CN113100740A

CN113100740A - Abdominal component analyzer and analysis method thereof

Info

Publication number: CN113100740A
Application number: CN202110380744.0A
Authority: CN
Inventors: 许川佩; 陈帅印; 江林; 余英铨; 石坚; 权慧
Original assignee: Guilin Gemred Sensor Tech Ltd; Guilin University of Electronic Technology
Current assignee: Guilin Gemred Sensor Tech Ltd; Guilin University of Electronic Technology
Priority date: 2021-04-09
Filing date: 2021-04-09
Publication date: 2021-07-13

Abstract

The invention discloses an abdominal component analyzer and an analysis method thereof, wherein an abdominal component analysis model of a maximum posterior estimation method is firstly used, then the model is trained by detecting human abdominal bioelectrical impedance of a sample, and finally the human abdominal bioelectrical impedance of a tested person is sent into the trained model to obtain the abdominal component content of the tested person; meanwhile, in the measurement process of the bioelectrical impedance of the human abdomen, the accuracy of impedance data measurement is improved by adopting a multi-frequency detection and self-correction mode, and experimental results show that the error of a measured value of the human abdomen can be controlled within 10%. In addition, compared with doctors and common large-scale human body composition analyzers, the abdominal fat analyzer is more targeted, more miniaturized, more noninvasive, safe, simple, convenient and low-cost, and is a feasible solution for household detection at present.

Description

Abdominal component analyzer and analysis method thereof

Technical Field

The invention relates to the technical field of analytical instruments, in particular to an abdominal component analyzer and an analytical method thereof.

Background

With the gradual improvement of living standard of people, obesity becomes a serious problem in modern society, especially for people with abdominal obesity, the patients are easy to suffer from cardiovascular and cerebrovascular diseases such as hypertension, hyperlipidemia, diabetes, fatty liver, coronary heart disease and the like, and even more, the patients develop a plurality of serious diseases such as myocardial infarction, cerebral infarction, apoplexy, hemiplegia, liver cirrhosis and the like. For this reason, specialized instruments and equipment are required to detect and analyze the abdominal components of human body so that people can understand the development conditions of their own physical qualities.

The balanced and standard human body component distribution can improve the health index of human beings, so that the human body components can be accurately and quickly measured, and a tester can be helped to judge the self health state. On a molecular level, the human body is composed of water, protein, fat, and minerals such as vitamins, minerals, cellulose, etc., water accounts for about 60%, carbohydrates and fat account for about 14%, and proteins account for about 17%. The body fluid of the human body has many ions to make it conductive, which makes the body have resistance characteristics. The cells of the human body have a double-layer membrane, on both sides of which there is a conductive body fluid and between which there is a non-conductive medium, and this structure is similar to a capacitor in an electric circuit. Although the human body can be simplified into a resistance-capacitance model, different impedance characteristics are shown under the condition of applying different frequencies, and the physiological characteristic information of the human body can be obtained theoretically according to the impedance characteristics. However, due to the complexity of the human body, the impedance of a certain part of the human body cannot be independently and accurately tested, so how to accurately obtain the impedance of the abdomen of the human body is the key problem to be solved.

Disclosure of Invention

The invention aims to solve the problems of detection and analysis of abdominal components and provides an abdominal component analyzer and an analysis method thereof.

In order to solve the problems, the invention is realized by the following technical scheme:

an abdominal component analysis method, comprising the steps of:

step 1, establishing an abdominal component analysis model of a maximum posterior estimation method:

wherein fat represents abdominal fat content, water represents abdominal water content, protein represents abdominal protein content, inSalt represents abdominal inorganic salt content, muscle represents abdominal muscle content, light represents height, weight represents body weight, BMI represents body mass index, impedence 1, impedence 2 and impedence 3 represent abdominal impedance values measured at three different frequencies, w is a weight vector, and δ is an expected error vector of the model;

step 2, measuring basic parameters, abdominal component content and abdominal impedance values of each sample for sample sets consisting of different crowds to obtain sample data sets corresponding to the sample sets;

step 3, training the abdominal component analysis model constructed in the step 1 by using the sample data set obtained in the step 2, and determining a weight vector of the abdominal component analysis model and an expected error vector of the model by using a maximum posterior estimation method to obtain a trained abdominal component analysis model;

step 4, for the tested person, measuring the basic parameters and the impedance value of the abdomen of the tested person;

step 5, sending the basic parameters and the abdominal impedance values of the tested person obtained in the step 5 into the abdominal component analysis model trained in the step 3 to obtain the abdominal component content of the tested person;

the above basic parameters include height, weight and body mass index, the abdominal component content includes abdominal fat content, abdominal moisture content, abdominal protein content, abdominal inorganic salt content and abdominal muscle content, and the abdominal impedance value includes abdominal impedance values measured at three different frequencies.

