Method for ECG electrocardio measurement and control based on smart ring

Abstract

Provided is a method for ECG electrocardio measurement and control based on a smart ring, which comprises the following steps: dynamically adjusting a pressure of a sensing electrode of a heart rate ECG sensor integrated inside a smart ring, collecting real-time electrocardiosignals of a skin surface of a user in real time, and transmitting the real-time electrocardiosignals of the skin surface of the user to a signal processing unit; performing dynamic electrocardiosignal adjustment on the real-time electrocardiosignals of the skin surface of the user to obtain dynamically adjusted electrocardiosignals of the skin surface of the user; performing electrocardiogram waveform diagram drawing and heart rate abnormality interval monitoring on the dynamically adjusted electrocardiosignals of the skin surface of the user to obtain an electrocardiosignal heart rate abnormality interval of the user; and performing abnormality change trend analysis and heart rate abnormality alarm control on the electrocardiosignal heart rate abnormality interval of the user to generate a user heart rate abnormality alarm response signal, so as to execute a corresponding user heart rate abnormality alarm notification. The method for ECG electrocardio measurement and control based on a smart ring can achieve high-precision and convenient electrocardiogram monitoring.

Description

ECG measurement and control method based on smart ring TECHNICAL FIELD

[0001] The present application relates to the technical field of ECG measurement control, and particularly relates to an ECG measurement and control method based on a smart ring. BACKGROUND

[0002] In the field of ECG health monitoring, electrocardiogram (ECG) is a key physiological signal widely used for detecting and analyzing the electrical activity of the heart. In the prior art, a smart ring is a portable device with a small design that makes it suitable for daily wear. It integrates high-precision electrodes and sensors in the smart ring, designs a reasonable signal acquisition and amplification circuit to improve the quality of the ECG signal, and applies advanced signal processing algorithms such as noise filtering and signal enhancement techniques to optimize the clarity and accuracy of the signal. It also includes real-time data transmission and cloud processing to transmit the collected ECG data to the cloud platform for in-depth analysis, so as to generate detailed ECG abnormality warning or suggestion reports. However, traditional ECG measurement methods usually rely on fixed equipment in hospitals or portable monitors, which can provide accurate ECG data, but still have problems of insufficient ECG measurement accuracy and comfort.

[0003] SUMMARY

[0004] Therefore, it is necessary to provide an ECG measurement and control method based on a smart ring to solve at least one of the above technical problems.

[0005] To achieve the above-mentioned purpose, an ECG measurement and control method based on a smart ring comprises the following steps:

[0006] Step S1: dynamically adjusting the sensing electrode pressure of the heart rate ECG sensor integrated in the smart ring to obtain a heart rate ECG electrode dynamic adjustment sensor; collecting the ECG signal on the surface of the user's skin in real time through the heart rate ECG electrode dynamic adjustment sensor to obtain the real-time ECG signal on the surface of the user's skin; and transmitting the real-time ECG signal on the surface of the user's skin to a signal processing unit through the wireless communication module built-in the smart ring;

[0007] Step S2: performing noise suppression and signal amplification processing on the real-time ECG signal on the surface of the user's skin through the signal processing unit to obtain an ECG amplified signal on the surface of the user's skin; obtaining the ECG resistance on the surface of the user's skin, and dynamically adjusting the ECG amplified signal on the surface of the user's skin based on the ECG resistance on the surface of the user's skin to obtain a dynamically adjusted ECG signal on the surface of the user's skin;

[0008] Step S3: ECG waveform mapping and heart rate abnormal interval monitoring are performed on the user's skin surface ECG dynamic adjustment signal to obtain a user ECG signal heart rate abnormal interval; abnormal change trend analysis is performed on the user ECG signal heart rate abnormal interval to obtain user heart rate abnormal interval signal change trend data;

[0009] Step S4: Based on the user heart rate abnormal interval signal change trend data, heart rate abnormal alarm control is performed on the user ECG signal heart rate abnormal interval to generate a user heart rate abnormal alarm response signal to execute corresponding user heart rate abnormal alarm notification.

[0010] Further, step S1 includes the following steps:

[0011] Step S11: Real-time monitoring of the contact pressure between the smart ring and the user's skin surface is performed by the contact pressure sensor built-in the smart ring to obtain contact pressure real-time change data between the smart ring and the user's skin surface;

[0012] Step S12: Based on the contact pressure real-time change data between the smart ring and the user's skin surface, dynamic adjustment of the sensing electrode pressure of the heart rate ECG sensor integrated inside the smart ring is performed to obtain a heart rate ECG electrode dynamic adjustment sensor;

[0013] Step S13: Real-time acquisition of the ECG signal of the user's skin surface is performed by the heart rate ECG electrode dynamic adjustment sensor to obtain real-time ECG signal of the user's skin surface;

[0014] Step S14: The real-time ECG signal of the user's skin surface is transmitted to the signal processing unit by the wireless communication module built-in the smart ring.

[0015] Further, step S12 includes the following steps:

[0016] Step S121: Time series change curve drawing is performed on the contact pressure real-time change data between the smart ring and the user's skin surface to generate a smart ring surface contact pressure time series change curve;

[0017] Step S122: Dynamic change analysis of the contact pressure is performed on the smart ring surface contact pressure time series change curve to obtain smart ring surface contact pressure dynamic change trend data;

[0018] Step S123: Based on the smart ring surface contact pressure dynamic change trend data, sensing electrode contact comfort evaluation analysis is performed on the heart rate ECG sensor integrated inside the smart ring to obtain sensing electrode contact comfort of the ECG sensor under different contact pressure change trends;

[0019] Step S124: electrode pressure dynamic compensation analysis is performed on the contact comfort of the sensing electrode of the ECG sensor under different contact pressure change trends, to obtain an electrode pressure dynamic compensation mechanism of the ECG sensor under different contact pressure change trends.

[0020] Step S125: the heart rate ECG sensor integrated in the smart ring is dynamically adjusted in terms of the electrode pressure under the electrode pressure dynamic compensation mechanism of the ECG sensor under different contact pressure change trends, to obtain a heart rate ECG electrode dynamic adjustment sensor.

[0021] Further, step S123 includes the following steps:

[0022] The contact pressure change trend condition design is performed on the smart ring surface contact pressure dynamic change trend data, to obtain different smart ring surface contact pressure change trend conditions;

[0023] The contact pressure distribution analysis is performed on the different smart ring surface contact pressure change trend conditions, to obtain contact pressure distribution condition data under different contact pressure change trend conditions;

[0024] The stress distribution quantitative analysis is performed on the corresponding sensing electrode contact area in the heart rate ECG sensor integrated in the smart ring in terms of the contact pressure distribution condition data under different contact pressure change trend conditions, to obtain contact area stress distribution numerical values under different contact pressure change trend conditions;

[0025] The sensing electrode contact influence moment evaluation analysis is performed on the corresponding sensing electrode contact area in the heart rate ECG sensor in terms of the contact area stress distribution numerical values under different contact pressure change trend conditions, to obtain the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends;

[0026] The contact comfort quantitative calculation is performed on the corresponding sensing electrode contact area in the heart rate ECG sensor in terms of the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends by using the electrode contact comfort calculation formula, to obtain the sensing electrode contact comfort of the ECG sensor under different contact pressure change trends.

[0027] Further, the sensing electrode contact influence moment evaluation analysis performed on the corresponding sensing electrode contact area in the heart rate ECG sensor in terms of the contact area stress distribution numerical values under different contact pressure change trend conditions includes the following steps:

[0028] The spatial coordinate system conversion is performed on the corresponding sensing electrode contact area in the heart rate ECG sensor, to obtain an ECG sensing electrode contact area spatial coordinate system;

[0029] mapping and matching the stress distribution numerical values of the contact area under the condition of different contact pressure change trends to the stress distribution position of the spatial coordinate system of the ECG sensing electrode contact area, to obtain the stress spatial distribution coordinate position of the contact area under the condition of different contact pressure change trends;

[0030] performing point-by-point spatial vector conversion processing on the stress distribution numerical values of the contact area under the condition of different contact pressure change trends and the stress spatial distribution coordinate position of the contact area, to obtain the contact stress spatial distribution vector at each stress distribution point of the contact area under the condition of different contact pressure change trends;

[0031] According to the contact stress spatial distribution vector at each stress distribution point of the contact area under the condition of different contact pressure change trends, the contact influence moment of the corresponding sensing electrode contact area in the heart rate ECG sensor is evaluated and calculated to obtain the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends.

[0032] Further, the electrode contact comfort degree calculation formula is specifically:

[0033] In the formula, S(p(x, y)) is the sensing electrode contact comfort degree of the ECG sensor under the contact pressure p(x, y), p(x, y) is the contact pressure on the coordinate point (x, y) in the sensing electrode contact area, x is the horizontal coordinate parameter in the sensing electrode contact area, y is the vertical coordinate parameter in the sensing electrode contact area, A is the total area of the sensing electrode contact area, a is the contact pressure influence weight parameter, u(p(x, y)) is the sensing electrode contact influence moment of the ECG sensor under the contact pressure p(x, y), F(p(x, y)) is the contact stress spatial distribution vector under the contact pressure p(x, y), k is the sensing electrode stiffness coefficient, x0 is the horizontal coordinate of the contact pressure deformation center point, y0 is the vertical coordinate of the contact pressure deformation center point, s is the contact pressure deformation distribution width, b is the contact pressure gradient influence weight parameter, and h is the correction coefficient of the sensing electrode contact comfort degree.

[0034] Further, step S2 includes the following steps:

[0035] Step S21: performing low-frequency interference elimination processing on the real-time electrocardio signal on the user's skin surface through the high-pass filter in the signal processing unit, to obtain the electrocardio low-frequency interference elimination signal on the user's skin surface;

[0036] Step S22: performing high-frequency noise elimination processing on the electrocardio low-frequency interference elimination signal on the user's skin surface through the low-pass filter in the signal processing unit, to obtain the electrocardio high-frequency noise elimination signal on the user's skin surface;

[0037] Step S23: baseline drift correction is performed on the user's skin surface electrocardio high frequency denoising signal to obtain a user's skin surface electrocardio drift correction signal;

[0038] Step S24: signal amplification processing is performed on the user's skin surface electrocardio drift correction signal to obtain a user's skin surface electrocardio amplification signal;

[0039] Step S25: the user's skin surface electrocardio resistance is obtained, and electrocardio signal dynamic adjustment is performed on the user's skin surface electrocardio amplification signal based on the user's skin surface electrocardio resistance to obtain a user's skin surface electrocardio dynamic adjustment signal.

[0040] Further, step S25 includes the following steps:

[0041] Step S251: electrocardio resistance measurement is performed on the user's skin surface by a high-precision resistance measurement instrument in the intelligent ring to obtain the user's skin surface electrocardio resistance;

[0042] Step S252: regression influence relationship mining analysis is performed on the user's skin surface electrocardio amplification signal based on the user's skin surface electrocardio resistance to obtain the regression influence relationship between the user's skin resistance and the skin surface electrocardio signal;

[0043] Step S253: dynamic adjustment coefficient quantification calculation is performed on the user's skin surface electrocardio resistance and the user's skin surface electrocardio amplification signal based on the regression influence relationship between the user's skin resistance and the skin surface electrocardio signal to obtain the signal adjustment coefficient of the user's skin surface electrocardio signal under different resistance values;

[0044] Step S254: electrocardio signal dynamic adjustment is performed on the user's skin surface electrocardio amplification signal according to the signal adjustment coefficient of the user's skin surface electrocardio signal under different resistance values to obtain a user's skin surface electrocardio dynamic adjustment signal.

