood pressure. They then calculate the pulse transit time (PTT) based on the ECG and PPG signals. Finally, the PTT is used to determine the blood pressure value.

[0051] Currently, there are not many smart wearable devices that can collect both ECG and PPG signals, and these smart wearable devices are generally expensive. This, to a certain extent, increases the cost of blood pressure measurement for users and causes great trouble for users.

[0052] To this end, an embodiment of the present disclosure provides a blood pressure measurement method that can be applied to a blood pressure measurement system. The blood pressure measurement system can use some easily collected signals to monitor blood pressure. For example, the easily collected signals are PPG signals collected by smart wearable devices and remote photoplethysmography (rPPG) signals collected by mobile phones to achieve blood pressure measurement. In this way, not only the user's blood pressure measurement comfort is greatly improved, but also the user's blood pressure measurement cost can be reduced, thereby helping more users to monitor their own blood pressure in a timely manner, so that when the blood pressure measurement data fluctuates or is abnormal, the user can receive timely treatment.

[0053] The specific architecture of the blood pressure measurement system involved in the technical solution provided in the embodiment of the present disclosure can be referred to Figure 1 The system architecture may include an electronic device 01 and a smart wearable device 02. The electronic device 01 and the smart wearable device 02 may be relatively close to each other (for example, the electronic device 01 is in the user's hand or pocket, and the smart wearable device 02 is worn on the user's body).

[0054] In an embodiment of the present disclosure, a wireless communication connection (such as a Bluetooth connection or a Wi-Fi connection) can be established between the electronic device 01 and the smart wearable device 02. The specific connection process can be determined by the user. Taking the wireless communication connection as a Bluetooth connection as an example, when the electronic device 01 and the smart wearable device 02 have not established a Bluetooth connection, the electronic device 01 and the smart wearable device 02 can respectively respond to the user's Bluetooth turning on operation and turn on the Bluetooth function respectively. Then, the electronic device 01 can establish a Bluetooth connection with the smart wearable device 02 in response to the user's Bluetooth pairing operation. Or the smart wearable device 02 establishes a Bluetooth connection with the electronic device 01 in response to the user's Bluetooth pairing operation. After the electronic device 01 and the smart wearable device 02 establish a Bluetooth connection for the first time, as long as the electronic device 01 and the smart wearable device 02 both have the Bluetooth function turned on and the distance is less than a certain threshold, a Bluetooth connection will be automatically established.

[0055] The electronic device 01 in the present disclosure may be a mobile phone, tablet computer, ultra-mobile personal computer (UMPC), netbook, cellular phone, personal digital assistant (PDA), personal computer (PC), augmented reality (AR) and virtual reality (VR) devices, etc., which can interact with other devices. The embodiment of the present disclosure does not impose any special restrictions on the specific form of the electronic device 01. The smart wearable device 02 in the present disclosure may be a smart watch, smart wristband, smart glasses, smart ring, etc., which can interact with the electronic device 01. The embodiment of the present disclosure does not impose any special restrictions on the specific form of the smart wearable device 02. Taking the mobile phone as the electronic device 01 and the smart watch as the smart wearable device 02 as an example, the specific architecture of the blood pressure measurement system can be referred to. Figure 2 shown.

[0056] For example, Figure 3 FIG. 1 shows a schematic structural diagram of the electronic device 01. Figure 3 As shown, the electronic device may include a processor 310, an external memory interface 320, an internal memory 321, a universal serial bus (USB) interface 330, a charging management module 340, a power management module 341, a battery 342, an antenna 1, an antenna 2, a mobile communication module 350, a wireless communication module 360, an audio module 370, a speaker 370A, a receiver 370B, a microphone 370C, an earphone interface 370D, a sensor module 380, a button 390, a motor 391, an indicator 392, a camera 393, a display screen 394, and a subscriber identification module (SIM) card interface 395, etc. Among them, the sensor module 380 may include a pressure sensor 380A, a gyroscope sensor 380B, an air pressure sensor 380C, a magnetic sensor 380D, an acceleration sensor 380E, a distance sensor 380F, a proximity light sensor 380G, a fingerprint sensor 380H, a temperature sensor 380J, a touch sensor 380K, an ambient light sensor 380L, a bone conduction sensor 380M, etc.

[0057] It should be understood that the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device. In other embodiments, the electronic device may include more or fewer components than shown, or may combine or separate certain components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0058] The processor 310 may include one or more processing units. For example, the processor 310 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU). The different processing units may be independent devices or integrated into one or more processors.

[0059] The controller can be the nerve center and command center of the electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to complete the control of instruction fetching and execution.

[0060] Processor 310 may also include a memory for storing instructions and data. In some embodiments, the memory in processor 310 is a cache memory. This memory can store instructions or data that have just been used or are being recycled by processor 310. If processor 310 needs to use the same instruction or data again, it can directly retrieve it from the memory. This avoids duplicate accesses, reduces processor 310 latency, and thus improves system efficiency.

[0061] In some embodiments, the processor 310 may include one or more interfaces. The interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface.

[0062] The wireless communication function of the electronic device can be implemented through antenna 1, antenna 2, mobile communication module 350, wireless communication module 360, modem processor and baseband processor.

[0063] Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in an electronic device can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In other embodiments, the antennas can be used in conjunction with a tuning switch.

[0064] The mobile communication module 350 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to electronic devices. The mobile communication module 350 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 350 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 350 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 350 can be set in the processor 310. In some embodiments, at least some of the functional modules of the mobile communication module 350 can be set in the same device as at least some of the modules of the processor 310.

[0065] The wireless communication module 360 can provide wireless communication solutions for electronic devices, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc. The wireless communication module 360 can be one or more devices that integrate at least one communication processing module. The wireless communication module 360 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 310. The wireless communication module 360 can also receive the signal to be sent from the processor 310, frequency modulate it, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0066] In some embodiments, antenna 1 of the electronic device is coupled to mobile communication module 350, and antenna 2 is coupled to wireless communication module 360, so that the electronic device can communicate with a network and other devices via wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology.

[0067] The electronic device implements display functionality through a GPU, display screen 394, and an application processor. A GPU is a microprocessor for image processing that connects display screen 394 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processor 310 may include one or more GPUs that execute program instructions to generate or modify display information.

[0068] Display screen 394 is used to display images, videos, and the like. Display screen 394 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a MiniLED, a MicroLED, a Micro-oLED, or a quantum dot light-emitting diode (QLED). In some embodiments, the electronic device can include one or N display screens 394, where N is a positive integer greater than one.

[0069] The electronic device can implement the shooting function through the ISP, camera 393, video codec, GPU, display 394 and application processor, etc. In some embodiments, the electronic device can include 1 or N cameras 393, where N is a positive integer greater than 1.

[0070] The internal memory 321 can be used to store computer executable program codes, and the executable program codes include instructions. The processor 310 executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory 321. The internal memory 321 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area can store data established during the use of the electronic device (such as audio data, a phone book, etc.), etc. In addition, the internal memory 321 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0071] In addition, an operating system runs on top of the above components, such as Etc. Application programs can be installed and run on the operating system. In other embodiments, there can be multiple operating systems running in the electronic device.

[0072] It should be understood that Figure 3The hardware modules included in the electronic device shown are merely illustrative and do not limit the specific structure of the electronic device. In fact, the electronic device provided by the embodiments of the present disclosure may also include other hardware modules that interact with the hardware modules illustrated in the figures, and these are not specifically limited here. For example, the electronic device may also include a flashlight, a micro-projector, and the like. For another example, if the electronic device is a PC, then the electronic device may also include components such as a keyboard and a mouse.

[0073] It is understandable that, generally speaking, the realization of electronic device functions requires not only hardware support but also software cooperation.

[0074] The software system of the electronic device can adopt a layered architecture, an event-driven architecture, a micro-kernel architecture, a micro-service architecture, or a cloud architecture. Taking the system as an example, the software structure of the electronic device is illustrated.

[0075] Figure 4 It is a software structure block diagram of the electronic device provided by the embodiment of the present disclosure.

[0076] A layered architecture divides software into several layers, each with distinct roles and responsibilities. Layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into four layers: from top to bottom: the application layer, the application framework layer, the Android runtime and system libraries (also called the native layer), and the kernel layer.

[0077] The application layer can include a series of application packages. Figure 4 As shown, the application package can include applications such as camera, gallery, calendar, phone, map, navigation, WLAN, Bluetooth, music, video, and short messaging. The application framework layer provides the application programming interface (API) and programming framework for the applications in the application layer. The application framework layer includes some predefined functions.

[0078] It is understood that the application layer may include multiple applications, and the types of these multiple applications may be the same or different. For example, the application layer may include multiple shopping applications, and these multiple shopping applications are of the same type. The application layer may also include a map application and a communication application, and the map application and the communication application are of different types.

[0079] like Figure 4 As shown, the application framework layer may include a window manager, a content provider, a view system, a phone manager, a resource manager, a notification manager, and the like.

[0080] The window manager is used to manage window programs. The window manager can obtain the display size, determine whether there is a status bar, lock the screen, take screenshots, etc.

[0081] Content providers are used to store and retrieve data and make it accessible to applications. Data can include videos, images, audio, calls made and received, browsing history and bookmarks, phone books, etc.

[0082] The view system includes visual controls, such as those for displaying text and images. The view system is used to build applications. A display interface can consist of one or more views. For example, a display interface containing a text notification icon might include a view for displaying text and a view for displaying images.

