Method and system for improving heart rate monitoring accuracy
The heart rate data is denoised and corrected through the data processing module in the heart rate monitoring system, which solves the problem of inaccurate monitoring in a noisy environment, and achieves high accuracy and risk warning of heart rate monitoring.
Patent Information
- Application Number
- CN202510511128.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-23
- Publication Date
- 2025-08-12
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
When existing heart rate monitoring equipment faces complex and variable noise environments, it is difficult to effectively improve the accuracy and reliability of monitoring results.
By setting up the original heart rate data measurement module, preprocessing module, frequency decomposition module, noise analysis module, data reconstruction module and risk judgment module, the heart rate sensor contacts the skin surface measurement data, performs data denoising, decomposition and correction, and combines the threshold to judge risks and generates early warning information.
Effectively reduce the impact of external interference on heart rate monitoring results, improve the accuracy and reliability of monitoring, and provide real-time risk analysis and early warning information.
Smart Images
Figure FT_1 
Figure FT_2 
Figure FDA0005371213730000021
Abstract
Description
Technical Field
[0001] The present invention relates to the field of big data technology, and more particularly to a method and system for improving the accuracy of heart rate monitoring. Background Art
[0002] As people's health awareness increases, more and more people begin to pay attention to their own health status. Heart rate monitoring, as a simple and easy way of health management, can help people understand their physical condition, adjust their living habits, and improve their quality of life. By monitoring heart rate changes, they can scientifically control exercise intensity, avoid injuries caused by excessive exercise, and improve exercise effects. Through continuous heart rate monitoring, abnormal heart rate can be detected in time, so that corresponding measures can be taken to intervene.
[0003] With the continuous advancement of sensor technology, heart rate monitoring devices are becoming increasingly smaller and more portable. The development of Internet of Things technology enables heart rate monitoring devices to interconnect with other health monitoring devices, smart phones and other terminal devices to achieve data sharing and remote monitoring, which provides users with more comprehensive and convenient health management services.
[0004] Heart rate monitoring devices often monitor heart rate by collecting heart rate signals. However, the original heart rate signals often contain various noise factors. Noise factors can cause deviations or abnormal fluctuations in heart rate monitoring data, affecting the accuracy and reliability of the monitoring results. Traditional heart rate monitoring methods often rely on a single signal processing algorithm and are difficult to effectively cope with complex and changing noise environments. Summary of the Invention
[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a system for improving the accuracy of heart rate monitoring to solve the problems existing in the above-mentioned background technology.
[0006] The present invention provides the following technical solution: a system for improving the accuracy of heart rate monitoring, comprising: a raw heart rate data measurement module, a raw heart rate data preprocessing module, a heart rate data frequency decomposition module, a frequency decomposition noise analysis module, a heart rate data post-analysis reconstruction module, a heart rate monitoring risk judgment module, and a risk analysis and warning output module;
[0007] The raw heart rate data measurement module measures the raw heart rate data by contacting the skin surface with the heart rate sensor, and transmits the raw heart rate data to the raw heart rate data preprocessing module;
[0008] The raw heart rate data preprocessing module receives the raw heart rate data measured by the raw heart rate data measurement module, performs data denoising on the raw heart rate data to obtain target heart rate data, and transmits the target heart rate data to the heart rate data frequency decomposition module;
[0009] The heart rate data frequency decomposition module decomposes the target heart rate data transmitted by the original heart rate data preprocessing module through a frequency decomposition mathematical model, calculates the heart rate decomposition coefficient, and transmits the heart rate decomposition coefficient to the frequency decomposition noise analysis module;
[0010] The frequency decomposition noise analysis module performs noise quantization on the heart rate decomposition coefficient based on the heart rate decomposition coefficient transmitted by the heart rate data frequency decomposition module, calculates the heart rate correction coefficient, and transmits the heart rate correction coefficient to the heart rate data post-analysis reconstruction module;
[0011] The heart rate data post-analysis reconstruction module performs data correction on the heart rate time series based on the heart rate correction coefficient transmitted by the frequency decomposition noise analysis module, and transmits the corrected heart rate time series to the heart rate monitoring risk judgment module;
[0012] The heart rate monitoring risk judgment module receives the corrected heart rate time series transmitted by the heart rate data analysis and reconstruction module, and performs risk judgment on the target heart rate data based on the threshold;
[0013] The risk analysis and warning output module completes the risk analysis of the target heart rate data and outputs the warning information to the user end.
