Heart rate detection method and system, electronic equipment and computer readable storage medium

Abstract

The invention discloses a heart rate detection method and system, electronic equipment and a computer readable storage medium. The method comprises the steps that first heart rate data obtained by measuring a to-be-detected object through a non-contact sensor and second heart rate data obtained by measuring the to-be-detected object through a contact sensor are obtained; calibrating the non-contact sensor based on the first heart rate data and the second heart rate data; obtaining an initial heart rate value obtained by measuring the to-be-detected object by the calibrated non-contact sensor and a corresponding signal distortion degree; and obtaining a heart rate detection result based on the initial heart rate value and the signal distortion degree. Through the method, the accuracy of heart rate detection can be improved, and the power consumption in the heart rate detection process can be reduced.

Description

Technical Field

[0001] This application relates to the field of intelligent device detection technology, and in particular to a heart rate detection method, system, electronic device, and computer-readable storage medium. Background Technology

[0002] Currently, the primary method for detecting changes in human heart rate is through cameras. These cameras extract and analyze facial information. However, due to the lack of scientific data to support accurate judgment criteria, the detection results are prone to large errors and have low accuracy. Furthermore, the cameras require an external power supply for extended periods during the detection process, resulting in significant power consumption. Therefore, improving the accuracy of heart rate detection and reducing power consumption during the detection process have become urgent problems to be solved. Summary of the Invention

[0003] The main technical problem addressed by this application is to provide a heart rate detection method, system, electronic device, and computer-readable storage medium that can improve the accuracy of heart rate detection and reduce the power consumption of the heart rate detection process.

[0004] To address the aforementioned technical problems, this application provides a heart rate detection method, comprising: acquiring first heart rate data obtained by measuring a target object using a non-contact sensor, and acquiring second heart rate data obtained by measuring the target object using a contact sensor; calibrating the non-contact sensor based on the first heart rate data and the second heart rate data; acquiring an initial heart rate value and corresponding signal distortion obtained by measuring the target object using the calibrated non-contact sensor; and obtaining a heart rate detection result based on the initial heart rate value and the signal distortion.

[0005] To address the aforementioned technical problems, a second aspect of this application provides a heart rate detection system. This system includes a first acquisition module, a calibration module, a second detection module, and a determination module. The first acquisition module acquires first heart rate data obtained by a non-contact sensor measuring the target object, and acquires second heart rate data obtained by a contact sensor measuring the target object. The calibration module calibrates the non-contact sensor based on the first and second heart rate data. The second acquisition module acquires the initial heart rate value and corresponding signal distortion obtained by the calibrated non-contact sensor measuring the target object. The determination module uses the initial heart rate value and the signal distortion to obtain a heart rate detection result.

[0006] To address the aforementioned technical problems, a third aspect of this application provides an electronic device comprising: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor invokes the program data to execute the method described in the first aspect.

[0007] To address the aforementioned technical problems, a fourth aspect of this application provides a computer-readable storage medium storing program data thereon, wherein the program data, when executed by a processor, implements the method described in the first aspect.

[0008] The beneficial effects of this application are as follows: Unlike the prior art, by using a contact sensor to calibrate a non-contact sensor, and then using the calibrated non-contact sensor to independently measure the object to be detected, the power consumption is greatly reduced while accurately measuring the corresponding heart rate detection results. This improves the accuracy of heart rate detection and reduces the power consumption of the heart rate detection process. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0010] Figure 1 This is a flowchart illustrating one embodiment of the heart rate detection method of this application;

[0011] Figure 2 This is a flowchart illustrating another embodiment of the heart rate detection method of this application;

[0012] Figure 3 This is a schematic diagram of one embodiment of the heart rate detection system of this application;

[0013] Figure 4 This is a schematic diagram of the structure of one embodiment of the electronic device of this application;

[0014] Figure 5 This is a schematic diagram of one embodiment of the computer-readable storage medium of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments, and different implementation methods can be adaptively combined. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] In this paper, the terms "system" and "network" are often used interchangeably. The term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Furthermore, "many" in this paper means two or more.

