PPG light leakage self-test method, wearable devices and computer-readable storage media

Abstract

This application discloses a PPG light leakage self-test method, a wearable device, and a computer-readable storage medium, relating to the field of device testing technology. The PPG light leakage self-test method is applied to a wearable device integrating a PPG sensor, which includes a photodetector and a light source. The method includes: receiving a PPG light leakage self-test command; acquiring first photoelectric data detected by the photodetector when the light source is on, and second photoelectric data detected when the light source is off, and determining PPG light leakage data based on the first and second photoelectric data; determining the current monitoring scenario, and dynamically determining a current light leakage data threshold based on the current monitoring scenario; and determining that the PPG sensor is leaking light when the PPG light leakage data exceeds the current light leakage data threshold. This application improves the convenience of light leakage detection while more accurately and sensitively determining whether the PPG sensor of a wearable device is leaking light.

Description

Technical Field

[0001] This application relates to the field of equipment testing technology, and in particular to a PPG light leakage self-testing method, wearable devices, and computer-readable storage media. Background Technology

[0002] Currently, most smart wearable devices are equipped with PPG (Photoplethysmography) sensors, which obtain physiological information by measuring the attenuation of a light beam as it passes through or is reflected back from human tissue. This allows for the non-invasive measurement of key health indicators such as blood pressure and heart rate, providing users with real-time health monitoring.

[0003] To ensure the accuracy and reliability of data acquired by PPG sensors, light leakage detection is essential. However, traditional PPG light leakage detection requires a strictly controlled darkroom environment to eliminate interference from natural light and other light sources. Creating such a darkroom environment necessitates the use of a sealed detection chamber and specialized light-shielding structures, resulting in high equipment costs and complex operating procedures. This makes it unsuitable for routine light leakage detection and significantly limits its application. Summary of the Invention

[0004] The main purpose of this application is to provide a PPG light leakage self-testing method, wearable device, and computer-readable storage medium, aiming to solve the technical problem that traditional PPG light leakage detection relies on a strictly controlled darkroom environment and has low convenience.

[0005] To achieve the above objectives, this application provides a PPG light leakage self-testing method, which is applied to a wearable device. The wearable device integrates a PPG sensor, which includes a photodetector and a light source. The method includes:

[0006] Receive PPG light leakage self-test command;

[0007] Acquire the first photoelectric data detected by the photodetector when the light source is turned on, and the second photoelectric data detected when the light source is turned off, and determine the PPG leakage data based on the first photoelectric data and the second photoelectric data;

[0008] The wearable device selects the current monitoring scenario to be activated from multiple health indicator monitoring scenarios. Based on the current monitoring scenario, the current light leakage data threshold is dynamically determined. The multiple health indicator monitoring scenarios include at least three of the following: heart rate monitoring scenario, blood oxygen saturation monitoring scenario, blood pressure monitoring scenario, and respiratory rate monitoring scenario. The current light leakage data threshold determined for different health indicator monitoring scenarios is different.

[0009] If the PPG leakage data is greater than the current leakage data threshold, it is determined that the PPG sensor is leaking light.

[0010] In one embodiment, the step of dynamically determining the current light leakage data threshold based on the current monitoring scenario includes:

[0011] Based on the current monitoring scenario, the light leakage data threshold of the current monitoring scenario is obtained by querying the preset scenario mapping threshold table;

[0012] The mapped light leakage data threshold is used as the current light leakage data threshold.

[0013] In one embodiment, the step of dynamically determining the current light leakage data threshold based on the current monitoring scenario includes:

[0014] The current optical sensitivity (CTR) of the PPG sensor is dynamically detected.

[0015] Based on the current monitoring scenario, determine the current light source color of the light source used in the current monitoring scenario;

[0016] The current light leakage data threshold is dynamically determined based on the current light source color and the current CTR.

[0017] In one embodiment, the step of dynamically determining the current light leakage data threshold based on the current light source color and the current CTR includes:

[0018] When the current light source color is green, the product of the current CTR and 3.7% is dynamically calculated to obtain the first product result, and the current light leakage data threshold is dynamically determined based on the dynamically calculated first product result.

[0019] When the current light source color is red, the product of the current CTR and 1.5% is dynamically calculated to obtain a second product result. Based on the dynamically calculated second product result, the current light leakage data threshold is dynamically determined.

[0020] In one embodiment, the step of determining the current light source color of the light source corresponding to the current monitoring scene based on the current monitoring scene includes:

[0021] When the current monitoring scenario is a blood oxygen saturation monitoring scenario, the current light source color of the light source used in the current monitoring scenario is determined to be red;

[0022] When the current monitoring scenario is a heart rate monitoring scenario, a blood pressure monitoring scenario, or a respiratory rate monitoring scenario, the current light source color of the light source used in the current monitoring scenario is determined to be green.

[0023] In one embodiment, the wearable device further integrates an ambient light detection device, and after the step of receiving the PPG light leakage self-test command, the method further includes:

[0024] The ambient light data detected by the ambient light detection device within a sliding time window is acquired, and the degree of fluctuation of the ambient light data is determined.

[0025] When the fluctuation level is the first fluctuation level, the step of acquiring the first photoelectric data detected by the photodetector when the light source is turned on is triggered.

[0026] When the fluctuation level is the second fluctuation level, a preset ambient light fluctuation prompt is output, wherein the second fluctuation level is higher than the first fluctuation level.

[0027] In one embodiment, the method further includes:

[0028] If the PPG leakage data is less than or equal to the current leakage data threshold and the PPG leakage data is not zero, PPG calibration data is generated based on the PPG leakage data, wherein the PPG calibration data is used to calibrate the PPG sensor.

[0029] In one embodiment, the photodetector includes a first photodetector and a second photodetector at different positions. The first photoelectric data includes a third photoelectric data detected by the first photodetector when the light source is turned on, and a fourth photoelectric data detected by the second photodetector when the light source is turned on. The second photoelectric data includes a fifth photoelectric data detected by the first photodetector when the light source is turned off, and a sixth photoelectric data detected by the second photodetector when the light source is turned off. The current light leakage data threshold includes a first light leakage data threshold dynamically determined based on the current monitoring scenario of the first photodetector, and a second light leakage data threshold dynamically determined based on the current monitoring scenario of the second photodetector.

[0030] The step of determining PPG leakage data based on the first photoelectric data and the second photoelectric data includes:

[0031] Based on the third photoelectric data and the fifth photoelectric data, determine the PPG leakage data corresponding to the first photodetector;

[0032] Based on the fourth photoelectric data and the sixth photoelectric data, determine the PPG leakage data corresponding to the second photodetector;

[0033] The step of determining PPG sensor light leakage when the PPG light leakage data is greater than the current light leakage data threshold includes:

[0034] If the PPG leakage data corresponding to the first photodetector is greater than the first leakage data threshold, or the PPG leakage data corresponding to the second photodetector is greater than the second leakage data threshold, then the PPG sensor is determined to be leaking light.

[0035] In addition, to achieve the above objectives, this application also provides a wearable device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the PPG light leakage self-test method as described above.

[0036] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the PPG light leakage self-test method as described above.

[0037] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the PPG light leakage self-test method as described above.

[0038] This application provides a PPG light leakage self-testing method, a wearable device, and a computer-readable storage medium. The PPG light leakage self-testing method is applied to a wearable device, which integrates a PPG sensor, which includes a photodetector and a light source. The technical solution of this application embodiment is to receive a PPG light leakage self-test command, acquire the first photoelectric data detected by the photodetector when the light source is turned on, and the second photoelectric data detected when the light source is turned off, and determine the PPG light leakage data based on the first and second photoelectric data. Then, the wearable device selects the current monitoring scenario to be activated from multiple health indicator monitoring scenarios, and dynamically determines the current light leakage data threshold based on the current monitoring scenario. The multiple health indicator monitoring scenarios include at least three of the following: heart rate monitoring scenario, blood oxygen saturation monitoring scenario, blood pressure monitoring scenario, and respiratory rate monitoring scenario. The current light leakage data threshold determined for different health indicator monitoring scenarios is different. If the PPG light leakage data is greater than the current light leakage data threshold, PPG sensor light leakage is determined. Thus, this application embodiment creatively transforms the physical shading of darkroom detection into a noise suppression model for photoelectric signal differential calculation, breaking through the traditional reliance on darkrooms. This allows end users to complete high-precision self-tests in everyday environments, solving the technical problems of traditional PPG light leakage detection relying on strictly controlled darkroom environments, high equipment costs, and complex operation procedures, which are not conducive to light leakage detection in daily use. This significantly improves the convenience of PPG light leakage detection.

