Signal processing method and related apparatus

Abstract

Provided in the present application are a signal processing method and a related apparatus. The signal processing method comprises: acquiring a first signal and a second signal, wherein the first signal is an echo signal reflected by a first body part of a target subject, the echo signal being a signal after a wireless signal emitted by a terminal device is reflected, and the second signal is an image signal of a second body part of the target subject; the first body part is closer to a heart side of the target subject than the second body part; determining, based on the first signal and the second signal, a propagation time of a pulse wave; and determining, based on the propagation time of the pulse wave, a physiological parameter of the target subject. The method can improve the accuracy of the detected physiological parameter of the target subject, and meanwhile is applicable to a portable terminal device.

Description

Signal processing methods and related devices

[0001] This application claims priority to Chinese Patent Application No. 202411440640.4, filed on October 14, 2024, entitled “Signal Processing Method and Related Apparatus”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of signal processing technology, and in particular to a signal processing method and related apparatus. Background Technology

[0003] With the development of terminal devices and people's increasing attention to health, smartphones and other terminal devices have gradually acquired the ability to detect physiological parameters of the human body, including those related to heart function. The detection principle is based on the fact that blood volume in capillaries, arteries, and veins increases during heart contraction and decreases during heart relaxation. Sensors are designed based on this characteristic to detect the changing trends of heart contraction and relaxation.

[0004] Based on the above principle, the terminal device can emit light into the skin. The light passes through the skin and is partially absorbed by red blood cells in the blood, while the unabsorbed portion is reflected back out of the skin. The photoplethysmography (PPG) sensor on the terminal device detects changes in light intensity, which can determine the trends of heart contraction and relaxation, and thus detect various physiological parameters. However, this method relies heavily on the accuracy of the signals detected by the PPG sensor. When interference factors cause inaccurate signal detection by the PPG sensor, the accuracy of the detected physiological parameters is also low.

[0005] In summary, there is currently a problem with inaccurate measurement results when measuring physiological parameters on terminal devices. Summary of the Invention

[0006] This application provides a signal processing method and related apparatus for improving the accuracy of physiological parameters measured by terminal devices.

[0007] Firstly, this application provides a signal processing method, which can be executed by a terminal device, or specifically by a chip, and the chip can be disposed in the terminal device. The signal processing method includes: acquiring a first signal and a second signal; wherein the first signal is an echo signal reflected from a first body part of a target object, the echo signal being a signal after a wireless signal emitted by the terminal device is reflected; the second signal is an image signal of a second body part of the target object, the first body part being closer to the heart side of the target object than the second body part. Based on the first and second signals, the transmission time of a pulse wave is determined. Based on the transmission time of the pulse wave, physiological parameters of the target object are determined.

[0008] In this application, the target object is an object whose physiological parameters need to be determined, such as the human body. The target object has multiple body parts, including a first body part and a second body part. The first body part is, for example, the chest or other parts near the heart, and the second body part is, for example, the fingers, wrists, face, or other parts with blood vessels. A wireless sensor on the terminal device can be used to transmit wireless signals. The wireless signals are reflected at the first body part to generate echo signals. Based on the echo signals, the trend of heart contraction or relaxation can be determined. As the heart contracts or relaxes, the blood volume in the target object's blood vessels increases or decreases accordingly, and the skin outside the blood vessels will show subtle changes that are not easily noticeable to the human eye. By capturing an image of the second body part, pixel information can be extracted from the image. Based on this pixel information, the subtle changes in blood volume in the blood vessels can be determined, and the trend of heart contraction or relaxation can be further determined.

[0009] In this application, a waveform of cardiac activity can be extracted based on a first signal, and a waveform of the pulse wave can be extracted based on a second signal. Cardiac activity includes things like heart contraction or relaxation, and the pulse wave is the pressure wave formed by blood flowing through blood vessels against the vessel walls; the pulse wave reflects heart contraction or relaxation. For the same cardiac activity, such as during the same heart contraction, because the first body part is closer to the heart than the second body part, the waveform of the cardiac activity corresponding to the first signal will change first, and the waveform of the pulse wave corresponding to the second signal will change later. The time difference between the two is the pulse wave transmission time. In other words, the interval between the time corresponding to the same cardiac activity in the first signal and the time corresponding to the second signal is the pulse wave transmission time.

[0010] In this application, physiological parameters are parameters describing cardiovascular-related physiological characteristics. Physiological parameters include pulse transit time (PTT), pulse wave velocity (PWV), blood pressure, and blood pressure variability. Over the same transmission distance, a longer pulse transit time indicates a slower pulse wave velocity; conversely, a shorter pulse transit time indicates a faster pulse wave velocity. A slower pulse wave velocity indicates better vascular elasticity, making it easier to buffer the pressure from the pulse wave and thus delaying its transmission; conversely, a faster pulse wave velocity indicates poorer vascular elasticity, making it difficult to buffer the pressure from the pulse wave. Furthermore, pulse transit time and blood pressure are negatively correlated. Blood pressure variability is the degree of blood pressure fluctuation over a certain period; excessively high blood pressure variability may indicate an increased risk of cardiovascular disease.

[0011] In the first aspect mentioned above, the pulse wave transmission time is determined based on the echo signal from a first body part close to the heart and the image signal from a second body part far from the heart. This transmission time reflects the time it takes for blood to flow from the proximal to the distal end of the heart after the heart pumps blood. Since this time is affected by physiological characteristics such as vascular stiffness, which are highly correlated with the unique individual characteristics of the target object, it accurately reflects the physiological state of the target object. Furthermore, determining the pulse wave transmission time using two different types of signals—echo signals and image signals—is complementary and provides stronger resistance to various external interference factors, thereby improving the accuracy of the signals collected from the target object. This further enhances the accuracy of the physiological parameters obtained from processing these signals. Moreover, both wireless and image signals are signals that terminal devices are well-compatible with. Therefore, measuring physiological parameters based on these two signals is highly applicable to terminal devices, further improving the accuracy of the measured physiological parameters on the terminal device.

[0012] In one possible implementation, before determining the pulse wave transmission time based on the first signal and the second signal, the method further includes: determining the fluctuation amplitude of the phase waveform of the first signal. Determining the pulse wave transmission time based on the first signal and the second signal further includes: if the fluctuation amplitude is less than or equal to a preset amplitude threshold, then determining the pulse wave transmission time based on the first signal and the second signal.

[0013] In this possible implementation, if the target's physical state is unstable—for example, due to exercise leading to physiological instability or emotional excitement causing psychological instability—the waveform of the first signal may exhibit abnormal changes, manifesting as significant fluctuations and severe waveform jitter. Analyzing the phase waveform of the first signal can determine whether it is abnormal; the phase waveform refers to the curve showing how the signal phase changes over time.

[0014] In this possible implementation, the phase waveform of the first signal can be obtained by unwinding the first signal. Unwinding methods include arctangent, differential and cross-multiply (DACM), or modified differential and cross-multiply (MDACM). The fluctuation amplitude of the phase waveform can be measured by parameters such as variance and standard deviation. A preset amplitude threshold is the threshold for the fluctuation amplitude when the target object's physical state is stable, and this threshold can be pre-calibrated and saved. If the variance of the phase waveform of the first signal is less than the preset variance, or the standard deviation of the phase waveform of the first signal is less than the preset standard deviation, it can be determined that the fluctuation degree of the first signal is small and the target object's state is stable. Therefore, by combining the first and second signals, the pulse wave transmission time can be determined, and physiological parameters can be further determined. The physiological parameters obtained in this way are those when the target object's physical state is stable, accurately representing the target object's daily physiological state.

[0015] In one possible implementation, after determining the fluctuation amplitude of the phase waveform of the first signal, the method further includes: if the fluctuation amplitude of the phase waveform is greater than a preset amplitude threshold, then outputting a first prompt message. The first prompt message is used to prompt the target object to maintain a quiet state.

[0016] In this possible implementation, if the fluctuation amplitude of the phase waveform exceeds a preset amplitude threshold—for example, if the variance or standard deviation of the phase waveform exceeds a preset threshold—it indicates that the fluctuation of the first signal is significant, and the target object's physical state is unstable. In this case, a first prompt message is used to prompt the target object to change its physical state to a stable state, reflecting its daily physical condition. The first prompt message can be in the form of audio, image, or text, and its content should be set to inform the target object that it needs to adjust to a quiet state. After the target object's physical state stabilizes, the physiological parameter measurement can be restarted. The terminal device reacquires the first and second signals, performs signal processing, and obtains the physiological parameters. This method allows the target object to flexibly adjust its physical state to a state suitable for measuring physiological parameters, improving interactivity.

[0017] In one possible implementation, determining the pulse wave transmission time based on a first signal and a second signal includes: processing the first signal to obtain an electrocardiogram (ECG) signal, wherein the first signal corresponds to a target time period; processing the second signal within the target time period to obtain a pulse wave signal; and determining the pulse wave transmission time based on the time corresponding to the first peak of the ECG signal and the second time corresponding to the second peak of the pulse wave signal. The first and second peaks are temporally adjacent, and the time corresponding to the first peak is earlier than the time corresponding to the second peak.

[0018] In this possible implementation, the electrocardiogram (ECG) signal characterizes the electrical activity of the heart. The heart's electrical activity can be indirectly inferred from the first signal to determine the ECG signal. The ECG signal can be extracted by unwinding the phase of the first signal and performing bandpass filtering according to the frequency of the heartbeat signal. The target time period is the time interval containing the signal to be processed. For example, it could be the time interval from the start to the end of acquiring the first signal, or the time interval from the start to the end of acquiring the second signal. For instance, if the first and second signals are acquired simultaneously, and acquisition of both signals ends simultaneously after 30 seconds (s), then the time interval within those 30 seconds is the target time period.

[0019] In this possible implementation, the second signal within the target time period includes multiple image signals. By extracting the pixel values ​​of the pixels in the image signals, the corresponding pulse wave signal value is determined based on the pixel values. A continuous pulse wave signal can be obtained by fitting multiple pulse wave signal values ​​into a curve. The ECG signal and pulse wave signal are searched to obtain the first and second peaks. During cardiac contraction, the ECG signal first shows a corresponding change, producing a corresponding peak, which is the first peak. The pulse wave signal then immediately shows a corresponding change, producing a corresponding peak, which is the second peak. The interval between the time corresponding to the first peak and the time corresponding to the second peak is the transmission time of the pulse wave corresponding to one cardiac contraction. Since the heart contracts multiple times within the target time period, multiple first peaks and multiple second peaks occur. Therefore, based on the times corresponding to multiple first peaks and multiple second peaks, the transmission time of multiple pulse waves can be obtained.

[0020] In this possible implementation, by processing the first and second signals within the same target time period, the electrocardiogram signal and the pulse wave signal can be located within the same time period, so the corresponding time period of cardiac activity is consistent. Based on this, the transmission time of the pulse wave can be determined by combining the earlier first peak and the later adjacent second peak. This transmission time can accurately reflect the time it takes for the pulse wave to travel from the proximal end of the heart to the distal end during a single cardiac contraction.

[0021] In one possible implementation, before determining the pulse wave transmission time based on the time corresponding to the first peak of the electrocardiogram (ECG) signal and the time corresponding to the second peak of the pulse wave signal, the method further includes: determining an effective time period within the target time period based on the amplitude of the pulse wave signal; wherein the effective time period is the time period during which the amplitude of the pulse wave signal changes stably. The first peak is determined based on the peaks of the ECG signal within the effective time period, and the second peak is determined based on the peaks of the pulse wave signal within the effective time period.

[0022] In this possible implementation, if the acquisition of the image signal of the second body part is interfered with, the amplitude of the pulse wave signal may exhibit unstable changes. These changes are not due to variations in the pulse wave of the second body part, but rather errors generated during the acquisition process, constituting invalid signals. The time period containing these invalid signals is the invalid time period. The time period outside the invalid time period within the target time period is the valid time period; in other words, the time period containing the valid signal within the target time period is the valid time period. The valid signal is a pulse wave signal with a stable amplitude. By determining the peak of the pulse wave signal within the valid time period as the second peak, the pulse wave waveform corresponding to cardiac contraction can be accurately reflected. Furthermore, by determining the peak of the electrocardiogram (ECG) signal within the valid time period as the first peak, the waveform corresponding to cardiac contraction in the ECG signal during the same period as the valid signal can be obtained. The first and second peaks obtained in this way can accurately reflect the pulse wave and cardiac electrical activity waveform corresponding to cardiac contraction.

[0023] In one possible implementation, the target time period includes multiple sub-time periods. Based on the amplitude of the pulse wave signal, the effective time period in the target time period is determined, including: calculating the similarity between the amplitude of the pulse wave signal in each sub-time period and the amplitude of the pulse wave signal in other sub-time periods; and determining the effective time period based on the sub-time period in which the pulse wave signal with a similarity greater than or equal to a similarity threshold is located.