[0017] The heart rate detection method provided in this application is used to detect heart rate detection results. Its corresponding execution subject is a processing unit capable of data processing. The processing unit is integrated into the smart terminal or exists independently of the smart terminal and interacts with the smart terminal for data.

[0018] Please see Figure 1 , Figure 1 This is a flowchart illustrating one embodiment of the heart rate detection method of this application, which includes:

[0019] S101: Acquire the first heart rate data obtained by non-contact sensor measurement of the object to be detected, and acquire the second heart rate data obtained by contact sensor measurement of the object to be detected.

[0020] Specifically, a non-contact sensor is used to measure the object under test to obtain the first heart rate data, and a contact sensor is used to measure the object under test to obtain the second heart rate data.

[0021] In one embodiment, a non-contact sensor is used to transmit a radio frequency signal and receive the reflected signal from the object to be detected. The first heart rate data is obtained by analyzing the phase change of the signal, and the second heart rate data is obtained by measuring the impedance change of the object to be detected using a contact sensor.

[0022] In some implementation scenarios, non-contact sensors include millimeter-wave radar. Millimeter-wave radar is used to measure the phase change of the reflected signal from the object being detected to obtain the initial heart rate data. The specific process is as follows:

[0023]

[0024] Where ΔR represents the distance change between the object to be detected and the millimeter-wave radar, and λ represents the wavelength of the signal. This represents the total phase change of the received signal.

[0025] Furthermore, the contact sensor includes a bioimpedance sensor, which uses a weak current applied through electrodes on the body surface to measure the impedance change of the subject and obtain a second heart rate data.

[0026] In one embodiment, a non-contact sensor is used to acquire facial information of the subject to be detected, and an image processing algorithm is used to analyze the facial information to obtain first heart rate data. A contact sensor is used to analyze the blood flow signal of the subject to be detected to measure second heart rate data.

[0027] It should be noted that both non-contact and contact sensors require synchronous data processing before measuring the object to be detected.

[0028] S102: Calibrate the non-contact sensor based on the first heart rate data and the second heart rate data.

[0029] Specifically, since contact measurement methods are more accurate than non-contact measurement methods, non-contact sensors need to be calibrated based on the first and second heart rate data.

[0030] In one embodiment, the difference between a first heart rate data and a second heart rate data is obtained, and the non-contact sensor is calibrated using the difference fitting.

[0031] In one embodiment, a mapping relationship is established between the first heart rate data and the second heart rate data, and the mapping relationship is used to calibrate the non-contact sensor.

[0032] S103: Obtain the initial heart rate value and corresponding signal distortion obtained by measuring the object under test using a calibrated non-contact sensor.

[0033] Specifically, after calibrating the non-contact sensor, the calibrated non-contact sensor is used to independently measure the object to be detected, obtain the initial heart rate value, and acquire the signal distortion corresponding to the non-contact sensor.

[0034] In one embodiment, after calibrating the non-contact sensor, the calibrated non-contact sensor transmits a radio frequency signal, receives the reflected signal from the object to be detected, obtains the initial heart rate value by analyzing the phase change of the signal, and acquires the signal distortion corresponding to the non-contact sensor.

[0035] In one embodiment, after calibrating the non-contact sensor, the facial information of the object to be detected is acquired using the calibrated non-contact sensor. The facial information is then analyzed using an image processing algorithm to obtain the initial heart rate value and the signal distortion corresponding to the non-contact sensor.

[0036] S104: Based on the initial heart rate value and signal distortion, the heart rate detection result is obtained.

[0037] Specifically, based on the initial heart rate value and signal distortion, the heart rate detection results of the subject under test are analyzed.

[0038] In one embodiment, the detection frequency of the non-contact sensor is determined based on the initial heart rate value, and after the detection frequency is adjusted based on the signal distortion, the object to be detected is detected according to the adjusted detection frequency to obtain the heart rate detection result.

[0039] In one embodiment, the detection parameters of the non-contact sensor, such as sampling rate and gain, are set based on the initial heart rate value. The detection parameters are then adjusted based on the signal distortion until the signal distortion meets the threshold condition, at which point the adjustment stops. The non-contact sensor with the adjusted detection parameters is then used to detect the object to be detected, and the heart rate detection result is obtained.

