Health detection method and related device

Abstract

The application provides a health detection method and related equipment, the method comprises the following steps: acquiring physiological characteristics of a user; and performing polycystic ovary syndrome risk assessment according to the physiological characteristics of the user. According to the health detection method provided in the embodiments of the application, the user does not need to go to a hospital for polycystic ovary syndrome screening, and can perform polycystic ovary syndrome risk assessment by himself / herself, realizes real-time monitoring and management of the health status of the user, and reduces the risk of the user suffering from polycystic ovary syndrome.

Description

[0001] Cross-references to related applications This application is a divisional application. The original application has the application number 202111450385.8 and the original application date is November 30, 2021. The entire contents of the original application are incorporated herein by reference. Technical Field

[0002] This application relates to the field of terminal technology, and in particular to a health detection method and related equipment. Background Technology

[0003] Polycystic ovary syndrome (PCOS) is a common female disease, most often occurring in adolescent and reproductive-age women. Its main clinical manifestations include obesity, hirsutism, acne, menstrual irregularities, infertility, and polycystic ovarian changes. Currently, PCOS screening and diagnosis are primarily performed by doctors through medical examinations such as blood glucose tests, hormone tests, and ultrasound. It is difficult for individuals to make a diagnosis on their own. Summary of the Invention

[0004] This application discloses a health testing method and related equipment, enabling users to conduct their own polycystic ovary syndrome (PCOS) risk assessment and reduce their risk of developing PCOS.

[0005] The first aspect of this application discloses a health detection method, the method comprising: acquiring the user's physiological characteristics, including at least one of heart rate, body temperature, sleep score, RR interval, heart rate variability, standard deviation of normal RR interval, low-frequency power spectral density, and high-frequency power spectral density; and performing a risk assessment of polycystic ovary syndrome based on the user's physiological characteristics.

[0006] According to this health testing method, electronic devices can assess the risk of polycystic ovary syndrome (PCOS) based on the user's physiological characteristics. Users do not need to go to the hospital for PCOS screening; they can conduct PCOS risk assessments themselves, learn about their PCOS status, and achieve real-time monitoring and management of their health status. This allows users to make reasonable adjustments to their lifestyle and diet, reducing their risk of developing PCOS.

[0007] In some optional implementations, the method further includes: acquiring a facial image of the user; the risk assessment of polycystic ovary syndrome based on the user's physiological characteristics includes: performing a risk assessment of polycystic ovary syndrome based on the user's physiological characteristics and the facial image.

[0008] Polycystic ovary syndrome (PCOS) can present with features such as hirsutism and acne. Combining a user's physiological characteristics with facial images for PCOS risk assessment can improve the accuracy of PCOS risk assessment.

[0009] In some optional implementations, the method further includes: obtaining the user's body composition index; the risk assessment of polycystic ovary syndrome based on the user's physiological characteristics includes: performing a risk assessment of polycystic ovary syndrome based on the user's physiological characteristics and body composition index.

[0010] Polycystic ovary syndrome (PCOS) is characterized by obesity. Combining a user's physiological characteristics and body composition indicators in PCOS risk assessment can improve the accuracy of PCOS risk assessment.

[0011] In some optional implementations, the method further includes: acquiring a facial image of the user; acquiring the user's body composition index; and the risk assessment of polycystic ovary syndrome based on the user's physiological characteristics includes: assessing the risk of polycystic ovary syndrome based on the user's physiological characteristics, body composition index, and facial image.

[0012] Polycystic ovary syndrome (PCOS) can present with features such as hirsutism, acne, and obesity. Combining a user's physiological characteristics, body composition indicators, and facial images to conduct PCOS risk assessment can improve the accuracy of PCOS risk assessment.

[0013] In some optional implementations, acquiring the user's physiological characteristics includes: acquiring the user's physiological characteristics over a continuous time period; calculating the average value of each physiological characteristic over the continuous time period; and performing the polycystic ovary syndrome risk assessment includes: using the average value of each physiological characteristic as an input feature of the machine learning model to perform the polycystic ovary syndrome risk assessment.

[0014] By conducting polycystic ovary syndrome risk assessment based on users' physiological characteristics over a continuous period of time, the impact of erroneous data can be reduced, resulting in more accurate assessment results.

[0015] In some optional implementations, the method further includes: calculating the highest and lowest body temperature values ​​within the continuous time period; the polycystic ovary syndrome (PCOS) risk assessment includes: using the highest and lowest body temperature values ​​as input features of the machine learning model to assess the risk of PCOS.

[0016] In some optional embodiments, the method further includes: performing a first analysis on the facial image to obtain the probability that the user has acne on their face, and / or performing a second analysis on the facial image to obtain the probability that the user has excessive hair on their upper lip and lower jaw; the polycystic ovary syndrome risk assessment includes: using the probability that the user has acne on their face and / or the probability that the user has excessive hair on their upper lip and lower jaw as input features of a machine learning model to assess the risk of polycystic ovary syndrome.

[0017] In some optional implementations, the method further includes: obtaining medical examination results related to polycystic ovary syndrome (PCOS) of the user, the medical examination results including at least one of the following: whether there is a risk of hyperandrogenemia, whether the blood glucose index is high, whether the blood LH concentration is high, whether the LH / FSH ratio is high, whether the AMH and AND levels are high, whether the follicle diameter is >10 mm, and whether a corpus luteum is present; the PCOS risk assessment includes: performing a PCOS risk assessment in conjunction with the medical examination results.

[0018] Combining medical examination results with polycystic ovary syndrome (PCOS) risk assessment can improve the accuracy of PCOS risk assessment.

[0019] In some optional implementations, the risk assessment of polycystic ovary syndrome (PCOS) based on the medical examination results includes: obtaining a first probability value for PCOS based on the user's physiological characteristics, or the user's physiological characteristics and body composition indicators, or the user's physiological characteristics and facial images, or the user's physiological characteristics, body composition indicators, and facial images; assigning a weight to each medical examination item in the medical examination results; calculating a second probability value for PCOS based on the medical examination results, wherein the second probability value is equal to the sum of the probability components corresponding to all medical examination items in the medical examination results, and the probability component corresponding to each medical examination item is equal to the product of the weight of that medical examination item and the examination result of that medical examination item; and calculating a final probability value for PCOS based on the first probability value and the second probability value.

[0020] The second aspect of this application discloses a computer-readable storage medium including computer instructions that, when executed on an electronic device, cause the electronic device to perform the health detection method as described in the first aspect.

[0021] A third aspect of this application discloses an electronic device comprising a processor and a memory, the memory being used to store instructions, and the processor being used to invoke the instructions in the memory to cause the electronic device to perform the health detection method as described in the first aspect.