[0083] The phone manager is used to provide communication functions for electronic devices, such as call status management (including answering, hanging up, etc.).

[0084] The resource manager provides various resources for applications, such as localized strings, icons, images, layout files, video files, and so on.

[0085] The Notification Manager allows applications to display notifications in the status bar. These messages can be displayed briefly and then disappear automatically without user interaction. For example, the Notification Manager is used to notify users of completed downloads and message reminders. The Notification Manager can also display notifications in the top status bar of the system as icons or scrolling text, such as notifications from background applications, or as dialog windows on the screen. Examples include text messages in the status bar, beeps, vibrations on electronic devices, and flashing indicator lights.

[0086] The application framework layer may also include an acquisition module, a signal quality detection module, and a signal processing module. The acquisition module is used to acquire rPPG signals. The signal quality detection module is used to perform signal quality detection on the rPPG signals to obtain a signal quality detection result. If the signal quality detection result of the rPPG signal indicates that the quality is substandard, the acquisition module is notified to reacquire the rPPG signal. If the signal quality detection result of the rPPG signal indicates that the quality is substandard, the signal processing module is used to perform signal filtering and peak detection on the rPPG signal to obtain the second R wave and the peak information of the second R wave; obtain the peak information of the first R wave and the peak information of the first R wave; and derive the user's blood pressure value based on the first R wave, the peak information of the first R wave, the peak information of the second R wave, and the peak information of the second R wave.

[0087] The Android runtime consists of core libraries and a virtual machine (VM). The Android runtime is responsible for scheduling and management of the Android system. The core library consists of two parts: one for Java-based functions and the other for the Android core library. The application layer and application framework layer run in the VM. The VM executes Java files from the application layer and application framework layer as binary files. The VM is responsible for performing functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.

[0088] The system library can include multiple functional modules, such as surface manager, media library, 3D graphics processing library (such as OpenGL ES), 2D graphics engine (such as SGL), etc.

[0089] The surface manager is used to manage the display subsystem and provide the fusion of two-dimensional and three-dimensional layers for multiple applications.

[0090] The media library supports playback and recording of a variety of common audio and video formats, as well as static image files. The media library can support multiple audio and video encoding formats.

[0091] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0092] A 2D graphics engine is a drawing engine for 2D drawings.

[0093] The kernel layer is the layer between hardware and software. The kernel layer includes at least display driver, camera driver, audio driver, and sensor driver.

[0094] The methods in the following embodiments can all be implemented in an electronic device having the above hardware structure or software structure.

[0095] Figure 5 Schematic diagram of the structure of a smart wearable device 05 is shown. Figure 5 As shown, the smart wearable device 05 may include components such as a processor 510, a memory 520, a display screen 530, a microphone 540, a speaker 550, a wireless communication module 560, an antenna, a power supply 570, a sensor 580, and a photo plethysmography (PPG) module 590.

[0096] The processor 510 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). The different processing units may be independent devices or integrated into the same processor. The processor 510 may be configured to perform signal quality detection, signal filtering, and peak detection on the collected PPG signal to obtain the first R wave and peak information of the first R wave. The processor 510 may also be configured to determine whether the PPG signal passes the signal quality detection and, if the PPG signal fails the signal quality detection, send a second notification to the PPG module to control the PPG module to reacquire the PPG signal.

[0097] Processor 510 is the decision-maker that directs the various components of smart wearable device 05 to coordinate operations according to instructions. It is the nerve center and command center of smart wearable device 05. Based on the instruction opcode and timing signals, the controller generates operation control signals to control instruction fetching and execution.

[0098] Processor 510 may also include a memory for storing instructions and data. In some embodiments, the memory in processor 510 is a cache memory. This can store instructions or data that have just been used or are being recycled by the processor. If the processor needs to use the instruction or data again, it can directly call it from the memory. This avoids duplicate accesses, reduces processor latency, and thus improves system efficiency.

[0099] In some embodiments, the processor 510 may include one or more interfaces. The interfaces may include an inter-integrated circuit (I5C) interface, an inter-integrated circuit sound (I5S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface.

[0100] The memory 520 can be used to store computer executable program code, and the executable program code includes instructions. The processor 510 executes various functional applications and data processing of the smart wearable device 05 by running the instructions stored in the memory 520. The memory 520 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area can store data created during the use of the smart wearable device 05 (such as audio data, a phone book, etc.), etc. In addition, the memory 520 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, other volatile solid-state storage devices, a universal flash storage (UFS), etc.

[0101] Display screen 530 is used to display images, videos, etc. The display screen includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a mini-LED, a micro-LED, a micro-o-LED, a quantum dot light-emitting diode (QLED), etc.

[0102] Microphone 540, also known as a "microphone" or "microphone," is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak by bringing their mouth close to the microphone, inputting the sound signal into the microphone. The smart wearable device 05 can be equipped with at least one microphone.

[0103] The speaker 550, also called a "speaker", is used to convert the audio electrical signal into a sound signal. The smart wearable device 05 can listen to music or listen to hands-free calls through the speaker.

[0104] Antennas are used to transmit and receive electromagnetic wave signals.

[0105] The wireless communication module 560 can provide a communication processing module for wireless communication solutions including wireless local area networks (WLAN) (for example, wireless fidelity (Wi-Fi)), Bluetooth, global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc., which are applied to the smart wearable device 05. The wireless communication module 560 can be one or more devices that integrate at least one communication processing module. The communication module receives electromagnetic waves via an antenna, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor. The wireless communication module 560 can also receive the signal to be sent from the processor, frequency modulate it, amplify it, and convert it into electromagnetic waves for radiation through the antenna.

[0106] In some embodiments, the antenna of the smart wearable device 05 is coupled to the wireless communication module 560, so that the smart wearable device 05 can communicate with the network and other devices through wireless communication technology.

[0107] The sensor 580 may include a gyro sensor, an acceleration sensor, a touch sensor, a bone conduction sensor, a photoelectric sensor, a blood oxygen sensor, and the like.

[0108] Among them, the gyroscope sensor and the acceleration sensor can be used in combination to determine the movement direction, movement speed and other motion data of the user wearing the smart wearable device 05.

[0109] The acceleration sensor can detect the magnitude of acceleration in various directions (generally three axes) of the smart wearable device 05. The touch sensor, also known as a "touch panel", can be set on the display screen.

[0110] The touch sensor detects touches applied on or near the display. After detecting a touch, it can pass the touch event to a processor to determine the type of touch event and display the corresponding visual output on the display. Bone conduction sensors can also capture vibration signals.

[0111] Bone conduction sensors can capture vibration signals from vibrating bones in the human body. They can also contact the human pulse and receive blood pressure signals. The processor can then interpret the blood pressure signals from the bone conduction sensors to determine heart rate, enabling heart rate detection.

[0112] The photoelectric sensor can collect the pulse wave waveform of the part of the human body where the smart wearable device 05 is worn.

[0113] The blood oxygen concentration sensor can include two light-emitting diodes and a photodiode, which respectively emit red light with a wavelength of 660nm and infrared light at 880nm toward the wrist. The photodiode on the other side receives the reflected light. The blood oxygen concentration sensor calculates the human body's blood oxygen concentration based on the difference in the emitted and received light intensities.

[0114] The smart wearable device in this embodiment further includes a blood pressure measurement module, wherein the blood pressure measurement module may specifically include a photo plethysmography (PPG) module 590. The smart wearable device may collect PPG signals through the PPG module to determine the user's blood pressure value using the PPG signals.

[0115] The PPG module may include components such as a red light source, an infrared light source, a green light source, and a photoelectric sensor. The PPG module may use photoplethysmography technology to detect blood pressure.

[0116] It should be noted that the structures illustrated in the embodiments of the present invention do not limit the smart wearable device. It may include more or fewer components than shown, or some components may be combined or separated, or arranged differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0117] The following is a detailed description of a blood pressure measurement method provided by the embodiment of the present disclosure in conjunction with the accompanying drawings. The method can be applied to the above-mentioned blood pressure measurement system, taking the electronic device as a mobile phone and the smart wearable device as a smart watch as an example, referring to Figure 6 As shown, the blood pressure measurement method provided by the embodiment of the present disclosure may include steps 601 to 621:

[0118] Step 601: In response to the user's blood pressure measurement operation, the PPG module of the smart watch collects PPG signals.

[0119] The blood pressure measurement operation may be a user triggering a blood pressure measurement button on a smartwatch. The PPG signal is used to determine the user's blood pressure. The blood pressure measurement button may be a button within a blood pressure measurement application, or a button within another application within the smartwatch that enables blood pressure measurement.

[0120] When a user needs to measure their blood pressure, they can use a smartwatch and a mobile phone to measure their blood pressure. For example, the user can wear a smartwatch and use the smartwatch to collect their physical characteristics, thereby obtaining a blood pressure value based on the user's physical characteristics. The user's physical characteristics include a PPG signal.

[0121] In some examples, the process of the PPG module of the smart watch collecting PPG signals may include: first, the user activates the blood pressure measurement function of the smart watch. Then, after the blood pressure measurement function is turned on, the smart watch may prompt the user to perform a first preset behavioral action and detect whether the user has performed the first preset behavioral action. Exemplarily, the first preset behavioral action may be wearing the smart watch on a specified part of the user's body, such as a wrist. When it is detected that the user has performed the first preset behavioral action, the smart watch controls the PPG module to collect PPG signals. It is understandable that when it is detected that the user has triggered the first preset behavioral action, the smart watch can also prompt the user to perform the first preset behavioral action more standardizedly through the voice module, for example, prompting the user to stay still.