[0040] In a specific implementation scenario, the non-contact sensor is a millimeter-wave radar, installed on a learning machine to measure students' heart rates in a smart classroom. Each student has a corresponding learning machine on their desk. The contact sensor is a bioimpedance sensor, installed on a smart watch, and each student wears one on their wrist. This sensor measures the student's heart rate to calibrate the millimeter-wave radar on the learning machine, addressing the issue of excessive interference during heart rate detection. The system acquires the first heart rate data from the millimeter-wave radar and the second heart rate data from the bioimpedance sensor. A fitting curve is created based on the difference between the first and second heart rate data to calibrate the millimeter-wave radar. The calibrated millimeter-wave radar is then used to independently measure each student's heart rate, obtaining initial heart rate values ​​and acquiring the corresponding millimeter-wave signal distortion. The detection frequency of the millimeter-wave radar is determined based on the initial heart rate values ​​and adjusted according to the signal distortion. The adjusted detection frequency is then used to detect each student, yielding the final heart rate result.

[0041] The above solution uses a contact sensor to calibrate a non-contact sensor, and then uses the calibrated non-contact sensor to independently measure the object to be detected. While accurately measuring the corresponding heart rate detection results, it greatly reduces power consumption, thereby improving the accuracy of heart rate detection and reducing the power consumption of the heart rate detection process.

[0042] Please see Figure 2 , Figure 2 This is a flowchart illustrating another embodiment of the heart rate detection method of this application, the method comprising:

[0043] S201: Acquire the first heart rate data obtained by non-contact sensor measurement of the object to be detected, and acquire the second heart rate data obtained by contact sensor measurement of the object to be detected.

[0044] Specifically, a non-contact sensor is used to measure the object under test to obtain the first heart rate data, and a contact sensor is used to measure the object under test to obtain the second heart rate data.

[0045] S202: Based on the first heart rate data, determine the heart rate zone corresponding to the first heart rate data.

[0046] Specifically, the corresponding heart rate zone is determined based on the first heart rate data measured by a non-contact sensor.

[0047] In some implementation scenarios, the heart rate is set to the following 5 intervals: [40-60], [60-80],

[0048] [80-100], [100-120], [120-140].

[0049] In other implementation scenarios, the heart rate can also be set to other intervals corresponding to different numbers, depending on actual needs. This application does not impose specific restrictions here.

[0050] S203: Obtain the calibration coefficient that matches the heart rate zone corresponding to the first heart rate data.

[0051] Specifically, a matching calibration coefficient is obtained based on the heart rate zone corresponding to the first heart rate data.

[0052] In one embodiment, a calibration coefficient lookup table is pre-set, which stores the correlation between heart rate intervals and calibration coefficients α. After obtaining the first heart rate data, the matching calibration coefficient α is obtained through the calibration coefficient lookup table.

[0053] It should be noted that the calibration coefficient α is the value used for calibration of the non-contact sensor during factory testing, and the value of the calibration coefficient α is different for each non-contact sensor in each heart rate zone.

[0054] In some implementation scenarios, the calibration coefficient matching the heart rate range [40-60] is α1, the calibration coefficient matching the heart rate range [60-80] is α2, the calibration coefficient matching the heart rate range [80-100] is α3, the calibration coefficient matching the heart rate range [100-120] is α4, and the calibration coefficient matching the heart rate range [120-140] is α5. For example, when the first heart rate data obtained is 85, the corresponding heart rate range is [80-100], and the matching calibration coefficient is α3.

[0055] Optionally, each non-contact sensor has its own calibration coefficient α, which can be the same or different, and this application does not impose any specific restrictions on it.

[0056] S204: Calibrate the non-contact sensor based on the first heart rate data, the second heart rate data, and the calibration coefficient.

[0057] Specifically, the non-contact sensor is calibrated based on the first heart rate data, the second heart rate data, and the corresponding calibration coefficients.

[0058] In some implementation scenarios, step S204 specifically includes: acquiring the heart rate difference between the first heart rate data and the second heart rate data; obtaining the heart rate compensation value based on the heart rate difference and the calibration coefficient; and calibrating the non-contact sensor using the heart rate compensation value corresponding to each heart rate zone.

