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WO2022237377A1 - 生理参数测量方法、系统及电子设备 - Google Patents

生理参数测量方法、系统及电子设备 Download PDF

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Publication number
WO2022237377A1
WO2022237377A1 PCT/CN2022/084397 CN2022084397W WO2022237377A1 WO 2022237377 A1 WO2022237377 A1 WO 2022237377A1 CN 2022084397 W CN2022084397 W CN 2022084397W WO 2022237377 A1 WO2022237377 A1 WO 2022237377A1
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WO
WIPO (PCT)
Prior art keywords
parameter
person
tested
electronic device
photo
Prior art date
Application number
PCT/CN2022/084397
Other languages
English (en)
French (fr)
Inventor
刘畅
严家兵
赵帅
任慧超
Original Assignee
华为技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to EP22806347.5A priority Critical patent/EP4324386A4/en
Publication of WO2022237377A1 publication Critical patent/WO2022237377A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0537—Measuring body composition by impedance, e.g. tissue hydration or fat content
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4869—Determining body composition
    • A61B5/4872—Body fat
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6898—Portable consumer electronic devices, e.g. music players, telephones, tablet computers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1072—Measuring physical dimensions, e.g. size of the entire body or parts thereof measuring distances on the body, e.g. measuring length, height or thickness

Definitions

  • the present application relates to the technical field of terminals, and in particular to a physiological parameter measurement method, system and electronic equipment.
  • fatty liver (Fatty liver) has gradually become the largest liver disease in humans, and the prevalence rate is gradually increasing. Among them, the formation of fatty liver is mainly due to excessive accumulation of fat in liver cells, which leads to liver lesions. Generally speaking, if the fat in the liver exceeds 5% of the liver weight or more than 50% of the liver cells have fatty degeneration in histology, it can be called fatty liver, so liver fat is an important feature for detecting fatty liver.
  • the present application provides a physiological parameter measurement method, system and electronic equipment, which can enable the user to conveniently and accurately measure the user's own physiological parameters.
  • the present application provides a method for measuring a physiological parameter, the method comprising: determining a first physical parameter and a second physical parameter of a person to be tested, and the first physical parameter and the second physical parameter are measured by a first electronic device , the second body parameter is measured based on at least 8 electrodes of the first electronic device; in response to the input of the person to be tested, determine the third body parameter; according to the first body parameter and the third body parameter, determine the fourth body parameter ; Determine the first body shape of the person to be tested according to the second body parameter; Determine the first physiological parameter according to at least the first body shape and the fourth body parameter; Display the first physiological parameter.
  • the first body parameter can be body weight
  • the second body parameter can be body impedance or raw data (such as voltage, current, etc.) required for calculating body impedance
  • the third body parameter can be height
  • the fourth body parameter It may be a body mass index (BMI)
  • the first physiological parameter may be a liver fat level.
  • body impedances may include impedances associated with the arms, impedances associated with the legs, and impedances associated with the torso.
  • the impedance related to the arms may include the impedance of the arms, the impedance of the left arm, or the impedance of the right arm, etc.
  • the impedance related to the legs may include the impedance of the legs, the impedance of the left leg, or the impedance of the right leg, etc.
  • the impedance related to the trunk may include torso impedance, or other impedances including torso impedance, or the like.
  • the first body parameter measured by the first electronic device is used to determine the body shape of the person to be tested, and the body shape is combined with the first body parameter measured by the first electronic device and the first body parameter determined based on the data input by the user.
  • the three body parameters are combined to determine the physiological parameters of the person to be tested, thereby realizing accurate measurement of the physiological parameters of the person to be tested.
  • determining the first body shape of the person to be tested according to the second body parameter specifically includes: determining the fifth body parameter according to the second body parameter; if the fifth body parameter belongs to the first interval, According to the fourth body parameter, determine the first body shape of the person to be tested; if the fifth body parameter belongs to the second interval, determine the first body shape of the person to be tested according to the sixth body parameter, wherein the sixth body parameter is based on the first body shape
  • the fifth body parameter may be the waist-to-hip ratio
  • the sixth body parameter may be the muscle mass or fat mass of both arms of the person to be tested. Therefore, after the fifth body parameter is determined based on the second body parameter, an appropriate body parameter can be selected based on the interval to which the fifth body parameter belongs to determine the body shape of the person to be measured, thereby improving the accuracy of determining the body shape.
  • determining the fifth body parameter according to the second body parameter specifically includes: determining a seventh body parameter according to the second body parameter, and determining the fifth body parameter according to the seventh body parameter.
  • the seventh body parameter may be the fat mass of each segment of the body, such as left arm fat mass, right arm fat mass, trunk fat mass, left leg fat mass, right leg fat mass, and the like.
  • the fifth body parameter is obtained through the second body parameter.
  • determining the first physiological parameter specifically includes: according to the first body shape, querying the correspondence between the predetermined body shape and the detection model, and determining A detection model corresponding to the first body shape is obtained, wherein in the corresponding relationship, different body shapes correspond to different detection models; at least the fourth body parameter is input into the detection model corresponding to the first body shape to determine the first physiological parameter. Therefore, based on the body shape of the person to be tested, a detection model suitable for the body shape is determined, and then the detection model is used to detect the physiological parameters of the person to be tested, so that users with different body shapes can be detected using different detection models. parameters, improving the accuracy of physiological parameters.
  • the method further includes: in response to the first operation of the person to be tested, determining a first photo and a second photo of the person to be tested, the first photo is The person is taken when the person makes the first preset action, and the second photo is taken when the person to be tested makes the second preset action; according to the first photo and the second photo, the eighth physical parameter of the person to be tested is determined; at least according to The first body shape, the fourth body parameter and the eighth body parameter, re-determining the first physiological parameter; displaying the first physiological parameter.
  • the eighth physical parameter may be waist circumference. Therefore, the physical parameters strongly correlated with the physiological parameters of the person to be tested are determined through the photo of the person to be tested, and the physical parameters are used to accurately measure the physiological parameters of the person to improve the detection accuracy of the physiological parameters.
  • determining the second physiological parameter specifically includes: according to the first body shape, querying a predetermined body shape and detection model Correspondence relationship, determine the detection model corresponding to the first body shape, wherein, in the second correspondence relationship, different body shapes correspond to different detection models; at least the fourth body parameter and the eighth body parameter are input to the same body shape as the first A detection model corresponding to the body shape to redetermine the first physiological parameter. Therefore, the accuracy of physiological parameter detection is improved.
  • the eighth body parameter before inputting at least the fourth body parameter and the eighth body parameter into the detection model corresponding to the first body shape, it also includes: determining the A ninth body parameter; according to the eighth body parameter and the ninth body parameter, determine the second body shape of the person to be tested.
  • the ninth body parameter may be hip circumference. Since the body shape of the person to be tested can be accurately presented on the photo, the body shape of the person to be tested can be accurately determined through the photo of the person to be tested, thereby improving the accuracy of body shape detection.
  • the method further includes: if the second body shape is inconsistent with the first body shape; according to the second body shape, query the corresponding relationship, determine the detection model corresponding to the second body shape, and select the corresponding The detection model corresponding to the second body shape re-determines the first physiological parameter. Therefore, the physiological parameters of the person to be tested are detected based on the body shape of the person to be tested detected through the photo, thereby improving the accuracy of the detection of the physiological parameters.
  • determining the first photo and the second photo of the person to be tested specifically includes: determining the action difference between the action of the person to be tested on the currently acquired target photo and the target preset action, And determine that the action difference is within a preset range; wherein, the target photo is the first photo, the target preset action is the first preset action, or the target photo is the second photo, and the target preset action is the second preset action . Therefore, when the person to be tested makes a preset action, the collected photos are used for detection, which improves the accuracy of physiological parameter detection.
  • the method further includes: displaying the body shape of the person to be tested.
  • the person to be tested can view his own body shape.
  • the second body parameter is measured by the first electronic device controlling at least 8 electrodes to generate electrical signals of at least two different frequencies. In this way, the second body parameter is measured through electrical signals of different frequencies, which improves the accuracy of subsequent detection.
  • the method is executed by the first electronic device. Therefore, the physiological parameters of the person to be tested can be detected through a device.
  • the method is executed by the second electronic device, wherein the second electronic device is connected to the first electronic device in communication; the first body parameter and the second body parameter of the person to be tested are determined, specifically The method includes: the second electronic device receives the first body parameter and the second body parameter sent by the first electronic device.
  • the physiological parameters of the person to be tested can be detected through a combination of multiple electronic devices.
  • the present application provides a physiological parameter measurement system, the system includes a first electronic device and a second electronic device, the first electronic device and the second electronic device are connected in communication, and the first electronic device has at least 8 electrodes ;
  • the first electronic device is used to determine the first body parameter and the second body parameter of the person to be tested, and send the first body parameter and the second body parameter to the second electronic device, and the second body parameter is based on at least 8 electrodes Measured.
  • the second electronic device is used for determining the third body parameter in response to the input of the person to be tested.
  • the second electronic device is also used for determining the fourth body parameter according to the first body parameter and the third body parameter in response to receiving the first body parameter and the second body parameter;
  • the second electronic device is also used for determining the fourth body parameter according to the second body parameter
  • the second electronic device is also used for determining the first physiological parameter and displaying the first physiological parameter at least according to the first body shape and the fourth body parameter.
  • the second electronic device is further configured to: determine the fifth body parameter according to the second body parameter; if the fifth body parameter belongs to the first interval, determine the First body shape; if the fifth body parameter belongs to the second interval, determine the first body shape of the person to be tested according to the sixth body parameter, wherein the sixth body parameter is obtained based on the second body parameter.
  • the second electronic device is further configured to: determine a seventh body parameter according to the second body parameter, and determine a fifth body parameter according to the seventh body parameter.
  • the second electronic device is further configured to: according to the first body shape, query the correspondence between the predetermined body shape and the detection model, and determine the detection model corresponding to the first body shape, wherein, In the corresponding relationship, different body shapes correspond to different detection models; at least the fourth body parameter is input into the detection model corresponding to the first body shape to determine the first physiological parameter.
  • the second electronic device is further configured to: determine the first photo and the second photo of the person to be tested in response to the first operation of the person to be tested, the first The photo is taken when the person to be tested makes the first preset action, and the second photo is taken when the person to be tested makes the second preset action; according to the first photo and the second photo, determine the eighth position of the person to be tested Body parameters; re-determining a first physiological parameter based on at least the first body shape, the fourth body parameter and the eighth body parameter; displaying the first physiological parameter.
  • the second electronic device is further configured to: according to the first body shape, query the correspondence between the predetermined body shape and the detection model, and determine the detection model corresponding to the first body shape, wherein, In the second correspondence, different body shapes correspond to different detection models; at least the fourth body parameter and the eighth body parameter are input into the detection model corresponding to the first body shape to determine the first physiological parameter.
  • the second electronic device before the second electronic device inputs the fourth body parameter and the eighth body parameter into the detection model corresponding to the first body shape, it is further configured to: according to the first photo and the second photo, Determine the ninth body parameter of the person to be tested; determine the second body shape of the person to be tested according to the eighth body parameter and the ninth body parameter.
  • the second electronic device is also used for: if the second body shape is inconsistent with the first body shape; query the corresponding relationship according to the second body shape, and determine the detection model corresponding to the second body shape , and selecting a detection model to determine the first physiological parameter.
  • the second electronic device is also used to: determine the action difference between the action of the person to be tested on the currently acquired target photo and the target preset action, and determine that the action difference is within the preset Within the range; wherein, the target photo is the first photo, the target preset action is the first preset action, or the target photo is the second photo, and the target preset action is the second preset action.
  • the second electronic device is also used to: display the body shape of the person to be tested.
  • the first electronic device is also used to: control at least eight electrodes to generate electrical signals of at least two different frequencies; respectively determine the body parameters measured based on the electrical signals of each frequency to obtain the second body parameter.
  • the present application provides a device for measuring physiological parameters, characterized in that the device includes:
  • a determining module configured to determine a first physical parameter and a second physical parameter of the person to be tested, the first physical parameter and the second physical parameter are measured by the first electronic device, and the second physical parameter is based on at least Measured by 8 electrodes;
  • an input module configured to determine a third body parameter in response to the input of the person to be tested
  • a processing module configured to determine a fourth body parameter according to the first body parameter and the third body parameter
  • the processing module is also used to determine the first body shape of the person to be tested according to the second body parameter;
  • the processing module is further configured to determine a first physiological parameter based on at least the first body shape and the fourth body parameter;
  • the display module is used for displaying the first physiological parameter.
  • the processing module is further configured to determine a fifth body parameter according to the second body parameter; wherein, if the fifth body parameter belongs to the first interval, the processing module determines the waiting period according to the fourth body parameter. The first body shape of the tester. If the fifth body parameter belongs to the second interval, the processing module determines the first body shape of the person to be tested according to the sixth body parameter, wherein the sixth body parameter is obtained based on the second body parameter.
  • the processing module is further configured to determine a seventh body parameter according to the second body parameter, and determine a fifth body parameter according to the seventh body parameter.
  • the processing module is further configured to query the correspondence between the predetermined body shape and the detection model according to the first body shape, and determine the detection model corresponding to the first body shape, wherein, in the corresponding Different body shapes in the relationship correspond to different detection models; at least the fourth body parameter is input into the detection model corresponding to the first body shape to determine the first physiological parameter.
  • the processing module is further configured to determine the first photo and the second photo of the person to be tested in response to the first operation of the person to be tested, the first The photo is taken when the person to be tested makes the first preset action, and the second photo is taken when the person to be tested makes the second preset action; according to the first photo and the second photo, determine the eighth position of the person to be tested A body parameter; re-determining a first physiological parameter based on at least the first body shape, the fourth body parameter, and the eighth body parameter.
  • the display module is also used to display the first physiological parameter.
  • processing module is further configured to query the correspondence between the predetermined body shape and the detection model according to the first body shape, and determine the detection model corresponding to the first body shape, wherein, in the second correspondence, different body shapes correspond to different detection models; at least the fourth body parameter and the eighth body parameter are input into the detection model corresponding to the first body shape, and the first physiological parameter is re-determined.
  • the processing module is further configured to determine the ninth physical parameter of the person to be tested according to the first photo and the second photo; determine the ninth physical parameter of the person to be tested according to the eighth physical parameter and the ninth physical parameter. Second body form.
  • the processing module queries the corresponding relationship according to the second body shape, determines the detection model corresponding to the second body shape, and selects the detection model corresponding to the first body shape.
  • the detection model corresponding to the two body shapes re-determines the first physiological parameter.
  • the processing module is further configured to determine the degree of action difference between the action of the person to be tested on the currently acquired target photo and the target preset action, and determine that the degree of action difference is within a preset range .
  • the target photo is the first photo
  • the target preset action is the first preset action
  • the target photo is the second photo
  • the target preset action is the second preset action
  • the display module is also used to display the body shape of the person to be tested.
  • the second body parameter is measured by the first electronic device controlling at least 8 electrodes to generate electrical signals of at least two different frequencies.
  • the apparatus is deployed on the first electronic device.
  • the apparatus is deployed on the second electronic device, where the second electronic device is communicatively connected to the first electronic device.
  • the second electronic device may receive the first body parameter and the second body parameter sent by the first electronic device from the first electronic device.
  • the present application provides an electronic device, including: at least one memory for storing programs; at least one processor for executing the programs stored in the memory, and when the programs stored in the memory are executed, the processor is used for executing The method provided in the first aspect.
  • the present application provides a computer storage medium, in which instructions are stored, and when the instructions are run on a computer, the computer is made to execute the method provided in the first aspect.
  • the present application provides a computer program product containing instructions, and when the instructions are run on a computer, the computer is made to execute the method provided in the first aspect.
  • Figure 1a is a schematic diagram of the system architecture of a liver fat level detection system provided by the embodiment of the present application.
  • Figure 1b is a schematic diagram of the system architecture of another liver fat level detection system provided by the embodiment of the present application.
  • Fig. 2 is a schematic diagram of the hardware structure of a body fat scale provided by the embodiment of the present application;
  • FIG. 3 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present application.
  • Fig. 4 is a schematic diagram of a scene where a person to be tested uses a body fat scale provided by an embodiment of the present application;
  • Fig. 5 is a schematic diagram of the resistance corresponding to each segment of the body of the person to be tested provided by the embodiment of the present application;
  • FIG. 6 is a schematic diagram of a display interface of an electronic device provided in an embodiment of the present application.
  • FIG. 7 is a schematic diagram of a display interface of an electronic device provided in an embodiment of the present application.
  • FIG. 8 is a schematic diagram of a display interface of an electronic device provided in an embodiment of the present application.
  • Fig. 9a is a schematic diagram of a display interface of an electronic device provided in an embodiment of the present application.
  • Fig. 9b is a schematic diagram of a display interface of an electronic device provided in an embodiment of the present application.
  • Fig. 10a is a schematic diagram of a display interface of a mobile phone provided by an embodiment of the present application.
  • Fig. 10b is a schematic diagram of a display interface of a mobile phone provided by an embodiment of the present application.
  • Fig. 10c is a schematic diagram of a display interface of a mobile phone provided by an embodiment of the present application.
  • Fig. 10d is a schematic diagram of a display interface of a mobile phone provided by an embodiment of the present application.
  • Fig. 10e is a schematic diagram of a display interface of a mobile phone provided by an embodiment of the present application.
  • Fig. 10f is a schematic diagram of a display interface of a mobile phone provided by an embodiment of the present application.
  • Fig. 11a is a schematic diagram of a display interface of a large screen provided by an embodiment of the present application.
  • Fig. 11b is a schematic diagram of a large-screen display interface provided by an embodiment of the present application.
  • Fig. 11c is a schematic diagram of a large-screen display interface provided by an embodiment of the present application.
  • Fig. 11d is a schematic diagram of a large-screen display interface provided by an embodiment of the present application.
  • Fig. 11e is a schematic diagram of a large-screen display interface provided by an embodiment of the present application.
  • Fig. 11f is a schematic diagram of a large-screen display interface provided by an embodiment of the present application.
  • Fig. 11g is a schematic diagram of a large-screen display interface provided by the embodiment of the present application.
  • Fig. 12 is a schematic flow chart of a physiological parameter measurement method provided in the embodiment of the present application.
  • Fig. 13 is a schematic diagram of the steps for accurately measuring the first physiological parameter of the person to be tested provided by the embodiment of the present application;
  • Fig. 14 is a schematic structural diagram of a physiological parameter measurement system provided in an embodiment of the present application.
  • words such as “exemplary”, “for example” or “for example” are used to represent examples, illustrations or illustrations. Any embodiment or design described as “exemplary”, “for example” or “for example” in the embodiments of the present application shall not be construed as being more preferred or more advantageous than other embodiments or designs. Rather, the use of words such as “exemplary”, “for example” or “for example” is intended to present related concepts in a specific manner.
  • the term "and/or" is only an association relationship describing associated objects, indicating that there may be three relationships, for example, A and/or B may indicate: A exists alone, A exists alone There is B, and there are three cases of A and B at the same time.
  • the term "plurality" means two or more. For example, multiple systems refer to two or more systems, and multiple electronic devices refer to two or more electronic devices.
  • first and second are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly specifying indicated technical features. Thus, a feature defined as “first” and “second” may explicitly or implicitly include one or more of these features.
  • the terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless specifically stated otherwise.
  • Fig. 1a is a schematic diagram of a system architecture of a liver fat level detection system provided by an embodiment of the present application. As shown in FIG. 1 a , the system includes: a body fat scale 11 and a mobile phone 12 . A connection can be established between the body fat scale 11 and the mobile phone 12 via Bluetooth.
  • the body fat scale 11 can detect the body weight and body impedance of the person to be measured; wherein, the body impedance can include the impedance of the arms, the impedance of the legs and the impedance of the trunk. In one example, after the body fat scale 11 obtains the weight and body impedance of the person to be measured, it can send the weight and body impedance to the mobile phone 12 via Bluetooth.
  • the body fat scale 11 does not establish a connection with the mobile phone 12 when the body fat scale 11 measures body weight and body impedance, then after the body fat scale 11 and the mobile phone 12 establish a connection, the connection between the body fat scale 11 and the mobile phone 12 The data can be synchronized, that is, the body fat scale 11 sends the measured data to the mobile phone 12 .
  • the body fat scale 11 can also send the detected basic data required to calculate the body impedance to the mobile phone 12, and then the mobile phone 12 can calculate the body impedance based on these basic data.
  • the mobile phone 12 can determine the body parameters of the person to be tested based on the body weight and body impedance detected by the body fat scale 11 .
  • the body parameters may include: body mass index (BMI), body fat percentage, visceral fat level, fat mass of each segment of the body, waist-to-hip ratio, or body shape and the like.
  • the body shape may include: apple shape, pear shape, pepper shape, hourglass shape, or inverted triangle shape and so on.
  • the mobile phone 12 may also determine a level determination model corresponding to the body shape of the person to be tested based on the body shape of the person to be tested.
  • the mobile phone 12 can input the body parameters of the person to be tested into the level determination model to obtain the liver fat level of the person to be tested.
