WO2021233019A1 - 一种体成分检测方法、电子设备和计算机可读存储介质 - Google Patents

一种体成分检测方法、电子设备和计算机可读存储介质 Download PDF

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Publication number
WO2021233019A1
WO2021233019A1 PCT/CN2021/086968 CN2021086968W WO2021233019A1 WO 2021233019 A1 WO2021233019 A1 WO 2021233019A1 CN 2021086968 W CN2021086968 W CN 2021086968W WO 2021233019 A1 WO2021233019 A1 WO 2021233019A1
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WIPO (PCT)
Prior art keywords
gender
current user
mass
body composition
fat
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PCT/CN2021/086968
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English (en)
French (fr)
Inventor
赵帅
杨斌
任慧超
李玥
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华为技术有限公司
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Publication of WO2021233019A1 publication Critical patent/WO2021233019A1/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
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
    • G01G19/44—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing persons
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01G—WEIGHING
    • G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
    • G01G19/52—Weighing apparatus combined with other objects, e.g. furniture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present invention relates to the technical field of body composition detection, in particular to a computer-readable storage medium, a body composition detection method, electronic equipment, and a computer-readable storage medium.
  • body composition detection based on Data Independent Acquisition (DIA) quantitative technology usually requires information including impedance, weight, gender, age, and height. Among them, impedance and weight are determined by body fat. The weight measurement module and fat measurement module of the scale are measured, and the gender, age, and height are input by the user.
  • DIA Data Independent Acquisition
  • gender is a necessary parameter for calculating body composition.
  • users must input gender information to obtain accurate body composition.
  • inputting too much information will affect the user experience, especially the application experience of the user in the visitor mode, resulting in a lack of intelligence in body composition detection.
  • the present invention provides a body composition detection method, electronic equipment, and computer-readable storage medium, which can effectively reduce user input, improve user experience, and enhance intelligence by actively identifying the gender of a user.
  • an embodiment of the present invention provides a body composition detection method, including:
  • the final body composition of the current user is calculated.
  • the method before the calculation of the temporary body composition of the current user according to the acquired weight, impedance, and non-gender parameters of the current user, the method further includes:
  • the non-gender parameter includes height; in an optional implementation manner, the non-gender parameter includes height and age;
  • the calculating the temporary body composition of the current user according to the acquired weight, impedance, and non-gender parameters of the current user includes:
  • the gender-related characteristic body components include right upper limb muscle and right upper limb fat; or, the gender-related characteristic body components include visceral fat grade, total body water, and fat removal. Body weight, bone mineral content, basal metabolic rate and muscle mass.
  • the gender of the current user is identified according to the characteristic body components.
  • a machine learning algorithm may be used to identify the gender of the current user according to the characteristic body composition.
  • other algorithms can also be used to identify the gender of the current user.
  • algorithms for identifying gender include but are not limited to support vector machines, logistic regression, XGBoost, and neural networks.
  • the commonly used algorithm for gender recognition includes a support vector machine.
  • the selected gender-related feature body components include right upper limb muscles and right upper limb fat as an example to determine the right upper limb muscles Whether it is greater than the preset first threshold, and whether the right upper limb fat is greater than the preset second threshold, if it is determined that the right upper limb muscle is greater than the preset first threshold, and the right upper limb fat is greater than the preset second threshold, then it is determined that the current user’s
  • the gender is male.
  • the value ranges of the preset first threshold and the preset second threshold are not limited, and can be set according to requirements.
  • the calculating the final body composition of the current user according to the weight, impedance, non-gender parameters, and the gender of the current user includes:
  • the final body composition includes fat percentage, fat mass, lean body mass, bone salt mass, muscle mass, skeletal muscle mass, total body water, protein, basal metabolic rate, and visceral fat grade.
  • the 5 muscle mass includes left arm muscle mass, right arm muscle mass, left leg muscle mass, and right leg muscle mass
  • Trunk muscle mass the five segmental fat masses include left arm fat mass, right arm fat mass, left leg fat mass, right leg fat mass, and trunk fat mass.
  • the method further includes:
  • an exercise recommendation is generated.
  • an embodiment of the present invention provides an electronic device, and the device includes:
  • the final body composition of the current user is calculated.
  • the device when the instruction is executed by the device, the device is caused to specifically execute the following steps:
  • the non-gender parameters include height and age
  • the device When the instruction is executed by the device, the device specifically executes the following steps:
  • Y i temp b i1 W t +b i2 Z+b i3 H t +b i4 Age+b i5 .
  • the gender-related characteristic body components include right upper limb muscle and right upper limb fat; or, the gender-related characteristic body components include visceral fat grade, total body water, and fat removal. Body weight, bone mineral content, basal metabolic rate and muscle mass.
  • the device when the instruction is executed by the device, the device is caused to specifically execute the following steps:
  • the final body composition includes fat percentage, fat mass, lean body mass, bone salt mass, muscle mass, skeletal muscle mass, total body water, protein, basal metabolic rate, and visceral fat grade.
  • the 5 muscle mass includes left arm muscle mass, right arm muscle mass, left leg muscle mass, and right leg muscle mass
  • Trunk muscle mass the five segmental fat masses include left arm fat mass, right arm fat mass, left leg fat mass, right leg fat mass, and trunk fat mass.
  • the device when the instruction is executed by the device, the device is caused to specifically execute the following steps:
  • an exercise recommendation is generated.
  • an embodiment of the present invention provides an electronic device, including: a display screen; one or more processors; a memory; a plurality of application programs; and one or more computer programs, wherein the one or more computers A program is stored in the memory, and the one or more computer programs include instructions, which when executed by the device, cause the device to execute the body composition detection method in any one of the possible implementations of any of the above aspects .
  • an embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium is used for program code executed by the device, and the program code includes the program code for executing the first aspect or any of the first aspect.
  • a method instruction in a possible implementation.
  • the temporary body composition of the current user is calculated according to the acquired weight, impedance, and non-gender parameters of the current user, and the characteristic body composition related to gender is selected from the temporary body composition.
  • Body composition identifies the gender of the current user, calculates the current user’s final body composition based on the current user’s weight, impedance, non-gender parameters, and gender, displays the current user’s gender and final body composition, and actively recognizes the user’s gender. It can effectively reduce user input, improve user experience, and improve the efficiency of body composition detection.
  • FIG. 1 is a structural diagram of a body composition detection system provided by an embodiment of the present invention
  • FIG. 2 is a flowchart of a body composition detection method provided by an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of an electronic device interface provided by an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a gender display method provided by an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of another gender display mode provided by an embodiment of the present invention.
  • FIG. 6 is a schematic diagram of an electronic device interface provided by an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of another electronic device provided by an embodiment of the present invention.
  • the body composition of the user is usually calculated by impedance, weight, gender, age, and height. Among them, the impedance and weight are measured by the weight measurement module and the fat measurement module of the body fat scale. The gender, age, and height are input by the user. However, inputting too much information will affect the user experience, especially the application experience of the user in the visitor mode, resulting in a lack of intelligence in body composition detection. Therefore, the embodiment of the present invention provides a body composition detection system to solve the above-mentioned problems.
  • FIG. 1 is a structural diagram of a body composition detection system provided by an embodiment of the present invention. As shown in FIG. 1, the system 100 includes: an electronic device 110 and a terminal 120.
  • the electronic device 110 includes a body fat scale.
  • the terminal 120 may be a handheld terminal of the current user, for example, a mobile phone terminal, a tablet computer, a notebook computer, augmented reality (AR) or AR/virtual reality (VR), etc.
  • the electronic device 110 includes a detection unit 111, an interaction unit 112, a processing unit 113, a display unit 114, an evaluation unit 115 and a display screen 116.
  • the detection unit 111 is used to detect the current user's weight and impedance.
  • the detection unit 111 includes a weight measurement module and a fat measurement module.
  • the weight measurement module is used to detect the weight of the current user, and the fat measurement module is used to detect the current user's impedance.
  • the interaction unit 112 is configured to receive non-gender parameters of the current user input by the terminal 120. It should be noted that the previous interaction mode between the terminal 120 and the electronic device 110 may be Bluetooth, wifi, etc., for data transmission.
