A method, apparatus, and system for filtering motion interference of a pulse signal

Abstract

The application provides a pulse signal motion interference filtering method, device and system, the method comprising: using a green light source to irradiate the skin of a target human body currently in a motion state in real time to collect a corresponding pulse signal from the target human body in real time; preprocessing the collected pulse signal; based on the acceleration data of the target human body collected in real time, adaptively filtering the preprocessed pulse signal to filter out the motion artifact interference of the pulse signal. The application can effectively improve the accuracy and effectiveness of filtering the pulse signal motion interference of a person in a motion state without a large amount of data, a simple calculation process and multiple PPG sensors, and can effectively improve the real-time performance of filtering the pulse signal motion interference, thereby effectively improving the accuracy and reliability of heart rate measurement based on the pulse signal after filtering the motion interference.

Description

Technical Field

[0001] This application relates to the field of pulse signal processing technology, and in particular to a method, apparatus and system for filtering motion interference in pulse signals. Background Technology

[0002] Photoplethysmography (PPG) signals are widely used for heart rate (HR) monitoring and are a key technology in many existing wearable products and clinical devices. To obtain this signal, an LED is used to illuminate human skin, and then a photodiode is used to measure the changes in reflected light intensity caused by blood flow. The pulse signal morphology is similar to the arterial blood pressure (ABP) waveform, allowing for non-invasive heart rate monitoring. The periodicity of the pulse signal corresponds to the heart rhythm. Therefore, heart rate can be estimated from the pulse signal. However, heart rate estimation performance is reduced by poor blood perfusion, ambient light, and, most importantly, motion artifacts (MA). Therefore, many research institutions and companies have conducted extensive design and research on the noise interference problem of pulse signals. Since motion artifacts are the biggest source of noise interference in PPG signal wearable devices, they, along with ambient light, have become a core research issue.

[0003] Currently, existing methods for filtering pulse signal interference include: methods based on non-motion reference frames, methods that introduce motion information from motion sensors as a source of noise signals, methods based on artificial neural networks, methods using multi-channel PPG sensors, methods for feature extraction using variance discriminant analysis, and general framework algorithms for heart rate monitoring. However, regardless of the method, each suffers from at least one of the following problems: large data requirements, complex computation, numerous PPG sensors, the ability to measure only static data, and the limitation to post-processing. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method, apparatus and system for filtering motion interference in pulse signals to eliminate or improve one or more defects existing in the prior art.

[0005] One aspect of this application provides a method for filtering motion interference in pulse signals, comprising:

[0006] A green light source is used to illuminate the skin of a target human body that is currently in motion in real time in order to collect the corresponding pulse signal from the target human body in real time.

[0007] The acquired pulse signals are preprocessed;

[0008] Based on the real-time collected acceleration data of the target human body, the preprocessed pulse signal is adaptively filtered to remove motion artifact interference.

[0009] In some embodiments of this application, the step of using a green light source to illuminate the skin of a target human body currently in motion in real time to collect the corresponding pulse signal from the target human body in real time includes:

[0010] The PPG sensor, which emits green light, is controlled to illuminate the skin of a moving target human body in real time and to collect the corresponding pulse signal in real time.

[0011] In some embodiments of this application, the preprocessing of the acquired pulse signal includes:

[0012] The acquired pulse signals were processed for baseline data and high-frequency noise removal.

[0013] In some embodiments of this application, the baseline data and high-frequency noise removal processing of the acquired pulse signal includes:

[0014] The infinite impulse response filter is controlled to perform high-pass filtering on the acquired pulse signal to remove baseline data from the pulse signal, and low-pass filtering on the pulse signal to remove high-frequency noise from the pulse signal.

[0015] In some embodiments of this application, before performing adaptive filtering on the preprocessed pulse signal based on the real-time acquired acceleration data of the target human body to filter out motion artifact interference in the pulse signal, the method further includes:

[0016] The accelerometer is controlled to collect the acceleration data of the target human body in real time.