[0122] For example, Figure 7As shown, a user wears a smartwatch 02 on their wrist. The smartwatch may be equipped with a display screen and can receive a blood pressure measurement request initiated by the user. In response to this request, the smartwatch initiates the PPG signal acquisition process described above. For example, the smartwatch's display interface includes a blood pressure measurement icon. In response to the user clicking the blood pressure measurement icon, the smartwatch launches a blood pressure measurement application. Once launched, the blood pressure measurement application first invokes the PPG module in the smartwatch to acquire PPG signals.

[0123] The click operation can be any one of a single click operation, a double click operation, a knuckle tap, and a multi-finger selection operation. It should be noted that the operation performed by the user on the smartwatch can be all possible operations, including but not limited to at least one operation such as a single click, a double click, a three-finger tap, a long press, and launching an application. This disclosure does not limit parameters such as the type and number of user operations.

[0124] For another example, a smartwatch may also be equipped with a microphone, allowing it to receive user-initiated voice commands to initiate the aforementioned PPG signal acquisition process. For example, the user can speak a designated activation command to activate the smartwatch's voice command acquisition mode. For example, if the user says "Hi, watch," the smartwatch will respond through the speaker with "I'm here." In this case, the smartwatch can monitor voice commands through the microphone. For example, if the user says "measure blood pressure," the smartwatch will recognize the user's voice command as initiating a blood pressure measurement. In response to the blood pressure measurement, the smartwatch can search for the corresponding application in its local application program library and launch the corresponding application to perform the aforementioned PPG signal acquisition process.

[0125] In some examples, such as Figure 8 As shown, the smartwatch collects data through the PPG module to acquire PPG signals. This process involves the smartwatch controlling the light source in the PPG module to emit light (red, infrared, or green) onto a specific part of the user's body, thereby generating a corresponding PPG signal. The PPG signal is then collected using a photoelectric sensor in the PPG module. This PPG signal includes red, green, and infrared light signals.

[0126] In some examples, the light source in the PPG module includes a red light source, an infrared light source, and a green light source. Generally, the green light source is used to illuminate the user's body to obtain the PPG signal.

[0127] The reasons are as follows. First, PPG is a technology that monitors biological parameters such as heart rate and blood oxygen saturation by measuring changes in light absorption caused by blood flow in the skin. Because different wavelengths of light have different transmission and reflection characteristics in skin tissue, different light sources will affect the quality and accuracy of the PPG signal.

[0128] Secondly, green light has relatively strong skin penetration, adapting well to the absorption characteristics of skin tissue. Compared to red and infrared light, green light has a lower absorption rate, resulting in a stronger reflected signal even at shallower skin depths. This gives green light better signal quality and stability when monitoring biometric parameters such as pulse oximetry and heart rate.

[0129] Furthermore, green light reflection is less affected by hemoglobin in the skin. Hemoglobin absorbs red light more strongly and green light less strongly. Therefore, using a green light source can reduce the effect of hemoglobin, resulting in a less noisy PPG signal and a clearer, more reliable one. Therefore, using a green light source can produce a more accurate and stable PPG signal. This PPG signal can then be used to more accurately determine biometric parameters such as heart rate, blood oxygen level, and blood pressure.

[0130] In some examples, when the PPG module collects PPG signals, the user can be in a variety of different states. For example, the user can be in one or more of a sitting state, a lying state, a standing state, a mental activity state, a relaxation / resting state, an anaerobic exercise state, or an aerobic exercise state. Accordingly, the user's environmental state can also be a variety of different states, for example, one or more of a cold state and a hot and humid state.

[0131] Step 602: The PPG module sends a first notification to the processor of the smart watch.

[0132] The first notification is used to instruct the processor to perform a signal quality test on the PPG signal to determine whether the PPG signal needs to be re-collected. The first notification includes the PPG signal.

[0133] To ensure the accuracy of blood pressure values determined based on PPG signals, the present disclosure further performs a signal quality test on the PPG signal after acquisition. If the PPG signal passes the signal quality test, further processing can be performed on the PPG signal. If the PPG signal fails the signal quality test, steps 605 to 607 must be repeated to reacquire the PPG signal until the acquired PPG signal meets the signal quality test requirements. This ensures that the acquired PPG signal meets both signal quality and stability requirements.

[0134] Step 603: The processor receives a first notification.

[0135] Step 604: In response to the first notification, the processor performs a signal quality test on the PPG signal to obtain a signal quality test result.

[0136] like Figure 8 As shown, after collecting the PPG signal, the PPG module can send a first notification to the processor, so that the processor performs signal quality detection on the collected PPG signal.

[0137] In some examples, the processor performs a signal quality test on the PPG signal to obtain a signal quality test result. Specifically, the processor may calculate the correlation between the red light channel, the infrared light channel, and the green light channel in the PPG signal. If the correlation exceeds a first preset threshold, the PPG signal is considered to have passed the signal quality test, and the signal quality test result is considered to be of satisfactory quality. If the correlation does not exceed the first preset threshold, the PPG signal is considered to have failed the signal quality test, and the signal quality test result is considered to be substandard quality, and the signal quality test result does not meet the requirements.

[0138] In some examples, the correlation between the red light channel, the infrared light channel, and the green light channel can be characterized by a correlation coefficient. For example, the correlation coefficient can be either a Pearson correlation coefficient or a Spearman correlation coefficient. The Pearson correlation coefficient is used to measure the linear correlation between two continuous variables. The Spearman correlation coefficient is used to measure the rank correlation between two variables.

[0139] By setting a first preset threshold for signal quality testing, low-quality or noisy signals can be eliminated, ensuring that only high-quality PPG signals are used. This helps reduce data interference and improves signal quality and reliability. It also reduces the workload for subsequent processing and analysis, improving signal quality measurement efficiency and saving time and costs.

[0140] Step 605: If the signal quality detection result is that the quality does not meet the standard, the processor sends a second notification to the PPG module.

[0141] The second notification is used to instruct the PPG module to re-collect the PPG signal.

[0142] If the signal quality detection result is that the quality does not meet the standard, it means that the quality of the PPG signal does not meet the requirements and the PPG signal needs to be re-collected. In this case, the processor can send a second notification to the PPG module to instruct the PPG module to re-collect the PPG signal using the second notification.

[0143] In some examples, if the signal quality test result is that the quality meets the standard, the processor will continue to process the PPG signal to facilitate the subsequent use of the PPG signal to obtain the user's blood pressure value.

[0144] Step 606: The PPG module receives the second notification.

[0145] Step 607: In response to the second notification, the PPG module re-collects the PPG signal and executes steps 602 to 604 again to obtain a signal quality detection result corresponding to the re-collected PPG signal.

[0146] After receiving the second notification, the PPG module may reacquire the PPG signal in response to the second notification. After reacquiring the PPG signal, the second notification may be sent to the processor. Upon receiving the second notification, the processor further performs signal quality testing on the reacquired PPG signal in the second notification, thereby obtaining a signal quality test result corresponding to the reacquired PPG signal. It will be appreciated that the process of reacquiring the PPG signal by the PPG module is similar to the process of acquiring the PPG signal in step 601 and will not be further described here.

[0147] If the signal quality detection result is that the quality does not meet the standard, the PPG module needs to continue collecting PPG signals until the signal quality detection result of the collected PPG signals meets the standard.

[0148] Step 608: When the PPG signal meets the detection conditions, the processor processes the PPG signal to obtain the first R wave and the peak information of the first R wave.

[0149] In some examples, such as Figure 8 As shown, the processor processes the PPG signal including performing signal filtering and peak detection on the PPG signal. The peak information of the first R wave includes multiple first peaks and a timestamp of the occurrence position of each first peak in the multiple first peaks.

[0150] In some examples, performing signal filtering on the PPG signal may include performing noise removal and baseline drift operations on the PPG signal. Signal filtering on the PPG signal can be implemented in a variety of ways. For example, the signal filtering operation can be implemented using a digital filter, a moving average filter, or a wavelet transform.

[0151] Using a digital filter to implement signal filtering is an example for illustration. Signal filtering on a PPG signal can involve the processor first using a digital filter (such as a low-pass filter) to remove high-frequency noise from the signal, and then using a high-pass filter to remove low-frequency baseline drift. Alternatively, a bandpass filter can be used to simultaneously remove high-frequency noise and low-frequency baseline drift. By filtering the PPG signal, not only high-frequency noise but also low-frequency baseline drift can be removed. This makes the PPG signal clearer and more accurate, facilitating subsequent peak detection.

[0152] In some examples, peak detection is performed on the basis of signal filtering performed on the PPG signal. Commonly used peak detection algorithms include the threshold method, the difference method, and the peak-to-valley method. Among them, the threshold method detects the peak in the signal by setting a threshold. The difference method detects the peak by calculating the first-order or second-order difference of the signal. The peak-to-valley method determines the peak by detecting the peaks and troughs of the signal. Based on the above peak detection method, the peak position and occurrence position timestamp of the PPG signal can be obtained. By analyzing the peak position in the PPG signal and its corresponding timestamp, the peak position of each first peak in multiple first peaks and the occurrence position timestamp of each first peak can be obtained. Subsequently, the user's blood pressure changes can be further analyzed based on the multiple first peaks and the occurrence position timestamp of each first peak.