  • the processing unit 113 is used to identify the gender of the current user. Specifically, the processing unit 113 is configured to calculate the temporary body composition of the current user based on the acquired weight, impedance, and non-gender parameters of the current user through the gender-free multiple linear regression model; and filter out gender-related information from the temporary body composition.
  • Characteristic body composition Identify the current user’s gender based on the characteristic body composition; use a multiple linear regression model to calculate the current user’s final body composition based on the current user’s weight, impedance, non-gender parameters, and gender.
  • the display unit 114 is used to control the display screen 116 to display the gender and final body composition of the current user.
  • the evaluation unit 115 is configured to generate a body assessment based on the final body composition, and generate exercise recommendations based on the body assessment and gender.
  • the display unit 114 is also used to control the display screen 114 to display exercise recommendations.
  • the interaction module 112 is also used to send the final body composition exercise recommendation of the current user to the terminal 120.
  • the interaction module 112 is also used to send the current user's weight and impedance measured by the detection unit 111 to the terminal 120, so that the terminal 120 can identify the current user's gender and calculate the current user's weight and impedance according to the current user's weight and impedance.
  • the user's body composition display the user's gender and final body composition, and generate body assessments and exercise recommendations.
  • Fig. 2 is a flowchart of a body composition detection method provided by an embodiment of the present invention. As shown in Fig. 2, the method includes:
  • Step 202 Detect the weight and impedance of the current user.
  • each step is executed by the electronic device 110.
  • the electronic device 110 may include a body fat scale including a weight measurement module and a fat measurement module.
  • the weight measurement module is used to detect the weight of the current user, and the fat measurement module is used to detect the impedance of the current user.
  • the method before step 202, the method further includes: step 201, starting the body fat scale.
  • Step 204 Receive the non-gender parameter of the current user input by the terminal.
  • the non-gender parameter includes other user parameters except gender.
  • the non-gender parameter may include height.
  • the non-gender parameters may include height and age. As shown in Figure 3, the current user enters the current user's nickname, birth date, and height on the APP registration page of the terminal, and the current user does not need to enter the gender. Through the current user inputting non-gender parameters in the terminal, so as to facilitate the interaction between the terminal 120 and the electronic device 110, the electronic device 110 can receive the non-gender parameters of the current user input by the terminal 120.
  • the non-gender parameters may also include other parameters besides height and age, which are not limited in the present invention.
  • the prior art usually requires the user to input the current user's height, age, and gender in the electronic device 110 or the terminal 120, so as to calculate the user's body composition through the measured impedance, weight, and the input height, age, and gender.
  • the user does not need to input gender parameters, thereby avoiding the problem that inputting excessive information will affect the user's experience; and the gender of the current user is identified in the subsequent steps, and the visitor mode is improved.
  • the user does not need to input gender parameters, thereby avoiding the problem that inputting excessive information will affect the user's experience; and the gender of the current user is identified in the subsequent steps, and the visitor mode is improved. Of the user’s application experience.
  • Step 206 Calculate the temporary body composition of the current user according to the acquired weight, impedance, and non-gender parameters of the current user.
  • step 206 may include: calculating the temporary body composition of the current user according to the obtained weight, impedance, and non-gender parameters of the current user through a gender-free multiple linear regression model.
  • the gender-free multiple linear regression model is a pre-trained model.
  • the non-gender parameters may include height and age
  • step 206 may specifically include: using a genderless multiple linear regression model
  • Y i temp b i1 W t +b i2 Z+b i3 H t +b i4 Age+b i5 , calculate the temporary body composition of the current user according to weight, impedance, height and age, where Y i temp is expressed as temporary body composition, W t represents the body weight, Z represents an impedance, H t is expressed as the height, Age is age-expressed, b i is expressed as the regression coefficient is acquired, the experimental data obtained from the training.
  • the temporary body composition includes but is not limited to right upper limb muscle and right upper limb fat; if the body fat scale includes a four-electrode body fat scale, the temporary body composition includes But it is not limited to visceral fat grade, total body water, lean body mass, bone salt mass, basal metabolic rate, and muscle mass.
  • the temporary body composition can also include other parameters, and the calculated body composition can be determined according to the type of actual body fat scale.
  • the regression coefficient bi can be obtained by training a gender-free multiple linear regression model.
  • the prior art usually uses a multiple linear regression model with gender to calculate the user's body composition.
  • this solution will cause the user to input a large amount of information, resulting in poor user experience and low body composition detection efficiency.
  • the gender of the current user is identified through the temporary body composition, thereby reducing the amount of user input and improving the visitor mode The user’s application experience.
  • Step 208 Screen out the characteristic body components related to gender from the temporary body components.
  • the embodiment of the present invention is obtained through a large amount of research data: if the body fat scale includes an eight-electrode body fat scale, the characteristic body components related to gender include right upper limb muscle and right upper limb fat, when the right upper limb muscle and right upper limb fat are used as the and Gender-related feature body components can more accurately identify the gender of the current user. If the body fat scale includes a four-electrode body fat scale, the gender-related characteristic body components include visceral fat grade, total body water, lean body mass, bone salt, basal metabolic rate, and muscle mass.
  • the characteristic body composition related to gender can also include other parameters, which can be determined according to the type of body fat scale currently in use. The present invention does not limit this.
  • Step 210 Identify the gender of the current user according to the characteristic body components.
  • a machine learning algorithm may be used to identify the gender of the current user according to the characteristic body composition.
  • other algorithms can also be used to identify the gender of the current user.
  • algorithms for identifying gender include but are not limited to support vector machines, logistic regression, XGBoost, and neural networks.
  • the commonly used algorithm for gender recognition includes a support vector machine.
  • the selected gender-related feature body components include right upper limb muscles and right upper limb fat as an example to determine the right upper limb muscles Whether it is greater than the preset first threshold, and whether the right upper limb fat is greater than the preset second threshold, if it is determined that the right upper limb muscle is greater than the preset first threshold, and the right upper limb fat is greater than the preset second threshold, then it is determined that the current user’s
  • the gender is male.
  • the value ranges of the preset first threshold and the preset second threshold are not limited, and can be set according to requirements.
  • Step 212 Calculate the final body composition of the current user according to the current user's weight, impedance, non-gender parameters, and gender.
  • step 212 may include: calculating the final body composition of the current user according to the weight, impedance, non-gender parameters, and gender of the current user through a multiple linear regression model corresponding to gender.
  • step 212 may specifically include:
  • Step 2121 obtain a multiple linear regression model corresponding to the gender
  • D is the regression coefficient i comprising d i1, d i2, d i3 , d i4, case d i5, for example, when the gender include male gender corresponding regression coefficient d i comprising d i1 M, d i2 M, d i3 M , di4 M , di5 M , where M is used to represent males.
  • sex includes female gender corresponding regression coefficient d i comprising d i1 F, d i2 F, d i3 F, d i4 F, d i5 F, where F denotes a female.
  • Step 2122 Through the multiple linear regression model corresponding to gender
  • the user’s gender is identified through the body fat scale, and the multiple linear regression model corresponding to the gender is obtained, so that the final body composition of the current user can be calculated more accurately, and the body composition detection accuracy and detection are improved. efficient.
  • the method further includes:
  • Step 213 Display the gender and final body composition of the current user.
  • the final body composition includes but not limited to fat percentage, fat mass, lean body mass, bone salt mass, muscle mass, skeletal muscle mass, total body water, protein, basal metabolic rate, visceral fat grade, 5 items Segmental muscle mass, 5 segmental fat mass, 5 segmental muscle mass including left arm muscle mass, right arm muscle mass, left leg muscle mass, right leg muscle mass, trunk muscle mass, and 5 segmental fat mass including Left arm fat mass, right arm fat mass, left leg fat mass, right leg fat mass, and trunk fat mass, in addition to other parameters, may also include other parameters.
  • the present invention does not limit the parameters included in the final body composition. Can be set according to needs.
  • the manner of displaying the gender of the current user may include displaying gender text, displaying gender graphics, displaying gender colors, displaying gender logos, and so on.
  • dots of different colors indicate different genders, for example, blue dots indicate males, and red dots indicate males. Dots indicate women.
  • the personal information of the current user is displayed, and the personal information includes the gender of the user.