[0059] Specifically, the heart rate difference between the first heart rate data and the second heart rate data is calculated. Based on the heart rate difference and the calibration coefficient, a heart rate compensation value is calculated. The non-contact sensor is calibrated using the heart rate compensation value corresponding to each heart rate interval, which can more accurately correct the non-contact sensor and thus improve the measurement accuracy of the non-contact sensor.

[0060] In a specific implementation scenario, the heart rate difference β between the first heart rate data and the second heart rate data is calculated. The heart rate difference β is multiplied by the calibration coefficient α to obtain the heart rate compensation value. The heart rate compensation value corresponding to each heart rate interval is added to the first heart rate data corresponding to each heart rate interval to calibrate the non-contact sensor.

[0061] In other implementation scenarios, corresponding weights can be set for the heart rate compensation values ​​corresponding to each heart rate zone. The non-contact sensor can be calibrated together with the weight values ​​to further improve the measurement accuracy of the non-contact sensor, thereby improving the accuracy of subsequent heart rate detection.

[0062] Alternatively, once the non-contact sensor has been calibrated, the contact sensor can be set to sleep mode, thereby reducing power consumption during the heart rate detection process.

[0063] S205: Obtain the initial heart rate value and corresponding signal distortion obtained by measuring the object under test using a calibrated non-contact sensor.

[0064] Specifically, after calibrating the non-contact sensor, the object to be tested is remeasured to obtain the initial heart rate value, and the signal distortion corresponding to the non-contact sensor is obtained.

[0065] In some implementation scenarios, the non-contact sensor includes a first radar sensor, which is installed on the learning machine. Step S205 specifically includes: using the calibrated first radar sensor on the learning machine to determine the target object matched by the learning machine; putting the first radar sensor on the learning machine that has not matched the target object into sleep mode; and obtaining the initial heart rate value and corresponding signal distortion obtained by the calibrated first radar sensor on the learning machine from the target object.

[0066] Specifically, the first radar sensor is a low-frequency millimeter-wave radar. Each learning machine is equipped with a low-frequency millimeter-wave radar. The low-frequency millimeter-wave radar on the learning machine is calibrated to determine the target to be detected that the learning machine is matched with. The low-frequency millimeter-wave radar on the learning machine that is not matched with the target to be detected is set to sleep mode, thereby further reducing the power consumption of the heart rate detection process.

[0067] Optionally, when the low-frequency millimeter-wave radar on the learning machine is measuring the object to be detected, all application functions on the learning machine that are not related to learning are turned off, thereby further reducing the power consumption of the heart rate detection process.

[0068] Furthermore, the initial heart rate value of the target object measured by the calibrated low-frequency millimeter-wave radar on the learning machine, as well as the signal distortion of the low-frequency millimeter-wave radar, are obtained.

[0069] In some implementation scenarios, the non-contact sensor preferably includes a second radar sensor. The second radar sensor and the first radar sensor are installed on a heart rate detection screen. After obtaining the initial heart rate value and corresponding signal distortion obtained by the first radar sensor calibrated on the learning machine from the measured object, the method further includes: determining the first and second radar sensors on the heart rate detection screen that are not obstructed; obtaining the first signal sent by the unobstructed first radar sensor and the second signal sent by the unobstructed second radar sensor; determining the object to be detected based on the first and second signals; obtaining the reference heart rate value of the object measured by the first radar sensor calibrated on the heart rate detection screen; and updating the initial heart rate value using the reference heart rate value.

[0070] Specifically, the second radar sensor is a high-frequency millimeter-wave radar. Multiple low-frequency and high-frequency millimeter-wave radars are placed around the screen of the heart rate detection screen, and all millimeter-wave radars avoid the display area of ​​the screen. The high-frequency millimeter-wave radar is used to detect obstacles such as walls, while the low-frequency millimeter-wave radar is used to detect all objects and is compared with the high-frequency millimeter-wave radar to filter out the objects to be detected.

[0071] In some implementation scenarios, multiple sensors are arranged with high- and low-frequency vibration sources spaced apart.

