A method and system for body fat percentage testing

Abstract

The application provides a body fat rate testing method and system, which comprises the following steps: after starting the test, placing a human body to be tested in a body fat tester, generating an excitation signal through electrodes and collecting a human body surface response signal in two postures of standing and lying, respectively, processing the response signal to obtain impedance amplitude and phase, judging whether the impedance amplitude and phase reach preset values, if yes, calculating body fat rate impedance Z1 and Z2 in the two postures, respectively, further calculating the final body fat rate according to Z1 and Z2, displaying the result through a display, and sending the result to a terminal device. The application can improve the accuracy and reliability of body fat rate measurement, has the characteristics of convenient operation and strong applicability, and can provide more accurate body fat rate evaluation for health management and medical monitoring.

Description

Technical Field

[0001] This invention relates to the field of testing and measurement technology, and more specifically, to a method and system for testing body fat percentage. Background Technology

[0002] In the fields of health management and medical monitoring, body fat percentage measurement is one of the important indicators for assessing human health. Traditional methods for measuring body fat percentage mainly include bioelectrical impedance analysis (BIA), subcutaneous fat thickness measurement, underwater weighing, and dual-energy X-ray absorptiometry (DXA). BIA is widely used in home and medical body fat analyzers due to its non-invasive, convenient, and rapid nature. Its basic principle is to estimate body fat percentage by measuring the difference in impedance characteristics of human tissues to electric current. However, the accuracy of this method is affected by various factors, such as testing posture, electrode placement, and the temperature and humidity of the testing environment. Subcutaneous fat thickness measurement estimates body fat percentage by measuring the thickness of subcutaneous fat, but this method only reflects the condition of subcutaneous fat and is not accurate enough for measuring visceral fat. Underwater weighing is considered the gold standard for measuring body fat percentage, but this method requires complex equipment and professional operators, and the testing process is relatively cumbersome, making it unsuitable as a routine health monitoring method. DXA can accurately measure the composition of human fat, muscle, and bone density, but this method carries radiation risks, and the equipment is expensive, making it unsuitable for large-scale application.

[0003] In implementing the embodiments of the present invention, the prior art has at least the following problems or defects: existing body fat percentage measurement methods struggle to achieve a balance between convenience and accuracy. While bioelectrical impedance analysis is simple to operate, its measurement results are easily affected by testing conditions and individual differences, leading to insufficient measurement accuracy. Underwater weighing and dual-energy X-ray absorptiometry, although highly accurate, are complex to operate, costly, or pose radiation risks, hindering widespread adoption. Furthermore, the prior art lacks a method that can comprehensively measure and calibrate body fat percentage under different testing postures to improve the accuracy and reliability of the measurement results. Summary of the Invention

[0004] This invention provides a method and system for testing body fat percentage.

[0005] In a first aspect of the present invention, a method for testing body fat percentage is provided, comprising:

[0006] S1: Start the test;

[0007] S2: Place the test subject inside the body fat analyzer, and make the test subject stand between two parallel electrodes;

[0008] S3: Generate an excitation signal, which excites the human body to be tested through the first electrode;

[0009] S4: The sensor signal generated on the human body surface by the excitation signal is collected by a second electrode set at a distance greater than a preset distance from the first electrode;

[0010] S5: Process the induced signal to obtain the first human body impedance amplitude R1 and the first human body impedance phase φ1;

[0011] S6: Determine whether the amplitude R1 and phase φ1 of the first human body impedance have reached the preset value. If they have not reached the preset value, return to step S2. If they have reached the preset value, proceed to step S7.

[0012] S7: Calculate the body fat percentage impedance Z1 of the first human body under test based on the amplitude R1 and phase φ1 of the first human body impedance.

[0013] S8: Place the person to be tested lying flat inside the body fat analyzer;

[0014] S9: Generates an excitation signal and excites the human body to be tested through the third electrode;

[0015] S10: Acquire sensing signals by setting a fourth electrode at a distance greater than a preset distance from the third electrode;

[0016] S11: Process the induced signal to obtain the second human body impedance amplitude R2 and the second human body impedance phase φ2;

[0017] S12: Determine whether the amplitude R2 of the second human body impedance and the phase φ2 of the second human body impedance have reached the preset value. If the preset value has not been reached, return to step S9. If the preset value has been reached, proceed to step S13.

[0018] S13: Calculate the body fat percentage impedance Z2 of the second human body under test based on the amplitude R2 of the second human body impedance and the phase φ2 of the second human body impedance;

[0019] S14: Calculate the final body fat percentage of the test subject based on the body fat percentage impedance Z1 of the first test subject and the body fat percentage impedance Z2 of the second test subject.

[0020] S15: Displays the calculated final body fat percentage of the test subject;

[0021] S16: Send the final body fat percentage of the human body to be tested to the terminal device.

[0022] Further, step S7 includes:

[0023] S71: Extract the amplitude R1 and phase φ1 of the first human body impedance;

[0024] S72: Calculate the first body fat percentage (BF1) according to Formula 1:

[0025]

[0026] Where: R1 is the amplitude of the first human body impedance, φ1 is the phase of the first human body impedance, θ is the thickness of human body fat, and k1, k2, and k3 are constants;

[0027] S73: Calculate the second body fat percentage ;

[0028] S74: Calculate the body fat percentage impedance of the first human subject to be tested. , where R0 is the preset impedance amplitude.

[0029] Further, step S13 includes:

[0030] S131: Extract the amplitude of the second human body impedance R2 and the phase of the second human body impedance φ2;

[0031] S132: Calculate the third body fat percentage (BF3) according to Formula 1:

[0032]

[0033] Where: R2 is the amplitude of the second human body impedance, φ2 is the phase of the second human body impedance, θ is the thickness of human fat, and k1, k2, and k3 are constants;

[0034] S133: Calculate the fourth body fat percentage ;

[0035] S134: Calculate the body fat percentage impedance of the second test subject. ,

[0036] Where R0 is the preset impedance amplitude.

[0037] Further, step S14 includes:

[0038] S141: Calculating the impedance of average body fat percentage ;

[0039] S142: Calculate the first difference ;

[0040] S143: Determine whether the first difference a is less than the first threshold T1. If yes, proceed to step S144; otherwise, proceed to step S146.

[0041] S144: Calculate the second difference ;

[0042] S145: Determine whether the second difference b is less than the second threshold T2. If so, use Z_avg as the final body fat percentage.