[0022] The fourth aspect of this application discloses a chip system applied to an electronic device; the chip system includes an interface circuit and a processor; the interface circuit and the processor are interconnected via a line; the interface circuit is used to receive signals from the memory of the electronic device and send signals to the processor, the signals including computer instructions stored in the memory; when the processor executes the computer instructions, the chip system performs the health detection method as described in the first aspect.

[0023] The fifth aspect of this application discloses a computer program product that, when run on a computer, causes the computer to perform the health detection method as described in the first aspect.

[0024] The sixth aspect of this application discloses an apparatus having the function of implementing the electronic device behavior described in the method of the first aspect. The function can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the described function.

[0025] It should be understood that the computer-readable storage medium described in the second aspect, the electronic device described in the third aspect, the chip system described in the fourth aspect, the computer program product described in the fifth aspect, and the device described in the sixth aspect all correspond to the method described in the first aspect. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here. Attached Figure Description

[0026] Figure 1 This is a schematic diagram illustrating an application scenario of the health detection method provided in the embodiments of this application.

[0027] Figure 2 This is a schematic diagram illustrating an application scenario of a health detection method provided in another embodiment of this application.

[0028] Figure 3 This is a flowchart of the health detection method provided in the embodiments of this application.

[0029] Figure 4 This is a graphical user interface diagram of the main interface for PCOS risk assessment displayed on an electronic device.

[0030] Figure 5 This is a diagram illustrating how a menstrual cycle notification pushed by an app redirects to the main interface of the PCOS risk assessment.

[0031] Figure 6 This is a schematic diagram of entering the physiological characteristics interactive interface.

[0032] Figure 7 This is a diagram showing how to access the survey questionnaire interface.

[0033] Figure 8 This is a flowchart of a health detection method provided in another embodiment of this application.

[0034] Figure 9 This is a flowchart of a health detection method provided in another embodiment of this application.

[0035] Figure 10 This is a flowchart of a health detection method provided in another embodiment of this application.

[0036] Figure 11 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application.

[0037] Figure 12 This is a schematic diagram of the software structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0038] For ease of understanding, exemplary descriptions of some concepts related to the embodiments of this application are provided for reference.

[0039] It should be noted that in this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or sequence.

[0040] To better understand the health detection method and related equipment provided in the embodiments of this application, the application scenarios of the health detection method of this application are described below.

[0041] Figure 1 This is a schematic diagram illustrating an application scenario of the health detection method provided in the embodiments of this application.

[0042] like Figure 1As shown, electronic device 10 establishes a communication connection with wearable device 11. Wearable device 11 detects the user's physiological characteristics and sends these characteristics to electronic device 10. Electronic device 10 receives the physiological characteristics sent by wearable device 11 and performs a polycystic ovary syndrome (PCOS) risk assessment on the user based on the detected physiological characteristics. In some embodiments, wearable device 11 can upload locally detected physiological characteristics to a server, and electronic device 10 receives the physiological characteristics from the server. Electronic device 10 then performs a PCOS risk assessment based on these physiological characteristics. In some embodiments, wearable device 11 may include a photoplethysmography (PPG) sensor and a temperature sensor. The PPG sensor is used to acquire PPG signals, and the temperature sensor is used to acquire skin temperature signals. Wearable device 11 can obtain the user's physiological characteristics based on the acquired PPG signals and skin temperature signals. In some embodiments, wearable device 11 can use more or fewer sensors to obtain the user's physiological characteristics. It should be understood that a PPG sensor includes a light-emitting diode (LED) and a photodiode (PD) photodetector. The LED in the PPG sensor emits green light that passes through the tissues, arteries, and veins in the skin, and is absorbed and reflected back to the PD photodetector. The absorption of light by muscles, bones, veins, and other tissues remains essentially constant. However, the absorption of light by arteries varies due to blood flow. The PPG sensor converts the light detected by the PD photodetector into an electrical signal. Since the absorption of light by arteries changes while the absorption by muscles, bones, veins, and other tissues remains essentially constant, the resulting electrical signal can be divided into a direct current (DC) signal and an alternating current (AC) signal. Extracting the AC signal (i.e., the PPG signal) reveals the characteristics of blood flow. A user's physiological characteristics may include heart rate, body temperature, respiratory rate, perfusion index, sleep status, RR interval (RRI), heart rate variability (HRV), standard deviation of normal-normal intervals (SDNN), systolic blood pressure (SBP), diastolic blood pressure (DBP), low-frequency power spectral density (LF), and high-frequency power spectral density (HF).

[0043] In one embodiment of this application, electronic device 10 captures a user's facial image. Based on the physiological characteristics detected by wearable device 11 and the facial image captured by electronic device 10, electronic device 10 performs a PCOS risk assessment on the user. Compared to using only the user's physiological characteristics for PCOS risk assessment, using both the user's physiological characteristics and facial image for PCOS risk assessment can improve the accuracy of PCOS risk assessment.

[0044] Figure 2 This is a schematic diagram illustrating an application scenario of a health detection method provided in another embodiment of this application.

[0045] like Figure 2 As shown, electronic device 10 establishes a communication connection with wearable device 11 and body fat scale 12. Wearable device 11 is used to detect the user's physiological characteristics. Body fat scale 12 is used to measure the body's impedance. Electronic device 10 calculates the user's body composition index based on the user's impedance and basic information, and performs a PCOS risk assessment on the user based on the user's physiological characteristics and body composition index. The user's body composition index may include body mass index (BMI), body fat percentage, basal metabolic rate, waist-to-hip ratio, etc.

[0046] In one embodiment of this application, electronic device 10 captures a user's facial image. Based on the physiological characteristics detected by wearable device 11, the body composition index calculated by electronic device 10, and the facial image captured by electronic device 10, electronic device 10 performs a PCOS risk assessment on the user. Compared to using the user's physiological characteristics and body composition index for PCOS risk assessment, using the user's physiological characteristics, body composition index, and facial image for PCOS risk assessment can improve the accuracy of PCOS risk assessment.

[0047] In other embodiments, wearable device 11 captures an image of the user's face and sends the image to electronic device 10 for PCOS risk assessment. For example, Figure 1 In the scenario shown, wearable device 11 captures an image of the user's face and sends the image to electronic device 10. Electronic device 10 then performs a PCOS risk assessment on the user based on the physiological characteristics detected by wearable device 11 and the captured facial image. For example, Figure 2 In the scenario shown, wearable device 11 captures a user's facial image and sends the facial image to electronic device 10. Electronic device 10 performs a PCOS risk assessment on the user based on the physiological characteristics detected by wearable device 11, the body composition index calculated by electronic device 10, and the facial image captured by wearable device 11.

[0048] Figure 1 , Figure 2The illustrated embodiment uses a mobile phone as an example for explanation. In other embodiments of this application, the electronic device 10 can be other terminal devices, such as a tablet or a laptop.