In the above step 2 and step 4, the method for measuring the impedance value of the abdomen is as follows:

step a, sinusoidal excitation signals of three frequencies are applied to the abdomen of a human body, the abdomen generates a corresponding electric field under the excitation of the sinusoidal excitation signals, and the electric field is collected to obtain measurement signals of the abdomen;

b, after amplifying, conditioning and amplitude-phase detecting the measurement signal, and performing analog-to-digital conversion, sampling to obtain a sampling impedance value of the abdomen;

and c, carrying out digital low-pass filtering on the sampled impedance value of the abdomen, and carrying out steady state judgment on the sampled impedance value by using the minimum mean square error to obtain the impedance value of the abdomen.

In the step a, the sinusoidal excitation signals with the three frequencies are sinusoidal excitation signals within the frequency range of 1KHz to 1 MHz.

An abdomen component analyzer for realizing the abdomen component analyzing method comprises a constant current source generating circuit, a signal collecting circuit, a signal amplifying circuit, a signal conditioning circuit, an amplitude and phase detecting circuit, an AD data collecting circuit, a microprocessor and a data processing platform; the control end of the microprocessor is connected with the constant current source generating circuit; the output end of the constant current source generating circuit is contacted with the abdomen of the human body, the input end of the signal acquisition circuit is contacted with the abdomen of the human body, and the output end of the constant current source generating circuit is separated from the input end of the signal acquisition circuit by a certain distance; the output end of the signal acquisition circuit is connected with the input end of the microprocessor after sequentially passing through the signal amplification circuit, the signal conditioning circuit, the amplitude-phase detection circuit and the AD data acquisition circuit, and the output end of the microprocessor is connected with the data processing platform.

The output end of the constant current source generating circuit outputs three sine excitation signals within the frequency range of 1 KHz-1 MHz.

The constant current source generating circuit consists of a DSS signal generator, an amplifying and filtering circuit and a voltage-controlled constant current source circuit; the DSS signal generator further comprises a phase accumulator, a waveform memory, a digital-to-analog converter and a low-pass filter; the control end of the microprocessor is connected with the input end of the phase accumulator, and the clock control ends of the phase accumulator and the digital-to-analog converter; the output end of the phase accumulator is connected with the input end of the waveform memory, the output end of the waveform memory is connected with the input end of the digital-to-analog converter, and the output end of the digital-to-analog converter is connected with the input end of the low-pass filter; the output end of the low-pass filter is connected with the input end of the amplifying and filtering circuit, the output end of the amplifying and filtering circuit is connected with the input end of the voltage-controlled constant current source circuit, and the output end of the voltage-controlled constant current source circuit forms the output end of the constant current source generating circuit.

The signal conditioning circuit consists of a wave trap and a band-pass filter; the input end of the wave trap forms the input end of the signal conditioning circuit, the output end of the wave trap is connected with the input end of the band-pass filter, and the output end of the band-pass filter forms the output end of the signal conditioning circuit.

The abdominal component analyzer further comprises an impedance network for calibration; the impedance network is formed by connecting more than 2 impedance matching branches in parallel, and each impedance matching branch is formed by connecting 1 precision resistor and 1 switch in series; two ends of the impedance network are connected in parallel with the output end of the constant current source generating circuit.

Compared with the prior art, the abdomen component analysis model of the maximum posterior estimation method is firstly used, then the model is trained by detecting the human abdomen bioelectrical impedance of the sample, and finally the human abdomen bioelectrical impedance of the tested person is sent into the trained model to obtain the abdomen component content of the tested person; meanwhile, in the measurement process of the bioelectrical impedance of the human abdomen, the accuracy of impedance data measurement is improved by adopting a multi-frequency detection and self-correction mode, and experimental results show that the error of a measured value of the human abdomen can be controlled within 10%. In addition, compared with doctors and common large-scale human body composition analyzers, the abdominal fat analyzer is more targeted, more miniaturized, more noninvasive, safe, simple, convenient and low-cost, and is a feasible solution for household detection at present.

Drawings

FIG. 1 is a single cell impedance model.

Fig. 2 is a simplified model of fig. 1.

Fig. 3 is a system block diagram of an abdominal component analyzer.

Fig. 4 is a schematic diagram of a constant current source generating circuit.

Fig. 5 is a schematic diagram of a DSS signal generator.

Fig. 6 is a schematic diagram of a voltage controlled constant current source circuit.

Fig. 7 is a schematic diagram of a signal conditioning circuit.

