Physiological data monitoring method and system and wearable device

Abstract

The invention relates to the technical field of data monitoring, and discloses a physiological data monitoring method and system and wearable device.The physiological data monitoring method comprises the steps that physiological data collected by a plurality of sampling channels are obtained; acquiring data quality parameters corresponding to the physiological data; determining the credibility of the corresponding physiological data based on the data quality parameters, wherein the credibility is the reliability degree of the corresponding physiological data collected by each sampling channel; determining a measurement result based on the credibility of the physiological data; and a measurement result is fed back to the user side. According to the physiological data monitoring method, the accuracy of physiological data measurement can be remarkably improved, the influence of a low-quality sampling channel is automatically inhibited in common degradation scenes such as motion artifacts, wearing looseness, ambient light interference and individual blood vessel differences, and the reliability of a physiological data measurement result is improved.

Description

Technical Field

[0001] This application relates to the field of data monitoring technology, and in particular to a physiological data monitoring method, system and wearable device. Background Technology

[0002] With increasing health awareness, people are paying more and more attention to the detection and management of personal physiological data. Currently, various physiological data monitoring devices exist on the market, primarily relying on PPG (Photoplethysmography) technology. However, PPG technology has some shortcomings in this physiological data monitoring device and its application. For example, it is prone to insecure wearing, susceptible to ambient light interference, motion artifacts, and vascular differences, all of which result in poor signal quality, affecting the accuracy of physiological data measurements. This can directly lead to significant deviations in key health indicators, making it difficult to meet the requirements for accurate health indicator monitoring. Summary of the Invention

[0003] In view of this, embodiments of this application provide a physiological data monitoring method, system, and wearable device, which can effectively solve problems such as inaccurate monitoring of key health indicators and significant biases.

[0004] In a first aspect, embodiments of this application provide a method for monitoring physiological data, including: Acquire physiological data from several sampling channels; Obtain the data quality parameters corresponding to the physiological data; The reliability of the corresponding physiological data is determined based on the data quality parameters, wherein the reliability is the degree of reliability of the physiological data collected by each sampling channel. The measurement results are determined based on the reliability of the physiological data. The measurement results are then fed back to the user.

[0005] In a first possible embodiment of the first aspect, each of the sampling channels is used to collect several physiological data of the same type, and the method further includes: Based on the reliability of the physiological data collected from each of the sampling channels, the measurement results for the same type of physiological data are determined.

[0006] In a second possible embodiment of the first aspect, each sampling channel is used to collect several different types of physiological data, and the method further includes: The measurement results for each type of physiological data are determined based on the reliability of several of the physiological data for each type.

[0007] In a third possible embodiment of the first aspect, determining the reliability of the corresponding physiological data based on the data quality parameters includes: Determine the parameter weight for each of the data quality parameters, wherein the parameter weight is the contribution strength coefficient of the data quality parameter to the credibility; The credibility is obtained by weighting and summing all the data quality parameters of each physiological data based on the parameter weights.

[0008] In a fourth possible embodiment of the first aspect, the physiological data is a digital result of sampling and quantizing physiological data signals, and the step of obtaining the data quality parameters corresponding to the physiological data includes: Acquire the physiological data signal when the physiological data is collected in each of the sampling channels; The data quality parameters are calculated based on the physiological data signals.

[0009] In a fifth possible embodiment of the first aspect, the data quality parameter includes one or more of signal-to-noise ratio, peak-to-peak value, RMS value, variance, and standard deviation, and the calculation of the data quality parameter based on the physiological data signal includes at least one of the following: The signal-to-noise ratio (SNR) of the physiological data signal is obtained by calculating the ratio of the mean signal power spectral density to the mean noise power spectral density. The difference between the maximum and minimum AC components in the physiological data signal is calculated to obtain the peak-to-peak value of the signal. The effective value of the signal is obtained by calculating the square root of the arithmetic mean of the squares of the AC components at each signal sampling point in the physiological data signal. The unbiased sample variance of the AC component at each of the signal sampling points in the physiological data signal is calculated to obtain the signal variance; The standard deviation of the signal is obtained by calculating the square root of the variance of the signal.