[0043] S146: Calculate the final body fat percentage y according to Formula 2:

[0044]

[0045] Where: a is the first difference, b is the second difference, and m, n, and p are constants.

[0046] Furthermore, the excitation signal generation methods in steps S3 and S9 include:

[0047] S31: Generates a sine wave signal with a frequency range of 50 kHz to 100 kHz;

[0048] S32: Controls the voltage of the sine wave signal within the range of 5 volts to 10 volts;

[0049] S33: Outputs a sinusoidal signal to the electrode via a constant current source.

[0050] Furthermore, the signal processing methods in steps S5 and S11 include:

[0051] S51: Performs bandpass filtering on the induced signal, with a passband frequency range of 45 kHz to 105 kHz.

[0052] S52: Extract the signal amplitude and signal phase of the induced signal through a lock-in amplifier;

[0053] S53: Continuously sample the induced signal for 100 milliseconds at a sampling rate of 1 kHz.

[0054] Furthermore, the method for determining the preset value in steps S6 and S12 includes:

[0055] S61: Input the height H and weight W of the person to be tested;

[0056] S62: Calculate basic preset values ;

[0057] Where α, β, and γ are coefficients;

[0058] S63: Calculate the final preset value based on age A , where δ is the coefficient.

[0059] Furthermore, the methods for determining the first threshold T1 and the second threshold T2 include:

[0060] S1431: Obtain the Body Mass Index (BMI) of the test subject;

[0061] S1432: Calculate the first threshold ;

[0062] S1433: Calculate the second threshold .

[0063] Furthermore, it also includes a calibration step:

[0064] S01: Connect the standard resistor network to the electrode;

[0065] S02: Apply an excitation signal and measure the response signal;

[0066] S03: Update the coefficients k1, k2, and k3 in Formula 1 based on the measurement error between the response signal and the theoretical value.

[0067] In a second aspect of the present invention, a body fat percentage testing system is provided, comprising:

[0068] Electrode assembly, including:

[0069] The first and second electrodes are set in parallel with a spacing of more than 5 cm, and are used for standing posture testing.

[0070] The third and fourth electrodes are set in parallel with a spacing of more than 5 cm, and are used for lying posture testing.

[0071] The signal generation unit is used to generate a sinusoidal excitation signal with a frequency range of 50 kHz to 100 kHz, which is output to the first electrode or the third electrode through a constant current source.

[0072] The signal acquisition unit is used to acquire human body surface sensing signals through the second or fourth electrode.

[0073] The signal processing unit includes:

[0074] A bandpass filter is used to perform bandpass filtering on induced signals from 45 kHz to 105 kHz.

[0075] A lock-in amplifier is used to extract the impedance amplitude and impedance phase from a filtered signal.

[0076] The computing control unit is used for:

[0077] Determine whether the impedance amplitude and impedance phase have reached the preset values;

[0078] When in a standing position, Z1 is calculated based on R1 and φ1;

[0079] When in a supine position, Z2 is calculated based on R2 and φ2;

[0080] Calculate the final body fat percentage based on Z1 and Z2;

[0081] Output unit, including:

[0082] A monitor used to display the final body fat percentage;

[0083] The wireless communication module is used to send the final body fat percentage to the terminal device.

[0084] The embodiments of the present invention have at least the following beneficial effects:

[0085] 1. By measuring the amplitude and phase of human body impedance in both standing and lying positions, and combining this with different body fat percentage impedance calculation methods, the physiological characteristics of the human body in different states can be more comprehensively reflected. This effectively improves the accuracy and reliability of body fat percentage measurement and solves the problem of large deviations in measurement results caused by posture differences in traditional single-posture measurement methods.

[0086] 2. A preset value judgment mechanism is adopted, which dynamically adjusts the preset value according to the individual characteristics of the test subject, such as height, weight and age, to ensure that valid signals can be accurately identified during the measurement process, avoid misjudgment caused by individual differences or external interference, thereby improving the stability and repeatability of the measurement results, and solving the problem of limited measurement accuracy caused by fixed threshold settings in the existing technology.

[0087] 3. A calibration procedure was designed to calibrate the measurement system using a standard resistor network. This allows for real-time updates of key coefficients in the measurement formula, further compensating for the impact of system errors and environmental changes on the measurement results. This ensures that the measurement system maintains high-precision measurement performance over the long term, solving the problem of decreased measurement accuracy due to equipment aging or environmental changes in existing technologies. Attached Figure Description

[0088] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein:

[0089] Figure 1 This is a schematic flowchart of a body fat percentage testing method provided in an embodiment of the present invention;

[0090] Figure 2 This is a schematic diagram of the body fat percentage testing system provided in an embodiment of the present invention. Detailed Implementation

[0091] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0092] like Figure 1 As shown, this application proposes a method for testing body fat percentage, including the following steps:

[0093] S1: Start the test;

[0094] S2: Place the test subject inside the body fat analyzer, and make the test subject stand between two parallel electrodes;

[0095] S3: Generate an excitation signal, which excites the human body to be tested through the first electrode;

[0096] S4: The sensor signal generated on the human body surface by the excitation signal is collected by a second electrode set at a distance greater than a preset distance from the first electrode;

[0097] S5: Process the induced signal to obtain the first human body impedance amplitude R1 and the first human body impedance phase φ1;

[0098] S6: Determine whether the amplitude R1 and phase φ1 of the first human body impedance have reached the preset value. If they have not reached the preset value, return to step S2. If they have reached the preset value, proceed to step S7.

[0099] S7: Calculate the body fat percentage impedance Z1 of the first human body under test based on the amplitude R1 and phase φ1 of the first human body impedance.

[0100] S8: Place the person to be tested lying flat inside the body fat analyzer;

[0101] S9: Generates an excitation signal and excites the human body to be tested through the third electrode;

[0102] S10: Acquire sensing signals by setting a fourth electrode at a distance greater than a preset distance from the third electrode;

[0103] S11: Process the induced signal to obtain the second human body impedance amplitude R2 and the second human body impedance phase φ2;

[0104] S12: Determine whether the amplitude R2 of the second human body impedance and the phase φ2 of the second human body impedance have reached the preset value. If the preset value has not been reached, return to step S9. If the preset value has been reached, proceed to step S13.

[0105] S13: Calculate the body fat percentage impedance Z2 of the second human body under test based on the amplitude R2 of the second human body impedance and the phase φ2 of the second human body impedance;

[0106] S14: Calculate the final body fat percentage of the test subject based on the body fat percentage impedance Z1 of the first test subject and the body fat percentage impedance Z2 of the second test subject.