[0024] In this possible implementation, the target time period is divided into multiple sub-time periods, and the pulse wave signal within each sub-time period represents the physiological activity of the target object during that specific time period. For example, if the target time period is 30 seconds and is divided into 10 sub-time periods, then each sub-time period is 3 seconds. The amplitude of the pulse wave signal reflects the magnitude of fluctuations in blood flow caused by the periodic beating of the heart. Similarity can be used to measure whether the amplitude changes of the pulse wave signal are consistent between two time periods. If the amplitude changes of the pulse wave signal are similar in two sub-time periods, it means that the physiological state of the target object is relatively stable and the cardiovascular activity is relatively consistent during these time periods. The similarity between the amplitudes of the pulse wave signals in different sub-time periods can be calculated using cosine similarity. If the similarity is high, it indicates that the pulse wave signals in these time periods are stable and reliable, and suitable for further physiological parameter calculations.

[0025] In this possible implementation, after similarity calculation, sub-time periods with similarity scores greater than or equal to a set similarity threshold are selected. The similarity threshold is a pre-defined standard used to judge the stability of the pulse wave signal. Sub-time periods above this threshold are considered valid time periods, during which the pulse wave signal is stable and accurately reflects the physiological state of the target object. These valid time periods serve as the basis for subsequent pulse wave transmission time calculations and other physiological parameter inferences, enabling the terminal device to filter out invalid signals during measurement, thereby improving the accuracy of pulse wave transmission time and other physiological parameters. By selecting valid time periods based on similarity, the terminal device can better cope with changes in the target object's physiological state and interference from the external environment or measurement conditions on signal acquisition.

[0026] In one possible implementation, the physiological parameters of the target object include target blood pressure. The method further includes: modifying a preset blood pressure mapping relationship based on the target object's standard blood pressure and standard pulse wave transit time to obtain a target blood pressure mapping relationship. Here, standard blood pressure is the blood pressure measured using a standard blood pressure testing device, standard pulse wave transit time is the pulse wave transit time historically measured by the target object using a terminal device, and the preset blood pressure mapping relationship is the mapping relationship between preset blood pressure and preset pulse wave transit time. Determining the target object's physiological parameters based on the pulse wave transit time includes: determining the target blood pressure mapped by the pulse wave transit time based on the target blood pressure mapping relationship.

[0027] In this possible implementation, a standard blood pressure testing device, such as a medical sphygmomanometer, can be used to measure standard blood pressure under standard testing conditions, reflecting the target subject's blood pressure level at a specific time point. The standard pulse wave transit time (CWT) is the pulse wave transit time recorded by the target subject through multiple past measurements using a terminal device; it reflects the target subject's vascular state, blood flow characteristics, and other physiological trends. The target subject can obtain the standard pulse wave transit time by measuring the pulse wave transit time within a similar timeframe to the standard blood pressure measurements, such as within a few tens of minutes or a few hours.

[0028] In this possible implementation, the preset blood pressure mapping relationship is a pre-stored correspondence between blood pressure and pulse wave transit time. This mapping relationship can be obtained through large-scale human data testing, medical research, or clinical trials. Furthermore, due to differences in the physiological state of each individual, the preset blood pressure mapping relationship can be modified to more accurately reflect the blood pressure of a specific target subject. By modifying the preset blood pressure mapping relationship based on the target subject's own standard blood pressure and standard pulse wave transit time, a more personalized blood pressure mapping relationship can be obtained. This modification process involves adjusting the parameters in the preset mapping relationship to better match the target subject's physiological characteristics. If the target subject's standard blood pressure is higher or lower than the average level, the modified mapping relationship can appropriately reflect this difference, thereby improving the accuracy of estimating target blood pressure based on pulse wave transit time.

[0029] In this possible implementation, the target individual can periodically undergo standard blood pressure measurements, and the pulse wave transmission time can be periodically collected by a terminal device. Using this data, the terminal device can dynamically adjust the blood pressure mapping relationship, ensuring that the blood pressure value calculated from the pulse wave transmission time consistently reflects the target individual's actual blood pressure status. Using the corrected target blood pressure mapping relationship, the pulse wave transmission time is converted into the corresponding blood pressure value, i.e., the target blood pressure. In this way, the terminal device can non-invasively monitor changes in the target individual's blood pressure, providing accurate blood pressure data.

[0030] In one possible implementation, the method further includes: outputting a second prompt message. The second prompt message is used to prompt the target object on how to operate the terminal device, including placing a fingertip on the terminal device's camera while simultaneously placing the terminal device in front of the target object's chest.

[0031] In this possible implementation, the terminal device is equipped with a camera and wireless sensors. By prompting the target to place their fingertip on the camera, the terminal device can capture an image of the finger and determine the pulse wave signal. By prompting the target to place the terminal device against their chest, the wireless sensors can collect echo signals from the heart. The second prompt can be output in various forms, such as audio, text, image, or vibration prompts, to help the target quickly understand how to operate the device. Placing the terminal device against the target's chest allows for the wireless collection of heart echo signals. When the terminal device is close to the chest, it can better receive signals related to the target's heart activity, thereby improving the accuracy of measuring electrocardiogram waveforms and related physiological parameters. Furthermore, the target naturally placing their fingertip on the camera and the terminal device against their chest is an easy-to-implement operation method, providing a convenient way for the target to operate the terminal device to measure physiological parameters.

[0032] In one possible implementation, the method is applied to a terminal device, and acquiring the first signal and the second signal includes: receiving the first signal based on a wireless sensor of the terminal device; wherein the wireless sensor includes an ultra-wideband sensor or a millimeter-wave sensor, and the wireless signal is emitted by the wireless sensor; and determining the second signal based on multiple images captured by the camera of the terminal device.

[0033] In this possible implementation, the wireless sensor is a sensor capable of emitting and receiving high-frequency wireless signals. Ultra-wideband (UWB) sensors, due to their wide spectral bandwidth, can provide high-precision distance and position detection over short distances, and can be used to detect minute object displacements, such as the minute vibrations of the skin caused by pulse waves. Millimeter-wave sensors, operating in the millimeter-wave band, can better penetrate clothing and skin to capture internal human motion information; for example, by analyzing minute chest movements caused by heartbeats, they can obtain electrocardiographic activity signals. The first signal can be obtained by the wireless sensor capturing physiological parameters of the target object. The signal emitted by the wireless sensor is reflected from the target object's body surface and returns to the wireless sensor, where signal processing methods are used to extract key information such as electrocardiographic activity.

[0034] In this possible implementation, the second signal is determined by capturing multiple images using a camera on the terminal device. The camera can capture skin color changes caused by blood circulation by photographing the skin surface of the target object, thereby determining the pulse wave signal. By analyzing the brightness or color changes of certain key pixels in these image frames, the pulse wave signal of the target object can be extracted. The images captured by the camera are fitted with a continuous pulse wave signal using an algorithm. This implementation not only allows for the acquisition of electrocardiogram signals through non-contact wireless sensors but also enables the determination of pulse wave signals through image analysis, further improving the accuracy and stability of physiological parameter measurements. The combination of the two signal sources effectively eliminates the uncertainty caused by errors from a single signal, enhancing the reliability of physiological parameters.

[0035] In one possible implementation, the frame rate of the wireless sensor transmitting wireless signals is greater than or equal to the frame rate of the camera capturing images.

[0036] In this possible implementation, the frame rate of the wireless sensor refers to the frequency at which it transmits and receives wireless signals, i.e., how many signal pulses it can transmit and receive reflected signals per second. The frame rate of the camera refers to the number of image frames it can capture per second. In cardiac activity monitoring, the electrocardiogram (ECG) signal changes relatively quickly, while the pulse wave signal occurs later. A higher frame rate for the wireless sensor can more sensitively capture subtle fluctuations in the ECG signal. Meanwhile, even with a lower frame rate from the camera, the pulse wave signal can still be extracted from fewer image frames and matched with the ECG signal, thus accurately calculating the pulse wave transmission time. A frame rate for the wireless sensor greater than or equal to the frame rate of the camera ensures that critical physiological signals are not missed due to insufficient wireless signal during synchronous processing of the two signal sources. Simultaneously, such a frame rate setting optimizes the coordination of signal processing, thereby improving the overall signal accuracy and consistency. This effectively enhances the measurement accuracy of physiological parameters, achieves consistent results when multiple signals are fused, and strengthens the measurement capabilities of the terminal device.

[0037] A second aspect of this application provides a terminal device, the terminal device comprising:

[0038] A wireless sensor is used to transmit wireless signals and receive a first signal. The first signal is the echo signal reflected from a first body part of the target object; the echo signal is the reflected signal of the wireless signal emitted by the terminal device.

[0039] A pulse wave sensor is used to acquire a second signal; wherein the second signal is an image signal of a second body part of the target object; the first body part is closer to the heart side of the target object than the second body part.

[0040] The processor is connected to both the wireless sensor and the pulse wave sensor, and is used to execute the method of the first aspect or any possible implementation thereof.

[0041] In this implementation, the terminal device is equipped with a wireless sensor and a pulse wave sensor to acquire a first signal and a second signal. The processor processes the first signal and the second signal to obtain the physiological parameters of the target object, enabling convenient and accurate measurement of the target object's physiological parameters.

[0042] In one possible implementation, the wireless sensor includes an ultra-wideband sensor or a millimeter-wave sensor.

[0043] In one possible implementation, an ultra-wideband (UWB) sensor can emit a series of extremely short electromagnetic pulse signals with a wide spectral bandwidth. UWB sensors can penetrate the surface layer of the human body to acquire internal reflected signals, accurately measuring the arrival time of the signals to detect minute body displacements, such as chest expansion caused by heartbeat or respiration. This type of sensor has high temporal resolution and penetration, effectively detecting cardiac echo signals from the target object. Furthermore, UWB sensors have strong resistance to environmental noise, enabling accurate measurements in complex environments. Millimeter-wave sensors can receive millimeter-wave signals reflected from the first body part of the target object. Millimeter-wave sensors enable high-precision measurements and are suitable for non-contact measurement scenarios. Millimeter-wave sensors have good environmental penetration and can operate stably in low-light conditions or under clothing. By using UWB or millimeter-wave sensors, terminal devices can provide high-precision ECG signal detection, further improving the accuracy and reliability of physiological parameter measurements when combined with other physiological parameter sensors.

[0044] In one possible implementation, the pulse wave sensor includes a camera.

[0045] In this possible implementation, a camera is used to acquire image signals from a second body part of the target object, and pulse wave information is obtained by analyzing these image signals. To improve measurement accuracy, the camera can be set to a higher resolution and frame rate. Higher resolution allows the camera to capture every detail of the skin surface, especially accurately identifying subtle changes in light caused by blood flow even under varying lighting conditions. A higher frame rate ensures the camera captures continuous image data quickly enough, and the higher sampling frequency results in more accurate and complete pulse waves. Using the camera as a pulse wave sensor allows the target object to acquire sufficient image information without direct contact with the device; the camera can be positioned at a certain distance. For example, when the target object places the camera in front of their face and takes a photo of their face at a certain distance, the camera can measure pulse wave information by detecting the pulsation of blood under the facial skin. Using this method, the camera can not only be used for everyday image capture but also function as a highly efficient pulse wave sensor, greatly enhancing the versatility and practicality of terminal devices in the field of physiological parameter detection.

[0046] A third aspect of this application provides a terminal device, which includes modules for implementing the first aspect or any possible implementation of the first aspect.

[0047] In one possible implementation, the terminal device includes:

[0048] The acquisition module is used to acquire a first signal and a second signal. The first signal is the echo signal reflected by a first body part of the target object, which is the signal after the wireless signal emitted by the terminal device is reflected. The second signal is the image signal of a second body part of the target object; the first body part is closer to the heart side of the target object than the second body part.

[0049] The first determining module is used to determine the transmission time of the pulse wave based on the first signal and the second signal.

[0050] The second determining module is used to determine the physiological parameters of the target object based on the transmission time of the pulse wave.

[0051] In one possible implementation, the terminal device further includes a third determining module, wherein:

[0052] The third determining module is used to determine the fluctuation amplitude of the phase waveform of the first signal;

[0053] The first determining module is also used to determine the transmission time of the pulse wave based on the first signal and the second signal if the fluctuation amplitude is less than or equal to a preset amplitude threshold.

[0054] In one possible implementation, the terminal device further includes:

[0055] The first output module is also used to output a first prompt message if the fluctuation amplitude is greater than a preset amplitude threshold; wherein the first prompt message is used to prompt the target object to keep its body state in a quiet state.

[0056] In one possible implementation, the first determining module is further configured to: process the first signal to obtain an electrocardiogram (ECG) signal; wherein the ECG signal corresponds to a target time period; process the second signal within the target time period to obtain a pulse wave signal; and determine the transmission time of the pulse wave based on the time corresponding to the first peak of the ECG signal and the time corresponding to the second peak of the pulse wave signal; wherein the first peak and the second peak are temporally adjacent, and the time corresponding to the first peak is earlier than the time corresponding to the second peak.

[0057] In one possible implementation, the first determining module is further configured to: determine an effective time period within the target time period based on the amplitude of the pulse wave signal. The effective time period is the time period during which the amplitude of the pulse wave signal changes stably; determine a first peak based on the peaks of the electrocardiogram signal within the effective time period; and determine a second peak based on the peaks of the pulse wave signal within the effective time period.

[0058] In one possible implementation, the first determining module is further configured to: calculate the similarity between the amplitude of the pulse wave signal in each sub-time period and the amplitude of the pulse wave signal in other sub-time periods; and determine the effective time period based on the sub-time period in which the pulse wave signal with a similarity greater than or equal to the similarity threshold is located.