[0153] like Figure 9 As shown, the first R wave includes multiple first wave peaks. Peak detection can be used to determine the peak position and occurrence timestamp of each of the multiple first wave peaks. The peak position and occurrence timestamp of each of the multiple first wave peaks are then recorded. PTT can then be calculated based on the occurrence timestamp of each first wave peak corresponding to the first R wave to determine the user's blood pressure.

[0154] For example, the occurrence position timestamp of each first wave peak in the plurality of first wave peaks can be obtained by {P1 1 ,P1 2 ,...,P1 n} represents. Among them, P1 1 Used to indicate the timestamp of the occurrence position of the first peak in the first R wave. 2 Used to indicate the timestamp of the occurrence position of the second peak in the first R wave. n Indicates the timestamp of the occurrence of the nth peak in the first R wave.

[0155] By performing signal filtering, peak detection, and other processing on the PPG signal, a clearer and more accurate physiological signal can be extracted. The first R wave and its peak information can then be used to further analyze and understand the body's physiological state, facilitating timely and accurate medical diagnosis based on the user's physiological condition. It should be noted that the aforementioned signal filtering, peak detection, and other processes can be adjusted and optimized based on different application scenarios and requirements, and this disclosure does not impose any limitations on this.

[0156] Step 609: In response to the user's blood pressure measurement operation, the acquisition module of the mobile phone acquires rPPG signals.

[0157] The blood pressure measurement operation can be performed by a user triggering a blood pressure measurement button on a mobile phone. The rPPG signal is used to determine the user's blood pressure value. The blood pressure measurement button can be a button in a blood pressure measurement application or a button in another application within the mobile phone that enables blood pressure measurement.

[0158] When a user needs to measure their blood pressure, they can use their phone and smartwatch to measure their blood pressure. Combining steps 601 through 608, the first R wave and its peak information output by the smartwatch can be obtained. This can then be combined with the rPPG signal collected by the phone to determine the user's blood pressure value.

[0159] In some examples, when a user measures blood pressure using a mobile phone, the phone may prompt the user to place a finger on the phone's camera, which then captures the user's physical characteristics, thereby deriving the user's blood pressure value based on the user's physical characteristics, including rPPG signals.

[0160] In some examples, when the phone includes a single camera, the user simply places their finger on the camera. When the phone includes multiple cameras, the user needs to place their finger on the main camera and the flash.

[0161] It is understandable that a fixed-length video captured by a camera of a user's finger can be used to obtain an rPPG signal. Based on the rPPG signal, subtle changes in the user's skin color can be analyzed to derive the user's physiological parameters. The use of rPPG signals can be considered a non-contact measurement method that does not require any sensors to come into direct contact with the body, reducing interference and discomfort to the user. Furthermore, this method does not require the wearing of any hardware devices or sensors; measurements can be performed solely through the camera, improving user convenience and comfort. After the camera captures the video, it can be analyzed in real time, helping to monitor and assess the user's physiological state. In other words, capturing the user's finger with a camera to obtain an rPPG signal enables non-contact, convenient, and real-time blood pressure measurement, providing a new and feasible solution for personal health monitoring and the medical field.

[0162] In some examples, in response to a user's blood pressure measurement operation, the process of the mobile phone's acquisition module acquiring rPPG signals may include: first, the user activates the blood pressure measurement function of the mobile phone. Then, after the blood pressure measurement function is turned on, the mobile phone may prompt the user to perform a second preset action and detect whether the user has performed the second preset action. Exemplarily, the second preset action may be the user placing a finger on the camera of the mobile phone. When it is detected that the user has performed the second preset action, the camera is called by the mobile phone's acquisition module to shoot a video of a fixed length. Based on the video, the rPPG signal is determined. The fixed-length video can be used for example, and the fixed length can be 30 seconds. The fixed length can be adjusted based on actual conditions, and the present disclosure is not limited to this.

[0163] In addition, when the mobile phone detects that the user has triggered a preset action, the mobile phone can also prompt the user to perform a more standardized preset action through the voice module, for example, prompting the user to remain still.

[0164] Videos consist of multiple frames. In some examples, the process for determining rPPG signals from a video can include first separating each frame in the video to obtain three independent channels: the r channel, the g channel, and the b channel. The pixel values of the r channel, g channel, and b channel corresponding to each frame are then summed frame by frame to obtain the r, g, and b channel rPPG signals.

[0165] In some examples, the mobile phone may receive a click operation triggered by the user. In response to the click operation, the above-mentioned operation of collecting rPPG signals is started. For example, in response to the user's sliding operation, such as Figure 10As shown in (a) of FIG, the mobile phone displays an interface 1000, which includes multiple application icons, such as a clock icon, a calendar icon, a gallery icon, a memo icon, a file management icon, an email icon, a music icon, a calculator icon, and a blood pressure measurement icon. In response to the user clicking on the blood pressure measurement icon, as shown in FIG. Figure 10 As shown in (b) of FIG. 1 , the mobile phone displays interface 1001, including areas 1002 and 1003. Area 1002 includes a visual prompt 10021 and a text prompt 10022. Visual prompt 10021 helps the user understand how to perform the action corresponding to the text prompt. Text prompt 10022 prompts the user on how to obtain the user's blood pressure. For example, text prompt 10022 reads: "Please place your index finger on the camera and flash." Area 1003 includes a note 10023. Note 10023 prompts the user on how to correctly perform the action indicated by text prompt 10022 so that the phone can accurately capture the user's rPPG signal. For example, note 10023 may include three items: Note 1: Place naturally, without pressing. Note 2: Do not move your finger when facing sideways. Note 3: When multiple cameras are used, cover the main camera and flash.

[0166] For another example, a mobile phone can initiate the aforementioned rPPG signal acquisition process by receiving a user-initiated voice command. For example, a user can speak a designated activation command to activate the phone's voice command acquisition mode. For example, if a user says "Hi, phone," the phone's speaker will respond with "I'm here." In this scenario, the phone can monitor voice commands through its microphone. For example, if a user says "measure blood pressure," the phone will recognize the user's voice command as initiating a measurement. In response to the measurement command, the phone can search for the corresponding application in its local application program library and launch it to perform the aforementioned rPPG signal acquisition.

[0167] Since the blood pressure measurement method disclosed in this disclosure requires the use of a smartwatch and a mobile phone. Specifically, the mobile phone is used to collect rPPG signals, and the smartwatch is used to collect PPG signals. Therefore, when implementing the blood pressure measurement method disclosed in this disclosure, the rPPG signal can be collected by the mobile phone first, and then the PPG signal can be collected by the smartwatch. In other words, the user can first refer to Figure 10The operation shown in (b) is to place the finger on the camera of the mobile phone. After the mobile phone collects the rPPG signal, the user no longer needs to place the finger on the camera of the mobile phone. At this time, the user only needs to wear a smart watch. It is also possible to use the smart watch to collect PPG signals first, and then use the mobile phone to collect rPPG signals. That is, the user wears the smart watch first, and after the smart watch collects the PPG signal, the user no longer needs to wear the smart watch. At this time, the user only needs to place the finger on the camera of the mobile phone. Alternatively, while using the mobile phone to collect rPPG signals, use the smart watch to collect PPG signals at the same time. Figure 7 As shown, users can wear a smart watch on their wrist and refer to Figure 10 The operation shown in (b) is to place your finger on the camera of the mobile phone. The present disclosure does not limit the order of collecting rPPG signals and PPG signals, and users can flexibly handle it according to their own needs.

[0168] In some examples, when it is detected that the user triggers the second preset action, the acquisition module of the mobile phone calls the camera to shoot a video of a fixed length, and the process of extracting the rPPG signal based on the video can be: first, as shown in FIG. Figure 8 As shown, the camera is called by the acquisition module of the mobile phone to collect data, and the camera captures a video containing R channel, G channel and B channel. The video includes multiple frames of images. The subject of the multiple frames of images is the user's finger covering the mobile phone camera. Secondly, in each frame of the image, a region of interest (ROI) is selected. The ROI is used to extract the rPPG signal. Then, the light intensity change signal of the ROI in each frame is calculated over time. The light intensity change signal is used to indicate the pulse change of blood in the skin. The light intensity change of the ROI in each frame can be determined by comparing the pixel value in the ROI with the average value of the frame image. Finally, the light intensity change signal is subjected to frequency analysis to extract the rPPG signal. The frequency analysis of the light intensity change signal can be achieved by Fourier transform.

[0169] It is understood that the process of collecting rPPG signals may be affected by factors such as ambient light, camera quality, and the movements of the subject being collected. Therefore, the collected rPPG signals can be subsequently subjected to signal quality testing to improve signal quality and accuracy.

[0170] In other examples, when a camera captures a video containing R, G, and B channels, the R (red) channel is often used as the primary reference when extracting rPPG signals from the video. This is because red light is absorbed and scattered more significantly by blood than green and blue light. Hemoglobin in blood absorbs red light more strongly, meaning that changes in light intensity in the R channel more easily reflect pulse signals. Furthermore, red light is less susceptible to interference from ambient light. Compared to blue and green light, red light has a longer wavelength and is less subject to scattering and reflection when penetrating the skin. This allows the light signal in the R channel to focus more on changes in blood pulses at the skin's surface, less susceptible to ambient light interference. Therefore, when extracting rPPG signals from video signals, the R channel can be selected as the primary reference to improve signal quality and accuracy. Meanwhile, data from the G (green) and B (blue) channels can also serve as auxiliary information to enhance signal stability and reliability.