  • the mobile phone 12 can present the determined liver fat level to the person to be tested.
  • different level determination models can be used to calculate the user's liver fat level for users with different body shapes, so that different processing methods can be used for different groups of people, and the liver fat level can be improved.
  • the accuracy of fat level evaluation avoids the situation that everyone adopts a liver fat level evaluation method.
  • part or all of the functions implemented by the mobile phone 12 can also be implemented by the body fat scale 11 or by other electronic devices except the body fat scale 11, which is not limited here.
  • the body fat percentage can also be calculated by the body fat scale 11; that is to say, the body fat scale 11 can not only transmit the measured data to the mobile phone 12, but also can calculate the calculated data after obtaining some data.
  • the data is transmitted to the mobile phone 12 .
  • the system may further include a smart screen 13 .
  • the connection between the body fat scale 11 and the mobile phone 12 can be established through Bluetooth, and the connection between the mobile phone 12 and the smart screen 13 can also be established through a wireless network.
  • the smart screen 13 can realize some functions realized by the mobile phone 12, for example, present the liver fat level determined by the mobile phone 12 to the person to be tested. For example, after the mobile phone 12 determines the liver fat level of the person to be measured based on the body impedance and weight of the person to be measured based on the body fat scale 11, it can project the determined liver fat level to the Smart screen 13; afterward, the smart screen 13 can present the level of liver fat to the person to be tested.
  • the body fat scale 11 can also be replaced with other electronic devices, and the electronic device can realize the functions realized by the body fat scale 11 in this solution, and there is no limitation here.
  • the electronic device replacing the body fat scale 11 may at least have the function of detecting the body impedance and body weight of the person to be measured.
  • the mobile phone 12 can also be replaced with other electronic devices, and the electronic device can realize the functions realized by the mobile phone 12 in this solution, which is not limited here.
  • the electronic device replacing the mobile phone 12 may be a tablet computer, a wearable device, a smart TV, a smart screen, and the like.
  • the smart screen 13 can also be replaced with other electronic devices, and the electronic device can realize the functions realized by the smart screen 13 in this solution, which is not limited here.
  • the electronic device replacing the smart screen 13 may be a tablet computer, a wearable device, a smart TV, and the like.
  • connection methods can also be used to establish connections between the body fat scale 11 and the mobile phone 12, and other connection methods can also be used to establish connections between the mobile phone 12 and the smart screen 13, which are not limited here.
  • Exemplary, short-distance wireless connection technology or long-distance wireless connection technology can be used to establish connection between body fat scale 11 and mobile phone 12;
  • short-distance wireless connection technology can include ZigBee (ZigBee) etc.; Technologies may include wireless fidelity (wireless fidelity, WIFI), cellular mobile communication (cellular mobile communication) and the like.
  • the mobile phone 12 and the smart screen 13 can also adopt short-distance wireless connection technology or long-distance wireless connection technology to establish a connection; wherein, the short-distance wireless connection technology can include ZigBee (ZigBee), etc.; the long-distance wireless connection technology can include wireless Fidelity technology (wireless fidelity, WIFI), cellular mobile communication (cellular mobile communication) and so on.
  • the short-distance wireless connection technology can include ZigBee (ZigBee), etc.
  • the long-distance wireless connection technology can include wireless Fidelity technology (wireless fidelity, WIFI), cellular mobile communication (cellular mobile communication) and so on.
  • the body fat scale may be the body fat scale 11 shown in Fig. 1a or Fig. 1b.
  • Fig. 2 is a schematic diagram of a hardware structure of a body fat scale provided by an embodiment of the present application.
  • the body fat scale 200 may include: a scale body 21 and a handle 22 .
  • the scale body 21 and the handle 22 can be connected by a cable 23 .
  • At least four electrodes 211 may be provided on the scale body 21 .
  • At least four electrodes 221 may be provided on the handle 22 .
  • at least two electrodes on the scale body 21 are in contact with the user's left foot, and at least two electrodes are in contact with the user's right foot; at least two electrodes on the handle 22 are in contact with the user's left hand. contact, and at least two electrodes are in contact with the user's right hand.
  • a pressure sensor (not shown in the figure) may also be arranged in the scale body 21 .
  • the user's weight can be detected by the pressure sensor.
  • a display screen 222 may also be provided on the handle 22, and the display screen 222 may display the user's weight, body fat percentage and so on.
  • a processor (not shown in the figure) and a communication module (not shown in the figure) may also be provided in the body fat scale 200 .
  • the processor in the body fat scale 200 can determine the user's body weight based on the signal detected by the pressure sensor in the scale body 21; determine the user's body impedance based on the signals detected by the electrodes on the scale body 21 and the handle 22 ; and based on the determined body impedance, determine the user's body fat percentage and the like.
  • the communication module in the body fat scale 200 can provide short-distance communication or long-distance communication for the body fat scale 200 to realize information exchange between the body fat scale 200 and other electronic devices.
  • the communication module in the body fat scale 200 can be Bluetooth (Bluetooth), ZigBee (ZigBee), wireless fidelity (wireless fidelity, WIFI), cellular mobile communication (cellular mobile communication) and so on.
  • the structure shown in Figure 2 of this solution does not constitute a specific limitation on the body fat scale.
  • the body fat scale may include more or fewer components than shown in the illustration, or combine certain components, or separate certain components, or arrange different components.
  • the illustrated components can be realized in hardware, software or a combination of software and hardware.
  • the electronic device may be the mobile phone 12 shown in FIG. 1a or FIG. 1b, or may be the smart screen 13 shown in FIG. 1b.
  • FIG. 3 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present application.
  • the electronic device 300 may include: a processor 301 , a memory 302 and a communication module 303 .
  • the processor 301, the memory 302, and the communication module 303 may be connected through a bus or in other ways.
  • the processor 301 is the calculation core and control core of the electronic device.
  • the processor 301 may include one or more processing units, for example, the processor 301 may include an application processor (application processor, AP), a modem (modem), a graphics processing unit (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural network processor (neural-network processing unit, NPU), etc. one or more of .
  • different processing units may be independent devices, or may be integrated in one or more processors.
  • the processor 301 can implement the liver fat level detection method provided in this solution.
  • the processor 301 can determine the body parameters of the person to be tested based on the body weight and body impedance detected by the body fat scale; the processor 301 can also determine the body parameters corresponding to the body shape Level determination model; the processor 301 may also input the body parameters of the person to be tested into the level determination model to obtain the liver fat level of the person to be tested, and so on.
  • the memory 302 is a storage device of the electronic device, and is used to store programs and data, such as storing the position of the electronic device itself and the positions of other electronic devices received by the electronic device. It can be understood that the memory 302 this time can be a high-speed RAM memory or a non-volatile memory (non-volatile memory); optionally, the memory 302 can also be at least one storage device located away from the aforementioned processor 301 .
  • the memory 302 can provide a storage space, which can store the operating system and executable program code of the electronic device, which can include but not limited to: Windows system (an operating system), Linux system (an operating system), Hongmeng system ( An operating system) and the like are not limited here. Exemplarily, a grade determination model for determining liver fat grade may be stored in the memory 302 .
  • the communication module 303 can provide short-distance communication or long-distance communication for the electronic device, so as to realize information exchange between the electronic device and other electronic devices (such as body fat scales, etc.).
  • the communication module 303 may be Bluetooth (Bluetooth), ZigBee (ZigBee), wireless fidelity (wireless fidelity, WIFI), cellular mobile communication (cellular mobile communication) and the like.
  • the electronic device 300 may further include a display screen 304 .
  • the display screen 304 may display the liver fat level, body parameters, etc. of the person to be tested.
  • the electronic device 300 may further include a camera 305 .
  • the camera 305 can be used to capture still images or videos, for example, to collect images of people to be tested.
  • the structure shown in FIG. 3 of this solution does not constitute a specific limitation on the electronic device.
  • the electronic device may include more or fewer components than shown in the figure, or combine some components, or separate some components, or arrange different components.
  • the illustrated components can be realized in hardware, software or a combination of software and hardware.
  • liver fat level detection system involved in this solution and the hardware structure of the electronic equipment involved in the system.
  • liver fat level detection scheme provided by this scheme will be introduced in detail.
  • the body weight and body impedance of the person to be measured can be determined through the body fat scale 200 shown in FIG. 2 .
  • the subject A can stand on the scale body 21 of the body fat scale 200 and hold the handle 22 of the body fat scale 200 .
  • the left foot of the person to be measured A can be in contact with the two electrodes 211 on the scale body 21, and the right foot of the person to be measured A can also be in contact with the two electrodes 211 on the scale body 21;
  • the right hand of the person A to be tested may also be in contact with the two electrodes (not shown in the figure) on the handle 22 .
  • each electrode on the body fat scale 200 can generate electrical signals of a specific frequency to measure the body impedance of the person A to be tested.
  • the body impedance can include: left arm impedance, right arm impedance, left leg impedance, right leg impedance, trunk impedance, etc.; among them, the impedance of different regions of the body can be acquired simultaneously or in time-sharing, which can be determined according to It depends on the actual situation and is not limited here.
  • the body fat scale 200 when the body fat scale 200 measures the body impedance of the person A to be tested, it can first turn on the left and right electrodes on the handle 22, and then measure the impedance according to the energized current and energized voltage. Arm impedance R1. Next, the body fat scale 200 can stop conducting the electrodes on its handle 22 and conduct on the electrodes on its body 21 , and then measure the impedance R2 of both legs according to the energized current and energized voltage.
  • the body fat scale 200 can stop conducting the electrodes on the left and right sides of its body 21, and conduct the electrodes on the left side of its handle 22 and the electrodes on the right side of its body 21, and then according to the energizing current and the energizing Voltage measured left oblique body impedance R3. Then, the body fat scale 200 can stop conducting the electrode on the left side of its handle 22 and the electrode on the right side of its body 21, and conduct the electrode on the right side of its handle 22 and the electrode on the left side of its body 21. , and then measure the right oblique body impedance R4 according to the energized current and energized voltage.
  • the body fat scale 200 can stop conducting the electrode on the right side of its handle 22 and the electrode on the left side of its body 21, and conduct the electrode on the left side of its handle 22 and the electrode on the left side of its body 21, and then Then measure the impedance R5 of the left half of the body according to the energized current and energized voltage. Then, the body fat scale 200 can stop conducting the electrode on the left side of its handle 22 and the electrode on the left side of its body 21, and conduct the electrode on the right side of its handle 22 and the electrode on the right side of its body 21, and then Then measure the impedance R6 of the right half of the body according to the energized current and energized voltage.
  • the body fat scale 200 can determine the body weight of the person to be tested based on the measured impedance R1 of both arms, impedance R2 of both legs, impedance R3 of the left oblique body, impedance R4 of the right oblique body, impedance R5 of the left half of the body and impedance R6 of the right half of the body.
  • the left arm 31 of the person to be tested can be equivalent to a resistance (ie, a resistance 311)
  • the right arm 32 of the person to be tested A can be equivalent to a Resistance (being resistance 321)
  • the torso 33 of the person to be tested A is equivalent to a resistance (being resistance 331)
  • the left leg 34 of the person to be measured A is equivalent to a resistance (being resistance 341)
  • the person to be measured is The right leg 35 of A is equivalent to a resistor (ie resistor 351).
  • the impedance of both arms can be the sum of the resistance values of resistor 311 and resistor 321, that is, the impedance of both arms can be the sum of the impedance of the left arm and the impedance of the right arm; the impedance of both legs can be the sum of resistor 341 and resistor 321.
  • the sum of the resistance values of 351 that is, the impedance of both legs may be the sum of the impedance of the left leg and the right leg; the impedance of the torso may be the resistance value of the resistor 341 .
  • the body fat scale 200 measures the impedance R1 of both arms, the impedance R2 of both legs, the impedance R3 of the left oblique half of the body, the impedance R4 of the right oblique half of the body, the impedance R5 of the left half of the body and the impedance R6 of the right half of the body, the following data can be obtained:
  • R1 R311+R321
  • R2 R341+R351;
  • R3 R311+R331+R351;
  • R4 R321+R331+R341;
  • R5 R311+R331+R341;
  • R6 R321+R331+R351
  • R311 (R1+R5-R4)/2
  • R321 (R1+R6-R3)/2
  • R331 (R3+R4-R1-R2)/2
  • R341 (R2+R5-R3)/ 2.
  • R351 (R2+R6-R4)/2
  • R311 can be the impedance of the left arm
  • R321 can be the impedance of the right arm
  • R331 can be the impedance of the trunk
  • R341 can be the impedance of the left leg
  • R351 can be the impedance of the right leg.
  • the body fat scale 200 can use the pressure sensor inside to measure the body weight of the subject A.
  • the electrodes on the body fat scale 200 can be controlled to generate electrical signals of various frequencies to measure the body impedance of the person to be tested, and thus the intracellular fluid flowing through the body can be obtained. Impedance at time and impedance of intracellular fluid not flowing through the body, and then measure the body impedance of the person to be tested from multiple dimensions to improve detection accuracy.
  • the frequency of the electrical signal generated by the electrodes on the body fat scale 200 may be 50 kilohertz (kHz) and 250 kilohertz (kHz).
  • the order in which the electrical signals of different frequencies are generated may be different, which is not limited here; for example, the electrical signal of 50 kHz is generated first, and then the electrical signal of 250 kHz is generated, or the electrical signal of 250 kHz is generated first, and then the electrical signal of 50 kHz is generated.
  • the frequency of the 50kHz electrical signal is relatively low, when the electrical signal of this frequency is used for measurement, it is difficult for the electrical signal to penetrate the intracellular fluid, that is, the body impedance measured at this time is that the electrical signal does not flow through the cells in the body
  • the impedance of the liquid because the frequency of the 250kHz electrical signal is relatively high, when the electrical signal of this frequency is used for measurement, the electrical signal can penetrate the intracellular fluid, that is, the body impedance measured at this time is the electrical signal flowing through the body. Impedance in intracellular fluid.
  • the liver fat level of the person to be measured can be determined based on the body weight and body impedance of the person to be measured.
  • the body fat scale 200 shown in FIG. 2 can transmit the body weight and body impedance of the person A to be tested to FIG. 3
  • the liver fat level of the subject A is determined through the electronic device 300 .
  • the body impedance of person A to be measured can also be calculated by the mobile phone based on the initial data measured by the body fat scale 200; wherein, the body fat scale 200 can measure the impedance R1 of the arms, the impedance R2 of the legs, and the left Oblique half-body impedance R3, right oblique half-body impedance R4, left half-body impedance R5, and right half-body impedance R5 are sent to the electronic device 300; after that, the electronic device 300 calculates the left arm impedance, right arm impedance, left leg impedance, right Leg impedance, and body impedance such as torso impedance.
  • the electronic device 300 may first determine basic parameters such as the height of the person A to be tested.
  • basic parameters such as the height of the person A to be tested.
  • an application program related to liver fat level such as Huawei Sports Health, etc.
  • the electronic device 300 After inputting the basic parameters such as height, he can select the "OK" button in the area 51 to enter the basic parameters such as his height into the application program related to the liver fat level.
  • the electronic device 300 After the electronic device 300 obtains the basic parameters such as the height of the person to be tested, it can combine the body weight and body impedance of the person to be tested to determine the physical parameters of the person to be tested, such as: body mass index BMI, body fat rate BFR, The amount of visceral fat in the trunk, the amount of fat in each segment of the body, the waist-to-hip ratio, or, body shape, etc.
  • the visceral fat mass in the trunk can be understood as the fat content of most viscera in the trunk, or the fat content of all viscera in the trunk.
  • body mass index BMI W/H 2 , where W is weight and H is height.
  • Body fat percentage can be calculated by the following formula.
  • the formula (hereinafter referred to as "Formula 1") is:
  • BFR ⁇ 1 Z1 50 + ⁇ 2 Z1 250 + ⁇ 3 Z2 50 + ⁇ 4 Z2 250 + ⁇ 5 Z3 50 + ⁇ 6 Z3 250 + ⁇ 7 w t + ⁇ 8 H t + ⁇ 9
  • BFR is the body fat rate
  • ⁇ 1 ,..., ⁇ 9 are preset coefficients, which can be obtained through experiments
  • Z1 50 is the impedance of both legs at 50KHz
  • Z1 250 is the impedance of both legs at 250KHz
  • Z2 50 is 50KHz arm impedance
  • Z2 250 is 250KHz arm impedance
  • Z3 50 is 50KHz trunk impedance
  • Z3 250 is 250KHz trunk impedance
  • w t is weight
  • H t is height.
  • the body fat scale can also send the body fat percentage to the electronic device 300, so that the electronic device 300 can directly obtain the body fat percentage.
  • Z 50 and Z 250 in Formula 1 can also be replaced by impedances of other frequencies, which is not limited here.
  • the visceral fat mass within the trunk can be calculated by the following formula.
  • the formula (hereinafter referred to as "Formula 2") is:
  • X is the amount of visceral fat in the trunk; ⁇ 1 ,..., ⁇ 9 are preset coefficients, which can be obtained through experiments; Z1 50 is the impedance of both legs at 50KHz; Z1 250 is the impedance of both legs at 250KHz; Z2 50 is the impedance of both arms at 50KHz; Z2 250 is the impedance of both arms at 250KHz; Z3 50 is the impedance of the trunk at 50KHz; Z3 250 is the impedance of the trunk at 250KHz; w t is weight; H t is height. It can be understood that Z 50 and Z 250 in Formula 2 can also be replaced by impedances of other frequencies, which is not limited here.
  • the fat mass of each segment of the body can be calculated by the following formula.
  • the formula (hereinafter referred to as "Formula 3") is:
  • P is body segment fat mass, which can be left arm fat mass, right arm fat mass, left leg fat mass, right leg fat mass, or trunk fat mass; ⁇ 1 ,..., ⁇ 5 are preset The fixed coefficient can be obtained by experiment; Z 50 is the impedance of 50KHz; Z 250 is the impedance of 250KHz; w t is body weight; H t is height.
  • Z 50 is the impedance of the left arm at 50KHz
  • Z 250 is the impedance of the left arm at 250KHz
  • P is the mass of fat in the right arm
  • Z 50 is the impedance of the right arm at 50KHz
  • Z 250 is the impedance of the right arm at 250KHz
  • P is the fat mass of the left leg
  • Z 50 is the impedance of the left leg at 50KHz
  • Z 250 is the impedance of the left leg at 250KHz
  • P is the fat mass of the right leg, Z 50 is the impedance of the right leg at 50KHz
  • P is the trunk fat mass
  • Z 50 is the trunk impedance at 50KHz
  • Z 250 is the trunk impedance at 250KHz.
  • the parameter ⁇ in Formula 3 when determining the fat mass of different body segments, the parameter ⁇ in Formula 3 may be partly the same, all may be the same, or all may be different. The details may be determined according to the actual situation, and it is understandable that there is no limitation here. , Z 50 and Z 250 in Formula 3 can also be replaced by impedances of other frequencies, which is not limited here.
  • the waist-to-hip ratio can be calculated by the following formula.
  • the formula (hereinafter referred to as "Formula 4") is:
  • Y is the waist-to-hip ratio
  • ⁇ 1 ,..., ⁇ 11 are preset coefficients, which can be obtained through experiments
  • L1 is the muscle mass of the left arm
  • L2 is the fat mass of the left arm
  • L3 is the muscle mass of the right arm
  • L4 is right arm fat mass
  • L5 is left leg muscle mass
  • L6 is left leg fat mass
  • L7 is right leg muscle mass
  • L8 is right leg fat mass
  • L9 is trunk muscle mass
  • L10 is trunk fat mass.
  • the "left arm muscle mass L1", “right arm muscle mass L3", “left leg muscle mass L5", "right leg muscle mass L7" and “trunk muscle mass L9" in Formula 4 can be adaptive selection, without limitation.
  • M ⁇ 1 Z 50 + ⁇ 2 Z 250 + ⁇ 3 W t + ⁇ 4 H t + ⁇ 5
  • M is the muscle mass of each segment of the body, which can be the muscle mass of the left arm, the muscle mass of the right arm, the muscle mass of the left leg, the muscle mass of the right leg, or the fat mass of the trunk ;
  • the set coefficients can be obtained by experiment;
  • Z 50 is the impedance of 50KHz;
  • Z 250 is the impedance of 250KHz;
  • w t is body weight;
  • H t is height.
  • Z 50 is the impedance of the left leg at 50KHz
  • Z 250 is the impedance of the left leg at 250KHz
  • M is the muscle mass of the right leg
  • Z 50 is the impedance of the right leg at 50KHz
  • Z 250 is the impedance of the right leg at 250KHz
  • M is the muscle mass of the left leg
  • Z 50 is the impedance of the left leg at 50KHz
  • Z 250 is the impedance of the left leg at 250KHz
  • M is the muscle mass of the trunk
  • Z 50 is the trunk impedance of 50KHz
  • Z 250 is the trunk impedance of 250KHz.
  • the parameter ⁇ in Formula 5 when determining the muscle mass of different body segments, the parameter ⁇ in Formula 5 may be partly the same, all may be the same, or all may be different. The details may be determined according to the actual situation. , Z 50 and Z 250 in Formula 5 can also be replaced by impedances of other frequencies, which is not limited here.