[0059] In one possible implementation, the terminal device further includes a correction module, wherein:

[0060] The correction module is used to correct the preset blood pressure mapping relationship based on the target object's standard blood pressure and the target object's standard pulse wave transmission time to obtain the target blood pressure mapping relationship. The standard blood pressure is the blood pressure measured by a standard blood pressure testing device, the standard pulse wave transmission time is the pulse wave transmission time of the target object measured historically by the terminal device, and the preset blood pressure mapping relationship is the mapping relationship between the preset blood pressure and the preset pulse wave transmission time.

[0061] The second determining module is also used to determine the target blood pressure based on the target blood pressure mapping relationship, which is mapped to the transmission time of the pulse wave.

[0062] In one possible implementation, the terminal device further includes:

[0063] The second output module is used to output a second prompt message; wherein the second prompt message is used to prompt the target object on how to operate the terminal device, and the method of operating the terminal device includes placing the fingertip on the camera of the terminal device and placing the terminal device in front of the target object's chest.

[0064] In one possible implementation, the acquisition module is further configured to: receive a first signal based on a wireless sensor of the terminal device; wherein the wireless sensor includes an ultra-wideband sensor or a millimeter-wave sensor, and the wireless signal is emitted by the wireless sensor; and determine a second signal based on multiple images captured by the camera of the terminal device.

[0065] In one possible implementation, the frame rate of the wireless sensor transmitting wireless signals is greater than or equal to the frame rate of the camera capturing images.

[0066] The fourth aspect of this application provides a computer-readable storage medium storing a computer program or instructions that, when executed by a terminal device, implement the first aspect of this application or any possible implementation thereof.

[0067] The fifth aspect of this application provides a computer program product that, when run on a terminal device, causes the terminal device to execute the method of the first aspect of this application or any possible implementation thereof.

[0068] The sixth aspect of this application provides a chip including a processor, which is used to support a terminal device in implementing the method of the first aspect of this application or any possible implementation of the first aspect.

[0069] In one possible implementation, the chip further includes a memory for storing program instructions and data necessary for the execution or training device. This chip system can be composed of a single chip or may include chips and other discrete components.

[0070] The technical effects of any of the second to sixth aspects can be found in the first aspect or any of the implementation methods described above, and will not be repeated here. Attached Figure Description

[0071] Figure 1 is a schematic diagram of the structure of a terminal device provided in an embodiment of this application;

[0072] Figure 2 is a schematic flowchart of a signal processing method provided in an embodiment of this application;

[0073] Figure 3 is an exemplary schematic diagram of a target handheld terminal device provided in an embodiment of this application;

[0074] Figure 4 is an exemplary schematic diagram of a method for collecting relevant signals of a target object according to an embodiment of this application;

[0075] Figure 5a is an exemplary schematic diagram of another method for collecting relevant signals of a target object according to an embodiment of this application;

[0076] Figure 5b is an exemplary schematic diagram of a portion of a face region provided in an embodiment of this application;

[0077] Figure 6 is an exemplary schematic diagram of a portion of the central pixel region in an image provided in an embodiment of this application;

[0078] Figure 7 is an exemplary schematic diagram of a pulse wave signal provided in an embodiment of this application;

[0079] Figure 8 is an exemplary schematic diagram of electrocardiogram signal and pulse wave waveform signal provided in an embodiment of this application;

[0080] Figure 9 is a flowchart illustrating a method for measuring blood pressure via a smartphone according to an embodiment of this application;

[0081] Figure 10 is a schematic diagram of the structure of a terminal device provided in an embodiment of this application;

[0082] Figure 11 is another structural schematic diagram of a terminal device provided in an embodiment of this application;

[0083] Figure 12 is another structural schematic diagram of a terminal device provided in an embodiment of this application. Detailed Implementation

[0084] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0085] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0086] The signal processing method of this application embodiment can be applied to a terminal device, as shown in FIG1. ​​The terminal device 100 includes a processor 101, a wireless sensor 102, and a pulse wave sensor 103. The processor 101 is connected to the wireless sensor 102 and the pulse wave sensor 103. The wireless sensor 102 is used to transmit wireless signals and collect a first signal of the target object. The pulse wave sensor 103 is used to collect a second signal of the target object. The processor 101 is used to perform signal processing on the first signal and the second signal to obtain the physiological parameters of the target object.

[0087] Terminal devices include, for example, smartphones, tablets, wearable devices, virtual reality (VR) devices, and augmented reality (AR) devices. Wearable devices include, for example, smartwatches and smart glasses. Terminal devices can be equipped with wireless sensors to receive a first signal, and can be equipped with or connected to a camera to collect a second signal. The following are some examples of terminal devices:

[0088] Smartphones, such as those running Android, iOS, or HarmonyOS, are equipped with wireless sensors, such as UWB sensors. UWB sensors can be used for ranging or positioning, and in this embodiment, they are also used to collect a first signal. The wireless sensor can be positioned on top of the smartphone (on the side where the camera is located) to accommodate the user's (target's) operating habits, but it can also be positioned in other locations on the terminal device. Smartphones generally have a camera, such as a front-facing or rear-facing camera, which can be used to collect a second signal. A flash can also be located near the camera on the smartphone to increase brightness during photography, thereby improving the accuracy of the collected second signal. Furthermore, the smartphone can be connected to an external camera to collect the second signal, increasing operational flexibility. Smartphones are highly portable and convenient for measuring the physiological parameters of a target.

[0089] Tablet computers are similar in structure and function to the aforementioned smartphones. The main difference is that tablet computers are often larger than smartphones, allowing them to display richer visuals and offer more flexible interaction methods.

[0090] Smartwatches, such as those running the aforementioned operating system, can be equipped with wireless sensors and cameras. A smartphone collects the first and second signals, processes them, and obtains the target's physiological parameters. The smartwatch can pair with a smartphone or tablet. After collecting the first and second signals, it can send them to the smartphone or tablet, which then processes them to obtain the physiological parameters and sends them back to the smartwatch, thus adapting to smartwatches with limited processing power. Using a smartwatch to measure physiological parameters offers significant convenience.

[0091] VR glasses are often equipped with high-precision cameras and wireless sensors, making them highly practical for measuring physiological parameters.

[0092] In addition to the terminal devices exemplified above, other terminal devices not shown can also be used to implement the signal processing of the embodiments of this application, and are not limited here.

[0093] Wireless sensors 102 include, for example, millimeter-wave sensors, UWB sensors, Wi-Fi modules, or Bluetooth modules. These are described below:

[0094] Millimeter-wave sensors contain a high-frequency signal generator (such as an oscillator) whose operating frequency can be within the millimeter-wave range. Millimeter-wave radar sensors operating at 24GHz or 77GHz can be selected, offering high resolution and accuracy, capable of detecting millimeter-level displacements, thus improving the accuracy of the initial signal. Millimeter-wave sensors can be installed anywhere on the terminal device. To improve accuracy, they can be specifically positioned where they easily transmit wireless signals towards the area near the heart. For example, in the case of a smartphone, they can be installed on top of the smartphone (considering usage habits, the side with the camera is often considered the top, and the side with the universal serial bus interface is considered the bottom). When a target holds a smartphone, they often obstruct the view of the bottom of the smartphone to some extent. By placing the millimeter-wave sensor on top of the smartphone, its wireless signal transmission and reception are not obstructed. The millimeter-wave sensor transmits millimeter-wave signals towards the target area (human chest). Upon encountering the human body surface, the transmitted millimeter-wave signals are reflected back to the millimeter-wave sensor. Minor movements of the human chest and heart area cause slight changes in the phase, frequency, and intensity of the reflected signal. These reflected signals contain information about the human heart and respiratory activity and can be used as cardiac echo signals.

[0095] UWB sensors utilize high-frequency signal generators to produce wide-spectrum pulse signals. UWB sensors supporting high-resolution detection can be selected, such as those operating in the 3.1 to 10.6 GHz frequency range. The extremely short pulse width (nanosecond level) of UWB sensors gives them very high time resolution, enabling the detection of minute displacement changes. UWB sensors can be mounted on top of smartphones or in other easily accessible locations. When using a UWB sensor in a terminal device to acquire the initial signal, the user can be encouraged to set the distance between the device and the target object within 0.2-1 meter to ensure a sufficiently strong signal. The UWB sensor emits short UWB pulse signals towards the target area (human chest). When the UWB signal hits the human body surface, it is reflected back. The UWB sensor receives the reflected pulse signal and calculates the distance to the target object (human body surface) by accurately measuring the signal's arrival time. The UWB sensor can determine the distance change between the target and the sensor using either the time of arrival (ToA) or two-way ranging (TWR). By continuously measuring the time delay of the reflected signal (i.e., the time difference between the signal being transmitted and received), the sensor can capture subtle changes caused by the heartbeat.

[0096] The Wi-Fi module continuously transmits standard Wi-Fi signals (such as 802.11n / ac), which are reflected off the surface of a target. During signal propagation, these signals may encounter the chest or abdomen of a human body, generating reflected echoes. The Wi-Fi module receives these reflected signals. The Wi-Fi signal is affected by reflections from objects in the environment (including the human body) during propagation, and the Wi-Fi module can detect changes in multipath signals. The Wi-Fi module extracts channel state information (CSI) data from the received signal. CSI data contains the amplitude and phase information of each subcarrier, reflecting the changes in the signal's path during propagation. CSI data can capture the phase and amplitude changes caused by minute displacements on the human body surface. These changes can be used to detect heartbeats and respiration to obtain electrocardiogram (ECG) signals.

[0097] The Bluetooth module continuously transmits radio frequency signals (typically around 2.4 GHz). These signals are reflected off surrounding surfaces, including the human body. When the signal encounters the chest or heart area, a reflected signal is generated. This reflected signal carries information about minute movements on the body surface. The Bluetooth receiver module receives both the original signal and the signal reflected back from the body. The Bluetooth module primarily relies on the Received Signal Strength Indicator (RSSI) to reflect signal changes. The Bluetooth module continuously records RSSI changes, which reflect minute displacements in the chest or abdomen. The contraction and relaxation of the heart cause minute changes on the chest surface, resulting in periodic variations in the reflected signal strength (RSSI). Analyzing the RSSI data allows the detection of specific fluctuations in the time and frequency domains. By identifying the period and frequency of these fluctuations, waveforms related to the heartbeat can be extracted. Since the displacement amplitude caused by respiration is larger than that of the heartbeat, it can easily mask the heartbeat signal. Therefore, algorithms are needed to distinguish and remove the respiratory signal while retaining the higher-frequency heartbeat characteristics.

[0098] Pulse wave sensors 103 include devices such as cameras, multispectral imaging sensors, or infrared cameras. These are described below:

[0099] A camera includes an image sensor, lens, aperture, and electronic circuitry for imaging. When capturing images with a camera, ambient light can be used as the main light source to illuminate the target object, or the flash of the terminal device can be activated to provide an additional light source to improve the quality of the acquired secondary signal. When the target object places its finger in front of the camera, light shines onto the skin surface. Different components of the skin (such as blood, tissue, and fat) have different reflection and absorption characteristics for different wavelengths of light. The camera's image sensor receives the reflected light and converts the light signal into an electrical signal, forming a digital image. Furthermore, the terminal device analyzes the captured image data to determine changes in pixel values, especially changes in the green channel (due to the light absorption characteristics of blood, changes in the green channel are most pronounced). The beating of the heart causes periodic changes in blood flow, resulting in periodic changes in light intensity over time. By extracting the light intensity of the skin surface in each frame of the image, time-series data is formed to further obtain the trend of pulse wave changes over a period of time.

[0100] Multispectral imaging sensors, comprising image sensors, optical lenses, optical filters, or electronic circuitry for signal processing, are capable of simultaneously capturing light information at different wavelengths. When a multispectral imaging sensor is aimed at the target area, the light source of the terminal device illuminates the skin surface. The multispectral sensor can simultaneously emit and receive light of different wavelengths. It captures the reflected light signals of different wavelengths, generating a series of multispectral images. Each pixel in each image contains reflection information at different wavelengths, allowing for detailed analysis of the skin's optical properties. By analyzing the captured multispectral images, features related to blood flow, especially pulse wave-related wavelength channels, can be extracted. Different wavelengths of light have different absorption characteristics compared to blood when penetrating and reflecting, resulting in variations in reflection intensity; these variations can be used to extract pulse wave signals.

[0101] An infrared camera consists of an infrared detector (such as an uncooled or cooled detector), a lens, optical filters, or an image processing module. Infrared cameras capture thermal radiation emitted by the human body. The infrared camera is pointed at the target area, with a suitable distance between the camera and the skin surface to obtain a clear image. The infrared camera receives infrared radiation from the skin surface; changes in skin temperature and blood flow affect the intensity of this thermal radiation. With the contraction and relaxation of the heart, blood flow causes changes in local skin temperature, and these temperature fluctuations are reflected in the infrared image. The infrared camera generates a thermal image, where different colors represent different temperatures. By analyzing the infrared images, temperature change information related to the pulse wave is extracted. A series of consecutive infrared images are processed to capture the changes caused by the pulse wave.

[0102] In addition to the sensors mentioned above, the terminal device may also use other wireless sensors to collect the first signal, or other sensors to collect the second signal, without limitation.