[0017] In some embodiments of this application, the step of adaptively filtering the preprocessed pulse signal based on the real-time acquired acceleration data of the target human body to filter out motion artifact interference in the pulse signal includes:

[0018] Based on the real-time collected acceleration data of the target human body, the estimated value of the blood volume change caused by the movement of the target human body is determined;

[0019] The estimated blood volume change is subtracted from the value corresponding to the preprocessed pulse signal to filter out motion artifact interference in the pulse signal, thus obtaining the corresponding target pulse signal.

[0020] Another aspect of this application provides a filtering device for motion interference in pulse signals, comprising:

[0021] The signal acquisition module is used to illuminate the skin of a target human body that is currently in motion with a green light source in real time in order to collect the corresponding pulse signal from the target human body in real time.

[0022] A preprocessing module is used to preprocess the acquired pulse signals;

[0023] The interference filtering module is used to adaptively filter the preprocessed pulse signal based on the acceleration data of the target human body collected in real time, so as to filter out the motion artifact interference of the pulse signal.

[0024] The third aspect of this application provides a pulse signal motion interference filtering system, comprising: a microcontroller unit, a PPG sensor communicatively connected to the microcontroller unit, an infinite impulse response filter, and an acceleration sensor;

[0025] The microcontroller unit is used to perform the pulse signal motion interference filtering method;

[0026] The microcontroller controls the PPG sensor to use a green light source to illuminate the skin of the target human body currently in motion in real time so as to collect the corresponding pulse signal from the target human body in real time.

[0027] The microcontroller unit controls the infinite impulse response filter to perform baseline data removal and high-frequency noise removal processing on the acquired pulse signal, respectively.

[0028] The microcontroller unit controls the accelerometer to collect the acceleration data of the target human body in real time.

[0029] A fourth aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method for filtering motion interference of the pulse signal.

[0030] A fifth aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method for filtering motion interference of the pulse signal.

[0031] The pulse signal motion interference filtering method provided in this application uses a green light source to illuminate the skin of a target human body currently in motion in real time to collect the corresponding pulse signal from the target human body in real time; the collected pulse signal is preprocessed; based on the real-time collected acceleration data of the target human body, the preprocessed pulse signal is adaptively filtered to remove motion artifact interference from the pulse signal. It can effectively improve the accuracy and effectiveness of filtering motion interference from the pulse signal of a person in motion without requiring a large amount of data, with a simple calculation process and without requiring multiple PPG sensors, and can effectively improve the real-time performance of pulse signal motion interference filtering, thereby effectively improving the accuracy and reliability of heart rate measurement based on the pulse signal after motion interference filtering.

[0032] Additional advantages, objectives, and features of this application will be set forth in part in the description which follows, and will in part become apparent to those skilled in the art upon review of the following description, or may be learned by practice of the application. The objectives and other advantages of this application can be realized and obtained by means of the structures specifically pointed out in the specification and drawings.

[0033] Those skilled in the art will understand that the purposes and advantages that can be achieved with this application are not limited to those specifically described above, and that the above and other purposes that this application can achieve will be more clearly understood from the following detailed description. Attached Figure Description

[0034] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, do not constitute a limitation thereof. The components in the drawings are not drawn to scale but are merely for illustrating the principles of this application. For ease of illustration and description of certain parts of this application, corresponding portions in the drawings may be enlarged, i.e., may appear larger relative to other components in an exemplary device actually manufactured according to this application. In the drawings:

[0035] Figure 1 This is a schematic diagram of the first process of a pulse signal motion interference filtering method in one embodiment of this application.

[0036] Figure 2(a) is a schematic diagram of the absorption rate of human skin when irradiated with green light.

[0037] Figure 2(b) is a schematic diagram of the absorption rate of human skin under red light.

[0038] Figure 3(a) is a schematic diagram of the reflected light intensity of the water in the red cup illuminated by a green light source.

[0039] Figure 3(b) is a schematic diagram of the reflected light intensity when a green light source illuminates a large red cup of water.

[0040] Figure 3(c) is a schematic diagram of the reflected light intensity of the water in the small red cup illuminated by a red light source.

[0041] Figure 3(d) is a schematic diagram of the reflected light intensity of a large red cup of water illuminated by a red light source.