  • the personal information of the current user is displayed, and the personal information includes a female logo1 and a female avatar 2.
  • the user by displaying the user's gender on the body fat scale, the user can conveniently and intuitively see the gender recognition result, which further improves the user's experience effect.
  • the manner of displaying the gender and final body composition of the current user for example, as shown in FIG. Displays the current user's gender, weight, fat percentage, skeletal muscle mass, visceral fat level and other parameters, so that the user can intuitively and quickly obtain the body composition detection results, while automatically completing the user's gender, avoiding user needs Manual input causes the problem of excessive input that affects the user experience.
  • it further includes: sending the final body composition and gender of the current user to the terminal 120.
  • the user can obtain the body composition detection result in real time, avoiding the problem that the body composition result cannot be obtained by leaving the body fat scale.
  • the gender of the current user is sent to the terminal 120, so that it can be automatically displayed in the terminal. Complement and display the gender of the current user, so that the user can easily and intuitively see the gender recognition result, further improve the user experience effect, and avoid the problem of excessive user input.
  • Step 214 Generate a body assessment based on the final body composition.
  • step 214 may specifically include: determining the body assessment based on the comparison result of the final body composition and a preset threshold. For example, if it is determined that the muscle mass in the final body composition is within the preset range value, it indicates that the muscle mass of the user is normal, and the normal muscle mass is taken as the physical assessment of the user. For example, if it is determined that the body fat rate in the final body composition is greater than the preset threshold, it indicates that the user's body fat rate is too high, and the high body fat rate is taken as the user's body assessment.
  • Step 216 Generate an exercise recommendation according to the physical assessment and gender.
  • the physical assessment and the correspondence between gender and sports recommendation are established in advance, so that the corresponding sports recommendation can be queried according to the physical assessment and gender.
  • the corresponding exercise recommendation may include yoga, aerobics, lunges, squats, sit-ups, running, swimming and other sports.
  • Physical assessment includes when men’s trunk fat content is high, and the corresponding exercise recommendations can include push-ups, pull-ups, strength steps, squats, running, swimming and other sports.
  • exercise type recommendation can be made according to gender, which further improves the intelligence of the body fat scale.
  • the method further includes:
  • Step 217 Display physical assessment and exercise recommendation.
  • the body assessment and exercise recommendation are displayed in the lower area 3 of the final body composition.
  • the display mode may include: displaying "Your body is too fat, and yoga, aerobics, etc. are recommended. Low- and medium-intensity aerobic exercises such as swimming are used to reduce fat, and exercises such as lunges, squats, and sit-ups are used to shape the legs, hips, and waist.”
  • other display modes may also be included, and the foregoing examples of the present invention are all illustrative and not limited.
  • the execution process of step 220 and step 214 can be displayed sequentially or simultaneously, which is not limited in the present invention.
  • the temporary body composition of the current user is calculated according to the acquired weight, impedance, and non-gender parameters of the current user through a gender-free multiple linear regression model, and the temporary body composition is screened out from the temporary body composition.
  • Relevant characteristic body components identify the current user’s gender based on the characteristic body components, and calculate the current user’s final body composition based on the current user’s weight, impedance, non-gender parameters, and gender through a multiple linear regression model, and display the current user’s Gender and final body composition, by actively identifying the gender of the user, can effectively reduce user input, improve user experience, and improve the efficiency of body composition detection.
  • FIG. 7 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. It should be understood that the electronic device 110 can execute each step in the above-mentioned body composition detection method. In order to avoid repetition, it will not be described in detail here.
  • the electronic device 110 includes: a processing unit 801 and a detection unit 802.
  • the processing unit 801 is configured to calculate the temporary body composition of the current user according to the acquired weight, impedance, and non-gender parameters of the current user.
  • the processing unit 801 is also used to screen out characteristic body components related to gender from the temporary body components.
  • the processing unit 801 is further configured to identify the gender of the current user according to the characteristic body component.
  • the processing unit 801 is further configured to calculate the final body composition of the current user according to the current user's weight, impedance, non-gender parameters, and the gender.
  • the detection unit 802 is used to detect the weight and impedance of the current user; and receive the non-gender parameters of the current user input by the terminal.
  • the non-gender parameters include height and age
  • the weight, the impedance, the height, and the age are used to calculate the temporary body composition of the current user, where Y i temp is the temporary body composition, W t is the weight, and Z is the total body composition.
  • H t is the height
  • Age is the age
  • b i is the obtained regression coefficient.
  • the gender-related characteristic body components include right upper limb muscle and right upper limb fat; or, the gender-related characteristic body components include visceral fat grade, total body water, and lean body weight , Bone mineral content, basal metabolic rate and muscle mass.
  • the processing unit 801 is further configured to obtain a multiple linear regression model corresponding to the gender;
  • the weight, the impedance, the height, the age, and the gender are calculated to calculate the final body composition of the current user, where Y i final represents the final body composition, W t represents the weight, and Z represents said impedance, H t is expressed as the height, Age is expressed as the ages, gender expressed as the sex, d i represents gender as the regression coefficient corresponding to the acquired.
  • the final body composition includes fat percentage, fat mass, lean body mass, bone salt mass, muscle mass, skeletal muscle mass, total body water, protein, basal metabolic rate, visceral fat grade, One or any combination of the 5 segments of muscle mass and the 5 segments of fat mass, the 5 segments of muscle mass including left arm muscle mass, right arm muscle mass, left leg muscle mass, right leg muscle mass, Trunk muscle mass, the five segment fat masses include left arm fat mass, right arm fat mass, left leg fat mass, right leg fat mass, and trunk fat mass.
  • the processing unit 801 is further configured to generate a body assessment according to the final body composition; and generate an exercise recommendation according to the body assessment and the gender.
  • the electronic device 110 here is embodied in the form of a functional unit.
  • the term "unit” herein can be implemented in the form of software and/or hardware, which is not specifically limited.
  • a "unit” may be a software program, a hardware circuit, or a combination of the two that realizes the above-mentioned functions.
  • the hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, and a processor for executing one or more software or firmware programs (such as a shared processor, a dedicated processor, or a group processor). Etc.) and memory, merged logic circuits and/or other suitable components that support the described functions.
  • the units of the examples described in the embodiments of the present invention can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of the present invention.
  • the embodiment of the present invention also provides an electronic device, which may be a terminal device or a circuit device built in the terminal device.
  • the device can be used to execute the functions/steps in the above method embodiments.
  • FIG. 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.
  • the electronic device 900 includes a processor 910 and a transceiver 920.
  • the electronic device 900 may further include a memory 930.
  • the processor 910, the transceiver 920, and the memory 930 can communicate with each other through an internal connection path to transfer control and/or data signals.
  • the memory 930 is used to store computer programs, and the processor 910 is used to download from the memory 930 Call and run the computer program.
  • the electronic device 900 may further include an antenna 940 for transmitting the wireless signal output by the transceiver 920.
  • the above-mentioned processor 910 and the memory 930 may be integrated into a processing device, and more commonly, they are components independent of each other.
  • the processor 910 is configured to execute the program code stored in the memory 930 to implement the above-mentioned functions.
  • the memory 930 may also be integrated in the processor 910, or independent of the processor 910.
  • the processor 910 may correspond to the processing unit 801 in the electronic device 110 in FIG. 6.
  • the electronic device 900 may also include one or more of an input unit 960, a display unit 970, an audio circuit 980, a camera 990, and a sensor 901.
  • the audio The circuit may also include a speaker 982, a microphone 984, and the like.
  • the display unit 970 may include a display screen.
  • the aforementioned electronic device 900 may further include a power supply 950 for providing power to various devices or circuits in the terminal device.
  • the electronic device 900 shown in FIG. 8 can implement each process of the body composition detection method embodiment shown in FIG. 2.
  • the operations and/or functions of each module in the electronic device 900 are used to implement the corresponding processes in the foregoing method embodiments.
  • processor 910 in the electronic device 900 shown in FIG. 8 may be a system on a chip (SOC), and the processor 910 may include a central processing unit (CPU), and may further Including other types of processors. Each part of the processor cooperates to implement the previous method flow, and each part of the processor can selectively execute a part of the software driver.
