CN118662111A - Intelligent wearable watch and health monitoring control method thereof - Google Patents

Abstract

The application provides an intelligent wearable watch and a health monitoring control method thereof, wherein after heart rate monitoring of the intelligent wearable watch is started, an observed heart rate signal of a tested user is obtained, a heart rate fluctuation influence factor of the tested user is determined according to the observed heart rate signal, an interfered heart rate signal interval of the observed heart rate signal is determined according to the heart rate fluctuation influence factor, a heart rate differential signal is obtained by carrying out differential regulation on the observed heart rate signal through a preset heart rate differential coefficient, a heart rate fluctuation calibration gradient is determined by the heart rate differential signal and the observed heart rate signal, observed heart rate values in the interfered heart rate signal interval in the observed heart rate signal are reconstructed according to the heart rate fluctuation calibration gradient, an actual heart rate signal of the tested user is obtained, a heart rate state of the tested user is determined according to the actual heart rate signal, and a monitoring result is sent to a monitoring center, so that the accuracy of health monitoring can be improved.

Description

A smart wearable watch and health monitoring control method thereof

Technical Field

The present application relates to the technical field of smart watches, and more specifically, to a smart wearable watch and a health monitoring and control method thereof.

Background Art

Watches have continued to evolve and innovate in different historical periods and applications. From mechanical watches to smart watches, the development of watch technology has provided people with more choices and promoted the continuous progress of watch design and manufacturing. Smart watches are a new type of watch that has emerged in recent years. They combine the appearance of traditional watches with digital technology. Smart watches can be connected to smartphones to provide functions such as notifications, health tracking and navigation.

Health monitoring through smart wearable watches uses sensor technology to measure and collect data about physical condition. This data can provide information about health and lifestyle to help people better manage their health and make medical diagnoses. However, existing technologies have the problem of insufficient accuracy of health monitoring results.

Summary of the invention

The present application provides a smart wearable watch and a health monitoring control method thereof, which can improve the accuracy of health monitoring.

In a first aspect, the present application provides a health monitoring control method for a smart wearable watch, comprising the following steps:

Start the heart rate monitoring of the smart wearable watch to obtain the observed heart rate signal of the user being tested;

Determine a heart rate fluctuation influence factor of the measured user according to the observed heart rate signal, and then determine an interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation influence factor;

Performing differential adjustment on the observed heart rate signal by using a preset heart rate differential coefficient to obtain a heart rate differential signal, and then determining a heart rate fluctuation calibration gradient by using the heart rate differential signal and the observed heart rate signal;

Reconstructing the observed heart rate value in the interfered heart rate signal interval in the observed heart rate signal according to the heart rate fluctuation calibration gradient to obtain the actual heart rate signal of the measured user;

The heart rate status of the user being measured is determined according to the actual heart rate signal, and the monitoring result is sent to the monitoring center.

In some embodiments, reconstructing the observed heart rate value in the interfered heart rate signal interval in the observed heart rate signal according to the heart rate fluctuation calibration gradient to obtain the actual heart rate signal of the measured user specifically includes:

Determine the actual heart rate value in the interfered heart rate signal interval in the observed heart rate signal according to the heart rate fluctuation calibration gradient and the observed heart rate value;

Obtaining an undisturbed observed heart rate value in the observed heart rate signal;

Taking the actual heart rate value and the undisturbed observed heart rate value in the observed heart rate signal as the actual observed heart rate value;

The actual heart rate signal of the measured user is determined based on all the actually observed heart rate values.

In some embodiments, determining the heart rate fluctuation influencing factor of the measured user according to the observed heart rate signal specifically includes:

determining a heart rate sequence according to the observed heart rate signal;

Performing standardization on the heart rate sequence to obtain a reference heart rate sequence;

Determining a time weight value of the observed heart rate signal in a time direction;

Determining a heart rate weight value of the observed heart rate signal in a direction of the observed heart rate value according to the heart rate sequence;

Determining the intermediate moments of the observed heart rate signal and the intermediate weighted values corresponding to the intermediate moments;

Determine a neighborhood heart rate moment and a neighborhood weighted value corresponding to the neighborhood heart rate moment according to the intermediate moment;

The heart rate fluctuation influencing factor is determined based on the benchmark heart rate sequence, the time weight value of the observed heart rate signal in the time direction, the heart rate weight value of the observed heart rate signal in the observed heart rate value direction, the intermediate moment of the observed heart rate signal and the intermediate weighted value corresponding to the intermediate moment, and the neighborhood heart rate moment and the domain weighted value corresponding to the neighborhood heart rate moment.

In some embodiments, determining the interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation influencing factor specifically includes:

Determining the heart rate fluctuation smoothness according to the heart rate fluctuation influencing factor;

The interfered heart rate signal interval of the observed heart rate signal is determined according to the heart rate fluctuation smoothness.