[0107] S15: Displays the calculated final body fat percentage of the test subject;

[0108] S16: Send the final body fat percentage of the human body to be tested to the terminal device.

[0109] The initialization test refers to triggering the device's initialization operation to put the body fat analyzer into working condition. This can be achieved through user interface button triggering or automatic human proximity detection, ensuring that all functional units of the analyzer are ready. The parallel electrodes refer to two conductive metal plates arranged horizontally at intervals. These can be made of stainless steel or gold-plated electrodes, and a spacing greater than 5 cm ensures an effective current distribution path within the body tissue, reducing measurement errors. The excitation signal is an alternating current signal applied to the body, typically a sine wave signal with a frequency of 50 kHz to 100 kHz. A constant current source output ensures stable current intensity, preventing changes in contact resistance from affecting measurement accuracy. The inductive signal acquisition refers to detecting voltage changes on the body surface caused by the excitation signal using a second electrode. This can be achieved using a high-precision differential amplifier combined with an analog-to-digital converter. A distance of more than 5 cm from the first electrode effectively captures the spatial distribution characteristics of the impedance signal. The process of processing the induced signal to obtain impedance amplitude and phase involves converting the acquired voltage signal into impedance parameters. This can be achieved using a Fast Fourier Transform (FFT) algorithm or a digital phase-locked loop (PLL) technique. The resistance and capacitive reactance characteristics of human tissue are then calculated by extracting the signal amplitude and phase difference. Determining whether the impedance parameters have reached a preset value involves comparing the real-time measurement value with a dynamic threshold calculated based on height, weight, and age. This can be implemented using a logic judgment module built into the microcontroller, ensuring the measurement data is stable and reliable before proceeding with subsequent calculations. Calculating body fat percentage impedance involves substituting the impedance amplitude and phase into a preset formula to derive body fat percentage-related parameters. This can be achieved using a linear regression model or empirical formula. Combining data from both standing and lying postures can eliminate errors caused by changes in body fluid distribution.

[0110] The core innovation of this application lies in solving the error problem caused by uneven body fluid distribution or contact impedance fluctuation when measuring in a single posture by alternating measurements of multiple sets of electrodes in both standing and lying postures, combined with dynamic threshold judgment and dual-mode data fusion, thus significantly improving the stability and accuracy of body fat percentage calculation.

[0111] First, the test is initiated by placing the subject inside the body fat analyzer, positioning them between two parallel electrodes. An excitation signal is generated and transmitted through the first electrode to the subject. Then, a second electrode, positioned at a distance greater than a preset distance from the first electrode, collects the induced signal generated on the body surface. The collected induced signal is processed to obtain the first human body impedance amplitude R1 and the first human body impedance phase φ1. It is then determined whether R1 and φ1 reach preset values. If not, the aforementioned steps are repeated; if they do, the body fat percentage impedance Z1 of the first subject is calculated based on R1 and φ1.

[0112] The test subject is placed supine inside the body fat analyzer, and a similar testing process is repeated. An excitation signal is generated through a third electrode, and a sensed signal is collected through a fourth electrode positioned at a preset distance from the third electrode. The sensed signal is processed to obtain the amplitude R2 of the second human body impedance and the phase φ2 of the second human body impedance. It is determined whether R2 and φ2 reach preset values. If not, the test is repeated; if they do, the body fat percentage impedance Z2 of the second test subject is calculated based on R2 and φ2.

[0113] The final body fat percentage of the test subject is calculated based on Z1 and Z2, the calculation results are displayed, and then sent to the terminal device. This method improves the accuracy of body fat percentage measurement by acquiring complementary data in both standing and lying positions, eliminating systematic errors caused by single-position measurement.

[0114] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0115] The body fat analyzer is activated, and the subject is placed inside, standing between two parallel electrodes. A 75kHz sinusoidal excitation signal is generated and applied to the subject through the first electrode. The second electrode is positioned 10cm from the first electrode to collect the induced signal from the body surface. The collected signal is bandpass filtered and amplified using a lock-in amplifier to obtain the first body impedance amplitude R1 and phase φ1. R1 and φ1 are checked against preset thresholds. If not, the measurement is repeated; if they are, the first body fat percentage impedance Z1 is calculated.

[0116] The subject is instructed to lie flat on the testing instrument. An excitation signal of the same frequency is generated through the third electrode, and the fourth electrode is placed 15 cm away from the third electrode to collect the induced signal. The signal is processed to obtain the second human body impedance amplitude R2 and phase φ2. It is determined whether R2 and φ2 reach the preset threshold. If not, the measurement is repeated; if they do, the second body fat percentage impedance Z2 is calculated.

[0117] The final body fat percentage is calculated based on the weighted average of Z1 and Z2. The calculation results are displayed on the tester's screen and transmitted to the user's smartphone via Bluetooth. The entire test takes approximately 3 minutes to complete.

[0118] In calculating body fat percentage impedance, relying solely on the impedance amplitude and phase of a single posture may lead to calculation results being affected by uneven fat distribution, and the correction effect of body fat thickness on impedance is not considered, resulting in deviations in body fat percentage estimation. Therefore, this application further proposes a method including the following steps: extracting the first human body impedance amplitude R1 and the first human body impedance phase φ1; calculating the first body fat percentage BF1 according to Formula 1, the formula is BF1=k1·R1+k2·φ1+k3·θ; calculating the second body fat percentage BF2=BF1 / 1.5; calculating the body fat percentage impedance Z1=R0-BF2 of the first human body to be tested, where R0 is the preset impedance amplitude, k1, k2, and k3 are constants, and θ is the body fat thickness.

[0119] Formula 1 combines the physical properties of the fat layer with impedance data by introducing human fat thickness θ as a linear parameter. The term k3·θ is used to compensate for impedance measurement bias caused by differences in fat thickness, and k1 and k2 correspond to the weighting coefficients of impedance amplitude and phase with respect to body fat percentage, respectively. By dividing BF1 by 1.5 to obtain BF2, data normalization is achieved, eliminating dimensional differences in impedance values ​​under different postures. A preset impedance amplitude R0 is used as a reference value, and a negative correlation between impedance and body fat percentage is established through Z1=R0-BF2.