[0010] In a sixth possible embodiment of the first aspect, the measurement result includes a first measurement result, and determining the measurement result for the same type of physiological data includes: The credibility of each of the physiological data is compared, and the physiological data with the highest credibility is taken as the first measurement result.

[0011] In a seventh possible embodiment of the first aspect, the measurement result further includes a second measurement result, and determining the measurement result for the same type of physiological data further includes: The credibility of each physiological data point is normalized to obtain the normalized credibility. The normalized confidence level of each physiological data point is used as the data weight of each physiological data point. The second measurement result is obtained by weighting and summing the physiological data based on the data weight of each physiological data.

[0012] Secondly, embodiments of this application provide a wearable device, including: a plurality of sampling channels; The wearable device is used to send the physiological data collected by each of the sampling channels to the terminal device; The terminal device is used to perform the physiological data monitoring method described above.

[0013] Thirdly, embodiments of this application provide a physiological data monitoring system, including: a terminal device and at least one wearable device; Each of the wearable devices is used to collect the user's physiological data through several sampling channels; The terminal device is used to perform the physiological data monitoring method described above.

[0014] The embodiments of this application have the following beneficial effects: This embodiment of a physiological data monitoring method includes: acquiring physiological data collected from several sampling channels; acquiring data quality parameters corresponding to the physiological data; determining the reliability of the corresponding physiological data based on the data quality parameters, wherein the reliability is the degree of reliability of the physiological data collected by each sampling channel; determining a measurement result based on the reliability of the physiological data; and feeding the measurement result back to the user. Based on the above scheme, this physiological data monitoring method collects physiological data through multiple sampling channels, introduces data quality parameters to dynamically quantify the reliability of the physiological data collected by each sampling channel, and outputs the final measurement result based on the reliability. This significantly improves the accuracy of physiological data measurement. In common deterioration scenarios such as motion artifacts, loosening of the device, ambient light interference, and individual vascular differences, it automatically suppresses the influence of low-quality sampling channels, improves the reliability of the output measurement results, and enhances user trust in wearable devices in real-world usage environments. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic diagram of a physiological data monitoring system according to an embodiment of this application is shown; Figure 2 This paper illustrates a first flowchart of a physiological data monitoring method according to an embodiment of this application. Figure 3 A second flowchart of the physiological data monitoring method according to an embodiment of this application is shown.

[0017] Explanation of key component symbols: 100 - Physiological data monitoring system; 110 - Terminal equipment; 120 - Wearable device. Detailed Implementation

[0018] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0019] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0021] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0022] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0023] First, this application provides a physiological data monitoring system 100. Please refer to... Figure 1 This is a structural block diagram of the physiological data monitoring system 100 provided in this application embodiment. The physiological data monitoring system 100 may include: a terminal device 110 and at least one wearable device 120. The terminal device 110 and the wearable device 120 can be directly or indirectly connected via wireless communication or a communication interface to realize the transmission and interaction of physiological data.

[0024] In this embodiment, each wearable device 120 is used to collect the user's physiological data through several sampling channels. The terminal device 110 can process information and / or data related to the physiological data monitoring method to perform one or more functions described in this application. For example, the terminal device 110 can: acquire physiological data collected from several sampling channels; acquire data quality parameters corresponding to the physiological data; determine the reliability of the corresponding physiological data based on the data quality parameters, wherein the reliability is the degree of reliability of the physiological data collected from each sampling channel; determine the measurement result based on the reliability of the physiological data; and feed the measurement result back to the user. This enables the terminal device 110 to feed back accurate physiological data to the user, avoiding inaccurate physiological data fed back to the user due to wearing conditions or external environmental factors.