Fig. 8 is a schematic diagram of an amplitude detection circuit.

Fig. 9 is a schematic diagram of a phase detection circuit.

FIG. 10 is a graph showing the relationship between the output voltages of the amplitude detection circuit.

Fig. 11 is a diagram illustrating the relationship between the output voltages of the phase detection circuit.

Fig. 12 is a schematic diagram of an impedance network.

Fig. 13 is an impedance response curve of a human abdomen.

Fig. 14 is a graph after digital low-pass filtering.

FIG. 15 is a schematic diagram of a data queue.

Fig. 16 is a flowchart of the correction process.

Fig. 17 is a flowchart of an abdominal component analysis method.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to specific examples.

The abdomen component analyzer aims at detecting the impedance of the abdomen of a human body to obtain the components of abdomen fat, whole fat, water and the like, and the principle is similar to that of measuring the electrical resistivity of a conductor in electricity. Since each conductor has specific resistivity, for a material with a known length, the resistivity can be obtained by correlation calculation and the material of the composition can be found as long as the resistance is measured. Considering the volume of a conductor as a human body, the length of the conductor is replaced by measuring the length of human tissue, and the resistivity is the value of the resistance per unit volume of human tissue. The content of the related components in the body can be obtained by measuring the impedance value of the human body by an instrument. Fat in the human body conducts electricity weakly, while muscle moisture conducts electricity easily. If the fat content is high and the muscle content is low, the biological impedance value is relatively high when the current passes through; whereas the bio-impedance value is relatively low. Through the information, a mathematical model is established according to different ages, sexes and the like of people, and the human body components such as the fat content, the water content and the like of the trunk are obtained through quantitative analysis.

The biological tissue is composed of cells, the main components of the cells are cell membranes and cell sap, and extracellular fluid, intercellular substance and the like exist among different cells, so that the human body can be equivalent to an impedance model through electrochemical analysis. As shown in fig. 1. At frequencies less than 1MHZ, the equivalent resistance of the cell membrane can be considered open circuit, so the model of fig. 1 can be simplified as shown in fig. 2. The simplified expression is as follows:

where w is the angular frequency, Cm is the cell membrane capacitance, Ri is the intracellular fluid resistance, and Re is the extracellular fluid resistance.

The impedance modulus is:

the phase angle is:

the aim of the invention is to measure the above two parameters.

In a healthy human body, water is distributed both intracellularly and extracellularly and is relatively constant. But the water balance in the human body is damaged by old patients, people lacking nutrients, patients with heart diseases and the like. Since the electron sensitivity of intracellular and extracellular fluids is different, the electron sensitivity of extracellular fluid is much greater than that of intracellular fluid.

The electrical characteristics of the biological tissue show different regular characteristics along with different frequency bands of the frequency. The human body impedance measured by the high-frequency electronic signal reflects the total value of intracellular fluid and extracellular fluid, and the low-frequency electronic signal only reflects the resistance value of the extracellular fluid. Because the frequency of each excitation current passing through human tissues is different, low-frequency signals cannot pass through cells under excitation, the tissue outside human cells is measured, intermediate-frequency signals can pass through part of the cells, and high-frequency signals can directly penetrate through the cells. In the alpha frequency band, the electrical properties of biological tissues are mainly related to the cell membrane properties. In the beta band, the response is the characteristics of the cell fluid (including the intracellular fluid and the external fluid), and when the frequency is more than 1MHz, the response enters the gamma band, the band reflects the water molecule condition, and in clinic, most pathological conditions only occur in the alpha band and the beta band, and in order to ensure that the excitation can penetrate through the cell membrane, the frequency of the selected signal is more than 1 KHz.

At present, most of lipid measuring instruments adopt single 50KHz frequency signals, the conditions of all tissues of a human body cannot be more accurately reflected due to single frequency and single input parameters, and the multi-frequency bioelectrical impedance detection technology can jointly measure the intracellular fluid and the extracellular fluid of the water of the human body, so that the problem can be better solved. Therefore, the impedance of different tissue components of the human abdomen is measured by adopting a 3-sinusoidal signal method within the frequency range of 1KHz to 1MHz, so that the obtained result is more targeted, and the measurement accuracy is improved by a self-calibration mode.