  • the body parameters required to determine the current body shape can be determined based on the waist-to-hip ratio, and then the body shape can be determined based on the body parameters.
  • the required body parameters may be determined based on the interval to which the waist-to-hip ratio belongs.
  • BMI can be selected to determine the body shape.
  • the body shape is chili type.
  • the body shape is symmetrical; when the waist-to-hip ratio is in the preset range a2 (such as a2 ⁇ (0,0.78]), the muscles of the arms can be selected Determine the body shape by weight.
  • the muscle mass of both arms When the ratio to the total amount of body muscle is less than the preset threshold b2, it can be determined that the body shape is pear-shaped.
  • the waist-to-hip ratio can be combined with multiple other body parameters when determining the body shape; for example, the waist-to-hip ratio, the fat mass of each segment of the body can be used And muscle mass, BMI, the amount of visceral fat in the trunk, and more.
  • the parameters used to determine the body shape can be input into the machine learning classification model to obtain the body shape.
  • the electronic device 300 may determine, based on the body shape, from the correspondence between the preset body shape and the level determination model used to determine the liver fat level
  • the level determination model corresponding to the body shape of the person A to be tested can be obtained by training using a Gaussian process model, a neural network model, a support vector machine, etc.; in addition, the level determination model can also be a deviation function model , proportional function model, mixed function model, or other mathematical function models.
  • the correspondence between the preset body shape and the level determination model used to determine the liver fat level can be shown in Table 1. When the body shape is determined to be "apple-shaped", it can be obtained from Table 1 It can be seen that the grade determination model that should be selected at this time is "Model 2".
  • the body parameters such as BMI, body fat percentage, waist-to-hip ratio and other physical parameters of the person A to be tested can be input into the level determination model, and after being processed by the level determination model, it can be obtained The liver fat level of person A to be tested.
  • the parameters input into the grade determination model may also include other body parameters, such as the amount of visceral fat in the trunk, the amount of fat in each segment of the body, the shape of the body, etc., so as to improve the accuracy of detection.
  • the electronic device 300 can determine the liver risk level of the person A to be tested based on the correspondence between the liver fat level and the liver risk coefficient.
  • the correspondence between the preset liver fat level and the liver risk level can be shown in Table 2.
  • the liver fat level is determined to be "5", it can be seen from Table 2 that the current The liver risk level is "suspected risk”.
  • the electronic device 300 may present the detected person A's liver fat level to the person A to be tested. Exemplarily, as shown in FIG. 7 , it may be displayed on the electronic device 300 that the liver fat level of the person A to be tested is 7.8, and the screening result is medium-high risk. In addition, continuing to refer to FIG. 7 , the electronic device 300 can also display other parameters of the person A to be tested, such as displaying height, body shape (not shown in the figure), etc.; and displaying exercise suggestions, etc.
  • different level determination models can be used to calculate the user's liver fat level for users with different body shapes, so that different processing methods can be adopted for different groups of people. Improve the accuracy of liver fat level evaluation, avoiding the situation that everyone adopts a liver fat level evaluation method.
  • the liver fat level detection scheme in this scheme.
  • the user's body feature parameters can also be added during the detection process; where the body feature parameters include bust, waist, hip, etc.
  • the body feature parameter is a parameter strongly related to the fat content in the liver, thereby improving the accuracy of liver fat level detection.
  • the liver fat level detection system in this solution can be the system shown in FIG. 1a, wherein the mobile phone 12 can collect photos of the person to be tested to determine the physical characteristic parameters of the person to be tested.
  • the liver fat level detection system in this solution can be the system shown in Figure 1b, wherein the smart screen 13 can be equipped with a camera;
  • the mobile phone 12 can send to the smart screen 13 an instruction to collect the photo of the person to be tested, so as to collect the photo of the person to be tested through the camera on the smart screen 13; after that, the smart screen 13 sends the photo of the person to be tested to the mobile phone 12 to
  • the physical characteristic parameters of the person to be tested are determined through the mobile phone 12, and a more accurate liver fat level is obtained. See description below for details.
  • the electronic device 300 is a mobile phone, and this scenario can be understood as an application scenario under the system shown in FIG. 1a.
  • applications related to liver fat levels such as Huawei Sports Health, etc.
  • the person to be tested A can select the “next page” button at the area 61 .
  • the electronic device 300 may display an interface as shown in FIG. 8 to remind the person A to be tested that "you need to turn on the mobile phone to take a picture", and the person A to be tested can choose whether to measure liver fat more accurately. If the subject A selects the "cancel" button in the area 71, the accurate measurement of liver fat will be stopped, and the interface shown in FIG.
  • the person to be tested A selects the "OK" button in the area 72, the process of accurately measuring liver fat will be carried out.
  • the person A to be tested can also slide on the interface shown in FIG. Slide from left to right, from the right to the left of the electronic device 300, from the upper side of the electronic device 300 to the lower side, or from the lower side of the electronic device 300 to the upper side, etc., to display the 8 shows the interface.
  • the "next page” displayed in area 61 in Figure 7 can also be replaced with other content, for example, as shown in Figure 9, the "next page” in area 61 in Figure 7 can be replaced ” is replaced by the content in the area 62 in FIG. 9a, so that the person to be tested A selects the “OK” button, wherein, when the person to be tested A selects the “OK” button, the interface shown in FIG. 8 is entered.
  • the "next page" in area 61 in Figure 7 can be replaced by the content in area 63 in Figure 9b, so that the person A to be tested can choose "Yes” or "No", wherein, When the person to be tested A selects "Yes", the interface shown in FIG. 8 is entered.
  • the content of area 63 in Figure 9b can appear in the form of a pop-up window, wherein, after the person to be tested A selects "Yes", then enters the interface shown in Figure 8, when the person to be tested A selects "No ", then the pop-up window can be closed, that is, the content in the area 63 is no longer displayed.
  • the pop-up window may pop up after a period of time (such as 3 seconds, etc.) after the liver fat level is detected.
  • the mobile phone Since the whole body image of the user needs to be taken at a specific height when taking pictures, when the electronic device 300 is a mobile phone, the mobile phone needs to be fixed at a specific position.
  • the mobile phone can display the suggestion information before taking pictures, and the suggestion information can be "1. Please put Put the mobile phone in a fixed position, and make sure that the mobile phone can take a clear full-body photo. 2. Please wear tights, expose the waist and abdomen, and hang your hands down and don't stick them on your legs.” The accuracy of the measurement. Further, when the person A to be tested is ready, he can select the "OK" button at the area 1001 in Fig.
  • the mobile phone can first take a frontal photo of the person A to be tested. After the mobile phone captures the frontal photo of the person A to be tested, it can detect whether the photo meets the requirements. If it meets the requirements, it will enter the next process. If it does not, it will take a new frontal photo.
  • a human skeleton key point detection algorithm (Pictorial Structure) based on template matching and a human bone key point detection algorithm based on target detection, such as a cascaded feature network (cascaded feature network) can be used.
  • Feature network, CFN regional multi-person pose estimation (regional multi-person pose estimation, RMPE), cascaded pyramid network (cascaded pyramid network, CPN) and other algorithms, select human skeleton nodes from captured images, and construct limb vectors ; After that, compare the preset standard limb vector with the obtained limb vector to obtain the degree of motion difference between the two; then, determine whether the obtained image meets the requirements according to the degree of motion difference. For example, when the motion difference is within the preset range, it is determined that the requirements are met; when the motion difference is not within the preset range, it is determined that the requirements are not met.
  • the person image in FIG. 10 b is only a schematic frontal image of the person A to be tested currently collected by the mobile phone.
  • the mobile phone can take a side photo of the person to be tested A, and then display an interface as shown in FIG. 10c to take a side photo of the person to be tested A.
  • the detection method can refer to the method of detecting the front photo, and will not be repeated here. If it meets, go to the next process, if not, you can take a side photo again. It can be understood that the person image in FIG. 10c is only a schematic side image of the person A to be tested currently collected by the mobile phone.
  • the mobile phone When the mobile phone detects that both the front photo and the side photo meet the requirements.
  • the mobile phone can perform image segmentation on the captured frontal photos and side photos based on a neural network (such as Unet network, etc.), and extract human body portraits. After that, the mobile phone determines the corresponding human body node positions according to the bone nodes and outline nodes, such as armpit, groin, navel, thigh root and so on.
  • the human skeleton key point detection algorithm described above may be used to determine the corresponding human body node positions.
  • the mobile phone can combine the body proportion of the person A to be tested determined by the body portrait and height at the node position of the human body to determine the characteristic information such as chest width, chest thickness, waist width, waist thickness, hip width, and hip thickness. Then, based on the determined feature information, the mobile phone can infer body feature parameters such as bust, waist, and hip.
  • body feature parameters such as bust, waist, and hip.
  • the waist-to-hip ratio measured before the photo is calculated based on the formula set by experience, and the calculated waist-to-hip ratio at this time is not accurate; while the waist-to-hip ratio measured after the photo is based on the formula to be measured
  • the characteristic information of the person's body is calculated, which can truly reflect the physical condition of the person to be measured, that is, the waist-to-hip ratio calculated at this time has a high accuracy.
  • the mobile phone can input body parameters such as waist circumference and other body parameters, BMI, body fat percentage, waist-to-hip ratio and other physical parameters of the person A to be tested into the above determined level determination model, and after processing by the level determination model, it can be obtained The liver fat level of person A to be tested.
  • the waist-to-hip ratio input into the grade determination model at this time can be the waist-to-hip ratio estimated from the photo of the person to be tested, or the waist-to-hip ratio calculated before taking pictures; but in order to improve the accuracy of detection, It is best to use a waist-to-hip ratio estimated from a photograph of the person to be tested.
  • the waist-to-hip ratio can be compared with the waist-to-hip ratio determined based on body impedance. If the parameters of the body shape are in the same interval, the level determination model determined before taking the photo can be used; if the parameters required for the body shape are determined to be in the same interval, the waist-to-hip ratio determined based on the photo can be used Re-determine the body shape of the person A to be tested, and redefine the grade determination model, and use the re-determined grade determination model to calculate the liver fat grade of the person A to be tested. In one example, after re-determining the body shape of the person A to be tested, the re-determined body shape can also be used to replace the body shape determined before taking the photo, and presented to the person A to be tested.
  • the mobile phone after the mobile phone captures the frontal and side photos and before obtaining the liver fat level of the person to be tested, that is, in the process of calculating the liver fat level of the person to be tested, the mobile phone can display as shown in Figure 10d interface, so that the personnel under test can be notified of the processing progress.
  • the parameters input into the grade determination model may also include other parameters, such as the amount of visceral fat in the trunk, the amount of fat in each segment of the body, body shape, chest circumference, hip circumference, etc., so as to improve the detection the accuracy.
  • the "liver fat level” can be displayed on the mobile phone as shown in FIG. 10e.
  • the interface shown in Figure 10f can also be displayed on the mobile phone, so that the subject A can intuitively understand his own Bust, waist, hip, waist-to-hip ratio and other parameters. It can be understood that the display order of the interfaces shown in FIGS. 10e and 10f may depend on the situation, and is not limited here.
  • the electronic device 300 is a large screen at a fixed position, such as a smart screen, wherein an image acquisition device (such as a camera, etc.) is configured on the large screen to collect images of persons to be tested; Apps related to liver fat levels (such as Huawei Sports Health, etc.) can be installed on the phone.
  • an image acquisition device such as a camera, etc.
  • Apps related to liver fat levels such as Huawei Sports Health, etc.
  • the subject A can click on the area 61 where the "risk level of fatty liver" is located.
  • the electronic device 300 may display an interface as shown in FIG. 11a to remind the person A to be tested that "the large-screen camera function needs to be turned on", and the person A to be tested can choose whether to measure liver fat more accurately. If the subject A selects the "cancel" button in the area 91, the accurate measurement of liver fat will be stopped, and the interface shown in FIG. 7 will be returned.
  • the person to be tested A selects the "OK" button in the area 92, the process of accurately measuring liver fat will be carried out.
  • the person A to be tested can also slide on the interface shown in FIG. Slide from left to right, from the right to the left of the electronic device 300, from the upper side of the electronic device 300 to the lower side, or from the lower side of the electronic device 300 to the upper side, etc., to display the The interface shown in 11a.
  • the "next page” displayed in area 61 in Figure 7 can also be replaced with other content, for example, as shown in Figure 9, the "next page” in area 61 in Figure 7 can be replaced ” is replaced by the content in area 62 in FIG. 9, so that the person A to be tested can select “Yes” or “No”.
  • the interface shown in FIG. 11 a is entered.
  • the large screen can display the suggestion information before taking pictures. Don't stick it on the leg", so as to remind you to make an appropriate photo-taking action to improve the accuracy of the measurement. Further, when the person to be tested A is ready, he can select the "confirm” button at the area 93 in Fig. 11b, and enter into the photographing process. It can be understood that in this solution, when the tester A selects a button on the large screen, he can click to select, he can also select by voice, or he can select by gesture, which can be determined according to the actual situation and is not limited here.
  • the large screen can first use the camera 901 to take a frontal photo of the person A to be tested. After the large screen captures the frontal photo of the person A to be tested, it can detect whether the photo meets the requirements. Wherein, if it meets, enter the next process; if not, camera 901 can be used to take a new frontal photo. For the method of checking whether the photo meets the requirements, see the description in "Scene 1" above for details, so I won't go into details here. It can be understood that the person image in FIG. 11c is only a frontal image of the person A to be tested currently captured by the schematic large screen.
  • the large screen When the large screen detects that the captured image meets the requirements, the large screen takes a side photo of the person to be tested A, and the interface shown in Figure 11d can be displayed, and the camera 901 is used to take a side photo of the person to be tested A. After the large screen captures the side photo of the person A to be tested, it can detect whether the photo meets the requirements. If it meets, enter the next process, if not, camera 901 can be used to take the frontal photo again. It can be understood that the person image in FIG. 11d is only a frontal image of the person A to be tested currently captured by the schematic large screen.
  • the big screen can segment the frontal photos and side photos based on the neural network (such as the Unet network), and extract the portrait of the human body. After that, the large screen determines the corresponding human body node positions according to the bone nodes and outline nodes, such as armpit, groin, navel, thigh root and so on. Then, the large screen can combine the portrait and height of the human body at the node position of the human body to determine the characteristic information of chest width, chest thickness, waist width, waist thickness, hip width, hip thickness, thigh width, and thigh thickness. Then, based on the determined feature information, the large screen can infer the bust, waist, hip and other body feature parameters.
  • the neural network such as the Unet network
  • the waist circumference since the waist circumference is a quasi-ellipse, after obtaining the waist width and waist thickness, the waist circumference can be estimated based on mathematical operations. Further, after the body characteristic parameters are obtained, the waist-to-hip ratio can also be calculated based on the waist circumference and hip circumference in the body characteristic parameters.
  • the waist-to-hip ratio measured before the photo is calculated based on the formula set by experience, and the calculated waist-to-hip ratio at this time is not accurate; while the waist-to-hip ratio measured after the photo is based on the formula to be measured
  • the characteristic information of the person's body is calculated, which can truly reflect the physical condition of the person to be measured, that is, the waist-to-hip ratio calculated at this time has a high accuracy.
  • the parameters input into the grade determination model may also include other parameters, such as the amount of visceral fat in the trunk, the amount of fat in each segment of the body, body shape, chest circumference, hip circumference, etc., so as to improve the detection the accuracy.
  • the large screen can input body parameters such as waist circumference and other body parameters, BMI, body fat percentage, waist-to-hip ratio and other physical parameters of the person to be tested into the above-mentioned determined level determination model, which can be processed by the level determination model.
  • the liver fat level of the person A to be tested is obtained.
  • the waist-to-hip ratio input into the grade determination model at this time can be the waist-to-hip ratio estimated from the photo of the person to be tested, or the waist-to-hip ratio calculated before taking pictures; but in order to improve the accuracy of detection, It is best to use a waist-to-hip ratio estimated from a photograph of the person to be tested.
  • the waist-to-hip ratio can be compared with the waist-to-hip ratio determined based on body impedance. If the parameters of the body shape are in the same interval, the level determination model determined before taking the photo can be used; if the parameters required for the body shape are determined to be in the same interval, the waist-to-hip ratio determined based on the photo can be used Re-determine the body shape of the person A to be tested, and redefine the grade determination model, and use the re-determined grade determination model to calculate the liver fat grade of the person A to be tested. In one example, after re-determining the body shape of the person A to be tested, the re-determined body shape can also be used to replace the body shape determined before taking the photo, and presented to the person A to be tested.
  • the large screen can display as shown in Figure 11e The displayed interface, so that the personnel under test can be informed of the processing progress.
  • the "liver fat level” can be displayed on the large screen as shown in FIG. 11f.
  • the interface shown in Figure 11g can also be displayed on the large screen, so that the person A to be tested can intuitively understand his body size. Your own bust, waist, hip, waist-to-hip ratio and other parameters. It can be understood that, the display order of the interfaces shown in Figs. 11f and 11g may depend on the situation, and is not limited here.
  • the prompt information of each operation process in Fig. 11a to Fig. 11g can also be replaced with other prompt information, which can prompt the user to perform the same or similar action as the standard action, which is not mentioned here. Do limited.
  • the prompt information in other figures may also be replaced with other prompt information, which is not limited here.
  • This scenario is an application scenario under the system shown in FIG. 1 b , where an application program related to liver fat level (such as Huawei Sports Health, etc.) can be installed on the mobile phone 12 . .
  • an application program related to liver fat level such as Huawei Sports Health, etc.
  • the mobile phone 12 after the liver fat level is displayed on the mobile phone 12 (that is, the interface shown in FIG. 7, at this time, the electronic device 300 can be the mobile phone 12), the person A to be tested can click on the “next page” in FIG. area 61.
  • the mobile phone 12 may display a suggestion message before the test.
  • the suggestion message may be "1.
  • the large-screen camera function needs to be turned on, please ensure that the big screen is turned on. 2.
  • the big screen can be understood as the smart screen 13. If the subject A selects the "Cancel” button, the precise measurement of liver fat will be stopped, and the interface shown in FIG. 7 will be returned. If the person to be tested A selects the “OK” button, the mobile phone 12 sends a photographing instruction to the smart screen 13 . It can be understood that, in this solution, when the person to be tested A selects a button on the mobile phone, he can click to select, or select by voice, or select by gesture, which can be determined according to the actual situation, and is not limited here.
  • the smart screen 13 After receiving the photographing instruction sent by the mobile phone 12, the smart screen 13 can turn on the camera 1201 configured on it to enter the photographing process.
  • the smart screen 13 can first use the camera 1201 on it to take a frontal photo of the person A to be tested. After the smart screen 13 captures the frontal photo of the person A to be tested, the photo can be sent to the mobile phone 12, so that the mobile phone 12 can detect whether the photo meets the requirements.
  • the mobile phone 12 detects that the photo meets the requirements it sends an instruction to enter the next process to the smart screen 13; when the mobile phone 12 detects that the photo does not meet the requirements, it sends a re-shooting instruction to the smart screen 13 to Utilize camera 1201 to take the front photo again.
  • the mobile phone 12 to detect whether the photo meets the requirements see the description in "Scene 1" above for details, and will not repeat them here.
  • the smart screen 13 can use the camera 1201 to take a side photo of the person A to be tested.
  • the photo can be sent to the mobile phone 12, so that the mobile phone 12 can detect whether the photo meets the requirements.
  • the mobile phone 12 detects that the photo meets the requirements, it sends an instruction to the smart screen 13 to indicate the end of shooting; when the mobile phone 12 detects that the photo does not meet the requirements, it sends a re-shooting instruction to the smart screen 13, To utilize the camera 1201 to take a side photo again.
  • the smart screen 13 After receiving the instruction from the mobile phone 12 indicating the end of shooting, the smart screen 13 can turn off the camera and end the shooting work.
  • the mobile phone 12 can take pictures based on a neural network (such as the Unet network). Segment the obtained frontal photos and side photos to extract human body portraits. Afterwards, the mobile phone 12 determines the corresponding human body node positions according to the bone nodes and outline nodes, such as armpit, groin, navel, thigh root and so on. Next, the mobile phone 12 can combine the body portrait and height at the node positions of the human body to determine characteristic information such as chest width, chest thickness, waist width, waist thickness, hip width, hip thickness, thigh width, and thigh thickness.
  • a neural network such as the Unet network
  • the mobile phone 12 can infer body feature parameters such as chest circumference, waist circumference, and hip circumference.
  • body feature parameters such as chest circumference, waist circumference, and hip circumference.
  • the waist circumference is a quasi-ellipse, after obtaining the waist width and waist thickness, the waist circumference can be estimated based on mathematical operations.
  • the mobile phone 12 can input physical parameters such as waist circumference and other body parameters, BMI, body fat percentage, waist-to-hip ratio and other physical parameters of the person to be tested into the above-mentioned determined level determination model, which can be processed by the level determination model.
  • the liver fat level of the person A to be tested is obtained.
  • the waist-to-hip ratio can be compared with the waist-to-hip ratio determined based on body impedance.