  • SOC system on a chip
  • CPU central processing unit
  • each part of the processor or processing unit inside the processor 910 can cooperate to implement the previous method flow, and the corresponding software program of each part of the processor or processing unit can be stored in the memory 930.
  • the processor 910 involved may include, for example, a central processing unit (CPU), a microprocessor, a microcontroller, or a digital signal processor, and may also include a GPU, an NPU, and an ISP. It may also include necessary hardware accelerators or logic processing hardware circuits, such as application-specific integrated circuits (ASICs), or one or more integrated circuits for controlling the execution of the technical solutions of the present invention.
  • the processor may have a function of operating one or more software programs, and the software programs may be stored in the memory.
  • the present invention also provides a computer-readable storage medium that stores instructions in the computer-readable storage medium.
  • the computer executes each of the body composition detection methods shown in FIG. 2 above. step.
  • the memory can be read-only memory (ROM), other types of static storage devices that can store static information and instructions, random access memory (RAM), or other types that can store information and instructions
  • Dynamic storage devices can also be electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), or other optical disk storage, optical disc storage ( Including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or can be used to carry or store desired program codes in the form of instructions or data structures and can Any other medium accessed by the computer, etc.
  • EEPROM electrically erasable programmable read-only memory
  • CD-ROM compact disc read-only memory
  • optical disc storage Including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.
  • magnetic disk storage media or other magnetic storage devices or can be used to carry or store desired program codes in the form of instructions or data structures and can Any other medium
  • “at least one” refers to one or more, and “multiple” refers to two or more.
  • “And/or” describes the association relationship of the associated objects, indicating that there can be three relationships, for example, A and/or B, which can mean the existence of A alone, A and B at the same time, and B alone. Among them, A and B can be singular or plural.
  • the character “/” generally indicates that the associated objects before and after are in an “or” relationship.
  • “The following at least one item” and similar expressions refer to any combination of these items, including any combination of single items or plural items.
  • At least one of a, b, and c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.
  • any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of the present invention essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present invention.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (read-only memory, ROM), random access memory (random access memory, RAM), magnetic disks or optical disks and other media that can store program codes. .

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Abstract

一种体成分检测方法、电子设备(110)和计算机可读存储介质，根据获取的当前用户的体重、阻抗、非性别参数，计算出当前用户的临时体成分(206)，从临时体成分中筛选出与性别相关的特征体成分(208)，根据特征体成分识别出当前用户的性别(210)，根据当前用户的体重、阻抗、非性别参数以及性别，计算出当前用户的最终体成分(212)，显示当前用户的性别和最终体成分，通过主动识别用户的性别，从而能够有效减少用户输入，提高用户体验，提高体成分检测的效率。

Description

一种体成分检测方法、电子设备和计算机可读存储介质
本申请要求于2020年5月19日提交中国专利局、申请号为202010423640.9、申请名称为“一种体成分检测方法、电子设备和计算机可读存储介质”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
本发明涉及体成分检测技术领域，具体地涉及计算机可读存储介质一种体成分检测方法、电子设备和计算机可读存储介质。
背景技术
在相关技术中，基于数据非依赖性采集(Data independent acquisition，简称DIA)定量技术的体成分检测，通常需要的信息包括阻抗、体重、性别、年龄、身高，其中，阻抗、体重分别由体脂秤的测重模块和测脂模块测量得到，性别、年龄、身高由用户输入。
由于不同性别所对应的体成分模型不同，性别是计算体成分的必需参数，通常用户必须输入性别信息才能获得准确体成分。然而输入过量的信息会影响用户的体验，尤其影响访客模式下的用户的应用体验，从而造成体成分检测缺乏智能性的问题。
发明内容
有鉴于此，本发明提供了一种体成分检测方法、电子设备和计算机可读存储介质，通过主动识别用户的性别，从而能够有效减少用户输入，提升用户体验，提升智能性。
