Heart rate warning method and electronic device

Abstract

The application provides a heart rate warning method and an electronic device. The method comprises the following steps: obtaining a maximum heart rate value of a target user based on the age of the target user and a preset first algorithm model, wherein the first algorithm model is a nonlinear relationship between the maximum heart rate value and the age parameter; determining a warning heart rate value corresponding to the target user according to the maximum heart rate value of the target user; collecting a first heart rate value of the target user in real time, and starting timing if the first heart rate value is greater than or equal to the warning heart rate value and the current timing is not started; monitoring the first heart rate value of the target user collected after the timing is started, and issuing a heart rate warning information if the timing time reaches a first time length and all the first heart rate values monitored in the first time length are greater than or equal to the warning heart rate value. In this way, a more accurate maximum heart rate of the user can be obtained, the estimation accuracy of the maximum heart rate is improved, the accuracy of the heart rate warning is improved, and the health risk is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of terminal equipment, and in particular to a heart rate warning method and an electronic device. BACKGROUND

[0002] Heart rate is an important indicator of human health. High-intensity exercise will bring about rapid heart rate, which is easy to cause danger. However, most people are not aware of their own heart rate limit (i.e. maximum heart rate), and there is a risk of blind exercise, especially high-intensity exercise.

[0003] At present, many electronic devices, such as smart watches, smart bracelets, etc., have the function of measuring heart rate. With the help of such wearable electronic devices, users can obtain their own maximum heart rate and real-time heart rate, so as to monitor the heart rate in the process of exercise in real time. In the related art, the maximum heart rate is estimated by the linear data relationship between the maximum heart rate and the age, and the accuracy is low. SUMMARY

[0004] In order to solve the above technical problems, the present application provides a heart rate warning method and an electronic device, which can obtain more accurate user maximum heart rate, improve the accuracy of maximum heart rate, and further improve the accuracy of heart rate warning based on maximum heart rate, and reduce health risks.

[0005] In a first aspect, the present application provides a heart rate warning method. The method is applied to an electronic device, and the method comprises: obtaining a maximum heart rate value of a target user based on the age of the target user and a preset first algorithm model, wherein the first algorithm model has a non-linear relationship between the maximum heart rate value and an age parameter; determining a warning heart rate value corresponding to the target user according to the maximum heart rate value of the target user, wherein the warning heart rate value is less than the maximum heart rate value of the target user; collecting a first heart rate value of the target user in real time, and starting timing if the first heart rate value is greater than or equal to the warning heart rate value and the timing is not currently started; and monitoring the first heart rate value of the target user collected after the timing is started, and issuing a heart rate warning information if the timing time reaches a first time length and all the first heart rate values monitored in the first time length are greater than or equal to the warning heart rate value. In this way, more accurate user maximum heart rate can be obtained, the accuracy of maximum heart rate can be improved, and the accuracy of heart rate warning based on maximum heart rate can be improved, thereby reducing health risks.

[0006] According to the first aspect, before determining the target user's corresponding early warning heart rate value according to the target user's maximum heart rate value, further comprising: reading a current value of a calibration weight; calibrating the target user's maximum heart rate value according to the current value of the calibration weight to obtain a calibrated maximum heart rate value; and determining the target user's corresponding early warning heart rate value according to the calibrated maximum heart rate value. In this way, by calibration, the accuracy of the maximum heart rate value can be improved, thereby improving the accuracy of the heart rate warning.

[0007] According to the first aspect, the process of obtaining the maximum heart rate value based on the first algorithm model comprises: determining a target index value as a quotient obtained by dividing the difference between the target user's age and a first constant value by a second constant value; determining a first factor value as a sum of a product of the target index value of the natural constant raised to a power and a third constant value and a fourth constant value; determining a product value as a product of the first factor value and the target user's age; and determining the maximum heart rate value as a difference between a fifth constant value and the product value. In this way, the impact of physical decline with age on the maximum heart rate is considered, the user's maximum heart rate can be more accurately estimated, the estimation accuracy of the maximum heart rate is improved, and the accuracy of the heart rate warning based on the maximum heart rate is improved, thereby reducing the health risk.

[0008] According to the first aspect, the process of obtaining the maximum heart rate value based on the first algorithm model comprises: determining a first index value as a quotient obtained by dividing the difference between the target user's age and a first constant value by a second constant value; determining a first factor value as a sum of a product of the first index value of the natural constant raised to a power and a third constant value and a fourth constant value; determining a product value as a product of the first factor value and the target user's age; determining a second factor value as a difference between a fifth constant value and the product value; determining a physical fitness influence weight value of the target user according to the target user's basic physical fitness information and the first exercise data collected in the target user's most recent exercise statistical period; and determining the maximum heart rate value as a product of the target user's physical fitness influence weight value and the second factor value. In this way, not only the impact of physical decline with age on the maximum heart rate is considered, but also the impact of physical fitness and exercise habits on the maximum heart rate is considered, the estimation accuracy of the maximum heart rate is further improved, and the accuracy of the heart rate warning based on the maximum heart rate is improved, thereby reducing the health risk.

[0009] According to the first aspect, the body fitness influence weight value of the target user is determined according to the body fitness basic information of the target user and the first exercise data of the target user collected in the latest exercise statistical period, and includes: determining the exercise frequency of the target user in the latest exercise statistical period according to the first exercise data of the target user in the latest exercise statistical period; finding a first body fitness level value corresponding to the body fitness basic information of the target user and the exercise frequency from a preset first mapping table, wherein the first mapping table stores a mapping relationship between the body fitness basic information and the exercise frequency and the body fitness level value; and determining the body fitness influence weight value of the target user according to the first body fitness level value and the age of the target user.

[0010] According to the first aspect, the body fitness influence weight value of the target user is determined according to the first body fitness level value and the age of the target user, and includes: determining a second exponential value according to the first body fitness level value; determining a third factor value as a difference between a second exponential power of a natural constant and a sixth constant value; determining a third exponential value according to the age of the target user; determining a fourth factor value as a third exponential power of a natural constant; and determining the body fitness influence weight value of the target user as a sum of a product of the third factor value, the fourth factor value and a seventh constant value and an eighth constant value.