  • the level determination model determined before taking the photo can be used; if the parameters required for the body shape are determined to be in the same interval, the waist-to-hip ratio determined based on the photo can be used Re-determine the body shape of the person A to be tested, and redefine the grade determination model, and use the re-determined grade determination model to calculate the liver fat grade of the person A to be tested.
  • the re-determined body shape can also be used to replace the body shape determined before taking the photo, and presented to the person A to be tested.
  • the mobile phone 12 can obtain the frontal photo and side photo of the person to be tested and before obtaining the liver fat level of the person to be tested, that is, in the process of calculating the liver fat level of the person to be tested, the mobile phone 12 can The interface shown in FIG. 10d is displayed, so that the personnel to be tested can know the processing progress.
  • the parameters input into the grade determination model may also include other parameters, such as the amount of visceral fat in the trunk, the amount of fat in each segment of the body, body shape, chest circumference, hip circumference, etc., so as to improve the detection the accuracy.
  • the “liver fat level” can be displayed on the mobile phone 12 as shown in FIG. 10e.
  • the mobile phone 12 can also display an interface as shown in Figure 10f, so that the person A to be tested can intuitively understand his body size. Your own bust, waist, hip, waist-to-hip ratio and other parameters. It can be understood that the display order of the interfaces shown in FIGS. 10e and 10f may depend on the situation, and is not limited here.
  • the mobile phone 12 can also send the measured liver fat level and/or morphological characteristic parameters to the smart screen 13 , such as by projecting a screen to the smart screen 13 for display on the smart screen 13 .
  • liver fat level detection the physical characteristic parameters of the person to be tested are combined with parameters such as body shape, and different processing methods are adopted for different groups of people, and the accuracy of liver fat level evaluation is further improved. Accuracy, avoiding the situation that everyone adopts a liver fat level evaluation method.
  • a physiological parameter detection method provided in the embodiment of the present application is introduced. It can be understood that, the method is proposed based on the scheme for detecting the liver fat level described above, and part or all of the content of the method can be referred to the above description of the scheme for detecting the liver fat level.
  • Fig. 12 is a schematic flowchart of a method for measuring a physiological parameter provided by an embodiment of the present application. As shown in Figure 12, the method may include the following steps:
  • Step 101 Determine the first physical parameter and the second physical parameter of the person to be tested.
  • both the first body parameter and the second body parameter can be measured by the first electronic device.
  • the first electronic device may have at least 8 electrodes, and the second body parameter may be measured based on the at least 8 electrodes of the first electronic device.
  • the second body parameter can be measured by the first electronic device controlling at least 8 electrodes to generate electrical signals of at least two different frequencies, thereby measuring the second body parameter through the electrical signals of different frequencies, improving the efficiency of subsequent detection. precision.
  • the first electronic device may be the body fat scale 11 shown in FIG. 1a.
  • the first body parameter may be body weight
  • the second body parameter may be body impedance or raw data (such as voltage, current, etc.) required for calculating body impedance.
  • body impedances may include impedances associated with the arms, impedances associated with the legs, and impedances associated with the torso.
  • the impedance related to the arms may include the impedance of the arms, the impedance of the left arm, or the impedance of the right arm, etc.
  • the impedance related to the legs may include the impedance of the legs, the impedance of the left leg, or the impedance of the right leg, etc.
  • the impedance related to the trunk may include torso impedance, or other impedances including torso impedance, or the like.
  • Step 102 Determine a third body parameter in response to the input of the person to be tested.
  • the third body parameter may be height.
  • Step 103 Determine a fourth body parameter according to the first body parameter and the third body parameter.
  • the fourth body parameter may be BMI.
  • the fourth body parameter when the first body parameter is weight and the third body parameter is height, the fourth body parameter may be BMI.
  • Step 104 Determine the first body shape of the person to be tested according to the second body parameter.
  • the fifth body parameter when determining the first body shape of the person to be tested, may be determined first according to the second body parameter. Then, the interval to which the fifth body parameter belongs is judged. Wherein, if the fifth body parameter belongs to the first interval, the first body shape of the person to be tested is determined according to the fourth body parameter; if the fifth body parameter belongs to the second interval, the body shape of the person to be tested is determined according to the sixth body parameter. The first body shape, wherein the sixth body parameter is obtained based on the second body parameter.
  • the fifth body parameter may be the waist-to-hip ratio
  • the sixth body parameter may be the muscle mass or fat mass of the arms of the person to be tested; wherein, when the second body parameter is body impedance, it may be used first The formula three described above is based on the body impedance to determine the fat mass of each segment of the body of the person to be tested, and then the formula four described above is used to determine the waist-to-hip ratio based on the fat mass; then the waist-to-hip ratio is selected to use the body parameters, and finally the body shape is determined by the selected body parameters.
  • the selected body parameters may be BMI, muscle mass, fat mass and so on. For the calculation method of muscle mass, please refer to the description in Formula 5 above.
  • Step 105 Determine a first physiological parameter according to at least the first body shape and the fourth body parameter.
  • the corresponding relationship between the predetermined body shape and the detection model can be queried, and the detection model corresponding to the first body shape can be determined.
  • different body shapes correspond to different A detection model: at least the fourth body parameter is input into the detection model corresponding to the first body shape to determine the first physiological parameter. Therefore, based on the body shape of the person to be tested, a detection model suitable for the body shape is determined, and then the detection model is used to detect the physiological parameters of the person to be tested, so that users with different body shapes can be detected using different detection models. parameters, improving the accuracy of physiological parameters.
  • the detection model may be the level determination model described above, and the corresponding relationship may be as shown in Table 1 above.
  • the first physiological parameter may be liver fat level.
  • Step 106 displaying the first physiological parameter.
  • the first physiological parameter may be displayed, so that the person to be tested can visually view his own physiological parameter.
  • the first physiological parameter may be displayed on an interface as shown in FIG. 7 .
  • the first physiological parameter of the person to be tested can also be accurately measured. Specifically, as shown in Figure 13, the following steps are included:
  • Step 201 in response to the first operation of the person to be tested, determine a first photo and a second photo of the person to be tested.
  • the person to be tested may perform the first operation, and the first operation may be an operation of accurately measuring the first physiological parameter. Afterwards, in response to the first operation, the first photo and the second photo of the person to be tested can be determined. Wherein, in this solution, the first photo is taken when the person to be tested makes the first preset action, and the second photo is taken when the person to be tested makes the second preset action.
  • the first operation may be a click operation of the person to be tested at the area 61 in FIG. 7 .
  • the first photo can be a frontal photo of the person to be tested
  • the second photo can be a profile photo of the person to be tested.
  • the method of obtaining the frontal photo and the side photo is detailed in the descriptions in the above scenes 1 to 3, and will not be repeated here.
  • determining the first photo and the second photo of the person to be tested may specifically include: determining the action difference between the action of the person to be tested on the currently acquired target photo and the target preset action, and determining the action The degree of difference is within a preset range; wherein, the target photo is the first photo, the target preset action is the first preset action, or the target photo is the second photo, and the target preset action is the second preset action. Therefore, when the person to be tested makes a preset action, the collected photos are used for detection, which improves the accuracy of physiological parameter detection.
  • the human skeleton key point detection algorithm (Pictorial Structure) based on template matching described above and the human bone key point detection algorithm based on target detection, such as cascaded feature network (cascaded feature network, CFN), area Multi-person pose estimation (regional multi-person pose estimation, RMPE), cascaded pyramid network (cascaded pyramid network, CPN) and other algorithms to detect motion differences, see the above description for details.
  • Step 202 Determine the eighth physical parameter of the person to be tested according to the first photo and the second photo.
  • the eighth physical parameter of the person to be tested can be determined according to the first photo and the second photo.
  • the eighth physical parameter is a parameter strongly correlated with the first physiological parameter.
  • the eighth physical parameter may be waist circumference.
  • the method of determining the waist circumference please refer to the descriptions in the above scenarios 1 to 3 for details, so I won’t go into details here.
  • Step 203 Re-determine the first physiological parameter according to at least the first body shape, the fourth body parameter and the eighth body parameter.
  • the correspondence between the predetermined body shape and the detection model can be queried, and the detection model corresponding to the first body shape can be determined, wherein, in the second correspondence Different body shapes correspond to different detection models; at least the fourth body parameter and the eighth body parameter are input into the detection model corresponding to the first body shape, and the first physiological parameter is re-determined.
  • the detection model may be the level determination model described above, and the corresponding relationship may be as shown in Table 1 above.
  • the first physiological parameter may be liver fat level.
  • the ninth body parameter of the person to be tested may also be determined according to the first photo and the second photo; According to the eighth body parameter and the ninth body parameter, the second body shape of the person to be tested is determined.
  • the ninth body parameter may be hip circumference. Since the body shape of the person to be tested can be accurately presented on the photo, the body shape of the person to be tested can be accurately determined through the photo of the person to be tested, thereby improving the accuracy of body shape detection.
  • the method of determining the hip circumference please refer to the descriptions in the above scenes 1 to 3 for details, so I won't go into details here.
  • the corresponding relationship can be queried according to the second body shape, the detection model corresponding to the second body shape can be determined, and the detection model corresponding to the second body shape can be selected.
  • the detection model re-determines the first physiological parameter. Therefore, the physiological parameters of the person to be tested are detected based on the body shape of the person to be tested detected through the photo, thereby improving the accuracy of the detection of the physiological parameters.
  • Step 204 displaying the first physiological parameter.
  • the re-determined first physiological parameter may be displayed.
  • the re-displayed first physiological parameter may be displayed on the interface shown in Fig. 10e.
  • the re-determined physical shape of the person to be measured can also be displayed.
  • the method provided in FIG. 12 may be executed by the first electronic device, or may be executed by the second electronic device.
  • the second electronic device when executed by the second electronic device, there can be a communication connection between the second electronic device and the first electronic device; when the second electronic device determines the first body parameter and the second body parameter of the person to be tested, the second The electronic device may receive the first body parameter and the second body parameter sent by the first electronic device.
  • the physiological parameters of the person to be tested can be detected through a combination of multiple electronic devices.
  • the first electronic device may be the body fat scale 11 shown in FIG. 1a
  • the second electronic device may be the mobile phone 12 shown in FIG. 1a.
  • liver fat level detection scheme based on the liver fat level detection scheme described above, a physiological parameter detection system provided in the embodiment of the present application is introduced. It can be understood that the system is proposed based on the liver fat level detection scheme described above, and part or all of the content of the method can be referred to the above description of the liver fat level detection scheme.
  • Fig. 14 is a schematic structural diagram of a physiological parameter measurement system provided in an embodiment of the present application. As shown in FIG. 14 , the system includes: a first electronic device 1401 and a second electronic device 1402 , the communication connection between the first electronic device 1401 and the second electronic device 1402 , and the first electronic device 1401 has at least 8 electrodes.
  • the first electronic device 1401 can be used to determine the first body parameter and the second body parameter of the person to be tested, and send the first body parameter and the second body parameter to the second electronic device 1402, the second body parameter is based on at least Measured on 8 electrodes.
  • the second electronic device 1402 may be used to determine a third body parameter in response to the input of the person to be tested.
  • the second electronic device 1402 may also be configured to determine a fourth body parameter according to the first body parameter and the third body parameter in response to receiving the first body parameter and the second body parameter.
  • the second electronic device 1402 may also be used to determine the first body shape of the person to be tested according to the second body parameter.
  • the second electronic device 1402 may also be configured to determine a first physiological parameter and display the first physiological parameter according to at least the first body shape and the fourth body parameter.
  • the first electronic device 1401 may be the body fat scale 11 shown in FIG. 1a
  • the second electronic device 1402 may be the mobile phone 12 shown in FIG. 1a.
  • the second electronic device 1402 can also be used for:
  • the first body shape of the person to be tested is determined according to the sixth body parameter, wherein the sixth body parameter is obtained based on the second body parameter.
  • the second electronic device 1402 can also be used for:
  • a seventh body parameter is determined, and from the seventh body parameter, a fifth body parameter is determined.
  • the second electronic device 1402 can also be used for:
  • the first body shape query the correspondence between the predetermined body shape and the detection model, and determine the detection model corresponding to the first body shape, wherein in the corresponding relationship, different body shapes correspond to different detection models;
  • At least a fourth body parameter is input to a detection model corresponding to the first body shape to determine a first physiological parameter.
  • the second electronic device 1402 can also be used to:
  • a first photo and a second photo of the person to be tested are determined, the first photo is taken when the person to be tested makes a first preset action, and the second photo is taken when the person to be tested is doing Shooting when the second preset action is performed;
  • a first physiological parameter is displayed.
  • the second electronic device 1402 can also be used to: query the correspondence between the predetermined body shape and the detection model according to the first body shape, and determine the detection model corresponding to the first body shape. Different body shapes in the two correspondences correspond to different detection models; and inputting at least the fourth body parameter and the eighth body parameter into the detection model corresponding to the first body shape to determine the first physiological parameter.
  • the second electronic device 1402 before the second electronic device 1402 inputs the fourth body parameter and the eighth body parameter into the detection model corresponding to the first body shape, it may also be used to: The ninth body parameter of the person to be tested; and according to the eighth body parameter and the ninth body parameter, determine the second body shape of the person to be tested.
  • the second electronic device 1402 may also be used to: when the second body shape is inconsistent with the first body shape, query the corresponding relationship according to the second body shape, and determine the detection model corresponding to the second body shape, And selecting a detection model to determine the first physiological parameter.
  • the second electronic device 1402 can also be used to: determine the degree of difference between the action of the person to be tested on the currently acquired target photo and the target preset action, and determine that the degree of action difference is within a preset range .
  • the target photo is the first photo
  • the target preset action is the first preset action
  • the target photo is the second photo
  • the target preset action is the second preset action
  • the second electronic device 1402 may also be used to: display the body shape of the person to be tested.
  • the first electronic device 1401 can also be used to: control at least 8 electrodes to generate electrical signals of at least two different frequencies; respectively determine body parameters measured based on the electrical signals of each frequency to obtain second body parameters.
  • the first electronic device 1401 can control 8 electrodes to generate electrical signals of 50KHz and 250KHz.
  • the above-mentioned second electronic device can be used to execute the method in the above-mentioned embodiment, and its realization principle and technical effect are similar to those described in the above-mentioned method, and the working process of the second electronic device can refer to the corresponding The process will not be repeated here.
  • processor in the embodiments of the present application may be a central processing unit (central processing unit, CPU), and may also be other general processors, digital signal processors (digital signal processor, DSP), application specific integrated circuits (application specific integrated circuit, ASIC), field programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof.
  • CPU central processing unit
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • a general-purpose processor can be a microprocessor, or any conventional processor.
  • the method steps in the embodiments of the present application may be implemented by means of hardware, or may be implemented by means of a processor executing software instructions.
  • the software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (random access memory, RAM), flash memory, read-only memory (read-only memory, ROM), programmable read-only memory (programmable rom) , PROM), erasable programmable read-only memory (erasable PROM, EPROM), electrically erasable programmable read-only memory (electrically EPROM, EEPROM), register, hard disk, mobile hard disk, CD-ROM or known in the art any other form of storage medium.
  • An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium.
  • the storage medium may also be a component of the processor.
  • the processor and storage medium can be located in the ASIC.
  • all or part of them may be implemented by software, hardware, firmware or any combination thereof.
  • software When implemented using software, it may be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application will be generated in whole or in part.
  • the computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices.
  • the computer instructions may be stored in or transmitted via a computer-readable storage medium.
  • the computer instructions may be transmitted from one website site, computer, server, or data center to another website site by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) , computer, server or data center for transmission.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrated with one or more available media.
  • the available medium may be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (solid state disk, SSD)), etc.