[0014] Preferably, in the raw heart rate data measurement module, a fixed sampling time interval is set, the sampling time is divided into t segments, and the raw heart rate data is measured by contacting the skin surface with a heart rate sensor. The raw heart rate data includes an instantaneous heart rate value and a heart rate time series, and the heart rate time series represents the heart rate values continuously recorded during the tth sampling time period.
[0015] Preferably, in the raw heart rate data preprocessing module, the average value of the raw heart rate data collected at different sampling points in different time periods is calculated by sliding average filtering, and the raw heart rate data is subjected to data denoising to obtain the target heart rate data.
[0016] Preferably, in the heart rate data frequency decomposition module, based on the target heart rate data transmitted by the original heart rate data preprocessing module, the target heart rate data is decomposed and processed by a frequency decomposition mathematical model, and the specific content of the heart rate decomposition coefficient is calculated as follows:
[0017] Step S01: performing n-layer decomposition on the instantaneous heart rate values sampled at different sampling points i in different time periods, where i=1, 2, 3, ..., I, and n=0, 1, 2, ..., N;
[0018] Step S02: Calculate the instantaneous heart rate values obtained by sampling at different sampling points i in different time periods after being decomposed into n layers. The calculation formula is: Among them Hti (x) represents the instantaneous heart rate value sampled at different sampling points i in different time periods after being decomposed through n layers, x represents the instantaneous heart rate value sampled at different sampling points i in different time periods, and n represents the number of decomposition layers;
[0019] Step S03: Calculate the mean of the heart rate time series in different time periods. The calculation formula is: in represents the mean of the heart rate time series in different time periods, and i represents different sampling points in different time periods;
[0020] Step S04: Calculate the heart rate decomposition coefficients in different time periods. The calculation formula is: where α t Indicates the heart rate decomposition coefficient in different time periods.
[0021] Preferably, in the frequency decomposition threshold analysis module, based on the heart rate decomposition coefficient transmitted by the heart rate data frequency decomposition module, the heart rate decomposition coefficient is threshold quantized, and the specific content of the calculated heart rate correction coefficient is as follows:
[0022] Step S01: Calculate the noise intensity of the heart rate decomposition coefficient in different time periods. The calculation formula is: where θ t Represents the noise intensity of the heart rate decomposition coefficient in different time periods, m|lα t | represents the median of the absolute value of the decomposition scale of the heart rate decomposition coefficient in different time periods, and k represents a constant;
[0023] Step S02: performing noise quantization on the heart rate decomposition coefficients in different time periods. The calculation formula is: where λ t It represents the heart rate decomposition coefficient after noise quantization of the heart rate decomposition coefficient in different time periods, and n represents the number of decomposition layers;
[0024] Step S03: Calculate the heart rate correction coefficient in different time periods. The calculation formula is: where β t Indicates the heart rate correction factor in different time periods.
[0025] Preferably, in the heart rate data analysis and reconstruction module, the heart rate correction coefficient β in different time periods is used. t Correct the mean of the heart rate time series in different time periods. The calculation formula is: Among them D t represents the mean of the heart rate time series in different time periods after correction, β t Indicates the heart rate correction coefficient in different time periods, Represents the mean of the heart rate time series in different time periods.