[0025] For ease of understanding, the following embodiments of this application will be described in terms of... Figure 1 Taking the physiological data monitoring system 100 shown as an example, and in conjunction with the accompanying drawings, the physiological data monitoring method provided in this application embodiment will be described.

[0026] Please refer to Figure 2 , Figure 2 A flowchart of a physiological data monitoring method provided in an embodiment of this application is shown. This physiological data monitoring method can be applied to the aforementioned terminal device 110, and may include the following steps: S210, acquires physiological data collected from several sampling channels.

[0027] As an example, the sampling channel is an independent optical signal acquisition path built based on a PPG sensor. By acquiring physiological data signals (PPG signals), the PPG signals can be further decoupled or used to calculate relevant physiological data, including but not limited to key physiological indicators such as heart rate, blood oxygen, respiratory rate, and heart rate interval.

[0028] S220, obtain the data quality parameters corresponding to the physiological data.

[0029] In this embodiment, the data quality parameter is an intermediate feature index extracted in real time from the PPG signals of each sampling channel, which can quantify the reliability of the signal. The data quality parameter does not directly correspond to physiological meaning, but it can objectively reflect the degree to which the current sampling channel is affected by motion artifacts, poor contact, ambient light crosstalk, or differences in tissue optical properties. For example, a low signal-to-noise ratio indicates that noise dominates, resulting in poor original signal quality and difficulty in extracting clear physiological data. A signal peak-to-peak value that is too small indicates that the PPG sensor applies too much pressure to the skin or that poor contact results in insufficient pressure, leading to poor PPG signal quality.

[0030] In one embodiment, the physiological data is the digital result of sampling and quantizing the physiological data signal, and the physiological data signal is obtained when physiological data is collected in each sampling channel; data quality parameters are calculated based on the physiological data signal.

[0031] In this embodiment, after the raw physiological data signal is sampled and quantized into digital physiological data, multidimensional quality parameters can be calculated directly and in real time based on the physiological data signal. These data quality parameters include, but are not limited to, signal-to-noise ratio (SNR), peak-to-peak voltage (Vpp), root mean square (RMS), variance, and standard deviation.

[0032] For example, the signal-to-noise ratio (SNR) reflects the power ratio of useful physiological data signals to noise (motion artifacts, thermal noise, ambient light interference), while the peak-to-peak value characterizes the integrity of the physiological data signal. The RMS value, variance, and standard deviation of the signal characterize the energy distribution and fluctuation stability of the signal. By calculating data quality parameters, the reliability of physiological data collected by the sampling channel can be comprehensively quantified, further determining whether the physiological data is measured accurately.

[0033] In one implementation, such as Figure 3 As shown, calculating data quality parameters includes at least the following steps: S221, calculate the ratio of the mean signal power spectral density to the mean noise power spectral density of the physiological data signal to obtain the signal-to-noise ratio.

[0034] In this embodiment, the mean power spectral density of the signal is the average amplitude of the power spectral density within the frequency band strongly correlated with the heart rhythm in the PPG signal, representing the energy concentration of the effective physiological data signal. The mean noise power spectral density is the average amplitude of the power spectral density within the interference frequency band outside the main frequency band of the PPG signal, reflecting the energy level of non-physiological components such as motion artifacts, power supply interference, and thermal noise. The signal-to-noise ratio (SNR) is the ratio of the mean energy calculated based on the PPG signal power spectral density within the physiologically effective frequency band determined by human physiological characteristics, to the mean energy calculated within the interference-sensitive frequency band after excluding this physiological frequency band; its value represents the relative advantage of demodulizable components derived from real physiological characteristics in the signal captured by the current sampling channel relative to various environmental and biological interference components.

[0035] S222 calculates the difference between the maximum and minimum AC components in the physiological data signal to obtain the peak-to-peak value of the signal.

[0036] In one implementation, firstly, the direct current (DC) component in the physiological data signal is calculated: ; In the formula, It represents the arithmetic mean of the entire physiological data signal, and represents the DC bias in the physiological data signal that does not change with time, i.e., the DC component; This represents the total number of sampling points in the physiological data signal. Indicates the first The original signal values ​​of each sampling point.