[0039] It is worth mentioning that this application embodiment also employs a technical solution that determines the current monitoring scenario selected by the wearable device from multiple health indicator monitoring scenarios, and dynamically determines the current light leakage data threshold based on this current monitoring scenario. This allows the wearable device of this application embodiment to adaptively adjust the current light leakage data threshold when facing different health indicator monitoring scenarios (e.g., heart rate monitoring, blood oxygen saturation monitoring, blood pressure monitoring, or respiratory rate monitoring). This facilitates the selection of the most reasonable light leakage value as the current light leakage data threshold based on the current monitoring scenario, and allows for adjustment of the current light leakage threshold when the current monitoring scenario changes. The light leakage data threshold is dynamically and adaptively adjusted, so that no matter how the current monitoring scene changes, the current light leakage data threshold can always be matched with the most accurate and appropriate light leakage value based on the scene type of the current monitoring scene. This makes it easier to compare the most accurate and appropriate current light leakage data threshold with the actual detected PPG light leakage data, so as to more accurately and sensitively determine whether there is light leakage in the PPG sensor of the wearable device. This effectively improves the accuracy of PPG light leakage detection in daily use after it leaves the strictly controlled dark room environment. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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 the structures shown in these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the first embodiment of the PPG light leakage self-test method of this application;

[0043] Figure 2 This is a flowchart illustrating the second embodiment of the PPG light leakage self-test method of this application;

[0044] Figure 3 This is a schematic diagram showing the layout of the light source and photodetector in a specific embodiment of this application;

[0045] Figure 4 This is a schematic diagram of a PPG light leakage self-test scenario in a specific embodiment of this application;

[0046] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the PPG light leakage self-test method in this application embodiment.

[0047] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0049] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0050] Currently, traditional PPG light leakage detection methods require a strictly controlled darkroom environment. This typically involves using a closed detection chamber and a specially designed light-shielding structure to create a completely dark space to ensure that interference from natural light and other external light sources can be eliminated.

[0051] However, this method has significant limitations and challenges. First, constructing such an anechoic chamber environment is not only expensive in terms of equipment costs, but also requires additional space and resources, resulting in high hardware costs. Furthermore, these devices are often bulky and difficult to move and deploy. On the other hand, the operational procedures are complex, including environmental setup and calibration steps, which not only demands high levels of expertise from operators but also prolongs test preparation time and reduces testing efficiency. In addition, in practical applications, especially for end users, routine device self-testing becomes virtually impossible. Because ideal anechoic chamber conditions cannot be simulated at any time and place, end users find it difficult to promptly detect optical path degradation caused by long-term use, thus affecting the accuracy and reliability of health monitoring data.

[0052] The main solution of this application embodiment is a PPG light leakage self-test method. This method is applied to a wearable device that integrates a PPG sensor, which includes a photodetector and a light source. The method includes: receiving a PPG light leakage self-test command; acquiring first photoelectric data detected by the photodetector when the light source is turned on, and second photoelectric data detected when the light source is turned off, and determining PPG light leakage data based on the first and second photoelectric data; determining the current monitoring scenario selected by the wearable device from multiple health indicator monitoring scenarios, and dynamically determining a current light leakage data threshold based on the current monitoring scenario. The multiple health indicator monitoring scenarios include at least three of the following: heart rate monitoring, blood oxygen saturation monitoring, blood pressure monitoring, and respiratory rate monitoring. The current light leakage data threshold determined for different health indicator monitoring scenarios is different. If the PPG light leakage data is greater than the current light leakage data threshold, it is determined that the PPG sensor is leaking light.

[0053] This application's embodiments creatively transform the physical shading of darkroom detection into a noise suppression model for photoelectric signal differential calculation, breaking through the traditional reliance on darkrooms. This allows end users to complete high-precision self-tests in everyday environments, solving the technical problems of traditional PPG light leakage detection relying on strictly controlled darkroom environments, high equipment costs, and complex operation procedures, which are not conducive to light leakage detection in daily use. This significantly improves the convenience of PPG light leakage detection.

[0054] It is worth mentioning that this application embodiment also employs a technical solution that determines the current monitoring scenario selected by the wearable device from multiple health indicator monitoring scenarios, and dynamically determines the current light leakage data threshold based on this current monitoring scenario. This allows the wearable device of this application embodiment to adaptively adjust the current light leakage data threshold when facing different health indicator monitoring scenarios (e.g., heart rate monitoring, blood oxygen saturation monitoring, blood pressure monitoring, or respiratory rate monitoring). This facilitates the selection of the most reasonable light leakage value as the current light leakage data threshold based on the current monitoring scenario, and allows for adjustment of the current light leakage threshold when the current monitoring scenario changes. The light leakage data threshold is dynamically and adaptively adjusted, so that no matter how the current monitoring scene changes, the current light leakage data threshold can always be matched with the most accurate and appropriate light leakage value based on the scene type of the current monitoring scene. This makes it easier to compare the most accurate and appropriate current light leakage data threshold with the actual detected PPG light leakage data, so as to more accurately and sensitively determine whether there is light leakage in the PPG sensor of the wearable device. This effectively improves the accuracy of PPG light leakage detection in daily use after it leaves the strictly controlled dark room environment.

[0055] It should be noted that the execution subject of the embodiments of this application is a wearable device, which may include, but is not limited to, smartwatches, smart bracelets, smart helmets, smart glasses, smart collars, or any electronic device capable of realizing the above functions. The following description uses a wearable device as an example to illustrate the various embodiments of this application.

[0056] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0057] This application proposes a PPG light leakage self-testing method according to a first embodiment.

[0058] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the PPG light leakage self-testing method of this application.

[0059] In this embodiment, the PPG light leakage self-test method is applied to a wearable device that integrates a PPG sensor, which includes a photodetector and a light source. The method includes steps S100 to S400:

[0060] Step S100: Receive PPG light leakage self-test command;

[0061] As those skilled in the art will know, a photodetector is a device that converts light signals into electrical signals. A PPG sensor is an optical sensor used to measure changes in blood volume, typically applied to monitor physiological parameters such as heart rate and blood oxygen saturation. A PPG sensor mainly consists of a light source and a photodetector. Its working principle is as follows: the light source emits light of a specific wavelength (such as green, red, or infrared light) into human tissue; simultaneously, the photodetector detects the change in light intensity after absorption and scattering by the human tissue. Various physiological indicators are then measured by analyzing these changes in light intensity.

[0062] It should be noted that the PPG light leakage self-test command is a command triggered by the user or the system to activate the PPG light leakage self-test function of the wearable device. This PPG light leakage self-test command can be triggered manually or automatically under specific conditions, such as automatic triggering at set times, automatic triggering periodically, or automatic triggering after detecting that the wearable device has maintained a static state for a preset period of time.

[0063] Step S200: Obtain the first photoelectric data detected by the photodetector when the light source is turned on, and the second photoelectric data detected when the light source is turned off, and determine the PPG leakage data based on the first photoelectric data and the second photoelectric data.

[0064] Those skilled in the art will know that when the light source is turned on, the signal received by the photodetector includes light signals emitted by the light source and reflected or scattered by human tissue, as well as ambient light signals; when the light source is turned off, if the wearable device is not worn correctly, or if the photoelectric detection system of the wearable device is not tightly packaged, the photoelectric signal mainly consists of ambient light signals.

[0065] It should be noted that PPG light leakage data refers to the result calculated from the difference between the first photoelectric data and the second photoelectric data. This reflects whether, under the current ambient light conditions, there is any unexpected light leakage from the PPG sensor into the photodetector. This embodiment, by calculating the difference between the first and second photoelectric data, can effectively separate the effective signal components caused by the internal light source of the PPG sensor, thereby eliminating the interference of ambient light fluctuations on light leakage detection.

[0066] In this embodiment, after receiving the PPG light leakage self-test command, there are multiple ways to obtain the first photoelectric data and the second photoelectric data required for light leakage detection.