[0026] Preferably, in the heart rate monitoring risk judgment module, the mean D of the heart rate time series in different time periods after correction transmitted by the heart rate data analysis and reconstruction module is received. t , the mean D of the heart rate time series in different time periods after correction t Compare with the preset threshold, if the mean D of the heart rate time series t If the target heart rate data is within the preset threshold range, it is judged that the detection is normal. If the mean D of the heart rate time series is t If it is not within the preset threshold range, the target heart rate data detection is judged to be abnormal and there is a risk.
[0027] Preferably, in the risk analysis and warning output module, risk analysis is performed on the time period when the target heart rate data is judged to be abnormal, and based on the risk analysis result, the warning level is determined and warning information is generated, and the warning information is sent to the user end through the user interface.
[0028] A method for improving the accuracy of heart rate monitoring, comprising the following steps:
[0029] Step S1: measuring raw heart rate data by contacting the skin surface with a heart rate sensor;
[0030] Step S2: performing data denoising on the original heart rate data to obtain target heart rate data;
[0031] Step S3: Decomposing the target heart rate data using a frequency decomposition mathematical model to calculate a heart rate decomposition coefficient;
[0032] Step S4: performing noise quantization on the heart rate decomposition coefficient to calculate the heart rate correction coefficient;
[0033] Step S5: performing data correction on the heart rate time series according to the heart rate correction coefficient;
[0034] Step S6: performing risk assessment on the target heart rate data based on the corrected heart rate time series and the threshold value;
[0035] Step S7: Complete the risk analysis of the target heart rate data and output the warning information to the user end.
[0036] Technical effects and advantages of the present invention:
[0037] The present invention is provided with an original heart rate data measurement module, an original heart rate data preprocessing module, a heart rate data frequency decomposition module, a frequency decomposition noise analysis module, a heart rate data post-analysis reconstruction module, a heart rate monitoring risk judgment module and a risk analysis and early warning output module. The original heart rate data is measured by contacting the skin surface with a heart rate sensor, and the original heart rate data is subjected to data denoising processing to obtain target heart rate data. The instant heart rate values sampled and obtained at different sampling points in different time periods are decomposed to obtain heart rate decomposition coefficients for different time periods. The heart rate decomposition coefficients are subjected to noise quantization to obtain heart rate correction coefficients for different time periods. Data correction is performed on the heart rate time series of different time periods based on the heart rate correction coefficient. Based on the corrected heart rate time series and according to the threshold, risk judgment is performed on the target heart rate data, and risk analysis of the target heart rate data is completed, and early warning information is output to the user end. In short, a method and system for improving the accuracy of heart rate monitoring effectively reduces the influence of external interference on the heart rate monitoring results and further improves the accuracy of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] Figure 1 A flow chart of a system for improving the accuracy of heart rate monitoring.
[0039] Figure 2 A flow chart of a method for improving the accuracy of heart rate monitoring. DETAILED DESCRIPTION
[0040] The technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples. The method and system for improving the accuracy of heart rate monitoring involved in the present invention are not limited to the various structures described in the following embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
[0041] like Figure 1 As shown, the present invention provides a system for improving the accuracy of heart rate monitoring, comprising: an original heart rate data measurement module, an original heart rate data preprocessing module, a heart rate data frequency decomposition module, a frequency decomposition noise analysis module, a heart rate data post-analysis reconstruction module, a heart rate monitoring risk judgment module, and a risk analysis and warning output module;
[0042] The raw heart rate data measurement module measures the raw heart rate data by contacting the skin surface with the heart rate sensor, and transmits the raw heart rate data to the raw heart rate data preprocessing module;
[0043] The raw heart rate data preprocessing module receives the raw heart rate data measured by the raw heart rate data measurement module, performs data denoising on the raw heart rate data to obtain target heart rate data, and transmits the target heart rate data to the heart rate data frequency decomposition module;
[0044] The heart rate data frequency decomposition module decomposes the target heart rate data transmitted by the original heart rate data preprocessing module through a frequency decomposition mathematical model, calculates the heart rate decomposition coefficient, and transmits the heart rate decomposition coefficient to the frequency decomposition noise analysis module;
[0045] The frequency decomposition noise analysis module performs noise quantization on the heart rate decomposition coefficient based on the heart rate decomposition coefficient transmitted by the heart rate data frequency decomposition module, calculates the heart rate correction coefficient, and transmits the heart rate correction coefficient to the heart rate data post-analysis reconstruction module;
[0046] The heart rate data post-analysis reconstruction module performs data correction on the heart rate time series based on the heart rate correction coefficient transmitted by the frequency decomposition noise analysis module, and transmits the corrected heart rate time series to the heart rate monitoring risk judgment module;
[0047] The heart rate monitoring risk judgment module receives the corrected heart rate time series transmitted by the heart rate data analysis and reconstruction module, and performs risk judgment on the target heart rate data based on the threshold;
[0048] The risk analysis and warning output module completes the risk analysis of the target heart rate data and outputs the warning information to the user end.