[0045] Further, step S3 includes the following steps:

[0046] Step S31: electrocardio waveform drawing is performed on the user's skin surface electrocardio dynamic adjustment signal to generate a user's skin surface electrocardio signal waveform graph;

[0047] Step S32: R wave amplitude and QRS wave duration statistical analysis is performed on each time sequence point in the user's skin surface electrocardio signal waveform graph to obtain the electrocardio signal R wave amplitude and the electrocardio signal QRS wave duration at each time sequence point in the electrocardio signal waveform graph;

[0048] Step S33: Based on the R-wave amplitude of the electrocardiosignal at each time point in the electrocardiosignal waveform diagram and the QRS wave duration, the electrocardiosignal fluctuation calculation formula is used to quantitatively calculate the fluctuation value of each time point in the electrocardiosignal waveform diagram on the surface of the skin of the user, so as to obtain the electrocardiosignal fluctuation value at each time point in the electrocardiosignal waveform diagram.

[0049] The electrocardiosignal fluctuation calculation formula is specifically as follows:

[0050] In the formula, V(t) is the electrocardiosignal fluctuation value at time point t in the electrocardiosignal waveform diagram, t is a time point measurement parameter, A R (t) is the R-wave amplitude of the electrocardiosignal at time point t in the electrocardiosignal waveform diagram, μ R is the average change amplitude of the R-wave, σ R is the standard deviation of the R-wave amplitude, t0 is the starting time of the integral calculation range, T QRS is the QRS wave duration, t' is an integral time variable parameter, and ξ is a correction coefficient of the electrocardiosignal fluctuation value.

[0051] Step S34: According to the preset electrocardiosignal fluctuation abnormal threshold, the electrocardiosignal fluctuation value at each time point in the electrocardiosignal waveform diagram is compared and judged. When the electrocardiosignal fluctuation value is greater than or equal to the preset electrocardiosignal fluctuation abnormal threshold, the corresponding time point in the electrocardiosignal waveform diagram on the surface of the skin of the user is marked as an abnormal point. When the electrocardiosignal fluctuation value is less than the preset electrocardiosignal fluctuation abnormal threshold, the next time point in the electrocardiosignal waveform diagram on the surface of the skin of the user is continuously compared and judged until all the comparison and judgment are completed. Each abnormal point marked in the electrocardiosignal waveform diagram on the surface of the skin of the user is connected with adjacent abnormal points, and the user electrocardiosignal heart rate abnormal interval is obtained.

[0052] Step S35: The abnormal change trend of the user electrocardiosignal heart rate abnormal interval is analyzed, and the user heart rate abnormal interval signal change trend data is obtained.

[0053] Further, step S4 includes the following steps:

[0054] Step S41: The signal change abnormal alarm threshold of the user heart rate abnormal interval signal change trend data is set, and the user heart rate signal change trend abnormal alarm threshold is obtained.

[0055] Step S42: According to the user heart rate signal change trend abnormal alarm threshold, the heart rate abnormal alarm control of the user electrocardiosignal heart rate abnormal interval is performed, and the user heart rate abnormal alarm response signal is generated.

[0056] Step S43: the user heart rate anomaly alarm response signal is applied to the smart ring to formulate a heart rate signal anomaly alarm control strategy, so as to execute corresponding user heart rate anomaly alarm notification.

[0057] Advantages of the present application:

[0058] The dynamic adjustment of the sensor electrode pressure of the heart rate ECG sensor integrated in the smart ring is achieved by monitoring the contact pressure between the smart ring and the skin surface. According to the data feedback of the contact pressure, the sensor electrode pressure can be adjusted in real time to ensure the optimal contact state with the skin surface. The dynamic adjustment function can effectively respond to changes in the wearer's activity state, body size, or skin condition, avoiding data errors caused by insufficient or excessive pressure. By adjusting the electrode pressure, the sensor can maintain high-quality signal transmission under various conditions, improving the accuracy of electrocardiogram and heart rate data collection. This adaptive adjustment mechanism not only optimizes the data collection process but also enhances user experience, ensuring stable data quality during long-term wear and achieving more accurate and comfortable electrocardiogram monitoring and analysis, thereby improving the accuracy and comfort of the smart ring's electrocardiogram measurement. By using the heart rate ECG electrode dynamic adjustment sensor to collect real-time electrocardiogram signals from the user's skin surface, the heart rate ECG electrode can more accurately capture the electrocardiogram signals from the user's skin surface under the dynamically adjusted pressure. Real-time electrocardiogram signals provide important information about heart health, including heart rate, heart rate variability, and electrocardiogram changes. Through accurate capture of electrocardiogram signals, the smart ring can monitor the health status of the heart, detect abnormal heart rates or potential arrhythmias, and help users promptly understand their heart health status, providing a foundation for subsequent processing. At the same time, by using the built-in wireless communication module of the smart ring to transmit real-time electrocardiogram signals from the user's skin surface to the signal processing unit, seamless transmission and processing of electrocardiogram signals can be achieved. The smart ring effectively transmits real-time electrocardiogram signals to the signal processing unit through the built-in wireless communication module, ensuring the timeliness and accuracy of data transmission. The application of wireless communication technology allows users to conveniently perform daily activities without worrying about the limitations of data cables, and electrocardiogram signals can be quickly transmitted to the signal processing unit for further analysis. This real-time transmission and processing capability of electrocardiogram signals greatly improves the response speed and accuracy of health monitoring, providing users with more comprehensive and timely health feedback, thereby promoting personalized health management and the implementation of preventive measures.The user's skin surface real-time electrocardio signal after noise suppression is amplified, which can increase the amplitude of the electrocardio signal, improve the detectability of the signal, and make the electrocardio signal better in subsequent analysis and processing. Since the voltage level of the electrocardio signal itself is low, the amplification process can amplify the weak signal to a level that is easier to process, so that various electrocardiogram waveform features are more prominent, and the enhanced electrocardio signal can more clearly reflect the electrical activity of the heart, ensuring the accuracy of the signal and improving the resolution of the electrocardiogram. The skin surface resistance of the user is obtained, and the electrocardio signal of the user's skin surface is dynamically adjusted based on the electrocardio resistance of the user's skin surface, which can further improve the accuracy and consistency of the signal. Changes in skin resistance will affect the amplitude and shape of the electrocardio signal, so dynamic adjustment can correct the signal according to the actual resistance changes. This ensures that the signal remains consistent under different conditions, thereby improving the stability and comparability of the electrocardio signal. The dynamically adjusted electrocardio signal is more consistent with the characteristics of the actual electrocardiogram, making the electrocardiogram analysis result more reliable, thereby better protecting the user's heart health. Then, the electrocardio dynamic adjustment signal of the user's skin surface is plotted into an electrocardio waveform graph. The key of this step is to convert the original electrocardio signal into a visual waveform graph, which helps to clearly show the real-time changes of the electrocardio signal. The electrocardio waveform graph shows the electrical activity of the heart with each beat, and the R wave, QRS complex and other important waveform features in the electrocardiogram can be observed intuitively. This visualization makes the fluctuation trend and periodic changes of the electrocardio signal obvious at a glance. The plotted waveform graph is automatically detected for electrocardio signal abnormalities. When the electrocardio signal fluctuation value at the corresponding time point in the waveform graph exceeds the normal range, it is quickly identified as an abnormal point. At the same time, the labeled abnormal point is connected with adjacent abnormal points to form the corresponding heart rate abnormal interval. This process can significantly improve the intelligent level of electrocardiogram analysis, reduce the need for manual intervention, and timely detect the corresponding heart abnormality problem, providing accurate warning information for doctors. The abnormal change trend of the user's electrocardio signal heart rate abnormal interval is analyzed, which helps to deeply understand the dynamic changes of the heart rate health status. This analysis process can reveal the signal change trend of the heart rate abnormal interval, help to identify the potential abnormal patterns or development trend in the electrocardio signal, and through the analysis of these abnormal change trends, the depth and breadth of electrocardio signal analysis can be improved, providing more comprehensive scientific basis for subsequent abnormal monitoring and intervention.Finally, by combining the user's heart rate abnormal interval signal trend data to control the user's heart rate abnormal interval, this process can quickly identify the heart rate abnormality by monitoring the user's ECG signal in real time and comparing it with the abnormal alarm mechanism, so as to immediately respond to generate the corresponding abnormal alarm response signal. This timely response can provide timely health feedback to the user, help to take intervention measures in the early stage of heart rate abnormality, and reduce the potential health risk. This abnormal alarm control mechanism can effectively reduce false alarms, accurately filter out real abnormal situations by combining the ECG signal changes with specific alarm mechanisms, and reduce the user's distress from frequent and irrelevant alarms, thereby providing strong data support for the subsequent heart rate abnormality alarm notification process. The alarm response signal is combined with the smart ring, so that the user can receive real-time notification of heart rate abnormalities by wearing the ring. This immediate feedback mechanism can ensure that the user is quickly reminded when a health problem occurs, so as to take necessary action. The smart ring can integrate multiple alarm methods, such as vibration, sound prompt or visual display. Such diversified alarm strategies can meet the needs of different users and improve the effectiveness of heart rate abnormality alarm and user experience, so as to provide targeted suggestions and notifications to help users better manage their health status. BRIEF DESCRIPTION OF DRAWINGS

[0059] Other features, objects, and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments thereof, read in conjunction with the accompanying drawings:

[0060] Fig. 1 is a schematic diagram of the steps of the ECG heart rate measurement and control method based on the smart ring according to the present application;

[0061] Fig. 2 is a detailed schematic diagram of step S1 in Fig. 1;

[0062] Fig. 3 is a detailed schematic diagram of step S15 in Fig. 2. DETAILED DESCRIPTION

[0063] The technical method of the present application will be described in detail below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0064] Further, the accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application. In the drawings:

[0065] It should be understood that, although terms such as "first", "second", and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the scope of the example embodiments. The term "and / or" as used herein encompasses any and all combinations of one or more of the associated listed items.

[0066] To achieve the above object, referring to FIGS. 1 to 3, the present application provides an ECG electrocardiogram measurement and control method based on a smart ring, which comprises the following steps:

[0067] Step S1: dynamically adjusting the sensing electrode pressure of the heart rate ECG sensor integrated inside the smart ring to obtain a heart rate ECG electrode dynamic adjustment sensor; collecting the electrocardio signals on the surface of the user's skin in real time through the heart rate ECG electrode dynamic adjustment sensor to obtain the real-time electrocardio signals on the surface of the user's skin; and transmitting the real-time electrocardio signals on the surface of the user's skin to the signal processing unit through the wireless communication module built-in the smart ring.

[0068] Step S2: performing noise suppression and signal amplification processing on the real-time electrocardio signals on the surface of the user's skin through the signal processing unit to obtain the electrocardio amplified signals on the surface of the user's skin; obtaining the electrocardio resistance on the surface of the user's skin, and performing electrocardio signal dynamic adjustment on the electrocardio amplified signals on the surface of the user's skin based on the electrocardio resistance on the surface of the user's skin to obtain the electrocardio dynamic adjustment signals on the surface of the user's skin.