[0103] Based on the aforementioned terminal device, the signal processing method performed by the terminal device is described below, as shown in Figure 2. This method includes steps 201-203.

[0104] Step 201. Obtain the first signal and the second signal.

[0105] Before acquiring the first and second signals, the terminal device can guide the target subject through a series of preparatory steps, including adjusting posture or relaxing, to improve the accuracy of the acquired signals. Once the target subject confirms readiness, they can trigger the terminal device to acquire the signal. The terminal device interacts with the target subject by providing real-time prompts, guiding them on how to operate the device and maintain the desired state. This interaction not only improves the accuracy of physiological parameter measurements but also enhances the user experience. Alternatively, the target subject can consult the product manual or publicly available online resources to understand the operating methods and independently trigger the terminal device to measure physiological parameters, skipping the guided prompts. Through this flexible mechanism, the terminal device achieves high-quality signal acquisition while satisfying the target subject's initiative, improving the overall user experience, and is suitable for various signal acquisition scenarios.

[0106] Before acquiring the first signal and the second signal, the terminal device may output one or more of the following prompt messages:

[0107] The first type of information concerns how to hold the terminal device. There are various ways to hold the terminal device, and examples are given below.

[0108] As shown in Figure 3, the target can hold the terminal device with their left hand and place the fingertips of their left hand on the main camera. Fingertips have good tactile sensitivity and can apply appropriate pressure—natural and even pressure—which helps improve the stability and clarity of the signal acquired by the main camera. The terminal device also has a built-in wireless sensor that can collect a second signal from the fingertips when the target holds the device. The target can maintain a certain distance from their heart while holding the device. To improve the accuracy of the first signal, it is recommended that the target place the terminal device as close to their heart as possible. Being close to the heart reduces signal attenuation and interference during propagation, making the acquired first signal more accurate. Furthermore, being close to the heart increases sensitivity to signals related to heart activity, allowing the terminal device to capture subtle physiological changes. As shown in Figure 4, the target can place the terminal device in front of their chest, close to their heart, to acquire the first signal reflected from the chest, while simultaneously acquiring the second signal from the fingertips.

[0109] The target object in Figure 4 is in a seated position. Besides the seated position shown in Figure 4, the target object can also adopt other postures, such as lying flat. When the target object is lying flat, the terminal device can be placed against the chest. When lying flat, the body's natural weight helps stabilize the device and reduces the impact of hand tremors on the measurement results. At the same time, the lying position can reduce the interference of abdominal breathing on signal acquisition to some extent. Using fingers or wrists to place on the camera further stabilizes the device and improves signal clarity and quality. The terminal device can acquire high-quality primary signals, while improving the user experience and measurement accuracy.

[0110] As shown in Figure 5a, the target subject can sit in a chair, maintaining a certain distance between the terminal device and their body, for example, by using a stand to fix the terminal device in front of them. The stand can have stability and height adjustment functions to ensure the terminal device is in the optimal shooting position and stable without shaking, thus improving image quality. The stand can increase stability through aluminum alloy material or a center of gravity design, while an adjustable height mechanism allows the terminal device to be aligned with the target subject's eye level or slightly higher, resulting in the optimal shooting angle. The front-facing camera of the terminal device is used to capture facial photos to obtain a second signal. During the shooting process, the target subject can observe the preview screen of the front-facing camera in real time to check if their face is in clear focus. The terminal device uses autofocus technology to adjust the focus in real time, ensuring clear facial details. If the face in the photo is not in focus, the target subject can optimize the focus by adjusting the position of the stand or their own posture. In addition, the terminal device can use image segmentation algorithms to identify and extract the forehead or cheek areas (as shown in Figure 5b) as the extraction area for pulse wave signals, efficiently distinguishing different areas of the face.

[0111] Besides the methods of holding the terminal device mentioned above, other methods of holding the terminal device may also be used, and no restrictions are imposed here.

[0112] The second type of information concerns the target subject's physiological state. This information can be very specific, making it easy for the user to understand. For example, it might instruct the user to follow these steps: The target subject should sit quietly for at least 5 minutes before measurement to allow their body and heart rate to gradually stabilize, reduce sympathetic nerve excitability, and avoid physiological fluctuations caused by emotional excitement or tension. The target subject can sit in a comfortable chair with their back against the backrest and their feet flat on the ground. They should avoid smoking, drinking alcohol, or consuming caffeinated beverages within 30 minutes before measurement, as these substances may increase heart rate and blood pressure. During the measurement, the target subject should try to remain silent to minimize fluctuations in breathing rate and heart rate, and should avoid taking the measurement under stress, anxiety, or tension.

[0113] The third type of information is environmental information. Since a suitable environment can lead to more accurate measurements of the target's physiological parameters, it is advisable to recommend that the target be measured in a suitable environment, such as one or more of the following: a suitable temperature; excessively cold or hot environments may cause vasoconstriction or vasodilation, so the ambient temperature should be maintained between 20-25°C to ensure the target's blood vessels remain in a natural state. When using the terminal device's camera to measure pulse wave signals, lighting conditions are crucial. Choose a location with soft and even lighting, avoiding excessively strong or dim light. For example, measurements should be taken under natural light or soft indoor lighting, avoiding direct sunlight or flickering lights, so that the terminal device's camera can more accurately capture minute changes in blood circulation on the skin surface. Since the terminal device uses a wireless sensor to acquire the initial signal, strong sources of wireless interference, such as Wi-Fi routers or other electronic devices, should be avoided to improve signal stability.

[0114] In addition to the information mentioned above, the terminal device can also output other prompts to the target object. There are no restrictions on this. By outputting prompts, not only can the interactivity with the target object be increased, but the accuracy of the obtained physiological parameters can also be indirectly improved.

[0115] Terminal devices can transmit sensing signals via UWB sensors or millimeter-wave sensors. These sensing signals include, for example, the following:

[0116] Orthogonal frequency division multiplexing (OFDM) signals are characterized by subcarriers that are orthogonal to each other, preventing interference in the frequency domain. This orthogonality allows multiple signals to be transmitted simultaneously on the same channel, improving spectral efficiency. Utilizing the orthogonal subcarrier properties, OFDM effectively resists interference and multipath effects in the measurement environment. For example, in everyday environments, other wireless signals or reflected waves may affect measurement results; OFDM technology can reduce interference and maintain the stability of cardiac echo signals. By extending the duration of each symbol and inserting guard intervals between symbols, OFDM reduces the impact of multipath interference on the signal. OFDM divides data into lower-rate sub-signals that are transmitted simultaneously on multiple subcarriers, enabling high-speed data transmission. Furthermore, it allows for flexible adjustment of the number and bandwidth of subcarriers to adapt to different communication needs. OFDM signals can utilize high-frequency bands, such as millimeter-wave frequencies (30-300 GHz), enabling higher spatial resolution. High-frequency signals help to accurately detect minute movements and echo changes in the heart, thus capturing details of cardiac activity. In applications requiring high precision and real-time performance, such as measuring cardiac activity, terminal devices can achieve high-bandwidth data transmission using OFDM technology. By adjusting the frequency and bandwidth of the subcarriers, OFDM can flexibly adapt to different measurement distances and signal strength conditions, thereby improving the adaptability of the measurement.

[0117] Continuous wave (CW) signals offer high sensitivity and accuracy. When measuring cardiac echo signals, terminal devices can obtain highly sensitive echo signals, thereby improving the accuracy of cardiac activity monitoring. The simplicity of CW signals makes signal processing more direct, reducing processing delays and improving real-time monitoring capabilities, adapting to rapid changes in cardiac echo signals. CW signals have good penetration at lower frequencies, allowing them to better penetrate skin and tissue, effectively capturing the subtle echo signals generated by the heart. CW signals also have low power consumption, enabling low-power cardiac monitoring in terminal devices and extending device lifespan.

[0118] Frequency-modulated continuous wave (FMCW) signals enable precise distance measurement through frequency variations. The continuity and frequency modulation characteristics of FMCW signals allow terminal devices to achieve real-time monitoring and rapid response to changes in cardiac activity. The frequency modulation of FMCW signals allows for signal processing via frequency domain analysis, reducing reliance on complex time-domain signal processing and simplifying algorithm design and computational burden. FMCW signals effectively utilize the Doppler effect, efficiently capturing echo signals from the heart and exhibiting strong adaptability. Furthermore, FMCW signals can be used for effective measurements at lower power levels, extending the battery life of terminal devices.

[0119] After emitting a sensing signal, the terminal device receives the echo signal and treats it as the first signal, or processes the echo signal and uses the processed signal as the first signal. Specific processing steps include converting the received analog echo signal into a digital signal and amplifying it to enhance signal strength. Furthermore, the terminal device can remove noise from the signal and adjust the signal baseline, thereby improving signal accuracy.

[0120] The phase of the first signal contains motion information of specific body parts of the target object. Since the echo signal of the heart is within a specific frequency range, it can be extracted using frequencies related to cardiac activity. By analyzing the phase changes of the cardiac echo signal, dynamic information related to cardiac activity can be obtained. Because the phase cycles between 0 and 2π (or -π to π), this can lead to phase jumps (e.g., from 2π to 0), which need to be removed using unwinding techniques to obtain the true phase waveform. The unwound phase waveform can be used to analyze minute changes in the cardiac echo signal, providing dynamic information during cardiac systole and diastole. The signal obtained after unwinding can be used as a new first signal. An example of an unwinding method is as follows:

[0121] The arctangent (ACR) calculation includes the following steps: First, the phase value is calculated using the amplitude and phase of the acquired first signal. Since the phase value may be affected by wrapping, it's necessary to assess phase changes during the calculation. Specifically, the difference between adjacent phase values ​​is calculated; if the phase difference exceeds π, adjustments are needed, increasing or decreasing by 2π to eliminate wrapping. The entire signal's phase sequence is iteratively processed. For each pair of adjacent phase values, the phase difference is used to determine whether unwrapping is necessary, until no further unwrapping is required. After these steps, the final phase sequence is obtained.

[0122] Differential and cross-multiply (DACM) includes the following steps: At time t, calculate the real part I and imaginary part Q of the received signal of the first signal at time t. Then, multiply the I-path modulus value at time t by the time difference between the I-path and the Q-path modulus values ​​before and after the time difference, and add the Q-path modulus value at time t multiplied by the time difference between the I-path and the Q-path modulus values ​​before and after the time difference, thus obtaining the sum of the cross-multiplications at time t. Divide the sum of the cross-multiplications by the square of the I and Q-path modulus values ​​at time t to obtain the normalized phase value at the current time t. Then, accumulate this normalized phase value with the phase values ​​calculated before time t to obtain the final unwrapped phase value at time t. A phase sequence is obtained from the phase values ​​at different times. Refer to the following calculation method:

[0123] Where v(t) is the unwound phase value at time t, I(t) is the real part of the first signal at time t, and Q(t) is the imaginary part of the first signal at time t. I′(t) is the differential of I(t), Q′(t) is the differential of Q(t), and t is time.

[0124] The modified differential and cross-multiply (MDACM) is similar to DAM in that both obtain the phase solution value by differentially calculating the I and Q signals at different time points to obtain the true vital signs signal. The improvement of MDACM lies mainly in subtracting the I and Q signals after cross-multiplying them at different time points and then summing the results, ensuring that the demodulated phase sequence has a stable linear mapping relationship to the vital signs signal. Refer to the following calculation method:

[0125] Where X[n] is the phase value of the unwound signal at time n, λ is the wavelength of the first signal, I[k] is the real part of the first signal at time k, and Q[k] is the imaginary part of the first signal at time k. I[k-1] is the real part of the signal at the time preceding time k, Q[k-1] is the imaginary part of the signal at the time preceding time k, and k is a certain time, ranging from 2 to n.

[0126] Terminal devices can capture images via cameras and use them as a secondary signal, or process them before using them as a secondary signal. Specifically, after the terminal device's camera captures image signals in real time, it performs preprocessing such as denoising and contrast enhancement to improve image quality. During denoising, various image processing algorithms, such as Gaussian filtering or median filtering, can be used to help eliminate random noise in the image. Contrast enhancement can be achieved through methods such as histogram equalization, making details in the image more apparent and facilitating subsequent analysis and recognition. After preprocessing, the terminal device performs content recognition on the image signal to identify body parts contained within the image. By applying computer vision technology and deep learning algorithms, the terminal device can identify different body parts and perform different processing based on the recognition results. For example, if a finger is identified, the terminal device can extract a pulse wave signal by combining the features of the middle of the finger. If a facial feature is identified, the terminal device will further extract local facial features, such as the forehead or cheek area. Changes in blood flow in the forehead or cheek area can reflect heart activity; by extracting the pulse wave from the forehead features, physiological information related to heart health can be obtained.

[0127] Terminal devices can be configured with a time synchronization mechanism to ensure that the first and second signals are acquired within the same time period. For example, when acquiring cardiac echo signals and image signals, the terminal device can record the timestamp of each signal. The system clock can be used to obtain the timestamp, and the acquisition time of the signals can be aligned based on the timestamp, so that the acquired first and second signals are within the same time period.