[0042] Figure 4 This is a schematic diagram of a second process for filtering motion interference in pulse signals according to an embodiment of this application.

[0043] Figure 5 This is a schematic diagram illustrating the execution logic of step 320 in one embodiment of this application.

[0044] Figure 6 This is a schematic diagram of the structure of a pulse signal motion interference filtering device in another embodiment of this application.

[0045] Figure 7 This is a schematic diagram of the structure of a pulse signal motion interference filtering system in another embodiment of this application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and their descriptions are used to explain this application, but are not intended to limit it.

[0047] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the structures and / or processing steps closely related to the solution according to this application are shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0048] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0049] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0050] In the following description, embodiments of the present application will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0051] In one or more embodiments of this application, the infinite impulse response filter refers to an IIR filter. IIR filters have the characteristics of simple structure and low computational load, and are very suitable for digital signal front-end processing in embedded systems.

[0052] In one or more embodiments of this application, ACC data refers to acceleration data, which can be measured by an acceleration sensor.

[0053] In one or more embodiments of this application, the PPG sensor is a photoelectric sensor for monitoring cardiovascular vital signs, referred to as a cardiovascular vital signs photoelectric sensor (PPG sensor). The PPG sensor typically operates in a transmission or reflection manner. The PPG sensor consists of at least one pair of light-emitting diodes (LEDs) and a photodetector (PD), wherein the LEDs act as a light source illuminating the skin, and the PD detects the remaining transmitted or reflected light (another portion of the incident light is absorbed by blood and tissue during penetration). Due to changes in arterial blood volume during cardiac activity, the intensity of the transmitted or reflected light from the PD varies with arterial pulsation, and the PD then converts this into an electrical signal. The pulse signal contains alternating current (AC) and direct current (DC) components, generated by the light absorption of pulsating blood and non-pulsating tissue. Based on the light absorption of pulsating blood and non-pulsating tissue, the pulse signal provides information on heart rate, blood oxygen saturation, and blood pressure.

[0054] PPG sensors are mainly divided into two types: reflective and transmissive. Reflective PPG sensors obtain relatively weak signals. The light source of a reflective PPG sensor needs to pass through tissues such as skin, muscle, and capillaries before returning to the skin surface and finally being received by the sensor. Therefore, reflective PPG sensors are more susceptible to motion artifacts. According to existing literature, there are two main methods to eliminate motion artifacts: one is based on a non-motion reference frame, which extracts the main frequencies from the pulse wave signal and extracts information such as heart rate from the distorted pulse wave signal; the other is to introduce motion information from the motion sensor as a source of noise. For example, by using motion components as characteristic parameters of motion artifacts, filter optimization algorithms are used to actively eliminate noise components. Many related methods have been proposed to eliminate motion artifacts in pulse signals, but to effectively eliminate motion artifacts, not only is a superior algorithm required, but also careful design of the photoelectric sensor. Furthermore, the appropriate wavelength of incident light should be selected according to requirements to maximize the signal-to-noise ratio while meeting the requirements.

[0055] Reducing motion artifacts in pulse signals remains a challenge, leading to extensive research in this area. Some researchers have employed artificial neural networks (ANNs) to identify finger pulse signals containing motion artifacts. This method requires extensive training of the ANN with a large amount of clean pulse signals and necessitates generating a unique reference pulsation template for each subject; its generalizability still needs improvement. Other researchers have focused on the sensor itself, using a nine-channel PPG sensor and a truncated singular decomposition algorithm to estimate the pulse signal of subjects during high-intensity exercise.

[0056] In addition, some scholars have developed a reflective pulse signal acquisition system. Based on wearable devices, they used variance discriminant analysis to extract features from motion artifacts in the pulse signal. They then calculated physiological parameters such as blood oxygen saturation from the pulse signal, demonstrating the method's accuracy. However, this method only applies to static measurements and does not filter for motion artifacts during the subject's movement. Another scholar proposed a general framework algorithm for heart rate monitoring called Troika. This framework employs multiple algorithms to remove noise signals. It boasts high estimation accuracy and robustness against motion artifacts, with an average heart rate estimation error of 2.34 beats per minute and a Pearson correlation of 0.992 with the actual heart rate, exhibiting good adaptability. However, its computational process is complex.