[0069] Step S3: performing electrocardio waveform drawing and heart rate abnormal interval monitoring on the electrocardio dynamic adjustment signals on the surface of the user's skin to obtain the heart rate abnormal interval of the user's electrocardio signals; and performing abnormal change trend analysis on the heart rate abnormal interval of the user's electrocardio signals to obtain the signal change trend data of the heart rate abnormal interval of the user.

[0070] Step S4: based on the user heart rate abnormal interval signal change trend data, the user heart rate abnormal interval is controlled to generate a user heart rate abnormal alarm response signal to execute corresponding user heart rate abnormal alarm notification.

[0071] In the embodiment of the present application, please refer to the step flow diagram of the ECG electrocardiogram measurement and control method based on the smart ring shown in Figure 1. In this example, the ECG electrocardiogram measurement and control method based on the smart ring includes the following steps:

[0072] Step S1: dynamically adjust the sensing electrode pressure of the heart rate ECG sensor integrated in the smart ring to obtain a heart rate ECG electrode dynamic adjustment sensor; real-time collect the electrocardiogram signal on the surface of the user's skin through the heart rate ECG electrode dynamic adjustment sensor to obtain the real-time electrocardiogram signal on the surface of the user's skin; and transmit the real-time electrocardiogram signal on the surface of the user's skin to the signal processing unit through the wireless communication module built-in the smart ring.

[0073] In the embodiment of the present application, the contact pressure sensor built-in the smart ring is used to monitor the contact pressure change between the smart ring and the user's skin surface in real time. The working principle of the contact pressure sensor is based on capacitive or piezoelectric technology, which can measure and record the pressure change in real time. The circuit built-in the sensor can detect the pressure change on the surface of the sensor, and dynamically adjust the sensing electrode pressure of the heart rate ECG sensor integrated inside the smart ring by combining the previously monitored contact pressure change between the smart ring and the user's skin surface. The specific operation includes transmitting the contact pressure change to the control unit, which analyzes the data and calculates the necessary adjustment amount. Based on these calculation results, the control unit drives the electrode adjustment mechanism to adjust the pressure of the heart rate ECG sensor electrode through a micro motor or a servo mechanism. The adjustment of the electrode pressure aims to ensure that it is in close contact with the skin surface, so that it can effectively capture the electrocardio signal and ensure that the heart rate ECG sensor can maintain the best contact state when collecting the electrocardio signal, thereby obtaining the heart rate ECG electrode dynamic adjustment sensor. By using the heart rate ECG electrode dynamic adjustment sensor obtained after dynamic adjustment to collect the electrocardio signal of the user's skin surface in real time, the heart rate ECG sensor is equipped with high-sensitivity electrodes for sensing the electrophysiological signals on the skin surface. The electrodes of the sensor record the electrical activity of the user's heart and convert these signals into electrical signals, thereby obtaining the real-time electrocardio signal of the user's skin surface. Then, the real-time electrocardio signal of the user's skin surface obtained through real-time monitoring is transmitted to the signal processing unit by using the wireless communication module (including Bluetooth, Wi-Fi or other low-power wireless technology) built-in the smart ring, which is used for wireless transmission of the electrocardio signal. At the beginning of the signal transmission process, the electrocardio signal is packaged into a data packet and sent through a wireless communication protocol. The data packet is transmitted in the wireless network until it reaches the signal processing unit. After the signal processing unit receives the data, it unpacks and recovers the data for further analysis and processing.

[0074] Step S2: The real-time electrocardio signal of the user's skin surface is processed by the signal processing unit to suppress noise and amplify the signal, to obtain the electrocardio amplified signal of the user's skin surface; the electrocardio resistance of the user's skin surface is obtained, and the electrocardio amplified signal of the user's skin surface is dynamically adjusted based on the electrocardio resistance of the user's skin surface to obtain the electrocardio dynamically adjusted signal of the user's skin surface;

[0075] In the embodiment of the present application, the real-time ECG signal on the user's skin surface obtained through real-time monitoring is subjected to interference elimination processing by using a high-pass filter in the signal processing unit, a suitable cut-off frequency (for example, 0.5 Hz) is selected to remove interference below the frequency, and the signal obtained after high-pass filter processing is the ECG signal after low-frequency interference elimination. This signal can effectively reduce interference caused by movement or other low-frequency sources. The real-time ECG signal on the user's skin surface after low-frequency interference elimination is subjected to high-frequency noise elimination processing by using a low-pass filter in the signal processing unit. A low-pass filter is applied to remove high-frequency noise in the signal, and the cut-off frequency of the low-pass filter is usually between 20 Hz and 50 Hz. This can be achieved by designing a filter with appropriate bandwidth and attenuation characteristics, ensuring that the main features of the ECG signal are retained while high-frequency noise is removed. Thus, high-frequency noise caused by environmental electromagnetic interference or electromyographic signals is removed. The real-time ECG signal on the user's skin surface after noise elimination is subjected to signal amplification processing by using an amplifier circuit or a digital signal processing method. A suitable gain value (for example, 10 to 1000 times) is selected, and the signal is amplified by the amplifier circuit. If an analog amplifier such as an operational amplifier is used, a gain resistor network needs to be configured to set the gain value, thereby obtaining the user's skin surface ECG amplified signal. Then, the corresponding user's skin surface ECG resistance is obtained, which is usually measured in real time by a resistance sensor, and the user's skin surface ECG amplified signal obtained after amplification is dynamically adjusted according to the measured user's skin surface ECG resistance. Changes in resistance value will affect the amplitude and quality of the signal, so adjustments need to be made to compensate for these effects. In specific implementation, the amplitude of the signal can be adjusted according to the resistance value and a predefined compensation model (for example, a linear or nonlinear model). This can be calibrated by experimental data to ensure the accuracy of the adjustment, and it can be implemented in real-time systems to achieve real-time compensation of the signal. The adjusted signal is the ECG dynamic adjustment signal, which maintains good signal quality under various resistance conditions, ensuring the accuracy and reliability of ECG monitoring. Finally, the user's skin surface ECG dynamic adjustment signal is obtained.

[0076] Step S3: ECG waveform mapping and ECG rate abnormal interval monitoring are performed on the user's skin surface ECG dynamic adjustment signal to obtain the user's ECG signal rate abnormal interval; abnormal change trend analysis is performed on the user's ECG signal rate abnormal interval to obtain the user's ECG signal rate abnormal interval signal change trend data;

[0077] In the embodiment of the present application, the previously dynamically adjusted user skin surface electrocardio dynamic adjustment signal is drawn in real time waveform by using a special electrocardio waveform drawing tool (such as a digital oscilloscope or a special electrocardio drawing software), and in the drawing process, the electrocardio signal is converted into a waveform including P wave, QRS complex and T wave, etc., so as to draw and generate a corresponding user skin surface electrocardio signal waveform to show the timing change of the user electrocardio signal. At the same time, the image processing software is used to statistically analyze each timing point in the previously drawn and generated user skin surface electrocardio signal waveform, to identify and analyze the R wave peak value of each timing point, and the peak value detection algorithm is used to calculate the amplitude of the R wave, and for each timing point, the start and end positions of the QRS complex are used for statistics to quantitatively calculate the duration of the QRS complex, and according to the previously analyzed R wave amplitude and QRS complex duration, the specific fluctuation value of the electrocardio signal at each timing point is quantitatively calculated, and the previously quantitatively calculated electrocardio signal fluctuation value is compared and judged by using the pre-set electrocardio signal fluctuation abnormal threshold value, if the electrocardio signal fluctuation value at a certain timing point is greater than or equal to the threshold value, it means that the electrocardio signal fluctuation at the timing point is abnormal, then the timing point is marked as an abnormal point, otherwise the next timing point in the user skin surface electrocardio signal waveform is compared and judged, until all are compared, so as to compare the fluctuation value of each timing point one by one through the set threshold value, so as to mark all the points exceeding the threshold value, and the adjacent point connection processing of the marked abnormal points is carried out, if the corresponding abnormal points are adjacent, they are connected to form a continuous abnormal interval, at this time, all adjacent abnormal points are connected by using the connectivity analysis method (such as distance-based clustering analysis), so as to obtain the user electrocardio signal heart rate abnormal interval. Then, the previously determined user electrocardio signal heart rate abnormal interval is analyzed to identify the abnormal change trend by using the trend analysis algorithm (such as moving average method or regression analysis), so as to input the data of the abnormal interval into the trend analysis algorithm to statistically analyze the abnormal fluctuation trend, this process involves using the data visualization tool to generate a trend chart to analyze the fluctuation mode of the data to predict the future abnormal trend, all analysis operations are completed on the data processing platform to ensure the accuracy and visualization effect of the trend data, and finally the user heart rate abnormal interval signal change trend data is obtained.

[0078] Step S4: Based on the user heart rate abnormal interval signal change trend data, the user electrocardio signal heart rate abnormal interval is controlled to generate a user heart rate abnormal alarm response signal to execute a corresponding user heart rate abnormal alarm notification.

[0079] In the embodiment of the present application, the abnormal interval signal change trend data of the user's heart rate obtained by previous trend analysis is analyzed to determine the fluctuation trend range of the heart rate signal in the abnormal interval, and an abnormal signal change alarm threshold is set based on this trend range. When the signal change exceeds the threshold value, an abnormal alarm is triggered. Real-time monitoring of the heart rate change in the abnormal interval of the user's electrocardio signal is performed based on the previously set signal change abnormal alarm threshold. When the heart rate change in the abnormal interval of the user's electrocardio signal exceeds the preset threshold range during the collection process, an immediate heart rate abnormal alarm response signal is generated in response to the control. The specific implementation includes comparing the real-time collected electrocardio data with the set threshold value, using a data processing unit (such as a microcontroller or a computing unit) to analyze the data, and triggering an abnormal alarm signal if an abnormal fluctuation is detected. These signals can include audible and visual alarms, vibration notifications, or other preset warning methods, thereby generating a user heart rate abnormal alarm response signal. Then, the previously generated user heart rate abnormal alarm response signal is input into and applied to the control system of the smart ring to respond to the development of the corresponding abnormal alarm control strategy, which includes defining the alarm notification method (such as vibration, flashing indicator light, or sending to the paired device through Bluetooth), and the specific operation includes the smart ring receiving the abnormal alarm signal obtained from the heart rate monitoring and executing the predetermined alarm strategy based on this signal. For example, if the heart rate abnormal alarm signal indicates that the heart rate exceeds the set upper limit, the smart ring will emit continuous vibration or flashing light to attract the user's attention; if the heart rate is below the set lower limit, no corresponding warning will be given. The control strategy of the smart ring must be implemented through programming to ensure that appropriate alarm measures are taken in different types of abnormal situations, thereby executing the corresponding user heart rate abnormal alarm notification.