[0049] Figure 1 , Figure 2 The illustrated embodiment uses a smartwatch as an example to illustrate wearable device 11. In other embodiments of this application, wearable device 11 can also be a smart bracelet.

[0050] Electronic device 10 and wearable device 11 and body fat scale 12 can communicate wirelessly over short distances via Bluetooth, Wi-Fi (Wireless Fidelity), NFC (Near Field Communication), ZigBee, IrDA (Infrared Data Association), UWB (Ultra Wideband), and wireless USB (Universal Serial Bus).

[0051] In addition to PCOS risk assessment being performed by electronic device 10 (e.g., mobile phone), PCOS risk assessment can also be performed by wearable device 11 (e.g., watch).

[0052] In some embodiments, the health detection method provided in this application can be applied to scenarios including a wearable device 11. The wearable device 11 detects the user's physiological characteristics and performs a PCOS risk assessment on the user based on the physiological characteristics. Alternatively, the wearable device 11 detects the user's physiological characteristics and captures a facial image of the user, and performs a PCOS risk assessment on the user based on the user's physiological characteristics and facial image. Optionally, the wearable device 11 can send the assessed PCOS risk result to the electronic device 10.

[0053] In some embodiments, the health detection method provided in this application can be applied to scenarios including a wearable device 11 and a body fat scale 12. The wearable device 11 detects the user's physiological characteristics, and the body fat scale 12 measures the body's impedance. The wearable device 11 calculates the user's body composition index based on the user's impedance and basic information, and performs a PCOS risk assessment on the user based on the user's physiological characteristics and body composition index. Alternatively, the wearable device 11 detects the user's physiological characteristics and captures a facial image of the user, while the body fat scale 12 measures the body's impedance. The wearable device 11 calculates the user's body composition index based on the user's impedance and basic information, and performs a PCOS risk assessment on the user based on the user's physiological characteristics, body composition index, and facial image.

[0054] In some other embodiments, the body fat scale 12 can calculate the user's body composition index based on the measured impedance.

[0055] Figure 3 This is a flowchart of the health detection method provided in the embodiments of this application. Figure 3 In the illustrated embodiment, the electronic device 10 is communicatively connected to the wearable device 11 and the body fat scale 12 (see [reference]). Figure 2 (As shown). Electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics, body composition indicators, and facial images.

[0056] 301. Electronic device 10 obtains basic user information.

[0057] In one embodiment of this application, the electronic device 10 can display the PCOS risk assessment main interface, from which the user can enter the user information settings interface and set the user's basic information.

[0058] Figure 4 This is a graphical user interface diagram of the PCOS risk assessment main interface displayed on electronic device 10. (Example) Figure 4 As shown, the main interface for PCOS risk assessment may include a user information setting button 401, a PCOS risk screening button 402, a physiological characteristic button 403, a physiological characteristic synchronization button 404, a body composition button 405, a body composition synchronization button 406, a photo button 407, and a questionnaire button 408. In this embodiment, physiological characteristics are essential data for PCOS risk assessment, while body composition indicators, facial images, and questionnaire data (i.e., medical examination results, which may include blood glucose levels, hormone levels, ultrasound examination results, etc.) are optional data for PCOS risk assessment. Essential and optional data for PCOS risk assessment can be identified on the main interface. For example, the physiological characteristic button 403 can be marked "Required," and the body composition synchronization button 406, the photo button 407, and the questionnaire button 408 can be marked "Optional."

[0059] The user information settings button 401 is used to set basic user information. Users can click the user information settings button 401 to enter the user information settings interface and set their basic information. Basic user information can include gender, age, height, weight, etc. Age can be an age range, such as 20-30 years old, 30-40 years old, etc. Basic user information is used to calculate the user's body composition index.

[0060] In one embodiment of this application, the electronic device 10 may install a female menstrual cycle application (APP). The female menstrual cycle APP is used to predict the user's menstrual period to determine whether the user's menstrual cycle is normal. When the next menstrual period is predicted to begin, the female menstrual cycle APP can push a menstrual period reminder message to the electronic device 10. Based on the menstrual period reminder message pushed by the female menstrual cycle APP, the user can be redirected to the PCOS risk assessment main interface. Figure 5 This is a diagram illustrating how a menstrual cycle notification pushed by an app redirects to the main interface of the PCOS risk assessment. Figure 5 The interface consists of, in order: lock screen, menstrual cycle details, POCS risk assessment confirmation, and PCOS risk assessment main interface. Users can click on the menstrual cycle notification on the lock screen (e.g., "Friendly reminder: You have entered the first day of your period. Please take care of your body. See details >>") to enter the menstrual cycle details interface. The menstrual cycle details interface may include a "More Screening" button, which users can click to enter the POCS risk assessment confirmation interface. The POCS risk assessment confirmation interface may include POCS risk assessment notifications (e.g., "Please assess whether your current prediction result differs from your actual menstrual period by more than 3 days. If this occurs for two consecutive cycles, please try a polycystic ovary syndrome (PCOS) status assessment") and a POCS risk assessment start button. Clicking the POCS risk assessment start button will take the user to the PCOS risk assessment main interface.

[0061] It is understandable that menstrual period notification information can be displayed on other interfaces of the electronic device 10. For example, when the electronic device 10 is unlocked, menstrual period notification information can be displayed on the desktop of the electronic device 10, or on the notification bar of the electronic device 10.

[0062] In another embodiment of this application, the electronic device 10 may be equipped with a POCS risk assessment app, which is used to perform PCOS risk assessment. Users can access the PCOS risk assessment main interface by opening the POCS risk assessment app.

[0063] In another embodiment of this application, the POCS risk assessment app can predict a user's menstrual cycle. When the start of the next menstrual period is predicted, the POCS risk assessment app can issue a menstrual period reminder. Users can access the PCOS risk assessment main interface based on the menstrual period reminder issued by the POCS risk assessment app.

[0064] In one embodiment of this application, a user's menstrual cycle can be predicted using a calendar method (menstrual cycle method). The user's menstrual time (including start and end times) can be recorded for multiple consecutive cycles. Based on the recorded menstrual times from multiple consecutive cycles, a predicted value for the duration of menstruation (e.g., 5 days) and a predicted value for the menstrual cycle (e.g., 28 days) are determined. Based on the start time of the user's last menstrual period, the predicted duration of menstruation, and the predicted menstrual cycle, the menstrual time for the next cycle is calculated. The mean or mode of the duration of menstruation from multiple consecutive cycles can be used as the predicted duration of menstruation, and the mean or mode of the menstrual cycle from multiple consecutive cycles can be used as the predicted menstrual cycle. The mode is determined by dividing the data into intervals; the interval with the most data is the mean of the data in that interval.

[0065] In some embodiments, electronic device 10 can display a user's basic information by obtaining the user's account information. For example, electronic device 10 can synchronize a user's basic information from a server to its local display. Users only need to log in to their user account on different electronic devices 10 to obtain their basic information, avoiding multiple manual inputs.