[0057] Therefore, existing pulse signal motion interference filtering methods suffer from at least one of the following problems: large data volume required, complex calculation process, large number of PPG sensors, can only measure static data, can only be processed after the fact, and are not suitable for microcontroller units (MCUs). This application can filter out ambient light, baseline data, high-frequency noise, and motion artifact interference, and can eliminate motion artifacts during vigorous movement. It is also suitable for real-time processing during data acquisition and is suitable for processing using an MCU (i.e., the microcontroller unit mentioned in the following embodiments of this application).

[0058] The following examples will provide a detailed description.

[0059] This application provides a method for filtering motion interference in pulse signals, see [link to relevant documentation]. Figure 1 The pulse signal motion interference filtering method, which can be executed by the pulse signal motion interference filtering device, specifically includes the following:

[0060] Step 100: Illuminate the skin of the target human body in motion with a green light source in real time to collect the corresponding pulse signal from the target human body in real time.

[0061] Understandably, as shown in Figures 2(a) and 2(b), green light was chosen as the light source because our skin color is primarily caused by melanin, and different skin tones appear depending on the amount of melanin. Melanin absorbs green light very well, but is essentially transparent to red light. Furthermore, hemoglobin in the blood also has a high absorption rate for green light. Therefore, overall, the human body has a high absorption rate for green light, which is why no one has green skin—green light is absorbed by the body and doesn't reflect into our eyes. On the other hand, the human body absorbs red or infrared light much less readily.

[0062] Among them, see Figures 3(a) to 3(d) The advantages of using green light sources are as follows:

[0063] (1) Large signal amplitude

[0064] When green light of the same intensity is shone into a large cup and a small cup of red water, the intensity of the reflected light differs significantly due to the higher absorption rate of green light by the red water and the different amounts of water.

[0065] When red light of the same intensity is shone into a large cup and a small cup of red water, the difference in the intensity of the reflected light is small because red water has a low absorption rate for red light.

[0066] Now imagine blood vessels as water, with the amount of water increasing or decreasing with each heartbeat. If the incident light is green, the reflected light changes more significantly due to the heartbeat; if the incident light is red, the reflected light changes less significantly. In other words, the signal obtained using green light will show a greater range of change than the signal obtained using red light.

[0067] (2) Minimal impact from ambient light

[0068] Light intensity sensors receive not only reflected light from our own emitted light but also reflected light from ambient light. If we use green light with a green light sensor, the green component of the ambient light is almost entirely absorbed by the human body, and the reflection to the sensor is negligible. However, if we use red light (infrared light), because the human body absorbs less red light (infrared light), some of the red (infrared) component of the ambient light will still be reflected to the sensor, interfering with the effective signal. Therefore, green light is more resistant to the influence of ambient light than red light.

[0069] Combining the above two points, the green light signal has a large amplitude and low noise, which means it has a high signal-to-noise ratio.

[0070] A high signal-to-noise ratio makes detection easier and reduces the impact of some interferences (such as the wristband / watch not fitting properly or the person being in motion).

[0071] Step 200: Preprocess the acquired pulse signal.

[0072] It is well-known that pulse signals are susceptible to adverse blood perfusion and motion artifacts in surrounding tissues. To minimize the impact of these factors and avoid interfering with subsequent PPG analysis and heart rate estimation, a preprocessing stage is necessary. Bandpass filters can be used to eliminate high-frequency components (such as power supply) and low-frequency components (such as changes in capillary density and venous blood volume, temperature changes, etc.) of the pulse signal.

[0073] Step 300: Based on the real-time collected acceleration data of the target human body, adaptive filtering is performed on the preprocessed pulse signal to filter out motion artifact interference in the pulse signal.

[0074] As can be seen from the above description, the pulse signal motion interference filtering method provided in this application embodiment can effectively improve the accuracy and effectiveness of filtering motion interference from the pulse signal of a person in motion, without requiring a large amount of data, with a simple calculation process and without requiring multiple PPG sensors. It can also effectively improve the real-time performance of pulse signal motion interference filtering, thereby effectively improving the accuracy and reliability of heart rate measurement based on the pulse signal after motion interference filtering.