[0037] Then, the DC component is subtracted from the original value at each sampling point to obtain the AC component containing only fluctuations. The formula for calculating the AC component is: ; In the formula, Indicates the first The AC components at each sampling point can be used to determine the maximum value (maximum AC component) and the minimum value (minimum AC component) in the AC component sequence. Finally, the peak-to-peak value of the signal is calculated based on the maximum and minimum AC components. The formula for calculating the peak-to-peak value is as follows: ; In the formula, Indicates the peak-to-peak value of the signal. Indicates the maximum communication component. This represents the minimum communication component.

[0038] S223, calculate the square root of the arithmetic mean of the squares of the AC components at each sampling point in the physiological data signal to obtain the effective value of the signal.

[0039] In one embodiment, the formula for calculating the effective value of the signal is: ; In the formula, It represents the effective value of the signal, which quantifies the energy intensity of the AC component and accurately characterizes the activity and stability of physiological data signals.

[0040] S224, calculate the unbiased sample variance of the AC component at each signal sampling point in the physiological data signal to obtain the signal variance.

[0041] S225 calculates the square root of the signal variance to obtain the signal standard deviation.

[0042] In one embodiment, the formula for calculating the signal variance is: ; In the formula, The formulas for calculating the standard deviation of a signal, representing the variance, are: ; In the formula, This represents the standard deviation of the signal. In this embodiment, the dynamic stability of physiological data signals over time is objectively characterized by the signal variance and its square root standard deviation.

[0043] S230 determines the reliability of the corresponding physiological data based on data quality parameters. The reliability is the degree of reliability of the physiological data collected for each sampling channel.

[0044] For example, data quality parameters are used to characterize the acquisition quality of physiological data, providing quantifiable quality assessment criteria for physiological data from each sampling channel, thereby determining the reliability of physiological data, ensuring the accuracy and reliability of subsequent judgments of physiological data, and providing accurate and reliable physiological data to users.

[0045] In one embodiment, the parameter weight of each data quality parameter is determined, and the parameter weight is the contribution strength coefficient of the data quality parameter to the credibility; the credibility is obtained by weighted summation of all data quality parameters of each physiological data based on the parameter weight.

[0046] In this embodiment, parameter weights are used to reflect the differentiated contribution strength of each data quality parameter to the final credibility. For example, in ear-worn devices, motion artifacts dominate the error, so the parameter weights for the effective signal value and signal variance are relatively high; while in ring scenarios, pressure sensitivity is stronger, so the parameter weight for the peak-to-peak signal value is relatively high. Credibility is calculated based on the parameter weights of each data quality parameter using a weighted summation method. The formula for calculating credibility is: ; In the formula, Indicates credibility. The parameter weights represent the signal-to-noise ratio. Indicates the signal-to-noise ratio. The parameter weights represent the peak-to-peak value of the signal. The parameter weights representing the effective value of the signal The weights of the parameters representing the signal variance The parameter weights represent the standard deviation of the signal. .

[0047] S240, determine the measurement results based on the reliability of physiological data.

[0048] The S250 feeds the measurement results back to the user.

[0049] As an example, when the sampling channel in the wearable device 120 collects only one physiological data, the physiological data can be directly fed back to the user terminal, which is the terminal device 110 that ultimately presents the physiological measurement results and supports human-computer interaction.

[0050] In one embodiment, each sampling channel is used to collect several physiological data of the same type, and the measurement results of the same type of physiological data can be determined based on the reliability of the physiological data collected by each sampling channel.

[0051] In this embodiment, all sampling channels synchronously acquire PPG signals for the same physiological data (such as heart rate) and independently output the preliminary estimate and corresponding confidence level of the physiological data. This not only improves robustness by utilizing the redundancy of multiple sampling channels, but also determines the relative reliability of each sampling channel on the same type of physiological data through the confidence level, thus significantly improving the measurement accuracy of a single type of physiological data.