[0011] According to the first aspect, the first algorithm model is a trained maximum heart rate regression model; the maximum heart rate value of the target user is obtained based on the age of the target user and the preset first algorithm model, and the maximum heart rate value and the age parameter in the first algorithm model are in a non-linear relationship, and includes: obtaining the first exercise data of the target user collected in the latest exercise statistical period, and collecting the first heart rate data and the second exercise data of the target user; finding a first body fitness level value corresponding to the body fitness basic information of the target user and the exercise frequency from a preset first mapping table, wherein the first mapping table stores a mapping relationship between the body fitness basic information and the exercise frequency and the body fitness level value; determining a first cardiopulmonary health condition characteristic value corresponding to the target user according to the first heart rate data and the second exercise data; determining a first gender characteristic value corresponding to the target user according to the gender of the target user and a preset second mapping table, wherein the second mapping table stores a mapping relationship between the gender and the gender characteristic value; and inputting the first body fitness characteristic value, the first cardiopulmonary health condition characteristic value, the first gender characteristic value and the age of the target user into the maximum heart rate regression model to output the maximum heart rate value of the target user from the maximum heart rate regression model. In this way, not only the influence of the decline of physical ability with age on the maximum heart rate, the influence of body fitness and exercise habit on the maximum heart rate, but also the influence of gender factor and cardiopulmonary disease on the maximum heart rate are considered, the estimation accuracy of the maximum heart rate is further improved, the accuracy of the heart rate warning based on the maximum heart rate is improved, and the health risk is reduced.

[0012] According to the first aspect, the first cardiopulmonary health condition characteristic value corresponding to the target user is determined according to the first heart rate data and the second motion data, including: determining the current heart rate value and the current breathing data of the target user according to the first heart rate data and the second motion data; determining the first cardiopulmonary health condition characteristic value corresponding to the target user according to the current heart rate value and the current breathing data of the target user and the preset cardiopulmonary health condition evaluation algorithm.

[0013] According to the first aspect, the maximum heart rate value of the target user is calibrated according to the measured maximum heart rate value of the target user to obtain a calibrated maximum heart rate value, including: reading the current maximum heart rate evaluation weight value of the target user; and taking the product of the current maximum heart rate evaluation weight value of the target user and the maximum heart rate value of the target user as the calibrated maximum heart rate value.

[0014] According to the first aspect, the process of obtaining the maximum heart rate evaluation weight value of the target user includes: determining the initial maximum heart rate value of the target user according to the registration information filled in by the target user when the target user registers for the first time; obtaining the measured maximum heart rate value of the target user, the measured maximum heart rate value being obtained by the target user actually testing according to a preset maximum heart rate test process; and determining the maximum heart rate evaluation weight value of the target user according to the measured maximum heart rate value and the initial maximum heart rate value.

[0015] The second aspect of the present application provides an electronic device, including: a memory and a processor, the memory being coupled with the processor; the memory storing program instructions, when the program instructions are executed by the processor, the electronic device executes the heart rate warning method of any one of the first aspect.

[0016] The third aspect of the present application provides a computer readable storage medium, including a computer program, when the computer program runs on the electronic device, the electronic device executes the heart rate warning method of any one of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A structural schematic diagram of the smart bracelet A is shown for example;

[0018] Figure 2 An example graph of the relationship between age and maximum heart rate is shown for example;

[0019] Figure 3 Another structural schematic diagram of the smart bracelet A is shown for example;

[0020] Figure 4 An example graph of the relationship between age and maximum heart rate affected by physical fitness and exercise habits is shown for example;

[0021] Figure 5 Yet another structural schematic diagram of the smart bracelet A shown for example;

[0022] Figure 6 An example diagram of a heart rate warning process shown for example;

[0023] Figure 7 A schematic block diagram of an apparatus 900 according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0025] The term “and / or” in the present application is merely used to describe an association relationship of associated objects, and indicates that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone.

[0026] The terms “first” and “second” and the like in the description and claims of the embodiments of the present application are used to distinguish different objects, and are not used to describe a specific order of the objects. For example, the first target object and the second target object are used to distinguish different target objects, and are not used to describe a specific order of the target objects.

[0027] In the embodiments of the present application, the words “exemplary” or “for example” are used to mean serving as an example, instance, or illustration. Any embodiment or design presented as “exemplary” or “for example” in the embodiments of the present application should not be interpreted as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of “exemplary” or “for example” is merely intended to present related concepts in a particular manner.

[0028] In the description of the embodiments of the present application, unless otherwise specified, “a plurality of” means two or more. For example, a plurality of processing units means two or more processing units; a plurality of systems means two or more systems.

[0029] In the related art, the maximum heart rate is only related to age, and the relationship between the maximum heart rate and the age is a linear relationship. In the related art, the currently coexisting formulas for calculating the maximum heart rate are shown in the following formulas (1) to (5).

[0030] HR max = 220-Age (1)

[0031] HRmax = 216 - 0.88 * Age (2)

[0032] HR max = 227 - 1.067 * Age (3)

[0033] HR max = 209 - 0.587 * Age (4)

[0034] HR max = 209 - 0.7 * Age (5)

[0035] In the above formulae for calculating the maximum heart rate, only the age factor is considered, and the linear estimation is not considered the impact of physical decline with age.

[0036] Moreover, even for the same age, the maximum heart rate of people with different physical qualities also has a large difference.

[0037] In addition, cardiopulmonary diseases also have a great impact on the maximum heart rate that can be achieved.

[0038] The embodiments of the present application further consider at least one of the following factors on the basis of considering the age factor:

[0039] (1) the impact of physical decline with age

[0040] (2) physical quality and exercise habits

[0041] (3) cardiopulmonary health status

[0042] In various embodiments of the present application, the relationship between the maximum heart rate and the age is a nonlinear relationship.

[0043] The heart rate warning method of the present application will be described in detail below through embodiments.