[0075] To further improve the effectiveness and reliability of removing ambient light interference, a method for filtering motion interference from pulse signals is provided in this application embodiment, see [link to relevant documentation]. Figure 4 Step 100 in the pulse signal motion interference filtering method specifically includes the following:

[0076] Step 110: Control the PPG sensor used to emit green light source to illuminate the skin of the target human body that is currently in motion in real time, and collect the corresponding pulse signal in real time.

[0077] In other words, the PPG sensor needs to be adjusted before step 100 so that its light source is green.

[0078] To further improve the effectiveness and reliability of preprocessing, in a pulse signal motion interference filtering method provided in this application embodiment, step 200 of the pulse signal motion interference filtering method specifically includes the following:

[0079] Step 210: Perform baseline data and high-frequency noise removal processing on the acquired pulse signals.

[0080] To further improve the effectiveness and reliability of high-pass and low-pass filtering, in a pulse signal motion interference filtering method provided in this application embodiment, see [link to relevant documentation]. Figure 4 Step 210 of the pulse signal motion interference filtering method specifically includes the following:

[0081] Step 211: Control the infinite impulse response filter to perform high-pass filtering on the acquired pulse signal to remove baseline data from the pulse signal, and perform low-pass filtering on the pulse signal to remove high-frequency noise from the pulse signal.

[0082] Specifically, an infinite impulse response (IIR) filter can be used to perform a 1Hz high-pass processing on the pulse signal to remove baseline data. Then, an 8Hz low-pass IIR filter can be used to remove some high-frequency noise.

[0083] To further improve the effectiveness and reliability of filtering motion artifact interference from the pulse signal, a method for filtering motion interference from the pulse signal is provided in this application embodiment, see [link to relevant documentation]. Figure 4 The method for filtering motion interference in pulse signals also includes the following steps prior to step 300:

[0084] Step 010: Control the accelerometer to collect the acceleration data of the target human body in real time.

[0085] To further improve the efficiency and reliability of filtering motion artifact interference from pulse signals, a method for filtering motion interference from pulse signals is provided in this application embodiment, see [link to relevant documentation]. Figure 4 Step 300 in the pulse signal motion interference filtering method specifically includes the following:

[0086] Step 310: Based on the real-time collected acceleration data of the target human body, determine the estimated value of the blood volume change caused by the movement of the target human body;

[0087] Step 320: Subtract the estimated blood volume change from the value corresponding to the preprocessed pulse signal to filter out motion artifact interference in the pulse signal and obtain the corresponding target pulse signal.

[0088] Specifically, the acceleration data, or ACC signal, corresponds to the ACC value, which can be written as x(n). The ACC value represents the real-time change in acceleration in each direction during motion. The ACC value is then used to estimate the change in blood volume caused by motion. By subtracting the change in blood volume caused by motion from the real-time PPG value, interference can be removed.

[0089] See Figure 5 Step 320 may specifically include the following:

[0090] Taking a wrist sensor as an example, when we shake our arm, the acceleration ACC will definitely change because of the change in force. And because of the change in force, the volume of blood in the human body will also change with the change in force (the change in x(n)). Although their amplitudes are different, their frequencies are definitely the same.

[0091] S1: Determine the adaptive filter coefficients w, i.e., input the value x(n) of the ACC signal (which can be written as: acc signal) into the adaptive filter (which can be written as: Adaptive filter) to obtain the organization path estimate. (Can be written as: Estimatetissue path);

[0092] S2: Solve We know that the signal we ultimately want to remove is y(n), which is the change in blood volume caused by the transformation of ACC, as shown in formula (1):

[0093]

[0094] In formula (1), x n Represents an Nth-order filter; w T The remote input at time n and the N times prior to it is weighted by w to obtain the noise signal estimate at time n. The purpose of the entire system is to find the filter w.