[0066] 302, Electronic device 10 sends a first data acquisition command to wearable device 11.

[0067] The first data acquisition command is used to control the wearable device 11 to detect the user's physiological characteristics. See also Figure 4 As shown, the main interface for PCOS risk assessment may include a physiological characteristics button 403, which allows users to click on the physiological characteristics button 403 to send a first data collection command to the wearable device 11.

[0068] 303, Wearable device 11 detects the user's physiological characteristics.

[0069] Upon receiving the first data acquisition command, the wearable device 11 can detect the user's physiological characteristics through the PPG sensor and temperature sensor, such as detecting the user's physiological characteristics over a continuous period of time (e.g., three days).

[0070] In one embodiment of this application, the physiological characteristics detected by the wearable device 11 may include at least one of heart rate, body temperature, sleep score, RRI, HRV, SDNN, LF, HF, etc.

[0071] In other embodiments of this application, the physiological characteristics detected by the wearable device 11 may also include others, such as SBP, DBP, respiratory rate, and perfusion index.

[0072] 304. Wearable device 11 returns the detected physiological characteristics of the user to electronic device 10.

[0073] After detecting the user's physiological characteristics, the wearable device 11 can actively return the user's physiological characteristics to the electronic device 10.

[0074] Alternatively, the user can send a first synchronization command to the wearable device 11, causing the wearable device 11 to return the user's physiological characteristics to the electronic device 10. See also Figure 4 As shown, the PCOS risk assessment main interface may include a physiological characteristic synchronization button 404. Users can click the physiological characteristic synchronization button 404 to make the wearable device 11 return the user's physiological characteristics to the electronic device 10.

[0075] Wearable device 11 can return the physiological characteristics of a recently detected period of time to electronic device 10. For example, if wearable device 11 detects the user's physiological characteristics over ten consecutive days, and PCOS risk assessment requires the user's physiological characteristics over three consecutive days, wearable device 11 will return the user's physiological characteristics over the most recent three days to electronic device 10.

[0076] When a user clicks the physiological characteristic synchronization button 404, if the wearable device 11 does not detect sufficient physiological characteristics (e.g., less than three days), it can send a data shortage warning to the user. For example, it can prompt: "Current data is missing. Please wear it continuously for three days and try again."

[0077] After the wearable device 11 returns the user's physiological characteristics to the electronic device 10, the user's physiological characteristics can be viewed from the electronic device 10.

[0078] In some embodiments, the wearable device 11 uploads the collected user's physiological characteristic data to a server, and the electronic device 10 retrieves the user's physiological characteristic data from the server. For example, after a user logs into their user account on the electronic device 10, they can retrieve the user's physiological characteristic data associated with their user account from the server. By synchronizing data through the account, data loss caused by accidental disconnection of the real-time communication connection between the electronic device 10 and the wearable device 11 can be avoided, and the electronic device 10 can be kept connected to the wearable device 11 for extended periods, thus saving power consumption.

[0079] Figure 6 This is a diagram illustrating the process of accessing the physiological characteristics interactive interface. See also... Figure 6 As shown, after the wearable device 11 returns the user's physiological characteristics to the electronic device 10, if the user clicks the physiological characteristics button 404, they can enter the physiological characteristics interactive interface. The physiological characteristics interactive interface is used to display the user's physiological characteristics. For example... Figure 6As shown, the physiological characteristics interactive interface can include buttons for Women's Health, Sleep, Advanced Heart Rate, Blood Pressure, and Blood Sugar. The Women's Health button redirects to the Women's Menstrual Cycle app, the Sleep button displays the user's sleep score, the Advanced Heart Rate button displays information such as RRI, HRV, SDNN, LF, and HF, the Blood Pressure button displays information such as SBP and DBP, and the Blood Sugar button displays the user's blood sugar information. The physiological characteristics interactive interface can also display information such as the user's heart rate, respiratory rate, and body temperature.

[0080] 305, Electronic device 10 sends a second data acquisition command to body fat scale 12.

[0081] The Body Fat Scale 12 is based on bioelectrical impedance analysis (BIA). BIA measures the impedance of the human body by utilizing the different electrical conductivity of muscle and fat (muscle contains more water and therefore conducts more electricity; fat contains less water and therefore conducts less electricity).

[0082] The second data acquisition command is used to control the body fat scale 12 to measure the user's impedance.

[0083] In some embodiments, after the body fat scale measures the user's impedance, it sends the impedance data to the electronic device 10.

[0084] See Figure 4 As shown, the PCOS risk assessment main interface may include a body composition button 405, which allows users to click on the body composition button 405 to send a second data collection command to the body fat scale 12.

[0085] The electronic device 10 can send a second data acquisition command to the body fat scale 12 before the wearable device 11 detects the user's physiological characteristics, or after the wearable device 11 detects the user's physiological characteristics, or during the period when the wearable device 11 detects the user's physiological characteristics.

[0086] 306, Body Fat Scale 12 measures the user's impedance.

[0087] After receiving the second data acquisition command, the body fat scale 12 collects the user's body composition indicators.

[0088] 307, the body fat scale 12 returns the user's impedance to the electronic device 10.

[0089] See Figure 4 As shown, the PCOS risk assessment main interface may include a volume composition synchronization button 406. Users can click the volume composition synchronization button 406 to return the user's impedance to the electronic device 10.

[0090] 308, Electronic device 10 calculates the user's body composition index based on the user's basic information and the user's impedance.

[0091] In one embodiment of this application, the electronic device 10 calculates the user's BMI, body fat percentage, and basal metabolic rate based on the user's basic information and the user's impedance. In other embodiments of this application, the electronic device 10 can also calculate other body composition indicators of the user based on the user's basic information and the user's impedance, such as muscle mass, protein mass, bone mineral mass, waist-to-hip ratio, etc.

[0092] BMI is calculated as weight / (height squared). Fat percentage and basal metabolic rate can be obtained from a body composition model, whose inputs are sex, age, height, weight, and resistance.

[0093] 309. Electronic device 10 captures an image of the user's face.

[0094] See Figure 4 As shown, the main interface of the PCOS risk assessment may include a camera button 407, which the user can click to control the electronic device 10 to capture an image of the user's face.

[0095] Electronic device 10 can use a front-facing camera to capture an image of the user's face. After the user clicks the shutter button 407, electronic device 10 can prompt the user to assume the correct posture. For example, it can prompt the user to face the camera and also prompt the user not to cover their forehead. Electronic device 10 can include a preview frame, from which the user can preview the image to be captured after clicking the shutter button 407. Electronic device 10 can use an image recognition algorithm to detect whether the image in the preview frame meets the requirements (e.g., whether it is a complete face image, whether it is facing the camera directly, and whether the forehead is unobstructed). If the image displayed in the preview frame does not meet the requirements, it can prompt the user to make adjustments. For example, if the image in the preview frame is not a complete face image, it can prompt the user to "move further away from the camera".