一方面，本发明实施例提供了一种体成分检测方法，包括：
根据获取的当前用户的体重、阻抗、非性别参数，计算出所述当前用户的临时体成分；
从所述临时体成分中筛选出与性别相关的特征体成分；
根据所述特征体成分识别出所述当前用户的性别；
根据所述当前用户的体重、阻抗、非性别参数以及所述性别，计算出所述当前用户的最终体成分。
在一种可选的实现方式中，在所述根据获取的当前用户的体重、阻抗、非性别参数，计算出所述当前用户的临时体成分之前，还包括：
检测当前用户的体重以及阻抗；
接收终端输入的当前用户的非性别参数。
在一种可选的实现方式中，所述非性别参数包括身高；在一种可选的实现方式中，所述非性别参数包括身高和年龄；
所述根据获取的当前用户的体重、阻抗、非性别参数，计算出所述当前用户的临时体 成分，包括：
通过无性别多元线性回归模型Y i temp＝b i1W t+b i2Z+b i3H t+b i4Age+b i5，根据所述体重、所述阻抗、所述身高和所述年龄，计算出所述当前用户的临时体成分，其中，Y i temp表示为临时体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，bi表示为获取的回归系数。
在一种可选的实现方式中，所述与性别相关的特征体成分包括右上肢肌肉和右上肢脂肪；或者，所述与性别相关的特征体成分包括内脏脂肪等级、体总水、去脂体重、骨盐量、基础代谢率和肌肉量。
在一种可选的实现方式中，根据特征体成分识别出当前用户的性别。
本发明实施例中，作为一种可选方案，可通过机器学习算法，根据特征体成分识别出当前用户的性别。除此之外，还可以通过其他算法识别出当前用户的性别。例如识别性别的算法包括但不限于支持向量机、逻辑回归、XGBoost、神经网络。作为一种可选方案，通常采用的识别性别的算法包括支持向量机。
具体地，以机器学习算法为例，例如，以该体脂秤包括八电极体脂秤时，筛选出的与性别相关的特征体成分包括右上肢肌肉和右上肢脂肪为例，判断右上肢肌肉是否大于预设第一阈值，且右上肢脂肪是否大于预设第二阈值，若判断出右上肢肌肉大于预设第一阈值，且右上肢脂肪大于预设第二阈值，则判断出当前用户的性别为男性。对于预设第一阈值和预设第二阈值的取值范围不做限定，可根据需求进行设定。
在一种可选的实现方式中，所述根据所述当前用户的体重、阻抗、非性别参数以及所述性别，计算出所述当前用户的最终体成分，包括：
获取所述性别对应的多元线性回归模型；
通过所述性别对应的多元线性回归模型Y i final＝d i1W t+d i2Z+d i3H t+d i4Age+d i5,where d ij＝f ij(gender),j＝1,2,...,5，根据所述体重、所述阻抗、所述身高、所述年龄和所述性别，计算出所述当前用户的最终体成分，其中，Y i final表示为最终体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，gender表示为所述性别，d i表示为获取的所述性别对应的回归系数。
在一种可选的实现方式中，所述最终体成分包括脂肪率、脂肪量、去脂体重、骨盐量、肌肉量、骨骼肌量、体总水、蛋白质、基础代谢率、内脏脂肪等级、5项节段肌肉量、5项节段脂肪量的其中一项或任意组合，所述5项节段肌肉量包括左臂肌肉量、右臂肌肉量、左腿肌肉量、右腿肌肉量、躯干肌肉量，所述5项节段脂肪量包括左臂脂肪量、右臂脂肪量、左腿脂肪量、右腿脂肪量、躯干脂肪量。
在一种可选的实现方式中，在所述根据所述当前用户的体重、阻抗、非性别参数以及所述性别，计算出所述当前用户的最终体成分之后，还包括：
根据所述最终体成分生成身体评估；
根据所述身体评估以及所述性别，生成运动推荐。
第二方面，本发明实施例提供了一种电子设备，所述设备包括：
根据获取的当前用户的体重、阻抗、非性别参数，计算出所述当前用户的临时体成分；
从所述临时体成分中筛选出与性别相关的特征体成分；
根据所述特征体成分识别出所述当前用户的性别；
根据所述当前用户的体重、阻抗、非性别参数以及所述性别，计算出所述当前用户的最终体成分。
在一种可选的实现方式中，当所述指令被所述设备执行时，使得所述设备具体执行以下步骤：
检测当前用户的体重以及阻抗；
接收终端输入的当前用户的非性别参数。
在一种可选的实现方式中，所述非性别参数包括身高和年龄；
当所述指令被所述设备执行时，使得所述设备具体执行以下步骤：
通过无性别多元线性回归模型Y i temp＝b i1W t+b i2Z+b i3H t+b i4Age+b i5，根据所述体重、所述阻抗、所述身高和所述年龄，计算出所述当前用户的临时体成分，其中，Y i temp表示为临时体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，b i表示为获取的回归系数。
在一种可选的实现方式中，所述与性别相关的特征体成分包括右上肢肌肉和右上肢脂肪；或者，所述与性别相关的特征体成分包括内脏脂肪等级、体总水、去脂体重、骨盐量、基础代谢率和肌肉量。
在一种可选的实现方式中，当所述指令被所述设备执行时，使得所述设备具体执行以下步骤：
获取所述性别对应的多元线性回归模型；
通过所述性别对应的多元线性回归模型Y i final＝d i1W t+d i2Z+d i3H t+d i4Age+d i5,where d ij＝f ij(gender),j＝1,2,...,5，根据所述体重、所述阻抗、所述身高、所述年龄和所述性别，计算出所述当前用户的最终体成分，其中，Y i final表示为最终体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，gender表示为所述性别，d i表示为获取的所述性别对应的回归系数。
在一种可选的实现方式中，所述最终体成分包括脂肪率、脂肪量、去脂体重、骨盐量、肌肉量、骨骼肌量、体总水、蛋白质、基础代谢率、内脏脂肪等级、5项节段肌肉量、5项节段脂肪量的其中一项或任意组合，所述5项节段肌肉量包括左臂肌肉量、右臂肌肉量、左腿肌肉量、右腿肌肉量、躯干肌肉量，所述5项节段脂肪量包括左臂脂肪量、右臂脂肪量、左腿脂肪量、右腿脂肪量、躯干脂肪量。
在一种可选的实现方式中，当所述指令被所述设备执行时，使得所述设备具体执行以下步骤：
根据所述最终体成分生成身体评估；
根据所述身体评估以及所述性别，生成运动推荐。
第三方面，本发明实施例提供了一种电子设备，包括：显示屏；一个或多个处理器；存储器；多个应用程序；以及一个或多个计算机程序，其中所述一个或多个计算机程序被存储在所述存储器中，所述一个或多个计算机程序包括指令，当所述指令被所述设备执行时，使得设备执行上述任一方面任一项可能的实现中的体成分检测方法。
第四方面，本发明实施例提供了一种计算机可读存储介质，所述计算机可读存储介质用于设备执行的程序代码，所述程序代码包括用于执行第一方面或者第一方面的任一可能的实现方式中的方法的指令。
本发明实施例提供的技术方案中，根据获取的当前用户的体重、阻抗、非性别参数，计算出当前用户的临时体成分，从临时体成分中筛选出与性别相关的特征体成分，根据特征体成分识别出当前用户的性别，根据当前用户的体重、阻抗、非性别参数以及性别，计算出当前用户的最终体成分，显示当前用户的性别和最终体成分，通过主动识别用户的性别，从而能够有效减少用户输入，提高用户体验，提高体成分检测的效率。
附图说明
图1是本发明一实施例所提供的一种体成分检测系统的架构图；
图2是本发明一实施例所提供的一种体成分检测方法的流程图；
图3是本发明一实施例所提供的一种电子设备界面的示意图；
图4是本发明一实施例所提供的一种性别显示方式的示意图；
图5是本发明一实施例所提供的另一种性别显示方式的示意图；
图6是本发明一实施例所提供的电子设备界面的示意图；
图7是本发明一实施例所提供的一种电子设备的结构示意图；
图8为本发明一实施例所提供的另一种电子设备的结构示意图。
具体实施方式
为了更好的理解本发明的技术方案，下面结合附图对本发明实施例进行详细描述。
应当明确，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
在本发明实施例中使用的术语是仅仅出于描述特定实施例的目的，而非旨在限制本发明。在本发明实施例和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。
应当理解，本文中使用的术语“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，甲和/或乙，可以表示：单独存在甲，同时存在甲和乙，单独存 在乙这三种情况。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系。
由于不同性别所对应的体成分模型不同，因此性别是计算体成分的必需参数，用户必须输入性别信息。因此在相关技术的体成分检测方法中，通常采用阻抗、体重、性别、年龄、身高计算出用户的体成分，其中，阻抗、体重分别由体脂秤的测重模块和测脂模块测量得到，性别、年龄、身高由用户输入。然而输入过量的信息会影响用户的体验，尤其影响访客模式下的用户的应用体验，从而造成体成分检测缺乏智能性的问题。因此本发明实施例提供了一种体成分检测系统，用于解决上述问题。
图1为本发明一实施例提供的一种体成分检测系统的架构图，如图1所示，该系统100包括：电子设备110和终端120。