[0095] To mimic y(n), we use FIR adaptive filtering, modifying the parameter w in real time, and convolving it with the acceleration x(n) to obtain an estimated value of y(n), namely: based on formula (1) and the organization path estimate. The change in blood volume caused by motion is calculated, i.e., the estimated value of motion artifacts or motion noise. (Can be written as: Estimate noise of movement).

[0096] S3: Solve for the clean pulse signal based on formula (2). It is known that d(n) is the original pulse signal mixed with noise, while the environmental noise y(n) (which can be written as: noise of movement) has already been used. It was estimated, so in the end Calculate the final

[0097]

[0098] In formula (2), J min (w n ) is the LNS optimization function; This is the final output; d(n) is the original photoplethysmography signal containing noisy data. It is an estimate of the change in blood volume caused by changes in ACC, and the whole E is the mathematical expectation. Is this a normal request? The process, represented by linear algebra. Among them, The value roughly estimated by adaptive filtering h(t) is called y(n). In other words, y(n) (which can be written as noise of movement) is unknowable, but... This is an estimate of y(n), and subsequent calculations will use it. To perform the calculations.

[0099] From a software perspective, based on the aforementioned embodiments of the pulse signal motion interference filtering method, this application also provides an embodiment of a pulse signal motion interference filtering device for implementing the aforementioned pulse signal motion interference filtering method, see [link to relevant documentation]. Figure 6 The pulse signal motion interference filtering device specifically includes the following components:

[0100] The signal acquisition module 10 is used to illuminate the skin of a target human body that is currently in motion with a green light source in real time in order to collect the corresponding pulse signal from the target human body in real time.

[0101] The preprocessing module 20 is used to preprocess the acquired pulse signal.

[0102] The interference filtering module 30 is used to adaptively filter the preprocessed pulse signal based on the acceleration data of the target human body collected in real time, so as to filter out the motion artifact interference of the pulse signal.

[0103] The pulse signal motion interference filtering device provided in this application can be used to execute the processing flow of the pulse signal motion interference filtering method embodiment described above. Its function will not be repeated here, but can be referred to the detailed description of the pulse signal motion interference filtering method embodiment described above.

[0104] The pulse signal motion interference filtering device can perform the filtering of pulse signal motion interference in the controller, or in another practical application, all operations can be completed in the client device. The specific choice depends on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor for the specific processing of pulse signal motion interference filtering.

[0105] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0106] The server and the client device can communicate using any suitable network protocol, including those not yet developed as of the date of this application. Such network protocols may include, for example, TCP / IP, UDP / IP, HTTP, HTTPS, etc. Furthermore, such network protocols may also include RPC (Remote Procedure Call Protocol) and REST (Representational State Transfer Protocol) protocols used on top of the aforementioned protocols.

[0107] As can be seen from the above description, the pulse signal motion interference filtering device provided in this application embodiment can effectively improve the accuracy and effectiveness of filtering motion interference from the pulse signal of a person in motion without requiring a large amount of data, with a simple calculation process and without requiring multiple PPG sensors. It can also effectively improve the real-time performance of pulse signal motion interference filtering, thereby effectively improving the accuracy and reliability of heart rate measurement based on the pulse signal after motion interference filtering.

[0108] From a software perspective, based on the aforementioned embodiments of the pulse signal motion interference filtering method, this application also provides an embodiment of a pulse signal motion interference filtering system, see [link to relevant documentation]. Figure 7 The pulse signal motion interference filtering system specifically includes the following components:

[0109] Microcontroller 1, PPG sensor 2, infinite impulse response filter 3 and accelerometer 4, which are respectively connected to the microcontroller 1 in communication;

[0110] The microcontroller unit 1 is used to execute the pulse signal motion interference filtering method described in the foregoing embodiments;

[0111] The microcontroller unit 1 controls the PPG sensor 2 to use a green light source to illuminate the skin of the target human body currently in motion in real time so as to collect the corresponding pulse signal from the target human body in real time.

[0112] The microcontroller unit 1 controls the infinite impulse response filter 3 to perform baseline data and high-frequency noise removal processing on the acquired pulse signal, respectively.

[0113] The microcontroller unit 1 controls the accelerometer 4 to collect the acceleration data of the target human body in real time.