[0096] The electronic device 10 can capture a user's facial image before the wearable device 11 detects the user's physiological characteristics, or after the wearable device 11 detects the user's physiological characteristics, or during the period when the wearable device 11 detects the user's physiological characteristics.

[0097] In some embodiments, the user may also capture a facial image using the wearable device 11, and the wearable device 11 may send the facial image to the electronic device 10.

[0098] 310, Electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics, body composition indicators and facial images.

[0099] In one embodiment of this application, see Figure 4 As shown, if a user clicks the PCOS risk screening button 402 on the main interface of the PCOS risk assessment, a PCOS risk assessment will be performed.

[0100] The results of PCOS risk assessment can be categorized into three levels: no abnormalities, medium risk, and high risk.

[0101] In one embodiment of this application, when a user clicks the PCOS risk screening button 402, if the electronic device 10 has not obtained sufficient (e.g., less than three days) physiological characteristics, it can issue a message indicating that there is no data available.

[0102] Electronic device 10 can perform PCOS risk assessment using machine learning models (such as XGBoost, randomized forest, deep neural networks). The input to the machine learning model is the user's physiological characteristics, body composition indicators, and facial image-related features, and the output of the machine learning model is the probability value P1 of PCOS (which can be called the first probability value).

[0103] In one embodiment of this application, the electronic device can calculate the average value of various physiological characteristics over a continuous period of time (e.g., three consecutive days), and use the average value of the physiological characteristics as input features of the machine learning model corresponding to the physiological characteristics. For example, the electronic device 10 can calculate the average sleep score, average body temperature, average RRI, average HRV, average SDNN, average LF, and average HF, the probability value of acne, and BMI, body fat percentage, and basal metabolic rate. The electronic device 10 can also calculate the highest and lowest body temperature values ​​over a continuous period of time (e.g., three consecutive days), and use the highest and lowest body temperature values ​​as input features of the machine learning model.

[0104] In one embodiment of this application, the electronic device 10 can perform a first analysis on the user's facial image using an image recognition algorithm to obtain the probability that the user has acne on their face; and / or perform a second analysis on the user's facial image using an image recognition algorithm to obtain the probability that the user has excessive hair on their upper lip and lower jaw; and use the probability that the user has acne on their face and / or the probability that the user has excessive hair on their upper lip and lower jaw as input features of a machine learning model.

[0105] The electronic device 10 can use various body composition indicators (such as BMI, body fat percentage, and basal metabolic rate) as input features for a machine learning model.

[0106] The probability value P1 of PCOS output by the machine learning model can be used directly as the result of PCOS risk assessment. For example, if the probability value P1 of PCOS output by the machine learning model is 0.6, then the PCOS risk assessment result is a risk of 60%.

[0107] In one embodiment of this application, a judgment threshold can be given for the probability value P1 of PCOS: P1≤0.3---------------------No abnormalities found; 0.3 < P1 ≤ 0.8 -------------- Risk in PCOS; P1>0.8 ----------------------- High risk for PCOS.

[0108] For example, if the probability value P1 of PCOS output by the machine learning model is 0.6, then the PCOS risk assessment result can be output.

[0109] In one embodiment of this application, considering that the user may have recently undergone PCOS-related medical examinations at a hospital or medical examination center, PCOS risk assessment can be performed based on the medical examination results to improve the accuracy of the PCOS risk assessment. A questionnaire can be administered to the user based on the medical examination results. Figure 4 As shown, the main interface for PCOS risk assessment may include a questionnaire button 408, used to conduct a survey for users to provide their medical examination results. Users can click the questionnaire button 408 to enter the questionnaire interface and input relevant medical examination results.

[0110] Figure 7 This is a diagram illustrating how to access the survey questionnaire. See also... Figure 7 As shown, the questionnaire may include whether there is a risk of hyperandrogenemia, whether the blood glucose index is high, whether the blood LH concentration is high, whether the LH / FSH ratio is high, whether the AMH and AND levels are high, whether the follicle diameter is >10mm, and whether the corpus luteum is present.

[0111] A weight can be assigned to each medical examination item (i.e., questionnaire item). For example, the weights corresponding to the 7 medical examination items in the questionnaire are as follows: Is there a risk of hyperandrogenemia? ----------- 0.3; Is the blood sugar level too high? ----------------------- 0.05; Is the blood LH concentration too high? ----------------------0.2; Is the LH / FSH ratio too high? ------------------ 0.1; Are AMH and AND levels too high? ------------ 0.2; Is the follicle diameter > 10mm? ------------------- 0.1; Does the corpus luteum appear? -----------------------------0.05

[0112] The weightings for each factor are as follows: 0.3 for the risk of hyperandrogenemia, 0.05 for elevated glycemic index, 0.2 for elevated serum LH concentration, 0.1 for elevated LH / FSH ratio, 0.2 for elevated AMH and AND levels, 0.1 for follicle diameter > 10 mm, and 0.05 for presence of corpus luteum.

[0113] The probability value P2 (also known as the second probability value) of PCOS can be calculated based on the medical examination results. P2 is equal to the sum of the probability components corresponding to each medical examination item. The probability component corresponding to each medical examination item is equal to the product of the weight of that medical examination item and the result of that medical examination item. If the result of a medical examination item is yes (i.e., the answer to the questionnaire item is yes), then the result of that medical examination item is 1; otherwise, if the result of that medical examination item is no (i.e., the answer to the questionnaire item is no), then the result of that medical examination item is 0. For example, see... Figure 7 As shown, the probability value of PCOS, P2, is calculated as follows: P2 = 0.3 × risk of hyperandrogenemia + 0.05 × high blood glucose level + 0.2 × high blood LH concentration + 0.1 × high LH / FSH ratio + 0.2 × high AMH and AND levels + 0.1 × follicle diameter > 10 mm + 0.05 × presence of corpus luteum.

[0114] by Figure 7 For example, if a user has a risk of hyperandrogenemia, high blood sugar level, high blood LH concentration, high LH / FSH ratio, corpus luteum, high AMH and AND levels, and answers no to whether the follicle diameter is >10mm, then P2 = 0.3×1+0.05×1+0.2×1+0.1×LH / FSH1+0.2×0+0.1×0+0.05×1=0.7.

[0115] In one embodiment of this application, the final probability value of PCOS is P = P1 + 0.5 × (1 - P1) × P2.

[0116] In another embodiment of this application, the final probability value of PCOS is P = a × P1 + b × P2, where a + b = 1. a and b are constants and can be set as needed, for example, a = 0.8 and b = 0.2.

[0117] A threshold can be given for the final probability P of PCOS: P≤0.3---------------------No abnormalities found; 0.3 < P ≤ 0.8 -------------- Risk in PCOS; P>0.8 ----------------------- High risk for PCOS.