[0067] In one example, the data detected by the photodetector when the light source is turned on can be directly used as the first photoelectric data, and the data detected by the photodetector when the light source is turned off can be used as the second photoelectric data.

[0068] This example directly acquires single measurement data from the photodetector when the light source is on and off as the first and second photoelectric data, offering significant ease of operation and high efficiency. Firstly, at the operational level, this example eliminates the need for additional data processing steps such as multiple sampling, removal of extreme values, and calculation of averages, thus simplifying algorithm design and reducing processor performance requirements. Secondly, by reducing data acquisition and processing time, this example significantly speeds up the detection process, making it particularly suitable for applications requiring rapid feedback. Finally, for situations with extremely stable external environmental conditions and minimal interference, a single measurement can provide sufficiently accurate results, enabling the device to achieve an efficient self-test process while maintaining a certain level of accuracy. Therefore, this example demonstrates unique advantages in resource-constrained or real-time-critical applications.

[0069] In another example, multiple data points detected by the photodetector when the light source is turned on can be taken, the maximum and minimum values ​​can be removed, and the average value can be calculated. The average value can then be used as the first photoelectric data. Alternatively, multiple data points detected by the photodetector when the light source is turned off can be taken, the maximum and minimum values ​​can be removed, and the average value can then be calculated. The average value can then be used as the second photoelectric data.

[0070] This example uses multiple measurements, removing the maximum and minimum values ​​before averaging to obtain the first and second photoelectric data, significantly improving the accuracy and stability of the detection results. First, by sampling multiple times, this example effectively reduces the impact of random errors, especially in the presence of brief, unpredictable interference, more accurately reflecting the actual changes in the photoelectric signal. Second, removing extreme values ​​and averaging acts as a simple filter, helping to smooth data fluctuations, suppress random noise, thereby improving the signal-to-noise ratio and enhancing the stability of the output signal. Finally, this example is particularly suitable for applications requiring high measurement accuracy, such as ensuring the accuracy of health monitoring data or eliminating various uncertain interference factors in complex and changing environments. In summary, this example, through refined data processing, provides more reliable and accurate detection results, making it ideal for applications with high precision requirements.

[0071] Step S300: Determine the current monitoring scenario to be activated from multiple health indicator monitoring scenarios for the wearable device. Based on the current monitoring scenario, dynamically determine the current light leakage data threshold. The multiple health indicator monitoring scenarios include at least three of the following: heart rate monitoring scenario, blood oxygen saturation monitoring scenario, blood pressure monitoring scenario, and respiratory rate monitoring scenario. The current light leakage data threshold determined for different health indicator monitoring scenarios is different.

[0072] It should be noted that health indicator monitoring scenarios refer to various physiological parameter monitoring scenarios that wearable devices can perform through their integrated PPG sensors, including but not limited to heart rate monitoring, blood oxygen saturation monitoring, blood pressure monitoring, and respiratory rate monitoring. Specifically, heart rate monitoring is a scenario specifically designed to measure the number of heartbeats per minute; blood oxygen saturation monitoring is a scenario specifically designed to measure the oxygen saturation level in the blood; blood pressure monitoring is a scenario specifically designed to measure blood pressure levels; and respiratory rate monitoring is a scenario specifically designed to measure respiratory rate.

[0073] It should also be noted that the current monitoring scenario refers to the health indicator monitoring scenario that the user is currently using or that the device automatically selects. The current light leakage data threshold refers to the light leakage data threshold corresponding to the current monitoring scenario, used to determine whether the PPG sensor has a light leakage problem under the current monitoring scenario.

[0074] Understandably, different monitoring scenarios have varying tolerances for light leakage. For example, in blood oxygen saturation monitoring, which relies on changes in light signals at specific wavelengths, the tolerance for light leakage is low, resulting in a relatively small current light leakage data threshold. In contrast, heart rate monitoring focuses primarily on the overall trend of light intensity changes, allowing for potentially larger light leakage amounts without affecting the final measurement results. Therefore, the current light leakage data threshold will vary depending on the monitoring scenario to ensure that the most suitable light leakage standard can be used for detection in various applications, thereby accurately detecting potential light leakage problems and effectively improving the accuracy of light leakage detection.

[0075] Upon receiving the PPG light leakage self-test command, this embodiment first identifies the current health indicator monitoring scenario and selects the optimal light leakage data threshold applicable to the scenario as the current light leakage data threshold. This allows for the subsequent comparison of the PPG light leakage data calculated based on the difference between the first photoelectric data and the second photoelectric data with the current light leakage data threshold, thereby determining whether the PPG sensor has a light leakage problem in the current monitoring scenario.

[0076] Step S400: If the PPG leakage data is greater than the current leakage data threshold, PPG sensor leakage is determined.

[0077] This embodiment acquires first photoelectric data detected by the photodetector when the light source is on and second photoelectric data detected when the light source is off, under the condition that the ambient light is in a stable state (i.e., the fluctuation level of the ambient light data is the first fluctuation level). The influence of ambient light is eliminated by using a differential method, effectively separating the signal changes caused by light leakage to obtain PPG light leakage data. At the same time, the current light leakage data threshold is dynamically determined according to the current monitoring scenario. The current light leakage data threshold is used to determine whether there is a risk of light leakage in the PPG sensor under the current monitoring scenario. This ensures that the most suitable light leakage standard can be used under various monitoring scenarios to accurately detect potential light leakage problems, effectively improving the accuracy of light leakage detection. In this way, accurate light leakage detection can be achieved under natural light conditions, breaking through the dependence of traditional PPG light leakage detection on dark room environment, and greatly improving the convenience and practicality of PPG light leakage detection.

[0078] This embodiment creatively transforms the physical shading of darkroom detection into a noise suppression model for photoelectric signal differential calculation, breaking through the traditional reliance on darkrooms. This allows end users to perform high-precision self-tests in everyday environments, solving the technical problems of traditional PPG light leakage detection relying on strictly controlled darkroom environments, high equipment costs, and complex operation procedures, which are not conducive to light leakage detection in daily use. This significantly improves the convenience of PPG light leakage detection.

[0079] It is worth mentioning that this embodiment also employs a technical solution that determines the current monitoring scenario selected by the wearable device from multiple health indicator monitoring scenarios, and dynamically determines the current light leakage data threshold based on this current monitoring scenario. This allows the wearable device in this embodiment to adaptively adjust the current light leakage data threshold when facing different health indicator monitoring scenarios (such as heart rate monitoring, blood oxygen saturation monitoring, blood pressure monitoring, or respiratory rate monitoring). This facilitates the selection of the most reasonable light leakage value as the current light leakage data threshold based on the current monitoring scenario, and allows for adjustments to the current light leakage threshold when the current monitoring scenario changes. The data threshold is dynamically and adaptively adjusted, so that no matter how the current monitoring scene changes, the current light leakage data threshold can always be dynamically and adaptively matched with the most accurate and appropriate light leakage value as the current light leakage data threshold based on the scene type of the current monitoring scene. This facilitates subsequent comparison of the most accurate and appropriate current light leakage data threshold with the actual detected PPG light leakage data, thereby more accurately and sensitively determining whether there is light leakage in the PPG sensor of the wearable device. This effectively improves the accuracy of PPG light leakage detection in daily use after it leaves the strictly controlled darkroom environment.

[0080] In a first feasible implementation, step S300, which involves dynamically determining the current light leakage data threshold based on the current monitoring scenario, may include steps S310 to S320:

[0081] Step S310: Based on the current monitoring scene, query the light leakage data threshold of the current monitoring scene mapping from the preset scene mapping threshold table;

[0082] It should be noted that, in order to achieve the goal of adaptively adjusting the light leakage data threshold according to different health indicator monitoring scenarios, this implementation method pre-constructs a scenario mapping threshold table. This table records in detail the mapping relationship between each health indicator monitoring scenario (such as heart rate monitoring, blood oxygen saturation monitoring, blood pressure monitoring, and respiratory rate monitoring) and the corresponding light leakage data threshold. For example, in the blood oxygen saturation monitoring scenario, because it has high requirements for the accuracy of the light signal, the corresponding light leakage data threshold is set relatively small; while in the heart rate monitoring scenario, considering that the main focus is on the overall trend of light intensity changes, a larger amount of light leakage is allowed without affecting the final heart rate measurement result, so the corresponding light leakage data threshold can be set larger.