[0171] Step 610: The acquisition module sends a third notification to the signal quality detection module in the mobile phone.

[0172] The third notification is used to instruct the signal quality detection module to perform signal quality detection on the rPPG signal to determine whether the rPPG signal needs to be re-collected. The third notification includes the rPPG signal.

[0173] To ensure the accuracy of blood pressure values determined based on rPPG signals, the present disclosure further performs a signal quality test on the rPPG signal after acquiring it. If the rPPG signal passes the signal quality test, further processing of the rPPG signal can be continued. If the rPPG signal fails the signal quality test, steps 613 to 615 are continued to reacquire the rPPG signal until the acquired rPPG signal meets the signal quality test requirements. This ensures that the signal quality and signal stability of the acquired rPPG signal meet the requirements, thereby facilitating the subsequent determination of a more accurate PTT.

[0174] Step 611: The signal quality detection module receives the third notification.

[0175] Step 612: In response to the third notification, the signal quality detection module performs a signal quality detection on the rPPG signal to obtain a signal quality detection result.

[0176] like Figure 8 As shown, after collecting the rPPG signal, the collection module may send a third notification to the signal quality detection module, so that the signal quality detection module performs signal quality detection on the collected rPPG signal.

[0177] In some examples, the signal quality detection module performs a signal quality test on the rPPG signal to obtain a signal quality test result. Specifically, the signal quality detection module may calculate the correlation between the g channel, b channel, and r channel in the rPPG signal. If the correlation exceeds a second preset threshold, the rPPG signal is considered to have passed the signal quality test, and the signal quality test result is considered to be of satisfactory quality. If the correlation does not exceed the second preset threshold, the rPPG signal is considered to have failed the signal quality test, and the signal quality test result is considered to be substandard quality, and the signal quality test result does not meet the requirements.

[0178] In other examples, the correlation between the g channel, the b channel, and the r channel can be characterized by a correlation coefficient. For example, the correlation coefficient can be either a Pearson correlation coefficient or a cross-correlation coefficient. Generally, the closer the correlation coefficient is to 1 or -1, the stronger the correlation between the signals, that is, the better the signal quality. When the correlation coefficient is close to 0, it means that the correlation between the signals is weak, and there may be more noise or poor signal quality. It is understood that in actual applications, other signal quality detection indicators and algorithms can also be combined to comprehensively evaluate the signal quality detection results of the rPPG signal, and the present disclosure is not limited to this.

[0179] By setting a second preset threshold for signal quality testing, low-quality or noisy signals can be eliminated, ensuring that only high-quality rPPG signals are used. This helps reduce data interference, improving signal quality and reliability. It also reduces the workload for subsequent processing and analysis, improving signal quality measurement efficiency and saving time and costs.

[0180] Step 613: If the signal quality detection result is that the quality does not meet the standard, send a fourth notification to the acquisition module.

[0181] The fourth notification is used to instruct the acquisition module to re-acquire the rPPG signal.

[0182] If the signal quality detection result is substandard, indicating that the rPPG signal quality does not meet the requirements, the rPPG signal needs to be re-collected. In this case, the signal quality detection module can send a fourth notification to the acquisition module to instruct the acquisition module to re-collect the rPPG signal using the fourth notification.

[0183] In some examples, if the signal quality detection result is that the quality meets the standard, the signal quality detection module will send the rPPG signal to the signal filtering module for further processing, so that the rPPG signal can be used to obtain the user's blood pressure value.

[0184] Step 614: The collection module receives the fourth notification.

[0185] Step 615: In response to the fourth notification, the acquisition module re-acquires the rPPG signal and executes steps 610 to 612 again to obtain a signal quality detection result corresponding to the re-acquired rPPG signal.

[0186] After the acquisition module receives the fourth notification, it can reacquire the rPPG signal in response to the fourth notification. After reacquiring the rPPG signal, it can continue to send a fourth notification to the signal quality detection module. Upon receiving the fourth notification, the signal quality detection module continues to perform signal quality detection on the reacquired rPPG signal in the fourth notification, thereby obtaining a signal quality detection result corresponding to the reacquired rPPG signal. It will be appreciated that the process of reacquiring the rPPG signal by the acquisition module is similar to the process of acquiring the rPPG signal in step 609 and will not be further described here.

[0187] If the signal quality detection result is that the quality does not meet the standard, the acquisition module needs to continue to acquire the rPPG signal until the signal quality detection result of the acquired rPPG signal meets the standard.

[0188] Step 616: When the rPPG signal meets the detection condition, the signal quality detection module sends a fifth notification to the signal processing module.

[0189] The fifth notification is used to instruct the signal processing module to process the rPPG signal to obtain the second R wave and the peak information of the second R wave. The fifth notification includes the rPPG signal.

[0190] In some examples, such as Figure 8 As shown, the signal processing module processes the rPPG signal by performing signal filtering and peak detection on the rPPG signal. The peak information of the second R wave includes multiple second peaks and a timestamp of the occurrence position of each second peak in the multiple second peaks.

[0191] Step 617: The signal processing module receives the fifth notification.

[0192] Step 618: In response to the fifth notification, the signal processing module processes the rPPG signal to obtain the second R wave and the peak information of the second R wave.

[0193] The signal processing module processes the rPPG signal by performing signal filtering, peak detection, etc. The peak information of the second R wave includes the timestamps of the occurrence positions of multiple peaks.

[0194] In some examples, the process of filtering the rPPG signal involves, after acquiring the rPPG signal, inputting it into a filter to pass signals within a specific frequency range while suppressing or attenuating signals at other frequencies. This ensures that low-frequency drift and high-frequency noise in the rPPG signal can be effectively removed after passing through the filter.

[0195] Exemplarily, a specific frequency range can be set by a starting frequency and a cutoff frequency. For example, the starting frequency and the cutoff frequency are 0.5 Hz and 2.5 Hz, respectively. The frequency range of the filter can be determined based on the starting frequency and the cutoff frequency. Based on this frequency range, signals within this frequency range can be retained, while signals below 0.5 Hz and above 2.5 Hz will be suppressed. It is understood that the starting frequency and the cutoff frequency of the filter can be flexibly selected according to actual needs, and the present disclosure does not limit this.

[0196] In some examples, peak detection is performed based on signal filtering of the rPPG signal. Common peak detection algorithms include fixed threshold-based peak detection, moving average-based peak detection, differential-based peak detection, and waveform template matching-based peak detection. Based on the above peak detection methods, the peak position and occurrence location timestamp of the rPPG signal can be obtained. By analyzing the peak position and its corresponding timestamp in the rPPG signal, the peak position and occurrence location timestamp of each second peak in multiple second peaks can be obtained. Subsequently, the peak position and occurrence location timestamp of each second peak in the multiple second peaks can be used to further analyze the user's blood pressure changes.

[0197] like Figure 11 As shown, the second R wave includes multiple second peaks. Peak detection can be used to determine the peak position and occurrence timestamp of each of the multiple second peaks. The peak position and occurrence timestamp of each of the multiple second peaks are then recorded. PTT can then be calculated based on the occurrence timestamp of each second peak corresponding to the second R wave to determine the blood pressure value.

[0198] For example, the occurrence position timestamp of each second wave peak in the plurality of second wave peaks can be obtained by Indicates. Among them, Indicates the timestamp of the first peak in the second R wave. Indicates the timestamp of the second peak in the second R wave. Indicates the timestamp of the occurrence of the nth peak in the second R wave.

[0199] By performing signal filtering and peak detection on the rPPG signal, clearer and more accurate physiological signals can be extracted. The second R wave and the timestamps of the occurrence of multiple second wave peaks within the second R wave can then be used to further analyze and understand the human body's physiological state, enabling medical personnel to make timely and accurate medical diagnoses based on the user's physiological condition.

[0200] It should be noted that the embodiment of the present disclosure does not limit the execution order of steps 601-608 and steps 609-618. For example, steps 601-608 may be executed first, followed by steps 609-618; steps 609-618 may be executed first, followed by steps 601-608; or steps 601-608 and steps 609-618 may be executed simultaneously. The specific execution order may be determined based on actual usage requirements.

[0201] Step 619: The smartwatch and the mobile phone establish a Bluetooth connection.

[0202] Mobile phones generally have Bluetooth functionality. In step 619, when the PPG signal collected by the smartphone is needed to obtain a blood pressure value, the smartphone can use its Bluetooth functionality to establish a Bluetooth connection with the smartwatch. Of course, if this is the first time the smartphone is establishing a Bluetooth connection with the smartwatch, the smartphone will need to perform pairing procedures with the smartwatch before establishing a Bluetooth connection.

[0203] It should be noted that the present embodiment does not limit the order of step 619 and steps 601-618. For example, step 619 may be performed first, followed by steps 601-618; steps 601-618 may be performed first, followed by step 619; or steps 619 and steps 601-618 may be performed simultaneously. The specific order can be determined based on actual usage requirements.

[0204] Step 620: In response to the acquisition request of the mobile phone, the processor of the smart watch sends the first R wave and the peak information of the first R wave to the information processing module of the mobile phone.

[0205] The acquisition request is used to request the acquisition of the first R wave and the peak information of the first R wave.