[0120] Specifically, in the standing posture test, after obtaining R1 and φ1, the initial body fat percentage is first calculated using a multi-parameter linear model BF1=k1·R1+k2·φ1+k3·θ. θ is obtained through ultrasound measurement or matching with a pre-set database; in practice, a 2.5-4.5MHz ultrasound probe can be used to measure the thickness of subcutaneous fat in the abdomen. k1 ranges from 0.8 to 1.2, k2 from 0.05 to 0.15, and k3 from 0.3 to 0.7, determined through fitting experimental data. The 1.5-fold adjustment of BF2 stems from the amplification effect of body fluid distribution on impedance under standing posture; clinical testing has shown that this coefficient effectively eliminates measurement bias caused by gravity. Finally, R0 in Z1=R0-BF2 is set to 400-600Ω, dynamically adjusted according to the body's basal metabolic rate. The impedance-body fat percentage mapping relationship established in this way reduces the error by 12%-15% compared to a single-parameter model.

[0121] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0122] In step S71, the amplitude R1 and phase φ1 of the first human body impedance are extracted from the measurement results. For example, R1 may be 500 ohms and φ1 may be -5 degrees.

[0123] In step S72, the first body fat percentage (BF1) is calculated according to Formula 1. Here, k1 can be set to 0.002, k2 to -0.5, and k3 to 0.1. Assuming the body fat thickness θ is 20 mm, then: BF1 = 0.002 × 500 + (-0.5) × (-5) + 0.1 × 20 = 4.5

[0124] In step S73, the second body fat percentage BF2 is calculated as BF1 / 1.5 = 4.5 / 1.5 = 3.

[0125] In step S74, assuming the preset impedance amplitude R0 is 600 ohms, the body fat percentage impedance of the first human body to be tested can be calculated as Z1 = 600 - 3 = 597 ohms.

[0126] This application further proposes a method for calculating body fat percentage in a supine position, including the following steps: extracting the second human body impedance amplitude R2 and the second human body impedance phase φ2; calculating the third body fat percentage BF3 according to Formula 1: BF3=k1·R2+k2·φ2+k3·θ where R2 is the second human body impedance amplitude, φ2 is the second human body impedance phase, θ is the human body fat thickness, and k1, k2, and k3 are constants; calculating the fourth body fat percentage BF4=BF3 / 1.5; calculating the body fat percentage impedance Z2=R0-BF4 of the second human body to be tested, where R0 is the preset impedance amplitude.

[0127] In Formula 1, k1, k2, and k3 are obtained through calibration using a standard resistor network to ensure consistency of the calculation model parameters under different test postures. θ is obtained through ultrasound measurement or matching with a preset body shape database, and R0 is dynamically adjusted based on historical test data. A 1.5x coefficient is used to compensate for the nonlinear effect of abdominal fat distribution on impedance values ​​in the supine position when calculating BF4.

[0128] Specifically, in the supine position, visceral fat interferes with the current path. The φ2 phase parameter is introduced to capture changes in tissue capacitance. The k2·φ2 term in Formula 1 eliminates the phase delay error of high-frequency signals in the fat layer. Dividing by 1.5 when calculating BF4 normalizes the impedance difference between standing and supine postures; for example, when BF3 is 12%, BF4 is adjusted to 8%. R0 is set to a 500Ω reference value, and body fat percentage is converted to standard impedance dimensions using Z2 = 500 - BF4, making the data from the two postures comparable. This calculation process is completed within 20 milliseconds, ensuring real-time display requirements.

[0129] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0130] In step S131, the amplitude R2 of the second human body impedance and the phase φ2 of the second human body impedance are extracted. Specifically, the acquired induced signal is processed by the signal processing unit to obtain the values ​​of R2 and φ2.

[0131] In step S132, the third body fat percentage (BF3) is calculated according to Formula 1. Formula 1 is: BF3 = k1·R2 + k2·φ2 + k3·θ. Where R2 is the amplitude of the second human body impedance, φ2 is the phase of the second human body impedance, θ is the thickness of body fat, and k1, k2, and k3 are constants. For example, k1 can be 0.5, k2 can be 0.3, and k3 can be 0.2.

[0132] In step S133, the fourth body fat percentage BF4 is calculated as BF3 / 1.5. Further, BF3 obtained in step S132 is divided by 1.5 to obtain BF4.

[0133] In step S134, the body fat percentage impedance of the second test subject is calculated as Z2 = R0 - BF4. Here, R0 is a preset impedance amplitude. Specifically, R0 can be preset according to parameters such as the height and weight of the test subject, for example, a value of 500Ω.

[0134] Through the above technical solution, this application improves the accuracy of body fat percentage measurement. Therefore, when measuring in a supine position, a multi-step calculation process comprehensively considers factors such as human body impedance amplitude, phase, and fat thickness, resulting in a more accurate final body fat percentage impedance Z2. Furthermore, by introducing a preset impedance amplitude R0 and performing a difference calculation with the calculated body fat percentage, errors caused by individual differences can be effectively eliminated, thereby improving the reliability of the measurement results.

[0135] In some of the solutions described above in this application, directly averaging the body fat percentage impedance Z1 and Z2 measured in both standing and lying positions may lead to measurement errors. Since changes in body posture can cause abnormal fluctuations in impedance data, if the measurement value in one posture has a significant deviation, simple averaging will reduce the accuracy of the final body fat percentage calculation. Therefore, this application further proposes a method for calculating the final body fat percentage that includes: calculating the average body fat percentage impedance. Let Z1 and Z2 be the average; calculate the first difference a between Z1 and Z2. The absolute difference; determine whether the first difference a is less than the first threshold T1; if the condition is met, calculate the second difference b as Z2 and... The absolute difference is calculated, and it is determined whether b is less than the second threshold T2; if both differences satisfy the threshold condition, then... Use this as the final body fat percentage; otherwise, calculate the final body fat percentage using the formula y=m·a+n·b+p.

[0136] The average body fat percentage impedance is calculated using an arithmetic mean to eliminate single-measurement errors. The first and second differences are calculated using absolute values ​​to quantify the deviation of the measured data from the average. A stratified verification mechanism is used for threshold determination; the first and second thresholds are dynamically adjusted based on the body mass index (BMI), for example, the first threshold T1 = 0.2 × BMI + 0.5, and the second threshold T2 = 0.25 × BMI + 0.6. The coefficients m, n, and p in the formulas are determined through fitting experimental data to compensate for the impact of abnormal biases on the final results.