本发明实施例中，电子设备110包括体脂秤。终端120可以是当前用户的手持终端，例如，手机终端、平板电脑、笔记本电脑、增强现实(augmented reality，简称AR)或者AR/虚拟现实(virtual reality，简称VR)等。如图1所示，电子设备110包括检测单元111、交互单元112、处理单元113、显示单元114、评估单元115以及显示屏116。
检测单元111用于检测当前用户的体重以及阻抗，具体地，检测单元111包括测重模块和测脂模块，测重模块用于检测当前用户的体重，测脂模块用于检测当前用户的阻抗。
交互单元112用于接收终端120输入的当前用户的非性别参数。需要说明的是，终端120和电子设备110之前的交互方式可采用蓝牙、wifi等方式，进行数据传输。
处理单元113用于识别当前用户的性别。具体地，处理单元113用于通过无性别多元线性回归模型，根据获取的当前用户的体重、阻抗、非性别参数，计算出当前用户的临时体成分；从临时体成分中筛选出与性别相关的特征体成分；根据特征体成分识别出当前用户的性别；通过多元线性回归模型，根据当前用户的体重、阻抗、非性别参数以及性别，计算出当前用户的最终体成分。
显示单元114用于控制显示屏116显示当前用户的性别和最终体成分。
评估单元115用于根据最终体成分生成身体评估，根据身体评估以及性别，生成运动推荐。
显示单元114还用于控制显示屏114显示运动推荐。
交互模块112还用于将当前用户的最终体成分运动推荐发送至终端120。
在一种实现方式中，交互模块112还用于将检测单元111测量的当前用户的体重以及阻抗发送至终端120，以使终端120根据当前用户的体重以及阻抗，识别当前用户的性别、计算当前用户的体成分、显示用户性别和最终体成分以及生成身体评估和运动推荐。
在本发明实施例中，基于上述的系统架构100，通过主动识别用户的性别，从而能够有效减少用户输入，提高用户体验，提高体成分检测的效率。下面结合图2，包括步骤202至步骤220，对体成分检测方法的过程进行详细的说明。
图2为本发明一实施例提供的一种体成分检测方法的流程图，如图2所示，该方法包 括：
步骤202、检测当前用户的体重以及阻抗。
本发明实施例中，各步骤由电子设备110执行。电子设备110可包括体脂秤，该体脂秤包括测重模块和测脂模块，测重模块用于检测当前用户的体重，测脂模块用于检测当前用户的阻抗。
本发明实施例中，在步骤202之前，还包括：步骤201、启动体脂秤。
步骤204、接收终端输入的当前用户的非性别参数。
本发明实施例中，非性别参数包括除性别之外的其他用户参数，作为一种可选方案，非性别参数可包括身高。作为另一种可选方案，非性别参数可包括身高和年龄。如图3所示，当前用户在终端的APP注册页面输入当前用户的昵称、出生年月以及身高，当前用户无需输入性别。通过当前用户在终端输入非性别参数，以便于通过终端120和电子设备110之间的交互，电子设备110能够接收终端120输入的当前用户的非性别参数。除此之外，非性别参数还可以包含除身高、年龄之外的其他参数，本发明对此不做限定。
在实际应用中，现有技术通常需要用户在电子设备110或者终端120中输入当前用户的身高、年龄以及性别，从而通过测量的阻抗、体重以及输入的身高、年龄以及性别计算出用户的体成分，然而输入过量的信息会影响用户的体验，尤其影响访客模式下的用户的应用体验，从而造成体成分检测缺乏智能性的问题。在本发明实施例中，如图3所示，用户无需输入性别参数，从而避免了输入过量的信息会影响用户的体验的问题；并通过后续步骤中识别出当前用户的性别，提高访客模式下的用户的应用体验。
步骤206、根据获取的当前用户的体重、阻抗、非性别参数，计算出当前用户的临时体成分。
本发明实施例中，步骤206可包括：通过无性别多元线性回归模型，根据获取的当前用户的体重、阻抗、非性别参数，计算出当前用户的临时体成分。
本发明实施例中，无性别多元线性回归模型为预先训练好的模型。在非性别参数可包括身高和年龄的情况下，步骤206可具体包括：通过无性别多元线性回归模型
Y i temp＝b i1W t+b i2Z+b i3H t+b i4Age+b i5，根据体重、阻抗、身高和年龄，计算出当前用户的临时体成分，其中，Y i temp表示为临时体成分，W t表示为体重，Z表示为阻抗，H t表示为身高，Age表示为年龄，b i表示为获取的回归系数，由实验数据训练得到。
需要说明的是，若该体脂秤包括八电极体脂秤时，临时体成分包括但不限于右上肢肌肉、右上肢脂肪，若该体脂秤包括四电极体脂秤时，临时体成分包括但不限于内脏脂肪等级、体总水、去脂体重、骨盐量、基础代谢率、肌肉量。除此之外，临时体成分还可以包括其他参数，具体计算出的体成分可根据实际体脂秤的类型决定。此外，本发明实施例中，可通过训练无性别多元线性回归模型获取回归系数bi。
在实际应用中，现有技术通常采用带有性别的多元线性回归模型计算用户的体成分， 然而该方案会造成用户需要输入大量的信息，从而导致用户体验效果差，体成分检测效率低的问题，而本发明实施例中，通过无性别多元线性回归模型所计算出当前用户的临时体成分之后，通过临时体成分识别出当前用户的性别，从而能够减少用户的输入量，提高访客模式下的用户的应用体验。
步骤208、从临时体成分中筛选出与性别相关的特征体成分。
本发明实施例通过大量研究数据得到：若该体脂秤包括八电极体脂秤时，与性别相关的特征体成分包括右上肢肌肉和右上肢脂肪，当采用右上肢肌肉和右上肢脂肪作为与性别相关的特征体成分时，能够更为准确的识别出当前用户的性别。若该体脂秤包括四电极体脂秤时，与性别相关的特征体成分包括内脏脂肪等级、体总水、去脂体重、骨盐量、基础代谢率和肌肉量，当采用内脏脂肪等级、体总水、去脂体重、骨盐量、基础代谢率和肌肉量作为与性别相关的特征体成分时，能够更为准确的识别出当前用户的性别。除此之外，与性别相关的特征体成分还可以包括其他参数，可根据当前使用的体脂秤的类型决定。本发明对此不做限定。
步骤210、根据特征体成分识别出当前用户的性别。
本发明实施例中，作为一种可选方案，可通过机器学习算法，根据特征体成分识别出当前用户的性别。除此之外，还可以通过其他算法识别出当前用户的性别。例如识别性别的算法包括但不限于支持向量机、逻辑回归、XGBoost、神经网络。作为一种可选方案，通常采用的识别性别的算法包括支持向量机。
具体地，以机器学习算法为例，例如，以该体脂秤包括八电极体脂秤时，筛选出的与性别相关的特征体成分包括右上肢肌肉和右上肢脂肪为例，判断右上肢肌肉是否大于预设第一阈值，且右上肢脂肪是否大于预设第二阈值，若判断出右上肢肌肉大于预设第一阈值，且右上肢脂肪大于预设第二阈值，则判断出当前用户的性别为男性。对于预设第一阈值和预设第二阈值的取值范围不做限定，可根据需求进行设定。
步骤212、根据当前用户的体重、阻抗、非性别参数以及性别，计算出当前用户的最终体成分。
本发明实施例中，步骤212可包括：通过性别对应的多元线性回归模型，根据当前用户的体重、阻抗、非性别参数以及性别，计算出当前用户的最终体成分。
本发明实施例中，在非性别参数可包括身高和年龄的情况下，步骤212可具体包括：
步骤2121、获取性别对应的多元线性回归模型；
本发明实施例中，由于不同的性别所对应的回归系数不同，因此不同的性别所对应的多元线性回归模型不同。在回归系数d i包括d i1，d i2，d i3，d i4，d i5的情况下，例如，当性别包括男性时，性别对应的回归系数d i包括d i1 M，d i2 M，d i3 M，d i4 M，d i5 M，其中M用于表示男性。当性别包括女性时，性别对应的回归系数d i包括d i1 F，d i2 F，d i3 F，d i4 F，d i5 F，其中F用于表示女性。
步骤2122、通过性别对应的多元线性回归模型
Y i final＝d i1W t+d i2Z+d i3H t+d i4Age+d i5,where d ij＝f ij(gender),j＝1,2,...,5，根据体重、阻抗、身高、年龄和性别，计算出当前用户的最终体成分，其中，Y i final表示为最终体成分，W t表示为体重，Z表示为阻抗，H t表示为身高，Age表示为年龄，gender表示为性别，d i表示为获取的性别对应的回归系数。具体地，性别包括女性时，女性所对应的多元线性回归模型：Y i final＝d i1 FW t+d i2 FZ+d i3 FH t+d i4 FAge+d i5 F，where d ij＝f ij(gender)，j＝1,2，...，5，性别包括男性时，男性所对应的多元线性回归模型：
Y i final＝d i1 MW t+d i2 MZ+d i3 MH t+d i4 MAge+d i5 M，where d ij＝f ij(gender)，j＝1,2，...，5。本发明实施例中，通过体脂秤识别出用户的性别，并根据获取性别对应的多元线性回归模型，从而能够更加精确的计算出当前用户的最终体成分，提高了体成分的检测精度和检测效率。
本发明实施例中，在步骤212之后，还包括：
步骤213、显示当前用户的性别和最终体成分。
本发明实施例中，最终体成分包括但不限于脂肪率、脂肪量、去脂体重、骨盐量、肌肉量、骨骼肌量、体总水、蛋白质、基础代谢率、内脏脂肪等级、5项节段肌肉量、5项节段脂肪量，5项节段肌肉量包括左臂肌肉量、右臂肌肉量、左腿肌肉量、右腿肌肉量、躯干肌肉量，5项节段脂肪量包括左臂脂肪量、右臂脂肪量、左腿脂肪量、右腿脂肪量、躯干脂肪量，除此之外，还可以包括其他参数，本发明对于最终体成分所包含的参数并不做限定，可根据需求设定。