[0080] Further, step S1 includes the following steps:

[0081] Step S11: Real-time monitoring of the contact pressure between the smart ring and the user's skin surface is performed by the contact pressure sensor built-in the smart ring to obtain real-time contact pressure change data between the smart ring and the user's skin surface;

[0082] Step S12: Dynamic adjustment of the sensing electrode pressure of the heart rate ECG sensor integrated inside the smart ring is performed based on the real-time contact pressure change data between the smart ring and the user's skin surface to obtain a heart rate ECG electrode dynamic adjustment sensor;

[0083] Step S13: Real-time collection of the electrocardio signal of the user's skin surface is performed by the heart rate ECG electrode dynamic adjustment sensor to obtain real-time electrocardio signal of the user's skin surface;

[0084] Step S14: transmit the real-time ECG signal of the user's skin surface to the signal processing unit by using the wireless communication module built in the smart ring.

[0085] As an embodiment of the present application, referring to FIG. 2, a detailed step flow diagram of step S1 in FIG. 1 is shown. In this embodiment, step S1 includes the following steps:

[0086] Step S11: Real-time monitoring of the contact pressure between the smart ring and the user's skin surface by using the contact pressure sensor built in the smart ring to obtain the real-time change data of the contact pressure between the smart ring and the user's skin surface;

[0087] In the embodiment of the present application, the contact pressure between the smart ring and the user's skin surface is monitored in real time by using the contact pressure sensor built in the smart ring to ensure that the smart ring is correctly in contact with the skin surface when worn on the user's finger. The working principle of the contact pressure sensor is based on capacitive or piezoelectric technology, which can measure and record pressure changes in real time. The circuit built in the sensor detects the pressure changes on the surface of the sensor and converts these changes into electrical signals. The electrical signals are converted into digital data by an analog-to-digital converter for further processing. The real-time data acquisition module continuously records the changes in contact pressure, and finally obtains the real-time change data of the contact pressure between the smart ring and the user's skin surface.

[0088] Step S12: Dynamic adjustment of the sensing electrode pressure of the heart rate ECG sensor integrated inside the smart ring based on the real-time change data of the contact pressure between the smart ring and the user's skin surface to obtain a heart rate ECG electrode dynamic adjustment sensor;

[0089] In the embodiment of the present application, the sensing electrode pressure of the heart rate ECG sensor integrated inside the smart ring is dynamically adjusted based on the previously monitored real-time change data of the contact pressure between the smart ring and the user's skin surface. The specific operation includes transmitting the real-time change data of the contact pressure to the control unit, which analyzes the data and calculates the necessary adjustment amount. Based on these calculation results, the control unit drives the electrode adjustment mechanism to adjust the pressure of the heart rate ECG sensor electrode through a micro motor or a servo mechanism. The adjustment of the electrode pressure aims to ensure that it is in close contact with the skin surface, so that the ECG signal can be effectively captured. The data feedback in this adjustment process will continuously optimize the contact pressure of the electrode, ensuring that the heart rate ECG sensor can maintain the best contact state when collecting the ECG signal, and finally obtaining a heart rate ECG electrode dynamic adjustment sensor.

[0090] Step S13: Real-time acquisition of the ECG signal of the user's skin surface by the heart rate ECG electrode dynamic adjustment sensor to obtain the real-time ECG signal of the user's skin surface;

[0091] In the embodiment of the present application, the heart rate ECG electrode dynamic adjustment sensor is used to collect the real-time electrocardio signal on the user's skin surface through dynamic adjustment. The heart rate ECG sensor is internally equipped with a high-sensitivity electrode for sensing the electrophysiological signal on the skin surface. The electrode of the sensor records the electrical activity of the user's heart and converts the signal into an electrical signal to obtain the real-time electrocardio signal on the user's skin surface.

[0092] Step S14: The real-time electrocardio signal on the user's skin surface is transmitted to the signal processing unit by using the wireless communication module built in the smart ring.

[0093] In the embodiment of the present application, the real-time electrocardio signal on the user's skin surface obtained through real-time monitoring is transmitted to the signal processing unit by using the wireless communication module (including Bluetooth, Wi-Fi or other low-power wireless technology) built in the smart ring for wireless transmission of the electrocardio signal. At the beginning of the signal transmission process, the electrocardio signal is packaged into a data packet and sent through a wireless communication protocol. The data packet is transmitted in the wireless network until it reaches the signal processing unit. After the signal processing unit receives the data, it is unpacked and recovered for further analysis and processing.

[0094] Further, step S12 includes the following steps:

[0095] Step S121: Time sequence change curve drawing is performed on the real-time change data of the contact pressure between the smart ring and the user's skin surface to generate a time sequence change curve of the contact pressure on the surface of the smart ring;

[0096] Step S122: Dynamic change analysis of the contact pressure is performed on the time sequence change curve of the contact pressure on the surface of the smart ring to obtain dynamic change trend data of the contact pressure on the surface of the smart ring;

[0097] Step S123: Based on the dynamic change trend data of the contact pressure on the surface of the smart ring, the sensing electrode contact comfort of the heart rate ECG sensor integrated in the smart ring is evaluated and analyzed to obtain the sensing electrode contact comfort of the ECG sensor under different contact pressure change trends;

[0098] Step S124: Electrode pressure dynamic compensation analysis is performed on the sensing electrode contact comfort of the ECG sensor under different contact pressure change trends to obtain the electrode pressure dynamic compensation mechanism of the ECG sensor under different contact pressure change trends;

[0099] Step S125: The sensing electrode pressure of the heart rate ECG sensor integrated in the smart ring is dynamically adjusted according to the electrode pressure dynamic compensation mechanism of the ECG sensor under different contact pressure change trends to obtain a heart rate ECG electrode dynamic adjustment sensor.

[0100] As an embodiment of the present invention, referring to FIG3, which is a detailed flowchart of step S12 in FIG2, step S12 in this embodiment includes the following steps:

[0101] Step S121: Plot the time-series variation curve of the real-time change data of the contact pressure between the smart ring and the user's skin surface to generate the time-series variation curve of the contact pressure on the surface of the smart ring.

[0102] In this embodiment of the invention, a time-series change curve is plotted on the real-time change data of the contact pressure between the smart ring and the user's skin surface obtained from previous analysis using a data visualization tool. The processed data is then input into plotting software or visualization tools to plot the time-series change curve of the pressure. This curve represents the change of the contact pressure between the smart ring and the skin during the wearing process, and finally, the time-series change curve of the contact pressure on the surface of the smart ring is generated.

[0103] Step S122: Perform dynamic change analysis on the contact pressure time-series change curve of the smart ring surface to obtain dynamic change trend data of the smart ring surface contact pressure;

[0104] In this embodiment of the invention, the time-series variation curve of the surface contact pressure of the smart ring is analyzed by using data analysis tools (such as data analysis libraries in MATLAB or Python), including calculating the mean, standard deviation, peak value and frequency of pressure changes, and identifying the main trends and patterns of pressure changes, such as periodicity and sudden changes in pressure fluctuations, by using time series analysis methods (such as Fourier transform or wavelet analysis), and finally obtaining the dynamic trend data of the surface contact pressure of the smart ring.

[0105] Step S123: Based on the dynamic change trend data of the surface contact pressure of the smart ring, evaluate and analyze the contact comfort of the sensing electrode of the heart rate ECG sensor integrated inside the smart ring, and obtain the contact comfort of the sensing electrode of the ECG sensor under different contact pressure change trends.

[0106] In the embodiment of the present application, the contact pressure dynamic change trend of the smart ring in the actual wearing condition obtained through previous analysis is used to design the corresponding contact pressure change trend conditions, which include but are not limited to pressure change rate, fluctuation amplitude and periodic change, etc., and the contact pressure distribution on the surface of the smart ring is analyzed in detail after obtaining different contact pressure change trend conditions by using professional pressure distribution analysis tools (such as the contact pressure module in ANSYS Workbench), to analyze and record the pressure distribution conditions under each condition through simulation analysis, at the same time, the corresponding sensor electrode contact area inside the heart rate ECG sensor integrated in the smart ring is analyzed in detail for stress distribution statistical analysis by using stress analysis software (such as ABAQUS or Altair HyperWorks) combined with the contact pressure distribution conditions obtained through previous analysis, to quantitatively calculate the specific values of the stress distribution of the electrode contact area under different pressure conditions, and the contact influence moment of the corresponding sensor electrode contact area inside the heart rate ECG sensor is evaluated and calculated by using mechanical analysis tools (such as Simulink of MATLAB or Mathematica) combined with the stress distribution data obtained through previous analysis, to analyze the moment effect of each electrode contact point according to the stress distribution data obtained through previous analysis, thereby quantitatively calculating the contact influence moment of the sensor under different pressure conditions, and the contact comfort of the corresponding sensor electrode contact area inside the heart rate ECG sensor is quantitatively calculated according to the contact influence moment obtained through previous quantitative calculation, to quantitatively calculate the comfort performance of the ECG sensor under different pressure changes, and finally obtain the sensor electrode contact comfort of the ECG sensor under different contact pressure change trends.

[0107] Step S124: electrode pressure dynamic compensation analysis is performed on the sensor electrode contact comfort of the ECG sensor under different contact pressure change trends, to obtain the electrode pressure dynamic compensation mechanism of the ECG sensor under different contact pressure change trends.

[0108] In the embodiment of the present application, the size of the sensor electrode contact comfort of the ECG sensor under different contact pressure change trends obtained through previous quantitative calculation is combined to study the compensation mechanism of the electrode under different contact pressures, to define a dynamic compensation algorithm which should adjust the electrode position or pressure of the ECG sensor to maintain stable comfort, and the compensation mechanism can adjust the electrode pressure in real time, which needs to consider the rate and amplitude of pressure change and calculate the required adjustment amount to compensate the pressure size of the electrode at both ends of the ECG sensor, and finally obtain the electrode pressure dynamic compensation mechanism of the ECG sensor under different contact pressure change trends.

[0109] Step S125: dynamically adjusting the electrode pressure of the heart rate ECG sensor integrated in the smart ring according to the electrode pressure dynamic compensation mechanism under different contact pressure change trends to obtain a heart rate ECG electrode dynamic adjustment sensor.

[0110] In the embodiment of the present application, by automatically adjusting the electrode pressure of the heart rate ECG sensor integrated in the smart ring according to the real-time pressure data obtained by previous monitoring and the analysis obtained electrode pressure dynamic compensation mechanism under different contact pressure change trends, the compensation mechanism should be able to respond to the input of the pressure sensor in real time and adjust, and verify the performance of the dynamically adjusted sensor under different contact pressure conditions, record the comfort data after adjustment, and evaluate whether it meets the requirements, if not, continue to adjust the pressure between the sensing electrodes, otherwise, do not need to adjust, and finally adjust to obtain a heart rate ECG electrode dynamic adjustment sensor.

[0111] Further, step S123 comprises the following steps:

[0112] The contact pressure change trend condition design is performed on the smart ring surface contact pressure dynamic change trend data to obtain different smart ring surface contact pressure change trend conditions.

[0113] In the embodiment of the present application, in order to accurately simulate and analyze the contact pressure dynamic change trend of the smart ring in the actual wearing condition, it is necessary to design the contact pressure change trend condition, and set different contact pressure load conditions by using finite element analysis software (such as ANSYS or COMSOL Multiphysics), including static and dynamic pressure conditions. The static pressure condition can be simulated by setting a constant contact pressure, while the dynamic pressure condition can be simulated by applying a time-varying pressure load to simulate the pressure fluctuation in the actual wearing process. These conditions include but are not limited to pressure change rate, fluctuation amplitude and periodic change, etc. Finally, different smart ring surface contact pressure change trend conditions are obtained.