[0118] Figure 3 In the illustrated embodiment, the electronic device 10 combines the user's physiological characteristics, body composition indicators, and facial images to perform PCOS risk assessment, which can obtain relatively accurate assessment results. According to the health detection method provided in this application embodiment, users do not need to go to the hospital for PCOS screening; they can conduct their own PCOS risk assessment, learn about their own PCOS status, and achieve real-time monitoring and management of their health status. This allows users to make reasonable adjustments to their lifestyle and diet, reducing their risk of developing PCOS.

[0119] Figure 8 This is a flowchart of a health detection method provided in another embodiment of this application. Figure 8 In the illustrated embodiment, the electronic device 10 is communicatively connected to the wearable device 11 and the body fat scale 12 (see [reference]). Figure 2 (As shown). Figure 3 In the flowchart shown, electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics, body composition indicators, and facial images. Figure 8 In the flowchart shown, electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics and body composition indicators (PCOS risk assessment is not performed using facial images).

[0120] 801, Mobile phone obtains basic user information.

[0121] In one embodiment of this application, the electronic device 10 can display the PCOS risk assessment main interface, from which the user can enter the user information settings interface and set the user's basic information.

[0122] User basic information may include gender, age, height, weight, etc. Age can be a range, such as 20-30 years old, 30-40 years old, etc. This user basic information is used to calculate the user's body composition index.

[0123] 802, Electronic device 10 sends a first data acquisition command to wearable device 11.

[0124] 803, Wearable device 11 detects the user's physiological characteristics.

[0125] 804, Wearable device 11 returns the user's physiological characteristics to electronic device 10.

[0126] 805, Electronic device 10 sends a second data acquisition command to body fat scale 12.

[0127] 806, Body Fat Scale 12 measures the user's impedance.

[0128] 807, the body fat scale 12 returns the user's impedance to the electronic device 10.

[0129] 808, Electronic device 10 calculates the user's body composition index based on the user's basic information and the user's impedance.

[0130] 809, Electronic devices 10 conduct PCOS risk assessment based on the user's physiological characteristics and body composition indicators.

[0131] Electronic device 10 can perform PCOS risk assessment using machine learning models (such as XGBoost, randomized forest, and deep neural networks). The input to the machine learning model is the user's physiological characteristics and features related to body composition indicators, and the output of the machine learning model is the probability value P1 of PCOS.

[0132] Electronic device 10 can combine medical examination results to perform PCOS risk assessment, thereby improving the accuracy of PCOS risk assessment. The specific process of electronic device 10 combining medical examination results to perform PCOS risk assessment can be found in [reference needed]. Figure 3 The relevant description of 310 will not be repeated here.

[0133] Figure 8 In the illustrated embodiment, the electronic device 10 combines the user's physiological characteristics and body composition indicators to perform a PCOS risk assessment. According to the health monitoring method provided in this application embodiment, users do not need to go to the hospital for PCOS screening; they can perform a PCOS risk assessment themselves, learn about their own PCOS status, and achieve real-time monitoring and management of their health status. This allows users to make reasonable adjustments to their lifestyle and diet, reducing their risk of developing PCOS.

[0134] Figure 9 This is a flowchart of a health detection method provided in another embodiment of this application. Figure 9 In the illustrated embodiment, electronic device 10 is communicatively connected to wearable device 11 (see [reference]). Figure 1 (As shown). Figure 3 In the flowchart shown, electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics, body composition indicators, and facial images. Figure 9 In the flowchart shown, electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics (without using the user's body composition indicators and facial images for PCOS risk assessment).

[0135] 901, Electronic device 10 sends a first data acquisition command to wearable device 11.

[0136] 902, Wearable device 11 detects the user's physiological characteristics.

[0137] 903, Wearable device 11 returns the user's physiological characteristics to electronic device 10.

[0138] 904, Electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics.

[0139] Electronic device 10 can perform PCOS risk assessment using machine learning models (such as XGBoost, randomized forest, deep neural networks). The input to the machine learning model is features related to the user's physiological characteristics, and the output of the machine learning model is the probability value P1 of PCOS.

[0140] Electronic device 10 can combine medical examination results to perform PCOS risk assessment, thereby improving the accuracy of PCOS risk assessment. The specific process of electronic device 10 combining medical examination results to perform PCOS risk assessment can be found in [reference needed]. Figure 3 The relevant description of 310 will not be repeated here.

[0141] Figure 9 In the illustrated embodiment, the electronic device 10 performs a PCOS risk assessment based on the user's physiological characteristics. According to the health monitoring method provided in this application embodiment, users do not need to go to the hospital for PCOS screening; they can perform a PCOS risk assessment themselves, learn about their own PCOS status, and achieve real-time monitoring and management of their health status. This allows users to make reasonable adjustments to their lifestyle and diet, reducing their risk of developing PCOS.

[0142] Figure 10 This is a flowchart of a health detection method provided in another embodiment of this application. Figure 10 In the illustrated embodiment, electronic device 10 is communicatively connected to wearable device 11 (see [reference]). Figure 1 (As shown). Figure 3 In the flowchart shown, electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics, body composition indicators, and facial images. Figure 10 In the flowchart shown, electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics and facial image (the user's body composition index is not used for PCOS risk assessment).

[0143] 1001, Electronic device 10 sends a first data acquisition command to wearable device 11.

[0144] 1002, Wearable device 11 detects the user's physiological characteristics.

[0145] 1003, Wearable device 11 returns the user's physiological characteristics to electronic device 10.

[0146] 1004, Electronic device 10 captures an image of the user's face.

[0147] 1005, Electronic device 10 performs PCOS risk assessment based on the user's physiological characteristics and facial image.

[0148] Electronic device 10 can perform PCOS risk assessment using machine learning models (such as XGBoost, randomized forest, deep neural networks). The input to the machine learning model is features related to the user's physiological characteristics, and the output of the machine learning model is the probability value P1 of PCOS.

[0149] Electronic device 10 can combine medical examination results to perform PCOS risk assessment, thereby improving the accuracy of PCOS risk assessment. The specific process of electronic device 10 combining medical examination results to perform PCOS risk assessment can be found in [reference needed]. Figure 3 The relevant description of 310 will not be repeated here.

[0150] Figure 10 In the illustrated embodiment, the electronic device 10 performs a PCOS risk assessment based on the user's physiological characteristics and facial image. According to the health monitoring method provided in this application embodiment, users do not need to go to the hospital for PCOS screening; they can perform a PCOS risk assessment themselves, learn about their own PCOS status, and achieve real-time monitoring and management of their health. This allows users to make reasonable adjustments to their lifestyle and diet, reducing their risk of developing PCOS.