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Abstract

一种生理参数测量方法、系统及电子设备。方法包括：确定待测人员的第一身体参数和第二身体参数，第一身体参数和第二身体参数由第一电子设备（1401）测得，第二身体参数基于第一电子设备（1401）所具有的至少8个电极（211、221）测得（S101）；响应待测人员的输入，确定第三身体参数（S102）；根据第一身体参数和第三身体参数，确定第四身体参数（S103）；根据第二身体参数，确定待测人员的第一身体形态（S104）；至少根据第一身体形态和第四身体参数，确定第一生理参数（S105）；显示第一生理参数（S106）。由此通过电子设备测得的身体参数确定待测人员的身体形态，并将身体形态和通过电子设备测得的身体参数及基于用户输入的数据确定的身体参数相结合确定待测人员的生理参数，实现对待测人员的生理参数准确测量。

Description

生理参数测量方法、系统及电子设备
本申请要求于2021年5月14日提交至中国国家知识产权局、申请号为202110531351.5、申请名称为“生理参数测量方法、系统及电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及终端技术领域，尤其涉及一种生理参数测量方法、系统及电子设备。
背景技术
随着生活水平的不断提升，脂肪肝(Fatty liver)逐渐成为了人类的第一大肝脏疾病，且患病率呈逐渐增高趋势。其中，脂肪肝的形成主要是由于肝细胞内脂肪堆积过多而导致肝脏病变造成的。一般来说，如果肝内脂肪超过肝重量的5％或在组织学上肝细胞50％以上有脂肪变性时，就可称为脂肪肝，所以肝脏脂肪是检测脂肪肝的重要特征。
相关技术中，除了医学上操作复杂的腹部B超脂肪肝筛查方法外，平时我们还可以通过身体质量指数(body mass index,BMI)和体脂率(body fat ratio，BFR)筛查脂肪肝。其中，在进行脂肪肝筛查时，可以依据设定的BMI和体脂率阈值，来对肝脏脂肪等级进行评估。但根据医学调研发现，即使体脂率相同的两个人，他们的脂肪肝风险等级也不相同。此外，“BMI”也不能十分准确地反映一个人的肥胖情况。也即是说，简单的通过BMI和体脂率难以准确的评估肝脏脂肪的等级。因此，如何使用户能够便捷且准确的获取到其自身的肝脏脂肪等级是目前亟需解决的技术问题。
发明内容
本申请提供了一种生理参数测量方法、系统及电子设备，可以实现使用户能够便捷且准确的测量到用户自身的生理参数。
第一方面，本申请提供了一种生理参数测量方法，该方法包括：确定待测人员的第一身体参数和第二身体参数，第一身体参数和第二身体参数由第一电子设备测得，第二身体参数基于第一电子设备所具有的至少8个电极测得；响应于待测人员的输入，确定第三身体参数；根据第一身体参数和第三身体参数，确定第四身体参数；根据第二身体参数，确定待测人员的第一身体形态；至少根据第一身体形态和第四身体参数，确定第一生理参数；显示第一生理参数。示例性的，第一身体参数可以为体重，第二身体参数可以为身体阻抗或者是计算身体阻抗所需的原始数据(如电压、电流等)，第三身体参数可以为身高，第四身体参数可以为身体质量指数BMI，第一生理参数可以为肝脏脂肪等级。在一个例子中，身体阻抗可以包括与双臂相关的阻抗，与双腿相关的阻抗，和与躯干相关的阻抗。示例性的，与双臂相关的阻抗可以包括双臂阻抗、左臂阻抗或右臂阻抗等，与双腿相关的阻抗可以包括双腿阻抗、左腿阻抗或右腿阻抗等，与躯干相关的阻抗可以包括躯干阻抗，或者包含躯干阻抗的其他阻抗等。
由此，通过第一电子设备测得的第一身体参数确定出待测人员的身体形态，以及将身体形态和通过第一电子设备测得的第一身体参数及基于用户输入的数据确定的第三身体参数相结合确定出待测人员的生理参数，从而实现了对待测人员的生理参数进行准确测量。
在一种可能的实现方式中，根据第二身体参数，确定待测人员的第一身体形态，具体包括：根据第二身体参数，确定第五身体参数；若第五身体参数属于第一区间，根据第四身体参数，确定待测人员的第一身体形态；若第五身体参数属于第二区间，根据第六身体参数，确定待测人员的第一身体形态，其中，第六身体参数基于第二身体参数得到。示例性的，第五身体参数可以为腰臀比，第六身体参数可以为待测人员的双臂的肌肉量或者脂肪量等等。由此，在基于第二身体参数确定出第五身体参数后，可以基于第五身体参数所属的区间范围，选择适宜的身体参数确定待测人员的身体形态，提升确定身体形态的准确度。
在一种可能的实现方式中，根据第二身体参数，确定第五身体参数，具体包括：根据第二身体参数，确定第七身体参数，以及根据第七身体参数，确定第五身体参数。示例性的，第七身体参数可以为身体各节段的脂肪量，如左臂脂肪量，右臂脂肪量，躯干脂肪量，左腿脂肪量，或右腿脂肪量等等。由此，通过第二身体参数得到第五身体参数。
在一种可能的实现方式中，至少根据第一身体形态和第四身体参数，确定第一生理参数，具体包括：根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在对应关系中不同的身体形态对应有不同的检测模型；至少将第四身体参数输入至与第一身体形态对应的检测模型，确定第一生理参数。由此，基于待测人员的身体形态确定出与其身体形态适配的检测模型，进而利用该检测模型对待测人员的生理参数进行检测，从而实现对不同身体形态的用户使用不同的检测模型检测生理参数，提升了生理参数的准确性。
在一种可能的实现方式中，显示第一生理参数之后，方法还包括：响应于待测人员的第一操作，确定待测人员的第一照片和第二照片，第一照片为在待测人员做出第一预设动作时拍摄，第二照片为在待测人员做出第二预设动作时拍摄；根据第一照片和第二照片，确定待测人员的第八身体参数；至少根据第一身体形态，第四身体参数和第八身体参数，重新确定第一生理参数；显示第一生理参数。示例性的，第八身体参数可以为腰围。由此，以通过待测人员的照片确定出与待测人员的生理参数强相关的身体参数，并利用该身体参数对其生理参数进行精确测量，提升生理参数的检测精度。
在一种可能的实现方式中，至少根据第一身体形态，第四身体参数和第八身体参数，确定第二生理参数，具体包括：根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在第二对应关系中不同的身体形态对应有不同的检测模型；至少将第四身体参数和第八身体参数输入至与第一身体形态对应的检测模型，重新确定第一生理参数。由此，以提升生理参数检测的准确度。
在一种可能的实现方式中，至少将第四身体参数和第八身体参数输入至与第一身体形态对应的检测模型之前，还包括：根据第一照片和第二照片，确定待测人员的第九身体参数；根据第八身体参数和第九身体参数，确定待测人员的第二身体形态。示例性的，第九身体参数可以为臀围。由于照片上可以准确的呈现待测人员的身体形态，因此可以通过待测人员的照片准确的确定出待测人员的身体形态，提升身体形态检测的准确度。
在一种可能的实现方式中，方法还包括：若第二身体形态与第一身体形态不一致；根据第二身体形态，查询对应关系，确定出与第二身体形态对应的检测模型，以及选用与第二身体形态对应的检测模型重新确定第一生理参数。由此，以通过照片检测的待测人员的身体形态为准，并对待测人员的生理参数进行检测，提升了生理参数检测的准确度。
在一种可能的实现方式中，确定待测人员的第一照片和第二照片，具体包括：确定当前 获取到的目标照片上待测人员的动作与目标预设动作之间的动作差异度，以及确定动作差异度处于预设范围内；其中，目标照片为第一照片，目标预设动作为第一预设动作，或者，目标照片为第二照片，目标预设动作为第二预设动作。由此，以使得在待测人员做出预设动作时再利用采集到的照片进行检测，提升了生理参数检测的准确度。
在一种可能的实现方式中，方法还包括：显示待测人员的身体形态。由此，以使待测人员可以查看到其自身的身体形态。
在一种可能的实现方式中，第二身体参数由第一电子设备控制至少8个电极产生至少两种不同频率的电信号测得。由此通过不同频率的电信号测得第二身体参数，提升后续检测的精准度。
在一种可能的实现方式中，该方法由第一电子设备执行。由此以通过一个设备检测出待测人员的生理参数。
在一种可能的实现方式中，该方法由第二电子设备执行，其中，第二电子设备和第一电子设备之间通信连接；确定待测人员的第一身体参数和第二身体参数，具体包括：第二电子设备接收第一电子设备发送的第一身体参数和第二身体参数。由此，以通过多个电子设备结合检测出待测人员的生理参数。
第二方面，本申请提供了一种生理参数测量系统，系统包括第一电子设备和第二电子设备，第一电子设备和第二电子设备之间通信连接，第一电子设备具有至少8个电极；
其中，第一电子设备用于确定待测人员的第一身体参数和第二身体参数，以及将第一身体参数和第二身体参数发送至第二电子设备，第二身体参数基于至少8个电极测得。
其中，第二电子设备用于响应于待测人员的输入，确定第三身体参数。第二电子设备还用于响应接收第一身体参数和第二身体参数，根据第一身体参数和第三身体参数，确定第四身体参数；第二电子设备还用于根据第二身体参数，确定待测人员的第一身体形态；第二电子设备还用于至少根据第一身体形态和第四身体参数，确定第一生理参数，以及显示第一生理参数。
在一种可能的实现方式中，第二电子设备还用于：根据第二身体参数，确定第五身体参数；若第五身体参数属于第一区间，根据第四身体参数，确定待测人员的第一身体形态；若第五身体参数属于第二区间，根据第六身体参数，确定待测人员的第一身体形态，其中，第六身体参数基于第二身体参数得到。
在一种可能的实现方式中，第二电子设备还用于：根据第二身体参数，确定第七身体参数，以及根据第七身体参数，确定第五身体参数。
在一种可能的实现方式中，第二电子设备还用于：根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在对应关系中不同的身体形态对应有不同的检测模型；至少将第四身体参数输入至与第一身体形态对应的检测模型，确定第一生理参数。
在一种可能的实现方式中，第二电子设备在显示第一生理参数之后，还用于：响应于待测人员的第一操作，确定待测人员的第一照片和第二照片，第一照片为在待测人员做出第一预设动作时拍摄，第二照片为在待测人员做出第二预设动作时拍摄；根据第一照片和第二照片，确定待测人员的第八身体参数；至少根据第一身体形态，第四身体参数和第八身体参数，重新确定第一生理参数；显示第一生理参数。
在一种可能的实现方式中，第二电子设备还用于：根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在第二对应关系中不同的身体形态对应有不同的检测模型；至少将第四身体参数和第八身体参数输入至与第一身体形态对应的检测模型，确定第一生理参数。
在一种可能的实现方式中，第二电子设备在将第四身体参数和第八身体参数输入至与第一身体形态对应的检测模型之前，还用于：根据第一照片和第二照片，确定待测人员的第九身体参数；根据第八身体参数和第九身体参数，确定待测人员的第二身体形态。
在一种可能的实现方式中，第二电子设备还用于：若第二身体形态与第一身体形态不一致；根据第二身体形态，查询对应关系，确定出与第二身体形态对应的检测模型，以及选用检测模型确定第一生理参数。
在一种可能的实现方式中，第二电子设备还用于：确定当前获取到的目标照片上待测人员的动作与目标预设动作之间的动作差异度，以及确定动作差异度处于预设范围内；其中，目标照片为第一照片，目标预设动作为第一预设动作，或者，目标照片为第二照片，目标预设动作为第二预设动作。
在一种可能的实现方式中，第二电子设备还用于：显示待测人员的身体形态。
在一种可能的实现方式中，第一电子设备还用于：控制至少8个电极产生至少两种不同频率的电信号；分别确定基于各个频率的电信号测得的身体参数，得到第二身体参数。
第三方面，本申请提供了一种生理参数测量装置，其特征在于，该装置包括：
确定模块，用于确定待测人员的第一身体参数和第二身体参数，第一身体参数和第二身体参数由第一电子设备测得，第二身体参数基于第一电子设备所具有的至少8个电极测得；
输入模块，用于响应于待测人员的输入，确定第三身体参数；
处理模块，用于根据第一身体参数和第三身体参数，确定第四身体参数；
处理模块，还用于根据第二身体参数，确定待测人员的第一身体形态；
处理模块，还用于至少根据第一身体形态和第四身体参数，确定第一生理参数；
显示模块，用于显示第一生理参数。
在一种可能的实现方式中，处理模块，还用于根据第二身体参数，确定第五身体参数；其中，若第五身体参数属于第一区间，则处理模块根据第四身体参数，确定待测人员的第一身体形态。若第五身体参数属于第二区间，处理模块则根据第六身体参数，确定待测人员的第一身体形态，其中，第六身体参数基于第二身体参数得到。
在一种可能的实现方式中，处理模块，还用于根据第二身体参数，确定第七身体参数，以及根据第七身体参数，确定第五身体参数。
在一种可能的实现方式中，处理模块，还用于根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在对应关系中不同的身体形态对应有不同的检测模型；至少将第四身体参数输入至与第一身体形态对应的检测模型，确定第一生理参数。
在一种可能的实现方式中，在显示模块显示第一生理参数之后，处理模块，还用于响应于待测人员的第一操作，确定待测人员的第一照片和第二照片，第一照片为在待测人员做出第一预设动作时拍摄，第二照片为在待测人员做出第二预设动作时拍摄；根据第一照片和第二照片，确定待测人员的第八身体参数；至少根据第一身体形态，第四身体参数和第八身体 参数，重新确定第一生理参数。
所述显示模块，还用于显示第一生理参数。
6.根据权利要求5的方法，其特征在于，处理模块，还用于根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在第二对应关系中不同的身体形态对应有不同的检测模型；至少将第四身体参数和第八身体参数输入至与第一身体形态对应的检测模型，重新确定第一生理参数。
在一种可能的实现方式中，处理模块，还用于根据第一照片和第二照片，确定待测人员的第九身体参数；根据第八身体参数和第九身体参数，确定待测人员的第二身体形态。
在一种可能的实现方式中，处理模块在第二身体形态与第一身体形态不一致时则根据第二身体形态，查询对应关系，确定出与第二身体形态对应的检测模型，以及选用与第二身体形态对应的检测模型重新确定第一生理参数。
在一种可能的实现方式中，处理模块，还用于确定当前获取到的目标照片上待测人员的动作与目标预设动作之间的动作差异度，以及确定动作差异度处于预设范围内。
其中，目标照片为第一照片，目标预设动作为第一预设动作，或者，目标照片为第二照片，目标预设动作为第二预设动作。
在一种可能的实现方式中，显示模块，还用于显示待测人员的身体形态。
在一种可能的实现方式中，第二身体参数由第一电子设备控制至少8个电极产生至少两种不同频率的电信号测得。
在一种可能的实现方式中，该装置部署于第一电子设备上。
在一种可能的实现方式中，该装置部署于第二电子设备上，其中，第二电子设备和第一电子设备之间通信连接。其中，第二电子设备可以从第一电子设备处接收第一电子设备发送的第一身体参数和第二身体参数。
第四方面，本申请提供了一种电子设备，包括：至少一个存储器，用于存储程序；至少一个处理器，用于执行存储器存储的程序，当存储器存储的程序被执行时，处理器用于执行第一方面中提供的方法。
第五方面，本申请提供了一种计算机存储介质，计算机存储介质中存储有指令，当指令在计算机上运行时，使得计算机执行第一方面中提供的方法。
第六方面，本申请提供了一种包含指令的计算机程序产品，当指令在计算机上运行时，使得计算机执行第一方面中提供的方法。
附图说明
图1a是本申请实施例提供的一种肝脏脂肪等级检测系统的系统架构示意图；
图1b是本申请实施例提供的另一种肝脏脂肪等级检测系统的系统架构示意图；
图2是本申请实施例提供的一种体脂秤的硬件结构示意图；
图3是本申请实施例提供的一种电子设备的硬件结构示意图；
图4是本申请实施例提供的一种待测人员使用体脂秤的场景示意图；
图5是本申请实施例提供的一种待测人员身体各节段对应的电阻的示意图；
图6是本申请实施例提供的一种电子设备的显示界面示意图；
图7是本申请实施例提供的一种电子设备的显示界面示意图；
图8是本申请实施例提供的一种电子设备的显示界面示意图；
图9a是本申请实施例提供的一种电子设备的显示界面示意图；
图9b是本申请实施例提供的一种电子设备的显示界面示意图；
图10a是本申请实施例提供的一种手机的显示界面示意图；
图10b是本申请实施例提供的一种手机的显示界面示意图；
图10c是本申请实施例提供的一种手机的显示界面示意图；
图10d是本申请实施例提供的一种手机的显示界面示意图；
图10e是本申请实施例提供的一种手机的显示界面示意图；
图10f是本申请实施例提供的一种手机的显示界面示意图；
图11a是本申请实施例提供的一种大屏的显示界面示意图；
图11b是本申请实施例提供的一种大屏的显示界面示意图；
图11c是本申请实施例提供的一种大屏的显示界面示意图；
图11d是本申请实施例提供的一种大屏的显示界面示意图；
图11e是本申请实施例提供的一种大屏的显示界面示意图；
图11f是本申请实施例提供的一种大屏的显示界面示意图；
图11g是本申请实施例提供的一种大屏的显示界面示意图；
图12是本申请实施例提供的一种生理参数测量方法的流程示意图；
图13是本申请实施例提供的一种对待测人员的第一生理参数进行精准测量的步骤示意图；
图14是本申请实施例中提供的一种生理参数测量系统的架构示意图。
具体实施方式
为了使本申请实施例的目的、技术方案和优点更加清楚，下面将结合附图，对本申请实施例中的技术方案进行描述。
在本申请实施例的描述中，“示例性的”、“例如”或者“举例来说”等词用于表示作例子、例证或说明。本申请实施例中被描述为“示例性的”、“例如”或者“举例来说”的任何实施例或设计方案不应被解释为比其它实施例或设计方案更优选或更具优势。确切而言，使用“示例性的”、“例如”或者“举例来说”等词旨在以具体方式呈现相关概念。
在本申请实施例的描述中，术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，单独存在B，同时存在A和B这三种情况。另外，除非另有说明，术语“多个”的含义是指两个或两个以上。例如，多个系统是指两个或两个以上的系统，多个电子设备是指两个或两个以上的电子设备。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多个该特征。术语“包括”、“包含”、“具有”及它们的变形都意味着“包括但不限于”，除非是以其他方式另外特别强调。
图1a是本申请实施例提供的一种肝脏脂肪等级检测系统的系统架构示意图。如图1a所示，该系统中包括：体脂秤11和手机12。体脂秤11和手机12之间可以通过蓝牙建立连接。
体脂秤11可以检测待测人员的体重和身体阻抗；其中，身体阻抗可以包括双臂阻抗，双腿阻抗和躯干阻抗。在一个例子中，体脂秤11在获取到待测人员的体重和身体阻抗后，可以 通过蓝牙将体重和身体阻抗发送至手机12。此外，若在体脂秤11测得体重和身体阻抗时，体脂秤11和手机12未建立连接，则后续在体脂秤11和手机12建立连接后，体脂秤11和手机12之间可以同步数据，即体脂秤11将其测得的数据发送至手机12。此外，体脂秤11也可以将其检测到的计算身体阻抗所需的基本数据发送至手机12，之后，手机12可以由这些基本数据计算得到身体阻抗。
手机12可以基于体脂秤11检测到的体重和身体阻抗，确定出待测人员的身体参数。示例性的，身体参数可以包括：身体质量指数BMI，体脂率，内脏脂肪等级，身体各节段脂肪量，腰臀比，或，身体形态等等。示例性的，身体形态可以包括：苹果型，梨型，辣椒型，沙漏型，或，倒三角型等等。接着，手机12还可以基于待测人员的身体形态，确定出与该身体形态对应的等级确定模型。之后，手机12可以将待测人员的身体参数，输入至该等级确定模型，得到待测人员的肝脏脂肪等级。最后，手机12可以将确定出的肝脏脂肪等级呈现给待测人员。
本方案中，在确定肝脏脂肪等级的过程中，可以针对不同身体形态的用户，采用不同的等级确定模型计算用户的肝脏脂肪等级，从而实现了针对不同的人群采用不同的处理方式，提升了肝脏脂肪等级评测的准确性，避免了所有人均采用一种肝脏脂肪等级评测方式的情况出现。
可以理解的是，本方案中，手机12所实现的部分或全部功能也可以由体脂秤11或者由除体脂秤11之外的其他电子设备实现，在此不作限定。示例性的，体脂率也可以由体脂秤11计算得到；也即是说，体脂秤11不但可以把测量的数据传输给手机12，还可以在计算获得一些数据之后，将计算后的数据传输给手机12。示例性的，如图1b所示，该系统中还可以包括智慧屏13。示例性的，体脂秤11和手机12之间可以通过蓝牙建立连接，手机12和智慧屏13之间也可以通过无线网络建立连接。其中，该智慧屏13可以实现手机12所实现的部分功能，例如，将手机12确定出的肝脏脂肪等级呈现给待测人员等。举例来说，手机12在基于体脂秤11发送的待测人员的身体阻抗和体重，确定出待测人员的肝脏脂肪等级后，可以通过投屏的方式将确定出的肝脏脂肪等级投屏到智慧屏13；之后，智慧屏13可以将肝脏脂肪等级呈现给待测人员。
可以理解的是，本方案中，体脂秤11也可以替换为其他的电子设备，该电子设备可以实现体脂秤11在本方案中所实现的功能即可，在此不作限定。示例性的，替换体脂秤11的电子设备可以至少具有检测待测人员的身体阻抗和体重的功能。手机12也可以替换为其他的电子设备，该电子设备可以实现手机12在本方案中所实现的功能即可，在此不作限定。示例性的，替换手机12的电子设备可以为平板电脑、可穿戴设备、智能电视、智慧屏等。智慧屏13也可以替换为其他的电子设备，该电子设备可以实现智慧屏13在本方案中所实现的功能即可，在此不作限定。示例性的，替换智慧屏13的电子设备可以为平板电脑、可穿戴设备、智能电视等。