[0128] The camera's image capture frame rate can be set according to the maximum frame rate supported by the terminal device. For example, if the terminal device supports a maximum image capture frame rate of 120Hz, then the image capture frame rate can be set to 120Hz. The frame rate of the wireless sensor can be greater than or equal to 120Hz. Furthermore, the camera's image capture frame rate can also be set to other values, such as 60Hz or 30Hz, without limitation here. By setting the maximum frame rate, the camera can capture a richer set of secondary signals, thereby more accurately determining the trend of pulse wave changes.

[0129] Because the target's physical state may be disturbed when in motion, and an unstable physical state cannot reflect the target's daily physiological condition, to ensure more accurate measurement of physiological parameters, the target's physical state can be monitored after acquiring the first signal. Physiological parameters should only be measured when the target's physical state is stable. An example of how to determine the target's physical state is as follows:

[0130] The target object's physical state is determined based on the fluctuation amplitude of the phase waveform of the first signal. The phase waveform of the first signal demonstrates how the signal shifts phase over time. The phase waveform of the cardiac echo signal acquired by the wireless sensor reveals changes in the signal at different time points, thus inferring cardiac motion information. The fluctuation amplitude of the phase waveform can be quantified using variance or standard deviation. A larger variance indicates more drastic fluctuations and a larger amplitude; conversely, a smaller variance indicates more concentrated phase fluctuations and a smaller amplitude. If the fluctuation amplitude is less than or equal to a preset amplitude threshold, the target object is considered to be in a quasi-static state; if the fluctuation amplitude is greater than the preset amplitude threshold, the target object is considered to be in motion. By statistically analyzing the fluctuation amplitude of the phase waveform in both motion and quasi-static states, the boundary between the two can be clearly defined, allowing for the setting of an appropriate amplitude threshold, which is then preset in the terminal device. Furthermore, to improve measurement accuracy, the preset amplitude threshold can be updated periodically to adapt to changes in the target object's physical condition.

[0131] The body state of a target object is determined by combining Doppler velocity. Doppler velocity is the speed of motion of a target object measured based on the Doppler effect. Velocity information is calculated by detecting the movement of an object (such as the human body surface) relative to a sensor. The terminal device can calculate the Doppler frequency shift of the signal based on the phase change and time interval of the first signal. Based on the Doppler frequency shift and the frequency of the wireless signal transmitted by the wireless sensor, the Doppler velocity is calculated. Doppler velocity reflects the speed of heart activity. A higher Doppler velocity indicates more intense body movement. Therefore, a Doppler velocity threshold can be set. If the calculated Doppler velocity is greater than or equal to the set threshold, the target object is determined to be in motion; if the calculated Doppler velocity is less than the set threshold, the target object is determined to be in a quasi-static state.

[0132] When the terminal device determines that the target object is in a quasi-static state, the pulse wave transmission time can be determined based on the first and second signals to further obtain the target object's physiological parameters. When the terminal device determines that the target object is in motion, a prompt message can be output, prompting the target object to keep its body in a resting state. At this time, the target object can rest for a period of time and then re-measure. If the target object is in motion, the terminal device does not process the first and second signals acquired at this time to avoid interference from the target object's movement on the measurement of physiological parameters. Once the target object returns to a quasi-static state, the first and second signals are re-acquired.

[0133] Step 202. Determine the transmission time of the pulse wave based on the first signal and the second signal.

[0134] The main principle for determining the pulse wave transmission time by combining the first and second signals is as follows: When the heart contracts, it pumps blood from the heart to the whole body. The pressure wave generated in this process propagates in the blood vessels, forming a pulse wave. The pulse wave travels from the heart along the arteries to other parts of the body, such as the fingers. The time difference of this process, that is, the time required for the pulse wave to reach a specific location, is called the pulse wave transmission time. UWB or millimeter-wave sensors on the terminal device are used to collect echo signals from the heart, which reflect cardiac activity and related physiological changes. Through signal processing, the characteristics of cardiac activity, especially the R wave, can be extracted. A camera on the terminal device is used to capture images of areas through which blood flows (fingers, wrists, or face, etc.), detecting changes in skin color caused by changes in blood flow, and extracting the pulse wave waveform. To synchronize the time of the cardiac echo signal and the image signal, the acquisition times of the two signals can be aligned. By processing the cardiac echo signal, the timing of the R wave is determined. Simultaneously, by processing the image signal, the timing of the pulse wave peak is identified. Calculate the time difference between two points in time, which reflects the time it takes for the pulse wave to travel from the heart to other parts of the body.

[0135] The terminal device processes the first signal to obtain an electrocardiogram (ECG) signal. This processing includes: removing noise above a certain frequency using a low-pass filter, retaining low-frequency cardiac signal components; setting a band-pass filter between 0.8Hz and 2Hz to retain frequency components relevant to cardiac activity, further obtaining the ECG signal. The first signal acquired by the terminal device is within a certain time period, i.e., the target time period, and the ECG signal obtained after processing is also within this target time period, such as 30 seconds. The target time period can be the entire period from the start to the end of signal acquisition, or a portion of the time period between the start and end of signal acquisition.

[0136] The terminal device processes the second signal to obtain a pulse wave signal. The second signal processed by the terminal device is from the same target time period as the first signal, such as signals collected within the same 30-second period, so that the first and second signals reflect cardiac activity within the same time frame. After obtaining the second signal, the terminal device identifies the type of body feature within it, extracts a portion of the image based on this type, and processes the color information of this portion to obtain the pulse wave signal. Furthermore, the terminal device can perform high-pass filtering (above 0.1Hz) on the obtained pulse wave signal to extract signals that better reflect cardiac activity.

[0137] The terminal device classifies the second signal to identify the type of body features within it, such as skin features and facial features. If the target object places their fingers or wrist directly on the terminal device's camera, the image captured by the camera will reflect skin features because the camera is directly covered by skin. In this case, the terminal device can calculate the average value of a certain color channel (e.g., the green channel) and use this average value as the pulse wave feature of that frame. As shown in Figure 6, for pixels in the central region of the image, the values ​​of the green channel for multiple pixels (155, 150, 153, 152) can be taken, and the average value (152.5) ​​can be calculated to determine the pulse wave value of the image. If the target object takes a facial image with the terminal device at a certain distance from their body, the image captured by the camera will contain the contours and details of the face. In this case, it is identified as a human face. Further, areas with significant blood flow changes, such as the forehead or cheeks, can be extracted. The average value of a certain color channel is calculated for the pixels in the extracted areas, and this average value is used as the pulse wave feature of that frame. The pulse wave signal for the target time period is constructed by analyzing the pulse wave features of all images within the target time period.

[0138] After receiving the ECG signal, the terminal device can detect the R wave as the first peak, as it is the highest amplitude and easiest to detect part of the QRS complex. The location of each R wave is located using an ECG signal analysis algorithm. After receiving the pulse wave signal, the terminal device selects a prominent peak or characteristic point as a reference, such as the first major peak as the second peak. This peak represents the instant the pulse wave reaches the finger or other measurement site. For each detected R wave, its peak time is recorded. For the pulse wave signal, the time of its characteristic peak (the peak time of the pulse wave) is recorded. Each R wave time point in the ECG signal is paired with the corresponding peak time point in the pulse wave signal. The pairing method is based on temporally adjacent peaks: after selecting each R wave time point, the nearest subsequent pulse wave peak time point is found. The difference between these two time points is calculated, i.e., PTT (Pulse Tone-Up). The average of multiple PTT values ​​over a period of time can be calculated to improve the accuracy of PTT.

[0139] Because the operation of the target object may be unstable, the second signal collected by the terminal device may also be unstable, further leading to instability in the pulse wave signal. As shown in Figure 7, the pulse wave signal and signal period are stable when the finger presses the camera with uniform force, but when the pressure changes, the signal period varies, accompanied by an increase or decrease in waveform amplitude. To obtain a stable pulse wave signal and improve the accuracy of physiological parameter measurements, the time period during which the signal changes stably within the target time period can be extracted as the effective time period. Subsequent processing of the pulse wave signal within the effective time period is then performed, including extracting the peak of the pulse wave signal within the effective time period as the second peak. Simultaneously, it is necessary to maintain temporal consistency between the electrocardiogram (ECG) signal and the pulse wave signal, ensuring they are signals within the same time period. Therefore, subsequent processing based on the ECG signal within the effective time period is required, including extracting the peak within the effective time period as the first peak.

[0140] Terminal devices can determine the valid time period within the target time period in the following ways:

[0141] The target time period is divided into multiple sub-time periods. The similarity between the amplitude of the pulse wave signal in each sub-time period and the amplitude of the pulse wave signal in other sub-time periods is calculated. The valid time periods are determined based on the sub-time periods containing pulse wave signals whose similarity is greater than or equal to a similarity threshold. For example, if the target time period is divided into n sub-time periods, including t1, t2, t3...tn, t1, t2, t3...tn can be used as the sub-time periods to be determined for validity. All other sub-time periods can be used as other sub-time periods. The length of each sub-time period can be the same or different. The length of other sub-time periods can be 1 to 3 times the length of the sub-time period to be determined for validity. If the amplitude similarity of a signal in a certain sub-time period is high compared to the signals in other sub-time periods, it indicates that the signal changes little from that sub-time period to other sub-time periods, therefore the signal in that sub-time period is a stable signal. Conversely, if the amplitude similarity of a signal in a certain sub-time period is low compared to the signals in other sub-time periods, it indicates that the signal changes significantly from that sub-time period to other sub-time periods, therefore the signal in that sub-time period is an unstable signal.

[0142] The terminal device uses a Fast Fourier Transform (FFT) to convert the pulse wave signal from the time domain to the frequency domain, obtaining the amplitude value of each frequency component in the frequency domain signal to obtain the signal amplitude in different sub-time periods. The two signal segments are then normalized. For the normalized signals, cosine similarity is used to measure the directional similarity; alternatively, Euclidean distance is used to calculate the distance between the two signals, with smaller distances indicating greater similarity; or Pearson correlation coefficient is used to measure the linear relationship between the two signals, with larger correlation coefficients indicating greater similarity. Subsequently, a similarity threshold is set to filter signals with a similarity greater than or equal to the threshold as valid signals, and the time period containing the valid signals is designated as the valid time period. Signals with a similarity less than the threshold are designated as invalid signals, and the time period containing the invalid signals is designated as the invalid time period.

[0143] After determining the valid time period, the terminal device performs a peak search on the pulse wave signal within that period. As shown in Figure 8, this includes the signal waveform during the dynamic time period and the signal waveform during the quasi-static time period where the pulse wave is determined to be valid. The signal waveforms include both the electrocardiogram (ECG) signal and the pulse wave signal. During the dynamic time period, the ECG signal waveform exhibits significant fluctuations and poor stability, while during the quasi-static time period, the ECG signal shows a stable trend. Within the quasi-static time period, the signal between every two adjacent peaks of the pulse wave signal constitutes a period, such as segment 1, segment 2, or segment 3. The starting point of this period is the peak with the earlier time, and the ending point is the peak with the later time. By searching for the ECG signal peaks between the starting and ending points, the peak corresponding to one cardiac contraction can be obtained. The peak corresponding to the ending point is the pulse wave signal peak corresponding to that cardiac contraction. Calculating the time difference between the two peaks yields the pulse wave transmission time. Because the pulse wave signal has multiple cycles, the transmission time of multiple pulse waves can be calculated, such as PTT1, PTT2, or PTT3. The calculated transmission times of multiple pulse waves can form a set.

[0144] The terminal device can further determine the total length of all valid time periods within the target time period. If the total length is less than the preset length threshold, such as less than 5 seconds, it indicates that most of the signals collected during this period are unstable or have a lot of noise. At this time, the target object can be prompted to remeasure the physiological parameters.

[0145] In addition to the above method of combining ECG and pulse wave signals to determine the pulse wave transmission time, terminal devices can also determine the pulse wave transmission time in the following way: ECG signals are measured by a wireless sensor, and heartbeat signals at key facial areas such as the cheeks and forehead are measured by a camera. The time difference between the peak of the heartbeat signal captured by the camera and the peak of the ECG signal measured by the wireless sensor is calculated to obtain the PTT from the heart to the face. Then, the mapping relationship between PTT and blood pressure is obtained through numerical fitting, thereby realizing blood pressure measurement.

[0146] Specifically, the terminal device measures the electrocardiogram (ECG) signal using the aforementioned wireless sensors. Then, using image processing algorithms, the terminal device analyzes subtle color changes or blood flow fluctuations on the skin surface, thereby deriving the temporal heartbeat signal from the skin image. A high-precision time synchronization algorithm is used to directly calculate the time difference between these two signal peaks. Inference can also be performed using machine learning or deep learning models. With a large amount of training data (including cardiac echo signals, skin images, PTT data, etc.), a machine learning model can be used to establish a direct mapping relationship between cardiac echo signals and changes in blood flow on the skin surface. Training phase: A model is trained using a large amount of existing cardiac echo signals, skin images, and PTT data. By learning the complex relationships between these data, the model can directly infer blood pressure values ​​from cardiac echo signals and skin images during the inference phase. Inference phase: The user's ECG signal is collected via wireless sensors, and the heartbeat signal from the face is obtained by analyzing skin images captured by a camera. The PTT value is calculated, and the model infers the blood pressure value through inference.

[0147] Step 203. Determine the physiological parameters of the target object based on the pulse wave transmission time.

[0148] Physiological parameters of the target subject include pulse wave transit time, pulse wave velocity, target blood pressure, and blood pressure variability. These physiological parameters are described below.