[0114] Preferably, the contact pressure distribution analysis is performed on different smart ring surface contact pressure change trend conditions to obtain contact pressure distribution condition data under different contact pressure change trend conditions.

[0115] In the embodiment of the present application, by obtaining different contact pressure change trend conditions, the contact pressure distribution of the smart ring surface is analyzed in detail by using professional pressure distribution analysis tools (such as the contact pressure module in ANSYS Workbench), to calculate the pressure distribution map under each contact pressure change trend condition through simulation analysis. The tool generates a contact pressure distribution map showing the pressure intensity and its change trend at different positions. Special attention should be paid to the local high pressure area during the analysis, as these areas will affect the performance and comfort of the sensor. Thus, the pressure distribution data under each condition is recorded, and finally the contact pressure distribution data under different contact pressure change trend conditions is obtained.

[0116] Preferably, the stress distribution of the corresponding sensing electrode contact area in the heart rate ECG sensor integrated inside the smart ring is quantitatively analyzed according to the contact pressure distribution data under different contact pressure change trend conditions, to obtain the contact area stress distribution values under different contact pressure change trend conditions.

[0117] In the embodiment of the present application, by combining the contact pressure distribution data under different contact pressure change trend conditions obtained from the previous analysis, stress analysis software (such as ABAQUS or Altair HyperWorks) is used to perform detailed stress distribution statistical analysis on the corresponding sensing electrode contact area in the heart rate ECG sensor integrated inside the smart ring. The geometric model of the electrode inside the heart rate ECG sensor is set, and the stress distribution of the electrode contact area is calculated in combination with the pressure distribution data obtained from the previous analysis. This analysis can reveal the stress concentration of the electrode contact area under different pressure conditions, and provide numerical data of the stress distribution of each area. Finally, the contact area stress distribution values under different contact pressure change trend conditions are obtained.

[0118] Preferably, the sensing electrode contact influence moment of the corresponding sensing electrode contact area in the heart rate ECG sensor is evaluated and analyzed based on the contact area stress distribution values under different contact pressure change trend conditions, to obtain the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trend conditions.

[0119] In the embodiment of the present application, the contact influence moment of the corresponding sensing electrode contact area in the heart rate ECG sensor is evaluated and calculated by combining the stress distribution values of the contact area under different contact pressure change trends obtained by previous quantitative calculation using a mechanical analysis tool (such as MATLAB Simulink or Mathematica), to analyze the moment effect of each electrode contact point according to the stress distribution data obtained by previous analysis, and determine the specific influence of pressure on the function of the sensor. This evaluation includes calculating the acting moment of the sensor under different pressure conditions, and finally obtaining the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends.

[0120] Preferably, the contact comfort of the corresponding sensing electrode contact area in the heart rate ECG sensor is quantitatively calculated based on the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends using an electrode contact comfort calculation formula, to obtain the sensing electrode contact comfort of the ECG sensor under different contact pressure change trends.

[0121] In the embodiment of the present application, a suitable electrode contact comfort calculation formula is constructed by combining the contact pressure on the coordinate points in the sensing electrode contact area, the horizontal coordinate parameter, the vertical coordinate parameter, the total area, the contact pressure influence weight parameter, the sensing electrode contact influence moment, the contact stress space distribution vector, the sensing electrode stiffness coefficient, the contact pressure deformation center point horizontal coordinate, the contact pressure deformation center point vertical coordinate, the contact pressure deformation distribution width, the contact pressure gradient influence weight parameter and related parameters, to quantitatively calculate the contact comfort of the corresponding sensing electrode contact area in the heart rate ECG sensor under each condition, and finally obtain the sensing electrode contact comfort of the ECG sensor under different contact pressure change trends.

[0122] Further, the evaluation and analysis of the sensing electrode contact influence moment of the corresponding sensing electrode contact area in the heart rate ECG sensor based on the contact area stress distribution values under different contact pressure change trends includes the following steps:

[0123] The spatial coordinate system of the corresponding sensing electrode contact area in the heart rate ECG sensor is converted to obtain the spatial coordinate system of the ECG sensing electrode contact area;

[0124] In the embodiment of the present application, the spatial coordinates of the corresponding sensing electrode contact area in the previously determined heart rate ECG sensor are extracted by using a high-precision three-dimensional scanner, to obtain spatial coordinate data of the electrode contact area, and the spatial coordinate data obtained after scanning is imported into a coordinate conversion program by using a software tool, a conversion matrix is set according to the geometric parameters of the sensor and the actual working environment, the original scanning coordinate system is converted into a specified spatial coordinate system, the conversion takes into account the installation angle, offset and other factors of the sensor, ensures that the obtained contact area coordinate system can accurately represent the spatial position of the electrode contact area in the actual work, and finally obtains the ECG sensing electrode contact area spatial coordinate system.

[0125] Preferably, the ECG sensing electrode contact area spatial coordinate system is stress distribution position mapping matched based on the contact area stress distribution values under different contact pressure change trend conditions, to obtain the contact area stress spatial distribution coordinate positions under different contact pressure change trend conditions.

[0126] In the embodiment of the present application, the contact area stress distribution values under different contact pressure change trend conditions obtained by previous analysis are simulated by using a finite element analysis software, to simulate the stress distribution under different pressure conditions, and the simulated stress data is matched with the previously converted ECG sensing electrode contact area spatial coordinate system in terms of stress distribution position, to convert the stress distribution values into the spatial coordinate positions of the contact area by using a data mapping algorithm, which involves associating the stress distribution data with each point in the spatial coordinate system, thereby generating a stress spatial distribution coordinate position map under each pressure condition, so that the stress change under different pressure conditions can be directly observed, and finally the contact area stress spatial distribution coordinate positions under different contact pressure change trend conditions are obtained.

[0127] Preferably, the contact area stress distribution values under different contact pressure change trend conditions and the contact area stress spatial distribution coordinate positions are processed by point-by-point spatial vector conversion, to obtain the contact stress spatial distribution vector at each stress distribution point in the contact area under different contact pressure change trend conditions.

[0128] In the embodiment of the present application, the stress distribution values and the stress spatial distribution coordinate positions of the contact area under different contact pressure change trend conditions obtained through previous analysis are processed point by point, so as to convert the stress values of each stress distribution point into a spatial vector by using a vector conversion algorithm. In the specific implementation process, the stress value and the spatial coordinate of each point are determined, and then the vector conversion method is applied to convert the spatial stress vector of each point (that is, the stress distribution values and the coordinate positions of each stress distribution point are integrated into a spatial vector). The tool uses numerical calculation software and vector analysis tools to ensure that the stress vector of each point is accurately represented in space, and finally the contact stress spatial distribution vector of each stress distribution point in the contact area under different contact pressure change trend conditions is obtained.

[0129] Preferably, the contact stress spatial distribution vector of each stress distribution point in the contact area under different contact pressure change trend conditions is used to evaluate and calculate the contact influence moment of the corresponding sensing electrode contact area in the heart rate ECG sensor, so as to obtain the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends.

[0130] In the embodiment of the present application, the contact stress spatial distribution vector of each stress distribution point in the contact area under different contact pressure change trend conditions obtained through previous conversion is used to evaluate and calculate the contact influence moment of the corresponding sensing electrode contact area in the heart rate ECG sensor, so as to quantitatively calculate the influence moment of each stress point on the electrode contact area of the heart rate ECG sensor. The stress vector of each point is combined with the geometric shape of the electrode contact area by using mechanical and dynamic analysis tools, the contact moment of each stress point is calculated, and finally the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends is obtained.

[0131] Further, the electrode contact comfort degree calculation formula is specifically:

[0132] In the formula, S(p(x,y)) represents the contact comfort of the ECG sensor's sensing electrode under contact pressure p(x,y), p(x,y) represents the contact pressure at coordinate point (x,y) within the contact area of ​​the sensing electrode, x represents the abscissa parameter within the contact area of ​​the sensing electrode, y represents the ordinate parameter within the contact area of ​​the sensing electrode, A represents the total area of ​​the contact area of ​​the sensing electrode, α represents the contact pressure influence weighting parameter, u(p(x,y)) represents the contact influence torque of the ECG sensor's sensing electrode under contact pressure p(x,y), F(p(x,y)) represents the spatial distribution vector of contact stress under contact pressure p(x,y), k represents the sensing electrode stiffness coefficient, x0 represents the abscissa of the contact pressure deformation center point, y0 represents the ordinate of the contact pressure deformation center point, σ represents the width of the contact pressure deformation distribution, β represents the contact pressure gradient influence weighting parameter, and η represents the correction coefficient for the contact comfort of the sensing electrode.

[0133] This invention, through the use of a specific mathematical model and verification, derives a formula for calculating electrode contact comfort. This formula is used to quantitatively calculate the contact comfort of the corresponding sensing electrode contact area within a heart rate ECG sensor. The first part of this formula, α*p(x,y), reflects the direct impact of contact pressure p(x,y) on comfort. α is a weighting parameter for the influence of contact pressure; adjusting α can highlight or suppress the impact of contact pressure on comfort. The second part includes the influence of pressure gradient on comfort, and particularly the effect of the pressure change rate on comfort. This part is achieved through… The contributions of the pressure gradient and the second derivative of pressure are calculated. β is the gradient influence weighting parameter used to control the contribution of pressure changes to comfort. u(p(x,y)) represents the influence moment under contact pressure, which is calculated by the ratio of the contact stress spatial distribution vector F(p(x,y)) and the sensor electrode stiffness coefficient k to a Gaussian function. The product calculation shows that this Gaussian function describes the deformation distribution of contact pressure, and σ is the width of the deformation distribution, and x0 and y0 are the center points of the pressure deformation. This part helps to evaluate how the stress distribution in the contact area under different pressures affects comfort. In addition, by introducing a correction coefficient, it allows the calculation results to be adjusted in practical applications to better meet the actual situation or user needs, which provides a mechanism to adjust the comfort calculation results, so that the formula can adapt to different actual use environments and user preferences. By analyzing the comfort data under different contact pressure trends, we can identify weaknesses in the design, such as if the comfort is low under certain pressure conditions, we can optimize the contact comfort by adjusting the design parameters or materials. The electrode contact comfort calculation formula considers the contact pressure, pressure gradient, pressure deformation influence, and quantifies the influence moment through the Gaussian function, providing a systematic comfort evaluation method for ECG sensor design. This not only helps to understand the impact of pressure on comfort, but also allows for optimization of design through adjustment of relevant parameters, enabling products that better meet user needs. In summary, the formula fully considers the ECG sensor contact comfort S(p(x,y)) under contact pressure p(x,y), the contact pressure p(x,y) at the coordinate point (x,y) in the contact area of the sensor electrode, the horizontal coordinate parameter x in the contact area of the sensor electrode, the vertical coordinate parameter y in the contact area of the sensor electrode, the total area A of the contact area of the sensor electrode, the contact pressure influence weight parameter α, the contact influence moment u(p(x,y)) of the sensor electrode under contact pressure p(x,y), the contact stress space distribution vector F(p(x,y)) under contact pressure p(x,y), the sensor electrode stiffness coefficient k, the contact pressure deformation center point horizontal coordinate x0, the contact pressure deformation center point vertical coordinate y0, the contact pressure deformation distribution width σ, the contact pressure gradient influence weight parameter β, and the correction coefficient η of the contact comfort of the sensor electrode. Through the contact pressure p(x,y) at the coordinate point (x,y) in the contact area of the sensor electrode, the horizontal coordinate parameter x in the contact area of the sensor electrode, the vertical coordinate parameter y in the contact area of the sensor electrode, the contact stress space distribution vector F(p(x,y)) under contact pressure p(x,y), the sensor electrode stiffness coefficient k, the contact pressure deformation center point horizontal coordinate x0, the contact pressure deformation center point vertical coordinate y0, and the contact pressure deformation distribution width σ, a function relationship of the contact influence moment u(p(x,y)) of the ECG sensor under contact pressure p(x,y) is formed According to the interrelation between the contact comfort S(p(x,y)) of the ECG sensor under contact pressure p(x,y) and the above parameters, a function relationship is formed

[0134] The formula can realize the contact comfort quantitative calculation process of the corresponding sensing electrode contact area in the heart rate ECG sensor, and the introduction of the correction coefficient η of the sensing electrode contact comfort can adjust according to the error in the calculation process, thereby improving the accuracy and applicability of the electrode contact comfort calculation formula.