[0052] In another embodiment, each sampling channel is used to collect several different types of physiological data, and the measurement results for each type of physiological data are determined based on the reliability of several physiological data for each type.

[0053] In this embodiment, each sampling channel synchronously acquires the raw PPG signal through multi-wavelength LEDs and calculates various physiological data. Each physiological parameter is independently assessed for quality, thereby accurately obtaining the measurement results of each type of physiological data. This enables quality control of multidimensional physiological data and significantly improves the consistency and reliability of multi-physiological data monitoring. It is especially suitable for wearable scenarios that require the simultaneous output of multiple physiological data.

[0054] For example, for the collection results containing several physiological data, the physiological data can be screened or weighted according to their reliability. Only the physiological data measurement results that meet the preset reliability conditions can be output, or the physiological data measurement results with different reliability are assigned different weights and then merged for output, thereby improving the overall reliability and accuracy of the physiological data measurement results.

[0055] In one embodiment, the measurement result includes a first measurement result, which compares the reliability of various physiological data and selects the physiological data with the highest reliability as the first measurement result.

[0056] In this embodiment, the first measurement result is generated by a reliability optimization method. Among the same type of physiological data and their corresponding reliability output from all sampling channels, the physiological data with the highest reliability is directly selected as the final measurement result. Reliability is used as the sole screening criterion to ensure that the measurement result originates from the current optimal signal pathway.

[0057] In another embodiment, the measurement result also includes a second measurement result, wherein the confidence level of each physiological data is normalized to obtain a normalized confidence level; the normalized confidence level of each physiological data is used as the data weight of each physiological data; and the physiological data are weighted and summed based on the data weight of each physiological data to obtain the second measurement result.

[0058] In this embodiment, a data weighting system is constructed by normalizing the confidence level to achieve a weighted fusion output of the second measurement result. First, the confidence levels of each channel are linearly normalized so that the sum of the data weights is one. Then, the weighted sum of each physiological data point is used to calculate the second measurement result. The formula for calculating the data weights is: ; In the formula, Indicates the first The data weights of each physiological data point Indicates the first The reliability of physiological data This represents the total number of physiological data.

[0059] The formula for calculating the second measurement result is: ; In the formula, This indicates the second measurement result. Indicates the first Several physiological data points, among which... .

[0060] In this embodiment, while retaining the dominant position of high-reliability physiological data, the final measurement result is obtained by comprehensively calculating using medium- and low-reliability physiological data. This reduces the impact of transient anomalies in a single channel on the final physiological data measurement result, demonstrating the insensitivity of the measurement result to accidental interference. Even when faced with various uncontrollable interference factors in real-world wearing scenarios of wearable devices 120, this application can still continuously output stable, accurate, and reliable physiological data measurement results.

[0061] This application also provides a wearable device 120, which, exemplary, includes a plurality of sampling channels. In this embodiment, the wearable device 120 is used to send physiological data collected by each sampling channel to a terminal device 110; the terminal device 110 is used to execute the physiological data monitoring method of the above embodiment.

[0062] In one embodiment, the wearable device 120 can be a multi-terminal wearable device 120 or a single-terminal wearable device 120. The wearable device 120 includes, but is not limited to, smartwatches, TWS (True Wireless Stereo) wireless earphones (including a TWS left earphone and a TWS right earphone), smart rings, smart bracelets, and patch-type physiological monitoring devices. When the wearable device 120 is a multi-terminal wearable device 120, both the TWS left earphone and the TWS right earphone can acquire the aforementioned physiological data through several sampling channels.

[0063] This application also provides a terminal device 110, which, exemplary, includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the terminal device 110 to perform the aforementioned physiological data monitoring method. The terminal device 110 includes, but is not limited to, wearable terminals (e.g., the main control MCU and ring embedded processor of smartwatches, TWS wireless earphones, etc.), mobile terminals (e.g., smartphones, tablets, etc.), and medical terminals (e.g., portable monitors, home health all-in-one machines, etc.).