In some embodiments, determining the heart rate fluctuation smoothness according to the heart rate fluctuation influencing factor specifically includes:

Obtaining a baseline heart rate sequence;

Obtaining the heart rate fluctuation influencing factor;

The heart rate fluctuation smoothness is determined according to the reference heart rate sequence and the heart rate fluctuation influencing factor.

In some embodiments, determining the interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation smoothness specifically includes:

Dividing the observed heart rate signal according to a preset heart rate signal segmentation index to obtain a plurality of heart rate signal intervals;

An interference judgment is performed on each heart rate signal interval according to the heart rate fluctuation smoothness, thereby determining the interfered heart rate signal interval of the observed heart rate signal.

In some embodiments, performing differential adjustment on the observed heart rate signal by using a preset heart rate differential coefficient to obtain a heart rate differential signal specifically includes:

Preset heart rate differential coefficient;

Get the heart rate gradient value corresponding to each observed heart rate value in the heart rate sequence;

Each heart rate gradient value is jointly adjusted with the heart rate differential coefficient to obtain a heart rate differential signal.

In a second aspect, the present application provides a smart wearable watch, including a health monitoring control unit, wherein the health monitoring control unit includes:

An acquisition module is used to acquire the observed heart rate signal of the user being measured after the heart rate monitoring of the smart wearable watch is started;

a processing module, configured to determine a heart rate fluctuation influence factor of the measured user according to the observed heart rate signal, and further determine an interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation influence factor;

The processing module is further used to perform differential adjustment on the observed heart rate signal by using a preset heart rate differential coefficient to obtain a heart rate differential signal, and then determine a heart rate fluctuation calibration gradient by using the heart rate differential signal and the observed heart rate signal;

The processing module is further used to reconstruct the observed heart rate value in the interfered heart rate signal interval in the observed heart rate signal according to the heart rate fluctuation calibration gradient to obtain the actual heart rate signal of the measured user;

The monitoring module determines the heart rate status of the user under test according to the actual heart rate signal and sends the monitoring result to the monitoring center.

In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned health monitoring control method for the smart wearable watch.

In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned health monitoring control method for the smart wearable watch when executed.

The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

In the present application, after starting the heart rate monitoring of the smart wearable watch, the observed heart rate signal of the measured user is obtained, and the heart rate fluctuation influence factor of the measured user is determined according to the observed heart rate signal. The heart rate fluctuation influence factor in the present application is a parameter used to evaluate the degree of mutation of the observed heart rate signal. The larger the heart rate fluctuation influence factor, the greater the degree of mutation of the observed heart rate signal. Subsequently, the interfered heart rate signal interval of the observed heart rate signal is determined by the heart rate fluctuation influence factor, and the specific interval where the observed heart rate signal needs to be reconstructed is clarified. Then, the observed heart rate signal is differentially adjusted according to the preset heart rate differential coefficient to obtain a heart rate differential signal. The heart rate fluctuation calibration gradient is determined by the heart rate differential signal and the observed heart rate signal. The heart rate fluctuation calibration gradient is used to characterize the degree of change and the direction of change of the observed heart rate signal at the corresponding moment in the interfered heart rate signal interval. In the present application, the observed heart rate value in the interfered heart rate signal interval in the observed heart rate signal is reconstructed according to the heart rate fluctuation calibration gradient, thereby recovering the lost data. The data, i.e., the actual observed heart rate value corresponding to the actual heart rate signal of the user being measured, can more accurately reflect the real-time heart rate of the user being measured, enhance the accuracy and reliability of the real-time heart rate of the user being measured, and make the final health monitoring result more accurate.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

FIG1 is an exemplary flow chart of a health monitoring control method of a smart wearable watch according to some embodiments of the present application;

FIG2 is an exemplary flow chart of determining the smoothness of heart rate fluctuation according to some embodiments of the present application;

FIG3 is an exemplary flow chart of determining an actual heart rate signal according to some embodiments of the present application;

FIG4 is a schematic diagram of exemplary hardware and/or software of a health monitoring control unit according to some embodiments of the present application;

FIG5 is a schematic diagram of the structure of a computer device for implementing a health monitoring control method for a smart wearable watch according to some embodiments of the present application.

DETAILED DESCRIPTION

The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

The embodiment of the present application provides a smart wearable watch and a health monitoring control method thereof, the core of which is to start heart rate monitoring of the smart wearable watch, obtain an observed heart rate signal of a measured user, determine a heart rate fluctuation influencing factor of the measured user according to the observed heart rate signal, determine a disturbed heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation influencing factor, perform differential adjustment on the observed heart rate signal according to a preset heart rate differential coefficient to obtain a heart rate differential signal, determine a heart rate fluctuation calibration gradient through the heart rate differential signal and the observed heart rate signal, reconstruct the observed heart rate value within the disturbed heart rate signal interval in the observed heart rate signal through the heart rate fluctuation calibration gradient to obtain an actual heart rate signal of the measured user, determine the heart rate state of the measured user based on the actual heart rate signal, and send the monitoring result to a monitoring center, thereby improving the accuracy of health monitoring.