[0044] Embodiment one

[0045] In this embodiment, the electronic device can be a smart bracelet, a smart watch, or the like wearable device. The electronic device is installed with a PPG (Photoplethysmography) signal acquisition device and an ACC (Accelerometer) signal acquisition device. The PPG signal acquisition device can include a light source and a light sensor for acquiring the PPG signal formed by the light source. PPG is a non-invasive detection method for detecting blood vessel volume changes in living tissues using photoelectric means, and the original PPG signal contains physiological information including heart rate, blood oxygen saturation, blood pressure, and the like. The ACC signal acquisition device is used to acquire the ACC signal. The ACC signal can include acceleration values of X, Y, and Z three axes.

[0046] The ACC signal acquisition device can also be referred to as an acceleration sensor.

[0047] For example, the smart bracelet. Figure 1 A structure diagram of the smart bracelet A is exemplarily shown. As shown in the figure, Figure 1 The smart bracelet A includes a PPG signal acquisition device 100, an ACC signal acquisition device 200, a sports health module 300, and a heart rate monitoring module 400. The sports health module 300 corresponds to a sports health APP (Application), that is, the sports health APP is installed in the smart bracelet A.

[0048] Suppose user A wears the smart bracelet A. The process of the sports health APP on the smart bracelet A for heart rate warning of user A can include:

[0049] Step (11) obtains the maximum heart rate value of user A.

[0050] When user A registers the smart bracelet A for the first time, or when user A registers the sports health APP on the smart bracelet A for the first time, a questionnaire form is filled in, which can include the age of user A or the birth year of user A (the current age of user A can be obtained according to the current year and the birth year of user A). In this way, the smart bracelet A can obtain the age information of user A and store the age information of user A in the sports health module 300.

[0051] The sports health module 300 calculates the maximum heart rate value of user A according to the age of user A and the following formula (6) and formula (7).

[0052] HR max = C - Age loss (6)

[0053] In formula (6), C is a statistical value constant, Age loss is the true value converted in the maximum heart rate calculation process.

[0054]

[0055] In formula (7), a and b are constant parameters, and the values of a and b can be obtained based on big data fitting.

[0056] In one example, the value of a is 0.2 and the value of b is 10.

[0057] Figure 2 An example diagram of the relationship between age and maximum heart rate is exemplarily shown. Figure 2 In the figure, the horizontal axis is age and the vertical axis is maximum heart rate.

[0058] Figure 2 In the figure, curve A and curve B correspond to curves corresponding to the linear estimation of the maximum heart rate formula corresponding to formula (1) to formula (5), and curve C and curve D are curves corresponding to the nonlinear estimation of the maximum heart rate formula based on the value of the age of curve A or curve B (the weighting method is shown in formula (7)).

[0059] Step (12) calibrates the maximum heart rate value HR max of user A to obtain the calibrated maximum heart rate value HR' max .

[0060] HR' max The calculation method is shown in formula (8) as follows.

[0061] HR' max = wHR max (8)

[0062] In formula (8), w is the calibration weight. The way to obtain w is as follows:

[0063] According to the age of user A and the following formula (6) and formula (7), the maximum heart rate value HR max of user A is calculated, the maximum heart rate measured value HR real of user A is obtained, and according to HR max and HR real , the calibration weight w is calculated according to the following formula (9).

[0064]

[0065] It should be noted that user A can perform multiple maximum heart rate measurements, and after each measurement, the value of w can be updated and stored according to formula (9), for example, the updated w value can be stored in the sports health module 300. In this way, when the maximum heart rate value needs to be calibrated, the currently stored w value can be read, and the maximum heart rate value can be calibrated using the above formula (8).

[0066] Wherein, user A can obtain the maximum heart rate measured value by the following method (referred to as the maximum heart rate test scheme), that is, actually test the maximum heart rate:

[0067] Select a 400-meter long steep slope with a slope of not more than 5%, and then test according to the following steps:

[0068] Warm-up: Wear the smart bracelet A to jog for 10 minutes, and touch the pulse to confirm that the heart rate value displayed by the smart bracelet A is consistent with the actual heart rate value. The heart rate value displayed by the smart bracelet A is obtained from the PPG signal of the user A collected by the PPG signal acquisition device 100 in the smart bracelet A. The PPG signal includes the heart rate value.

[0069] First time: Run up the steep slope with 80% strength, and check the heart rate value displayed by the smart bracelet A at the top of the slope. Jog down the slope, and rest for 3 minutes at the bottom of the slope.

[0070] Second time: Run up the steep slope with 90% strength, and check the heart rate value displayed by the smart bracelet A at the top of the slope. Jog down the slope, and rest for 3 minutes at the bottom of the slope.

[0071] Third time: Run up the steep slope with full strength, and check the heart rate value displayed by the smart bracelet A at the top of the slope. Jog down the slope, and rest for 5 minutes at the bottom of the slope.

[0072] Fourth time: Run up the steep slope with full strength, and try to break the highest heart rate value measured before. If no higher heart rate value than the highest heart rate value measured before appears, end the test; if a higher heart rate value than the highest heart rate value measured before appears, jog down the slope, and rest for 5 minutes at the bottom of the slope, and then repeat the fourth time.

[0073] During the whole test, the smart bracelet A will record the real-time heart rate during the exercise, and generate a heart rate curve. The highest heart rate value appearing on the heart rate curve is the measured maximum heart rate value.

[0074] It should be noted that if the maximum heart rate cannot be maintained for more than 10 seconds, a peak heart rate slightly lower but maintained for more than 10 seconds is selected from the heart rate curve as the measured maximum heart rate value. For example, assuming that the peak heart rate on the heart rate curve is 180, but the duration is only 3s, and before and after the peak value 180, the heart rate value is greater than 178 for 10s, then 178 is taken as the maximum heart rate.

[0075] It should be noted that in other embodiments, the heart rate warning method can not include the above-mentioned step (12), in which case, the step (11) directly enters the following step (13).

[0076] Step (13) determines the warning heart rate value corresponding to the user A according to the maximum heart rate value of the user A.