[0083] This implementation method, which customizes light leakage data thresholds based on specific application scenario requirements, not only considers the different performance requirements of PPG sensors for monitoring various physiological parameters, but also ensures high accuracy and reliability in the light leakage detection process through refined management of light leakage standards for each monitoring scenario. Furthermore, by organizing these mapping relationships into an easily accessible scenario mapping threshold table, the process of querying and applying corresponding light leakage data thresholds during actual operation is greatly simplified.

[0084] Step S320: Use the mapped light leakage data threshold as the current light leakage data threshold.

[0085] By introducing a scene mapping threshold table and querying and using the corresponding light leakage data threshold according to the current monitoring scene, this implementation provides a simple and effective solution, enabling wearable devices to flexibly adjust the light leakage detection standard under different health indicator monitoring scenarios. This significantly enhances the applicability and accuracy of the PPG light leakage self-test method, not only solving the problem of the high dependence of traditional PPG light leakage detection on the dark room environment, but also realizing convenient and efficient light leakage detection in daily environments, providing users with a more reliable and user-friendly experience.

[0086] In a second feasible implementation, step S300, which involves dynamically determining the current light leakage data threshold based on the current monitoring scenario, may include steps S330 to S350:

[0087] Step S330: Dynamically detect the current optical sensitivity CTR of the PPG sensor;

[0088] It should be noted that optical sensitivity CTR (Current Transform Ratio) refers to the ability of the photodetector inside the PPG sensor to convert light signals emitted by a light source into electrical signals. Specifically, it measures the ratio of the light intensity received by the photodetector to the light intensity emitted by the light source.

[0089] Since the performance of PPG sensors may be affected by various factors, such as the increase in usage time and changes in environmental conditions, their actual optical sensitivity may change. This embodiment can correct the sensitivity decay caused by sensor aging or environmental factors in real time by dynamically detecting the current optical sensitivity CTR of the PPG sensor, avoid misjudging normal signals as light leakage due to reduced sensitivity, and prevent the failure to detect real light leakage due to abnormally increased sensitivity, thus significantly improving the reliability of detection.

[0090] Step S340: Determine the current light source color of the light source corresponding to the current monitoring scene based on the current monitoring scene;

[0091] It should be noted that the current light source color refers to the color of light emitted by the light source in the current monitoring scenario.

[0092] Those skilled in the art will recognize that the color of the light source used by a PPG sensor (e.g., green, red, or infrared light) has a direct impact on its monitoring performance. Different physiological parameter monitoring scenarios have different preferences for light source colors: for example, blood oxygen saturation monitoring typically relies on red and infrared light because these two types of light can penetrate the skin better and provide information about the oxygen content in the blood; while heart rate monitoring tends to use green light because it better matches the small changes in blood volume caused by the heartbeat.

[0093] For example, in one feasible implementation, step S340 may include steps S341 to S342:

[0094] Step S341: When the current monitoring scenario is a blood oxygen saturation monitoring scenario, determine that the current light source color of the light source used in the current monitoring scenario is red.

[0095] It should be noted that blood oxygen saturation monitoring relies on the absorption characteristics of different wavelengths of light (especially red and infrared light) after penetrating human tissue. The oxygen content in the blood affects the absorption rate of these lights. By analyzing the intensity differences of red and infrared light after passing through the fingers or other parts of the body, blood oxygen saturation can be calculated.

[0096] Therefore, red is chosen as the light source color when monitoring blood oxygen saturation based on its physical properties, which make it particularly effective in detecting oxygen levels in the blood.

[0097] In addition, red light sources are effective at penetrating tissues such as skin and muscle, providing stable and accurate measurement results.

[0098] Step S342: If the current monitoring scenario is a heart rate monitoring scenario, a blood pressure monitoring scenario, or a respiratory rate monitoring scenario, determine that the current light source color of the light source used in the current monitoring scenario is green.

[0099] Those skilled in the art will recognize that green light can be well matched with small changes in blood volume caused by heartbeats, effectively reflecting information about blood flow. That is, changes in blood volume lead to changes in the absorption rate of green light. Therefore, this change can be captured by a photodetector and converted into an electrical signal to measure heart rate and indirectly estimate blood pressure levels.

[0100] In addition, since the breathing process causes changes in the distribution of blood in the body, which in turn affects the morphology of the PPG signal, green light is also suitable for monitoring in this scenario.

[0101] Because photodetectors have different optical sensitivity (CTR) for different colors of light, even the same PPG sensor will have different abilities to convert light signals into electrical signals when using different colored light sources. This implementation further optimizes the setting of the light leakage data threshold by adjusting the current light source color, so that the threshold setting is deeply coupled with the physical properties of light waves, thereby obtaining a more accurate light leakage data threshold as the current light leakage data threshold, ensuring that a more accurate light leakage detection result can be obtained in the end.

[0102] Step S350: Dynamically determine the current light leakage data threshold based on the current light source color and the current CTR.

[0103] This implementation dynamically determines the current light leakage data threshold based on the current CTR and the current light source color, constructing an adaptive light leakage threshold decision system. This solves the threshold rigidity problem caused by sensor performance drift and fixed light wave parameters in traditional solutions, ensuring that the current light leakage data threshold is dynamically adjusted according to the sensor status and the current monitoring scenario. This enables light leakage detection to maintain high accuracy in monitoring scenarios with significant differences such as heart rate, blood oxygen, and blood pressure, promoting reliable self-testing of wearable devices in natural light environments around the clock.

[0104] Furthermore, in one feasible implementation, step S350 may include steps S351 to S352:

[0105] Step S351: When the current light source color is green, dynamically calculate the product of the current CTR and 3.7% to obtain the first product result, and dynamically determine the current light leakage data threshold based on the dynamically calculated first product result.

[0106] Step S352: When the current light source color is red, dynamically calculate the product of the current CTR and 1.5% to obtain the second product result, and dynamically determine the current light leakage data threshold based on the dynamically calculated second product result.

[0107] This implementation employs a dual mechanism of light source color-specific correction and real-time CTR determination to achieve a deep adaptation between the light leakage detection threshold and the actual performance of the photodetector and the monitoring scenario. This allows the system to flexibly and accurately adjust the current light leakage data threshold based on the specific light source color and the actual working efficiency of the photodetector (i.e., the current CTR). Specifically, the light source color-specific correction is based on the differences in the physical characteristics of different wavelengths of light. For example, green light, due to its shallow tissue penetration and weak environmental interference, uses a high correction coefficient to improve sensitivity, while red light, due to its deep penetration and strong environmental noise, uses a low correction coefficient to enhance anti-interference capabilities. This directly maps the optical propagation laws to the threshold decision logic, ensuring an essential correlation between the light leakage judgment standard and the characteristics of light waves. Simultaneously, the dynamic detection of CTR provides real-time feedback on changes in photoelectric conversion efficiency, avoiding false detections of light leakage due to sensor aging.

[0108] With the synergistic effect of the two, this implementation method constructs a dynamic threshold model driven by "photophysical characteristics-sensor status" in two dimensions. It achieves a balance between high-precision noise suppression and weak signal capture in scenarios such as blood oxygen and heart rate, breaking through the rigid limitations of traditional fixed thresholds. This enables light leakage detection to have environmental adaptability, sensor lifespan perception capability, and scenario-based accuracy optimization, providing a universal solution for reliable self-testing of wearable devices in complex lighting and long-term use scenarios.

[0109] It is worth mentioning that when dynamically determining the current light leakage data threshold based on the first or second product result obtained by dynamic calculation, the product result can be directly used as the current light leakage data threshold, or the product result can be adjusted based on other influencing factors before being used as the current light leakage data threshold. This implementation does not make specific limitations on this.

[0110] Based on the first embodiment described above, a PPG light leakage self-testing method according to the second embodiment of this application is proposed.

[0111] In the second embodiment of this application, the same or similar content as in the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0112] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the PPG light leakage self-testing method of this application.

[0113] In this embodiment, the wearable device also integrates an ambient light detection device. After receiving the PPG light leakage self-test command in step S100, the PPG light leakage self-test method may further include steps S500 to S700:

[0114] Step S500: Obtain ambient light data detected by the ambient light detection device within the sliding time window, and determine the degree of fluctuation of the ambient light data;

[0115] It should be noted that the ambient light detection device is a device used to detect ambient light. Ambient light data refers to the ambient light data detected by the ambient light detection device within a sliding time window after the wearable device receives the PPG light leakage self-test command. It mainly includes the light intensity and frequency of the ambient light within the sliding time window.