In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the drawings and specific implementation methods of the specification. Referring to Figure 1, this figure is an exemplary flow chart of a health monitoring control method for a smart wearable watch according to some embodiments of the present application. The health monitoring control method 100 of the smart wearable watch mainly includes the following steps:

In step 101, the heart rate monitoring of the smart wearable watch is started to obtain the observed heart rate signal of the user being measured.

In specific implementation, after starting the heart rate monitoring of the smart wearable watch, the built-in optical heart rate sensor in the smart wearable watch collects the heart rate value of the user being measured in real time by irradiating the skin of the user being measured and detecting changes in reflected light, and then uses the collected heart rate values as observed heart rate values. The data processor in the smart wearable watch generates an observed heart rate signal of the user being measured based on the obtained observed heart rate values and the collection time corresponding to the observed heart rate values.

It should be noted that in the present application, the heart rate value of the measured user is collected in real time at a specified frequency. In some embodiments, the specified frequency can be set according to actual needs. For example, when it is necessary to monitor the heart rate of the measured user at a high frequency, the heart rate value of the measured user is collected in real time at a frequency of once per second. When it is necessary to monitor the heart rate of the measured user at a low frequency, the heart rate value of the measured user is collected in real time at a frequency of once every five minutes. In other embodiments, other methods can also be used to set the specified frequency, which is not specifically limited here.

In step 102, a heart rate fluctuation influence factor of the measured user is determined according to the observed heart rate signal, and then a disturbed heart rate signal interval of the observed heart rate signal is determined according to the heart rate fluctuation influence factor.

In some embodiments, determining the heart rate fluctuation influencing factor of the measured user according to the observed heart rate signal can be implemented by the following steps:

determining a heart rate sequence according to the observed heart rate signal;

Performing standardization on the heart rate sequence to obtain a reference heart rate sequence;

Determining a time weight value of the observed heart rate signal in a time direction;

Determining a heart rate weight value of the observed heart rate signal in a direction of the observed heart rate value according to the heart rate sequence;

Determining the intermediate moments of the observed heart rate signal and the intermediate weighted values corresponding to the intermediate moments;

Determine a neighborhood heart rate moment and a neighborhood weighted value corresponding to the neighborhood heart rate moment according to the intermediate moment;

The heart rate fluctuation influence factor is determined according to the reference heart rate sequence, the time weight value of the observed heart rate signal in the time direction, the heart rate weight value of the observed heart rate signal in the observed heart rate value direction, the intermediate moment of the observed heart rate signal and the intermediate weighted value corresponding to the intermediate moment, and the neighborhood heart rate moment and the domain weighted value corresponding to the neighborhood heart rate moment, wherein the heart rate fluctuation influence factor can be determined according to the following formula:

in, represents the heart rate fluctuation influencing factor, Indicates the total number of reference heart rate values in the reference heart rate sequence. Indicates the first The baseline heart rate value, Indicates the first The baseline heart rate value, Represents the time weight value of the observed heart rate signal in the time direction, Indicates the heart rate weight value of the observed heart rate signal in the direction of the observed heart rate value, represents a constant, represents the standard deviation of the heart rate series, Indicates the middle moment The corresponding intermediate weighted value, Indicates the neighborhood heart rate moment The corresponding neighborhood weight value.

In specific implementation, the observed heart rate value corresponding to each acquisition time in the observed heart rate signal is obtained, and all the obtained observed heart rate values are arranged in the order of acquisition time to obtain a heart rate sequence; the maximum observed heart rate value in the heart rate sequence is obtained, the first observed heart rate value in the heart rate sequence is selected, the observed heart rate value is subtracted from the maximum observed heart rate value, and then the ratio of the difference obtained by the difference to the standard deviation of the heart rate sequence is used as the first reference heart rate value in the reference heart rate sequence, the second observed heart rate value in the heart rate sequence is selected, the observed heart rate value is subtracted from the maximum observed heart rate value, and then the ratio of the difference obtained by the difference to the standard deviation of the heart rate sequence is used as the second reference heart rate value in the reference heart rate sequence, and so on, traversing each observed heart rate value in the heart rate sequence, obtaining the reference heart rate value corresponding to each observed heart rate value, and thus obtaining the reference heart rate sequence; the calculus method can be used to calculate the derivative of the observed heart rate signal in the time direction to obtain multiple time gradient values, normalize each time gradient value, and calculate Calculate the average value of all normalized time gradient values to obtain the time gradient average value, subtract each normalized time gradient value from the time gradient average value to obtain multiple differences, and use the average of all differences as the time weight value of the observed heart rate signal in the time direction; select an observed heart rate value in the heart rate sequence, calculate the difference between the observed heart rate value and the previous observed heart rate value of the observed heart rate value, and use the difference as the heart rate gradient value of the observed heart rate value, repeat the above steps for the remaining observed heart rate values in the heart rate sequence to obtain the heart rate gradient values of the remaining observed heart rate values, normalize each heart rate gradient value, calculate the average value of all normalized heart rate gradient values to obtain the heart rate gradient average value, subtract each normalized heart rate gradient value from the heart rate gradient average value to obtain multiple normalized heart rate gradient differences, and use the average of all normalized heart rate gradient differences as the heart rate weight value of the observed heart rate signal in the direction of the observed heart rate value; obtain the middle moment in time of the observed heart rate signal , and then obtain the intermediate time The corresponding observed heart rate value and the normalized heart rate gradient difference corresponding to the observed heart rate value are converted into The product of the corresponding observed heart rate value and the normalized heart rate gradient difference corresponding to the observed heart rate value is taken as the intermediate moment The intermediate weighted value ; Obtain the collection time of the heart rate signal closest to the middle moment in time as the neighborhood heart rate moment , similarly, get the neighborhood heart rate time The corresponding observed heart rate value and the normalized heart rate gradient difference corresponding to the observed heart rate value are converted into the neighborhood heart rate moment The product of the corresponding observed heart rate value and the normalized heart rate gradient difference corresponding to the observed heart rate value is taken as the neighborhood heart rate moment The neighborhood weighted value of .