此外，本方案中，体脂秤11和手机12之间也可以采用其他的连接方式建立连接，手机12和智慧屏13之间亦可以采用其他的连接方式建立连接，在此不限定。示例性的，体脂秤11和手机12之间可以采用短距离无线连接技术或长距离无线连接技术建立连接；其中，短距离无线连接技术可以包括紫蜂(ZigBee)等等；长距离无线连接技术可以包括无线保真技术(wireless fidelity，WIFI)，蜂窝移动通信(cellular mobile communication)等等。手机12和智慧屏13之间也可以采用短距离无线连接技术或长距离无线连接技术建立连接； 其中，短距离无线连接技术可以包括紫蜂(ZigBee)等等；长距离无线连接技术可以包括无线保真技术(wireless fidelity，WIFI)，蜂窝移动通信(cellular mobile communication)等等。
接下来，介绍本方案中提供的一种体脂秤的硬件结构示意图。示例性的，该体脂秤可以为图1a或图1b中所示的体脂秤11。
图2是本申请实施例提供的一种体脂秤的硬件结构示意图。如图2所示，该体脂秤200可以包括：秤本体21和手柄22。秤本体21和手柄22之间可以通过线缆23连接。在秤本体21上可以设置有至少4个电极211。手柄22上可以设置有至少4个电极221。其中，用户使用体脂秤200时，秤本体21上的至少两个电极与用户的左脚接触，且至少两个电极与用户的右脚接触；手柄22上的至少两个电极与用户的左手接触，且至少两个电极与用户的右手接触。
可以理解的是，在秤本体21中还可以设置有压力传感器(图中未示出)。通过该压力传感器可以检测用户的体重。此外，在手柄22上还可以设置有显示屏222，该显示屏222可以显示用户的体重，体脂率等等。
本方案中，体脂秤200中还可以设置有处理器(图中未示出)和通信模块(图中未示出)。其中，体脂秤200中的处理器可以基于秤本体21中的压力传感器检测到的信号，确定出用户的体重；基于秤本体21和手柄22上的电极检测到的信号，确定用户的身体阻抗；以及基于确定出的身体阻抗，确定出用户的体脂率等等。
体脂秤200中的通信模块可以为体脂秤200提供短距离通信或长距离通信，以实现该体脂秤200与其他电子设备之间的信息交互。示例性的，体脂秤200中的通信模块可以为蓝牙(Bluetooth)，紫蜂(ZigBee)、无线保真技术(wireless fidelity，WIFI)、蜂窝移动通信(cellular mobile communication)等等。
可以理解的是，本方案图2示意的结构并不构成对体脂秤的具体限定。在本方案另一些实施例中，体脂秤可以包括比图示更多或更少的部件，或者组合某些部件，或者拆分某些部件，或者不同的部件布置。图示的部件可以以硬件，软件或软件和硬件的组合实现。
接下来，介绍本方案中提供的一种电子设备的硬件结构示意图。示例性的，该电子设备可以为图1a或图1b所示的手机12，也可以为图1b中所示的智慧屏13。
图3是本申请实施例提供的一种电子设备的硬件结构示意图。如图3所示，该电子设备300可以包括：可以包括：处理器301、存储器302和通信模块303。其中，处理器301，存储器302，及通信模块303可以通过总线或其他方式连接。
本方案中，处理器301是电子设备的计算核心及控制核心。处理器301可以包括一个或多个处理单元，例如，处理器301可可以包括应用处理器(application processor，AP)、调制解调器(modem)、图形处理器(graphics processing unit，GPU)、图像信号处理器(image signal processor，ISP)、控制器、视频编解码器、数字信号处理器(digital signal processor，DSP)、基带处理器、和/或神经网络处理器(neural-network processing unit，NPU)等中的一项或多项。其中，不同的处理单元可以是独立的器件，也可以集成在一个或多个处理器中。在一个例子中，该处理器301可以实现本方案中提供的肝脏脂肪等级检测方法。示例性的，处理器301可以基于体脂秤检测到的体重和身体阻抗，确定出待测人员的身体参数；处理器301也可以基于待测人员的身体形态，确定出与该身体形态对应的等级确定模型； 处理器301还可以将待测人员的身体参数，输入至该等级确定模型，得到待测人员的肝脏脂肪等级，等等。
存储器302是电子设备的记忆设备，用于存放程序和数据，例如存放电子设备自身的位置和电子设备接收到的其他电子设备的位置等。可以理解的是，此次的存储器302可以是高速RAM存储器，也可以是非易失性存储器(non-volatile memory)；可选地，存储器302还可以是至少一个位于远离前述处理器301的存储装置。存储器302可以提供存储空间，该存储空间可以存储电子设备的操作系统和可执行程序代码，可包括但不限于：Windows系统(一种操作系统)，Linux系统(一种操作系统)，鸿蒙系统(一种操作系统)等等，在此不做限定。示例性的，存储器302中可以存储有用于确定肝脏脂肪等级的等级确定模型。
通信模块303可以为电子设备提供短距离通信或长距离通信，以实现该电子设备与其他电子设备(如体脂秤等)之间的信息交互。示例性的，通信模块303可以为蓝牙(Bluetooth)，紫蜂(ZigBee)、无线保真技术(wireless fidelity，WIFI)、蜂窝移动通信(cellular mobile communication)等等。
可选地，电子设备300中还可以包括显示屏304。示例性的，显示屏304可以显示待测人员的肝脏脂肪等级，身体参数等。
可选地，电子设备300中还可以包括摄像头305。摄像头305可以用于捕获静态图像或视频，例如，采集待测人员的图像等。
可以理解的是，本方案图3示意的结构并不构成对电子设备的具体限定。在本方案另一些实施例中，电子设备可以包括比图示更多或更少的部件，或者组合某些部件，或者拆分某些部件，或者不同的部件布置。图示的部件可以以硬件，软件或软件和硬件的组合实现。
以上即是对本方案中涉及的肝脏脂肪等级检测系统，及该系统中涉及的电子设备的硬件结构的介绍。接下来基于上述描述的内容，对本方案提供的肝脏脂肪等级检测方案进行详细介绍。
(1)确定待测人员的体重和身体阻抗
本方案中，可以通过图2中所示的体脂秤200确定待测人员的体重和身体阻抗。如图4所示，待测人员A可以站立在体脂秤200的秤本体21上，并手持体脂秤200的手柄22。其中，待测人员A的左脚可以与秤本体21上的两个电极211接触，待测人员A的右脚也可以与秤本体21上的两个电极211接触；待测人员A的左手可以与手柄22上的两个电极(图中未示出)接触，待测人员A的右手也可以与手柄22上的两个电极(图中未示出)接触。在待测人员A启动检测后，体脂秤200上的各个电极可以产生特定频率的电信号测量待测人员A的身体阻抗。本方案中，身体阻抗可以包括：左臂阻抗，右臂阻抗，左腿阻抗，右腿阻抗，躯干阻抗等；其中，身体上不同区域的阻抗可以同时获取，也可以分时获取，具体可根据实际情况而定，在此不做限定。
在一个例子中，继续参阅图4，体脂秤200在测量待测人员A的身体阻抗时，可以先导通其手柄22上左侧和右侧的电极，然后再根据通电电流和通电电压测得双臂阻抗R1。接着，体脂秤200可以停止导通其手柄22上的电极，并导通其本体21上的电极，然后再根据通电电流和通电电压测得双腿阻抗R2。接着，体脂秤200可以停止导通其本体21上左侧和右侧的电极，并导通其手柄22的左侧的电极和其本体21上右侧的电极，然后再根据通电电流和通电电压测得左斜半身阻抗R3。接着，体脂秤200可以停止导通导通其手柄22的左侧的电 极和其本体21上右侧的电极，并导通其手柄22的右侧的电极和其本体21上左侧的电极，然后再根据通电电流和通电电压测得右斜半身阻抗R4。接着，体脂秤200可以停止导通其手柄22的右侧的电极和其本体21上左侧的电极，并导通其手柄22的左侧的电极和其本体21上左侧的电极，然后再根据通电电流和通电电压测得左半身阻抗R5。接着，体脂秤200可以停止导通其手柄22的左侧的电极和其本体21上左侧的电极，并导通其手柄22的右侧的电极和其本体21上右侧的电极，然后再根据通电电流和通电电压测得右半身阻抗R6。最后，体脂秤200可以基于测得的双臂阻抗R1，双腿阻抗R2，左斜半身阻抗R3，右斜半身阻抗R4，左半身阻抗R5和右半身阻抗R6，确定出待测人员A的左臂阻抗，右臂阻抗，左腿阻抗，右腿阻抗，躯干阻抗。需说明的是，本方案中，各个电极的通电顺序仅是示例性说明，具体可根据实际情况而定，在此并不做限定。
示例性的，如图5所示，在测量身体阻抗时，可以将待测人员A的左臂31等效于一个电阻(即电阻311)，将待测人员A的右臂32等效于一个电阻(即电阻321)，将待测人员A的躯干33等效于一个电阻(即电阻331)，将待测人员A的左腿34等效于一个电阻(即电阻341)，将待测人员A的右腿35等效于一个电阻(即电阻351)。继续参阅图5，本方案中，双臂阻抗可以为电阻311与电阻321的电阻值之和，即双臂阻抗可以为左臂阻抗和右臂阻抗之和；双腿阻抗可以为电阻341和电阻351的电阻值之和，即双腿阻抗可以为左腿阻抗和右腿阻抗之和；躯干阻抗可以为电阻341的电阻值。进一步地，体脂秤200测得的双臂阻抗R1，双腿阻抗R2，左斜半身阻抗R3，右斜半身阻抗R4，左半身阻抗R5和右半身阻抗R6后，可以得到以下数据：
R1＝R311+R321；
R2＝R341+R351；
R3＝R311+R331+R351；
R4＝R321+R331+R341；
R5＝R311+R331+R341；
R6＝R321+R331+R351；
进一步地，通过数学运算，即可以得到待测人员A左臂，右臂，左腿，右腿和躯干的阻抗。其中，R311＝(R1+R5-R4)/2，R321＝(R1+R6-R3)/2，R331＝(R3+R4-R1-R2)/2，R341＝(R2+R5-R3)/2，R351＝(R2+R6-R4)/2，R311可以为左臂阻抗，R321可以为右臂阻抗，R331可以为躯干阻抗，R341可以为左腿阻抗，R351可以为右腿阻抗。
此外，在待测人员A站立在体脂秤200上时，若体脂秤200处于开启状态，体脂秤200则可以利用其内的压力传感器测量出待测人员A的体重。
在一个例子中，在测量待测人员A的身体阻抗时，可以控制体脂秤200上的电极产生多种频率的电信号测量待测人员的身体阻抗，由此得到流经身体中细胞内液时的阻抗和未流经身体中细胞内液的阻抗，进而从多个维度测量待测人员的身体阻抗，提升检测准确度。示例性的，体脂秤200上的电极产生的电信号的频率可以为50千赫兹(kHz)和250千赫兹(kHz)。其中，不同频率的电信号产生的先后顺序可以不同，在此不做限定；如先产生50kHz的电信号，再产生250kHz的电信号，或者先产生250kHz的电信号，再产生50kHz的电信号。其中，由于50kHz的电信号的频率较低，因此使用该频率的电信号测量时，该电信号难以穿透细胞内液，即此时测得的身体阻抗是电信号未流经身体内细胞内液时的阻抗；由于250kHz的电信号的频率较高，因此使用该频率的电信号测量时，该电信号可以穿透细胞内液，即此时测得 的身体阻抗是电信号流经身体内细胞内液时的阻抗。
(2)确定待测人员的肝脏脂肪等级
本方案中，在利用体脂秤确定出待测人员的体重和身体阻抗后，即可以基于待测人员的体重和身体阻抗，确定待测人员的肝脏脂肪等级。
在一个例子中，利用图2中所示的体脂秤200确定出待测人员A的体重和身体阻抗后，该体脂秤200可以将待测人员A的体重和身体阻抗传输至图3中所示的电子设备300中，以通过电子设备300确定待测人员A的肝脏脂肪等级。此外，对于待测人员A的身体阻抗也可以由手机基于体脂秤200测得的初始数据计算得到；其中，体脂秤200可以将其测得的双臂阻抗R1，双腿阻抗R2，左斜半身阻抗R3，右斜半身阻抗R4，左半身阻抗R5，和右半身阻抗R5，发送至电子设备300；之后，再由电子设备300计算得到左臂阻抗，右臂阻抗，左腿阻抗，右腿阻抗，和躯干阻抗等身体阻抗。
其中，电子设备300在确定待测人员A的肝脏脂肪等级之前，可以先确定待测人员A的身高等基本参数。示例性的，电子设备300上可以安装有与肝脏脂肪等级相关的应用程序(如华为运动健康等)，其中，如图6所示，待测人员A可以在该与肝脏脂肪等级相关的应用程序上输入身高等基本参数，在其输入完毕后，其可以选在区域51处的“确定”按键，即可以将其身高等基本参数录入到与肝脏脂肪等级相关的应用程序中。
电子设备300在获知到待测人员A的身高等基本参数后，即可以结合待测人员的体重和身体阻抗，确定待测人员A的身体参数，如：身体质量指数BMI，体脂率BFR，躯干内的内脏脂肪量，身体各节段脂肪量，腰臀比，或，身体形态等等。示例性的，躯干内的内脏脂肪量可以理解为躯干内大部分内脏的脂肪含量，或者躯干内全部内脏的脂肪含量。
本方案中，身体质量指数BMI＝W/H 2，其中，W为体重，H为身高。
体脂率可以通过以下公式计算。该公式(以下简称“公式一”)为：
BFR＝α 1Z1 50+α 2Z1 250+α 3Z2 50+α 4Z2 250+α 5Z3 50+α 6Z3 250+α 7w t+α 8H t+α 9
其中，BFR为体脂率；α 1，...，α 9为预先设定的系数，可以由实验获取；Z1 50为50KHz的双腿阻抗；Z1 250为250KHz的双腿阻抗；Z2 50为50KHz的双臂阻抗；Z2 250为250KHz的双臂阻抗；Z3 50为50KHz的躯干阻抗；Z3 250为250KHz的躯干阻抗；w t为体重；H t为身高。在一个例子中，当体脂秤测量出体脂率后，体脂秤也可以将体脂率发送至电子设备300，由此电子设备300即可以直接获取到体脂率。可以理解的是，公式一中的Z 50和Z 250也可以替换为其他频率的阻抗，在此不做限定。
躯干内的内脏脂肪量可以通过以下公式计算。该公式(以下简称“公式二”)为：
X＝β 1Z1 50+β 2Z1 250+β 3Z2 50+β 4Z2 250+β 5Z3 50+β 6Z3 250+β 7W t+β 8H t+β 9
其中，X为躯干内的内脏脂肪量；β 1，...，β 9为预先设定的系数，可以由实验获取；Z1 50为50KHz的双腿阻抗；Z1 250为250KHz的双腿阻抗；Z2 50为50KHz的双臂阻抗；Z2 250为250KHz的双臂阻抗；Z3 50为50KHz的躯干阻抗；Z3 250为250KHz的躯干阻抗；w t为体重；H t为身高。可以理解的是，公式二中的Z 50和Z 250也可以替换为其他频率的阻抗，在此不做限定。
身体各节段脂肪量可以通过以下公式计算。该公式(以下简称“公式三”)为：
P＝δ 1Z 50+δ 2Z 250+δ 3w t+δ 4H t+δ 5
其中，P为身体节段脂肪量，其可以是左臂脂肪量，右臂脂肪量，左腿脂肪量，右腿脂肪量，或躯干脂肪量；δ 1，...，δ 5为预先设定的系数，可以由实验获取；Z 50为50KHz的阻抗；Z 250为250KHz的阻抗；w t为体重；H t为身高。可以理解的是，P为左臂脂肪量时，Z 50为50KHz 的左臂阻抗；Z 250为250KHz的左臂阻抗；P为右臂脂肪量时，Z 50为50KHz的右臂阻抗；Z 250为250KHz的右臂阻抗；P为左腿脂肪量时，Z 50为50KHz的左腿阻抗；Z 250为250KHz的左腿阻抗；P为右腿脂肪量时，Z 50为50KHz的右腿阻抗；P为躯干脂肪量时，Z 50为50KHz的躯干阻抗；Z 250为250KHz的躯干阻抗。其中，在确定不同身体节段的脂肪量时，公式三中的参数δ可以部分相同，也可以全部相同，亦可以全部不同，具体可根据实际情况而定，在此不做限定可以理解的是，公式三中的Z 50和Z 250也可以替换为其他频率的阻抗，在此不做限定。
腰臀比可以通过以下公式计算。该公式(以下简称“公式四”)为：
Y＝γ 1L1+γ 2L2+γ 3L3+γ 4L4+γ 5L5+γ 6L6+γ 7L7+γ 8L8+γ 9L9+γ 10L10+γ 11
其中，Y为腰臀比；γ 1，...，γ 11为预先设定的系数，可以由实验获取；L1为左臂肌肉量；L2为左臂脂肪量；L3为右臂肌肉量；L4为右臂脂肪量；L5为左腿肌肉量；L6为左腿脂肪量；L7为右腿肌肉量；L8为右腿脂肪量；L9为躯干肌肉量；L10为躯干脂肪量。在一个例子中，公式四中的“左臂肌肉量L1”，“右臂肌肉量L3”，“左腿肌肉量L5”，“右腿肌肉量L7”和“躯干肌肉量L9”可以适应性选取，在此不作限定。
本方案中，身体各节段肌肉量可以通过以下公式计算。该公式(以下简称“公式五”)为：
M＝θ 1Z 50+θ 2Z 250+θ 3W t+θ 4H t+θ 5
其中,M为身体各节段肌肉量，其可以是左臂肌肉量，右臂肌肉量，左腿肌肉量，右腿肌肉量，或躯干脂肪量；θ 1，...，θ 5为预先设定的系数，可以由实验获取；Z 50为50KHz的阻抗；Z 250为250KHz的阻抗；w t为体重；H t为身高。可以理解的是，M为左腿肌肉量时，Z 50为50KHz的左腿阻抗；Z 250为250KHz的左腿阻抗；M为右腿肌肉量时，Z 50为50KHz的右腿阻抗；Z 250为250KHz的右腿阻抗；M为左腿肌肉量时，Z 50为50KHz的左腿阻抗；Z 250为250KHz的左腿阻抗；M为右腿肌肉量时，Z 50为50KHz的右腿阻抗；M为躯干肌肉量时，Z 50为50KHz的躯干阻抗；Z 250为250KHz的躯干阻抗。其中，在确定不同身体节段的肌肉量时，公式五中的参数θ可以部分相同，也可以全部相同，亦可以全部不同，具体可根据实际情况而定，在此不做限定可以理解的是，公式五中的Z 50和Z 250也可以替换为其他频率的阻抗，在此不做限定。
本方案中，在确定出腰臀比后，可以基于腰臀比，确定出当前确定身体形态所需的身体参数，然后再由该身体参数，确定身体形态。在一个例子中，可以基于腰臀比所属的区间，确定所需的身体参数。示例性的，当腰臀比处于预设区间a1(如a1∈(0.78,0.85))时，可以选用BMI确定身体形态，此时，当BMI小于预设阈值b1(如b1＝21)时，身体形态为辣椒型，当BMI大于或等于预设阈值b1时，身体形态为匀称型；当腰臀比处于预设区间a2(如a2∈(0,0.78])时，可以选用双臂的肌肉量确定身体形态，此时，当双臂的肌肉量占身体肌肉总量的比值大于或等于预设阈值b2(如b2＝0.0981)时，可以确定身体形态为沙漏型，当双臂的肌肉量占身体肌肉总量的比值小于预设阈值b2时，可以确定身体形态为梨型。
可以理解的是，为了提升身体形态确定的准确度，在确定身体形态时，可以将腰臀比与多个其他的身体参数相结合；例如，可以使用腰臀比、身体各节段的脂肪量和肌肉量、BMI、躯干内的内脏脂肪量等等。其中，可以将用于确定身体形态的参数输入至机器学习分类模型，以得到身体形态。
进一步地，在确定出待测人员A的身体形态后，电子设备300可以基于该身体形态从预先设定的身体形态与用于确定肝脏脂肪等级的等级确定模型之间的对应关系中，确定出待测人员A的身体形态对应的等级确定模型。可以理解的是，本方案中，用于确定肝脏脂肪等级的等级确定模型可以可以使用高斯过程模型、神经网络模型、支持向量机等进行训练得到； 此外，等级确定模型也可以为偏差型函数模型，比例型函数模型，混杂型函数模型，或者其他的数学函数模型。示例性的，预先设定的身体形态与用于确定肝脏脂肪等级的等级确定模型之间的对应关系可以如表一所示，当确定出身体形态为“苹果型”时，由表一中可以看出，此时应该选用的等级确定模型为“模型二”。
表一
身体形态 等级确定模型
梨型 模型一
苹果型 模型二
沙漏型 模型三
辣椒型 模型四
进一步地，电子设备300确定出等级确定模型后，可以将待测人员A的BMI，体脂率，腰臀比等身体参数输入至该等级确定模型中，经等级确定模型进行处理后即可以得到待测人员A的肝脏脂肪等级。可以理解的是，输入至等级确定模型中的参数也可以包括其他的身体参数，例如躯干内的内脏脂肪量，身体各节段脂肪量，身体形态等等，由此以提升检测的准确度。
接着，电子设备300即可以基于肝脏脂肪等级与肝脏风险系数之间的对应关系，确定出待测人员A的肝脏风险等级。示例性的，预先设定的肝脏脂肪等级与肝脏风险等级之间的对应关系可以如表二所示，当确定出肝脏脂肪等级为“5”时，由表二中可以看出，此时的肝脏风险等级为“疑似风险”。
表二
肝脏脂肪等级 肝脏风险等级
0～4 正常
4～7 疑似风险
7～10 中高风险
其中，为便于待测人员A能够及时获知到其自身的肝脏脂肪等级，本方案中，电子设备300可以将其检测到的待测人员A的肝脏脂肪等级呈现给待测人员A。示例性的，如图7所示，在电子设备300上可以显示出待测人员A的肝脏脂肪等级为7.8，且筛查结果为中高风险。此外，继续参阅图7，电子设备300还可以显示待测人员A的其他的参数，如显示身高，身体形态(图中未示出)等等；以及显示运动建议等等。
由此，本方案中，在确定肝脏脂肪等级的过程中，可以针对不同身体形态的用户，采用不同的等级确定模型计算用户的肝脏脂肪等级，从而实现了针对不同的人群采用不同的处理方式，提升了肝脏脂肪等级评测的准确性，避免了所有人均采用一种肝脏脂肪等级评测方式的情况出现。