[0206] After the smartwatch and the mobile phone establish a Bluetooth connection and the smartwatch obtains the first R wave and the peak information of the first R wave, the information processing module of the mobile phone can send an acquisition request to the processor of the smartwatch through the Bluetooth module of the mobile phone. After the Bluetooth module of the smartwatch receives the acquisition request, it can send the acquisition request to the processor of the smartwatch. Figure 8As shown, the processor of the smart watch can respond to the acquisition request sent by the information processing module of the mobile phone, and send the first R wave and the peak information of the first R wave to the information processing module of the mobile phone through the Bluetooth module of the smart watch, so that the information processing module of the mobile phone can obtain the PPT based on the first R wave, the peak information of the first R wave, the second R wave and the peak information of the second R wave.

[0207] Step 621: The information processing module of the mobile phone receives the first R wave and its peak information, and determines the blood pressure value based on the first R wave and its peak information and the second R wave and its peak information.

[0208] After the signal processing module receives the first R wave and its peak information, it can determine the user's blood pressure value based on the first R wave, its peak information, the second R wave, and its peak information.

[0209] In some examples, such as Figure 8 As shown, the signal processing module determines the blood pressure value based on the first R wave, the peak information of the first R wave, the second R wave, and the peak information of the second R wave. This includes first determining the PTT based on the first R wave, the peak information of the first R wave, the second R wave, and the peak information of the second R wave. The PTT is then input into a blood pressure model to obtain a blood pressure value. The blood pressure model describes the relationship between the PTT and the blood pressure value.

[0210] In some examples, determining the PTT based on the first R wave, the peak information of the first R wave, the second R wave, and the peak information of the second R wave may include: first, deriving, based on the peak information of the first R wave and the peak information of the second R wave, a plurality of second peaks in the second R wave corresponding to the plurality of first peaks in the first R wave; then, calculating, based on the correspondence between the plurality of first peaks and the plurality of second peaks, the difference between the occurrence position timestamps of the plurality of first peaks and the occurrence position timestamps of the plurality of second peaks; and finally, deriving the PTT based on the difference between the occurrence position timestamps of the plurality of first peaks and the occurrence position timestamps of the plurality of second peaks.

[0211] In some examples, based on the peak information of the first R wave and the peak information of the second R wave, the multiple second peaks in the second R wave corresponding to the multiple first peaks in the first R wave can be obtained by: based on the occurrence location timestamp corresponding to one of the multiple first peaks, after the location where the first peak appears, searching the occurrence location timestamps corresponding to the multiple second peaks for the timestamp closest to the occurrence location timestamp of the first peak. The second peak corresponding to the found closest timestamp is then used as the second peak corresponding to the first peak. It is understood that determining the second peak corresponding to other first peaks in the multiple first peaks is similar to the method for determining the second peak corresponding to a first peak described above, and will not be repeated here.

[0212] Figure 12 This example illustrates a process where, based on the timestamp of the occurrence location of a first peak among multiple first peaks, after the first peak appears, a timestamp closest to the timestamp of the occurrence location of the first peak is searched among the timestamps of the occurrence locations of multiple second peaks, and then the second peak corresponding to the found closest timestamp is used as the second peak corresponding to the first peak. For example, for first peak 1 among multiple first peaks, the second peak corresponding to first peak 1 among multiple second peaks is second peak 1.

[0213] In some examples, based on the correspondence between the plurality of first wave peaks and the plurality of second wave peaks, calculating the difference between the occurrence position timestamps of the plurality of first wave peaks and the occurrence position timestamps of the plurality of second wave peaks may be: performing a difference processing on the occurrence position timestamp of each first wave peak and the occurrence position timestamp of the second wave peak adjacent to the first wave peak, thereby obtaining a plurality of initial PTTs. Exemplarily, the initial PTT may be obtained by {PTT1,PTT2,...,PTT n} means. For example, Figure 12 As shown, for the first wave peak 1, the initial PTT shown in the figure can be obtained by calculating the difference between the occurrence position timestamp of the first wave peak 1 and the occurrence position timestamp of the second wave peak 1.

[0214] Then, the multiple initial PTTs are averaged to obtain the PTT. For example, the calculation of PTT satisfies the following expression:

[0215]

[0216] Here, i represents the nth difference result, and n represents the total number of difference results.

[0217] By identifying multiple first wave peaks and their corresponding second wave peaks, the pulse start and end positions can be accurately captured, providing more accurate time difference measurements. Averaging these initial PTTs can then reduce the impact of individual noise or abnormal measurements on the final result, improving measurement stability and reliability. Furthermore, since PTT is used to estimate blood pressure, averaging the PTTs corresponding to multiple wave peaks can improve the accuracy of blood pressure estimation.

[0218] As can be seen from the preceding, the blood pressure model describes the relationship between PTT and blood pressure. Before inputting PTT into the blood pressure model to calculate blood pressure, it's helpful to first explain the relationship between PTT and blood pressure. Before explaining the relationship between PTT and blood pressure, it's helpful to first explain pulse wave velocity.

[0219] Pulse Wave Velocity (PWV): An important indicator used to assess arteriosclerosis and vascular health. PWV is based on the relationship between the speed of the pulse wave within blood vessels and blood pressure. Lower blood pressure indicates slower pulse wave propagation within blood vessels; conversely, higher blood pressure indicates faster pulse wave propagation. Therefore, measuring PWV can indirectly infer blood pressure levels.

[0220] The pulse wave is formed when blood is pumped into the aorta during heart contraction. Once formed, the pulse wave propagates outward along the arterial system. The pulse wave's velocity is closely related to changes in blood pressure. By measuring the pulse wave's arrival time difference (PTT) at different vascular locations and the distance (L) between different vascular locations, the PWV (Pulse Wave Volume) of the pulse wave within the blood vessels can be calculated. PWV satisfies the following expression.

[0221] PWV = L / PTT (Formula 1)

[0222] Since the Moens-Korteweg equation is used to describe the relationship between PWV and blood pressure in arterial vessels, the following expression can be established based on the Moens-Korteweg equation.

[0223]

[0224] Among them, E0 is the elastic modulus of the blood vessel; h is the thickness of the blood vessel wall; α is a constant; P is the blood pressure; ρ is the blood density; and r is the radius of the blood vessel.

[0225] Since E0, h, α, ρ, and r in the above formula are all fixed values, it can be seen that PWV is positively correlated with blood pressure. Combined with Formula 1, it can be seen that PWV and PTT are also correlated. Therefore, based on the above Formulas 1 and 2, a linear fitting relationship between blood pressure and PTT can be created using a large number of samples, thereby deriving a blood pressure model used to describe the relationship between PTT and blood pressure values. For example, the blood pressure model satisfies the following expression:

[0226] BP = α lnPTT + β (Formula 3)

[0227] Where α and β are constants. For example, α is 0.8 and β is 70.

[0228] It is understandable that the blood pressure model can also satisfy other expressions, which can be flexibly adjusted according to actual conditions, and the present disclosure does not impose any restrictions on this.

[0229] In some examples, before using the blood pressure model to calculate the blood pressure value, the parameters in the blood pressure model need to be calibrated. Figure 13 The present invention exemplifies a calibration process of a blood pressure model and an application process of the blood pressure model. The process of calibrating the parameters in the blood pressure model may include: first, measuring and recording the user's gold standard blood pressure. Then, implementing the test step (i.e., obtaining the first R wave, the peak information of the first R wave, the second R wave, and the peak information of the second R wave by executing steps 601-618. And determining PTT based on the first R wave, the peak information of the first R wave, the second R wave, and the peak information of the second R wave). Finally, based on PTT and the gold standard blood pressure, adjusting the parameters in the initial blood pressure model to obtain a blood pressure model. Exemplarily, measuring and recording the user's gold standard blood pressure may be measuring the user's blood pressure using a gold standard blood pressure meter (e.g., a mercury sphygmomanometer) and recording the measurement results. The measurement result may also be referred to as the gold standard blood pressure (i.e., the user's measured blood pressure).

[0230] Alternatively, the process of calibrating the parameters in the blood pressure model may further include: first, performing a testing step (i.e., obtaining the first R wave, the peak information of the first R wave, the second R wave, and the peak information of the second R wave by executing steps 601-618. And determining the PTT based on the first R wave, the peak information of the first R wave, the second R wave, and the peak information of the second R wave). Then, measuring and recording the user's gold standard blood pressure. Finally, adjusting the parameters in the initial blood pressure model based on the PTT and the gold standard blood pressure to obtain a blood pressure model.

[0231] It should be noted that to ensure the accuracy of the blood pressure model, the above calibration process can be repeated multiple times at different time periods to correct the parameters in the blood pressure model and improve the accuracy of the blood pressure model, so that more accurate blood pressure values of the user can be obtained using the blood pressure model in the future. The number of repetitions of the above test process can be flexibly set according to different blood pressure models and is not limited here.

[0232] After multiple calibrations of the parameters in the blood pressure model, the PTT can be input into the blood pressure model to obtain a blood pressure value. The application process of the blood pressure model may include: first, performing a test step (i.e., executing steps 601-618 to obtain the first R wave, the peak information of the first R wave, the second R wave, and the peak information of the second R wave. The PTT is then determined based on the first R wave, the peak information of the first R wave, the second R wave, and the peak information of the second R wave). Then, the user's blood pressure value is obtained by inputting the PTT into the blood pressure model.