[0151] Figure 11 This is a schematic diagram of the hardware structure of an electronic device 10 provided in an embodiment of this application. For example... Figure 11 As shown, the electronic device 10 may include components such as a radio frequency (RF) circuit 1101, a memory 1102, an input unit 1103, a display unit 1104, a sensor 1105, an audio circuit 1106, a Wi-Fi module 1107, a processor 1108, and a power supply 1109. Those skilled in the art will understand that... Figure 11 The structure shown does not constitute a limitation on the electronic device 10. The electronic device 10 may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0152] RF circuit 1101 can be used to send and receive information or, during a call, to receive and transmit signals. Specifically, after receiving downlink information from the base station, it forwards it to processor 1108 for processing; additionally, it transmits uplink data to the base station. Typically, RF circuit 1101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low-noise amplifier (LNA), a duplexer, etc.

[0153] The memory 1102 can be used to store software programs and modules. The processor 1108 executes various functional applications and data processing of the electronic device 10 by running the software programs and modules stored in the memory 1102. The memory 1102 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 10 (such as audio data, telephone book, etc.). In addition, the memory 1102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0154] The input unit 1103 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the electronic device 10. Specifically, the input unit 1103 may include a touch panel 11031 and other input devices 11032. The touch panel 11031, also known as a touch screen, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel 11031), and drive the corresponding connection devices according to a pre-set program. Optionally, the touch panel 11031 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, sends it to the processor 1108, and receives and executes commands from the processor 1108. In addition, the touch panel 11031 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch panel 11031, the input unit 1103 may also include other input devices 11032. Specifically, other input devices 11032 may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0155] Display unit 1104 can be used to display information input by the user or information provided to the user, as well as various menus of electronic device 10. Display unit 1104 may include display panel 11041, optionally configured as a liquid crystal display (LCD), organic light-emitting diode (OLED), or similar form. Further, touch panel 11031 may cover display panel 11041. When touch panel 11031 detects a touch operation on or near it, it transmits the information to processor 1108 to determine the type of touch event. Subsequently, processor 1108 provides corresponding visual output on display panel 11041 based on the type of touch event. Although in Figure 11 In this embodiment, the touch panel 11031 and the display panel 11041 are two separate components to realize the input and output functions of the electronic device 10. However, in some embodiments, the touch panel 11031 and the display panel 11041 can be integrated to realize the input and output functions of the electronic device 10.

[0156] The electronic device 10 may also include at least one sensor 1105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 11041 according to the ambient light level, and the proximity sensor can turn off the display panel 11041 and / or the backlight when the electronic device 10 is moved to the ear. As a type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used for applications that identify the posture of the electronic device 10 (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc. In addition, the electronic device 10 may also be equipped with other sensors such as a gyroscope, barometer, hygrometer, thermometer, and infrared sensor, which will not be described in detail here.

[0157] Audio circuit 1106, speaker 11061, and microphone 11062 provide an audio interface between the user and electronic device 10. Audio circuit 1106 converts received audio data into electrical signals and transmits them to speaker 11061, where speaker 11061 converts them into sound signals for output. On the other hand, microphone 11062 converts collected sound signals into electrical signals, which are received by audio circuit 1106, converted into audio data, and then processed by processor 1108 before being sent to another electronic device via RF circuit 1101, or the audio data can be output to memory 1102 for further processing.

[0158] Wi-Fi is a short-range wireless transmission technology. Electronic device 10, through Wi-Fi module 1107, can help users send and receive emails, browse web pages, and access streaming media, providing users with wireless broadband internet access. Although Figure 11 Wi-Fi module 1107 is shown, but it is understood that it is not an essential component of electronic device 10 and can be omitted as needed without changing the nature of the invention.

[0159] The processor 1108 is the control center of the electronic device 10. It connects various parts of the electronic device 10 via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 1102, and by calling data stored in the memory 1102, it performs various functions and processes data of the electronic device 10, thereby providing overall monitoring of the electronic device 10. Optionally, the processor 1108 may include one or more processing units; preferably, the processor 1108 may integrate an application processor and a modem, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem mainly handles wireless communication. It is understood that the aforementioned modem processor may not be integrated into the processor 1108.

[0160] The electronic device 10 also includes a power supply 1109 (such as a battery) that supplies power to various components. Optionally, the power supply can be logically connected to the processor 1108 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system.

[0161] Although not shown, electronic device 10 may also include a camera, Bluetooth module, etc., which will not be described in detail here.

[0162] Figure 11 The electronic device 10 described herein can be used to implement this application. Figure 3 , Figure 8-10 For some or all of the processes described in the embodiments, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0163] Figure 12 This is a schematic diagram of the software structure of the electronic device 10 provided in an embodiment of this application. In one embodiment of this application, the electronic device 10 is equipped with an Android system, which, from top to bottom, consists of an application layer, an application framework layer, native C / C++ libraries and the Android runtime, a hardware abstraction layer, and a kernel layer.

[0164] The application layer can include a series of application packages. For example... Figure 12 As shown, the application package may include gallery, calendar, maps, WLAN, music, SMS, calls, navigation, Bluetooth, video, etc.

[0165] The application framework layer may include a window manager, activity manager, input manager, resource manager, notification manager, view system, content provider, etc.

[0166] The window manager provides Window Manager Service (WMS), which can be used for window management, window animation management, surface management, and as a relay station for the input system.

[0167] The Activity Manager Service (AMS) can be used to start, switch, and schedule system components (such as activities, services, content providers, and broadcast receivers), as well as manage and schedule application processes.

[0168] The Input Manager Service (IMS) provides input management services, which can be used to manage system inputs such as touchscreen input, keypad input, and sensor input. IMS retrieves events from input device nodes and, through interaction with the WMS, distributes these events to the appropriate windows.

[0169] The file explorer provides applications with various resources, such as localized strings, icons, images, layout files, video files, etc.

[0170] The notification manager allows applications to display notifications in the status bar. These notifications can be used to convey informational messages and can disappear automatically after a short pause, requiring no user interaction. For example, the notification manager can be used to notify users of download completion or message alerts. The notification manager can also display notifications as icons or scrolling text in the top status bar, such as notifications from background applications, or as dialog boxes on the screen. Examples include displaying text messages in the status bar, emitting sounds, vibrating electronic devices, and flashing indicator lights.

[0171] A view system includes visual controls, such as controls for displaying text and controls for displaying images. View systems can be used to build applications. A display interface can consist of one or more views. For example, a display interface including a text notification icon could include views for displaying text and views for displaying images.

[0172] Content providers store and retrieve data, making that data accessible to applications. This data can include videos, images, audio, phone calls made and received, browsing history and bookmarks, phone books, etc.

[0173] Native C / C++ libraries can include multiple functional modules. Examples include: surface manager, media framework, C standard library (libc), OpenGL ES, SQLite, Webkit, etc.