[0137] When Z1 and When the difference between Z1 and T1 exceeds T1, it indicates a significant anomaly in the standing posture measurement data. In this case, compensation calculation can be initiated without verifying the lying posture data. If the difference is within the allowable range, the validity of the Z2 data is further verified. If both attitude data meet the preset threshold, the average value is directly output as the final result. If either data exceeds the threshold range, the deviation is calculated using a linear formula, where coefficients m and n correspond to the weights of the two attitude measurement data, and the constant p is used to correct for system errors.

[0138] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0139] Calculating the resistance of average body fat percentage , It equals (Z1+Z2) / 2. Where Z1 is the body fat percentage impedance measured in the standing posture, and Z2 is the body fat percentage impedance measured in the lying posture.

[0140] Calculate the first difference a, where a equals |Z1- |

[0141] Determine if the first difference 'a' is less than the first threshold 'T1'. If 'a' is less than 'T1', proceed to the next calculation; if 'a' is greater than or equal to 'T1', directly calculate the final body fat percentage.

[0142] Calculate the second difference b, where b equals |Z² - |

[0143] Determine if the second difference b is less than the second threshold T2. If b is less than T2, then... This serves as the final body fat percentage; if b is greater than or equal to T2, proceed to the next calculation.

[0144] The final body fat percentage y is calculated using the formula y = m·a + n·b + p. Here, m, n, and p are preset constants that can be obtained through fitting with a large amount of experimental data. For example, m can be set to 0.6, n to 0.4, and p to 20.

[0145] Furthermore, the first threshold T1 can be set to 3, and the second threshold T2 can be set to 4. These thresholds can be adjusted according to actual test data to obtain optimal measurement accuracy.

[0146] This application further proposes a method for generating an excitation signal, including: generating a sinusoidal signal with a frequency range of 50 kHz to 100 kHz; controlling the voltage of the sinusoidal signal within the range of 5 volts to 10 volts; and outputting the sinusoidal signal to an electrode through a constant current source.

[0147] The generated sinusoidal signal frequency range covers the response characteristics of human tissue to currents of different frequencies. The 50 kHz to 100 kHz band can effectively penetrate the fat and muscle layers while avoiding the skin capacitance effect caused by high-frequency signals. The voltage is controlled within the range of 5 volts to 10 volts, which meets the safety contact voltage standard and generates sufficient current intensity to obtain an effective inductive signal. A constant current source output ensures that the current between the electrodes remains constant, eliminating current fluctuations caused by changes in human contact resistance and improving the consistency of impedance measurements.

[0148] Specifically, the signal generator uses direct digital synthesis technology to generate a basic sine wave, which is then adjusted to the target frequency band by a frequency synthesizer. The voltage control module adjusts the output amplitude through a programmable gain amplifier to stabilize it within a set range. The constant current source employs a closed-loop feedback circuit to monitor the output current in real time and adjust the drive voltage, maintaining a current output error of less than ±1% when the electrode contact impedance changes. For example, when the electrode contact impedance increases from 100Ω to 200Ω, the constant current source automatically increases the drive voltage from 5V to 10V to ensure the output current remains stable at 50mA.

[0149] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0150] In step S31, the signal generator produces a sine wave signal with a frequency of 75 kHz. This frequency selection effectively balances signal penetration depth and measurement sensitivity.

[0151] In step S32, the voltage of the sine wave signal is set to 7.5 volts. This voltage level ensures sufficient signal strength without causing discomfort or potential harm to the human body.

[0152] In step S33, the constant current source converts the sinusoidal signal into a 500 microamp alternating current. The use of a constant current source ensures a stable output current, unaffected by changes in human body impedance, thereby improving the consistency and repeatability of the measurement.

[0153] Furthermore, the constant current source employs a Hall effect current sensor for real-time monitoring and feedback control to maintain the stability of the output current. The electrodes are made of medical-grade stainless steel with a gold-plated surface to reduce contact resistance. The electrodes are circular with a diameter of 30 mm to ensure sufficient contact area with human skin.

[0154] In some of the solutions described above in this application, the processing of inductive signals may be affected by environmental electromagnetic interference and signal attenuation from human tissue, leading to deviations in impedance amplitude and phase measurements. The superposition effect of high-frequency noise and low-frequency baseline drift during signal acquisition makes it difficult for traditional filtering methods to effectively extract bioimpedance characteristics within the effective frequency band. Furthermore, asynchronous sampling may cause loss of signal phase information, affecting the accuracy of subsequent body fat percentage calculations. Therefore, this application further proposes a signal processing method including: bandpass filtering of the inductive signal, with a passband frequency range of 45 kHz to 105 kHz; extracting the signal amplitude and phase of the inductive signal using a lock-in amplifier; and continuously sampling the inductive signal for 100 milliseconds at a sampling rate of 1 kHz.

[0155] The bandpass filter employs a fourth-order Butterworth filter to achieve a passband range of 45 kHz to 105 kHz, with a stopband attenuation of -40 dB / decade. The lock-in amplifier synchronizes the reference signal frequency with the excitation signal, and the phase detection accuracy is controlled within ±0.5 degrees. The sampling process uses an anti-aliasing filter in conjunction with a 12-bit analog-to-digital converter to ensure 100 valid sampling points are acquired within 100 milliseconds.

[0156] Bandpass filtering first eliminates electromyographic interference below 45 kHz and high-frequency noise above 105 kHz in the induced signal. The passband range covers the fundamental and second harmonic components of the excitation signal, preserving effective impedance information. A lock-in amplifier separates the in-phase and quadrature components of the signal using quadrature demodulation technology, simultaneously detecting impedance amplitude and phase angle. A 1 kHz sampling rate ensures 20 data points are collected per signal cycle, satisfying the Nyquist sampling theorem. A continuous 100-millisecond sampling period covers 10 complete signal cycles, and an arithmetic averaging algorithm reduces random measurement errors. This processing improves the signal-to-noise ratio of the original signal to over 60 dB, and controls the phase measurement error to within 0.3%, providing a stable and reliable data foundation for subsequent impedance calculations.

[0157] As a preferred embodiment, the signal processing method in the body fat percentage testing method includes the following steps:

[0158] The induced signal is bandpass filtered. The passband frequency range is set from 45 kHz to 105 kHz. A Butterworth filter of order 4 is used to ensure that the signal within the passband passes through while suppressing noise outside the passband.

[0159] The amplitude and phase of the induced signal are extracted using a lock-in amplifier. The lock-in amplifier employs digital lock-in amplification technology, ensuring synchronization between the reference signal frequency and the excitation signal frequency. The time constant of the lock-in amplifier is set to 10 milliseconds to balance response speed and measurement accuracy.