[0072] In other implementation scenarios, the arrangement of multiple sensors can be flexibly set according to the actual situation, and the number of high-frequency millimeter-wave radars and low-frequency millimeter-wave radars can also be set according to actual needs. This application does not impose specific restrictions here.

[0073] Furthermore, in the application of heart rate monitoring screens, there are often interfering objects. For example, in the application of smart classrooms, it is necessary to monitor students' heart rates, and teachers are interfering objects. Teachers may obstruct students from different positions. Therefore, it is necessary to identify the low-frequency millimeter-wave radar and high-frequency millimeter-wave radar that are not obstructed by teachers on the heart rate monitoring screen, obtain the first signal sent by the unobstructed low-frequency millimeter-wave radar, and obtain the second signal sent by the unobstructed high-frequency millimeter-wave radar. The obstacle structure diagram formed by the second signal is subtracted from the structure diagram formed by the first signal to form a model diagram of the student, thereby determining the student's position. The reference heart rate value of the student is measured using the calibrated low-frequency millimeter-wave radar on the heart rate monitoring screen, and the initial heart rate value measured by the low-frequency millimeter-wave radar on the learning machine is updated using the reference heart rate value.

[0074] In a specific implementation scenario, corresponding initial weights are set for the learning machine and the low-frequency millimeter-wave radar on the heart rate detection screen. Since the learning machine is closer to the student than the heart rate detection screen, the initial weight of the learning machine should be higher than that of the heart rate detection screen. For example, the initial weight of the learning machine can be set to 80%, and the initial weight of the heart rate detection screen can be set to 20%. The two data are then fused through dynamic weighting to obtain the final initial heart rate value, thereby improving the reliability of the final detection result.

[0075] Optionally, when the learning machine does not match a student in the corresponding position, or the learning machine does not have a low-frequency millimeter-wave radar installed, or the learning machine is out of power and cannot detect, the weight of the heart rate detection screen can be adjusted to 100%, that is, the reference heart rate value measured by the heart rate detection screen can be directly used as the student's initial heart rate value, thereby ensuring that every student can be detected and avoiding omissions.

[0076] Understandably, a large heart rate monitoring screen can simultaneously measure the heart rate data of multiple different students over a wide range, and can continuously monitor students' physical condition and provide timely feedback on their health status.

[0077] S206: Based on the initial heart rate value and signal distortion, the heart rate detection result is obtained.

[0078] Specifically, based on the initial heart rate value and signal distortion, the heart rate detection results of the subject under test are analyzed.

[0079] In some implementation scenarios, step S206 specifically includes: determining the heart rate interval corresponding to the initial heart rate value based on the initial heart rate value; obtaining the initial scanning frequency of the current non-contact sensor based on the heart rate interval; and obtaining the heart rate detection result based on the signal distortion and the initial scanning frequency.

[0080] Specifically, the corresponding heart rate zone is determined based on the initial heart rate value.

[0081] Optionally, the heart rate can be set to the following 5 intervals: [40-60], [60-80], [80-100],

[0082] [100-120], [120-140].

[0083] In other implementation scenarios, the heart rate can also be set to other intervals corresponding to different numbers, depending on actual needs. This application does not impose specific restrictions here.

[0084] Furthermore, based on the heart rate range corresponding to the initial heart rate value, the initial scanning frequency of the current non-contact sensor is obtained.

[0085] In some implementation scenarios, a scan frequency lookup table is pre-set, which stores the correlation between heart rate zones and scan frequencies. Once the initial heart rate value is obtained, the matching scan frequency is obtained through this scan frequency lookup table.

[0086] In a specific implementation scenario, the scanning frequency matching the heart rate range [40-60] is 10Hz, the scanning frequency matching the heart rate range [60-80] is 16Hz, the scanning frequency matching the heart rate range [80-100] is 20Hz, the scanning frequency matching the heart rate range [100-120] is 24Hz, and the scanning frequency matching the heart rate range [120-140] is 24Hz, wherein the peak value of the scanning frequency is 24Hz.

