CN-121971059-A - Heart rate determining method, electronic device, computer readable storage medium

CN121971059ACN 121971059 ACN121971059 ACN 121971059ACN-121971059-A

Abstract

The application discloses a heart rate determining method, electronic equipment and a computer readable storage medium, and relates to the technical field of wearable equipment, wherein the heart rate determining method comprises the steps of acquiring heart rate data detected by a plurality of wearable equipment worn by a user at the same time; calculating a difference between heart rate data of any two wearable devices; based on the comparison result of the difference value and a preset consensus threshold value, determining whether consensus is achieved between any two wearable devices, forming a consensus group by the agreed wearable devices, dynamically adjusting the device credit value of each wearable device based on the forming result of the consensus group, and carrying out fusion calculation or selection on heart rate data of all the wearable devices based on the adjusted device credit values of all the wearable devices to obtain a final target heart rate value. The application introduces a unified and dynamically adjusted device credit value to replace the confidence coefficient, and enables the multi-device heart rate data fusion result to be more accurate and reliable based on a dynamic credit adjustment mechanism of a consensus group.

Inventors

SUN HAORAN
SUI CHENGHAO
PAN JUNJIE
LIU YAOCHENG

Assignees

歌尔科技有限公司

Dates

Publication Date: 20260505
Application Date: 20251229

Claims (13)

1. A heart rate determination method, the method comprising: Acquiring heart rate data detected by a plurality of wearable devices worn by a user at the same time; calculating a difference between heart rate data of any two wearable devices; Based on a comparison result of the difference value and a preset consensus threshold value, determining whether consensus is achieved between any two wearable devices, and forming a consensus group by the agreed wearable devices; Based on the formation result of the consensus group, dynamically adjusting the device credit value of each wearable device, wherein the device credit value of the wearable device which agrees with at least one other wearable device is increased, and the device credit value characterizes the reliability of heart rate data detected by the wearable device; and based on the adjusted device credit values of the wearable devices, carrying out fusion calculation or selection on heart rate data of all the wearable devices, and outputting to obtain a final target heart rate value.
2. The method of claim 1, wherein the method further comprises: assigning the same initial device credit value to each newly joined or reset wearable device; After the output of the target heart rate value is completed each time, storing the device credit value of each wearable device after the adjustment, and taking the device credit value as an initial device credit value of each wearable device participating in the next target heart rate value determination.
3. The method of claim 1, wherein for a wearable device that is not in consensus with any other wearable device, its device credit value is reduced.
4. The method of claim 1, wherein dynamically adjusting the device credit value of each wearable device based on the formation of the consensus group comprises: For each wearable device within the consensus group, increasing its device credit value by a first preset value, and/or, For a wearable device that does not agree with any other wearable device, its device credit value is reduced by a second preset value.
5. The method of claim 1, wherein the dynamically adjusting the device credit value for each wearable device based on the formation of the consensus group comprises: For a wearable device that has a consensus with at least one other wearable device, its device credit value is increased by a third preset value, and/or, For a wearable device that is not in agreement with any other wearable device, the magnitude of the decrease in its device credit value is positively correlated with the minimum difference between the heart rate data of that wearable device and the heart rate data of all other wearable devices.
6. The method of claim 1, wherein the performing a fusion calculation on the heart rate data of all the wearable devices based on the adjusted device credit values of the respective wearable devices, and outputting to obtain a final target heart rate value includes: correspondingly determining the weighting weight of each wearable device based on the adjusted device credit value of each wearable device; And carrying out weighted average calculation on heart rate data detected by each wearable device based on the weighted weights of the wearable devices to obtain a final target heart rate value.
7. The method of claim 1, wherein the performing a fusion calculation on the heart rate data of all the wearable devices based on the adjusted device credit values of the respective wearable devices, and outputting to obtain a final target heart rate value includes: correspondingly determining the weighting weight of each wearable device in the consensus group based on the adjusted device credit value of each wearable device in the consensus group; And carrying out weighted average calculation on heart rate data detected by each wearable device in the consensus group based on the weighted weights of each wearable device in the consensus group to obtain a final target heart rate value.
8. The method according to any one of claims 1 to 7, wherein selecting heart rate data of all the wearable devices based on the adjusted device credit values of the respective wearable devices, and outputting to obtain a final target heart rate value includes: If no consensus group is formed, acquiring current motion state information of the user; Determining the wearable equipment to be selected, which is matched with the current motion state information, from all the wearable equipment according to the current motion state information; Taking heart rate data of the wearable equipment to be selected as a final target heart rate value under the condition that the number of the wearable equipment to be selected is one; And under the condition that the number of the wearable devices to be selected is larger than one, correspondingly determining the weight of each wearable device to be selected in the consensus group based on the device credit value of each wearable device to be selected, and carrying out weighted average calculation on heart rate data detected by each wearable device to be selected based on the weight of each wearable device to be selected to obtain a final target heart rate value.
9. The method of claim 8, wherein the method further comprises: And punishment deduction is carried out on the device credit values of the other wearable devices except the wearable device to be selected in all the wearable devices.
10. The method according to claim 8 or 9, wherein said obtaining current motion state information of the user comprises: Acquiring data from an inertial measurement unit sensor of at least one of the plurality of wearable devices, determining current motion state information of the user based on the inertial measurement unit sensor data, and/or, A quality parameter of a photoplethysmograph signal from at least one of the plurality of wearable devices is acquired, and current motion state information of the user is determined from the quality parameter of the photoplethysmograph signal.
11. The method of claim 1, wherein the determining whether a consensus is reached between any two wearable devices based on a comparison of the difference with a preset consensus threshold comprises: If the difference value between the heart rate data of the two wearable devices is smaller than or equal to the preset consensus threshold value, determining that consensus is achieved between the two wearable devices; The wearable devices to be agreed to form an consensus group, comprising: wearable devices that agree with each other are grouped into the same consensus group.
12. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the heart rate determination method of any one of claims 1 to 11.
13. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the heart rate determination method according to any of claims 1 to 11.