It should be noted that the heart rate fluctuation impact factor in the present application is a parameter used to evaluate the degree of mutation of the observed heart rate signal. The larger the heart rate fluctuation impact factor, the greater the degree of mutation of the observed heart rate signal. The smaller the heart rate fluctuation impact factor, the smaller the degree of mutation of the observed heart rate signal.

In some embodiments, determining the interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation influencing factor can be implemented by the following steps:

Determining the heart rate fluctuation smoothness according to the heart rate fluctuation influencing factor;

The interfered heart rate signal interval of the observed heart rate signal is determined according to the heart rate fluctuation smoothness.

In some embodiments, referring to FIG. 2 , which is an exemplary flow chart of determining the smoothness of heart rate fluctuation in some embodiments of the present application, the determination of the smoothness of heart rate fluctuation in this embodiment can be implemented by the following steps:

First, in step 1021, a reference heart rate sequence is obtained;

Next, in step 1022, the heart rate fluctuation influencing factor is obtained;

Finally, in step 1023, the heart rate fluctuation smoothness is determined according to the reference heart rate sequence and the heart rate fluctuation influencing factor.

In the above embodiment, in specific implementation, the heart rate fluctuation smoothness can be determined according to the following formula:

in, Indicates the smoothness of heart rate fluctuations. represents the heart rate fluctuation influencing factor, Indicates the total number of reference heart rate values in the reference heart rate sequence. Indicates the first The baseline heart rate value, Indicates the first The baseline heart rate value, Indicates the first Base heart rate value.

It should be noted that the heart rate fluctuation smoothness in this application is an indicator used to measure the regularity of the observed heart rate signal fluctuations. The higher the heart rate fluctuation smoothness, the higher the regularity of the observed heart rate signal fluctuations. The lower the heart rate fluctuation smoothness, the lower the regularity of the observed heart rate signal fluctuations.

Among them, in some embodiments, determining the interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation smoothness can be specifically implemented by the following steps:

Dividing the observed heart rate signal according to a preset heart rate signal segmentation index to obtain a plurality of heart rate signal intervals;

An interference judgment is performed on each heart rate signal interval according to the heart rate fluctuation smoothness, thereby determining the interfered heart rate signal interval of the observed heart rate signal.

In specific implementation, the segmentation index of the heart rate signal can be preset according to the period of the observed heart rate signal, and the duration of one period of acquisition time corresponding to the observed heart rate signal can be used as the segmentation index of the heart rate signal; the observed heart rate signal can be divided according to the segmentation index of the heart rate signal. For example, assuming that the segmentation index of the heart rate signal is 0.4s and the total acquisition time of the observed heart rate signal is 60s, the time in The observed heart rate signal corresponding to the interval is taken as the first heart rate signal interval, and the time is The observed heart rate signal corresponding to the interval is taken as the second heart rate signal interval, and so on, until the 60s observed heart rate signal is completely divided, thereby obtaining multiple heart rate signal intervals; select a heart rate signal interval, compare the variance value of the observed heart rate value in the heart rate signal interval with the heart rate fluctuation smoothness, when the variance value exceeds the heart rate fluctuation smoothness, the heart rate signal interval is determined to be an interfered interval, repeat the above steps, perform interference judgment on the remaining heart rate signal intervals, and take the set of all heart rate signal intervals corresponding to the interfered intervals as the interfered heart rate signal interval of the observed heart rate signal.

It should be noted that by determining the interfered heart rate signal interval of the observed heart rate signal through the heart rate fluctuation smoothness, the specific interval where the observed heart rate signal needs to be reconstructed is clarified, thereby improving the accuracy and efficiency of subsequent reconstruction of the observed heart rate signal.

In step 103, the observed heart rate signal is differentially adjusted using a preset heart rate differential coefficient to obtain a heart rate differential signal, and then a heart rate fluctuation calibration gradient is determined using the heart rate differential signal and the observed heart rate signal.