[0174] The Surface Manager is used to manage the display subsystem and provides the blending of 2D and 3D layers for multiple applications.

[0175] The media framework supports playback and recording of various commonly used audio and video formats, as well as still image files. It supports multiple audio and video encoding formats, such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG.

[0176] The C standard library is a collection of all header files that match the standard in C programming, as well as commonly used function library implementations.

[0177] OpenGL ES provides drawing and manipulation of 2D and 3D graphics in applications.

[0178] SQLite provides a lightweight relational database for applications on electronic devices 10.

[0179] The Android runtime consists of the Android runtime itself and the core libraries. The Android runtime is responsible for converting source code into machine code. It primarily employs Ahead-of-Time (AOT) and Just-in-Time (JIT) compilation techniques. The core libraries mainly provide basic Java class library functionalities, such as libraries for fundamental data structures, mathematics, I / O, tools, databases, and networking. The core libraries provide APIs for users to develop Android applications.

[0180] The Hardware Abstraction Layer (HAL) runs in user space, encapsulates kernel-level drivers, and provides calling interfaces to the upper layers.

[0181] The kernel layer is the layer between hardware and software. The kernel layer includes at least display drivers, camera drivers, audio drivers, and sensor drivers.

[0182] This embodiment also provides a computer storage medium storing computer instructions. When the computer instructions are executed on the electronic device 10, the electronic device 10 performs the aforementioned related method steps to implement the health detection method in the above embodiment.

[0183] This embodiment also provides a computer program product that, when run on the electronic device 10, causes the electronic device 10 to perform the aforementioned related steps to implement the health detection method described in the above embodiment.

[0184] In addition, embodiments of this application also provide an apparatus, which may specifically be a chip, component, or module. The apparatus may include a connected processor and a memory; wherein the memory is used to store computer execution instructions, and when the apparatus is running, the processor may execute the computer execution instructions stored in the memory to cause the chip to execute the health detection methods in the above-described method embodiments.

[0185] In this embodiment, the electronic device, computer storage medium, computer program product or chip are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding method provided above, and will not be repeated here.

[0186] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0187] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0188] The unit described as a separate component may or may not be physically separate. The component shown as a unit can be one physical unit or multiple physical units, that is, it can be located in one place or distributed in multiple different places. Some or all of the units can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0189] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0190] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially or in other words, the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0191] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope 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 the claims.

Claims

1. A health detection method, characterized in that, The method includes: Acquire the user's physiological characteristics, which include at least one of heart rate, body temperature, sleep score, RR interval, heart rate variability, standard deviation of normal RR interval, low-frequency power spectral density, and high-frequency power spectral density; Polycystic ovary syndrome risk assessment was performed based on the user's physiological characteristics.

2. The health detection method as described in claim 1, characterized in that, The method further includes: Obtain the user's facial image; The risk assessment of polycystic ovary syndrome based on the user's physiological characteristics includes: Polycystic ovary syndrome risk assessment is performed based on the user's physiological characteristics and facial image.

3. The health detection method as described in claim 1, characterized in that, The method further includes: Obtain the user's body composition index; The risk assessment of polycystic ovary syndrome based on the user's physiological characteristics includes: Polycystic ovary syndrome risk assessment was conducted based on the user's physiological characteristics and body composition indicators.

4. The health detection method as described in claim 1, characterized in that, The method further includes: Obtain the user's facial image; Obtain the user's body composition index; The risk assessment of polycystic ovary syndrome based on the user's physiological characteristics includes: Polycystic ovary syndrome risk assessment was performed based on the user's physiological characteristics, body composition indicators, and facial images.

5. The health detection method as described in claim 1, characterized in that, The acquisition of user physiological characteristics includes: Acquire the physiological characteristics of the user over a continuous time period; Calculate the average value of each of the physiological characteristics over the continuous time period; The polycystic ovary syndrome risk assessment includes: The average value of each of the aforementioned physiological characteristics is used as the input feature for a machine learning model to assess the risk of polycystic ovary syndrome.

6. The health detection method as described in claim 5, characterized in that, The method further includes: Calculate the highest and lowest body temperature values ​​within the continuous time period; The polycystic ovary syndrome risk assessment includes: The highest and lowest body temperature values ​​are used as input features for the machine learning model to assess the risk of polycystic ovary syndrome.

7. The health detection method as described in claim 2 or 4, characterized in that, The method further includes: A first analysis is performed on the facial image to obtain the probability that the user has acne on their face, and / or a second analysis is performed on the facial image to obtain the probability that the user has excessive hair on their upper lip and lower jaw. The polycystic ovary syndrome risk assessment includes: The probability of the user having acne on their face and / or the probability of the user having excessive hair on their upper lip and chin are used as input features for a machine learning model to assess the risk of polycystic ovary syndrome.

8. The health detection method according to any one of claims 1 to 7, characterized in that, The method further includes: Obtain the medical examination results related to polycystic ovary syndrome of the user. The medical examination results include at least one of the following: whether there is a risk of hyperandrogenemia, whether the blood glucose index is high, whether the blood LH concentration is high, whether the LH / FSH ratio is high, whether the AMH and AND levels are high, whether the follicle diameter is >10mm, and whether the corpus luteum is present. The polycystic ovary syndrome risk assessment includes: A risk assessment for polycystic ovary syndrome (PCOS) should be conducted based on the aforementioned medical examination results.

9. The health detection method as described in claim 8, characterized in that, The risk assessment of polycystic ovary syndrome based on the aforementioned medical examination results includes: A first probability value for polycystic ovary syndrome is obtained based on the user's physiological characteristics, or the user's physiological characteristics and body composition indicators, or the user's physiological characteristics and facial image, or the user's physiological characteristics, body composition indicators and facial image. A weight is assigned to each medical examination item in the medical examination results; A second probability value for polycystic ovary syndrome is calculated based on the medical examination results. The second probability value is equal to the sum of the probability components corresponding to all medical examination items in the medical examination results. The probability component corresponding to each medical examination item is equal to the product of the weight of the medical examination item and the examination result of the medical examination item. The final probability value for polycystic ovary syndrome is calculated based on the first probability value and the second probability value.

10. A computer-readable storage medium, characterized in that, Includes computer instructions that, when executed on an electronic device, cause the electronic device to perform the health detection method as described in any one of claims 1 to 9.

11. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory being used to store instructions, and the processor being used to invoke the instructions in the memory to cause the electronic device to perform the health detection method as described in any one of claims 1 to 9.

12. A chip system, characterized in that, The chip system is applied to an electronic device; the chip system includes an interface circuit and a processor; the interface circuit and the processor are interconnected via a line; the interface circuit is used to receive signals from the memory of the electronic device and send signals to the processor, the signals including computer instructions stored in the memory; when the processor executes the computer instructions, the chip system performs the health detection method as described in any one of claims 1 to 9.

Images