[0087] In other implementation scenarios, the scanning frequency matching each heart rate zone can also be other values, which can be set according to the actual situation. This application does not impose specific restrictions here.

[0088] Furthermore, based on the signal distortion and initial scan frequency, the heart rate detection results are analyzed.

[0089] In some implementation scenarios, the steps for obtaining heart rate detection results based on signal distortion and initial scanning frequency specifically include: in response to the non-contact sensor's scanning time at the initial scanning frequency meeting a preset time threshold, reducing the non-contact sensor's scanning frequency and obtaining the first signal distortion of the non-contact sensor at the initial scanning frequency; obtaining the second signal distortion of the non-contact sensor after the scanning frequency is reduced; in response to the difference between the first and second signal distortions meeting a preset condition, keeping the non-contact sensor's scanning frequency unchanged and determining the corresponding heart rate detection result; in response to the difference between the first and second signal distortions not meeting the preset condition, increasing the non-contact sensor's scanning frequency until the difference meets the preset condition and determining the corresponding heart rate detection result.

[0090] Specifically, when the scanning time of the non-contact sensor at the initial scanning frequency meets a preset time threshold, the scanning frequency of the non-contact sensor is reduced, and the first signal distortion corresponding to the initial scanning frequency of the non-contact sensor is obtained. After the initial scanning frequency of the non-contact sensor is reduced, the second signal distortion corresponding to the non-contact sensor is obtained, and the difference between the first signal distortion and the second signal distortion is calculated. When the calculated difference meets a preset condition, the scanning frequency of the non-contact sensor is kept unchanged, and the corresponding heart rate data is determined, thereby obtaining the heart rate detection result of the subject to be detected. When the calculated difference does not meet the preset condition, the scanning frequency of the non-contact sensor is increased until the calculated difference meets the preset condition, and the corresponding heart rate data is determined, thereby obtaining the heart rate detection result of the subject to be detected.

[0091] In a specific implementation scenario, the initial heart rate of a student measured by a low-frequency millimeter-wave radar is 85, corresponding to an initial scanning frequency of 20Hz. After the low-frequency millimeter-wave radar remains at the 20Hz scanning frequency for more than 10 minutes, the scanning frequency of the low-frequency millimeter-wave radar is reduced to 16Hz, and the first signal distortion and the second signal distortion of the low-frequency millimeter-wave radar are acquired. When the difference between these two signal distortions is within ±2, the low-frequency millimeter-wave radar remains at 16Hz, and the corresponding heart rate data is determined, thereby obtaining the heart rate detection result of the subject. If the difference between these two signal distortions is beyond ±2, the scanning frequency of the low-frequency millimeter-wave radar is increased to 20Hz, and the corresponding heart rate data is determined, thereby obtaining the heart rate detection result of the subject.

[0092] Understandably, the scanning frequency of non-contact sensors can be adjusted to the lowest possible frequency, thereby further reducing the power consumption of the heart rate detection process.

[0093] It should be noted that if the scanning frequency of the non-contact sensor remains at a peak of 24Hz, it indicates that signal distortion has clearly occurred. In this case, a warning will be issued, indicating that the person being tested has an abnormal condition and needs immediate medical attention.

[0094] Please see Figure 3 , Figure 3 This is a schematic diagram of one embodiment of the heart rate detection system of this application. The heart rate detection system 30 includes a first acquisition module 301, a calibration module 302, a second acquisition module 303, and a determination module 304. The first acquisition module 301 is used to acquire first heart rate data obtained by non-contact sensor measurement of the target object, and to acquire second heart rate data obtained by contact sensor measurement of the target object. The calibration module 302 is used to calibrate the non-contact sensor based on the first and second heart rate data. The second acquisition module 303 is used to acquire the initial heart rate value and corresponding signal distortion obtained by the calibrated non-contact sensor measurement of the target object. The determination module 304 is used to obtain the heart rate detection result based on the initial heart rate value and the signal distortion.

[0095] The above solution uses a contact sensor to calibrate a non-contact sensor, and then uses the calibrated non-contact sensor to independently measure the object to be detected. While accurately measuring the corresponding heart rate detection results, it greatly reduces power consumption, thereby improving the accuracy of heart rate detection and reducing the power consumption of the heart rate detection process.