In some embodiments, differential adjustment of the observed heart rate signal according to a preset heart rate differential coefficient to obtain a heart rate differential signal can be specifically implemented by the following steps:

Preset heart rate differential coefficient;

Get the heart rate gradient value corresponding to each observed heart rate value in the heart rate sequence;

Each heart rate gradient value is jointly adjusted with the heart rate differential coefficient to obtain a heart rate differential signal.

It should be noted that the heart rate differential coefficient in the present application is a parameter used to indicate the degree of change and irregularity of the observed heart rate signal. In some embodiments, the heart rate differential coefficient can be preset according to the discreteness and irregularity of the observed heart rate signal. The heart rate differential coefficient generally takes a value in the range of 0.1 - 1. In other embodiments, other methods may also be used for preset, which is not limited here.

In specific implementation, each heart rate gradient value is jointly adjusted with the heart rate differential coefficient to obtain a heart rate differential signal, that is: the heart rate gradient value corresponding to the first observed heart rate value in the heart rate sequence is selected and multiplied by the heart rate differential coefficient, and the product value and the acquisition time corresponding to the first observed heart rate value constitute the first signal in the heart rate differential signal; the heart rate gradient value corresponding to the second observed heart rate value in the heart rate sequence is selected and multiplied by the heart rate differential coefficient, and the product value and the acquisition time corresponding to the second observed heart rate value constitute the second signal in the heart rate differential signal, and so on, the remaining observed heart rate values in the heart rate sequence are traversed to obtain the signals corresponding to the remaining observed heart rate values, thereby obtaining a heart rate differential signal.

In some embodiments, determining the heart rate fluctuation calibration gradient from the heart rate difference signal and the observed heart rate signal can be implemented by the following steps:

determining a heart rate fluctuation calibration gradient value according to the heart rate difference signal and the observed heart rate signal;

Determine the heart rate fluctuation calibration gradient direction;

The heart rate fluctuation calibration gradient is composed of the heart rate fluctuation calibration gradient value and the heart rate fluctuation calibration gradient direction.

In some embodiments, determining the heart rate fluctuation calibration gradient value according to the heart rate difference signal and the observed heart rate signal may be implemented by the following steps:

Determine a heart rate neighbor correlation coefficient for each adjacent observed heart rate value in the observed heart rate signal, and then determine a maximum heart rate neighbor correlation coefficient and a standard deviation of all heart rate neighbor correlation coefficients;

Acquire the heart rate difference signal, the heart rate difference coefficient, and the heart rate fluctuation smoothness;

A heart rate fluctuation calibration gradient value is determined according to the observed heart rate signal, the maximum heart rate neighbor correlation coefficient and the standard deviation of all heart rate neighbor correlation coefficients, the heart rate difference signal, the heart rate difference coefficient and the heart rate fluctuation smoothness, wherein the heart rate fluctuation calibration gradient value can be determined according to the following formula:

in, Indicates that the observed heart rate signal is collected at time The heart rate fluctuation calibration gradient value at Indicates the total acquisition time of observing the heart rate signal. Indicates that the observed heart rate signal is collected at time The observed heart rate value at represents the heart rate differential coefficient, Indicates that the heart rate differential signal is collected at time The value of Indicates the smoothness of heart rate fluctuations, represents the maximum heart rate proximity correlation coefficient, Represents the standard deviation of all heart rate neighbor correlation coefficients.

In a specific implementation, two adjacent observed heart rate values in the observed heart rate signal are taken as adjacent observed heart rate values, and so on, the adjacent observed heart rate values of each observed heart rate value in the heart rate signal are determined, the difference of the observed heart rate values in each adjacent observed heart rate value is determined, the average value of all the differences is calculated, one of the adjacent observed heart rate values is selected, and the ratio of the difference between the adjacent observed heart rate values to the average value is taken as the heart rate proximity correlation coefficient of the adjacent observed heart rate value, and the above steps are repeated for the remaining adjacent observed heart rate values to obtain the heart rate proximity correlation coefficients of the remaining adjacent observed heart rate values, and then the maximum heart rate proximity correlation coefficient and the standard deviation of all heart rate proximity correlation coefficients are determined.

It should be noted that the heart rate neighborhood correlation coefficient in this embodiment is an indicator of the correlation strength between adjacent observed heart rate values in the observed heart rate signal. The larger the heart rate neighborhood correlation coefficient, the greater the correlation strength between adjacent observed heart rate values in the observed heart rate signal. The smaller the heart rate neighborhood correlation coefficient, the smaller the correlation strength between adjacent observed heart rate values in the observed heart rate signal. In addition, in this embodiment, the heart rate difference signal is collected at a time of The value of , which is the heart rate sequence The product value obtained by multiplying the heart rate gradient value corresponding to the observed heart rate value at the moment by the heart rate differential coefficient.