[0149] The pulse wave transit time is a valuable physiological parameter, and therefore can be directly used as a physiological parameter for the target subject. An excessively long pulse wave transit time may indicate insufficient blood flow or heart failure; a short pulse wave transit time may indicate good heart function or a hypermetabolic state. If multiple pulse wave transit times are available, their average value can be calculated and used as the physiological parameter for the target subject.

[0150] The pulse wave velocity is the ratio of the pulse wave's travel distance to its travel time. A high pulse wave velocity may indicate stiff blood vessels and cardiovascular disease, while a low pulse wave velocity generally indicates good blood vessel elasticity, but an excessively low velocity may also indicate cardiovascular disease. The pulse wave velocity can be calculated by using the ratio of the pulse wave's travel distance to its travel time.

[0151] For example, when acquiring the first and second signals, the first signal is the echo signal from the heart, and the second signal is the image signal from the fingertip. The pulse wave transmission time is the time it takes for the pulse wave to travel from the heart to the fingertip. Correspondingly, the distance from the heart to the fingertip is the pulse wave transmission distance. For ease of measurement, users can measure their arm length using tools such as a flexible measuring band, using the arm length to represent the distance from the heart to the fingertip. This distance is then uploaded to the terminal device, which calculates the pulse wave transmission speed based on the uploaded distance. Alternatively, if the second signal is an image signal from the face, the user can measure and upload the distance from the heart to the face. Furthermore, if measurement may be inconvenient for the user, they can input their height, weight, gender, or body type into the terminal device. The terminal device then estimates the pulse wave transmission distance and calculates the pulse wave transmission speed based on this information and preset anatomical proportions.

[0152] Target blood pressure refers to the blood pressure measured for a specific target individual. Blood pressure is a commonly used physiological indicator; both excessively high and low blood pressure can indicate related health conditions. Target blood pressure includes systolic blood pressure (SBP) and diastolic blood pressure (DBP). SBP and DBP are calculated as follows:

[0153] In this context, PTT is the pulse wave transmission time of the target object determined by the first and second signals. SBP0 is the baseline systolic blood pressure, which is the systolic blood pressure measured for a specific object using standard testing equipment; it can be the average of multiple systolic blood pressure measurements taken for that object. DBP0 is the baseline diastolic blood pressure, which is the diastolic blood pressure measured for the same object using standard testing equipment; it can be the average of multiple diastolic blood pressure measurements taken for that object. Standard blood pressure testing equipment includes medical blood pressure monitors such as electronic blood pressure monitors, stethoscopes, or cuff combinations. PTT0 is the PTT measured for the corresponding object when measuring SBP0 or DBP0, using standard PTT testing equipment. Standard PTT testing equipment includes high-resolution electrocardiographs, ultrasound equipment, etc. γ is a weighting coefficient, the specific value of which is set based on actual statistical data and is not limited here.

[0154] The above method for calculating systolic or diastolic blood pressure maps blood pressure to PTT (post-traumatic pressure) for an unspecified individual. To better reflect individual differences within a specific target group, the existing mapping can be modified by incorporating the target group's unique parameters, resulting in a more accurate mapping for that target individual. This allows for further calculation of blood pressure more precisely for that target individual. The implementation is as follows:

[0155] The system collects the target subject's standard blood pressure and standard pulse wave transit time (PTT). The data is collected by the target subject using standard testing equipment to measure their own blood pressure and PTT. Both standard blood pressure and standard PTT are data obtained before the acquisition of the first and second signals; therefore, the currently measured first and second signals are historical data, not real-time data. Since the target subject's physiological state changes over time, and this change affects the mapping relationship between standard blood pressure and standard PTT, it is necessary to measure standard blood pressure and standard PTT when the target subject's physiological state remains unchanged. To achieve this, the target subject can measure standard blood pressure and standard PTT within a similar time period, such as the same morning. After the target subject measures their standard blood pressure and standard PTT, they upload these data to the terminal device.

[0156] After obtaining standard blood pressure and standard pulse time (PTT), the terminal device substitutes these values ​​into a preset mapping relationship and re-corrects the mapping relationship. The corrected preset mapping relationship becomes the target mapping relationship. After determining the target mapping relationship, the target blood pressure can be obtained by substituting the pulse wave transmission time previously determined based on the first and second signals into the target mapping relationship.

[0157] Furthermore, by combining the target individual's personal attributes such as age, gender, or physical condition, a corresponding preset blood pressure mapping relationship can be selected to determine the appropriate target blood pressure. For example, older individuals with weaker constitutions may have different PTT (post-traumatic stress test) and blood pressure correspondences compared to younger individuals with stronger constitutions. The target individual's personal attributes can be input into the terminal device, which can then select the target blood pressure mapping relationship from multiple preset relationships based on those attributes. This approach can, to some extent, reflect the individual physiological characteristics of the target individual and improve the accuracy of the measured blood pressure.

[0158] Blood pressure variability refers to the degree of fluctuation in blood pressure within a given time period. It can be measured using indicators such as the standard deviation of 24-hour mean systolic blood pressure, the standard deviation of 24-hour mean diastolic blood pressure, or the coefficient of variation (COP), which is the ratio of the standard deviation to the mean. Excessive blood pressure variability may increase the risk of cardiovascular disease. Terminal devices can determine the target blood pressure based on the pulse wave transmission time and then determine the blood pressure variability based on the target blood pressure.

[0159] In addition to the physiological parameters mentioned above, the terminal device can further calculate the mean or variance of blood pressure as an evaluation indicator of blood pressure.

[0160] After obtaining the first and second signals, the terminal device can send them to the cloud for processing to obtain the pulse wave transmission time or physiological parameters. The methods and principles by which the cloud processes the first and second signals to determine the pulse wave transmission time or further determine physiological parameters are similar to those used by the terminal device, and will not be elaborated here.

[0161] After obtaining the physiological parameters of the target object, the terminal device can output these parameters. Output methods include displaying or broadcasting them via voice on the terminal device. Furthermore, the terminal device can also send the physiological parameters to other devices.

[0162] Figure 9 shows a schematic diagram of a process for measuring blood pressure via a smartphone according to an embodiment of this application.

[0163] Step 901. The smartphone responds to the user's command and triggers a blood pressure measurement.

[0164] Users can initiate blood pressure measurement by touching relevant interface elements in their smartphone's blood pressure measurement app, such as clicking the "Blood Pressure Measurement" button. After measurement is triggered, the smartphone will provide a voice prompt: "Please start the blood pressure measurement while lying down or sitting still. Turn on the phone's flashlight and cover the main camera with the tip of your left index finger. Hold the phone flat against your chest." Once the user completes these steps, the smartphone will begin measuring blood pressure.

[0165] In addition, users need to operate in a relatively quiet environment. The smartphone can remind users via voice to avoid taking blood pressure measurements in noisy or excessively hot places to prevent affecting the results. If the smartphone detects that the user's environment is not suitable, such as being too bright or having strong external motion interference, it will further remind the user to change positions. The smartphone's gyroscope and accelerometer can detect whether the phone is placed in front of the chest and remains stationary. If the user does not follow the prompts, the smartphone will vibrate or provide another voice prompt to remind the user to place the phone and fingers correctly.

[0166] Step 902. The smartphone uses a UWB sensor to determine whether the user's physical state is in a quasi-static state.

[0167] If not, proceed to step 903; if yes, proceed to step 904. If the user's physical state is not quasi-static, the user may be in motion, in which case the measured blood pressure will be inaccurate, and blood pressure measurement will not be performed. If the user's physical state is quasi-static, the user can be prompted via voice to maintain this state, and blood pressure measurement will begin. Quasi-static refers to a state where minor movements of the user's body do not significantly affect the accuracy of blood pressure measurement.

[0168] Users might want to measure their blood pressure immediately after exercise. In this case, the smartphone, using UWB technology, detects that the user's activity level does not meet the quasi-static condition and will prompt the user via voice to wait a few minutes before measuring, allowing the heart rate and blood pressure to stabilize. If the user is resting in bed, the UWB sensor can detect minute breathing movements and will still classify it as quasi-static. However, if the user moves significantly, the system will prompt for readjustment. The smartphone uses the UWB sensor to acquire the user's real-time activity level. If the sensor detects an activity level exceeding a certain threshold (e.g., greater than 0.05 m / s), the user is determined to be in a dynamic state, unsuitable for blood pressure measurement.

[0169] Step 903. The smartphone prompts the user to restore their physical state to a near-static state.

[0170] Smartphones can use voice prompts to help users adjust their physical state to a suitable condition for blood pressure measurement. Voice prompts might include, "You are currently active; please rest for a moment before measuring your blood pressure." The smartphone can also prompt users to perform some relaxing breathing exercises while starting a timer that displays the suggested waiting time, such as, "We suggest you wait 2 minutes before measuring." After the specified time, the smartphone can recheck the user's condition to confirm that the conditions are met. If the conditions are met, the smartphone can prompt the user to trigger the blood pressure measurement again.

[0171] Step 904. The smartphone detects the electrocardiogram signal through a wireless sensor and simultaneously detects the pulse wave signal through the camera, both for 30 seconds.

[0172] The smartphone first detects the echo signal from the heart using wireless sensors, and then extracts the electrocardiogram (ECG) signal from it. An image is captured by the camera, and the pulse wave signal is extracted from the green channel pixels of the image. After 30 seconds, the smartphone prompts the user to end the blood pressure measurement. After the measurement is complete, the user can use the smartphone normally.

[0173] During pulse wave signal detection, the user's finger must evenly cover the camera to ensure stable light reflection. The smartphone can adjust the flash brightness in real time based on changes in light reflection to adapt to different skin colors or ambient light intensities. During the 30-second measurement, the smartphone can provide voice updates on the real-time measurement progress, such as "20 seconds remaining, please wait until the measurement is complete."

[0174] Step 905. The smartphone filters out invalid signals from the pulse wave signal within 30 seconds to obtain a valid pulse wave signal for 28 seconds.

[0175] During pulse wave signal acquisition, slight movements of the user's fingers, changes in ambient light, or even improper skin contact can all introduce noise into the signal. Therefore, smartphones need to process the acquired 30-second pulse wave signal, filtering out invalid portions to obtain a valid 28-second signal for subsequent blood pressure calculation.

[0176] Optical measurements of pulse wave signals are affected by ambient light. Smartphones can use the red, green, and blue channels of their camera sensors to compare changes in light intensity across different channels, filtering out ambient light interference. The green channel is most sensitive to blood reflection, and the signal primarily retains data from the green channel. If the user moves their finger even slightly during the measurement, the waveform of the pulse wave signal will change significantly. For example, a sudden spike or excessively large amplitude may appear on a smooth pulse waveform. Smartphones use algorithms to analyze the continuity and smoothness of the signal in real time, and when they detect similar artifacts, they mark these waveform segments as invalid.

[0177] Smartphones can detect poor contact by monitoring changes in signal amplitude. When the amplitude drops below a certain threshold (e.g., very small light reflection), the signal is marked as invalid. Normal pulse wave signals have a certain periodicity, reflecting the user's heartbeat cycle. Smartphones can analyze the frequency domain characteristics of the signal to determine if abnormal, aperiodic signals (such as irregular fluctuations caused by other body movements or external interference) are present; these signals will be filtered out.

[0178] During a 30-second measurement, the smartphone may dynamically segment the signal based on periods of invalid signal detection. For example, during the 2-second invalid signal period caused by finger movement, the signal is automatically removed from the signal stream, retaining the remaining 28 seconds of valid data. The smartphone can slide along the timeline in fixed-length windows (e.g., every 1 second) to analyze the signal quality within each window. Once the signal quality in some windows is detected to be significantly lower than in others, that portion of the data is considered invalid. For the remaining valid signal, the smartphone uses smoothing algorithms, such as low-pass filters or Kalman filters, to eliminate small-amplitude high-frequency noise, resulting in a smoother and clearer signal waveform.

[0179] In some cases, small segments of missing data in a signal can be filled in using interpolation methods. For example, if there is invalid data within 2 seconds, but the waveforms of the valid data at both ends have similar trends, a smartphone can generate an approximate signal for this portion using techniques such as linear interpolation. After signal filtering and processing, the smartphone performs a final quality check on the processed 28-second signal. For example, the terminal device checks the periodicity, amplitude range, and stability of the remaining signal. Only signals that pass all quality checks are confirmed as valid pulse wave signals and proceed to the next processing stage.

[0180] In one scenario, when a user was taking their blood pressure, the smartphone's sensors detected several slight tremors in the user's finger, causing distortion of the pulse wave signal for a few seconds. The phone automatically flagged these few seconds of data as invalid using motion artifact detection. Subsequently, the device repaired the missing signal using smoothing and interpolation algorithms, ultimately retaining 28 seconds of valid pulse wave data. The phone screen displayed: "Signal acquisition complete, processing data."

[0181] Step 906. The smartphone divides the 28 seconds into periods based on the ECG signal and effective pulse wave signal.

[0182] The effective pulse wave signal is divided into multiple cycles based on the fact that each two adjacent peaks of the effective pulse wave signal constitute a cycle, and the electrocardiogram signal is also divided into multiple cycles of electrocardiogram signals according to this cycle.