Description

Heart rate determining method, electronic device, computer readable storage medium Technical Field The application relates to the technical field of wearable equipment, in particular to a heart rate determining method, electronic equipment and a computer readable storage medium. Background With the popularization of wearable devices such as smart watches, smart rings, smart glasses, users often use multiple independent devices simultaneously to monitor physiological parameters such as heart rate. In order to improve the reliability of the measurement, many devices output a "confidence" that characterizes the confidence of the current measurement, while outputting a heart rate measurement. However, in practical applications, the related art scheme has obvious limitations. First, the "confidence" calculation methods and scale systems employed by different vendors and different types of devices tend to be non-uniform, resulting in a lack of comparability between confidence values output by different devices. For example, "90%" of one device may be comparable to "70%" of another device at an actual level of trustworthiness. This makes it difficult to directly compare and utilize these confidences. Secondly, in the process of multi-device data fusion or optimal result evaluation, a simple strategy is usually adopted in the related scheme, for example, the measurement result with the highest confidence value is directly selected, or the arithmetic average value is taken for the measurement results of all devices. The former has insufficient reliability due to non-uniform confidence level, and the latter can not effectively process the situation that a certain device has obvious measurement errors, and when the reading difference among the devices is large, the simple average value may lose physiological significance. Therefore, a data fusion method which is independent of confidence of the self-contained and different standards of the device, can adaptively evaluate the long-term reliability of the device, and is suitable for multiple device scenes is needed to improve the accuracy and the robustness of heart rate monitoring results. Disclosure of Invention The application mainly aims to provide a heart rate determining method, electronic equipment and a computer readable storage medium, which aim to solve the problems of inaccurate and unreliable fusion results caused by non-uniform confidence level and lack of dynamic evaluation of long-term reliability of equipment when multi-equipment heart rate data are fused in the related technology. To achieve the above object, the present application proposes a heart rate determination method, the method comprising: Acquiring heart rate data detected by a plurality of wearable devices worn by a user at the same time; calculating a difference between heart rate data of any two wearable devices; Based on a comparison result of the difference value and a preset consensus threshold value, determining whether consensus is achieved between any two wearable devices, and forming a consensus group by the agreed wearable devices; Based on the formation result of the consensus group, dynamically adjusting the device credit value of each wearable device, wherein the device credit value of the wearable device which agrees with at least one other wearable device is increased, and the device credit value characterizes the reliability of heart rate data detected by the wearable device; and based on the adjusted device credit values of the wearable devices, carrying out fusion calculation or selection on heart rate data of all the wearable devices, and outputting to obtain a final target heart rate value. In an embodiment, the method further comprises: assigning the same initial device credit value to each newly joined or reset wearable device; After the output of the target heart rate value is completed each time, storing the device credit value of each wearable device after the adjustment, and taking the device credit value as an initial device credit value of each wearable device participating in the next target heart rate value determination. In an embodiment, the dynamically adjusting the device credit value of each wearable device based on the forming result of the consensus group includes: For each wearable device within the consensus group, increasing its device credit value by a first preset value, and/or, For a wearable device that does not agree with any other wearable device, its device credit value is reduced by a second preset value. In an embodiment, the dynamically adjusting the device credit value of each wearable device based on the forming result of the consensus group includes: For a wearable device that has a consensus with at least one other wearable device, its device credit value is increased by a third preset value, and/or, For a wearable device that is not in agreement with any other wearable device, the magnitude of the decrease in its device credit value is po