In the specific implementation, the determination of the heart rate fluctuation calibration gradient direction can be implemented in the following manner, for example: based on the acquisition time of the observed heart rate signal The difference between the time difference corresponding to the observed heart rate value at the moment and the observed heart rate value at the previous moment and the observed heart rate value is calculated using trigonometric functions. The angle between the observed heart rate value at the time and the observed heart rate value at the previous moment is used as the heart rate fluctuation calibration gradient direction. In other embodiments, other methods can also be used to determine the heart rate fluctuation calibration gradient direction, which is not specifically limited here.

It should be noted that the heart rate fluctuation calibration gradient in the present application is composed of a heart rate fluctuation calibration gradient value and a heart rate fluctuation calibration gradient direction, and the heart rate fluctuation calibration gradient is used to characterize the degree of change and direction of change of the observed heart rate signal at the corresponding moment in the interfered heart rate signal interval, and can be used to restore the observed heart rate value lost in the interfered heart rate signal interval, wherein the heart rate fluctuation calibration gradient value corresponds to the degree of change of the observed heart rate signal at the corresponding moment in the interfered heart rate signal interval, and the heart rate fluctuation calibration gradient direction corresponds to the direction of change of the observed heart rate signal at the corresponding moment in the interfered heart rate signal interval.

In addition, it should be noted that by determining the heart rate differential signal, the sensitivity to heart rate changes can be enhanced, and heart rate changes can be responded to in a timely manner. Determining the heart rate fluctuation calibration gradient helps to stabilize and calibrate the heart rate signal, thereby providing more accurate heart rate monitoring.

In step 104, the observed heart rate value in the interfered heart rate signal interval in the observed heart rate signal is reconstructed according to the heart rate fluctuation calibration gradient to obtain the actual heart rate signal of the measured user.

In some embodiments, referring to FIG. 3 , which is an exemplary flow chart of determining an actual heart rate signal in some embodiments of the present application, determining the actual heart rate signal in this embodiment can be implemented by the following steps:

First, in step 1041, the actual heart rate value in the interfered heart rate signal interval in the observed heart rate signal is determined according to the heart rate fluctuation calibration gradient and the observed heart rate value;

Next, in step 1042, an undisturbed observed heart rate value in the observed heart rate signal is obtained;

Then, in step 1043, the actual heart rate value and the observed heart rate value without interference in the observed heart rate signal are both used as the actual observed heart rate value;

Finally, in step 1044, the actual heart rate signal of the measured user is determined based on all the actually observed heart rate values.

In specific implementation, the actual heart rate value in the interfered heart rate signal interval in the observed heart rate signal is determined according to the heart rate fluctuation calibration gradient and the observed heart rate value, that is, the heart rate fluctuation calibration gradient at each acquisition time can be used as the gradient at the corresponding moment in the observed heart rate signal, and the actual heart rate value at the corresponding moment in the interfered heart rate signal interval in the observed heart rate signal is determined by the gradient descent method in the prior art. In other embodiments, the actual heart rate value can also be determined by other methods, which are not limited here; in addition, the observed heart rate values outside the interfered heart rate signal interval in the observed heart rate signal are used as the undisturbed observed heart rate values in the observed heart rate signal; the actual heart rate signal of the measured user is determined according to all the actual observed heart rate values, that is, all the actual observed heart rate values and the acquisition times corresponding to the actual observed heart rate values are composed of the actual heart rate signal of the measured user.

It should be noted that obtaining the actual heart rate signal of the user being measured through the heart rate fluctuation calibration gradient can more accurately reflect the actual heart rate of the user being measured, enhance the accuracy and reliability of the actual heart rate of the user being measured, and can improve the accuracy and reliability of subsequent smart wearable watches to issue alarms based on the actual heart rate signal of the user being measured.

In step 105, the heart rate state of the user being measured is determined according to the actual heart rate signal, and the monitoring result is sent to the monitoring center.

In specific implementation, the abnormal heart rate threshold can be preset according to the heart rate of a normal person. Usually, the abnormal heart rate threshold is set to 125 beats/minute. The heart rate state of the measured user is determined according to the actual heart rate signal, that is, each actually observed heart rate value in the actual heart rate signal of the measured user is compared with the preset abnormal heart rate threshold. Only when the actual observed heart rate value of the measured user exceeds the abnormal heart rate threshold, and the time when the actual observed heart rate value exceeds the abnormal heart rate threshold exceeds the abnormal heart rate threshold for more than 5 consecutive minutes, it means that the heartbeat of the measured user continues to be too fast, and it is determined that the heart rate state of the measured user is abnormal. The smart wearable watch is controlled to send the monitoring result of the abnormal heart rate of the measured user to the monitoring center. In other cases, the heart rate of the measured user is regarded as normal, and no processing is performed or the monitoring result of the normal heart rate of the measured user is sent to the monitoring center.

It should be noted that by determining the heart rate status of the user being measured based on the actual heart rate signal and sending the monitoring results to the monitoring center, the probability of the smart wearable watch falsely reporting abnormal heart rate of the user being measured is reduced, and the accuracy of health monitoring of the user being measured is enhanced, thereby realizing accurate health monitoring of the smart wearable watch.