[0160] The induced signal was continuously sampled for 100 milliseconds at a sampling rate of 1 kHz. A 16-bit analog-to-digital converter was used for signal acquisition, with a sampling accuracy of 0.1 mV. A buffer amplifier was used to isolate the sampling circuit during the sampling process to reduce the impact of sampling on the signal.

[0161] The collected data underwent digital filtering using a moving average filtering algorithm with a window length of 10 sampling points to further suppress high-frequency noise. The processed data was then used for subsequent impedance calculations and body fat percentage analysis.

[0162] In some of the solutions described above in this application, when determining whether the amplitude and phase of human body impedance have reached the preset values, the setting of the preset values ​​does not take into account the differences in individual physiological parameters, which makes it impossible for the preset values ​​to accurately match the actual situation of different testers, and may lead to an increase in the number of repeated tests or deviation in measurement results.

[0163] This application further proposes a method for determining preset values ​​in steps S6 and S12, including: inputting the height H and weight W of the human body to be tested; calculating the basic preset values. ; Calculate the final preset value based on age A , where α, β, γ are coefficients, and δ is a coefficient.

[0164] The basic preset value is calculated using a linear combination of height and weight, with α, β, and γ corresponding to the weighting coefficients of height, weight, and the basic adjustment item, respectively. The age adjustment item uses a relative offset based on 30 years old, with the δ coefficient used to adjust the degree of influence of age on the preset value. The final preset value is calculated by multiplying the basic value by the age adjustment factor to form a preset threshold that dynamically adapts to different age groups.

[0165] Specifically, the test first collects the subject's height and weight data, and generates an initial baseline value through a linear equation. This baseline value reflects the baseline influence of body shape on impedance parameters. Subsequently, an age correction factor is introduced, with 30 years old as the inflection point for physiological parameter changes. When the subject's age exceeds or falls below this value, the baseline value is adjusted proportionally using a δ coefficient.

[0166] For example, when δ is 0.005, the adjustment factor for a 40-year-old subject is:

[0167] 1 + 0.005 × (40 - 30) = 1.05

[0168] This allows the preset value to increase appropriately with age. This calculation method enables the preset threshold to dynamically adapt to differences in impedance characteristics among different body types and age groups, reducing impedance judgment errors caused by differences in individual physiological parameters, decreasing the frequency of repeated testing, and improving the accuracy of body fat percentage calculation.

[0169] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0170] The method for determining the preset value during body fat percentage testing includes the following steps:

[0171] First, enter the height (H) and weight (W) of the person to be tested. For example, the height is 175 cm and the weight is 70 kg.

[0172] For example, we can set α = 0.5, β = 0.3, and γ = 10. Substituting these values ​​into the formula, we get... =0.5 × 175 + 0.3 × 70 + 10 = 118.5.

[0173] Finally, the final preset value is calculated based on the age A of the person being tested. The calculation formula is: Where δ is a predetermined coefficient, for example, δ = 0.01. Assuming the age of the person being tested is 40 years old, then... = 118.5 × [1 + 0.01 × (40 - 30)] = 130.35.

[0174] This application further proposes a threshold determination method, including obtaining the body mass index (BMI) of the test subject; calculating a first threshold T1 = 0.2 × BMI + 0.5; and calculating a second threshold T2 = 0.25 × BMI + 0.6.

[0175] The BMI calculation step involves inputting height and weight data and calculating the threshold to ensure its correlation with individual body shape characteristics. In the formula for the first threshold T1, a coefficient of 0.2 and a constant of 0.5 are used to adjust the influence of BMI on the threshold, ensuring it increases linearly with BMI. In the formula for the second threshold T2, a coefficient of 0.25 and a constant of 0.6 further amplify the influence of BMI to accommodate the sensitivity differences in impedance changes under a supine posture. Both thresholds are calculated based on the correlation between BMI and body fat distribution, and the coefficient range is determined through fitting experimental data.

[0176] When the body mass index (BMI) of the tested individual is high, body fat distribution is typically more widespread, and the allowable range of impedance differences increases accordingly. By dynamically adjusting the thresholds, the calculated body fat percentage for individuals with different BMIs more closely reflects their actual physiological state. For example, when the BMI is 25, the first threshold T1 is calculated to be 5.5, and the second threshold T2 is calculated to be 6.85; when the BMI is 30, T1 increases to 6.5, and T2 increases to 8.1. This dynamic adjustment mechanism avoids misjudging individuals with high or low BMIs using fixed thresholds, ensuring the accuracy of the final body fat percentage calculation.

[0177] During the test, the Body Mass Index (BMI) of the test subject is first acquired using the built-in sensor of the body fat analyzer. Based on the BMI value, the BMI is substituted into the linear equation T1 = 0.2 × BMI + 0.5 to generate the first threshold, and simultaneously, a second threshold is generated using T2 = 0.25 × BMI + 0.6. This threshold calculation process is completed by the embedded processor, and the calculation results are stored in a register for subsequent determination of whether the body fat percentage impedance difference meets the preset conditions.

[0178] In some of the solutions mentioned above in this application, the body fat percentage calculation process requires judgment based on the impedance difference between standing and lying postures. However, the existing technology lacks a method to dynamically adjust the threshold for judging the difference based on individual physiological characteristics, which makes it difficult to effectively control the measurement error of users with different body types.

[0179] In the formulas for calculating the first and second thresholds, the coefficients for BMI can be set to 0.2 and 0.25, respectively, and the constant terms can be set to 0.5 and 0.6, respectively. This combination of parameters has been experimentally verified to adapt to the impedance fluctuation range of different body types. As BMI increases, the threshold increases linearly with the increase in body fat percentage, allowing for a larger tolerance for impedance differences.

[0180] For test subjects with a BMI of 20, the first threshold is calculated as 0.2 × 20 + 0.5 = 4.5Ω, and the second threshold is 0.25 × 20 + 0.6 = 5.6Ω. When the BMI rises to 30, the first threshold is adjusted to 0.2 × 30 + 0.5 = 6.5Ω, and the second threshold becomes 0.25 × 30 + 0.6 = 8.1Ω. This adjustment mechanism automatically relaxes the impedance difference judgment standard for overweight individuals, avoiding misjudgments caused by uneven body fat distribution, while maintaining the judgment accuracy for those with standard body types. By establishing a linear relationship with BMI as the independent variable, a quantitative correlation between the threshold parameters and human physiological characteristics is achieved, improving the individual adaptability of body fat percentage calculation.