[0116] It should also be noted that the degree of fluctuation refers to the dispersion of ambient light data, which reflects the stability of ambient light within the sliding time window.

[0117] In this embodiment, the degree of fluctuation can be determined by statistical measures such as variance, standard deviation, and range.

[0118] For example, in one feasible implementation, the step of determining the fluctuation level of ambient light data in step S500 may include steps S510 to S520:

[0119] Step S510: Perform statistical analysis on the ambient light data to obtain the statistical measures of the ambient light data, wherein the statistical measures include at least one of variance, standard deviation and range;

[0120] As those skilled in the art will know, a statistic is a quantitative indicator used in statistics to describe or summarize the characteristics of a dataset.

[0121] In this implementation, statistics mainly refer to indicators used to quantify the dispersion of data, namely, parameters such as variance, standard deviation, and range.

[0122] Step S520: Determine the degree of fluctuation in ambient light data based on statistics.

[0123] In this embodiment, the fluctuation level can be pre-divided into multiple levels. The lower the level, the smaller the fluctuation. Specific judgment criteria are set for the fluctuation level of each level. The statistical quantity is then compared with the judgment criteria corresponding to each level to determine which level the fluctuation level of the ambient light data belongs to.

[0124] For example, the degree of volatility can be divided into two levels, namely the first level and the second level, with the volatility of the second level being higher than that of the first level. In this case, if the statistic is the range, the judgment criterion for the first level can be set as the statistic being less than a preset range threshold, and the judgment criterion for the second level can be set as the statistic being not less than a preset range threshold. If the statistic is variance and standard deviation, the judgment criterion for the first level can be set as the variance in the statistic being less than a preset variance threshold and the standard deviation in the statistic being less than a preset standard deviation threshold, and the judgment criterion for the second level can be set as the variance in the statistic being not less than a preset variance threshold or the standard deviation in the statistic being not less than a preset standard deviation threshold, and so on.

[0125] This implementation method quantifies the fluctuation of ambient light data by introducing statistical measures such as variance, standard deviation, and range, and divides the fluctuation into multiple levels, each corresponding to different judgment criteria, thereby significantly improving the accuracy and adaptability of PPG light leakage self-detection. Specifically, this implementation method first performs detailed statistical analysis on the ambient light data within a sliding time window to calculate key statistical measures reflecting the data dispersion; then, based on these statistical measures, it compares them with preset judgment criteria to determine which level the current ambient light fluctuation belongs to. This hierarchical fluctuation assessment mechanism can not only effectively identify the most suitable stable lighting conditions for light leakage detection, but also allows the system to flexibly adjust the judgment criteria according to actual application scenarios, greatly improving the reliability and efficiency of detection.

[0126] Understandably, when the fluctuation level is divided into a first level and a second level, the fluctuation level of the first level can be determined as the first fluctuation level, indicating that the fluctuation of the ambient light is relatively slight and in a relatively stable state, which can trigger light leakage detection. On the other hand, the fluctuation level of the second level can be determined as the second fluctuation level, indicating that the fluctuation of the ambient light is more obvious and in a state of frequent change, which is not suitable for triggering light leakage detection.

[0127] Correspondingly, when the levels of volatility are divided into more granular levels, volatility levels below a certain level can be designated as the first level of volatility, while volatility levels at other levels can be designated as the second level of volatility.

[0128] Furthermore, this embodiment can directly set corresponding judgment criteria for the first fluctuation degree and the second fluctuation degree, thereby directly determining whether the fluctuation degree of ambient light data is the first fluctuation degree or the second fluctuation degree based on statistics, reducing intermediate steps.

[0129] In addition to determining the degree of fluctuation through statistics, this embodiment can also calculate the rate of change of ambient light data at adjacent time points within a sliding time window, and then determine the degree of fluctuation through parameters such as the average, maximum, and minimum values ​​of the rate of change.

[0130] For example, when the maximum value of the rate of change is less than 1, the minimum value is greater than -1, and the average value is between -0.3 and 0.3, the fluctuation level of the ambient light data is determined to be the first fluctuation level; otherwise, the fluctuation level of the ambient light data is determined to be the second fluctuation level.

[0131] It is important to note that within this sliding time window, all light-emitting components of the wearable device (including but not limited to the display light-emitting module, indicator lights, and the light source in the PPG sensor) should remain in their original state. This is because if the state of the various light-emitting components of the wearable device changes continuously within the sliding time window, it may lead to large fluctuations in the ambient light data. This would cause the system to consistently consider the current ambient light to be unstable and unsuitable for light leakage detection. This would not only reduce the success rate and efficiency of light leakage detection but also increase algorithm complexity and energy consumption, affecting user experience and device performance.

[0132] This embodiment can control the various light-emitting elements inside the wearable device to remain unchanged after receiving the PPG light leakage self-test command and before starting the ambient light detection device to detect ambient light. This is to avoid introducing additional light interference during the ambient light data detection process within the sliding time window, which would cause the ambient light data to fail to accurately depict changes in external ambient light. This effectively avoids misjudgment of ambient light stability caused by changes in internal light sources, ensures that the system accurately judges the stability of ambient light, and thus improves the reliability and efficiency of light leakage detection.

[0133] In this embodiment, after receiving the PPG light leakage self-test command, the ambient light detection device is activated to detect the ambient light. By acquiring the ambient light data detected by the ambient light detection device within the sliding time window, the fluctuation of the ambient light is monitored in real time. Based on the fluctuation of the ambient light, it is determined whether the current ambient light is stable and whether it is suitable for light leakage detection by the PPG sensor, thus breaking the dependence of traditional solutions on the dark room environment.

[0134] Step S600: When the fluctuation level is the first fluctuation level, trigger the step of acquiring the first photoelectric data detected by the photodetector when the light source is turned on;

[0135] It should be noted that the first fluctuation level is a preset fluctuation level, indicating that the ambient light fluctuation is small and in a relatively stable state, which can trigger light leakage detection.

[0136] In this embodiment, when the fluctuation level of the ambient light data is the first fluctuation level, indicating that the current lighting environment is relatively stable, it is determined that light leakage detection can be triggered. Step S200 is executed to obtain the first photoelectric data detected by the photodetector when the light source is turned on and the second photoelectric data detected when the light source is turned off. Based on the first photoelectric data and the second photoelectric data, the PPG light leakage data is determined. Thus, by pre-screening through ambient light stability, the misjudgment of light leakage caused by dynamic lighting interference is avoided, and the detection success rate is improved.

[0137] It is worth mentioning that in this embodiment, once the fluctuation level of the ambient light data reaches the first fluctuation level and it is determined that light leakage detection can be triggered, the ambient light detection device can be turned off to stop acquiring ambient light data, thereby reducing energy consumption and extending the battery life of the wearable device. Alternatively, the ambient light detection device can remain on, and the judgment standard for the first fluctuation level can be increased. This reduces the impact of the on / off state of the light source in the PPG sensor on the judgment of ambient light stability during the light leakage detection process, ensuring that the light leakage detection result is determined based on data detected when the ambient light is stable. This avoids inaccurate detection results due to sudden changes in ambient light during the light leakage detection process, and ensures that the entire light leakage detection process is carried out when the ambient light is stable.

[0138] Step S700: When the fluctuation level is the second fluctuation level, output a preset ambient light fluctuation prompt, wherein the second fluctuation level is higher than the first fluctuation level.

[0139] It's important to note that the ambient light fluctuation alert is a notification or warning sent by the wearable device to the user when it detects that the current ambient light conditions are unsuitable for the PPG sensor's light leakage self-test. This alert aims to inform the user that the current ambient light is changing too frequently or has significant intensity variations, causing the photodetector to fail to accurately detect light leakage. Specifically, when the system analyzes the ambient light data within a sliding time window and finds that the fluctuation level reaches a preset second fluctuation level (i.e., large fluctuations indicate unstable ambient light), this alert is triggered. The ambient light fluctuation alert can be communicated to the user through various means, such as displaying text information on the device's screen, issuing an audible alarm, or providing a vibration notification, guiding the user to choose a more stable lighting environment to retry the light leakage detection. This not only helps avoid misjudgments or inaccurate detection results caused by ambient light interference but also improves the user experience and understanding of device operation. In this way, it ensures that light leakage detection is only performed under ideal environmental conditions, thereby guaranteeing the accuracy and reliability of the detection process.