[0077] If the heart rate warning method does not include the above-mentioned step (12), the maximum heart rate value of the user A in this step is the maximum heart rate value HR max obtained in the above-mentioned step (11). At this time, the warning heart rate value HR alarmIt can be calculated using the following formula (10).

[0078] HR alarm =HR max -D(10)

[0079] If the heart rate warning method includes the above step (12), the maximum heart rate value of user A in this step refers to the maximum heart rate value HR′ after calibration in step (12). max At this time, the warning heart rate value HR alarm It can be calculated using the following formula (11).

[0080] HR alarm =HR′ max -D(11)

[0081] In formula (10) and formula (11), D is a constant. Users can adjust the value of D according to actual needs.

[0082] For example, in one example, the value of D may be 10.

[0083] Step (14): collect the heart rate value of user A in real time. If the collected heart rate value is greater than or equal to the warning heart rate value HR alarm If the timer is not currently started, start the timer.

[0084] The PPG signal acquisition device 100 in smart wristband A collects user A's PPG signal in real time and sends it to the heart rate monitoring module 400. The ACC signal acquisition device 200 in smart wristband A collects user A's ACC signal in real time and sends it to the heart rate monitoring module 400. The heart rate monitoring module 400 determines user A's heart rate based on the PPG and ACC signals. The heart rate monitoring module 400 then sends user A's heart rate to the sports and health module 300. In this way, the sports and health module 300 obtains user A's real-time heart rate.

[0085] Among them, the method of determining the heart rate value based on the PPG signal and the ACC signal can adopt the method in the relevant technology, which will not be repeated here.

[0086] Step (15) monitors the heart rate value of user A collected after the timing is started. If the timing time reaches the first time length T, and all the heart rate values ​​of user A monitored within the first time length T are greater than or equal to the warning heart rate value HR alarm , and send out heart rate warning information.

[0087] The value of T can be adjusted by the user according to actual needs.

[0088] In one example, the value of T may be 20 seconds.

[0089] The manner of issuing the heart rate warning information, i.e., the manner of heart rate warning, can be issuing voice reminder information, displaying pop-up information, vibration, etc. The present embodiment does not limit the manner of heart rate warning.

[0090] For example, when the real-time heart rate value of user A reaches HR alarm The above, i.e., the start of timing, if the real-time heart rate value of user A is greater than or equal to the warning heart rate value HR alarm for 20 consecutive seconds after the start of timing, the motion health module 300 in the smart bracelet A issues voice reminder information, for example, the voice reminder information can be "the current exercise is high-intensity exercise, please rest". Alternatively, the smart bracelet A displays pop-up information through the display screen, for example, the pop-up information can be "the current exercise is high-intensity exercise, please rest".

[0091] In the present embodiment, the relationship between the maximum heart rate and the age is nonlinear, considering the impact of the decline in physical fitness with age, the maximum heart rate of the user can be more accurately estimated, the estimation accuracy of the maximum heart rate is improved, and the accuracy of the heart rate warning based on the maximum heart rate is improved, and the health risk is reduced.

[0092] Embodiment Two

[0093] In addition to the age factor, physical fitness and exercise habits can also affect the maximum heart rate. In the present embodiment, in addition to considering the impact of the decline in physical fitness with age, the impact of physical fitness and exercise habits on the maximum heart rate is also considered.

[0094] In the present embodiment, the electronic device can be a smart bracelet, a smart watch, or other wearable devices. The electronic device is installed with a PPG signal acquisition device and an ACC signal acquisition device. The PPG signal acquisition device can include a light source and a light sensor for acquiring the PPG signal formed by the light source. The ACC signal acquisition device is used to acquire the ACC signal. The ACC signal can include X, Y, and Z three-axis acceleration values.

[0095] Still taking the smart bracelet as an example. In the present embodiment, the structure of the smart bracelet A can be as shown in Figure 3 . Figure 3 Another structure diagram of the smart bracelet A is shown in Figure 3 for example. The smart bracelet A includes a PPG signal acquisition device 100, an ACC signal acquisition device 200, a motion health module 300, a heart rate monitoring module 400, a motion monitoring module 500, and a motion habit recording module 600.

[0096] The PPG signal acquisition device 100 acquires the PPG signal of the user A in real time and sends the PPG signal to the heart rate monitoring module 400. The ACC signal acquisition device 200 acquires the ACC signal of the user A in real time and sends the ACC signal to the heart rate monitoring module 400 and the exercise monitoring module 500. The heart rate monitoring module 400 determines the heart rate value of the user A according to the PPG signal and the ACC signal. Then, the heart rate monitoring module 400 sends the heart rate value of the user A to the exercise health module 300.

[0097] It is assumed that the user A wears the smart bracelet A. In this embodiment, the process of the exercise health APP on the smart bracelet A for heart rate warning of the user A can include:

[0098] Step (21) obtains the maximum heart rate value of the user A.

[0099] When the user A registers the smart bracelet A for the first time, or when the user A registers the exercise health APP on the smart bracelet A for the first time, a questionnaire form is filled out, which can include the age of the user A (or the birth year of the user A, and the current age of the user A can be obtained according to the current year and the birth year of the user A) and the basic information of the physical fitness of the user A. In this way, the smart bracelet A can obtain the age information and the basic information of the physical fitness of the user A and store them in the exercise health module 300.

[0100] The basic information of the physical fitness can be one of “poor physical fitness, not suitable for exercise”, “general physical fitness”, and “good physical fitness with continuous exercise habit”.

[0101] The ACC signal acquisition device 200 acquires the ACC signal of the user A and sends it to the exercise monitoring module 500. The exercise monitoring module 500 determines the exercise state of the user A according to the ACC signal of the user A. The exercise state can include exercise and non-exercise. Then, the exercise monitoring module 500 sends the exercise state of the user A to the exercise habit recording module 600. The exercise habit recording module 600 records the exercise state information of the user A and counts the number of exercises of the user A in the last exercise statistical period according to the exercise state information, and then sends the number of exercises to the exercise health module 300.