[0049] In this embodiment, it should be specifically explained that in the raw heart rate data measurement module, a fixed sampling time interval is set, the sampling time is divided into t segments, and the raw heart rate data is measured by the heart rate sensor contacting the skin surface. The raw heart rate data includes the instantaneous heart rate value and the heart rate time series. The heart rate time series represents the heart rate values continuously recorded during the t-th sampling time period. In actual applications, the sampling time interval can be adjusted according to specific needs to ensure the accuracy and real-time nature of the data.
[0050] In this embodiment, it should be specifically explained that in the raw heart rate data preprocessing module, the average value of the raw heart rate data collected at different sampling points in different time periods is calculated by sliding average filtering, and the raw heart rate data is denoised to obtain target heart rate data, thereby eliminating noise interference caused by poor sensor contact and tiny movement factors on the skin surface. The target heart rate data after denoising more accurately reflects the true heart rate state of the subject.
[0051] In this embodiment, it should be specifically explained that, in the heart rate data frequency decomposition module, based on the target heart rate data transmitted by the original heart rate data preprocessing module, the target heart rate data is decomposed and processed by the frequency decomposition mathematical model, and the specific content of the heart rate decomposition coefficient calculated is as follows:
[0052] Step S01: performing n-layer decomposition on the instantaneous heart rate values sampled at different sampling points i in different time periods, where i=1, 2, 3, ..., I, and n=0, 1, 2, ..., N;
[0053] Step S02: Calculate the instantaneous heart rate values obtained by sampling at different sampling points i in different time periods after being decomposed into n layers. The calculation formula is: Among them H ti (x) represents the instantaneous heart rate value sampled at different sampling points i in different time periods after being decomposed through n layers, x represents the instantaneous heart rate value sampled at different sampling points i in different time periods, and n represents the number of decomposition layers;
[0054] Step S03: Calculate the mean of the heart rate time series in different time periods. The calculation formula is: in represents the mean of the heart rate time series in different time periods, i represents different sampling points in different time periods, and H ti (x) represents the instantaneous heart rate value sampled at different sampling points i in different time periods after being decomposed into n layers. The heart rate time series represents the heart rate values continuously recorded in the tth sampling time period. If i samples are sampled in each time period, the heart rate time series is the set of i sampling values.
[0055] Step S04: Calculate the heart rate decomposition coefficients in different time periods. The calculation formula is: where α t Indicates the heart rate decomposition coefficient in different time periods, Represents the mean of the heart rate time series in different time periods.