本发明实施例中，显示当前用户的性别的方式可包括显示性别文字、显示性别图形、显示性别颜色、显示性别logo等。例如，在一种可选方案中，如图4所示，通过在电子设备110的显示屏116中显示圆点，不同颜色的圆点表示不同的性别，例如，蓝色圆点表示男性，红色圆点表示女性。例如，在另一种可选方案中，如图5所示，显示当前用户的个人信息，个人信息中包括用户的性别。例如，在另一种可选方案中，如图6所示，显示当前用户的个人信息，个人信息中包括女性logo1和女性头像2。本发明实施例中，通过在体脂秤中显示用户的性别，使得用户能够方便直观地看到性别识别结果，进一步提高用户的体验效果。
本发明实施例中，显示当前用户的性别和最终体成分的方式，例如，如图6所示，通过执行上述步骤，能够识别出当前用户的性别之后，能够在电子设备110的显示屏116中显示当前用户的性别、体重、脂肪率、骨骼肌量、内脏脂肪等级等参数，以便于通过用户能够直观快速的获取体成分的检测结果的同时，能够自动补全用户的性别，避免了用户需要手动输入，造成输入量过多影响用户体验的问题。
本发明实施例中，进一步地，还包括：将当前用户的最终体成分和性别发送至终端120。通过执行该步骤，以便于用户能够实时获取体成分的检测结果，避免了离开体脂秤导致无法获取体成分结果的问题，同时，将当前用户的性别发送至终端120，从而能够在终端中自动补全并显示当前用户的性别，使得用户能够方便直观地看到性别识别结果，进一步提高用户的体验效果，避免了用户输入量过分的问题。
步骤214、根据最终体成分生成身体评估。
本发明实施例中，步骤214可具体包括：通过最终体成分与预设阈值的比较结果，确定出身体评估。例如，若判断出最终体成分中的肌肉量处于预设范围值，表明该用户的肌肉量正常，将肌肉量正常作为该用户的身体评估。例如，若判断出最终体成分中的体脂率大于预设阈值，表明该用户的体脂率偏高，将体脂率偏高作为该用户的身体评估。
步骤216、根据身体评估以及性别，生成运动推荐。
本发明实施例中，预先建立身体评估以及性别与运动推荐之间的对应关系，从而根据身体评估以及性别，能够查询出对应的运动推荐。例如，身体评估包括女性躯干脂肪含量高时，查询出对应的运动推荐可包括瑜伽、健美操、弓箭步下蹲、仰卧起坐、跑步、游泳等运动。身体评估包括男性躯干脂肪含量高时，查询出对应的运动推荐可包括俯卧撑、引体向上、力量台阶、深蹲、跑步、游泳等运动。本发明实施例中，通过执行步骤218，能够根据性别进行运动类型推荐，进一步提升了体脂秤的智能性。
本发明实施例中，在步骤216之后，还包括：
步骤217、显示身体评估和运动推荐。
本发明实施例中，如图6所示，在最终体成分的下方区域3显示身体评估和运动推荐，具体地，显示方式可包括：显示“您的身材偏胖，推荐您瑜伽、健美操、游泳等中低强度的有氧运动进行减脂，以及弓箭步下蹲、仰卧起坐等运动进行腿部、臀部、腰部的塑形”的字样。除此之外，还可以包括其他的显示方式，本发明上述例子均为举例说明并不做限定。另外需要说明的是，步骤220和步骤214的执行过程可分先后显示或同时显示，本发明对此不做限定。
本发明实施例提供的技术方案中，通过无性别多元线性回归模型，根据获取的当前用户的体重、阻抗、非性别参数，计算出当前用户的临时体成分，从临时体成分中筛选出与性别相关的特征体成分，根据特征体成分识别出当前用户的性别，通过多元线性回归模型，根据当前用户的体重、阻抗、非性别参数以及性别，计算出当前用户的最终体成分，显示当前用户的性别和最终体成分，通过主动识别用户的性别，从而能够有效减少用户输入，提高用户体验，提高体成分检测的效率。
图7是本发明一实施例所提供的一种电子设备的结构示意图，应理解，电子设备110能够执行上述体成分检测方法中的各个步骤，为了避免重复，此处不再详述。电子设备110包括：处理单元801、检测单元802。
所述处理单元801用于根据获取的当前用户的体重、阻抗、非性别参数，计算出所述当前用户的临时体成分。所述处理单元801还用于从所述临时体成分中筛选出与性别相关的特征体成分。所述处理单元801还用于根据所述特征体成分识别出所述当前用户的性别。所述处理单元801还用于根据所述当前用户的体重、阻抗、非性别参数以及所述性别，计算出所述当前用户的最终体成分。
在一种可能的实现方式中，所述检测单元802用于检测当前用户的体重以及阻抗；接 收终端输入的当前用户的非性别参数。
在一种可能的实现方式中，所述非性别参数包括身高和年龄；
在一种可能的实现方式中，所述处理单元801还用于通过无性别多元线性回归模型Y i temp＝b i1W t+b i2Z+b i3H t+b i4Age+b i5，根据所述体重、所述阻抗、所述身高和所述年龄，计算出所述当前用户的临时体成分，其中，Y i temp表示为临时体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，b i表示为获取的回归系数。
在一种可能的实现方式中，所述与性别相关的特征体成分包括右上肢肌肉和右上肢脂肪；或者，所述与性别相关的特征体成分包括内脏脂肪等级、体总水、去脂体重、骨盐量、基础代谢率和肌肉量。
在一种可能的实现方式中，所述处理单元801还用于获取所述性别对应的多元线性回归模型；
通过所述性别对应的多元线性回归模型
Y i final＝d i1W t+d i2Z+d i3H t+d i4Age+d i5,where d ij＝f ij(gender),j＝1,2,...,5，根据所述体重、所述阻抗、所述身高、所述年龄和所述性别，计算出所述当前用户的最终体成分，其中，Y i final表示为最终体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，gender表示为所述性别，d i表示为获取的所述性别对应的回归系数。
在一种可能的实现方式中，所述最终体成分包括脂肪率、脂肪量、去脂体重、骨盐量、肌肉量、骨骼肌量、体总水、蛋白质、基础代谢率、内脏脂肪等级、5项节段肌肉量、5项节段脂肪量的其中一项或任意组合，所述5项节段肌肉量包括左臂肌肉量、右臂肌肉量、左腿肌肉量、右腿肌肉量、躯干肌肉量，所述5项节段脂肪量包括左臂脂肪量、右臂脂肪量、左腿脂肪量、右腿脂肪量、躯干脂肪量。
在一种可能的实现方式中，所述处理单元801还用于根据所述最终体成分生成身体评估；根据所述身体评估以及所述性别，生成运动推荐。
应理解，这里的电子设备110以功能单元的形式体现。这里的术语“单元”可以通过软件和/或硬件形式实现，对此不作具体限定。例如，“单元”可以是实现上述功能的软件程序、硬件电路或二者结合。所述硬件电路可能包括应用特有集成电路(application specific integrated circuit，ASIC)、电子电路、用于执行一个或多个软件或固件程序的处理器(例如共享处理器、专有处理器或组处理器等)和存储器、合并逻辑电路和/或其它支持所描述的功能的合适组件。
因此，在本发明的实施例中描述的各示例的单元，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
本发明实施例还提供一种电子设备，该电子设备可以是终端设备也可以是内置于所述终端设备的电路设备。该设备可以用于执行上述方法实施例中的功能/步骤。
图8为本发明一实施例提供的一种电子设备的结构示意图，如图8所示，电子设备900包括处理器910和收发器920。可选地，该电子设备900还可以包括存储器930。其中，处理器910、收发器920和存储器930之间可以通过内部连接通路互相通信，传递控制和/或数据信号，该存储器930用于存储计算机程序，该处理器910用于从该存储器930中调用并运行该计算机程序。
可选地，电子设备900还可以包括天线940，用于将收发器920输出的无线信号发送出去。
上述处理器910可以和存储器930可以合成一个处理装置，更常见的是彼此独立的部件，处理器910用于执行存储器930中存储的程序代码来实现上述功能。具体实现时，该存储器930也可以集成在处理器910中，或者，独立于处理器910。该处理器910可以与图6中电子设备110中的处理单元801对应。
除此之外，为了使得电子设备900的功能更加完善，该电子设备900还可以包括输入单元960、显示单元970、音频电路980、摄像头990和传感器901等中的一个或多个，所述音频电路还可以包括扬声器982、麦克风984等。其中，显示单元970可以包括显示屏。
可选地，上述电子设备900还可以包括电源950，用于给终端设备中的各种器件或电路提供电源。
应理解，图8所示的电子设备900能够实现图2所示的体成分检测方法实施例的各个过程。电子设备900中的各个模块的操作和/或功能，分别为了实现上述方法实施例中的相应流程。具体可参见上述方法实施例中的描述，为避免重复，此处适当省略详细描述。
应理解，图8所示的电子设备900中的处理器910可以是片上系统(system on a chip，SOC)，该处理器910中可以包括中央处理器(central processing unit，CPU)，还可以进一步包括其他类型的处理器。各部分处理器配合工作实现之前的方法流程，并且每部分处理器可以选择性执行一部分软件驱动程序。
总之，处理器910内部的各部分处理器或处理单元可以共同配合实现之前的方法流程，且各部分处理器或处理单元相应的软件程序可存储在存储器930中。