[0183] Within a valid 28-second signal, a smartphone needs to divide the ECG and pulse wave signals into multiple corresponding cycles. Each cycle typically corresponds to a complete heartbeat. This cycle division is necessary to accurately match the heartbeat events in the ECG signal with the blood flow waveform in the pulse wave signal and to calculate the pulse-to-twitch (PTT) time. Assuming a user's heart rate is 80 beats per minute, the phone will detect approximately 37 heartbeats within 28 seconds, each constituting one ECG signal cycle.

[0184] Within a valid 28-second duration, the smartphone simultaneously acquired both electrocardiogram (ECG) and pulse wave signals. Both signals are generated based on changes in cardiac activity, but they differ in their representation and temporal characteristics. The ECG signal reflects the heart's electrical activity (the moment the heartbeat is triggered), while the pulse wave signal reflects the fluctuations of blood flowing through blood vessels after the heartbeat. Therefore, signal processing must ensure that these two signals are synchronized on the timeline to avoid data offset.

[0185] Step 907. The smartphone calculates the PTT based on the ECG signal and pulse wave signal within the same cycle within 28 seconds, and obtains multiple PTTs.

[0186] Because the R wave in an electrocardiogram (ECG) signal represents the moment the heart begins to contract, while the peak of a pulse wave signal represents the moment the blood wave reaches the finger, the R wave typically occurs before the peak. Smartphones need to calculate the time difference between these two signals, known as PTT. PTT is usually measured in milliseconds (ms).

[0187] ECG and pulse wave signals are divided into multiple cycles, and the smartphone calculates the PTT (post-traumatic stress test) for each cycle. For example, in 28 seconds of valid data, if a user's heart rate is 80 beats per minute, there are approximately 37 cycles, resulting in 37 independent PTT values. Within each cycle, the smartphone extracts the corresponding R-wave timestamp and pulse wave peak timestamp, calculating the difference between these two timestamps. This difference is the PTT for that cycle. For example, in a certain cycle, if the ECG R-wave occurs at 5 seconds and the pulse wave peak occurs at 5.25 seconds, the PTT would be: 5.25 seconds - 5 seconds = 250 milliseconds.

[0188] In some cases, due to signal noise or finger movement, PTT values ​​in individual cycles may be abnormally high or low, significantly deviating from the normal range. To improve data accuracy, a reasonable range for PTT can be set to automatically identify and eliminate abnormal PTT values. For example, the PTT value can be set between 100-300 milliseconds, depending on the user's blood pressure and arteriosclerosis.

[0189] After individual PTT values ​​are identified as abnormal and removed, the terminal device can fill in these missing PTT values ​​using interpolation techniques (such as linear interpolation) to ensure that all cycles have corresponding PTT data. For example, in the 10th cycle, due to slight finger movement by the user, the peak position of the pulse wave signal was detected abnormally, resulting in a PTT calculation of 600 milliseconds, far exceeding the reasonable range. The smartphone automatically removed the PTT value for that cycle and used the PTT values ​​from the 9th and 11th cycles through an interpolation algorithm to estimate this missing value.

[0190] To reduce fluctuations and instabilities during measurement, smartphones can smooth all PTT values, further eliminating noise interference in individual periods. For example, low-pass filters, moving averages, or Kalman filters can be used to smooth the PTT sequence, resulting in more stable results. In some cases, certain PTT periods may have higher signal quality, while others may have lower quality. Smartphones can assign higher weights to high-quality periods, prioritizing their PTT values ​​during smoothing and averaging. For instance, a smartphone performed a moving average on 37 PTT periods, smoothing out minor anomalies caused by signal fluctuations in individual periods, ultimately resulting in a more stable PTT value sequence.

[0191] Within a 28-second timeframe, the smartphone calculates multiple PTT values ​​(assuming 37 cycles correspond to 37 PTT values). These PTT values ​​are stored in the phone's memory as the basis for subsequent blood pressure calculations. If the user takes multiple blood pressure measurements consecutively, the smartphone can compare the PTT value sequences from different measurement periods to determine the user's vascular condition or blood pressure trends. For example, if the PTT value decreases over time, it may indicate that the user's blood pressure is rising.

[0192] Step 908. The smartphone obtains multiple target blood pressures based on the mapping relationship between blood pressure and PTT (Pulse Tolerance Test) inputs from multiple PTTs.

[0193] Terminal devices utilize the mapping relationship between PTT (post-contraction time) and blood pressure, converting each PTT into a corresponding blood pressure value. PTT and blood pressure are generally negatively correlated; a shorter PTT corresponds to higher blood pressure, and vice versa. Therefore, blood pressure can be indirectly inferred by measuring PTT. To derive blood pressure from PTT, a mapping relationship is pre-stored within the smartphone application. This mapping relationship can be a mathematical model built from historical data in biomedical research or personal health records, including linear regression models, multinomial regression models, or more complex nonlinear models based on machine learning. More complex mapping models can also be used, considering variables such as individual age, gender, and vascular stiffness, and using multiple regression or neural networks to predict blood pressure; such models require extensive training data.

[0194] Blood pressure consists of two values: systolic pressure (high pressure) and diastolic pressure (low pressure). Depending on the mapping model, a smartphone can calculate these two blood pressure values ​​separately. For example, a smartphone might estimate the systolic pressure as 105 mmHg using a PTT (Physical Time Tolerance) app, while the diastolic pressure might be extrapolated to 70 mmHg using a model.

[0195] After receiving multiple target blood pressure readings, the smartphone can examine these values. If a blood pressure value deviates significantly from the others or the normal range (e.g., a sudden spike or drop in blood pressure), the smartphone marks it as abnormal and processes it accordingly. This processing may involve discarding the abnormal value or interpolating it based on surrounding blood pressure readings. For example, in a user's blood pressure measurement, the smartphone detects a reading of 150 mmHg in one cycle, significantly higher than readings in other cycles. The smartphone considers this an abnormal value and discards it.

[0196] Multiple target blood pressure values ​​calculated by the smartphone can be temporarily stored for averaging and display in subsequent steps. In practical applications, users do not need to know every instantaneous blood pressure value; the ultimate goal is to provide an accurate and stable average blood pressure value (as described in step 909). After the smartphone calculates multiple target blood pressure values, this data is stored for subsequent averaging calculations.

[0197] Step 909. The smartphone calculates the average blood pressure for multiple target blood pressures to obtain the average blood pressure.

[0198] The smartphone calculates multiple target blood pressure values ​​using multiple pulse-times (PTTs). To obtain the final blood pressure result, the smartphone needs to process these target blood pressure values ​​and calculate an average. Signal quality can be considered when calculating the average to create a weighted average. Signal quality can be evaluated in various ways, such as the stability of the pulse wave signal, the clarity of the waveform, and the degree of interference from the electrocardiogram (ECG) signal. For example, in the user's measurement, the smartphone detects that the signal quality of the third cycle is better, so it assigns it a larger weight (e.g., a weight of 1.5), while the signal quality of the fifth cycle is poorer, so it assigns a smaller weight (e.g., 0.8). Finally, the smartphone calculates a more accurate average blood pressure based on the weighted result.

[0199] If the smartphone doesn't use weighted processing, it can simply average all the target blood pressure values. For example, if a user measures 37 target blood pressure values, such as 120 mmHg, 121 mmHg, and 118 mmHg, the smartphone adds these 37 values ​​together, resulting in 4400 mmHg, and then divides by 37 to get the final average blood pressure of 119 mmHg.

[0200] To further reduce the impact of instantaneous blood pressure fluctuations, smartphones can smooth these target blood pressure values. For example, methods such as moving averages and exponential smoothing can be used to further reduce noise and fluctuations in the data, making the final calculated average blood pressure more stable.

[0201] Step 910. The smartphone displays the average blood pressure.

[0202] Smartphones can display calculated average blood pressure to users intuitively and clearly. This display goes beyond simple numerical output; it also considers user interface design, user interaction experience, and how to effectively convey information to users. Furthermore, different users have different levels of health awareness, so the display needs to be both accurate and easy to understand.

[0203] When displaying average blood pressure, smartphones should present a clear, concise, and easy-to-understand interface. Blood pressure measurement apps should design a dedicated results interface, divided into several modules to display different blood pressure information. The interface design should consider font size, color, contrast, and the arrangement of charts and data to ensure that all users, regardless of their health knowledge level, can quickly understand the displayed results. The average blood pressure value (e.g., 120 / 80 mmHg) should be displayed in large font in the center of the screen so that users can see the key information at a glance. For example, a smartphone would display "120 / 80 mmHg," emphasizing this value in font and color, using large black font on a light background to make the number stand out.

[0204] Smartphones can clearly distinguish between systolic blood pressure (high pressure) and diastolic blood pressure (low pressure) and label their meanings to avoid user confusion. Smartphones can differentiate these two values ​​using icons or different colors, such as red for systolic blood pressure and blue for diastolic blood pressure, along with a brief explanation. For example, a smartphone might display 120 mmHg (systolic pressure) in red and 80 mmHg (diastolic pressure) in blue, labeling each value as "systolic / diastolic pressure".

[0205] In addition to displaying instantaneous average blood pressure values, smartphones can also generate simple charts showing recent blood pressure trends (such as multiple measurements taken on the same day, blood pressure changes over the past week, etc.). For example, a smartphone might display a line graph showing blood pressure changes over the past week, clearly marking peak and low blood pressure values ​​and using color to distinguish high blood pressure risk areas.

[0206] Blood pressure data may not be intuitive enough for the average user, so smartphones can include a simple health explanation module. For example, a smartphone could prompt the user with "normal blood pressure," "high blood pressure," or "low blood pressure," and provide health advice based on relevant health standards. This would improve the user experience for older adults or users unfamiliar with health terminology. For instance, if a user's measurement result is 120 / 80 mmHg, a green notification box could appear on the smartphone displaying "Normal blood pressure, continue to maintain healthy lifestyle habits."

[0207] If blood pressure is too high or too low, a smartphone can provide simple lifestyle suggestions based on the measurement data. For users with persistent high blood pressure, the smartphone can also remind them to see a doctor regularly. For example, if a user's blood pressure is higher than the standard value, the smartphone will display "High blood pressure, please reduce your intake of high-sodium foods and maintain exercise."

[0208] Users may want to share their blood pressure results with their doctors, family, or keep them for themselves. Therefore, smartphones can provide a share button, allowing users to export their blood pressure reports via social media platforms. These reports can include information such as current measurements, historical trend data, and health recommendations. For example, a user's phone interface could have a "share" button, allowing them to choose to send the report to their doctor for tracking and analysis.

[0209] Smartphones or health apps can automatically sync measurement data to health management platforms or third-party health applications, helping users manage their health data more comprehensively and create long-term health records. For example, a user's measurement results can be automatically synced to their personal health record for easy reference and management later.

[0210] For elderly users or those with visual impairments, smartphones can include voice announcements when displaying average blood pressure. The smartphone will clearly tell the user their measurement results and provide explanations. For example, the smartphone could say, "Your blood pressure is 120 / 80 mmHg, which is within the normal range. Please continue to maintain your healthy lifestyle." Smartphones can also offer accessibility features such as zoom, color inversion, and screen reading to help elderly or visually impaired users better view and understand the measurement results.

[0211] In one scenario, a user measures their blood pressure via their smartphone after waking up in the morning. After the measurement, the smartphone screen clearly displays the result "120 / 80 mmHg," with systolic and diastolic pressure marked in red and blue respectively. Simultaneously, the smartphone shows a blood pressure trend graph from the past week and indicates "blood pressure normal." If desired, the user can share this report with their doctor, and the results will be automatically synced to their health management platform. Furthermore, the smartphone provides a voice prompt telling the user, "Blood pressure is normal; continue maintaining healthy habits."

[0212] Using the above method, blood pressure can be measured via smartphone. Users do not need to purchase additional blood pressure testing equipment, and the measured blood pressure is highly accurate.

[0213] Figure 10 is a schematic diagram of a terminal device according to an embodiment of this application. Referring to Figure 10, the terminal device 1000 includes an acquisition module 1001, a first determination module 1002, and a second determination module 1003.

[0214] The acquisition module 1001 is used to acquire a first signal and a second signal. The first signal is the echo signal reflected by a first body part of the target object, which is the signal after the wireless signal emitted by the terminal device is reflected. The second signal is the image signal of a second body part of the target object. The first body part is closer to the heart side of the target object than the second body part.

[0215] The first determining module 1002 is used to determine the transmission time of the pulse wave based on the first signal and the second signal.

[0216] The second determining module 1003 is used to determine the physiological parameters of the target object based on the transmission time of the pulse wave.

[0217] In one possible implementation, the terminal device 1000 further includes a third determining module, wherein:

[0218] The third determining module is used to determine the fluctuation amplitude of the phase waveform of the first signal.

[0219] The first determining module 1002 is further configured to determine the transmission time of the pulse wave based on the first signal and the second signal if the fluctuation amplitude is less than or equal to a preset amplitude threshold.

[0220] In one possible implementation, the terminal device 1000 further includes:

[0221] The first output module is also used to output a first prompt message if the fluctuation amplitude is greater than a preset amplitude threshold; wherein the first prompt message is used to prompt the target object to keep its body state in a quiet state.