以上即是对本方案中肝脏脂肪等级检测方案的介绍。在得到肝脏脂肪等级后，用户若想要更加精确的结果，本方案中还可以在检测过程中增加用户的形体特征参数；其中，形体特征参数包括胸围，腰围，臀围等等。可以理解的是，该形体特征参数为与肝脏中脂肪含量强相关的参数，由此以提升肝脏脂肪等级检测的精准度。在一个例子中，此时本方案中的肝脏脂肪等级检测系统可以为图1a中所示的系统，其中，手机12可以采集待测人员的照片，以确定出待测人员的形体特征参数。此外，此时本方案中的肝脏脂肪等级检测系统可以为图1b 中所示的系统，其中，智慧屏13上可以配置有摄像头；在待测人员从手机12上确定精确测量肝脏脂肪等级后，手机12可以向智慧屏13发送采集待测人员的照片的指令，以通过智慧屏13上的摄像头采集待测人员的照片；之后，智慧屏13再将待测人员的照片发送至手机12，以通过手机12确定出待测人员的形体特征参数，并得到更为精准的肝脏脂肪等级。详见下文描述。
下面分场景对增加用户的形体特征参数的方案进行详细介绍。
场景一
在该场景下电子设备300为手机，该场景可以理解为是在图1a所示的系统下的应用场景。其中，在手机上可以安装有与肝脏脂肪等级相关的应用程序(如华为运动健康等)。继续参阅图7，此时待测人员A可以选择区域61处的“下一页”按键。然后，电子设备300上可以显示如图8所示的界面，以提示待测人员A“需打开手机照相”，并由待测人员A选择是否更精准测量肝脏脂肪。若待测人员A选择区域71处的“取消”按键，则停止精准测量肝脏脂肪，如返回到图7所示的界面。若待测人员A选择区域72处的“确定”按键，则进行精准测量肝脏脂肪流程。此外，待测人员A除了选择图7中区域61以显示出图8所示的界面外，待测人员A还可以通过在图7中所示的界面上滑动，如从电子设备300的屏幕的左侧向右侧滑动，从电子设备300的右侧向左侧滑动，从电子设备300的上侧向下侧滑动，或者，从电子设备300的下侧向上侧滑动等等，以显示出图8所示的界面。可以理解的是，本方案中，图7中区域61显示的“下一页”也可以替换为其他的内容，例如，如图9所示，可以将图7中区域61中的“下一页”替换为图9a中区域62中的内容，以让待测人员A选择“确定”按键，其中，当待测人员A选择“确定”按键后，则进入到图8所示的界面。此外，如图9b所示，可以将图7中区域61中的“下一页”替换为图9b中区域63中的内容，以让待测人员A选择“是”或“否”，其中，当待测人员A选择“是”后，则进入到图8所示的界面。在一个例子中，图9b中区域63的内容可以以弹出窗口的形式出现，其中，待测人员A选择“是”后，则进入到图8所示的界面，当待测人员A选择“否”后，则可以关闭该弹出窗口，即不再显示区域63中的内容。示例性的，该弹出窗口可以在检测出肝脏脂肪等级一段时间(如3秒钟等)后弹出。
由于拍照时需要在特定的高度拍摄用户的全身图像，因此，当电子设备300为手机时，需要将手机固定于特定的位置处。如图10a所示，待测人员A在图8中所示的手机上选择区域72处的“确定”按键后，手机上可以显示拍照前的建议信息，该建议信息可以为“1.请将手机放在固定位置，并确保手机可以拍到清晰的全身照片。2.请穿紧身衣，露出腰腹部，双手下垂且不要贴在腿上”，由此以提示做出适宜的拍照动作，提升测量的准确度。进一步地，当待测人员A准备好后，可以选取图10a中区域1001处的“确定”按键，并进入拍照流程。可以理解的是，本方案中，待测人员A在手机上选择按键时，可以点击选择，也可以语音选择，亦可以通过手势选择，具体可根据实际情况而定，在此不做限定。
接着，进入拍照流程后，如图10b所示，手机可以先拍摄待测人员A的正面照片。手机在拍摄到待测人员A的正面照片后，可以检测该照片是否符合要求，如果符合则进入下一流程，如果不符合，则重新拍摄正面照片。示例性的，在拍摄到待测人员A的正面照片后，可以基于模版匹配的人体骨骼关键点检测算法(Pictorial Structure)和基于目标检测的人体骨骼关键点检测算法，如级联特征网络(cascaded feature network，CFN)，区域多人姿态预测(regional multi-person pose estimation，RMPE)，级联金字塔网络(cascaded pyramid  network，CPN)等算法，从拍摄到的图像选取人体骨骼节点，并构建肢体向量；之后，再将预先设定的标准肢体向量与得到的肢体向量进行对比，得到两者的动作差异度；接着，根据该动作差异度确定得到的图像是否符合要求。例如，当动作差异度处于预设范围内时，则确定符合要求；当动作差异度未处于预设范围内时，则确定不符合要求。可以理解的是，图10b中的人物图像仅是示意的手机当前采集到的待测人员A的正面图像。
当手机检测到拍到的图像符合要求后，手机即可以拍摄待测人员A的侧面照片，即显示如图10c所示的界面，以拍摄到待测人员A的侧面照片。手机在拍摄到待测人员A的侧面照片后，可以检测该照片是否符合要求；其中，检测方法可参见检测正面照片的方式，在此就不再一一赘述。如果符合则进入下一流程，如果不符合，则可以重新拍摄侧面照片。可以理解的是，图10c中的人物图像仅是示意的手机当前采集到的待测人员A的侧面图像。
当手机检测到拍摄的正面照片和侧面照片均符合要求后。手机可以基于神经网络(如Unet网络等)对拍摄到的正面照片和侧面照片进行图像分割，提取出人体画像。之后，手机再根据骨骼节点和轮廓节点确定对应的人体节点位置，如腋窝，腹股沟，肚脐，大腿根部等等。示例性的，可以采用上文描述的人体骨骼关键点检测算法确定对应的人体节点位置。接着，手机可以在人体节点位置结合由人体画像和身高确定出的待测人员A的身体比例，确定出胸宽，胸厚，腰宽，腰厚，臀宽，臀厚等的特征信息。接着，手机基于确定出的特征信息，即可以推测出胸围，腰围，臀围等形体特征参数。示例性的，以腰围为例，由于腰围是一个类椭圆形，因此，得到腰宽和腰厚后，即可以基于数学运算推测得到腰围。进一步地，在得到形体特征参数后，也可以基于形体特征参数中的腰围和臀围，计算得到腰臀比。可以理解的是，在拍照前测得的腰臀比为根据经验设定的公式计算得到，此时计算的腰臀比精确到较差；而在拍照后测得的腰臀比是根据待测人员的身体的特征信息计算得到，其能够真实的反映出待测人员的身体情况，即此时计算得到的腰臀比精确度较高。
进一步地，手机可以将待测人员A的腰围等形体特征参数，BMI，体脂率，腰臀比等身体参数输入至上述确定出的等级确定模型中，经等级确定模型进行处理后即可以得到待测人员A的肝脏脂肪等级。其中，此时输入至等级确定模型中的腰臀比可以是由待测人员的照片推测出的腰臀比，也可以是由拍照前计算得到的腰臀比；但为了提升检测的准确度，最好是选用由待测人员的照片推测出的腰臀比。在一个例子中，在利用正面照片和侧面照片，确定出待测人员的腰臀比后，可以将该腰臀比与基于身体阻抗确定出的腰臀比进行对比，若在确定身体形态所需的参数时两者所属的区间一致，则可以继续使用拍照前确定出的等级确定模型；若在确定身体形态所需的参数时两者所属的区间不一致，则可以使用基于照片确定的腰臀比重新确定待测人员A的身体形态，并重新确定等级确定模型，以及使用重新确定的等级确定模型计算待测人员A的肝脏脂肪等级。在一个例子中，在重新确定待测人员A的身体形态后，也可以利用该重新确定的身体形态替换拍照前确定出的身体形态，并呈现给待测人员A。
本方案中，在手机拍摄到正面照片和侧面照片后且在得到待测人员A的肝脏脂肪等级之前，即在计算待测人员A的肝脏脂肪等级的过程中，手机可以显示如图10d所显示的界面，以使待测人员获知到处理进度。可以理解的是，输入至等级确定模型中的参数也可以包括其他的参数，例如躯干内的内脏脂肪量，身体各节段脂肪量，身体形态，胸围，臀围等等，由此以提升检测的准确度。
在手机计算出待测人员A的肝脏脂肪等级后，可以如图10e所示，在手机上处显示“肝脏脂肪等级”。此外，为了使待测人员A能够获知到其自身的形体特征参数(即身体尺寸信息)， 手机上还可以显示如图10f所示的界面，以使待测人员A能够直观的了解到其自身的胸围，腰围，臀围，腰臀比等参数。可以理解的是，图10e和10f所示的界面的显示顺序可视情况而定，在此不作限定。
场景二
在该场景下电子设备300为处于固定位置的大屏，如智慧屏，其中，在该大屏上配置有图像采集装置(如摄像头等)，以采集待测人员的图像；此外，在大屏上可以安装有与肝脏脂肪等级相关的应用程序(如华为运动健康等)。该场景相当于是将图1a中的手机12替换为了大屏，在该场景下是大屏与体脂秤之间的交互，其中，大屏和体脂秤之间可以通过蓝牙建立连接。继续参阅图7，此时待测人员A可以点击“脂肪肝脏风险等级”所在的区域61。示例性的，图7中区域61处的颜色可以区别于图6中其他区域的颜色，以使待测人员A可以获知区域61处可以被点击，例如，图7中其他区域的颜色可以为灰色或白色，而区域61处的颜色可以为绿色或蓝色。然后，电子设备300上可以显示如图11a所示的界面，以提示待测人员A“需打开大屏照相功能”，并由待测人员A选择是否更精准测量肝脏脂肪。若待测人员A选择区域91处的“取消”按键，则停止精准测量肝脏脂肪，如返回到图7所示的界面。若待测人员A选择区域92处的“确定”按键，则进行精准测量肝脏脂肪流程。此外，待测人员A除了选择图7中区域61以显示出图11a所示的界面外，待测人员A还可以通过在图7中所示的界面上滑动，如从电子设备300的屏幕的左侧向右侧滑动，从电子设备300的右侧向左侧滑动，从电子设备300的上侧向下侧滑动，或者，从电子设备300的下侧向上侧滑动等等，以显示出图11a所示的界面。可以理解的是，本方案中，图7中区域61显示的“下一页”也可以替换为其他的内容，例如，如图9所示，可以将图7中区域61中的“下一页”替换为图9中区域62中的内容，以让待测人员A选择“是”或“否”，其中，当待测人员A选择“是”后，则进入到图11a所示的界面。
在待测人员A在大屏上选择“确定”按键后，如图11b所示，大屏可以显示拍照前的建议信息，该建议信息可以为“请穿紧身衣，露出腰腹部，双手下垂且不要贴在腿上”，由此以提示做出适宜的拍照动作，提升测量的准确度。进一步地，当待测人员A准备好后，可以选择图11b中区域93处的“确定”按键，并进入拍照流程。可以理解的是，本方案中，待测人员A在大屏上选择按键时，可以点击选择，也可以语音选择，亦可以通过手势选择，具体可根据实际情况而定，在此不做限定。
接着，进入拍照流程后，如图11c所示，大屏可以先利用摄像头901拍摄待测人员A的正面照片。大屏在拍摄到待测人员A的正面照片后，可以检测该照片是否符合要求。其中，如果符合则进入下一流程，如果不符合，则可以利用摄像头901重新拍摄正面照片。对于检测照片是否符合要求的方式，详见上文“场景一”中的描述，在此就不再一一赘述。可以理解的是，图11c中的人物图像仅是示意的大屏当前采集到的待测人员A的正面图像。
当大屏检测到拍到的图像符合要求后，大屏拍摄待测人员A的侧面照片，即可以显示如图11d所示的界面，并利用摄像头901拍摄到待测人员A的侧面照片。大屏在拍摄到待测人员A的侧面照片后，可以检测该照片是否符合要求。如果符合则进入下一流程，如果不符合，则可以利用摄像头901重新拍摄正面照片。可以理解的是，图11d中的人物图像仅是示意的大屏当前采集到的待测人员A的正面图像。
当大屏检测到拍摄的正面照片和侧面照片均符合要求后。大屏可以基于神经网络(如Unet 网络)对拍摄到的正面照片和侧面照片进行图像分割，提取出人体画像。之后，大屏再根据骨骼节点和轮廓节点确定对应的人体节点位置，如腋窝，腹股沟，肚脐，大腿根部等等。接着，大屏可以在人体节点位置结合人体画像和身高，确定出胸宽，胸厚，腰宽，腰厚，臀宽，臀厚，大腿宽，大腿厚等的特征信息。接着，大屏基于确定出的特征信息，即可以推测出胸围，腰围，臀围等形体特征参数。示例性的，以腰围为例，由于腰围是一个类椭圆形，因此，得到腰宽和腰厚后，即可以基于数学运算推测得到腰围。进一步地，在得到形体特征参数后，也可以基于形体特征参数中的腰围和臀围，计算得到腰臀比。可以理解的是，在拍照前测得的腰臀比为根据经验设定的公式计算得到，此时计算的腰臀比精确到较差；而在拍照后测得的腰臀比是根据待测人员的身体的特征信息计算得到，其能够真实的反映出待测人员的身体情况，即此时计算得到的腰臀比精确度较高。可以理解的是，输入至等级确定模型中的参数也可以包括其他的参数，例如躯干内的内脏脂肪量，身体各节段脂肪量，身体形态，胸围，臀围等等，由此以提升检测的准确度。
进一步地，大屏可以将待测人员A的腰围等形体特征参数，BMI，体脂率，腰臀比等身体参数输入至上述确定出的等级确定模型中，经等级确定模型进行处理后即可以得到待测人员A的肝脏脂肪等级。其中，此时输入至等级确定模型中的腰臀比可以是由待测人员的照片推测出的腰臀比，也可以是由拍照前计算得到的腰臀比；但为了提升检测的准确度，最好是选用由待测人员的照片推测出的腰臀比。在一个例子中，在利用正面照片和侧面照片，确定出待测人员的腰臀比后，可以将该腰臀比与基于身体阻抗确定出的腰臀比进行对比，若在确定身体形态所需的参数时两者所属的区间一致，则可以继续使用拍照前确定出的等级确定模型；若在确定身体形态所需的参数时两者所属的区间不一致，则可以使用基于照片确定的腰臀比重新确定待测人员A的身体形态，并重新确定等级确定模型，以及使用重新确定的等级确定模型计算待测人员A的肝脏脂肪等级。在一个例子中，在重新确定待测人员A的身体形态后，也可以利用该重新确定的身体形态替换拍照前确定出的身体形态，并呈现给待测人员A。
本方案中，在大屏拍摄到正面照片和侧面照片后且在得到待测人员A的肝脏脂肪等级之前，即在计算待测人员A的肝脏脂肪等级的过程中，大屏可以显示如图11e所显示的界面，以使待测人员获知到处理进度。
在大屏计算出待测人员A的肝脏脂肪等级后，可以如图11f所示，在大屏上显示“肝脏脂肪等级”。此外，为了使待测人员A能够获知到其自身的形体特征参数(即身体尺寸信息)，大屏上还可以显示如图11g所示的界面，以使待测人员A能够直观的了解到其自身的胸围，腰围，臀围，腰臀比等参数。可以理解的是，图11f和11g所示的界面的显示顺序可视情况而定，在此不作限定。
需说明的是，本方案中，图11a至图11g中的各个操作流程的提示信息也可以替换为其他提示信息，其可以提示用户做出与规范动作相同或相似的动作即可，在此不做限定。此外，其它图中的提示信息也可以替换为其他提示信息，在此不做限定。
场景三
该场景是在图1b所示的系统下的应用场景，其中，在手机12上可以安装有与肝脏脂肪等级相关的应用程序(如华为运动健康等)。。在该场景下，在手机12上显示出肝脏脂肪等级(即图7所示的界面，此时电子设备300可以为手机12)后，待测人员A可以点击图7中“下一页”所在的区域61。然后，如图10a所示，手机12上可以显示显示检测前的建议信息， 该建议信息可以为“1.需打开大屏照相功能，请确保大屏已开启。2.请穿紧身衣，露出腰腹部，双手下垂且不要贴在腿上”，其中，大屏可以理解为智慧屏13。若待测人员A选择“取消”按键，则停止精准测量肝脏脂肪，如返回到图7所示的界面。若待测人员A选择“确定”按键，手机12则向智慧屏13发送拍照指令。可以理解的是，本方案中，待测人员A在手机上选择按键时，可以点击选择，也可以语音选择，亦可以通过手势选择，具体可根据实际情况而定，在此不做限定。
智慧屏13接收到手机12发送的拍照指令后，可以开启其上配置的摄像头1201，进入拍照流程。在拍照流程中，智慧屏13可以先利用其上的摄像头1201拍摄待测人员A的正面照片。智慧屏13在拍摄到待测人员A的正面照片后，可以将该照片发送至手机12，以由手机12检测该照片是否符合要求。其中，当手机12检测到该照片符合要求时，则向智慧屏13发送进入下一流程的指令；当手机12检测到该照片不符合要求时，则向智慧屏13发送重新拍摄的指令，以利用摄像头1201重新拍摄正面照片。对于手机12检测照片是否符合要求的方式，详见上文“场景一”中的描述，在此就不再一一赘述。
智慧屏13在接收到手机12发送的进入下一流程的指令后，智慧屏13可以利用摄像头1201拍摄到待测人员A的侧面照片。智慧屏13在拍摄到待测人员A的侧面照片后，可以将该照片发送至手机12，以由手机12检测该照片是否符合要求。其中，当手机12检测到该照片符合要求时，则向智慧屏13发送用于指示拍摄结束的指令；当手机12检测到该照片不符合要求时，则向智慧屏13发送重新拍摄的指令，以利用摄像头1201重新拍摄侧面照片。
智慧屏13接收到手机12发送的用于指示拍摄结束的指令后，可以关闭摄像头，并结束拍摄工作。
进一步地，手机12在接收到智慧屏13发送的待测人员A的正面照片和侧面照片，且检测到正面照片和侧面照片均符合要求后，手机12可以基于神经网络(如Unet网络)对拍摄到的正面照片和侧面照片进行图像分割，提取出人体画像。之后，手机12再根据骨骼节点和轮廓节点确定对应的人体节点位置，如腋窝，腹股沟，肚脐，大腿根部等等。接着，手机12可以在人体节点位置结合人体画像和身高，确定出胸宽，胸厚，腰宽，腰厚，臀宽，臀厚，大腿宽，大腿厚等的特征信息。接着，手机12基于确定出的特征信息，即可以推测出胸围，腰围，臀围等形体特征参数。示例性的，以腰围为例，由于腰围是一个类椭圆形，因此，得到腰宽和腰厚后，即可以基于数学运算推测得到腰围。
进一步地，手机12可以将待测人员A的腰围等形体特征参数，BMI，体脂率，腰臀比等身体参数输入至上述确定出的等级确定模型中，经等级确定模型进行处理后即可以得到待测人员A的肝脏脂肪等级。在一个例子中，在利用正面照片和侧面照片，确定出待测人员的腰臀比后，可以将该腰臀比与基于身体阻抗确定出的腰臀比进行对比，若在确定身体形态所需的参数时两者所属的区间一致，则可以继续使用拍照前确定出的等级确定模型；若在确定身体形态所需的参数时两者所属的区间不一致，则可以使用基于照片确定的腰臀比重新确定待测人员A的身体形态，并重新确定等级确定模型，以及使用重新确定的等级确定模型计算待测人员A的肝脏脂肪等级。在一个例子中，在重新确定待测人员A的身体形态后，也可以利用该重新确定的身体形态替换拍照前确定出的身体形态，并呈现给待测人员A。
本方案中，手机12在得到待测人员A的正面照片和侧面照片后且在得到待测人员A的肝脏脂肪等级之前，即在计算待测人员A的肝脏脂肪等级的过程中，手机12可以显示如图10d所显示的界面，以使待测人员获知到处理进度。可以理解的是，输入至等级确定模型中的参 数也可以包括其他的参数，例如躯干内的内脏脂肪量，身体各节段脂肪量，身体形态，胸围，臀围等等，由此以提升检测的准确度。
在手机12计算出待测人员A的肝脏脂肪等级后，可以如图10e所示，在手机12上显示“肝脏脂肪等级”。此外，为了使待测人员A能够获知到其自身的形体特征参数(即身体尺寸信息)，手机12上还可以显示如图10f所示的界面，以使待测人员A能够直观的了解到其自身的胸围，腰围，臀围，腰臀比等参数。可以理解的是，图10e和10f所示的界面的显示顺序可视情况而定，在此不作限定。另外，手机12也可以将测得的肝脏脂肪等级和/或形态特征参数发送至智慧屏13，如以投屏的方式发生至智慧屏13，以在智慧屏13上进行显示。
由此，在肝脏脂肪等级检测过程中，将待测人员的形体特征参数和身体形态等参数相结合，在实现了针对不同的人群采用不同的处理方式的同时，进一步提升了肝脏脂肪等级评测的准确性，避免了所有人均采用一种肝脏脂肪等级评测方式的情况出现。
接下来，基于上文所描述的肝脏脂肪等级检测方案，对本申请实施例提供的一种生理参数检测方法进行介绍。可以理解的是，该方法是基于上文所描述的肝脏脂肪等级检测方案提出，该方法中的部分或全部内容可以参见上文对肝脏脂肪等级检测方案的描述。
图12是本申请实施例提供的一种生理参数测量方法的流程示意图。如图12所示，该方法可以包括以下步骤：
步骤101、确定待测人员的第一身体参数和第二身体参数。
本方案中，第一身体参数和第二身体参数均可以由第一电子设备测得。其中，第一电子设备可以具有至少8个电极，第二身体参数可以基于第一电子设备所具有的至少8个电极测得。示例性的，第二身体参数可以由第一电子设备控制至少8个电极产生至少两种不同频率的电信号测得，由此通过不同频率的电信号测得第二身体参数，提升后续检测的精准度。示例性的，第一电子设备可以为图1a中所示的体脂秤11。
在一个例子中，第一身体参数可以为体重，第二身体参数可以为身体阻抗或者是计算身体阻抗所需的原始数据(如电压、电流等)。在一个例子中，身体阻抗可以包括与双臂相关的阻抗，与双腿相关的阻抗，和与躯干相关的阻抗。示例性的，与双臂相关的阻抗可以包括双臂阻抗、左臂阻抗或右臂阻抗等，与双腿相关的阻抗可以包括双腿阻抗、左腿阻抗或右腿阻抗等，与躯干相关的阻抗可以包括躯干阻抗，或者包含躯干阻抗的其他阻抗等。
步骤102、响应于待测人员的输入，确定第三身体参数。
示例性的，第三身体参数可以为身高。
步骤103、根据第一身体参数和第三身体参数，确定第四身体参数。
示例性的，第四身体参数可以为身体质量指数BMI。其中，当第一身体参数为体重，第三身体参数为身高时，第四身体参数可以为BMI。确定BMI的公式为：身体质量指数BMI＝W/H 2，其中，W为体重，H为身高。
步骤104、根据第二身体参数，确定待测人员的第一身体形态。
在一个例子中，确定待测人员的第一身体形态时，可以先根据第二身体参数，确定第五身体参数。然后，在判断第五身体参数所属的区间。其中，若第五身体参数属于第一区间，则根据第四身体参数，确定待测人员的第一身体形态；若第五身体参数属于第二区间，根据第六身体参数，确定待测人员的第一身体形态，其中，第六身体参数基于第二身体参数得到。 示例性的，第五身体参数可以为腰臀比，第六身体参数可以为待测人员的双臂的肌肉量或者脂肪量等等；其中，当第二身体参数为身体阻抗时，可以先利用上文所描述的公式三基于身体阻抗确定待测人员的身体各节段的脂肪量，然后再利用上文描述的公式四基于脂肪量，确定腰臀比；接着由腰臀比选用使用的身体参数，最后再由选取的身体参数确定身体形态。示例性的，选取的身体参数可以为BMI，肌肉量，脂肪量等等。对于肌肉量的计算方法可以参见上文公式五中的描述。
步骤105、至少根据第一身体形态和第四身体参数，确定第一生理参数。