以上各实施例中，涉及的处理器910可以例如包括中央处理器(central processing unit，CPU)、微处理器、微控制器或数字信号处理器，还可包括GPU、NPU和ISP，该处理器还可包括必要的硬件加速器或逻辑处理硬件电路，如特定应用集成电路(application-specific integrated circuit，ASIC)，或一个或多个用于控制本发明技术方案程序执行的集成电路等。此外，处理器可以具有操作一个或多个软件程序的功能，软件程序可以存储在存储器中。
本发明还提供了一种计算机可读存储介质，该计算机可读存储介质中存储有指令，当该指令在计算机上运行时，使得计算机执行如上述图2所示的体成分检测方法中的各个步骤。
存储器可以是只读存储器(read-only memory，ROM)、可存储静态信息和指令的其它类型的静态存储设备、随机存取存储器(random access memory，RAM)或可存储信息和指令的其它类型的动态存储设备，也可以是电可擦可编程只读存储器(electrically erasable programmable read-only memory，EEPROM)、只读光盘(compact disc read-only memory，CD-ROM)或其他光盘存储、光碟存储(包括压缩光碟、激光碟、光碟、数字通用光碟、蓝光光碟等)、磁盘存储介质或者其它磁存储设备，或者还可以是能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何其它介质等。
本发明实施例中，“至少一个”是指一个或者多个，“多个”是指两个或两个以上。“和/或”，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、同时存在A和B、单独存在B的情况。其中A，B可以是单数或者复数。字符“/”一般表示前后关联对象是一种“或”的关系。“以下至少一项”及其类似表达，是指的这些项中的任意组合，包括单项或复数项的任意组合。例如，a，b和c中的至少一项可以表示：a,b,c,a-b,a-c,b-c,或a-b-c，其中a,b,c可以是单个，也可以是多个。
本领域普通技术人员可以意识到，本文中公开的实施例中描述的各单元及算法步骤，能够以电子硬件、计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本发明所提供的几个实施例中，任一功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read-only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本发明的具体实施方式，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。本发明的保护范围应以所述权利要求的保护范围为准。

Claims (15)

  1. 一种体成分检测方法，其特征在于，包括：
    根据获取的当前用户的体重、阻抗、非性别参数，计算出所述当前用户的临时体成分；
    从所述临时体成分中筛选出与性别相关的特征体成分；
    根据所述特征体成分识别出所述当前用户的性别；
    根据所述当前用户的体重、阻抗、非性别参数以及所述性别，计算出所述当前用户的最终体成分。
  2. 根据权利要求1所述的方法，其特征在于，在所述根据获取的当前用户的体重、阻抗、非性别参数，计算出所述当前用户的临时体成分之前，还包括：
    检测当前用户的体重以及阻抗；
    接收终端输入的当前用户的非性别参数。
  3. 根据权利要求2所述的方法，其特征在于，所述非性别参数包括身高，或者身高和年龄；
    所述根据获取的当前用户的体重、阻抗、非性别参数，计算出所述当前用户的临时体成分，包括：
    通过无性别多元线性回归模型Y i temp＝b i1W t+b i2Z+b i3H t+b i4Age+b i5，根据所述体重、所述阻抗、所述身高和所述年龄，计算出所述当前用户的临时体成分，其中，Y i temp表示为临时体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，b i表示为获取的回归系数。
  4. 根据权利要求1所述的方法，其特征在于，所述与性别相关的特征体成分包括右上肢肌肉和右上肢脂肪；或者，所述与性别相关的特征体成分包括内脏脂肪等级、体总水、去脂体重、骨盐量、基础代谢率和肌肉量。
  5. 根据权利要求3所述的方法，其特征在于，所述根据所述当前用户的体重、阻抗、非性别参数以及所述性别，计算出所述当前用户的最终体成分，包括：
    获取所述性别对应的多元线性回归模型；
    通过所述性别对应的多元线性回归模型Y i final＝d i1W t+d i2Z+d i3H t+d i4Age+d i5,where d ij＝f ij(gender),j＝1,2,...,5，根据所述体重、所述阻抗、所述身高、所述年龄和所述性别，计算出所述当前用户的最终体成分，其中，Y i final表示为最终体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，gender表示为所述性别，d i表示为获取的所述性别对应的回归系数。
  6. 根据权利要求5所述的方法，其特征在于，所述最终体成分包括脂肪率、脂肪 量、去脂体重、骨盐量、肌肉量、骨骼肌量、体总水、蛋白质、基础代谢率、内脏脂肪等级、5项节段肌肉量、5项节段脂肪量的其中一项或任意组合，所述5项节段肌肉量包括左臂肌肉量、右臂肌肉量、左腿肌肉量、右腿肌肉量、躯干肌肉量，所述5项节段脂肪量包括左臂脂肪量、右臂脂肪量、左腿脂肪量、右腿脂肪量、躯干脂肪量。
  7. 根据权利要求1所述的方法，其特征在于，在所述根据所述当前用户的体重、阻抗、非性别参数以及所述性别，计算出所述当前用户的最终体成分之后，还包括：
    根据所述最终体成分生成身体评估；
    根据所述身体评估以及所述性别，生成运动推荐。
  8. 一种电子设备，其特征在于，包括：
    显示屏；一个或多个处理器；存储器；以及一个或多个计算机程序，其中所述一个或多个计算机程序被存储在所述存储器中，所述一个或多个计算机程序包括指令，当所述指令被所述设备执行时，使得所述设备执行以下步骤：
    根据获取的当前用户的体重、阻抗、非性别参数，计算出所述当前用户的临时体成分；
    从所述临时体成分中筛选出与性别相关的特征体成分；
    根据所述特征体成分识别出所述当前用户的性别；
    根据所述当前用户的体重、阻抗、非性别参数以及所述性别，计算出所述当前用户的最终体成分。
  9. 根据权利要求8所述的设备，其特征在于，当所述指令被所述设备执行时，使得所述设备具体执行以下步骤：
    检测当前用户的体重以及阻抗；
    接收终端输入的当前用户的非性别参数。
  10. 根据权利要求9所述的设备，其特征在于，所述非性别参数包括身高，或者身高和年龄；
    当所述指令被所述设备执行时，使得所述设备具体执行以下步骤：
    通过无性别多元线性回归模型Y i temp＝b i1W t+b i2Z+b i3H t+b i4Age+b i5，根据所述体重、所述阻抗、所述身高和所述年龄，计算出所述当前用户的临时体成分，其中，Y i temp表示为临时体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，b i表示为获取的回归系数。
  11. 根据权利要求8所述的设备，其特征在于，所述与性别相关的特征体成分包括右上肢肌肉和右上肢脂肪；或者，所述与性别相关的特征体成分包括内脏脂肪等级、体总水、去脂体重、骨盐量、基础代谢率和肌肉量。
  12. 根据权利要求10所述的设备，其特征在于，当所述指令被所述设备执行时， 使得所述设备具体执行以下步骤：
    获取所述性别对应的多元线性回归模型；
    通过所述性别对应的多元线性回归模型Y i final＝d i1W t+d i2Z+d i3H t+d i4Age+d i5,where d ij＝f ij(gender),j＝1,2,...,5，根据所述体重、所述阻抗、所述身高、所述年龄和所述性别，计算出所述当前用户的最终体成分，其中，Y i final表示为最终体成分，W t表示为所述体重，Z表示为所述阻抗，H t表示为所述身高，Age表示为所述年龄，gender表示为所述性别，d i表示为获取的所述性别对应的回归系数。
  13. 根据权利要求12所述的设备，其特征在于，所述最终体成分包括脂肪率、脂肪量、去脂体重、骨盐量、肌肉量、骨骼肌量、体总水、蛋白质、基础代谢率、内脏脂肪等级、5项节段肌肉量、5项节段脂肪量的其中一项或任意组合，所述5项节段肌肉量包括左臂肌肉量、右臂肌肉量、左腿肌肉量、右腿肌肉量、躯干肌肉量，所述5项节段脂肪量包括左臂脂肪量、右臂脂肪量、左腿脂肪量、右腿脂肪量、躯干脂肪量。
  14. 根据权利要求8所述的设备，其特征在于，当所述指令被所述设备执行时，使得所述设备具体执行以下步骤：
    根据所述最终体成分生成身体评估；
    根据所述身体评估以及所述性别，生成运动推荐。
  15. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质包括存储的程序，其中，在所述程序运行时控制所述计算机可读存储介质所在设备执行权利要求1至7中任意一项所述的体成分检测方法。
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