[0140] In this embodiment, when the ambient light data fluctuation level is detected to be at the second fluctuation level (i.e., the ambient light is unstable and fluctuates significantly), a preset ambient light fluctuation prompt is output. This step aims to enhance the user experience and improve the reliability of the detection process. By promptly informing the user that the current environment is not suitable for accurate light leakage detection, misjudgments or inaccurate results caused by unstable external light conditions are avoided. Specifically, when the system determines that the ambient light is in a relatively fluctuating state, it automatically pauses the light leakage detection process and displays corresponding prompts through the wearable device's user interface, informing the user to try again in a more stable lighting environment. This not only increases user trust and satisfaction but also ensures that light leakage detection is performed only under ideal conditions, thereby guaranteeing the accuracy and reliability of the detection results.

[0141] In one feasible implementation, the PPG light leakage self-test method may further include step S800:

[0142] If the PPG leakage data is less than or equal to the current leakage data threshold and the PPG leakage data is not zero, PPG calibration data is generated based on the PPG leakage data. The PPG calibration data is used to calibrate the PPG sensor.

[0143] To further improve the accuracy and reliability of the PPG sensor, this embodiment introduces an additional calibration step based on the premise that the PPG leakage data is less than or equal to the current leakage data threshold, i.e., confirming that the current PPG sensor does not have obvious leakage problems. By using the acquired PPG leakage data to generate specific PPG calibration data, the PPG sensor can be accurately calibrated during subsequent use of the wearable device to compensate for minor performance drift or errors caused by long-term use or other factors.

[0144] For example, if the PPG light leakage data is greater than the current light leakage data threshold, an alarm message is output indicating that the wearable device is not accurately detecting human health monitoring data.

[0145] The human health monitoring data may include physiological parameters such as heart rate, blood oxygen saturation, or sleep, and this embodiment does not impose specific limitations.

[0146] If the PPG light leakage data exceeds the current light leakage data threshold, it is determined that the PPG sensor is leaking light, or more specifically, the PPG sensor has uncalibrable light leakage. This means that the wearable device's function of monitoring human health indicators is no longer qualified. At this point, even if the PPG sensor is compensated based on the PPG light leakage data, accurate calibration cannot be achieved. This is because a large amount of light leakage data results in the loss of too much PPG photoelectric data. This lost PPG photoelectric data often contains a lot of detection information related to health indicators such as heart rate, blood oxygen, or sleep. Therefore, when the PPG light leakage data exceeds the current light leakage data threshold, even if PPG calibration data is generated based on the PPG light leakage data to compensate for the PPG sensor, effective calibration cannot be achieved. Consequently, it can no longer accurately monitor physiological parameters such as heart rate, blood oxygen saturation, or sleep, and the defective product needs to be reworked and repaired.

[0147] In one feasible implementation, the wearable device also integrates an inertial sensor. Before the step S500 of acquiring the ambient light data detected by the ambient light detection device within the sliding time window, the PPG light leakage self-test method may further include steps A10 to A20:

[0148] Step A10: Determine the motion state of the wearable device based on the data detected by the inertial sensor;

[0149] Step A20: When the motion state is stationary, perform the step of acquiring ambient light data detected by the ambient light detection device within the sliding time window.

[0150] As those skilled in the art will know, an inertial sensor is a device capable of sensing changes in the acceleration and angular velocity of an object, typically including components such as an accelerometer and a gyroscope. By analyzing this data, it is possible to accurately determine whether a wearable device is currently stationary or in motion.

[0151] This implementation utilizes data from an inertial sensor to assess whether the wearable device is stationary, ensuring that the device is stable before ambient light data acquisition. Once the wearable device is confirmed to be stationary (i.e., without significant displacement or rotation), the system triggers the step of acquiring ambient light data detected by the ambient light detection device within a sliding time window, thus initiating the collection of ambient light data. This is because when the wearable device is in motion, its relative position to the ambient light source may change rapidly, causing the data collected by the ambient light detection device to contain dynamic interference components, such as high-frequency fluctuations in ambient light intensity caused by arm movements. Such dynamic interference significantly increases the volatility of ambient light data within the sliding time window, causing the system to misjudge the ambient light as unstable and frequently interrupt the light leakage detection process, ultimately leading to an increased detection failure rate and wasted energy.

[0152] This implementation strongly correlates the triggering conditions for judging ambient light stability with the stationary state of the device, effectively filtering out transient noise in ambient light caused by motion, so that the fluctuation of ambient light data can truly reflect the stability of ambient light.

[0153] Based on the second embodiment described above, a PPG leakage self-testing method according to the third embodiment of this application is proposed.

[0154] In the third embodiment of this application, the same or similar content as the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0155] In this embodiment, the photodetector includes a first photodetector and a second photodetector at different positions. The first photoelectric data includes the third photoelectric data detected by the first photodetector when the light source is turned on, and the fourth photoelectric data detected by the second photodetector when the light source is turned on. The second photoelectric data includes the fifth photoelectric data detected by the first photodetector when the light source is turned off, and the sixth photoelectric data detected by the second photodetector when the light source is turned off. The current light leakage data threshold includes the first light leakage data threshold dynamically determined based on the current monitoring scenario of the first photodetector, and the second light leakage data threshold dynamically determined based on the current monitoring scenario of the second photodetector.

[0156] Step S200, which involves determining the PPG leakage data based on the first photoelectric data and the second photoelectric data, may include steps S210 to S220:

[0157] Step S210: Determine the PPG leakage data corresponding to the first photodetector based on the third photoelectric data and the fifth photoelectric data;

[0158] Step S220: Determine the PPG leakage data corresponding to the second photodetector based on the fourth photoelectric data and the sixth photoelectric data;

[0159] Step S400, where the PPG leakage data is greater than the current leakage data threshold, determines that the PPG sensor is leaking light, and may include step S410:

[0160] Step S410: If the PPG leakage data corresponding to the first photodetector is greater than the first leakage data threshold, or the PPG leakage data corresponding to the second photodetector is greater than the second leakage data threshold, then PPG sensor leakage is determined.

[0161] In this embodiment, when there is more than one photoelectric sensor in the PPG sensor, the data detected by each photoelectric sensor when the light source is turned on and off can be acquired separately. The influence of ambient light can be eliminated by differential calculation. Then, combined with the current light leakage data threshold set individually for each photoelectric detector, the light leakage situation at each photoelectric detector can be accurately quantified, thereby realizing independent light leakage detection of different photoelectric detectors and improving the sensitivity and adaptability of PPG light leakage detection.

[0162] It is worth mentioning that after realizing independent light leakage detection based on photodetectors, it is possible to further determine which photodetector in the PPG sensor has light leakage, thus facilitating targeted repair during subsequent maintenance.

[0163] Furthermore, when the PPG leakage data corresponding to each photodetector is less than or equal to its corresponding current leakage data threshold and is not zero, PPG calibration data corresponding to each photodetector can be generated, thereby achieving photodetector-level calibration accuracy when calibrating the PPG sensor.

[0164] Based on the second embodiment described above, a PPG light leakage self-testing method according to the fourth embodiment of this application is proposed.

[0165] In the fourth embodiment of this application, the same or similar content as the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0166] In this embodiment, the light source includes a first light source and a second light source at different positions, and the first photoelectric data includes a seventh photoelectric data detected by the photodetector when the first light source is turned on and the second light source is turned off, and an eighth photoelectric data detected by the photodetector when the second light source is turned on and the first light source is turned off.

[0167] Step S200, which involves determining the PPG leakage data based on the first photoelectric data and the second photoelectric data, may further include steps S230 to S240:

[0168] Step S230: Determine the PPG leakage data corresponding to the first light source based on the seventh photoelectric data and the second photoelectric data;

[0169] Step S240: Determine the PPG leakage data corresponding to the second light source based on the eighth photoelectric data and the second photoelectric data;

[0170] Step S400, where the PPG leakage data is greater than the current leakage data threshold, determines that the PPG sensor is leaking light, and may further include step S420:

[0171] Step S420: If the PPG leakage data corresponding to the first light source is greater than the current leakage data threshold, or the PPG leakage data corresponding to the second light source is greater than the current leakage data threshold, then PPG sensor leakage is determined.