[0135] Further, step S2 includes the following steps:

[0136] Step S21: The real-time ECG signal on the user's skin surface is subjected to low-frequency interference elimination processing by a high-pass filter in the signal processing unit, and a user's skin surface ECG low-frequency interference elimination signal is obtained.

[0137] In the embodiment of the application, the previously obtained real-time ECG signal on the user's skin surface is subjected to interference elimination processing by using a high-pass filter in the signal processing unit, and a suitable cutoff frequency (for example, 0.5 Hz) is selected to remove interference below the frequency. The design of the high-pass filter can be based on a digital filter or an analog filter, depending on the needs of the smart ring. In the implementation of a digital filter, FIR (Finite Impulse Response) or IIR (Infinite Impulse Response) filter structures are applied, and the high-pass characteristic is defined by designing filter coefficients. The filter can be designed and optimized by tools such as MATLAB. After high-pass filter processing, the obtained signal is the ECG signal after low-frequency interference elimination. This signal can effectively reduce interference caused by motion or other low-frequency sources, and finally obtain the user's skin surface ECG low-frequency interference elimination signal.

[0138] Step S22: The user's skin surface ECG low-frequency interference elimination signal is subjected to high-frequency noise elimination processing by a low-pass filter in the signal processing unit, and a user's skin surface ECG high-frequency noise elimination signal is obtained.

[0139] In the embodiment of the application, the user's skin surface ECG low-frequency interference elimination signal after low-frequency interference elimination is subjected to high-frequency noise elimination processing by using a low-pass filter in the signal processing unit. The cutoff frequency of the low-pass filter is usually between 20 Hz and 50 Hz, which can be achieved by designing a filter with appropriate bandwidth and attenuation characteristics. The low-pass filter can be based on a digital filter, for example, designed using Butterworth, Chebyshev or Bessel filters. During the filter design process, filter design toolboxes can be used for parameter optimization to ensure that the main features of the ECG signal are retained while removing high-frequency noise. Thus, the high-frequency noise generated by environmental electromagnetic interference or electromyographic signals is removed, and finally the user's skin surface ECG high-frequency noise elimination signal is obtained.

[0140] Step S23: baseline drift correction is performed on the user's skin surface electrocardio high-frequency denoising signal to obtain a user's skin surface electrocardio drift correction signal;

[0141] In the embodiment of the present application, the baseline drift correction is performed on the user's skin surface electrocardio high-frequency denoising signal which has been previously denoised, wherein the baseline drift is usually caused by device stability problems or environmental factors. In order to correct the baseline drift, a baseline correction algorithm can be used, such as the sliding average method or the polynomial fitting method. In the specific implementation, a sliding window can be used to calculate the local average of the user's skin surface electrocardio high-frequency denoising signal, and the average value is subtracted to remove the drift. In the polynomial fitting method, an appropriate polynomial (such as a first-order or second-order polynomial) is selected to fit the baseline part of the signal, and then the fitting curve is subtracted from the original signal. The calculation is performed through a programming environment such as MATLAB or Python. The corrected signal is the baseline drift correction signal, and the user's skin surface electrocardio drift correction signal is finally obtained.

[0142] Step S24: signal amplification processing is performed on the user's skin surface electrocardio drift correction signal to obtain a user's skin surface electrocardio amplification signal;

[0143] In the embodiment of the present application, the signal amplification processing is performed on the user's skin surface electrocardio drift correction signal which has been previously drift corrected by using an amplifier circuit or a digital signal processing method. By selecting an appropriate gain value (such as 10 to 1000), the signal is amplified through an amplifier circuit. If an analog amplifier such as an operational amplifier is used, a gain resistor network needs to be configured to set the gain value, so that the electrocardio amplification signal is obtained by amplification, and the user's skin surface electrocardio amplification signal is finally obtained.

[0144] Step S25: the user's skin surface electrocardio resistance is obtained, and the electrocardio signal dynamic adjustment is performed on the user's skin surface electrocardio amplification signal based on the user's skin surface electrocardio resistance to obtain a user's skin surface electrocardio dynamic adjustment signal.

[0145] In the embodiment of the present application, by acquiring the corresponding user skin surface electrocardio resistance, the resistance value is usually measured in real time by a resistance sensor, and by dynamically adjusting the user skin surface electrocardio amplified signal obtained after previous amplification according to the measured user skin surface electrocardio resistance, the change of the resistance value will affect the amplitude and quality of the signal, so adjustment is needed to compensate for these effects. In specific implementation, the amplitude of the signal can be adjusted according to the resistance value and a predefined compensation model (such as a linear or nonlinear model), which can be calibrated by experimental data to ensure the accuracy of the adjustment, and can be implemented in a real-time system to achieve real-time compensation of the signal. The adjusted signal is the electrocardio dynamic adjustment signal, which maintains good signal quality under various resistance conditions, ensuring the accuracy and reliability of electrocardio monitoring, and ultimately obtaining the user skin surface electrocardio dynamic adjustment signal.

[0146] Further, step S25 includes the following steps:

[0147] Step S251: Measure the electrocardio resistance of the user's skin surface by a high-precision resistance measuring instrument in the smart ring to obtain the electrocardio resistance of the user's skin surface;

[0148] In the embodiment of the present application, the electrocardio resistance of the user's skin surface is measured by using a high-precision resistance measuring instrument built in the smart ring. The smart ring uses resistance sensor technology, and its resistance measuring instrument has micron-level precision, which can accurately detect the resistance change of the skin surface. The smart ring is attached to the user's skin through electrodes, excites a weak current, and measures the voltage drop through the skin. According to Ohm's law, the measured voltage and the known current value are used to calculate the skin resistance. This process converts the data into the corresponding resistance value through the built-in processing unit, and finally obtains the electrocardio resistance of the user's skin surface.

[0149] Step S252: Perform regression influence relationship mining analysis on the user skin surface electrocardio amplified signal based on the electrocardio resistance of the user's skin surface to obtain the regression influence relationship between the user skin resistance and the skin surface electrocardio signal;

[0150] In the embodiment of the present application, the user skin surface electrocardio amplified signal obtained after amplification is subjected to regression analysis by combining the previously measured electrocardio resistance of the user's skin surface to collect a series of electrocardio signal data under different resistance values, and a statistical regression analysis method such as least squares method is used to establish a mathematical model of the relationship between the resistance value and the electrocardio signal amplification. The model includes a regression equation of each resistance value on the electrocardio signal, and the influence relationship of the resistance on the electrocardio signal is obtained through a fitting algorithm. This regression model is used to reveal the specific influence of skin resistance on electrocardio signal amplification effect, and finally the regression influence relationship between the user skin resistance and the skin surface electrocardio signal is obtained.

[0151] Step S253: Quantitative calculation of the dynamic adjustment coefficient of the user's skin surface electrocardio resistance and the user's skin surface electrocardio amplification signal based on the regression influence relationship between the user's skin resistance and the skin surface electrocardio signal, to obtain the signal adjustment coefficient of the user's skin surface electrocardio signal under different resistance values;

[0152] In the embodiment of the application, the regression influence relationship between the user's skin resistance and the skin surface electrocardio signal obtained by previous mining analysis is combined to quantitatively calculate the dynamic adjustment coefficient of the corresponding user's skin surface electrocardio resistance and the user's skin surface electrocardio amplification signal, so that the signal adjustment coefficient under different resistance values is calculated by using the resistance influence coefficient obtained from the regression analysis. The specific operation includes inputting the regression equation, combining the real-time resistance value, and using a calculation tool such as MATLAB or Python for numerical solution. This process generates the adjustment coefficient under the corresponding resistance condition, which indicates how the electrocardio signal should be adjusted under each resistance condition to ensure the accuracy and consistency of the signal. Finally, the signal adjustment coefficient of the user's skin surface electrocardio signal under different resistance values is obtained.

[0153] Step S254: Dynamically adjusting the electrocardio signal of the user's skin surface electrocardio amplification signal according to the signal adjustment coefficient of the user's skin surface electrocardio signal under different resistance values, to obtain the user's skin surface electrocardio dynamic adjustment signal.

[0154] In the embodiment of the application, the regression influence relationship between the user's skin resistance and the skin surface electrocardio signal obtained by previous mining analysis is combined to quantitatively calculate the dynamic adjustment coefficient of the corresponding user's skin surface electrocardio resistance and the user's skin surface electrocardio amplification signal, so that the signal adjustment coefficient under different resistance values is calculated by using the resistance influence coefficient obtained from the regression analysis. The specific operation includes inputting the regression equation, combining the real-time resistance value, and using a calculation tool such as MATLAB or Python for numerical solution. This process generates the adjustment coefficient under the corresponding resistance condition, which indicates how the electrocardio signal should be adjusted under each resistance condition to ensure the accuracy and consistency of the signal. Finally, the signal adjustment coefficient of the user's skin surface electrocardio signal under different resistance values is obtained.

[0155] Further, step S3 includes the following steps:

[0156] Step S31: Drawing an electrocardio waveform graph of the user's skin surface electrocardio dynamic adjustment signal to generate a user's skin surface electrocardio signal waveform graph;

[0157] In the embodiment of the present application, the previously dynamically adjusted user skin surface electrocardio dynamic adjustment signal is drawn in real time by using a special electrocardio waveform drawing tool (such as a digital oscilloscope or a special electrocardio drawing software), and in the drawing process, the electrocardio signal is converted into a waveform including P wave, QRS wave group and T wave, etc., so as to draw and generate a corresponding electrocardio waveform to show the timing change of the user electrocardio signal, and finally generate the user skin surface electrocardio signal waveform.

[0158] Step S32: R wave amplitude and QRS wave duration statistical analysis is performed on each timing point in the user skin surface electrocardio signal waveform to obtain the electrocardio signal R wave amplitude and electrocardio signal QRS wave duration at each timing point in the electrocardio signal waveform.