In summary, in the present application, after starting the heart rate monitoring of the smart wearable watch, the observed heart rate signal of the measured user is obtained; the heart rate fluctuation influence factor of the measured user is determined according to the observed heart rate signal, and the heart rate fluctuation influence factor is a parameter used to evaluate the degree of mutation of the observed heart rate signal. The larger the heart rate fluctuation influence factor, the greater the degree of mutation of the observed heart rate signal. The interfered heart rate signal interval of the observed heart rate signal is determined by the heart rate fluctuation influence factor, and the specific interval where the observed heart rate signal needs to be reconstructed is clarified. Then, the observed heart rate signal is differentially adjusted according to the preset heart rate differential factor to obtain a heart rate differential signal, and the heart rate differential signal is obtained by the The heart rate differential signal and the observed heart rate signal determine the heart rate fluctuation calibration gradient, and the heart rate fluctuation calibration gradient is used to characterize the degree of change and the direction of change of the observed heart rate signal at the corresponding moment in the interfered heart rate signal interval. In the present application, the real-time heart rate value in the interfered heart rate signal interval in the observed heart rate signal is reconstructed according to the heart rate fluctuation calibration gradient, thereby recovering the lost data. The data, i.e., the actual observed heart rate value corresponding to the actual heart rate signal of the user being measured, can more accurately reflect the real-time heart rate of the user being measured, enhance the accuracy and reliability of the real-time heart rate of the user being measured, and make the final health monitoring result more accurate.

In addition, in another aspect of the present application, in some embodiments, the present application provides a smart wearable watch, the system also includes a health monitoring control unit, refer to Figure 4, which is a schematic diagram of exemplary hardware and/or software of the health monitoring control unit shown in some embodiments of the present application, the health monitoring control unit 400 includes: an acquisition module 401, a processing module 402 and a monitoring module 403, which are described as follows:

Acquisition module 401, in this application, acquisition module 401 is mainly used to obtain the observed heart rate signal of the user being measured after starting the heart rate monitoring of the smart wearable watch;

Processing module 402, in the present application, the processing module 402 is mainly used to determine the heart rate fluctuation influence factor of the measured user according to the observed heart rate signal, and then determine the interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation influence factor;

It should be noted that the processing module 402 in the present application is also used to perform differential adjustment on the observed heart rate signal by using a preset heart rate differential coefficient to obtain a heart rate differential signal, and then determine the heart rate fluctuation calibration gradient by using the heart rate differential signal and the observed heart rate signal;

In addition, the processing module 402 in the present application is also used to reconstruct the observed heart rate value in the interfered heart rate signal interval in the observed heart rate signal according to the heart rate fluctuation calibration gradient to obtain the actual heart rate signal of the measured user;

Monitoring module 403: In the present application, monitoring module 403 is mainly used to determine the heart rate status of the user being measured based on the actual heart rate signal, and send the monitoring result to the monitoring center.

The above describes in detail the examples of smart wearable watches and health monitoring control methods thereof provided by the embodiments of the present application. It can be understood that the corresponding device includes hardware structures and/or software modules corresponding to the execution of each function in order to realize the above functions. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

In some embodiments, the present application also provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned health monitoring control method for the smart wearable watch.

In some embodiments, referring to FIG5, the dotted line in the figure indicates that the unit or the module is optional, and the figure is a schematic diagram of the structure of a computer device for implementing a health monitoring control method for a smart wearable watch according to an embodiment of the present application. The health monitoring control method for a smart wearable watch in the above embodiment can be implemented by a computer device as shown in FIG5, and the computer device 500 includes at least one processor 501, a memory 502, and at least one communication unit 505, and the computer device 500 can be a terminal device, a server, or a chip.

The processor 501 may be a general-purpose processor or a special-purpose processor. For example, the processor 501 may be a central processing unit (CPU), which may be used to control the computer device 500, execute software programs, and process data of the software programs. The computer device 500 may also include a communication unit 505 to implement input (reception) and output (transmission) of signals.

For example, the computer device 500 may be a chip, the communication unit 505 may be an input and/or output circuit of the chip, or the communication unit 505 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other devices.

For another example, the computer device 500 may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

The computer device 500 may include one or more memories 502, on which a program 504 is stored. The program 504 can be executed by the processor 501 to generate instructions 503, so that the processor 501 performs the method described in the above method embodiment according to the instructions 503. Optionally, data (such as a target audit model) can also be stored in the memory 502. Optionally, the processor 501 can also read the data stored in the memory 502, and the data can be stored at the same storage address as the program 504, or the data can be stored at a different storage address from the program 504.

The processor 501 and the memory 502 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned health monitoring control method for the smart wearable watch when executed.

Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims (10)

1. A health monitoring control method for a smart wearable watch, characterized in that it comprises the following steps: Start the heart rate monitoring of the smart wearable watch to obtain the observed heart rate signal of the user being tested; Determine a heart rate fluctuation influence factor of the measured user according to the observed heart rate signal, and then determine an interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation influence factor; Performing differential adjustment on the observed heart rate signal by using a preset heart rate differential coefficient to obtain a heart rate differential signal, and then determining a heart rate fluctuation calibration gradient by using the heart rate differential signal and the observed heart rate signal; Reconstructing the observed heart rate value in the interfered heart rate signal interval in the observed heart rate signal according to the heart rate fluctuation calibration gradient to obtain the actual heart rate signal of the measured user; The heart rate status of the user being measured is determined according to the actual heart rate signal, and the monitoring result is sent to the monitoring center. 2. The method according to claim 1, wherein reconstructing the observed heart rate value in the interfered heart rate signal interval in the observed heart rate signal according to the heart rate fluctuation calibration gradient to obtain the actual heart rate signal of the measured user specifically comprises: Determine the actual heart rate value in the interfered heart rate signal interval in the observed heart rate signal according to the heart rate fluctuation calibration gradient and the observed heart rate value; Obtaining an undisturbed observed heart rate value in the observed heart rate signal; Taking the actual heart rate value and the undisturbed observed heart rate value in the observed heart rate signal as the actual observed heart rate value; The actual heart rate signal of the measured user is determined based on all the actually observed heart rate values. 3. The method according to claim 1, wherein determining the heart rate fluctuation influencing factor of the measured user according to the observed heart rate signal specifically comprises: determining a heart rate sequence according to the observed heart rate signal; Performing standardization on the heart rate sequence to obtain a reference heart rate sequence; Determining a time weight value of the observed heart rate signal in a time direction; Determining a heart rate weight value of the observed heart rate signal in a direction of the observed heart rate value according to the heart rate sequence; Determining the intermediate moments of the observed heart rate signal and the intermediate weighted values corresponding to the intermediate moments; Determine a neighborhood heart rate moment and a neighborhood weighted value corresponding to the neighborhood heart rate moment according to the intermediate moment; The heart rate fluctuation influencing factor is determined based on the benchmark heart rate sequence, the time weight value of the observed heart rate signal in the time direction, the heart rate weight value of the observed heart rate signal in the observed heart rate value direction, the intermediate moment of the observed heart rate signal and the intermediate weighted value corresponding to the intermediate moment, and the neighborhood heart rate moment and the domain weighted value corresponding to the neighborhood heart rate moment. 4. The method according to claim 1, wherein determining the interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation influencing factor specifically comprises: Determining the heart rate fluctuation smoothness according to the heart rate fluctuation influencing factor; The interfered heart rate signal interval of the observed heart rate signal is determined according to the heart rate fluctuation smoothness. 5. The method according to claim 4, wherein determining the heart rate fluctuation smoothness according to the heart rate fluctuation influencing factor specifically comprises: Obtaining a baseline heart rate sequence; Obtaining the heart rate fluctuation influencing factor; The heart rate fluctuation smoothness is determined according to the reference heart rate sequence and the heart rate fluctuation influencing factor. 6. The method according to claim 4, wherein determining the interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation smoothness specifically comprises: Dividing the observed heart rate signal according to a preset heart rate signal segmentation index to obtain a plurality of heart rate signal intervals; An interference judgment is performed on each heart rate signal interval according to the heart rate fluctuation smoothness, thereby determining the interfered heart rate signal interval of the observed heart rate signal. 7. The method according to claim 1, wherein the step of differentially adjusting the observed heart rate signal by a preset heart rate differential coefficient to obtain a heart rate differential signal specifically comprises: Preset heart rate differential coefficient; Get the heart rate gradient value corresponding to each observed heart rate value in the heart rate sequence; Each heart rate gradient value is jointly adjusted with the heart rate differential coefficient to obtain a heart rate differential signal. 8. A smart wearable watch, characterized in that it includes a health monitoring control unit, the health monitoring control unit includes: An acquisition module is used to acquire the observed heart rate signal of the user being measured after the heart rate monitoring of the smart wearable watch is started; a processing module, configured to determine a heart rate fluctuation influence factor of the measured user according to the observed heart rate signal, and further determine an interfered heart rate signal interval of the observed heart rate signal according to the heart rate fluctuation influence factor; The processing module is further used to perform differential adjustment on the observed heart rate signal by using a preset heart rate differential coefficient to obtain a heart rate differential signal, and then determine a heart rate fluctuation calibration gradient by using the heart rate differential signal and the observed heart rate signal; The processing module is further used to reconstruct the observed heart rate value in the interfered heart rate signal interval in the observed heart rate signal according to the heart rate fluctuation calibration gradient to obtain the actual heart rate signal of the measured user; The monitoring module determines the heart rate status of the user under test according to the actual heart rate signal and sends the monitoring result to the monitoring center. 9. A computer device, characterized in that the computer device comprises a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the health monitoring control method of the smart wearable watch according to any one of claims 1 to 7. 10. A computer-readable storage medium, wherein instructions or codes are stored in the computer-readable storage medium. When the instructions or codes are executed on a computer, the computer implements the health monitoring control method of the smart wearable watch as described in any one of claims 1 to 7.