[0222] In one possible implementation, the first determining module 1002 is further configured to: process the first signal to obtain an electrocardiogram (ECG) signal; wherein the ECG signal corresponds to a target time period; process the second signal within the target time period to obtain a pulse wave signal; and determine the transmission time of the pulse wave based on the time corresponding to the first peak of the ECG signal and the time corresponding to the second peak of the pulse wave signal; wherein the first peak and the second peak are temporally adjacent, and the time corresponding to the first peak is earlier than the time corresponding to the second peak.

[0223] In one possible implementation, the first determining module 1002 is further configured to: determine an effective time period within the target time period based on the amplitude of the pulse wave signal; wherein the effective time period is a time period in which the amplitude of the pulse wave signal changes stably; determine a first peak based on the peak of the electrocardiogram signal within the effective time period; and determine a second peak based on the peak of the pulse wave signal within the effective time period.

[0224] In one possible implementation, the first determining module 1002 is further configured to: calculate the similarity between the amplitude of the pulse wave signal in each sub-time period and the amplitude of the pulse wave signal in other sub-time periods; and determine the effective time period based on the sub-time period in which the pulse wave signal with a similarity greater than or equal to the similarity threshold is located.

[0225] In one possible implementation, the terminal device 1000 further includes a correction module, wherein:

[0226] The correction module is used to correct the preset blood pressure mapping relationship based on the target object's standard blood pressure and the target object's standard pulse wave transmission time to obtain the target blood pressure mapping relationship. The standard blood pressure is the blood pressure measured by a standard blood pressure testing device, the standard pulse wave transmission time is the pulse wave transmission time of the target object measured historically by the terminal device, and the preset blood pressure mapping relationship is the mapping relationship between the preset blood pressure and the preset pulse wave transmission time.

[0227] The second determining module 1003 is also used to determine the target blood pressure mapped by the pulse wave transmission time based on the target blood pressure mapping relationship.

[0228] In one possible implementation, the terminal device 1000 further includes:

[0229] The second output module is used to output a second prompt message; wherein the second prompt message is used to prompt the target object on how to operate the terminal device, and the method of operating the terminal device includes placing the fingertip on the camera of the terminal device and placing the terminal device in front of the target object's chest.

[0230] In one possible implementation, the acquisition module 1001 is further configured to: receive a first signal based on a wireless sensor of the terminal device; wherein the wireless sensor includes an ultra-wideband sensor or a millimeter-wave sensor, and the wireless signal is emitted by the wireless sensor; and determine a second signal based on multiple images captured by the camera of the terminal device.

[0231] In one possible implementation, the frame rate of the wireless sensor transmitting wireless signals is greater than or equal to the frame rate of the camera capturing images.

[0232] It should be understood that the specific procedures for each module to perform the above-mentioned corresponding processes have been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0233] This application embodiment also provides another terminal device. FIG11 is another structural schematic diagram of the terminal device according to this application embodiment. Referring to FIG11, the terminal device 1100 includes a processor 1101, a wireless sensor 1102, and a pulse wave sensor 1103.

[0234] Optionally, the terminal device 1100 also includes a memory 1104. The memory 1104 is used to store data such as the first signal, the second signal, the transmission time of the pulse wave, or physiological parameters.

[0235] Optionally, the terminal device 1100 may also include a transceiver 1105. The transceiver 1105 may be used to send or receive the transmission time of the pulse wave or other physiological parameters.

[0236] Optionally, the terminal device 1100 also includes a speaker 1106. The speaker 1106 is used to play prompts to remind the user to measure physiological parameters.

[0237] In one possible implementation, the processor 1101, wireless sensor 1102, pulse wave sensor 1103, memory 1104, transceiver 1105 and speaker 1106 are connected via a bus, and the memory 1104 stores computer instructions.

[0238] Optionally, the first determining module 1002 or the second determining module 1003 in the embodiment shown in FIG10 may be the processor 1101, and the acquiring module 1001 in the embodiment shown in FIG10 may be the transceiver 1105.

[0239] This application also provides a terminal device. Figure 12 is another structural schematic diagram of the terminal device according to an embodiment of this application. Referring to Figure 12, the terminal device 1200 can be the terminal device in the above method embodiments, or it can be a component (e.g., a chip), module, or unit of the terminal device in the above method embodiments. The terminal device 1200 can be used to perform the steps of the above method embodiments, and the relevant descriptions in the above method embodiments can be referred to.

[0240] The processor is mainly used to process data or signals, such as processing the first signal and the second signal, as well as controlling terminal devices, executing corresponding software programs, and processing the data of the software programs.

[0241] The memory is mainly used to store software programs and data. The radio frequency (RF) circuit is mainly used for the conversion between baseband signals and RF signals, as well as the processing of RF signals.

[0242] Antennas are mainly used to transmit and receive radio frequency signals in the form of electromagnetic waves, such as transmitting wireless signals from wireless sensors and receiving echo signals from the first body part, such as echo signals from the heart.

[0243] Optionally, the terminal device 1200 also includes input / output devices, such as a touchscreen, a display screen, and a keyboard, primarily used to receive user input data and output data to the user. For example, the touchscreen can be used to allow the user to control the entire process of measuring physiological parameters through touch operation, and the display screen can be used to display physiological parameters or various prompts.

[0244] When data needs to be sent, the processor performs baseband processing on the data to be sent and outputs the baseband signal to the radio frequency (RF) circuit. The RF circuit then processes the baseband signal and transmits it outward as electromagnetic waves through the antenna. When data is sent to the terminal device, the RF circuit receives the RF signal through the antenna, converts it into a baseband signal, and outputs the baseband signal to the processor. The processor then converts the baseband signal back into data and processes it.

[0245] For ease of explanation, only one memory and processor are shown in Figure 12. In actual terminal device products, there may be one or more processors and one or more memories. Memory may also be called storage medium or storage device, etc. Memory may be set up independently of the processor or integrated with the processor; this application embodiment does not limit this.

[0246] In this embodiment, the antenna and radio frequency circuit with transceiver functions can be regarded as the transceiver unit of the terminal device, and the processor with processing functions can be regarded as the processing unit of the terminal device. As shown in FIG12, the terminal device 1200 includes a transceiver unit 1210 and a processing unit 1220. The transceiver unit can also be called a transceiver, transceiver machine, transceiver device, etc. The processing unit can also be called a processor, processing board, processing module, processing device, etc.

[0247] Optionally, the devices in transceiver unit 1210 used for receiving functions can be considered as receiving units, and the devices in transceiver unit 1210 used for transmitting functions can be considered as transmitting units. That is, transceiver unit 1210 includes both receiving and transmitting units. A transceiver unit can also be called a transceiver, transceiver circuit, etc. A receiving unit can also be called a receiver, receiver, or receiving circuit, etc. A transmitting unit can also be called a transmitter, transmitter, or transmitting circuit, etc.

[0248] When the terminal device is a chip, the chip includes a transceiver unit and a processing unit. The transceiver unit can be an input / output circuit or a communication interface; the processing unit is a processor, microprocessor, integrated circuit, or logic circuit integrated on the chip. In the above method embodiment, the sending operation corresponds to the output of the input / output circuit, and the receiving operation corresponds to the input of the input / output circuit.

[0249] This application also provides a computer program product including computer instructions, which, when run on a computer, causes the computer to perform the methods of the embodiments shown in Figures 2 to 9 above.

[0250] This application also provides a computer-readable storage medium including computer instructions that, when executed on a computer, cause the computer to perform the methods of the embodiments shown in Figures 2 to 9 above.

[0251] This application also provides a chip device, including a processor, for calling a computer program or computer instructions stored in a memory, so that the processor executes the method of the embodiments shown in Figures 2 to 9 above.

[0252] Optionally, the processor is coupled to the memory via an interface.

[0253] Optionally, the chip device may also include a memory in which computer programs or computer instructions are stored.

[0254] The processor mentioned above can be a general-purpose central processing unit, a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of a program for controlling the methods of the embodiments shown in Figures 2 to 9. The memory mentioned above can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, such as random access memory (RAM).

[0255] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0256] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0257] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0258] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the part of the technical solution that makes an essential contribution, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods described in the various embodiments of this application.

[0259] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A signal processing method, characterized by, The method includes: Acquire a first signal and a second signal; wherein the first signal is an echo signal reflected by a first body part of the target object, the echo signal being a signal after a wireless signal emitted by a terminal device is reflected, and the second signal is an image signal of a second body part of the target object; the first body part is closer to the heart side of the target object than the second body part. Based on the first signal and the second signal, the transmission time of the pulse wave is determined; The physiological parameters of the target object are determined based on the transmission time of the pulse wave.

2. The method of claim 1, wherein, Before determining the pulse wave transmission time based on the first signal and the second signal, the method further includes: Determine the fluctuation amplitude of the phase waveform of the first signal; Determining the pulse wave transmission time based on the first signal and the second signal includes: If the fluctuation amplitude is less than or equal to a preset amplitude threshold, the transmission time of the pulse wave is determined based on the first signal and the second signal.

3. The method of claim 2, wherein, After determining the fluctuation amplitude of the phase waveform of the first signal, the method further includes: If the fluctuation amplitude is greater than the preset amplitude threshold, a first prompt message is output; wherein, the first prompt message is used to prompt the target object to keep its body in a quiet state.

4. The method according to any one of claims 1 to 3, characterized in that, Determining the pulse wave transmission time based on the first signal and the second signal includes: The first signal is processed to obtain an electrocardiogram (ECG) signal; wherein the ECG signal corresponds to a target time period. The second signal within the target time period is processed to obtain a pulse wave signal; Based on the time corresponding to the first peak of the electrocardiogram signal and the time corresponding to the second peak of the pulse wave signal, the transmission time of the pulse wave is determined; wherein the first peak and the second peak are adjacent in time, and the time corresponding to the first peak is earlier than the time corresponding to the second peak.

5. The method of claim 4, wherein, Before determining the transmission time of the pulse wave based on the time corresponding to the first peak of the electrocardiogram signal and the time corresponding to the second peak of the pulse wave signal, the method further includes: Based on the amplitude of the pulse wave signal, an effective time period within the target time period is determined; wherein, the effective time period is the time period during which the amplitude of the pulse wave signal changes stably; The first peak is determined based on the peak of the electrocardiogram signal within the effective time period; The second peak is determined based on the peak of the pulse wave signal within the effective time period.

6. The method of claim 5, wherein, The target time period includes multiple sub-time periods, and determining the effective time period within the target time period based on the amplitude of the pulse wave signal includes: Calculate the similarity between the amplitude of the pulse wave signal in each sub-time period and the amplitude of the pulse wave signal in other sub-time periods; The effective time period is determined based on the sub-time period in which the pulse wave signal with a similarity greater than or equal to the similarity threshold is located.

7. The method according to any one of claims 1 to 6, characterized in that, The physiological parameters of the target object include target blood pressure, and the method further includes: Based on the target object's standard blood pressure and standard pulse wave transmission time, the preset blood pressure mapping relationship is corrected to obtain the target blood pressure mapping relationship; wherein, the standard blood pressure is the blood pressure measured by a standard blood pressure testing device, the standard pulse wave transmission time is the pulse wave transmission time of the target object historically measured by the terminal device, and the preset blood pressure mapping relationship is the mapping relationship between preset blood pressure and preset pulse wave transmission time. Determining the physiological parameters of the target object based on the transmission time of the pulse wave includes: Based on the target blood pressure mapping relationship, the target blood pressure is determined by the transmission time mapping of the pulse wave.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Output a second prompt message; wherein the second prompt message is used to prompt the target object on how to operate the terminal device, the method of operating the terminal device includes placing the fingertip on the camera of the terminal device, and placing the terminal device in front of the target object's chest.

9. The method according to any one of claims 1 to 8, characterized in that, Applied to the terminal device, the acquisition of the first signal and the second signal includes: The terminal device receives the first signal based on its wireless sensor; wherein the wireless sensor includes an ultra-wideband sensor or a millimeter-wave sensor, and the wireless signal is emitted by the wireless sensor. The second signal is determined based on multiple images captured by the camera of the terminal device.

10. The method of claim 9, wherein, The frame rate at which the wireless sensor transmits the wireless signal is greater than or equal to the frame rate at which the camera captures the image.

11. A terminal device, comprising: include: A wireless sensor is used to transmit a wireless signal and receive a first signal; wherein the first signal is an echo signal reflected by a first body part of a target object, and the echo signal is the signal after the wireless signal emitted by the terminal device is reflected. A pulse wave sensor is used to acquire a second signal; wherein the second signal is an image signal of a second body part of the target object; the first body part is closer to the heart side of the target object than the second body part. A processor, connected to the wireless sensor and the pulse wave sensor respectively, is used to execute the method according to any one of claims 1-10.

12. The terminal device according to claim 11, characterized by The wireless sensor includes an ultra-wideband sensor or a millimeter-wave sensor.

13. The terminal device of claim 11, wherein, The pulse wave sensor includes a camera.

14. A terminal device, comprising: Includes a module for performing the method according to any one of claims 1-10.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or instructions that, when executed by a terminal device, implement the method as described in any one of claims 1-10.

16. A computer program product, characterised in that, When the computer program product is run on a terminal device, it causes the terminal device to perform the method as described in any one of claims 1-10.

17. A chip, characterized by Includes a processor, said processor being configured to support a terminal device in implementing the method according to any one of claims 1-10.

Images