[0102] The way of determining the exercise state (such as walking, running, cycling, etc.) according to the ACC signal can adopt the corresponding way in the related art, which is not described here.

[0103] The ACC signal is the exercise data of the user.

[0104] The exercise statistic period can be determined as needed. For example, in one example, the exercise statistic period is one week. In this way, the exercise habit recording module 600 can always count the number of exercises in the last one week.

[0105] The exercise health module 300 finds, from a preset first mapping table, a first physical fitness level value corresponding to the physical fitness basic information of user A and the number of exercises of user A, wherein the first mapping table stores a mapping relationship between the physical fitness basic information and the number of exercises and the physical fitness level value.

[0106] The first mapping table can be as shown in Table 1.

[0107] Table 1

[0108] Physical fitness basic information and exercise frequency Physical fitness grade value Poor physical fitness, not suitable for exercise 0 Average physical fitness, no exercise per week 1 Average physical fitness, 1-2 times of exercise per week 2 Average physical fitness, 3-4 times of exercise per week 3 Average physical fitness, more than 5 times of exercise per week 4 Good physical fitness, with continuous exercise habit 5

[0109] According to Table 1, in the case of general physical fitness basic information, the physical fitness level value can be determined in combination with the physical fitness basic information and the number of exercises, and in the case of "poor physical fitness, not suitable for exercise" or "good physical fitness, with continuous exercise habits", the physical fitness level value can be directly determined according to the physical fitness basic information.

[0110] It should be noted that when the smart bracelet A first uses the manner in this embodiment to calculate the maximum heart rate, since the smart bracelet A has not stored the ACC data that can be used to analyze the exercise habit of user A, the exercise habit in the questionnaire form can be used, and after the ACC data is stored in the smart bracelet A, the exercise habit is determined according to the ACC data.

[0111] Then, the exercise health module 300 determines the physical fitness influence weight value of user A according to the first physical fitness level value and the age of user A.

[0112] The formula for determining the physical fitness influence weight value FitnessWeight according to the age Age and the physical fitness level value fitness is as shown in formula (12) or formula (13).

[0113]

[0114] It can be seen that if the age of user A is less than 35 years old, the formula (12) is used to calculate the physical fitness influence weight value FitnessWeight of user A, and if the age of user A is greater than or equal to 35 years old, the formula (13) is used to calculate the physical fitness influence weight value FitnessWeight of user A. After obtaining the physical fitness weight value, the maximum heart rate value HR max .

[0115] HR max = FitnessWeight * (C - Age loss )(14)

[0116] In formula (14), Age loss and the constant C are obtained as described in the foregoing embodiment one, which will not be repeated here.

[0117] Figure 4 An example of the relationship between age and maximum heart rate affected by physical fitness and exercise habits is shown. Figure 4 In the figure, the horizontal axis is age, and the vertical axis is maximum heart rate.

[0118] Figure 4 In the figure, curve A and curve B correspond to curves corresponding to linear estimation of maximum heart rate formulas similar to formulas (1) to (5), and curve C, curve D, curve E, curve F, and curve G are curves obtained using the maximum heart rate nonlinear estimation method shown in formula (14). Among them, the fitness values of curve C, curve D, curve E, curve F, and curve G are different, for example, the results calculated when the physical fitness level value is equal to 0, 1, 2, 3, and 4, respectively.

[0119] Step (22) calibrates the maximum heart rate value HR max of user A to obtain the calibrated maximum heart rate value HR' max .

[0120] After calculating the maximum heart rate value HR max according to formula (14), in this step, the maximum heart rate value HR max may be calibrated according to the foregoing formula (8) to obtain the calibrated maximum heart rate value HR' max . The detailed process of calibrating the maximum heart rate value is described in the foregoing embodiment one, which will not be repeated here.

[0121] It should be noted that in other embodiments, the heart rate warning method can not include the above-mentioned step (22), in which case, the foregoing step (21) directly enters the following step (23).

[0122] Step (23) determines the warning heart rate value corresponding to user A according to the maximum heart rate value of user A.

[0123] If the heart rate warning method does not include the above-mentioned step (22), the maximum heart rate value of user A in this step is the maximum heart rate value HR max obtained in the foregoing step (21). At this time, the warning heart rate value HR alarm may be calculated by the foregoing formula (10).

[0124] If the heart rate warning method comprises the step (22) described above, the maximum heart rate value of the user A in this step refers to the maximum heart rate value HR' calibrated in the step (22) max At this time, the warning heart rate value HR alarm The warning heart rate value HR

[0125] Step (24), the heart rate value of the user A is collected in real time, if the collected heart rate value is greater than or equal to the warning heart rate value HR alarm , and the timing is not started at present, the timing is started.

[0126] This step is the same as the step (14) described above, and the related content can be referred to the description in the step (14) described above, which will not be repeated here.

[0127] Step (25), the heart rate value of the user A collected after the timing is started is monitored, if the timing time reaches the first time length T, and all the heart rate values of the user A monitored in the first time length T are greater than or equal to the warning heart rate value HR alarm , the heart rate warning information is sent.

[0128] This step is the same as the step (15) described above, and the related content can be referred to the description in the step (15) described above, which will not be repeated here.

[0129] In this embodiment, not only the influence of the decline of physical ability with the increase of age on the maximum heart rate is considered, but also the influence of the physical quality and exercise habit on the maximum heart rate is considered, which further improves the estimation accuracy of the maximum heart rate, and further improves the accuracy of the heart rate warning based on the maximum heart rate, and reduces the health risk.

[0130] Embodiment three

[0131] In addition to the age and the physical quality and the exercise habit, the gender, the heart-lung health status and other factors of the person can also influence the maximum heart rate of the person. In this embodiment, the influences of the age, the physical quality and the exercise habit, the gender, the heart-lung health status and other factors on the maximum heart rate are considered at the same time. The influence of the heart-lung disease on the maximum heart rate can be referred to Table 2.