[0159] In the embodiment of the present application, statistical analysis is performed on each timing point in the previously drawn and generated user skin surface electrocardio signal waveform by using image processing software to identify and analyze the R wave peak value of each timing point, and the amplitude of the R wave is calculated by using a peak value detection algorithm, and the starting and ending positions of the QRS complex wave are used for statistics for each timing point to quantitatively calculate the duration of the QRS wave group. These calculation results involve electrocardio signal feature extraction and waveform segmentation, and algorithms such as filter design and peak value detection are used to ensure accurate acquisition of the amplitude of the R wave and the duration of the QRS wave, and finally the electrocardio signal R wave amplitude and electrocardio signal QRS wave duration at each timing point in the electrocardio signal waveform are obtained.

[0160] Step S33: Based on the electrocardio signal R wave amplitude and electrocardio signal QRS wave duration at each timing point in the electrocardio signal waveform, a fluctuation value quantitative calculation is performed on each timing point in the user skin surface electrocardio signal waveform by using an electrocardio signal fluctuation calculation formula to obtain the electrocardio signal fluctuation value at each timing point in the electrocardio signal waveform.

[0161] In the embodiment of the present application, by combining the timing point measurement parameter, the electrocardio signal R wave amplitude, the R wave average change amplitude, the R wave amplitude change standard deviation, the integral calculation range starting time, the QRS wave duration, the integral time variable parameter and the related parameters, a suitable electrocardio signal fluctuation calculation formula is formed to quantitatively calculate the fluctuation value of the electrocardio signal at each timing point, so as to ensure that the fluctuation value can accurately reflect the change of the electrocardio signal, and finally the electrocardio signal fluctuation value at each timing point in the electrocardio signal waveform is obtained.

[0162] The electrocardio signal fluctuation calculation formula is specifically as follows:

[0163] In the formula, V(t) is the fluctuation value of the electrocardiosignal at the time point t in the electrocardiosignal waveform, t is the time point measurement parameter, A R (t) is the R wave amplitude of the electrocardiosignal at the time point t in the electrocardiosignal waveform, μ R is the average change amplitude of the R wave, σ R is the standard deviation of the R wave amplitude, t0 is the starting time of the integral calculation range, T QRS is the QRS wave duration, t' is the integral time variable parameter, and ξ is the correction coefficient of the electrocardiosignal fluctuation value.

[0164] The present application obtains an electrocardiosignal fluctuation calculation formula through the use of a specific mathematical model and verification, which is used for quantitatively calculating the fluctuation value of each time point in the electrocardiosignal waveform on the surface of the user's skin. The electrocardiosignal fluctuation calculation formula considers the average value and the standard deviation of the R wave amplitude to model the normal change range of the R wave, helps to identify the corresponding electrocardiosignal fluctuation abnormality, and smoothes the R wave amplitude through the Gaussian function to consider the normal physiological fluctuation and reduce the interference caused by individual differences. The formula integrates the Gaussian function within the QRS wave duration through integral calculation to obtain a standardization factor to balance the influence of the electrocardiosignal amplitude and the time window. In addition, the correction coefficient is introduced to correct the fluctuation value to fully consider the deviation or noise in the actual measurement, thereby improving the accuracy of the calculation result. Through the comprehensive consideration of these factors, the calculation formula can accurately calculate the electrocardiosignal fluctuation value of each time point, which helps to detect the abnormal change of the R wave amplitude, thereby identifying the potential problems in the electrocardiosignal and timely discovering the abnormal interval of the heart rate. The formula fully considers the electrocardiosignal fluctuation value V(t) at the time point t in the electrocardiosignal waveform, the time point measurement parameter t, the R wave amplitude A R (t) of the electrocardiosignal at the time point t in the electrocardiosignal waveform, the average change amplitude μ R of the R wave, the standard deviation σ R of the R wave amplitude, the starting time t0 of the integral calculation range, the QRS wave duration T QRS , the integral time variable parameter t', and the correction coefficient ξ of the electrocardiosignal fluctuation value, and forms a functional relationship according to the mutual relationship between the electrocardiosignal fluctuation value V(t) at the time point t in the electrocardiosignal waveform and the above parameters. The formula can realize the quantitative calculation process of the fluctuation value of each time point in the electrocardiosignal waveform on the surface of the user's skin. Meanwhile, the introduction of the correction coefficient ξ of the electrocardiosignal fluctuation value can adjust according to the error in the calculation process, thereby improving the accuracy and applicability of the electrocardiosignal fluctuation calculation formula.

[0165] Step S34: comparing and judging the heart electrical signal fluctuation value at each time point in the heart electrical signal waveform according to the preset heart electrical signal fluctuation abnormal threshold value, when the heart electrical signal fluctuation value is greater than or equal to the preset heart electrical signal fluctuation abnormal threshold value, the corresponding time point in the user skin surface heart electrical signal waveform is marked as an abnormal point; when the heart electrical signal fluctuation value is less than the preset heart electrical signal fluctuation abnormal threshold value, the next time point in the user skin surface heart electrical signal waveform is continuously compared and judged until all are compared and judged; each abnormal point in the user skin surface heart electrical signal waveform is connected with adjacent abnormal points to obtain a user heart electrical signal heart rate abnormal interval.

[0166] In the embodiment of the present application, the heart electrical signal fluctuation value at each time point in the previously quantitatively calculated heart electrical signal waveform is compared and judged by using the preset heart electrical signal fluctuation abnormal threshold value, if the heart electrical signal fluctuation value at a certain time point is greater than or equal to the threshold value, it indicates that the heart electrical signal fluctuation at the time point is abnormal, then the time point is marked as an abnormal point, otherwise the next time point in the user skin surface heart electrical signal waveform is continuously compared and judged until all are compared and judged, so that the fluctuation value at each time point is compared one by one by using the set threshold value, and all points exceeding the threshold value are marked, at the same time, the adjacent points of the marked abnormal points are connected, if the corresponding abnormal points are adjacent, they are connected to form a continuous abnormal interval, at this time, all adjacent abnormal points are connected by using the connectivity analysis method (such as distance-based clustering analysis), and finally the user heart electrical signal heart rate abnormal interval is obtained.

[0167] Step S35: performing abnormal change trend analysis on the user heart electrical signal heart rate abnormal interval to obtain user heart rate abnormal interval signal change trend data.

[0168] In the embodiment of the present application, the abnormal change trend of the previously determined user heart electrical signal heart rate abnormal interval is identified and analyzed by using the trend analysis algorithm (such as moving average method or regression analysis), the data of the abnormal interval is input into the trend analysis algorithm to statistically analyze the change trend of the abnormal fluctuation, this process involves using a data visualization tool to generate a trend chart, analyzing the fluctuation mode of the data to predict the future abnormal trend, all analysis operations are completed on the data processing platform to ensure the accuracy and visualization effect of the trend data, and finally the user heart rate abnormal interval signal change trend data is obtained.

[0169] Further, step S4 includes the following steps:

[0170] Step S41: signal change abnormality alarm threshold setting is performed on the user heart rate abnormal interval signal change trend data, to obtain a user heart rate signal change trend abnormality alarm threshold;

[0171] In the embodiment of the present application, the user heart rate abnormal interval signal change trend data obtained through previous change trend analysis is analyzed to determine the change fluctuation trend range of the heart rate signal in the abnormal interval, and a signal change abnormality alarm threshold is set based on this trend range, which is usually determined according to statistical quantities such as standard deviation and mean value. The specific operation includes using a data acquisition module to obtain continuous monitoring data of the heart rate signal, using a calculation model (such as weighted moving average or exponential smoothing) to analyze the data change trend, setting a threshold value, and triggering an abnormal alarm when the signal change exceeds the threshold value. Finally, the corresponding user heart rate signal change trend abnormality alarm threshold is set.

[0172] Step S42: heart rate abnormality alarm control is performed on the user electrocardio signal heart rate abnormal interval according to the user heart rate signal change trend abnormality alarm threshold, to generate a user heart rate abnormality alarm response signal;

[0173] In the embodiment of the present application, the heart rate change in the user electrocardio signal heart rate abnormal interval is monitored in real time according to the previously set user heart rate signal change trend abnormality alarm threshold. When the heart rate change in the user electrocardio signal heart rate abnormal interval exceeds the preset threshold range during the collection process, a heart rate abnormality alarm response signal is immediately generated in response to control. The specific implementation includes comparing the real-time collected electrocardio data with the set threshold value, using a data processing unit (such as a microcontroller or a calculation unit) to analyze the data, and triggering an abnormal alarm signal if an abnormal fluctuation is detected. These signals can include audible and visual alarms, vibration notifications, or other preset warning methods. Finally, the user heart rate abnormality alarm response signal is generated in response.

[0174] Step S43: the user heart rate abnormality alarm response signal is applied to the smart ring to formulate a heart rate signal abnormality alarm control strategy, to execute corresponding user heart rate abnormality alarm notification.

[0175] In the embodiments of the present application, the user heart rate abnormality alarm response signal generated previously is input into and acts on the control system of the smart ring to formulate a corresponding abnormality alarm control strategy, which includes defining the alarm notification mode (such as vibration, flashing indicator light or sending to the paired device through Bluetooth), and the specific operation includes that the smart ring receives the abnormality alarm signal obtained from the heart rate monitoring and executes the predetermined alarm strategy based on the signal, for example, if the heart rate abnormality alarm signal indicates that the heart rate exceeds the set upper limit, the smart ring will issue continuous vibration or flashing light to attract the user's attention; if the heart rate is lower than the set lower limit, no corresponding warning will be performed, and the control strategy of the smart ring must be realized by programming to ensure that appropriate alarm measures are taken in different types of abnormal situations, thereby executing the corresponding user heart rate abnormality alarm notification.

[0176] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims and not by the above description, therefore all variations falling within the meaning and scope of the equivalent requirements of the application file are intended to be included in the present application.

[0177] The above description is merely a specific implementation of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart ring based ECG electrocardiogram measurement and control method, characterized in that, Comprise the following steps: Step S1: The heart rate ECG sensor integrated in the smart ring is dynamically adjusted in sensing electrode pressure to obtain a heart rate ECG electrode dynamic adjustment sensor; the electrocardiogram signal on the surface of the user's skin is collected in real time by the heart rate ECG electrode dynamic adjustment sensor to obtain the real-time electrocardiogram signal on the surface of the user's skin; the real-time electrocardiogram signal on the surface of the user's skin is transmitted to the signal processing unit by using the wireless communication module built-in the smart ring; Step S2: The real-time electrocardiogram signal on the surface of the user's skin is processed by noise suppression and signal amplification by the signal processing unit to obtain the electrocardiogram amplification signal on the surface of the user's skin; the electrocardiogram resistance on the surface of the user's skin is obtained, and the electrocardiogram signal on the surface of the user's skin is dynamically adjusted based on the electrocardiogram resistance on the surface of the user's skin to obtain the electrocardiogram dynamic adjustment signal on the surface of the user's skin; Step S3: The electrocardiogram waveform diagram of the electrocardiogram dynamic adjustment signal on the surface of the user's skin is drawn and the heart rate abnormal interval of the electrocardiogram signal is monitored to obtain the heart rate abnormal interval of the electrocardiogram signal; the abnormal change trend of the heart rate abnormal interval of the electrocardiogram signal is analyzed to obtain the signal change trend data of the heart rate abnormal interval of the electrocardiogram signal; Step S4: The heart rate abnormal interval of the electrocardiogram signal is controlled based on the signal change trend data of the heart rate abnormal interval of the electrocardiogram signal to generate a heart rate abnormal alarm response signal to execute corresponding heart rate abnormal alarm notification of the user.