[0172] In this embodiment, when there is more than one light source in the PPG sensor, the data detected by the photoelectric sensor when different light sources are turned on individually can be obtained separately. The influence of ambient light can be eliminated by differential calculation, and the light leakage of each light source in the PPG sensor can be accurately quantified, thereby realizing independent light leakage detection of different light sources and improving the sensitivity and adaptability of PPG light leakage detection.

[0173] It is worth mentioning that after realizing independent light leakage detection based on the light source, it is possible to further determine which light source in the PPG sensor has light leakage, thus facilitating targeted repairs during subsequent maintenance.

[0174] Furthermore, when the PPG leakage data corresponding to each light source is less than or equal to the current leakage data threshold and is not zero, PPG calibration data corresponding to each light source can be generated, thereby achieving light source-level calibration accuracy when calibrating the PPG sensor.

[0175] To facilitate understanding of the PPG light leakage self-test method provided in the above embodiments of this application, a specific embodiment is provided below:

[0176] like Figure 3 As shown in this specific embodiment, PPG light leakage self-test mainly involves wearable devices integrating PPG sensors, control software (host computer software, mobile APP or internal program of wearable device) and cloud devices.

[0177] In this specific embodiment, testers or users can issue a PPG light leakage self-test command through the control software to trigger the wearable device to perform PPG light leakage detection and display the data and results of the PPG light leakage detection. Simultaneously, the wearable device will also transmit the PPG light leakage detection data and results to a cloud device for storage and recording, facilitating real-time monitoring of the PPG sensor's defect rate.

[0178] like Figure 4As shown in this specific embodiment, the PPG sensor mainly includes 3 LEDs (Light Emitting Diodes, i.e., light sources) and 4 PDs (Photo Diodes, i.e., photodetectors). A light-shielding structure such as foam is used between the LEDs and PDs to block light and prevent the light from the LEDs from directly entering the corresponding PDs. The 3 LEDs are LED1, LED2, and LED3, and the 4 PDs are PD1, PD2, PD3, and PD4. When LED1 emits light, it corresponds to PD1, PD2, PD3, and PD4; when LED2 emits light, it corresponds to PD3 and PD4; and when LED3 emits light, it corresponds to PD1 and PD2.

[0179] In this specific embodiment, after receiving the PPG self-test command, the wearable device first determines whether it is stationary and horizontal by reading data collected by the inertial sensor built into the wearable device. That is, the wearable device also integrates an inertial sensor. Before the step of acquiring ambient light data detected by the photodetector within the sliding time window, the method further includes: determining the motion state of the wearable device based on the data detected by the inertial sensor; and, if the motion state is stationary, performing the step of acquiring ambient light data detected by the ambient light detection device within the sliding time window.

[0180] When the wearable device is stationary and horizontal, this specific embodiment first determines the current ambient light stability using an ambient light detection device. After determining the current ambient light stability, it sequentially controls LED1 to turn on for one second, then off for one second, then on for one second, then off for one second, then on for one second, then off for one second, then off for one second, then on for one second, then off for one second (initially, all LEDs are off). During the time the LEDs are on and off, the data detected by the corresponding photodetector is read, thereby determining the photodetector data when each LED is on and off. That is, the fluctuation level of the ambient light data is determined, and when the fluctuation level is the first fluctuation level, the first photoelectric data detected by the photodetector when the light source is on, and the second photoelectric data detected when the light source is off are obtained.

[0181] For example, when LED1 is lit, the data detected by PD1, PD2, PD3, and PD4 can be read separately and averaged, i.e., PD(LED1 lit) = (PD1 + PD2 + PD3 + PD4) / 4, which is taken as the PD data when LED1 is lit. To avoid accidental interference, multiple operations can be performed within a specified time to obtain multiple PD(LED1 lit) values. Then, the highest and lowest values ​​are removed, and the remaining data are averaged again to obtain the final PD(LED1 lit). Similarly, the PD data when LED1 is off (PD(LED1 off),) when LED2 is lit (PD(LED2 lit),) when LED2 is off (PD(LED2 off),) when LED3 is lit (PD(LED3 lit), and) when LED3 is off (PD(LED3 off)) can be obtained.

[0182] Next, calculate the PPG light leakage data for each LED, using the following formula:

[0183] PD (LED1 light leakage) = PD (LED1 on) - PD (LED1 off);

[0184] PD (LED2 light leakage) = PD (LED2 on) - PD (LED2 off);

[0185] PD (LED3 light leakage) = PD (LED3 on) - PD (LED3 off);

[0186] Among them, PD(LED1 light leakage) is the PPG light leakage data corresponding to LED1, PD(LED2 light leakage) is the PPG light leakage data corresponding to LED2, and PD(LED3 light leakage) is the PPG light leakage data corresponding to LED3.

[0187] That is, PPG leakage data are determined based on the first photoelectric data and the second photoelectric data.

[0188] In this specific embodiment, after calculating the PPG leakage data for each LED, the PPG leakage data for each LED is sequentially compared with the current leakage data threshold PD (leakage threshold). If any PD (LED leakage) > PD (leakage threshold), the PPG sensor is considered to have leakage, and the product is deemed unqualified. Even if compensation is performed on the PPG sensor based on the leakage data, accurate calibration cannot be achieved. This is because a large amount of leakage data results in the loss of too much PPG photoelectric data. This lost PPG photoelectric data often contains a significant amount of detection information related to health indicators such as heart rate, blood oxygen, or sleep. Therefore, when the PPG leakage data exceeds the current leakage data threshold, even if PPG calibration data is generated based on the leakage data to compensate the PPG sensor, effective calibration is impossible, and consequently, accurate monitoring of heart rate and blood oxygen saturation becomes impossible. Physiological parameters such as temperature or sleep patterns require rework and repair of non-conforming products. If all PD (LED light leakage) values ​​are ≤ PD (light leakage threshold), the light leakage of the PPG sensor is considered to be within an acceptable error range, and the product does not require rework and repair. If the light leakage of the PPG sensor is within an acceptable error range but is not zero, then only the PPG sensor needs to be calibrated based on the light leakage data (i.e., when the PPG light leakage data is less than or equal to the current light leakage data threshold and the PPG light leakage data is not zero, PPG calibration data is generated based on the PPG light leakage data, which is used to optimize and calibrate the PPG sensor). In other words, if the PPG light leakage data corresponding to the first light source is greater than the current light leakage data threshold, or the PPG light leakage data corresponding to the second light source is greater than the current light leakage data threshold, then PPG sensor light leakage is determined.

[0189] The PD (light leakage threshold) setting can be referenced to the optical sensitivity (i.e., the current CTR) of the PPG sensor. Based on the difference in absorption and reflection of red and green light by human skin, and the actual needs of the algorithm, when the LED emits green light, the corresponding PD (light leakage threshold) can be set as: PD (light leakage threshold - green light) = 3.7% * CTR; and when the LED emits green light, the corresponding PD (light leakage threshold) can be set as: PD (light leakage threshold - red light) = 1.5% * CTR. That is, when the current light source color is green, the product of the current CTR and 3.7% is dynamically calculated to obtain the first product result, and the current light leakage threshold is dynamically determined based on this dynamically calculated first product result; when the current light source color is red, the product of the current CTR and 1.5% is dynamically calculated to obtain the second product result, and the current light leakage threshold is dynamically determined based on this dynamically calculated second product result.

[0190] In this specific embodiment, after determining that the light leakage of the PPG sensor is within an acceptable error range, it is determined that the PPG sensor does not leak light, or it can be said that the PPG sensor has a calibrable weak light leakage (when the PPG light leakage data is not zero). At this time, when the PPG light leakage data is not zero, it is still necessary to perform further data calibration on the PPG sensor based on the PPG light leakage data to eliminate the slight light leakage, without the need for rework and repair.

[0191] In one example, the calibration method is as follows: PPG leakage data for each LED is saved to memory. Each time PPG sensor data is acquired subsequently, the corresponding PPG leakage data is retrieved from memory based on the currently used LED and PD. The measured PD data is then subtracted from the corresponding PPG leakage data to obtain the calibrated PD data. In other words, if the PPG leakage data is less than or equal to the current leakage data threshold and is not zero, PPG calibration data is generated based on the PPG leakage data. This PPG calibration data is used to calibrate the PPG sensor.