[0056] In this embodiment, it should be specifically explained that, in the frequency decomposition threshold analysis module, based on the heart rate decomposition coefficient transmitted by the heart rate data frequency decomposition module, the heart rate decomposition coefficient is threshold-quantized and the heart rate correction coefficient is calculated as follows:
[0057] Step S01: Calculate the noise intensity of the heart rate decomposition coefficient in different time periods. The calculation formula is: where θ t Represents the noise intensity of the heart rate decomposition coefficient in different time periods, m|lα t | represents the median of the absolute value of the decomposition scale of the heart rate decomposition coefficient in different time periods, and k represents a constant;
[0058] Step S02: performing noise quantization on the heart rate decomposition coefficients in different time periods. The calculation formula is: where λ t represents the heart rate decomposition coefficient after noise quantization of the heart rate decomposition coefficient in different time periods, n represents the number of decomposition layers, θ t Indicates the noise intensity of the heart rate decomposition coefficient in different time periods, α t Indicates the heart rate decomposition coefficient in different time periods;
[0059] Step S03: Calculate the heart rate correction coefficient in different time periods. The calculation formula is: where β t Indicates the heart rate correction coefficient in different time periods, λ t represents the heart rate decomposition coefficient after noise quantization of the heart rate decomposition coefficient in different time periods, α t Indicates the heart rate decomposition coefficient in different time periods.
[0060] In this embodiment, it should be specifically explained that in the heart rate data analysis and reconstruction module, the heart rate correction coefficient β in different time periods is used. t Correct the mean of the heart rate time series in different time periods to eliminate the influence of noise on the heart rate monitoring results. The calculation formula is: Among them D t represents the mean of the heart rate time series in different time periods after correction, β t Indicates the heart rate correction coefficient in different time periods, Represents the mean of the heart rate time series in different time periods.
[0061] In this embodiment, it should be specifically explained that in the heart rate monitoring risk judgment module, the mean D of the heart rate time series in different time periods after correction transmitted by the heart rate data analysis and reconstruction module is received. t , the mean D of the heart rate time series in different time periods after correction t Compare with the preset threshold, if the mean D of the heart rate time series t If the target heart rate data is within the preset threshold range, it is judged that the detection is normal. If the mean D of the heart rate time series is t If it is not within the preset threshold range, the target heart rate data detection is considered abnormal and there is a risk;
[0062] According to the judgment results of the heart rate monitoring risk judgment module, the parameter settings of the heart rate monitoring system are adjusted in real time to adapt to the changes in the physiological state of the subject and ensure the accuracy and reliability of the monitoring data.
[0063] In this embodiment, it should be specifically explained that the risk analysis and warning output module performs a risk analysis on the time period in which the target heart rate data is judged to be abnormal, determines the warning level and generates warning information based on the risk analysis results, sends the warning information to the user terminal through the user interface, and displays the heart rate monitoring results and risk assessment results to the user through the user interface. At the same time, corresponding health advice and warning information are provided to help the user take timely measures to maintain good health.
[0064] Analyze the target heart rate data to detect the heart rate change trend during the abnormal period. The calculation formula is: where η t Indicates the trend of heart rate change, ΔD t It represents the mean change of the heart rate time series in different time periods after correction, Δt represents the time interval, and according to the heart rate change trend η t Determine the warning level, which is divided into three levels: low, medium and high. t The heart rate is compared with the preset warning level range to generate warning information, which includes key information such as warning level, heart rate change trend and abnormal time period.
[0065] like Figure 2 As shown, in this embodiment, it should be specifically explained that a method for improving the accuracy of heart rate monitoring includes the following steps:
[0066] Step S1: measuring raw heart rate data by contacting the skin surface with a heart rate sensor;
[0067] Step S2: performing data denoising on the original heart rate data to obtain target heart rate data;
[0068] Step S3: Decomposing the target heart rate data using a frequency decomposition mathematical model to calculate a heart rate decomposition coefficient;
[0069] Step S4: performing noise quantization on the heart rate decomposition coefficient to calculate the heart rate correction coefficient;
[0070] Step S5: performing data correction on the heart rate time series according to the heart rate correction coefficient;
[0071] Step S6: performing risk assessment on the target heart rate data based on the corrected heart rate time series and the threshold value;
[0072] Step S7: Complete the risk analysis of the target heart rate data and output the warning information to the user end.