[0114] As can be seen from the above description, the pulse signal motion interference filtering system provided in this application embodiment can effectively improve the accuracy and effectiveness of filtering motion interference from the pulse signal of a person in motion without requiring a large amount of data, with a simple calculation process and without requiring multiple PPG sensors. It can also effectively improve the real-time performance of pulse signal motion interference filtering, thereby effectively improving the accuracy and reliability of heart rate measurement based on the pulse signal after motion interference filtering.

[0115] To further illustrate this solution, and to reduce the interference of motion artifacts and other noise on the PPG waveform during real-time data acquisition by the photoelectric sensor, thus facilitating subsequent heart rate variability (HRV) processing of the PPG data, this application also provides a specific application example of a pulse signal motion interference filtering method, which includes the following:

[0116] (a) Green light source selection

[0117] The green light signal has a large amplitude and low noise, meaning it has a high signal-to-noise ratio.

[0118] A high signal-to-noise ratio makes detection easier and reduces the impact of some interferences (such as the wristband / watch not fitting properly or the person being in motion).

[0119] (ii) Removing motion artifacts during data acquisition

[0120] Although green light provides a high signal-to-noise ratio (SNR) for detection, motion artifacts are still unavoidable when the tester is moving. Therefore, algorithms are needed to filter the data.

[0121] 1. High-pass and low-pass processing

[0122] An infinite impulse response (IIR) filter is used to perform a 1Hz high-pass processing on the pulse signal to remove baseline data. Then, an 8Hz low-pass IIR filter is used to remove some high-frequency noise.

[0123] 2. LMS Adaptive Filtering

[0124] This step requires ACC data to estimate motion artifacts.

[0125] First, we need to understand what motion artifacts are. Green light detection primarily measures the relative difference in blood volume between the skin and capillaries. When we are stationary during the test, the total volume of capillaries changes due to the heartbeat. However, if we move, the capillaries are easily affected by motion. For example, sudden acceleration can cause the blood to be subjected to forces from surrounding tissues, thus altering its volume. Additionally, sudden increases in acceleration during movement, along with the force exerted by jewelry or other equipment on the skin, can also lead to changes in blood volume.

[0126] The ACC value is a measure of the changes in acceleration in all directions during real-time motion. We then use the ACC value to estimate the changes in blood volume caused by motion, and by subtracting the change in blood volume caused by motion from the real-time PPG value, we can remove interference.

[0127] In summary, the application examples of this application can effectively improve the accuracy and effectiveness of filtering motion interference from the pulse signal of a person in motion, without requiring a large amount of data, a simple calculation process, or multiple PPG sensors. It can also effectively improve the real-time performance of filtering motion interference from the pulse signal, thereby effectively improving the accuracy and reliability of heart rate measurement based on the pulse signal after filtering motion interference.

[0128] This application also provides an electronic device (i.e., a computer device), which may include a processor, a memory, a receiver, and a transmitter. The processor is used to execute the pulse signal motion interference filtering method mentioned in the above embodiments. The processor and memory can be connected via a bus or other means, taking a bus connection as an example. The receiver can be connected to the processor and memory via wired or wireless means.

[0129] The processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0130] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the pulse signal motion interference filtering method in the embodiments of this application. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the pulse signal motion interference filtering method in the above method embodiments.

[0131] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0132] The one or more modules are stored in the memory, and when executed by the processor, they perform the pulse signal motion interference filtering method in the embodiment.

[0133] In some embodiments of this application, the user equipment may include a processor, a memory, and a transceiver unit. The transceiver unit may include a receiver and a transmitter. The processor, memory, receiver, and transmitter may be connected via a bus system. The memory is used to store computer instructions, and the processor is used to execute the computer instructions stored in the memory to control the transceiver unit to send and receive signals.

[0134] As one implementation method, the functions of the receiver and transmitter in this application can be implemented by transceiver circuits or dedicated transceiver chips, and the processor can be implemented by dedicated processing chips, processing circuits or general-purpose chips.