[0233] In addition, after the information processing module calculates the blood pressure value, the information processing module can send the blood pressure value to the display module of the mobile phone so that the blood pressure value can be displayed to the user through the display module of the mobile phone. The information processing module can also send the blood pressure value to the display module of the smart watch via the Bluetooth module of the mobile phone so that the blood pressure value can be displayed to the user through the smart watch. Alternatively, the information processing module can send the blood pressure value to the display module of the mobile phone and the display module of the smart watch simultaneously so that the blood pressure value can be displayed to the user through the mobile phone and the smart watch respectively. The present disclosure does not limit the display method of the blood pressure value.

[0234] Based on the above scheme, it can be seen that the present disclosure uses a blood pressure model to determine the user's blood pressure value. Before using the blood pressure model, it is necessary to first determine PTT. PTT is obtained by collecting rPPG signals through a mobile phone and PPG signals through a smart wearable device. Specifically, the occurrence position timestamps of multiple first wave peaks in the first R wave are obtained based on the PPG signal; the occurrence position timestamps of multiple second wave peaks in the second R wave are obtained based on the rPPG signal. Then, based on the occurrence position timestamps of multiple first wave peaks in the first R wave and the occurrence position timestamps of multiple second wave peaks in the second R wave, PTT is obtained, and then the user's blood pressure value is obtained.

[0235] In other words, the present disclosure utilizes easily acquired signals for blood pressure monitoring. For example, these easily acquired signals include PPG signals collected by smart wearable devices and rPPG signals collected by mobile phones. During rPPG signal acquisition, since the phone's camera only needs to capture the user's finger, non-contact acquisition is possible, avoiding the interference and discomfort caused by contact between the sensor and the user. Furthermore, this method does not require the wearer to wear any hardware devices or sensors, improving user convenience and comfort.

[0236] In addition, since the devices used to measure blood pressure in this disclosure are electronic devices (e.g., mobile phones) and smart wearable devices (e.g., smart watches) that most people use, the user's cost of use will be lower compared to existing smart wearable devices that support collecting ECG and PPG signals. This will help more users monitor their blood pressure in a timely manner and receive targeted treatment accordingly.

[0237] For ease of understanding, the following Figure 14 The data processing method provided by the embodiment of the present disclosure is described. Figure 14 As shown, the data processing method may include the following steps 1401-1404.

[0238] Step 1401: In response to a blood pressure measurement operation of a user, remote photoplethysmography (rPPG) signals are collected, and occurrence position timestamps of multiple first wave peaks in a first R wave from a smart wearable device are obtained.

[0239] Among them, the rPPG signal is determined based on the video captured by the electronic device, and the first R wave is determined based on the photoplethysmography (PPG) signal collected by the smart wearable device.

[0240] In response to the user's blood pressure measurement operation in the embodiment of the present disclosure, the remote photoplethysmography rPPG signal can be collected by referring to the above Figure 6 Step 609 in the illustrated embodiment will not be described in detail in the embodiment of the present disclosure.

[0241] The time stamps of the occurrence positions of the multiple first wave peaks in the first R wave from the smart wearable device in the embodiment of the present disclosure can refer to the above Figure 6 Steps 601 to 608 and steps 619 to 621 in the illustrated embodiment will not be described in detail in the embodiment of the present disclosure.

[0242] In some examples, rPPG signals are collected in response to a user's blood pressure measurement operation, including: prompting the user to perform a specified action in response to the user's blood pressure measurement operation; the specified action is the user covering the camera of the electronic device with his finger; when it is detected that the user has performed the specified action, a video of a fixed length is collected; and the rPPG signal is determined based on the video.

[0243] In response to the user's blood pressure measurement operation in the embodiment of the present disclosure, the user is prompted to perform a specified action; the specified action is that the user covers the camera of the electronic device with his finger; when it is detected that the user has performed the specified action, the rPPG signal can be collected by referring to the above Figure 6 Step 609 in the embodiment shown in the figure is not described in detail in the embodiment of the present disclosure. Figure 6 The second preset action in step 609 in the illustrated embodiment is similar.

[0244] Step 1402: Determine the occurrence position timestamps of multiple second wave peaks in the second R wave based on the rPPG signal.

[0245] In some examples, before determining the occurrence position timestamps of multiple second wave peaks in the second R wave based on the rPPG signal, the method further includes: performing a signal quality test on the rPPG signal to obtain a signal quality test result; if the signal quality test result does not meet the requirements, re-collecting the rPPG signal until the signal quality test result of the rPPG signal meets the requirements.

[0246] In the embodiment of the present disclosure, the signal quality of the rPPG signal is detected to obtain a signal quality detection result; if the signal quality detection result does not meet the requirements, the rPPG signal is re-collected until the signal quality detection result of the rPPG signal meets the requirements. Figure 6 Steps 612 to 615 in the illustrated embodiment will not be described in detail in the embodiment of the present disclosure.

[0247] In some examples, before the signal quality detection result does not meet the requirements, the method further includes: determining whether the signal quality detection result exceeds a preset threshold; if it does not exceed the preset threshold, determining that the signal quality detection result does not meet the requirements.

[0248] In the embodiment of the present disclosure, it is determined whether the signal quality detection result exceeds the preset threshold; if it does not exceed the preset threshold, it can be determined that the signal quality detection result does not meet the requirements. Figure 6 Step 612 in the embodiment shown in the figure will not be described in detail in the embodiment of the present disclosure. Figure 6 The second preset threshold in step 612 in the illustrated embodiment is similar.

[0249] In some examples, determining the occurrence position timestamps of multiple second wave peaks in the second R wave based on the rPPG signal includes: performing signal filtering and peak detection on the rPPG signal to obtain the second R wave and the occurrence position timestamps of the multiple second wave peaks in the second R wave.

[0250] In the embodiment of the present disclosure, the signal filtering and peak detection of the rPPG signal are performed to obtain the second R wave and the occurrence position timestamps of multiple second wave peaks in the second R wave. Figure 6 Step 618 in the illustrated embodiment will not be described in detail in the embodiment of the present disclosure.

[0251] Step 1403: Determine the pulse wave transmission time PTT based on the occurrence position timestamps of the multiple first wave peaks in the first R wave and the occurrence position timestamps of the multiple second wave peaks in the second R wave.

[0252] In some examples, the pulse wave transmission time PTT is determined based on the occurrence position timestamps of multiple first wave peaks in the first R wave and the occurrence position timestamps of multiple second wave peaks in the second R wave, including: determining the second wave peak corresponding to each first wave peak in the multiple first wave peaks; determining multiple initial PTTs based on the occurrence position timestamp of each first wave peak and the occurrence position timestamp of the second wave peak corresponding to each first wave peak; and averaging the multiple initial PTTs to obtain the PTT.

[0253] In the embodiment of the present disclosure, a second peak corresponding to each of the multiple first peaks is determined; multiple initial PTTs are determined based on the occurrence position timestamp of each first peak and the occurrence position timestamp of the second peak corresponding to each first peak; the multiple initial PTTs are averaged to obtain the PTT, which can be referred to the above Figure 6 Step 621 in the illustrated embodiment will not be described in detail in the embodiment of the present disclosure.

[0254] In some examples, based on the occurrence position timestamp of each first wave peak and the occurrence position timestamp of the second wave peak corresponding to each first wave peak, determining multiple initial PTTs includes: performing difference processing on the occurrence position timestamp of each first wave peak and the occurrence position timestamp of the second wave peak corresponding to each first wave peak to obtain multiple initial PTTs.

[0255] In the embodiment of the present disclosure, the occurrence position timestamp of each first wave peak and the occurrence position timestamp of each second wave peak corresponding to the first wave peak are subjected to difference processing to obtain multiple initial PTTs. Figure 6 Step 621 in the illustrated embodiment will not be described in detail in the embodiment of the present disclosure.

[0256] Step 1404: Input the PTT into the blood pressure model to obtain the user's blood pressure value and display the blood pressure value. The blood pressure model is used to describe the relationship between PTT and blood pressure.

[0257] In some examples, before inputting PTT into the blood pressure model, the method further includes: obtaining the user's gold standard blood pressure; and calibrating parameters in the initial blood pressure model based on the gold standard blood pressure and PTT to obtain the blood pressure model.

[0258] In the embodiment of the present disclosure, the user's gold standard blood pressure is obtained; the parameters in the initial blood pressure model are calibrated based on the gold standard blood pressure and PTT to obtain the blood pressure model. Figure 6 Step 621 in the illustrated embodiment will not be described in detail in the embodiment of the present disclosure.

[0259] For example, Figure 15 A schematic diagram of the structure of a blood pressure measuring device 1500 is shown. Figure 15 As shown, the blood pressure measurement device 1500 may include: an acquisition module 1501, a determination module 1502, and a display module 1503, etc.

[0260] Among them, the acquisition module 1501 is configured to collect remote photoplethysmography (rPPG) signals in response to the user's blood pressure measurement operation, and obtain the occurrence position timestamps of multiple first wave peaks in the first R wave from the smart wearable device. The rPPG signal is determined based on the video captured by the electronic device, and the first R wave is determined based on the photoplethysmography (PPG) signal collected by the smart wearable device.

[0261] The determination module 1502 is configured to determine the occurrence position timestamps of multiple second wave peaks in the second R wave based on the rPPG signal; and determine the pulse wave transmission time PTT based on the occurrence position timestamps of multiple first wave peaks in the first R wave and the occurrence position timestamps of multiple second wave peaks in the second R wave.

[0262] The display module 1503 is configured to input the PTT into a blood pressure model, obtain the user's blood pressure value, and display the blood pressure value. The blood pressure model is used to describe the relationship between PTT and blood pressure.