[0132] Table 2

[0133]

[0134]

[0135] In this embodiment, the electronic device can be a smart bracelet, a smart watch, or the like wearable device. The electronic device is provided with a PPG signal acquisition device and an ACC signal acquisition device. The PPG signal acquisition device can include a light source and a light sensor for acquiring the PPG signal formed by the light source. The ACC signal acquisition device is used to acquire the ACC signal. The ACC signal can include acceleration values of X, Y, and Z axes.

[0136] Still taking the smart bracelet as an example.

[0137] Suppose user A wears the smart bracelet A. In this embodiment, the process of the motion health APP on the smart bracelet A for heart rate warning of user A can include:

[0138] Step (31) acquires the maximum heart rate value of user A.

[0139] In this embodiment, the structure of the smart bracelet A can be as shown in Figure 5 . Figure 5 Another structure diagram of the smart bracelet A is shown by way of example. As shown in Figure 5 , the smart bracelet A includes a PPG signal acquisition device 100, an ACC signal acquisition device 200, a motion health module 300, a heart rate monitoring module 400, a motion monitoring module 500, a motion habit recording module 600, a respiration monitoring module 700, and a cardiopulmonary disease identification module 800.

[0140] The overall flow of this embodiment can be as shown in Figure 6 . Figure 6 An example diagram of the heart rate warning process is shown by way of example. The embodiment will be described below in combination with Figure 5 and Figure 6 .

[0141] Please refer to Figure 5 and Figure 6 . The PPG signal acquisition device 100 acquires the PPG signal of user A in real time, and sends the PPG signal to the heart rate monitoring module 400 and the respiration monitoring module 700. The ACC signal acquisition device 200 acquires the ACC signal of user A in real time, and sends the ACC signal to the heart rate monitoring module 400, the respiration monitoring module 700, and the motion monitoring module 500.

[0142] The heart rate monitoring module 400 determines the heart rate value of user A according to the PPG signal and the ACC signal. Then, the heart rate monitoring module 400 sends the heart rate value of user A to the cardiopulmonary disease identification module 800.

[0143] The respiration monitoring module 700 determines the respiration information of the user A according to the PPG signal and the ACC signal, and sends the respiration information of the user A to the cardiopulmonary disease identification module 800. The respiration information can be a respiration frequency. The method of determining the respiration frequency according to the PPG signal and the ACC signal is known in the art, and will not be described here.

[0144] The cardiopulmonary disease identification module 800 identifies the cardiopulmonary health status of the user A according to the heart rate value of the user A and the respiration information of the user A, obtains a first cardiopulmonary health status characteristic value of the user A, and then sends the first cardiopulmonary health status characteristic value and the heart rate value of the user A to the exercise health module 300. According to the PPG signal, the heartbeat waveform can be obtained while detecting the heart rate, so that whether there is atrial fibrillation, premature beat, arrhythmia, etc. can be analyzed according to the heartbeat waveform; and the lung health condition can be judged according to the respiration frequency / blood oxygen information, etc.

[0145] The cardiopulmonary health status can be with cardiopulmonary disease or without cardiopulmonary disease. If the cardiopulmonary health status is with cardiopulmonary disease, the cardiopulmonary health status characteristic value is 1; if the cardiopulmonary health status is without cardiopulmonary disease, the cardiopulmonary health status characteristic value is 0, as shown in Table 3.

[0146] When the user A registers the smart bracelet A for the first time, or when the user A registers the exercise health APP on the smart bracelet A for the first time, a questionnaire form is filled in, which can include the age of the user A (or the birth year of the user A, the current age of the user A can be obtained according to the current year and the birth year of the user A), gender, and basic information of physical fitness. In this way, the smart bracelet A can obtain the age information, gender, and basic information of physical fitness of the user A, and store them in the exercise health module 300.

[0147] Table 3

[0148] Cardiopulmonary health status Cardiopulmonary health status characteristic value With cardiopulmonary disease 1 Without cardiopulmonary disease 0

[0149] According to the gender of the user A and the preset second mapping table, the first gender characteristic value corresponding to the user A can be determined, wherein the second mapping table stores the mapping relationship between the gender and the gender characteristic value, as shown in Table 4.

[0150] Table 4

[0151] Gender Gender characteristic value Male 1 Female -1

[0152] Please continue to see Figure 5The motion monitoring module 500 determines the motion state of the user A according to the ACC signal of the user A. The motion state can include motion and non-motion. Then, the motion monitoring module 500 sends the motion state of the user A to the motion habit recording module 600. The motion habit recording module 600 records the motion state information of the user A, and counts the number of motions of the user A in a recent motion statistical period according to the motion state information, and then sends the number of motions to the motion health module 300.

[0153] The motion health module 300 finds a first physical fitness level value corresponding to the basic physical fitness information of the user A and the number of motions of the user A from a preset first mapping table (as shown in Table 1), wherein the first mapping table stores a mapping relationship between the basic physical fitness information and the number of motions and the physical fitness level value. The motion health module 300 takes the first physical fitness level value as the first physical fitness feature value of the user A.

[0154] At this point, the motion health module 300 obtains the first physical fitness feature value, the first cardiopulmonary health condition feature value, and the first gender feature value of the user A. The motion health module 300 inputs the first physical fitness feature value, the first cardiopulmonary health condition feature value, and the first gender feature value of the user A together with the stored age of the user A into the trained maximum heart rate regression model, so as to output the maximum heart rate value of the user A from the maximum heart rate regression model.

[0155] Here, the maximum heart rate regression model is a pre-trained machine learning model. The embodiment does not limit the specific network of the maximum heart rate regression model. For example, the network of the maximum heart rate regression model can be a traditional machine learning network or a deep learning model network such as a fully connected network, a one-dimensional convolutional network, and a random forest (RF), and the loss function can be a mean square error loss function.

[0156] The training process of the maximum heart rate regression model can include the following steps:

[0157] A network model M is constructed, and the initial parameter value of the network model M is set. For example, the network model is a fully connected network model

[0158] A plurality of sets of sample data are collected, each set of sample data including a physical fitness feature value, a cardiopulmonary health condition feature value, a gender feature value, an age, and a maximum heart rate label value, wherein the maximum heart rate label value can be a measured maximum heart rate value.