2. The smart ring based ECG electrocardiogram measurement and control method according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: The contact pressure between the smart ring and the surface of the user's skin is monitored in real time by the contact pressure sensor built-in the smart ring to obtain the real-time change data of the contact pressure between the smart ring and the surface of the user's skin; Step S12: The heart rate ECG sensor integrated in the smart ring is dynamically adjusted in sensing electrode pressure based on the real-time change data of the contact pressure between the smart ring and the surface of the user's skin to obtain a heart rate ECG electrode dynamic adjustment sensor; Step S13: The electrocardiogram signal on the surface of the user's skin is collected in real time by the heart rate ECG electrode dynamic adjustment sensor to obtain the real-time electrocardiogram signal on the surface of the user's skin; Step S14: The real-time electrocardiogram signal on the surface of the user's skin is transmitted to the signal processing unit by using the wireless communication module built-in the smart ring.

3. The smart ring based ECG electrocardiogram measurement and control method according to claim 2, characterized in that, Step S12 includes the following steps: Step S121: The time sequence change curve of the real-time change data of the contact pressure between the smart ring and the surface of the user's skin is drawn to generate a time sequence change curve of the contact pressure on the surface of the smart ring; Step S122: The time sequence change curve of the contact pressure on the surface of the smart ring is analyzed to obtain the dynamic change trend data of the contact pressure on the surface of the smart ring; Step S123: The heart rate ECG sensor integrated in the smart ring is evaluated and analyzed in sensing electrode contact comfort based on the dynamic change trend data of the contact pressure on the surface of the smart ring to obtain the sensing electrode contact comfort of the ECG sensor under different contact pressure change trends; ​ Step S124: electrode pressure dynamic compensation analysis is performed on the sensing electrode contact comfort of the ECG sensor under different contact pressure change trends, to obtain an electrode pressure dynamic compensation mechanism of the ECG sensor under different contact pressure change trends. Step S125: the heart rate ECG sensor integrated in the smart ring is dynamically adjusted in terms of sensing electrode pressure according to the electrode pressure dynamic compensation mechanism of the ECG sensor under different contact pressure change trends, to obtain a heart rate ECG electrode dynamic adjustment sensor.

4. The smart ring based ECG electrocardiogram measurement and control method according to claim 3, characterized in that, Step S123 includes the following steps: The contact pressure change trend condition design is performed on the smart ring surface contact pressure dynamic change trend data, to obtain different smart ring surface contact pressure change trend conditions. The contact pressure distribution analysis is performed on the different smart ring surface contact pressure change trend conditions, to obtain contact pressure distribution condition data under different contact pressure change trend conditions. The stress distribution quantitative analysis is performed on the corresponding sensing electrode contact area in the heart rate ECG sensor integrated in the smart ring according to the contact pressure distribution condition data under different contact pressure change trend conditions, to obtain contact area stress distribution values under different contact pressure change trend conditions. The sensing electrode contact influence moment evaluation analysis is performed on the corresponding sensing electrode contact area in the heart rate ECG sensor based on the contact area stress distribution values under different contact pressure change trend conditions, to obtain the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends. The contact comfort quantitative calculation is performed on the corresponding sensing electrode contact area in the heart rate ECG sensor based on the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends by using the electrode contact comfort calculation formula, to obtain the sensing electrode contact comfort of the ECG sensor under different contact pressure change trends.

5. The smart ring based ECG electrocardiogram measurement and control method according to claim 4, characterized in that, The sensing electrode contact influence moment evaluation analysis performed on the corresponding sensing electrode contact area in the heart rate ECG sensor based on the contact area stress distribution values under different contact pressure change trend conditions includes the following steps: The spatial coordinate system conversion is performed on the corresponding sensing electrode contact area in the heart rate ECG sensor, to obtain an ECG sensing electrode contact area spatial coordinate system. The stress distribution position mapping matching is performed on the ECG sensing electrode contact area spatial coordinate system based on the contact area stress distribution values under different contact pressure change trend conditions, to obtain contact area stress spatial distribution coordinate positions under different contact pressure change trend conditions. The point-by-point spatial vector conversion processing is performed on the contact area stress distribution values under different contact pressure change trend conditions and the contact area stress spatial distribution coordinate positions, to obtain the contact stress spatial distribution vector at each stress distribution point in the contact area under different contact pressure change trend conditions. The contact stress spatial distribution vector at each stress distribution point in the contact area under different contact pressure change trend conditions. According to the contact stress space distribution vector of each stress distribution point in the contact area under the condition of different contact pressure change trends, the contact influence moment of the corresponding sensing electrode contact area in the ECG sensor is evaluated and calculated, so as to obtain the sensing electrode contact influence moment of the ECG sensor under different contact pressure change trends.

6. The smart ring based ECG electrocardiogram measurement and control method of claim 4, wherein, The electrode contact comfort calculation formula is specifically: In the formula, S(p(x, y)) is the sensing electrode contact comfort of the ECG sensor under the contact pressure p(x, y), p(x, y) is the contact pressure on the coordinate point (x, y) in the sensing electrode contact area, x is the horizontal coordinate parameter in the sensing electrode contact area, y is the vertical coordinate parameter in the sensing electrode contact area, A is the total area of the sensing electrode contact area, a is the contact pressure influence weight parameter, u(p(x, y)) is the sensing electrode contact influence moment of the ECG sensor under the contact pressure p(x, y), F(p(x, y)) is the contact stress space distribution vector under the contact pressure p(x, y), k is the sensing electrode stiffness coefficient, x0 is the horizontal coordinate of the contact pressure deformation center point, y0 is the vertical coordinate of the contact pressure deformation center point, s is the contact pressure deformation distribution width, b is the contact pressure gradient influence weight parameter, and h is the correction coefficient of the sensing electrode contact comfort.

7. The smart ring based ECG electrocardiogram measurement and control method of claim 1, wherein, Step S2 includes the following steps: Step S21: The real-time ECG signal of the user's skin surface is subjected to low-frequency interference elimination processing through a high-pass filter in the signal processing unit, to obtain an ECG low-frequency interference elimination signal of the user's skin surface; Step S22: The ECG low-frequency interference elimination signal of the user's skin surface is subjected to high-frequency noise elimination processing through a low-pass filter in the signal processing unit, to obtain an ECG high-frequency noise elimination signal of the user's skin surface; Step S23: The ECG high-frequency noise elimination signal of the user's skin surface is subjected to baseline drift correction, to obtain an ECG drift correction signal of the user's skin surface; Step S24: The ECG drift correction signal of the user's skin surface is subjected to signal amplification processing, to obtain an ECG amplified signal of the user's skin surface; Step S25: The ECG resistance of the user's skin surface is obtained, and the ECG amplified signal of the user's skin surface is subjected to ECG signal dynamic adjustment based on the ECG resistance of the user's skin surface, to obtain an ECG dynamic adjustment signal of the user's skin surface.

8. The smart ring based ECG electrocardiogram measurement and control method according to claim 7, characterized in that, Step S25 includes the following steps: Step S251: The ECG resistance of the user's skin surface is measured through a high-precision resistance measuring instrument in the intelligent ring, to obtain the ECG resistance of the user's skin surface; Step S252: The regression influence relationship between the ECG resistance of the user's skin surface and the ECG signal of the user's skin surface is obtained through regression influence relationship mining analysis of the ECG amplified signal of the user's skin surface based on the ECG resistance of the user's skin surface; Step S253: The dynamic adjustment coefficient of the ECG signal of the user's skin surface under different resistance values is obtained through dynamic adjustment coefficient quantitative calculation of the ECG resistance of the user's skin surface and the ECG amplified signal of the user's skin surface based on the regression influence relationship between the ECG resistance of the user's skin surface and the ECG signal of the user's skin surface. Step S254: According to the signal adjustment coefficient of the user's skin surface electrocardio signal under different resistance values, the electrocardio signal of the user's skin surface electrocardio amplification signal is dynamically adjusted to obtain the user's skin surface electrocardio dynamic adjustment signal.

9. The smart ring based ECG electrocardiogram measurement and control method of claim 1, wherein, Step S3 includes the following steps: Step S31: The user's skin surface electrocardio dynamic adjustment signal is plotted into an electrocardio waveform graph to generate a user's skin surface electrocardio signal waveform graph; Step S32: The R wave amplitude and QRS wave duration of each time sequence point in the user's skin surface electrocardio signal waveform graph are statistically analyzed to obtain the electrocardio signal R wave amplitude and electrocardio signal QRS wave duration at each time sequence point in the electrocardio signal waveform graph; Step S33: Based on the electrocardio signal R wave amplitude and electrocardio signal QRS wave duration at each time sequence point in the electrocardio signal waveform graph, the fluctuation value of each time sequence point in the user's skin surface electrocardio signal waveform graph is quantitatively calculated by using the electrocardio signal fluctuation calculation formula to obtain the electrocardio signal fluctuation value at each time sequence point in the electrocardio signal waveform graph; In the formula, the fluctuation of the electrocardiosignal is calculated according to the following formula: In the formula, V(t) is the fluctuation value of the electrocardiosignal at the time point t in the electrocardiosignal waveform, t is the time point measurement parameter, A R (t) is the R wave amplitude of the electrocardiosignal at the time point t in the electrocardiosignal waveform, μ R is the average change amplitude of the R wave, σ R is the standard deviation of the R wave amplitude, t0 is the starting time of the integral calculation range, T QRS is the QRS wave duration, t' is the integral time variable parameter, and ξ is the correction coefficient of the electrocardiosignal fluctuation value. Step S34: According to the preset electrocardio signal fluctuation abnormal threshold, the electrocardio signal fluctuation value at each time sequence point in the electrocardio signal waveform graph is compared and judged. When the electrocardio signal fluctuation value is greater than or equal to the preset electrocardio signal fluctuation abnormal threshold, the corresponding time sequence point in the user's skin surface electrocardio signal waveform graph is marked as an abnormal point. When the electrocardio signal fluctuation value is less than the preset electrocardio signal fluctuation abnormal threshold, the next time sequence point in the user's skin surface electrocardio signal waveform graph is compared and judged until all the time sequence points are compared and judged. The marked each abnormal point in the user's skin surface electrocardio signal waveform graph is connected with adjacent abnormal points to obtain the user's electrocardio signal heart rate abnormal interval; Step S35: The abnormal change trend of the user's electrocardio signal heart rate abnormal interval is analyzed to obtain the user's heart rate abnormal interval signal change trend data. Step S4 includes the following steps:

10. The smart ring based ECG electrocardiogram measurement and control method of claim 1, wherein, Step S41: The signal change abnormal alarm threshold of the user's heart rate abnormal interval signal change trend data is set to obtain the user's heart rate signal change trend abnormal alarm threshold; Step S42: According to the user's heart rate signal change trend abnormal alarm threshold, the heart rate abnormal alarm control of the user's electrocardio signal heart rate abnormal interval is performed to generate the user's heart rate abnormal alarm response signal; Step S43: The user's heart rate abnormal alarm response signal is applied to the intelligent ring to formulate a heart rate signal abnormal alarm control strategy to execute the corresponding user's heart rate abnormal alarm notification. ​

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