[0064] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0065] Memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM). Memory is used to store computer programs, and the processor can execute these programs upon receiving execution instructions.

[0066] This application also provides a computer-readable storage medium for storing computer programs used in the terminal device 110 described above. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0067] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0068] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0069] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0070] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring physiological data, characterized in that, include: Acquire physiological data from several sampling channels; Obtain the data quality parameters corresponding to the physiological data; The reliability of the corresponding physiological data is determined based on the data quality parameters, wherein the reliability is the degree of reliability of the physiological data collected by each sampling channel. The measurement results are determined based on the reliability of the physiological data. The measurement results are then fed back to the user.

2. The physiological data monitoring method according to claim 1, characterized in that, Each of the aforementioned sampling channels is used to collect several physiological data of the same type, and the method further includes: Based on the reliability of the physiological data collected from each of the sampling channels, the measurement results for the same type of physiological data are determined.

3. The physiological data monitoring method according to claim 1, characterized in that, Each of the sampling channels is used to collect several different types of physiological data, and the method further includes: The measurement results for each type of physiological data are determined based on the reliability of several of the physiological data for each type.

4. The physiological data monitoring method according to claim 1, characterized in that, Determining the reliability of the corresponding physiological data based on the data quality parameters includes: Determine the parameter weight for each of the data quality parameters, wherein the parameter weight is the contribution strength coefficient of the data quality parameter to the credibility; The credibility is obtained by weighting and summing all the data quality parameters of each physiological data based on the parameter weights.

5. The physiological data monitoring method according to claim 1, characterized in that, The physiological data is the digital result of sampling and quantizing physiological data signals. The step of obtaining the data quality parameters corresponding to the physiological data includes: Acquire the physiological data signal when the physiological data is collected in each of the sampling channels; The data quality parameters are calculated based on the physiological data signals.

6. The physiological data monitoring method according to claim 5, characterized in that, The data quality parameters include one or more of the following: signal-to-noise ratio, peak-to-peak value, RMS value, variance, and standard deviation. The calculation of the data quality parameters based on the physiological data signals includes at least one of the following: The signal-to-noise ratio (SNR) of the physiological data signal is obtained by calculating the ratio of the mean signal power spectral density to the mean noise power spectral density. The difference between the maximum and minimum AC components in the physiological data signal is calculated to obtain the peak-to-peak value of the signal. The effective value of the signal is obtained by calculating the square root of the arithmetic mean of the squares of the AC components at each signal sampling point in the physiological data signal. The unbiased sample variance of the AC component at each of the signal sampling points in the physiological data signal is calculated to obtain the signal variance; The standard deviation of the signal is obtained by calculating the square root of the variance of the signal.

7. The physiological data monitoring method according to claim 2, characterized in that, The measurement results include a first measurement result, and determining the measurement results for the same type of physiological data includes: The credibility of each of the physiological data is compared, and the physiological data with the highest credibility is taken as the first measurement result.

8. The physiological data monitoring method according to claim 2, characterized in that, The measurement results also include a second measurement result, and the determination of the measurement results for the same type of physiological data further includes: The credibility of each physiological data point is normalized to obtain the normalized credibility. The normalized confidence level of each physiological data point is used as the data weight of each physiological data point. The second measurement result is obtained by weighting and summing the physiological data based on the data weight of each physiological data.

9. A wearable device, characterized in that, include: Several sampling channels; The wearable device is used to send the physiological data collected by each of the sampling channels to the terminal device; The terminal device is used to perform the physiological data monitoring method as described in any one of claims 1-8.

10. A physiological data monitoring system, characterized in that, include: Terminal equipment and at least one wearable device; Each of the wearable devices is used to collect the user's physiological data through several sampling channels; The terminal device is used to perform the physiological data monitoring method as described in any one of claims 1-8.

Images