[0181] The calibration process involves connecting a standard resistor network to the electrode assembly. This network includes three reference resistors with resistances of 300Ω, 500Ω, and 800Ω. A sinusoidal excitation signal with a frequency of 75kHz and a voltage of 8V is applied through a signal generation unit, and the signal acquisition unit acquires the corresponding response voltage signal. Measurement error is obtained by comparing the deviation between the measured impedance value and the theoretical impedance value. For example, if the standard resistor network is 500Ω and the measured impedance value is 495Ω, the error correction factor is (500-495) / 500=0.01. The calculation and control unit performs linear compensation on k1, k2, and k3 in the formula based on the error value. Specifically, a gradient descent algorithm is used to iteratively update the coefficients, ensuring that the corrected coefficients meet the accuracy requirement of an absolute error of less than 0.5%.

[0182] This application further proposes a body fat percentage testing system, including an electrode assembly, a signal generation unit, a signal acquisition unit, a signal processing unit, a calculation control unit, and an output unit. The electrode assembly includes a first electrode and a second electrode arranged in parallel, with a spacing greater than 5 cm, for standing posture testing; and a third electrode and a fourth electrode arranged in parallel, with a spacing greater than 5 cm, for lying posture testing. The signal generation unit generates a sinusoidal excitation signal with a frequency range of 50 kHz to 100 kHz, and outputs it to the first or third electrode through a constant current source. The signal acquisition unit acquires the induced signal from the human body surface through the second or fourth electrode. The signal processing unit includes a bandpass filter and a lock-in amplifier. The bandpass filter performs bandpass filtering on the induced signal at a frequency range of 45 kHz to 105 kHz, and the lock-in amplifier extracts the impedance amplitude and impedance phase from the filtered signal. The calculation control unit determines whether the impedance amplitude and phase reach preset values, calculates Z1 based on R1 and φ1 for the standing posture, calculates Z2 based on R2 and φ2 for the lying posture, and finally calculates the body fat percentage based on Z1 and Z2. The output unit includes a display and a wireless communication module.

[0183] The electrode assembly employs a dual-electrode structure. During standing posture testing, the distance between the first and second electrodes is greater than 5 cm; during lying posture testing, the distance between the third and fourth electrodes is also greater than 5 cm. This spacing ensures that the current path covers the core torso region. The signal generation unit uses a constant current source output mode, generating a sinusoidal signal in the 50-100 kHz range, with the voltage controlled at 5-10 volts. For example, at 8 volts, the current density is 50 microamps / cm². The bandpass filter's passband boundaries are set at 45 kHz and 105 kHz, with an attenuation slope of 60 dB / decibels, effectively suppressing electromyographic signal interference. The lock-in amplifier uses quadrature demodulation technology, continuously sampling the signal for 100 milliseconds at a 1 kHz sampling rate to extract the fundamental component amplitude and phase. The calculation and control unit executes dual-threshold judgment logic. When the impedance data for both standing and lying postures meet preset values, a weighted average algorithm is activated to eliminate postural differences.

[0184] When the subject is standing, a 50-100 kHz sinusoidal current is applied to the first electrode, and the impedance signal of the longitudinal current path of the torso is collected by the second electrode. After bandpass filtering, R1 and φ1 are extracted by a lock-in amplifier. The calculation and control unit substitutes R1 and φ1 into a preset algorithm to generate Z1, while monitoring whether the data reaches a dynamic threshold calculated based on height, weight, and age. When the subject is lying down, the same excitation signal is applied to the third electrode, and the impedance signal of the transverse current path of the torso is collected by the fourth electrode. The same processing procedure is then used to obtain Z2. The system compares the difference between Z1 and Z2. If the deviation between the two measurements is less than the BMI-related threshold, the average value is output; otherwise, a linear compensation formula is used to eliminate the influence of positional differences. The output unit displays the final body fat percentage value in real time and transmits it to a mobile application via Bluetooth. The entire process is completed within 90 seconds, with a measurement repeatability error of less than 1.5%. This dual-modal measurement mechanism obtains multi-dimensional impedance data through orthogonal current paths, effectively overcoming the error caused by tissue anisotropy during single-position measurement, and improving the accuracy of body fat percentage measurement to within ±3%.

[0185] like Figure 2 As shown, in a preferred embodiment, the solution of this application is implemented as follows: The electrode assembly 201 includes two sets of parallel electrode groups. The first set consists of a first electrode plate and a second electrode plate made of stainless steel, spaced six centimeters apart and vertically fixed to the bottom of the test chamber, for contact with the feet of the human body during standing tests. The third set consists of a third electrode plate and a fourth electrode plate made of copper alloy, spaced seven centimeters apart and horizontally embedded in the surface of the test bed, for contact with the back of the human body during lying tests. The signal generation unit 202 uses a direct digital frequency synthesizer to generate a 55 kHz sine wave signal, which is stably output as an 8-volt AC voltage by a voltage regulation module, and the current is controlled within the range of 500 microamps by a constant current circuit. The signal acquisition unit 203 uses a differential amplifier to receive the induced signals from the second and fourth electrodes, and performs digitization processing at a 16-bit resolution by an analog-to-digital converter. The signal processing unit 204 includes a Butterworth bandpass filter with a passband range of 48 kHz to 102 kHz, and a lock-in amplifier uses biphase demodulation technology to extract the impedance phase difference. The calculation and control unit 205 is equipped with an embedded processor to compare the impedance amplitude with a dynamic threshold calculated based on height and weight in real time. When testing the standing posture, it calls a linear regression model to calculate the body fat percentage impedance value; when testing the lying posture, it uses a multinomial fitting algorithm to generate the impedance value. Finally, it fuses the dual-posture data through a weighted average algorithm. The output unit 206 integrates an LCD screen to display the body fat percentage value in real time and sends the data to a smartphone application via a Bluetooth Low Energy module.

[0186] Through the above technical solution, this application effectively solves the problem of data deviation caused by a single testing posture in traditional body fat measurement devices. By using dual-posture impedance measurement and data fusion processing, the influence of body water distribution and positional changes on the measurement results is significantly reduced. The dynamic threshold judgment mechanism improves the reliability of signal acquisition, and the combination of bandpass filtering and lock-in amplification effectively suppresses environmental electromagnetic interference. The optimized design of the multi-material electrode group improves adaptability to different body surface contact sites. While ensuring ease of operation, the testing system improves the stability of body fat percentage measurement results, providing more accurate reference data for health monitoring.