[0096] In one embodiment, the calibration module 302 is further configured to determine the heart rate interval corresponding to the first heart rate data based on the first heart rate data; obtain a calibration coefficient that matches the heart rate interval corresponding to the first heart rate data; and calibrate the non-contact sensor based on the first heart rate data, the second heart rate data, and the calibration coefficient.

[0097] In one embodiment, the calibration module 302 is further configured to acquire the heart rate difference between the first heart rate data and the second heart rate data; obtain a heart rate compensation value based on the heart rate difference and the calibration coefficient; and calibrate the non-contact sensor using the heart rate compensation value corresponding to each heart rate interval.

[0098] In one embodiment, the determining module 304 is further configured to determine the heart rate interval corresponding to the initial heart rate value based on the initial heart rate value; obtain the initial scanning frequency of the current non-contact sensor based on the heart rate interval; and obtain the heart rate detection result based on the signal distortion and the initial scanning frequency.

[0099] In one embodiment, the determining module 304 is further configured to: reduce the scanning frequency of the non-contact sensor in response to the scanning time of the non-contact sensor at the initial scanning frequency meeting a preset time threshold, and obtain a first signal distortion degree of the non-contact sensor at the initial scanning frequency; obtain a second signal distortion degree of the non-contact sensor after the scanning frequency is reduced; maintain the scanning frequency of the non-contact sensor unchanged in response to the difference between the first signal distortion degree and the second signal distortion degree meeting a preset condition, and determine the corresponding heart rate detection result; and increase the scanning frequency of the non-contact sensor in response to the difference between the first signal distortion degree and the second signal distortion degree not meeting the preset condition, until the difference meets the preset condition, and determine the corresponding heart rate detection result.

[0100] In one embodiment, the non-contact sensor includes a first radar sensor mounted on the learning machine; the second acquisition module 303 is further configured to use the calibrated first radar sensor on the learning machine to determine the target object matched by the learning machine, put the first radar sensor on the learning machine that has not matched the target object into sleep mode; and acquire the initial heart rate value and corresponding signal distortion obtained by the calibrated first radar sensor on the learning machine from the target object.

[0101] In one embodiment, the non-contact sensor further includes a second radar sensor, which, along with the first radar sensor, is mounted on the heart rate detection screen. The second acquisition module 303 is further configured to determine the unobstructed first and second radar sensors on the heart rate detection screen, acquire a first signal sent by the unobstructed first radar sensor, and a second signal sent by the unobstructed second radar sensor; determine the object to be detected based on the first and second signals; acquire a reference heart rate value of the object to be detected measured by the calibrated first radar sensor on the heart rate detection screen, and update the initial heart rate value using the reference heart rate value.

[0102] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an embodiment of the electronic device of this application. The electronic device 40 includes a memory 401 and a processor 402 coupled to each other. The memory 401 stores program data (not shown in the figure), and the processor 402 calls the program data to implement the method in any of the above embodiments. For the description of the relevant content, please refer to the detailed description of the above method embodiments, which will not be repeated here.

[0103] Please see Figure 5 , Figure 5This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present application. The computer-readable storage medium 50 stores program data 500. When the program data 500 is executed by a processor, it implements the method in any of the above embodiments. For related descriptions, please refer to the detailed description of the above method embodiments, which will not be repeated here.

[0104] It should be noted that the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0106] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device...

[0107] The storage medium (which may be a personal computer, server, or network device, etc.) or processor executes all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0108] The above description is merely an embodiment of this application and does not limit the scope of protection of this application. Any equivalent structural or procedural transformations made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.

Claims

1. A heart rate detection method, characterized in that, include: Acquire first heart rate data from a non-contact sensor measuring the object under test, and acquire second heart rate data from a contact sensor measuring the object under test; The non-contact sensor is calibrated based on the first heart rate data and the second heart rate data. The initial heart rate value and corresponding signal distortion of the object to be detected are obtained by measuring the non-contact sensor after calibration. Based on the initial heart rate value and the signal distortion, the heart rate detection result is obtained.