本方案中，可以根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在对应关系中不同的身体形态对应有不同的检测模型；至少将第四身体参数输入至与第一身体形态对应的检测模型，确定第一生理参数。由此，基于待测人员的身体形态确定出与其身体形态适配的检测模型，进而利用该检测模型对待测人员的生理参数进行检测，从而实现对不同身体形态的用户使用不同的检测模型检测生理参数，提升了生理参数的准确性。示例性的，检测模型可以为上文所描述的等级确定模型，对应关系可以为上文表一所示。示例性的，第一生理参数可以为肝脏脂肪等级。
步骤106、显示第一生理参数。
本方案中，确定出待测人员的第一生理参数后，可以显示第一生理参数，以使待测人员可以直观的查看到其自身的生理参数。示例性的，当第一生理参数为肝脏脂肪等级时，可以以如图7所示的界面显示该第一生理参数。
在一个例子中，在显示第一生理参数之后，还可以对待测人员的第一生理参数进行精准测量。具体的，如图13所示，包括以下步骤：
步骤201、响应于待测人员的第一操作，确定待测人员的第一照片和第二照片。
其中，待测人员可以进行第一操作，该第一操作可以为对第一生理参数进行精准测量的操作。之后，即可以响应该第一操作，确定待测人员的第一照片和第二照片。其中，本方案中第一照片为在待测人员做出第一预设动作时拍摄，第二照片为在待测人员做出第二预设动作时拍摄。
示例性的，当第一生理参数为肝脏脂肪等级时，第一操作可以为待测人员在图7中区域61处的点击操作。第一照片可以为待测人员的正面照片，第二照片可以为待测人员的侧面照片。其中，获取正面照片和侧面照片的方式详见上文场景一至三中的描述，在此就不再一一赘述。
在一个例子中，确定待测人员的第一照片和第二照片，具体可以包括：确定当前获取到的目标照片上待测人员的动作与目标预设动作之间的动作差异度，以及确定动作差异度处于预设范围内；其中，目标照片为第一照片，目标预设动作为第一预设动作，或者，目标照片为第二照片，目标预设动作为第二预设动作。由此，以使得在待测人员做出预设动作时再利用采集到的照片进行检测，提升了生理参数检测的准确度。示例性的，可以通过上文描述的基于模版匹配的人体骨骼关键点检测算法(Pictorial Structure)和基于目标检测的人体骨骼关键点检测算法，如级联特征网络(cascaded feature network，CFN)，区域多人姿态预测(regional multi-person pose estimation，RMPE)，级联金字塔网络(cascaded pyramid network，CPN)等算法检测动作差异度，详见上文描述。
步骤202、根据第一照片和第二照片，确定待测人员的第八身体参数。
其中，在确定出第一照片和第二照片后，即可以根据第一照片和第二照片，确定待测人 员的第八身体参数。其中，第八身体参数为与第一生理参数强相关的参数。示例性的，第八身体参数可以为腰围。对于腰围的确定方式，详见上文场景一至三中的描述，在此就不再一一赘述。
步骤203、至少根据第一身体形态，第四身体参数和第八身体参数，重新确定第一生理参数。
其中，在确定出第八参数后，可以根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在第二对应关系中不同的身体形态对应有不同的检测模型；至少将第四身体参数和第八身体参数输入至与第一身体形态对应的检测模型，重新确定第一生理参数。示例性的，检测模型可以为上文所描述的等级确定模型，对应关系可以为上文表一所示。示例性的，第一生理参数可以为肝脏脂肪等级。
在一个例子中，在将第四身体参数和第八身体参数输入至与第一身体形态对应的检测模型之前，还可以根据第一照片和第二照片，确定待测人员的第九身体参数；根据第八身体参数和第九身体参数，确定待测人员的第二身体形态。示例性的，第九身体参数可以为臀围。由于照片上可以准确的呈现待测人员的身体形态，因此可以通过待测人员的照片准确的确定出待测人员的身体形态，提升身体形态检测的准确度。对于臀围的确定方式，详见上文场景一至三中的描述，在此就不再一一赘述。
在一个例子中，若第二身体形态与第一身体形态不一致，则可以根据第二身体形态，查询对应关系，确定出与第二身体形态对应的检测模型，以及选用与第二身体形态对应的检测模型重新确定第一生理参数。由此，以通过照片检测的待测人员的身体形态为准，并对待测人员的生理参数进行检测，提升了生理参数检测的准确度。
步骤204、显示第一生理参数。
其中，在确定出第一生理参数后，即可以显示重新确定的第一生理参数。示例性的，重新显示的第一生理参数可以以图10e所示的界面显示。此外，也可以显示重新确定出的待测人员的身体形态。
需要说明的是，图12中所提供的方法可以由第一电子设备执行，也可以由第二电子设备执行。其中，当由第二电子设备执行时，第二电子设备和第一电子设备之间可以通信连接；第二电子设备在确定确定待测人员的第一身体参数和第二身体参数时，第二电子设备可以接收第一电子设备发送的第一身体参数和第二身体参数。由此，以通过多个电子设备结合检测出待测人员的生理参数。示例性的，第一电子设备可以为图1a中所示的体脂秤11，第二电子设备可以为图1a中所示的手机12。
接下来，基于上文所描述的肝脏脂肪等级检测方案，对本申请实施例提供的一种生理参数检测系统进行介绍。可以理解的是，该系统是基于上文所描述的肝脏脂肪等级检测方案提出，该方法中的部分或全部内容可以参见上文对肝脏脂肪等级检测方案的描述。
图14是本申请实施例中提供的一种生理参数测量系统的架构示意图。如图14所示，该系统包括：第一电子设备1401和第二电子设备1402，第一电子设备1401和第二电子设备1402之间通信连接，第一电子设备1401具有至少8个电极。
其中，第一电子设备1401可以用于确定待测人员的第一身体参数和第二身体参数，以及将第一身体参数和第二身体参数发送至第二电子设备1402，第二身体参数基于至少8个电极测得。第二电子设备1402可以用于响应于待测人员的输入，确定第三身体参数。第二电子设 备1402还可以用于响应接收第一身体参数和第二身体参数，根据第一身体参数和第三身体参数，确定第四身体参数。第二电子设备1402还可以用于根据第二身体参数，确定待测人员的第一身体形态。第二电子设备1402还可以用于至少根据第一身体形态和第四身体参数，确定第一生理参数，以及显示第一生理参数。
示例性的，第一电子设备1401可以为图1a中所示的体脂秤11，第二电子设备1402可以为图1a中所示的手机12。
在一个例子中，第二电子设备1402还可以用于：
根据第二身体参数，确定第五身体参数；
若第五身体参数属于第一区间，根据第四身体参数，确定待测人员的第一身体形态；
若第五身体参数属于第二区间，根据第六身体参数，确定待测人员的第一身体形态，其中，第六身体参数基于第二身体参数得到。
在一个例子中，第二电子设备1402还可以用于：
根据第二身体参数，确定第七身体参数，以及根据第七身体参数，确定第五身体参数。
在一个例子中，第二电子设备1402还可以用于：
根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在对应关系中不同的身体形态对应有不同的检测模型；
至少将第四身体参数输入至与第一身体形态对应的检测模型，确定第一生理参数。
在一个例子中，第二电子设备1402在显示第一生理参数之后，还可以用于：
响应于待测人员的第一操作，确定待测人员的第一照片和第二照片，第一照片为在待测人员做出第一预设动作时拍摄，第二照片为在待测人员做出第二预设动作时拍摄；
根据第一照片和第二照片，确定待测人员的第八身体参数；
至少根据第一身体形态，第四身体参数和第八身体参数，重新确定第一生理参数；
显示第一生理参数。
在一个例子中，第二电子设备1402还可以用于：根据第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与第一身体形态对应的检测模型，其中，在第二对应关系中不同的身体形态对应有不同的检测模型；以及至少将第四身体参数和第八身体参数输入至与第一身体形态对应的检测模型，确定第一生理参数。
在一个例子中，第二电子设备1402在将第四身体参数和第八身体参数输入至与第一身体形态对应的检测模型之前，还可以用于：根据第一照片和第二照片，确定待测人员的第九身体参数；以及根据第八身体参数和第九身体参数，确定待测人员的第二身体形态。
在一个例子中，第二电子设备1402还可以用于：在第二身体形态与第一身体形态不一致时，根据第二身体形态，查询对应关系，确定出与第二身体形态对应的检测模型，以及选用检测模型确定第一生理参数。
在一个例子中，第二电子设备1402还可以用于：确定当前获取到的目标照片上待测人员的动作与目标预设动作之间的动作差异度，以及确定动作差异度处于预设范围内。
其中，目标照片为第一照片，目标预设动作为第一预设动作，或者，目标照片为第二照片，目标预设动作为第二预设动作。
在一个例子中，第二电子设备1402还可以用于：显示待测人员的身体形态。
在一个例子中，第一电子设备1401还可以用于：控制至少8个电极产生至少两种不同频率的电信号；分别确定基于各个频率的电信号测得的身体参数，得到第二身体参数。示例性 的，第一电子设备1401可以可以控制8个电极产生50KHz和250KHz的电信号。
应当理解的是，上述第二电子设备可以用于执行上述实施例中的方法，其实现原理和技术效果与上述方法中的描述类似，该第二电子设备的工作过程可参考上述方法中的对应过程，此处不再赘述。
可以理解的是，本申请的实施例中的处理器可以是中央处理单元(central processing unit，CPU)，还可以是其他通用处理器、数字信号处理器(digital signal processor，DSP)、专用集成电路(application specific integrated circuit，ASIC)、现场可编程门阵列(field programmable gate array，FPGA)或者其他可编程逻辑器件、晶体管逻辑器件，硬件部件或者其任意组合。通用处理器可以是微处理器，也可以是任何常规的处理器。
本申请的实施例中的方法步骤可以通过硬件的方式来实现，也可以由处理器执行软件指令的方式来实现。软件指令可以由相应的软件模块组成，软件模块可以被存放于随机存取存储器(random access memory，RAM)、闪存、只读存储器(read-only memory，ROM)、可编程只读存储器(programmable rom，PROM)、可擦除可编程只读存储器(erasable PROM，EPROM)、电可擦除可编程只读存储器(electrically EPROM，EEPROM)、寄存器、硬盘、移动硬盘、CD-ROM或者本领域熟知的任何其它形式的存储介质中。一种示例性的存储介质耦合至处理器，从而使处理器能够从该存储介质读取信息，且可向该存储介质写入信息。当然，存储介质也可以是处理器的组成部分。处理器和存储介质可以位于ASIC中。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者通过所述计算机可读存储介质进行传输。所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质(例如固态硬盘(solid state disk，SSD))等。
可以理解的是，在本申请的实施例中涉及的各种数字编号仅为描述方便进行的区分，并不用来限制本申请的实施例的范围。

Claims (27)

  1. 一种生理参数测量方法，其特征在于，所述方法包括：
    确定待测人员的第一身体参数和第二身体参数，所述第一身体参数和所述第二身体参数由第一电子设备测得，所述第二身体参数基于所述第一电子设备所具有的至少8个电极测得；
    响应于所述待测人员的输入，确定第三身体参数；
    根据所述第一身体参数和所述第三身体参数，确定第四身体参数；
    根据所述第二身体参数，确定所述待测人员的第一身体形态；
    至少根据所述第一身体形态和所述第四身体参数，确定第一生理参数；
    显示所述第一生理参数。
  2. 根据权利要求1所述的方法，其特征在于，所述根据所述第二身体参数，确定所述待测人员的第一身体形态，具体包括：
    根据所述第二身体参数，确定第五身体参数；
    若所述第五身体参数属于第一区间，根据所述第四身体参数，确定所述待测人员的第一身体形态；
    若所述第五身体参数属于第二区间，根据第六身体参数，确定所述待测人员的第一身体形态，其中，所述第六身体参数基于所述第二身体参数得到。
  3. 根据权利要求2所述的方法，其特征在于，根据所述第二身体参数，确定第五身体参数，具体包括：
    根据所述第二身体参数，确定第七身体参数，以及根据所述第七身体参数，确定所述第五身体参数。
  4. 根据权利要求1-3任一所述的方法，其特征在于，所述至少根据所述第一身体形态和所述第四身体参数，确定第一生理参数，具体包括：
    根据所述第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与所述第一身体形态对应的检测模型，其中，在所述对应关系中不同的身体形态对应有不同的检测模型；
    至少将所述第四身体参数输入至与所述第一身体形态对应的检测模型，确定所述第一生理参数。
  5. 根据权利要求1所述的方法，其特征在于，所述显示所述第一生理参数之后，所述方法还包括：
    响应于所述待测人员的第一操作，确定所述待测人员的第一照片和第二照片，所述第一照片为在所述待测人员做出第一预设动作时拍摄，所述第二照片为在所述待测人员做出第二预设动作时拍摄；
    所述根据所述第一照片和所述第二照片，确定所述待测人员的第八身体参数；
    至少根据所述第一身体形态，所述第四身体参数和所述第八身体参数，重新确定所述第一生理参数；
    显示所述第一生理参数。
  6. 根据权利要求5所述的方法，其特征在于，所述至少根据所述第一身体形态，所述第四身体参数和所述第八身体参数，确定第二生理参数，具体包括：
    根据所述第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与所 述第一身体形态对应的检测模型，其中，在所述第二对应关系中不同的身体形态对应有不同的检测模型；
    至少将所述第四身体参数和所述第八身体参数输入至与所述第一身体形态对应的检测模型，重新确定所述第一生理参数。
  7. 根据权利要求6所述的方法，其特征在于，所述至少将所述第四身体参数和所述第八身体参数输入至与所述第一身体形态对应的检测模型之前，还包括：
    根据所述第一照片和所述第二照片，确定所述待测人员的第九身体参数；
    根据所述第八身体参数和所述第九身体参数，确定所述待测人员的第二身体形态。
  8. 根据权利要求7所述的方法，其特征在于，所述方法还包括：
    若所述第二身体形态与所述第一身体形态不一致；
    根据所述第二身体形态，查询所述对应关系，确定出与所述第二身体形态对应的检测模型，以及选用与所述第二身体形态对应的检测模型重新确定所述第一生理参数。
  9. 根据权利要求5-8任一所述的方法，其特征在于，所述确定所述待测人员的第一照片和第二照片，具体包括：
    确定当前获取到的目标照片上所述待测人员的动作与目标预设动作之间的动作差异度，以及确定所述动作差异度处于预设范围内；
    其中，所述目标照片为所述第一照片，所述目标预设动作为所述第一预设动作，或者，所述目标照片为所述第二照片，所述目标预设动作为所述第二预设动作。
  10. 根据权利要求1-9任一所述的方法，其特征在于，所述方法还包括：
    显示所述待测人员的身体形态。
  11. 根据权利要求1-10任一所述的方法，其特征在于，所述第二身体参数由所述第一电子设备控制所述至少8个电极产生至少两种不同频率的电信号测得。
  12. 根据权利要求1-11任一所述的方法，其特征在于，所述方法由所述第一电子设备执行。
  13. 根据权利要求1-11任一所述的方法，其特征在于，所述方法由第二电子设备执行，其中，所述第二电子设备和所述第一电子设备之间通信连接；
    所述确定待测人员的第一身体参数和第二身体参数，具体包括：
    所述第二电子设备接收所述第一电子设备发送的所述第一身体参数和所述第二身体参数。
  14. 一种生理参数测量系统，其特征在于，所述系统包括第一电子设备和第二电子设备，所述第一电子设备和所述第二电子设备之间通信连接，所述第一电子设备具有至少8个电极；
    其中，所述第一电子设备用于确定待测人员的第一身体参数和第二身体参数，以及将所述第一身体参数和所述第二身体参数发送至所述第二电子设备，所述第二身体参数基于所述至少8个电极测得；
    所述第二电子设备用于响应于所述待测人员的输入，确定第三身体参数；
    所述第二电子设备还用于响应接收所述第一身体参数和所述第二身体参数，根据所述第一身体参数和所述第三身体参数，确定第四身体参数；
    所述第二电子设备还用于根据所述第二身体参数，确定所述待测人员的第一身体形态；
    所述第二电子设备还用于至少根据所述第一身体形态和所述第四身体参数，确定第一生 理参数，以及显示所述第一生理参数。
  15. 根据权利要求14所述的系统，其特征在于，所述第二电子设备还用于：
    根据所述第二身体参数，确定第五身体参数；
    确定所述第五身体参数对应的参数值属于第一区间，根据所述第四身体参数，确定所述待测人员的第一身体形态；或者
    确定所述第五身体参数对应的参数值属于第二区间，根据第六身体参数，确定所述待测人员的第一身体形态，其中，所述第六身体参数基于所述第二身体参数得到。
  16. 根据权利要求15所述的系统，其特征在于，所述第二电子设备还用于：
    根据所述第二身体参数，确定第七身体参数，以及根据所述第七身体参数，确定所述第五身体参数。
  17. 根据权利要求14-16任一所述的系统，其特征在于，所述第二电子设备还用于：
    根据所述第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与所述第一身体形态对应的检测模型，其中，在所述对应关系中不同的身体形态对应有不同的检测模型；
    至少将所述第四身体参数输入至与所述第一身体形态对应的检测模型，确定所述第一生理参数。
  18. 根据权利要求14所述的系统，其特征在于，所述第二电子设备在显示所述第一生理参数之后，还用于：
    响应于所述待测人员的第一操作，确定所述待测人员的第一照片和第二照片，所述第一照片为在所述待测人员做出第一预设动作时拍摄，所述第二照片为在所述待测人员做出第二预设动作时拍摄；
    根据所述第一照片和所述第二照片，确定所述待测人员的第八身体参数；
    至少根据所述第一身体形态，所述第四身体参数和所述第八身体参数，重新确定所述第一生理参数；
    显示所述第一生理参数。
  19. 根据权利要求18所述的系统，其特征在于，所述第二电子设备还用于：
    根据所述第一身体形态，查询预先确定的身体形态与检测模型的对应关系，确定出与所述第一身体形态对应的检测模型，其中，在所述第二对应关系中不同的身体形态对应有不同的检测模型；
    至少将所述第四身体参数和所述第八身体参数输入至与所述第一身体形态对应的检测模型，确定所述第一生理参数。
  20. 根据权利要求19所述的系统，其特征在于，所述第二电子设备在将所述第四身体参数和所述第八身体参数输入至与所述第一身体形态对应的检测模型之前，还用于：
    根据所述第一照片和所述第二照片，确定所述待测人员的第九身体参数；
    根据所述第八身体参数和所述第九身体参数，确定所述待测人员的第二身体形态，以及确定所述第二身体形态与所述第一身体形态一致。
  21. 根据权利要求20所述的系统，其特征在于，所述第二电子设备还用于：
    确定所述第二身体形态与所述第一身体形态不一致；
    根据所述第二身体形态，查询所述对应关系，确定出与所述第二身体形态对应的检测模型，以及选用所述检测模型确定所述第一生理参数。
  22. 根据权利要求18-20任一所述的系统，其特征在于，所述第二电子设备还用于：
    确定当前获取到的目标照片上所述待测人员的动作与目标预设动作之间的动作差异度，以及确定所述动作差异度处于预设范围内；
    其中，所述目标照片为所述第一照片，所述目标预设动作为所述第一预设动作，或者，所述目标照片为所述第二照片，所述目标预设动作为所述第二预设动作。
  23. 根据权利要求14-22任一所述的系统，其特征在于，所述第二电子设备还用于：
    显示所述待测人员的身体形态。
  24. 根据权利要求14-23任一所述的系统，其特征在于，所述第一电子设备还用于：
    控制所述至少8个电极产生至少两种不同频率的电信号；
    分别确定基于各个频率的电信号测得的身体参数，得到所述第二身体参数。
  25. 一种电子设备，其特征在于，包括：
    至少一个存储器，用于存储程序；
    至少一个处理器，用于执行所述存储器存储的程序，当所述存储器存储的程序被执行时，所述处理器用于执行如权利要求1-13任一所述的方法。
  26. 一种计算机存储介质，所述计算机存储介质中存储有指令，当所述指令在计算机上运行时，使得计算机执行如权利要求1-13任一所述的方法。
  27. 一种包含指令的计算机程序产品，当所述指令在计算机上运行时，使得所述计算机执行如权利要求1-13任一所述的方法。
PCT/CN2022/084397 2021-05-14 2022-03-31 生理参数测量方法、系统及电子设备 WO2022237377A1 (zh)

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