[0187] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for testing body fat percentage, characterized in that, Includes the following steps: S1: Start the test; S2: Place the test subject inside the body fat analyzer, and make the test subject stand between two parallel electrodes; S3: Generate an excitation signal, which excites the human body to be tested through the first electrode; S4: The sensor signal generated on the human body surface by the excitation signal is collected by a second electrode set at a distance greater than a preset distance from the first electrode; S5: Process the induced signal to obtain the first human body impedance amplitude R1 and the first human body impedance phase φ1; S6: Determine whether the amplitude R1 and phase φ1 of the first human body impedance have reached the preset value. If they have not reached the preset value, return to step S2. If they have reached the preset value, proceed to step S7. S7: Calculate the body fat percentage impedance Z1 of the first human body under test based on the amplitude R1 and phase φ1 of the first human body impedance. S8: Place the person to be tested lying flat inside the body fat analyzer; S9: Generates an excitation signal and excites the human body to be tested through the third electrode; S10: Acquire sensing signals by setting a fourth electrode at a distance greater than a preset distance from the third electrode; S11: Process the induced signal to obtain the second human body impedance amplitude R2 and the second human body impedance phase φ2; S12: Determine whether the amplitude R2 of the second human body impedance and the phase φ2 of the second human body impedance have reached the preset value. If the preset value has not been reached, return to step S9. If the preset value has been reached, proceed to step S13. S13: Calculate the body fat percentage impedance Z2 of the second human body under test based on the amplitude R2 of the second human body impedance and the phase φ2 of the second human body impedance; S14: Calculate the final body fat percentage of the test subject based on the body fat percentage impedance Z1 of the first test subject and the body fat percentage impedance Z2 of the second test subject. S15: Displays the calculated final body fat percentage of the test subject; S16: Send the final body fat percentage of the human body to be tested to the terminal device.

2. The body fat percentage testing method according to claim 1, characterized in that, Step S7 includes: S71: Extract the amplitude R1 and phase φ1 of the first human body impedance; S72: Calculate the first body fat percentage (BF1) according to Formula 1: Where: R1 is the amplitude of the first human body impedance, φ1 is the phase of the first human body impedance, θ is the thickness of human body fat, and k1, k2, and k3 are constants; S73: Calculate the second body fat percentage ; S74: Calculate the body fat percentage impedance of the first human subject to be tested. , where R0 is the preset impedance amplitude.

3. The body fat percentage testing method according to claim 1, characterized in that, Step S13 includes: S131: Extract the amplitude of the second human body impedance R2 and the phase of the second human body impedance φ2; S132: Calculate the third body fat percentage (BF3) according to Formula 1: Where: R2 is the amplitude of the second human body impedance, φ2 is the phase of the second human body impedance, θ is the thickness of human fat, and k1, k2, and k3 are constants; S133: Calculate the fourth body fat percentage ; S134: Calculate the body fat percentage impedance of the second test subject. , Where R0 is the preset impedance amplitude.

4. The body fat percentage testing method according to claim 1, characterized in that, Step S14 includes: S141: Calculating the impedance of average body fat percentage ; S142: Calculate the first difference ; S143: Determine whether the first difference a is less than the first threshold T1. If yes, proceed to step S144; otherwise, proceed to step S146. S144: Calculate the second difference ; S145: Determine whether the second difference b is less than the second threshold T2. If so, use Z_avg as the final body fat percentage. S146: Calculate the final body fat percentage y according to Formula 2: Where: a is the first difference, b is the second difference, and m, n, and p are constants.

5. The body fat percentage testing method according to claim 1, characterized in that, The methods for generating excitation signals in steps S3 and S9 include: S31: Generates a sine wave signal with a frequency range of 50 kHz to 100 kHz; S32: Controls the voltage of the sine wave signal within the range of 5 volts to 10 volts; S33: Outputs a sinusoidal signal to the electrode via a constant current source.

6. The body fat percentage testing method according to claim 1, characterized in that, The signal processing methods in steps S5 and S11 include: S51: Performs bandpass filtering on the induced signal, with a passband frequency range of 45 kHz to 105 kHz. S52: Extract the signal amplitude and signal phase of the induced signal through a lock-in amplifier; S53: Continuously sample the induced signal for 100 milliseconds at a sampling rate of 1 kHz.

7. The body fat percentage testing method according to claim 1, characterized in that, The methods for determining the preset values ​​in steps S6 and S12 include: S61: Input the height H and weight W of the person to be tested; S62: Calculate basic preset values ; Where α, β, and γ are coefficients; S63: Calculate the final preset value based on age A , where δ is the coefficient.

8. The body fat percentage testing method according to claim 4, characterized in that, The methods for determining the first threshold T1 and the second threshold T2 include: S1431: Obtain the Body Mass Index (BMI) of the test subject; S1432: Calculate the first threshold ; S1433: Calculate the second threshold .

9. The body fat percentage testing method according to claim 1, characterized in that, It also includes a calibration step: S01: Connect the standard resistor network to the electrode; S02: Apply an excitation signal and measure the response signal; S03: Update the coefficients k1, k2, and k3 in Formula 1 based on the measurement error between the response signal and the theoretical value.

10. A body fat percentage testing system, characterized in that, include: Electrode assembly, including: The first and second electrodes are set in parallel with a spacing of more than 5 cm, and are used for standing posture testing. The third and fourth electrodes are set in parallel with a spacing of more than 5 cm, and are used for lying posture testing. The signal generation unit is used to generate a sinusoidal excitation signal with a frequency range of 50 kHz to 100 kHz, which is output to the first electrode or the third electrode through a constant current source. The signal acquisition unit is used to acquire human body surface sensing signals through the second or fourth electrode. The signal processing unit includes: A bandpass filter is used to perform bandpass filtering on induced signals from 45 kHz to 105 kHz. A lock-in amplifier is used to extract the impedance amplitude and impedance phase from a filtered signal. The computing control unit is used for: Determine whether the impedance amplitude and impedance phase have reached the preset values; When in a standing position, Z1 is calculated based on R1 and φ1; When in a supine position, Z2 is calculated based on R2 and φ2; Calculate the final body fat percentage based on Z1 and Z2; Output unit, including: A monitor used to display the final body fat percentage; The wireless communication module is used to send the final body fat percentage to the terminal device.

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