[0192] For example, PD (LED1 calibration) = PD (LED1 measured) - PD (LED1 light leakage), where PD (LED1 calibration) is the PD data when LED1 is lit after calibration, and PD (LED1 measured) is the measured PD data when LED1 is lit.

[0193] Finally, after completing the light leakage detection or calibration, the wearable device or control software can push the corresponding data to the cloud device for storage, so as to monitor the defect rate of the PPG sensor in real time.

[0194] It should be noted that the above specific embodiments are only used to assist in understanding this application and do not constitute a limitation on the PPG light leakage self-testing method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0195] In addition, please refer to Figure 5 , Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the PPG light leakage self-test method in this application embodiment.

[0196] This application also provides a wearable device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the steps of the PPG light leakage self-test method in the above embodiments.

[0197] The following is for reference. Figure 5The diagram illustrates a structural schematic suitable for implementing the wearable device described in the embodiments of this application. The wearable device in the embodiments of this application may include, but is not limited to, smartwatches, smart bracelets, smart helmets, smart glasses, smart collars, or any electronic device capable of performing the aforementioned functions. Figure 5 The wearable device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0198] like Figure 5 As shown, the wearable device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the wearable device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the wearable device to communicate wirelessly or wiredly with other devices to exchange data. While wearable devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0199] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0200] The wearable device provided in this application employs the PPG light leakage self-testing method described in the above embodiments, which solves the technical problem that traditional PPG light leakage detection relies on a strictly controlled darkroom environment and has low convenience. Compared with the prior art, the beneficial effects of the wearable device provided in this application are the same as those of the PPG light leakage self-testing method provided in the above embodiments, and other technical features of the wearable device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0201] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0202] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the above claims.

[0203] In addition, this application also provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to perform the steps of the PPG light leakage self-test method in the above embodiments.

[0204] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (Radio Frequency), etc., or any suitable combination thereof.

[0205] The aforementioned computer-readable storage medium may be included in the wearable device; or it may exist independently and not assembled into the wearable device.

[0206] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the wearable device, the wearable device causes the wearable device to: receive a PPG leakage self-test command; acquire first photoelectric data detected by the photodetector when the light source is turned on, and second photoelectric data detected when the light source is turned off, and determine PPG leakage data based on the first photoelectric data and the second photoelectric data; determine the current monitoring scenario to be activated from multiple health indicator monitoring scenarios, and dynamically determine the current leakage data threshold based on the current monitoring scenario, wherein the multiple health indicator monitoring scenarios include at least three of the following: heart rate monitoring scenario, blood oxygen saturation monitoring scenario, blood pressure monitoring scenario, and respiratory rate monitoring scenario, and the current leakage data threshold determined for different health indicator monitoring scenarios is different; and determine PPG sensor leakage when the PPG leakage data is greater than the current leakage data threshold.

[0207] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0208] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0209] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0210] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for performing the steps of the above-described PPG light leakage self-testing method, which solves the technical problem that traditional PPG light leakage detection relies on a strictly controlled darkroom environment and has low convenience. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the PPG light leakage self-testing method provided in the above embodiments, and will not be repeated here.

[0211] Furthermore, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the PPG light leakage self-test method as described in the above embodiments.

[0212] The computer program product provided in this application solves the technical problem that traditional PPG light leakage detection relies on a strictly controlled darkroom environment, resulting in low convenience. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the PPG light leakage self-testing method provided in the above embodiments, and will not be repeated here.

[0213] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for self-testing PPG light leakage, characterized in that, The PPG light leakage self-detection method is applied to a wearable device, which integrates a PPG sensor. The PPG sensor includes a photodetector and a light source. The method includes: Receive PPG light leakage self-test command; Acquire the first photoelectric data detected by the photodetector when the light source is turned on, and the second photoelectric data detected when the light source is turned off, and determine the PPG leakage data based on the first photoelectric data and the second photoelectric data; The wearable device selects the current monitoring scenario to be activated from multiple health indicator monitoring scenarios. Based on the current monitoring scenario, the current light leakage data threshold is dynamically determined. The multiple health indicator monitoring scenarios include at least three of the following: heart rate monitoring scenario, blood oxygen saturation monitoring scenario, blood pressure monitoring scenario, and respiratory rate monitoring scenario. The current light leakage data threshold determined for different health indicator monitoring scenarios is different. If the PPG leakage data is greater than the current leakage data threshold, it is determined that the PPG sensor is leaking light. The step of dynamically determining the current light leakage data threshold based on the current monitoring scenario includes: Based on the current monitoring scenario, the light leakage data threshold of the current monitoring scenario is obtained by querying the preset scenario mapping threshold table; The mapped leakage data threshold is used as the current leakage data threshold; The step of dynamically determining the current light leakage data threshold based on the current monitoring scenario includes: Dynamically detect the current CTR of the PPG sensor; Based on the current monitoring scenario, determine the current light source color of the light source used in the current monitoring scenario; The current light leakage data threshold is dynamically determined based on the current light source color and the current CTR. The step of dynamically determining the current light leakage data threshold based on the current light source color and the current CTR includes: When the current light source color is green, the product of the current CTR and 3.7% is dynamically calculated to obtain the first product result, and the current light leakage data threshold is dynamically determined based on the dynamically calculated first product result. When the current light source color is red, the product of the current CTR and 1.5% is dynamically calculated to obtain a second product result. Based on the dynamically calculated second product result, the current light leakage data threshold is dynamically determined.

2. The PPG leakage self-test method as described in claim 1, characterized in that, The step of determining the current light source color of the light source corresponding to the current monitoring scene based on the current monitoring scene includes: When the current monitoring scenario is a blood oxygen saturation monitoring scenario, the current light source color of the light source used in the current monitoring scenario is determined to be red; When the current monitoring scenario is a heart rate monitoring scenario, a blood pressure monitoring scenario, or a respiratory rate monitoring scenario, the current light source color of the light source used in the current monitoring scenario is determined to be green.

3. The PPG leakage self-test method as described in any one of claims 1 to 2, characterized in that, The wearable device also integrates an ambient light detection device. After the step of receiving the PPG light leakage self-test command, the method further includes: The ambient light data detected by the ambient light detection device within a sliding time window is acquired, and the degree of fluctuation of the ambient light data is determined. When the fluctuation level is the first fluctuation level, the step of acquiring the first photoelectric data detected by the photodetector when the light source is turned on is triggered. When the fluctuation level is the second fluctuation level, a preset ambient light fluctuation prompt is output, wherein the second fluctuation level is higher than the first fluctuation level.

4. The PPG leakage self-test method as described in claim 3, characterized in that, The method further includes: If the PPG leakage data is less than or equal to the current leakage data threshold and the PPG leakage data is not zero, PPG calibration data is generated based on the PPG leakage data, wherein the PPG calibration data is used to calibrate the PPG sensor.

5. The PPG leakage self-test method as described in claim 3, characterized in that, The photodetector includes a first photodetector and a second photodetector at different positions. The first photoelectric data includes a third photoelectric data detected by the first photodetector when the light source is turned on, and a fourth photoelectric data detected by the second photodetector when the light source is turned on. The second photoelectric data includes a fifth photoelectric data detected by the first photodetector when the light source is turned off, and a sixth photoelectric data detected by the second photodetector when the light source is turned off. The current light leakage data threshold includes a first light leakage data threshold dynamically determined based on the current monitoring scenario of the first photodetector, and a second light leakage data threshold dynamically determined based on the current monitoring scenario of the second photodetector. The step of determining PPG leakage data based on the first photoelectric data and the second photoelectric data includes: Based on the third photoelectric data and the fifth photoelectric data, determine the PPG leakage data corresponding to the first photodetector; Based on the fourth photoelectric data and the sixth photoelectric data, determine the PPG leakage data corresponding to the second photodetector; The step of determining PPG sensor light leakage when the PPG light leakage data is greater than the current light leakage data threshold includes: If the PPG leakage data corresponding to the first photodetector is greater than the first leakage data threshold, or the PPG leakage data corresponding to the second photodetector is greater than the second leakage data threshold, then the PPG sensor is determined to be leaking light.

6. A wearable device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the PPG light leakage self-test method as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the PPG light leakage self-test method as described in any one of claims 1 to 5.

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