[0073] In this embodiment, it should be specifically explained that the main difference between this embodiment and the prior art is that this embodiment is provided with a raw heart rate data measurement module, a raw heart rate data preprocessing module, a heart rate data frequency decomposition module, a frequency decomposition noise analysis module, a heart rate data post-analysis reconstruction module, a heart rate monitoring risk judgment module, and a risk analysis and warning output module. The raw heart rate data is measured by contacting the skin surface with a heart rate sensor, the raw heart rate data is subjected to data denoising to obtain target heart rate data, the instantaneous heart rate values sampled and obtained at different sampling points in different time periods are decomposed to obtain heart rate decomposition coefficients for different time periods, the heart rate decomposition coefficients are subjected to noise quantization to obtain heart rate correction coefficients for different time periods, and data correction is performed on the heart rate time series for different time periods based on the heart rate correction coefficients. Based on the corrected heart rate time series and according to a threshold value, risk judgment is performed on the target heart rate data, and risk analysis of the target heart rate data is completed, and warning information is output to the user end. In short, a method and system for improving the accuracy of heart rate monitoring effectively reduces the impact of external interference on the heart rate monitoring results, further improving the accuracy of monitoring.
[0074] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
[0075] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.
Claims
1. A system for improving the accuracy of heart rate monitoring, characterized by: include: Original heart rate data measurement module, original heart rate data preprocessing module, heart rate data frequency decomposition module, frequency decomposition noise analysis module, heart rate data post-analysis reconstruction module, heart rate monitoring risk judgment module, and risk analysis and warning output module; The raw heart rate data measurement module measures the raw heart rate data by contacting the skin surface with the heart rate sensor, and transmits the raw heart rate data to the raw heart rate data preprocessing module; The raw heart rate data preprocessing module receives the raw heart rate data measured by the raw heart rate data measurement module, performs data denoising on the raw heart rate data to obtain target heart rate data, and transmits the target heart rate data to the heart rate data frequency decomposition module; The heart rate data frequency decomposition module decomposes the target heart rate data transmitted by the original heart rate data preprocessing module through a frequency decomposition mathematical model, calculates the heart rate decomposition coefficient, and transmits the heart rate decomposition coefficient to the frequency decomposition noise analysis module; The frequency decomposition noise analysis module performs noise quantization on the heart rate decomposition coefficient based on the heart rate decomposition coefficient transmitted by the heart rate data frequency decomposition module, calculates the heart rate correction coefficient, and transmits the heart rate correction coefficient to the heart rate data post-analysis reconstruction module; The heart rate data post-analysis reconstruction module performs data correction on the heart rate time series based on the heart rate correction coefficient transmitted by the frequency decomposition noise analysis module, and transmits the corrected heart rate time series to the heart rate monitoring risk judgment module; The heart rate monitoring risk judgment module receives the corrected heart rate time series transmitted by the heart rate data analysis and reconstruction module, and performs risk judgment on the target heart rate data based on the threshold; The risk analysis and warning output module completes the risk analysis of the target heart rate data and outputs the warning information to the user end.
2. A system for improving heart rate monitoring accuracy according to claim 1, characterized in that: In the raw heart rate data measurement module, a fixed sampling time interval is set, and the sampling time is divided into t segments. The raw heart rate data is measured by contacting the skin surface with a heart rate sensor. The raw heart rate data includes an instantaneous heart rate value and a heart rate time series. The heart rate time series represents the heart rate values continuously recorded during the tth sampling time period.
3. The system for improving the accuracy of heart rate monitoring according to claim 1, characterized in that: In the raw heart rate data preprocessing module, the average value of the raw heart rate data collected at different sampling points in different time periods is calculated by sliding average filtering, and the raw heart rate data is subjected to data denoising to obtain the target heart rate data.