2. The method according to claim 1, characterized in that, The calibration of the non-contact sensor based on the first heart rate data and the second heart rate data includes: Based on the first heart rate data, determine the heart rate zone corresponding to the first heart rate data; Obtain the calibration coefficient that matches the heart rate zone corresponding to the first heart rate data; The non-contact sensor is calibrated based on the first heart rate data, the second heart rate data, and the calibration coefficient.

3. The method according to claim 2, characterized in that, The calibration of the non-contact sensor based on the first heart rate data, the second heart rate data, and the calibration coefficient includes: Obtain the heart rate difference between the first heart rate data and the second heart rate data; Based on the heart rate difference and the calibration coefficient, a heart rate compensation value is obtained; The non-contact sensor is calibrated using the heart rate compensation value corresponding to each heart rate zone.

4. The method according to claim 1, characterized in that, The process of obtaining the heart rate detection result based on the initial heart rate value and the signal distortion includes: Based on the initial heart rate value, determine the heart rate interval corresponding to the initial heart rate value; Based on the heart rate zone, the initial scanning frequency of the non-contact sensor is obtained. The heart rate detection result is obtained based on the signal distortion and the initial scanning frequency.

5. The method according to claim 4, characterized in that, The process of obtaining the heart rate detection result based on the signal distortion and the initial scanning frequency includes: In response to the non-contact sensor's scanning time at the initial scanning frequency meeting a preset time threshold, the scanning frequency of the non-contact sensor is reduced, and the first signal distortion of the non-contact sensor at the initial scanning frequency is obtained. Obtain the second signal distortion corresponding to the non-contact sensor after the scanning frequency is reduced; In response to the difference between the first signal distortion and the second signal distortion satisfying a preset condition, the scanning frequency of the non-contact sensor remains unchanged, and the corresponding heart rate detection result is determined. In response to the fact that the difference between the first signal distortion and the second signal distortion does not meet the preset condition, the scanning frequency of the non-contact sensor is increased until the difference meets the preset condition, and the corresponding heart rate detection result is determined.

6. The method according to claim 1, characterized in that, The non-contact sensor includes a first radar sensor, which is mounted on the learning machine. The acquisition of the initial heart rate value and corresponding signal distortion obtained by the calibrated non-contact sensor from the object under test includes: Using the calibrated first radar sensor on the learning machine, the target object matched by the learning machine is determined, and the first radar sensor on the learning machine that is not matched with the target object is put into sleep mode. The initial heart rate value and the corresponding signal distortion obtained by measuring the object to be detected by the first radar sensor after calibration on the learning machine are acquired.

7. The method according to claim 6, characterized in that, The non-contact sensor also includes a second radar sensor, and the second radar sensor and the first radar sensor are installed on the heart rate detection screen. After acquiring the initial heart rate value and the corresponding signal distortion obtained by measuring the object to be detected from the first radar sensor calibrated on the learning machine, the method further includes: The first and second radar sensors on the heart rate detection screen that are not obstructed are identified, and the first signal sent by the first radar sensor that is not obstructed and the second signal sent by the second radar sensor that is not obstructed are acquired. The object to be detected is determined based on the first signal and the second signal; The reference heart rate value of the object to be detected is obtained by the first radar sensor after calibration on the heart rate detection screen, and the initial heart rate value is updated using the reference heart rate value.

8. A heart rate detection system, characterized in that, include: The first acquisition module is used to acquire first heart rate data obtained by non-contact sensor measurement of the object to be detected, and to acquire second heart rate data obtained by contact sensor measurement of the object to be detected. A calibration module is used to calibrate the non-contact sensor based on the first heart rate data and the second heart rate data; The second acquisition module is used to acquire the initial heart rate value and corresponding signal distortion obtained by the calibrated non-contact sensor from the object to be detected; The determination module is used to obtain the heart rate detection result based on the initial heart rate value and the signal distortion.

9. An electronic device, characterized in that, include: A memory and a processor are coupled to each other, wherein the memory stores program data, and the processor invokes the program data to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing program data thereon, characterized in that, When the program data is executed by the processor, the method as described in any one of claims 1-7 is implemented.

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