[0159] The network model M is trained using the sample data, and the trained network model M is taken as the maximum heart rate regression model.

[0160] In one example, the sample data is derived from subjects, wherein the subjects are in the age range of 18-55 years old, and the subjects include people without heart and lung diseases and people with mild heart and lung diseases. The same subjects are rested for a period of time before data collection, and the maximum heart rate label value is obtained based on the aforementioned maximum heart rate test scheme.

[0161] Step (32) calibrates the maximum heart rate value HR of user A max to obtain a calibrated maximum heart rate value HR' max .

[0162] After obtaining the maximum heart rate value HR of user A in step (31) max , in this step, the maximum heart rate value HR max may be calibrated according to the aforementioned formula (8) to obtain a calibrated maximum heart rate value HR' max . For details of the calibration of the maximum heart rate value, please refer to the description in the aforementioned embodiment one, which will not be repeated here.

[0163] It should be noted that in other embodiments, the heart rate warning method can not include the aforementioned step (32), in which case, the aforementioned step (31) directly enters the following step (33).

[0164] Step (33) determines the warning heart rate value corresponding to user A according to the maximum heart rate value of user A.

[0165] If the heart rate warning method does not include the aforementioned step (32), the maximum heart rate value of user A in this step is the maximum heart rate value HR max obtained in the aforementioned step (31). At this time, the warning heart rate value HR alarm may be calculated by the aforementioned formula (10).

[0166] If the heart rate warning method includes the aforementioned step (32), the maximum heart rate value of user A in this step refers to the calibrated maximum heart rate value HR' max in step (32). At this time, the warning heart rate value HR alarm may be calculated by the aforementioned formula (11).

[0167] Step (34) collects the heart rate value of user A in real time, and if the collected heart rate value is greater than or equal to the warning heart rate value HR alarm and the current timing is not started, the timing is started.

[0168] This step is the same as the aforementioned step (14), and the relevant content can be found in the description of the aforementioned step (14), which will not be repeated here.

[0169] Step (35), monitoring the heart rate value of the user A collected after starting the timing, if the timing time reaches the first time length T, and all the heart rate values of the user A monitored in the first time length T are greater than or equal to the early warning heart rate value HR alarm , issuing a heart rate early warning information.

[0170] This step is the same as the aforementioned step (15), and the related content can be referred to the description in the aforementioned step (15), which will not be repeated here.

[0171] In this embodiment, the influence of the decline of physical fitness with age on the maximum heart rate, the influence of physical fitness and exercise habits on the maximum heart rate, the influence of gender factors and cardiopulmonary diseases on the maximum heart rate are considered, which further improves the estimation accuracy of the maximum heart rate, and further improves the accuracy of the heart rate early warning based on the maximum heart rate, and reduces the health risk.

[0172] Although the smart bracelet is taken as an example in the foregoing description, it can be understood that the heart rate early warning method of this embodiment can also be executed in a mobile phone, a tablet and the like. For example, the user collects the PPG signal and the ACC signal through the smart bracelet or the smart watch and the like, the smart bracelet or the smart watch and the like sends the PPG signal and the ACC signal to the mobile phone or the tablet and the like, the mobile phone or the tablet and the like is provided with the aforementioned exercise health module 300, and the exercise health module 300 executes the heart rate early warning method flow according to the manner in the foregoing embodiment. In this case, the smart bracelet or the smart watch and the like can not be provided with the exercise health module 300.

[0173] In one example, Figure 7 A schematic block diagram of an apparatus 900 according to an embodiment of the present application is shown. The apparatus 900 can include a processor 901 and a transceiver / transceiver pin 902, and optionally further include a memory 903.

[0174] The various components of the apparatus 900 are coupled together by a bus 904, which can include a data bus, a power bus, a control bus, and a state signal bus. However, for the sake of clarity, the various buses are shown as a bus 904.

[0175] Optionally, the memory 903 can be used for instructions in the foregoing method embodiments. The processor 901 can be used to execute the instructions in the memory 903, and control the receiving pin to receive signals and control the sending pin to send signals.

[0176] The apparatus 900 can be an electronic device or a chip of an electronic device in the foregoing method embodiments.

[0177] All the related content of each step involved in the method embodiments can be referred to the function description of the corresponding function module, and will not be repeated here.

[0178] The embodiments of the present application also provide an electronic device, which comprises a memory and a processor, the memory is coupled with the processor, and the memory stores program instructions, which, when executed by the processor, cause the electronic device to perform the heart rate warning method.

[0179] It can be understood that, in order to implement the above functions, the electronic device comprises hardware and / or software modules corresponding to each function. The algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in hardware or computer software driven hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in combination with the embodiments, but such implementation should not be considered beyond the scope of the present application.

[0180] The embodiments also provide a computer storage medium, which stores computer instructions, when the computer instructions run on the electronic device, cause the electronic device to perform the related method steps to implement the heart rate warning method in the above embodiments.

[0181] The embodiments also provide a computer program product, when the computer program product runs on the computer, causes the computer to perform the related steps to implement the heart rate warning method in the above embodiments.

[0182] In addition, the embodiments of the present application also provide a device, which can be a chip, a component or a module, and the device can comprise a processor and a memory connected to each other; wherein the memory is used to store computer execution instructions, and when the device runs, the processor can execute the computer execution instructions stored in the memory to make the chip perform the heart rate warning method in the above method embodiments.

[0183] The electronic device, computer storage medium, computer program product or chip provided by the embodiments are used to perform the corresponding method provided above, and the beneficial effects that can be achieved are referred to the beneficial effects of the corresponding method provided above, and will not be repeated here.

[0184] Through the description of the above embodiments, those skilled in the art can understand that, for the convenience and brevity of description, only the division of the above functional modules is exemplified, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0185] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are only schematic, for example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0186] The units described as separate components can or can not be physically separate, and the components shown as units can be one physical unit or multiple physical units, that is, can be located in one place, or can be distributed to multiple different places. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0187] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0188] Any content of each embodiment of the present application, and any content of the same embodiment, can be freely combined. Any combination of the above is within the scope of the present application.