4. The system for improving heart rate monitoring accuracy according to claim 1, wherein: In the heart rate data frequency decomposition module, based on the target heart rate data transmitted by the original heart rate data preprocessing module, the target heart rate data is decomposed and processed by the frequency decomposition mathematical model, and the specific content of the heart rate decomposition coefficient is calculated as follows: Step S01: performing n-layer decomposition on the instantaneous heart rate values sampled at different sampling points i in different time periods, where i=1, 2, 3, ..., I, and n=0, 1, 2, ..., N; Step S02: Calculate the instantaneous heart rate values obtained by sampling at different sampling points i in different time periods after being decomposed into n layers. The calculation formula is: Among them H ti (x) represents the instantaneous heart rate value sampled at different sampling points i in different time periods after being decomposed through n layers, x represents the instantaneous heart rate value sampled at different sampling points i in different time periods, and n represents the number of decomposition layers; Step S03: Calculate the mean of the heart rate time series in different time periods. The calculation formula is: in represents the mean of the heart rate time series in different time periods, and i represents different sampling points in different time periods; Step S04: Calculate the heart rate decomposition coefficients in different time periods. The calculation formula is: where α t Indicates the heart rate decomposition coefficient in different time periods.
5. The system for improving the accuracy of heart rate monitoring according to claim 1, characterized in that: In the frequency decomposition threshold analysis module, based on the heart rate decomposition coefficient transmitted by the heart rate data frequency decomposition module, the heart rate decomposition coefficient is threshold quantized and the heart rate correction coefficient is calculated as follows: Step S01: Calculate the noise intensity of the heart rate decomposition coefficient in different time periods. The calculation formula is: where θ t Represents the noise intensity of the heart rate decomposition coefficient in different time periods, m | lα t | It represents the median of the absolute value of the decomposition scale of the heart rate decomposition coefficient in different time periods, and k represents a constant; Step S02: performing noise quantization on the heart rate decomposition coefficients in different time periods. The calculation formula is: where λ t It represents the heart rate decomposition coefficient after noise quantization of the heart rate decomposition coefficient in different time periods, and n represents the number of decomposition layers; Step S03: Calculate the heart rate correction coefficient in different time periods. The calculation formula is: where β t Indicates the heart rate correction factor in different time periods.
6. The system for improving the accuracy of heart rate monitoring according to claim 1, characterized in that: In the heart rate data analysis and reconstruction module, the heart rate correction coefficient β in different time periods is used. t Correct the mean of the heart rate time series in different time periods. The calculation formula is: Among them D t represents the mean of the heart rate time series in different time periods after correction, β t Indicates the heart rate correction coefficient in different time periods, Represents the mean of the heart rate time series in different time periods.
7. The system for improving heart rate monitoring accuracy according to claim 1, wherein: In the heart rate monitoring risk judgment module, the mean D of the heart rate time series in different time periods after correction transmitted by the heart rate data analysis and reconstruction module is received. t , the mean D of the heart rate time series in different time periods after correction t Compare with the preset threshold, if the mean D of the heart rate time series t If the target heart rate data is within the preset threshold range, it is judged that the detection is normal. If the mean D of the heart rate time series is t If it is not within the preset threshold range, the target heart rate data detection is judged to be abnormal and there is a risk.
8. The system for improving heart rate monitoring accuracy according to claim 1, characterized in that: In the risk analysis and warning output module, risk analysis is performed on the time period in which the target heart rate data is judged to be abnormal. According to the risk analysis result, the warning level is determined and warning information is generated, and the warning information is sent to the user end through the user interface.
9. A method for improving the accuracy of heart rate monitoring, for using the system for improving the accuracy of heart rate monitoring according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step S1: measuring raw heart rate data by contacting the skin surface with a heart rate sensor; Step S2: performing data denoising on the original heart rate data to obtain target heart rate data; Step S3: Decomposing the target heart rate data using a frequency decomposition mathematical model to calculate a heart rate decomposition coefficient; Step S4: performing noise quantization on the heart rate decomposition coefficient to calculate the heart rate correction coefficient; Step S5: performing data correction on the heart rate time series according to the heart rate correction coefficient; Step S6: performing risk assessment on the target heart rate data based on the corrected heart rate time series and the threshold value; Step S7: Perform risk judgment on the target heart rate data based on the corrected heart rate time series and according to the threshold.