[0135] As another implementation approach, the server provided in this application embodiment can be implemented using a general-purpose computer. That is, the program code implementing the processor, receiver, and transmitter functions is stored in memory, and the general-purpose processor implements the processor, receiver, and transmitter functions by executing the code in memory.

[0136] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned method for filtering motion interference in pulse signals. The computer-readable storage medium can be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.

[0137] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave.

[0138] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0139] In this application, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0140] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to the embodiments of this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for filtering motion interference in pulse signals, characterized in that, include: A green light source is used to illuminate the skin of a target human body that is currently in motion in real time in order to collect the corresponding pulse signal from the target human body in real time. The acquired pulse signals are preprocessed; Based on the real-time collected acceleration data of the target human body, the preprocessed pulse signal is adaptively filtered to remove motion artifact interference from the pulse signal. The method of adaptively filtering the preprocessed pulse signal based on the real-time acquired acceleration data of the target human body to remove motion artifact interference includes: Based on the real-time collected acceleration data of the target human body, the estimated value of the blood volume change caused by the movement of the target human body is determined; The estimated blood volume change is subtracted from the value corresponding to the preprocessed pulse signal to filter out motion artifact interference in the pulse signal, thus obtaining the corresponding target pulse signal.

2. The method for filtering motion interference in pulse signals according to claim 1, characterized in that, The method of using a green light source to illuminate the skin of a target human body currently in motion in real time to collect the corresponding pulse signal from the target human body in real time includes: The PPG sensor, which emits green light, is controlled to illuminate the skin of a moving target human body in real time and to collect the corresponding pulse signal in real time.

3. The method for filtering motion interference in pulse signals according to claim 1, characterized in that, The preprocessing of the acquired pulse signal includes: The acquired pulse signals were processed for baseline data and high-frequency noise removal.

4. The method for filtering motion interference in pulse signals according to claim 3, characterized in that, The process of removing baseline data and high-frequency noise from the acquired pulse signals includes: The infinite impulse response filter is controlled to perform high-pass filtering on the acquired pulse signal to remove baseline data from the pulse signal, and low-pass filtering on the pulse signal to remove high-frequency noise from the pulse signal.

5. The method for filtering motion interference in pulse signals according to claim 1, characterized in that, Before performing adaptive filtering on the preprocessed pulse signal based on the real-time acquired acceleration data of the target human body to remove motion artifact interference from the pulse signal, the method further includes: The accelerometer is controlled to collect the acceleration data of the target human body in real time.

6. A filtering device for motion interference in pulse signals, characterized in that, include: The signal acquisition module is used to illuminate the skin of a target human body that is currently in motion with a green light source in real time in order to collect the corresponding pulse signal from the target human body in real time. A preprocessing module is used to preprocess the acquired pulse signals; The interference filtering module is used to adaptively filter the preprocessed pulse signal based on the acceleration data of the target human body collected in real time, so as to filter out the motion artifact interference of the pulse signal. The method of adaptively filtering the preprocessed pulse signal based on the real-time acquired acceleration data of the target human body to remove motion artifact interference includes: Based on the real-time collected acceleration data of the target human body, the estimated value of the blood volume change caused by the movement of the target human body is determined; The estimated blood volume change is subtracted from the value corresponding to the preprocessed pulse signal to filter out motion artifact interference in the pulse signal, thus obtaining the corresponding target pulse signal.

7. A filtering system for motion interference in pulse signals, characterized in that, include: The microcontroller unit, the PPG sensor, the infinite impulse response filter, and the accelerometer are all communicatively connected to the microcontroller unit. The microcontroller unit is used to perform the pulse signal motion interference filtering method according to any one of claims 1 to 5; The microcontroller controls the PPG sensor to use a green light source to illuminate the skin of the target human body currently in motion in real time so as to collect the corresponding pulse signal from the target human body in real time. The microcontroller unit controls the infinite impulse response filter to perform baseline data removal and high-frequency noise removal processing on the acquired pulse signal, respectively. The microcontroller unit controls the accelerometer to collect the acceleration data of the target human body in real time.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the pulse signal motion interference filtering method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for filtering motion interference of pulse signals as described in any one of claims 1 to 5.

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