[0263] In a possible implementation, the determination module 1502 is further configured to perform a signal quality detection on the rPPG signal to obtain a signal quality detection result.

[0264] The acquisition module 1501 is further configured to re-acquire the rPPG signal if the signal quality detection result does not meet the requirements until the signal quality detection result of the rPPG signal meets the requirements.

[0265] In a possible implementation, the determination module 1502 is further configured to perform signal filtering and peak detection on the rPPG signal to obtain the second R wave and the occurrence position timestamps of multiple second wave peaks in the second R wave.

[0266] In one possible implementation, the determination module 1502 is further configured to determine a second peak corresponding to each first peak among multiple first peaks; determine multiple initial PTTs based on the occurrence position timestamp of each first peak and the occurrence position timestamp of the second peak corresponding to each first peak; and average the multiple initial PTTs to obtain PTT.

[0267] In a possible implementation, the determination module 1502 is further configured to perform a difference processing on the occurrence position timestamp of each first wave peak and the occurrence position timestamp of the second wave peak corresponding to each first wave peak to obtain multiple initial PTTs.

[0268] In a possible implementation, the acquisition module 1501 is further configured to obtain the gold standard blood pressure of the user.

[0269] The determination module 1502 is further configured to calibrate the parameters in the initial blood pressure model based on the gold standard blood pressure and PTT to obtain a blood pressure model.

[0270] In a possible implementation, the determination module 1502 is further configured to prompt the user to perform a specified action in response to the user's blood pressure measurement operation; the specified action is that the user covers the camera of the electronic device with his finger.

[0271] The acquisition module 1501 is further configured to acquire a video of a fixed length when it is detected that the user has performed a specified action; and determine the rPPG signal based on the video.

[0272] In a possible implementation, the determination module 1502 is further configured to determine whether the signal quality detection result exceeds a preset threshold; if it does not exceed the preset threshold, it is determined that the signal quality detection result does not meet the requirements.

[0273] It should be understood that the division of units or modules (hereinafter referred to as units) in the above devices is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a single physical entity, or physically separated. Furthermore, the units in the device may be implemented entirely in the form of software called through processing elements; entirely in the form of hardware; or partially in the form of software called through processing elements, and partially in the form of hardware.

[0274] For example, each unit can be a separately established processing element, or it can be integrated into a certain chip of the device. In addition, it can also be stored in a memory in the form of a program, and called by a certain processing element of the device to execute the function of the unit. In addition, all or part of these units can be integrated together or implemented independently. The processing element here can also be called a processor, which can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above units can be implemented by the integrated logic circuit of the hardware in the processor element or in the form of software called by the processing element.

[0275] It should be understood that each step in the above-mentioned method embodiments provided by the present disclosure can be completed by hardware integrated logic circuits in a processor or by software instructions. The method steps disclosed in conjunction with the embodiments of the present disclosure can be directly implemented as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in a processor.

[0276] In one example, the units in the above apparatus may be one or more integrated circuits configured to implement the above method, such as one or more ASICs, or one or more DSPs, or one or more FPGAs, or a combination of at least two of these integrated circuit forms.

[0277] For another example, when the units in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a CPU or other processor that can call programs. For another example, these units can be integrated together and implemented in the form of a system on a chip (SOC).

[0278] In one implementation, the units implementing the corresponding steps of the above method in the above apparatus may be implemented in the form of a processing element scheduling program. For example, the apparatus may include a processing element and a storage element, with the processing element invoking a program stored in the storage element to execute the method of the above method embodiment. The storage element may be a storage element on the same chip as the processing element, i.e., an on-chip storage element.

[0279] In another implementation, the program for executing the above method may be stored in a memory element on a different chip from the processing element, i.e., an off-chip memory element. In this case, the processing element calls or loads the program from the off-chip memory element to the on-chip memory element to call and execute the method of the above method embodiment.

[0280] For example, embodiments of the present disclosure may also provide an apparatus, such as an electronic device, which may include a processor and a memory for storing instructions executable by the processor. When the processor is configured to execute the instructions, the electronic device implements the data processing method of the aforementioned embodiment. The memory may be located within or outside the electronic device. The processor may include one or more processors.

[0281] In another implementation, the unit of the apparatus implementing each step of the above method may be configured as one or more processing elements, which may be provided on the corresponding electronic device. The processing elements may be integrated circuits, such as one or more ASICs, one or more DSPs, one or more FPGAs, or a combination of these integrated circuits. These integrated circuits may be integrated together to form a chip.

[0282] For example, embodiments of the present disclosure further provide a chip that can be used in the aforementioned electronic device. The chip includes one or more interface circuits and one or more processors; the interface circuits and processors are interconnected via circuits; the processors receive and execute computer instructions from the electronic device's memory via the interface circuits to implement the methods described in the aforementioned method embodiments.

[0283] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by an electronic device, the electronic device can implement the above-mentioned data processing method.

[0284] The embodiments of the present disclosure also provide a computer program product, including computer instructions for running on the electronic device as described above. When the computer instructions are run in the electronic device, the electronic device can implement the data processing method as described above. Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0285] In the several embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0286] Units described as separate components may or may not be physically separate, and components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0287] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0288] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present disclosure is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, such as a program. The software product is stored in a program product, such as a computer-readable storage medium, and includes a number of instructions for enabling a terminal device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute all or part of the steps of the various embodiments of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks or optical disks.

[0289] For example, the embodiments of the present disclosure may further provide a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by an electronic device, the electronic device implements the data processing method in the aforementioned method embodiment.

[0290] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or replacements within the technical scope disclosed in the present disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A blood pressure measurement method, characterized in that: Applied to an electronic device, the electronic device includes multiple cameras and a flash, the multiple cameras including a main camera, and the method includes: In response to a user placing a finger on the main camera and the flash, a video is captured by the main camera, the video including R channel data, G channel data, and B channel data, and the video includes multiple frames of images; In each of the multiple frames of images, a region of interest (ROI) is selected; Comparing the pixel values within the ROI in each frame of image with the average value of the pixel values of the frame of image to determine a light intensity change signal of the ROI in each frame of image over time, wherein the light intensity change signal is used to indicate a pulse change of blood in the skin; Using the R channel data as reference data and the G channel and B channel data as auxiliary data, performing Fourier transform on the light intensity change signal to obtain an rPPG signal; After acquiring the rPPG signal, in response to an operation of a user wearing the smart wearable device, obtaining timestamps of occurrence positions of multiple first wave peaks in a first R wave from the smart wearable device, wherein the rPPG signal is determined based on a video captured by the electronic device, and the first R wave is determined based on a photoplethysmography (PPG) signal acquired by the smart wearable device; determining, based on the rPPG signal, timestamps of occurrence positions of a plurality of second wave peaks in the second R wave; Determining a second peak corresponding to each of the plurality of first peaks; wherein determining the second peak corresponding to each of the plurality of first peaks comprises: determining, among a plurality of second peaks that appear after the first peak, a second peak that has a timestamp closest to that of the occurrence position of the first peak as the second peak corresponding to the first peak; Determine a plurality of initial PTTs based on a timestamp of a location of each first wave peak and a timestamp of a location of a second wave peak corresponding to each first wave peak; averaging the multiple initial PTTs to obtain the PTT; Inputting the PTT into a blood pressure model to obtain a blood pressure value of the user and displaying the blood pressure value, wherein the blood pressure model is used to describe the relationship between the PTT and blood pressure; Before determining the occurrence position timestamps of a plurality of second wave peaks in the second R wave based on the rPPG signal, the method further includes: Calculating the correlation between the g channel, the b channel, and the r channel in the rPPG signal; When the correlation among the g channel, the b channel, and the r channel in the rPPG signal does not exceed a preset threshold, the rPPG signal is reacquired until the correlation among the g channel, the b channel, and the r channel in the rPPG signal exceeds the preset threshold.

2. The method according to claim 1, characterized in that The step of determining occurrence position timestamps of a plurality of second wave peaks in the second R wave based on the rPPG signal includes: Signal filtering and peak detection are performed on the rPPG signal to obtain the second R wave and timestamps of occurrence positions of multiple second wave peaks in the second R wave.

3. The method according to claim 1, characterized in that The determining of a plurality of initial PTTs based on the occurrence position timestamp of each first wave peak and the occurrence position timestamp of the second wave peak corresponding to each first wave peak comprises: A difference process is performed on the occurrence position timestamp of each first wave peak and the occurrence position timestamp of the second wave peak corresponding to each first wave peak to obtain a plurality of the initial PTTs.

4. The method according to any one of claims 1 to 3, characterized in that Before inputting the PTT into the blood pressure model, the method further includes: Get the user's gold standard blood pressure; The parameters in the initial blood pressure model are calibrated based on the gold standard blood pressure and the PTT to obtain the blood pressure model.

5. The method according to any one of claims 1 to 4, characterized in that The collecting of the rPPG signal in response to the user's blood pressure measurement operation includes: In response to the user's blood pressure measurement operation, prompting the user to perform a specified action; the specified action is the user covering the camera of the electronic device with a finger; When it is detected that the user has performed the specified action, capturing the video of a fixed length; Based on the video, the rPPG signal is determined.

6. An electronic device, characterized in that: The electronic device comprises a processor and a memory for storing instructions executable by the processor; when the processor is configured to execute the instructions, the electronic device implements the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having computer program instructions stored thereon; characterized in that: When the computer program instructions are executed by an electronic device, the electronic device is caused to implement the method according to any one of claims 1 to 5.

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