[0189] If the integrated unit is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application essentially or say the parts that make contributions to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium, includes several instructions to make a device (which can be a single-chip microcomputer, a chip, etc.) or a processor execute all or part of the steps of the various embodiments of the method of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0190] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above-described specific embodiments, and the above-described specific embodiments are only illustrative, not restrictive. Those skilled in the art can make many forms under the inspiration of the present application without departing from the scope of the present application and the scope of protection of the claims, and all of them belong to the protection of the present application.

[0191] The steps of the method or algorithm described in combination with the embodiments of the present application can be implemented in hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in a random access memory (RAM), a flash memory, a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically EPROM (EEPROM), a register, a hard disk, a mobile hard disk, a compact disc (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor, so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC.

[0192] Those skilled in the art can understand that the functions described in the embodiments of the present application in the one or more examples above can be implemented in hardware, software, firmware or any combination thereof. When implemented in software, the functions can be stored in a computer readable medium or transmitted as one or more instructions or codes on a computer readable medium. The computer readable medium includes computer storage medium and communication medium, wherein the communication medium includes any medium that facilitates the transfer of computer program from one place to another. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0193] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the specific embodiments described above, which are merely illustrative rather than restrictive, and those of ordinary skill in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims.

Claims

1. A heart rate warning method, characterized in that: Applied to electronic equipment, the method includes: Obtaining a maximum heart rate value of the target user based on the age of the target user and a preset first algorithm model, wherein the maximum heart rate value and the age parameter in the first algorithm model have a nonlinear relationship; Determining a warning heart rate value corresponding to the target user according to the maximum heart rate value of the target user, wherein the warning heart rate value is less than the maximum heart rate value of the target user; collecting a first heart rate value of the target user in real time, and starting timing if the first heart rate value is greater than or equal to the warning heart rate value and timing is not currently started; monitoring the first heart rate value of the target user collected after starting the timing, and issuing a heart rate warning message if the timing reaches a first duration and all the first heart rate values ​​monitored within the first duration are greater than or equal to the warning heart rate value; The process of obtaining the maximum heart rate value based on the first algorithm model includes at least one of the following: The quotient obtained by dividing the difference between the target user's age and the first constant value by the second constant value is determined as the target index value; the product of the target index value raised to the power of the natural constant and the third constant value, and the sum of the product and the fourth constant value are determined as the first factor value; the product of the first factor value and the target user's age is determined as the subtrahend value; and the difference between the fifth constant value and the subtrahend value is determined as the maximum heart rate value; The quotient obtained by dividing the difference between the target user's age and the first constant value by the second constant value is determined as the first exponent value; the product of the first exponent value of the natural constant to the power of the third constant value and the sum of the fourth constant value are determined as the first factor value; the product of the first factor value and the target user's age is determined as the subtrahend value; the difference between the fifth constant value and the subtrahend value is determined as the second factor value; the physical fitness influence weight value of the target user is determined based on the basic physical fitness information of the target user and the first exercise data of the target user collected in the most recent exercise statistical period; the product of the physical fitness influence weight value of the target user and the second factor value is determined as the maximum heart rate value.

2. The method according to claim 1, characterized in that Before determining the warning heart rate value corresponding to the target user according to the maximum heart rate value of the target user, the method further includes: Read the current value of the calibration weight; Calibrate the maximum heart rate value of the target user according to the current value of the calibration weight to obtain a calibrated maximum heart rate value; Determining a warning heart rate value corresponding to the target user according to the maximum heart rate value of the target user includes: determining the warning heart rate value corresponding to the target user according to the calibrated maximum heart rate value.

3. The method according to claim 1, characterized in that Determining a physical fitness influence weight value of the target user according to the basic physical fitness information of the target user and the first exercise data of the target user collected in a recent exercise statistical period includes: Determining the number of times the target user exercises in the most recent exercise statistical period according to the first exercise data of the target user in the most recent exercise statistical period; searching, from a preset first mapping table, a first physical fitness level value corresponding to the basic physical fitness information of the target user and the number of exercises, wherein the first mapping table stores a mapping relationship between the basic physical fitness information, the number of exercises, and the physical fitness level value; The physical fitness impact weight value of the target user is determined according to the first physical fitness level value and the age of the target user.

4. The method according to claim 3, characterized in that Determining a physical fitness impact weight value of the target user according to the first physical fitness level value and the age of the target user includes: determining a second index value according to the first physical fitness level value; Determine the difference between the second exponential value raised to the power of the natural constant and the sixth constant value as the third factor value; determining a third index value according to the age of the target user; Determining the fourth factor value by raising the third exponential value of the natural constant to the power of the third exponential value; The product of the third factor value, the fourth factor value, and the seventh constant value and the sum of the eighth constant value are determined as the physical fitness impact weight value of the target user.

5. The method according to claim 2, characterized in that Reading a current value of the calibration weight, and calibrating the maximum heart rate value of the target user according to the current value of the calibration weight to obtain a calibrated maximum heart rate value, including: Reading the calibration weight of the target user; The product of the calibration weight of the target user and the maximum heart rate value of the target user is used as the calibrated maximum heart rate value.

6. The method according to claim 5, characterized in that The process of obtaining the calibration weight of the target user includes: Determining an initial maximum heart rate value of the target user according to the registration information filled in by the target user when registering for the first time; Obtaining a maximum heart rate measurement value of the target user, where the maximum heart rate measurement value is obtained by actually testing the target user according to a preset maximum heart rate test process; The calibration weight of the target user is determined according to the measured maximum heart rate value and the initial maximum heart rate value.

7. An electronic device, characterized in that: include: a memory and a processor, the memory being coupled to the processor; The memory stores program instructions, and when the program instructions are executed by the processor, the electronic device executes the heart rate warning method according to any one of claims 1 to 6.

8. A computer-readable storage medium comprising a computer program, characterized in that When the computer program is run on an electronic device, the electronic device is enabled to execute the heart rate warning method according to any one of claims 1 to 6.

Images