The invention relates to an abdomen component analyzer, which is composed of a constant current source generating circuit, an impedance acquisition circuit, a signal amplifying circuit, a signal conditioning circuit, an amplitude and phase detection circuit, an AD data acquisition circuit, a microprocessor and a data processing platform, as shown in figure 3. The control end of the microprocessor is connected with the constant current source generating circuit. The output end of the constant current source generating circuit is contacted with the human abdomen, the input end of the impedance acquisition circuit is contacted with the human abdomen, and the output end of the constant current source generating circuit is separated from the input end of the impedance acquisition circuit by a certain distance. The output end of the impedance acquisition circuit is connected with the input end of the microprocessor after sequentially passing through the signal amplification circuit, the signal conditioning circuit, the amplitude-phase detection circuit and the AD data acquisition circuit, and the output end of the microprocessor is connected with the data processing platform. The microprocessor generates a constant current excitation source signal by controlling the constant current source generating circuit, applies the excitation source signal to a detected part (abdomen) of a human body, generates a corresponding electric field at the detected part of the human body, acquires a measurement signal of the detected part by acquiring the electric field signal, performs amplitude-phase detection on the measurement signal after amplification and conditioning, transmits the measurement signal to the AD acquisition module, and sends the measurement signal to the microprocessor, wherein the microprocessor measures the impedance value of the abdomen of the human body. And sending the impedance value of the human abdomen into a data processing platform for data processing, and calculating the corresponding human body component content.

The constant current source generating circuit is composed of a DSS signal generator, an amplifying and filtering circuit and a voltage-controlled constant current source circuit, as shown in fig. 4. Wherein the DSS signal generator further comprises a phase accumulator, a waveform memory, a digital-to-analog converter, and a low pass filter, as shown in fig. 5. The control end of the microprocessor is connected with the input end of the phase accumulator, and the frequency control code output by the microprocessor is used for setting the jump step length and controlling the frequency of the signal. The control end of the microprocessor is connected with the phase accumulator and the clock control end of the digital-to-analog converter, an fclk signal output by the microprocessor is a reference clock of the DDS, and a point is output every time a clock period passes. The output end of the phase accumulator is connected with the input end of the waveform memory, the output end of the waveform memory is connected with the input end of the digital-to-analog converter, and the output end of the digital-to-analog converter is connected with the input end of the low-pass filter; the output end of the low-pass filter is connected with the input end of the amplifying and filtering circuit, the output end of the amplifying and filtering circuit is connected with the input end of the voltage-controlled constant current source circuit, and the output end of the voltage-controlled constant current source circuit forms the output end of the constant current source generating circuit. The phase accumulator is composed of an N-bit adder and an N-bit accumulation register, and can complete accumulation of phase values according to the frequency control code and input accumulated values into the waveform memory. The waveform memory takes the value of the phase accumulator as the current address, searches signal data corresponding to the phase value and outputs the signal data to the digital-to-analog converter. And the digital-to-analog converter is used for converting the digital quantity output by the waveform memory into the corresponding analog quantity. Because the digital-to-analog converter has quantization error and aliasing exists in an output waveform, a low-pass filter is required to be used for filtering at an output end, and the output performance of signals is improved. The frequency output formula of the DDS signal generator is as follows: fout ═ fclk ^ k/2^ n, wherein k is the frequency accuse word, and the resolution of output frequency is: fclk/2^ n. The signal output by the DDS signal generator is not necessarily the desired amplitude, and in addition, there may be a bias voltage, so that an amplifying filter circuit is added after DDS to remove the bias and noise ratio. The desired signal of frequency and amplitude magnitude has been obtained, but how to make the signal guarantee a constant current output. And finally, a voltage-controlled constant-current circuit is added to ensure the loading capacity of the excitation signal. Fig. 6 shows a voltage-controlled constant current circuit.

The errors of the constant current excitation circuit output according to the circuit design under different load conditions are shown in the following table 1:

TABLE 1 error of output signal under different loads

As can be seen from the above table, the signal output by the voltage-controlled constant current circuit has a small error under different load conditions. The signals output by the voltage-controlled constant-current circuit can meet our requirements. Although the error is already small, not exceeding 2% in the table, the trend of the error with increasing impedance can be found to be the relevant trend, and this error can be corrected again on the program. This also further ensures the accuracy of the measured impedance.

The microprocessor is used as a main control chip, and the output end of the constant current source generating circuit is contacted with the abdomen of the human body and used for generating an alternating excitation signal meeting the requirement. The input end of the impedance acquisition circuit is contacted with the human abdomen and used for acquiring the voltage fed back by the human abdomen so as to obtain the human abdomen impedance.

For the traditional method of solving impedance according to the magnitude of voltage and current, the attenuation of a circuit system to signals may cause the magnitude of excitation signals applied to two ends of the impedance to be measured to be inconsistent with the calculated value; as can also be seen from table 1, the current accuracy output when the loads are not simultaneously different may also be slightly different. Furthermore, since the voltage magnitude collected by the ADC and the ADC value are not necessarily in correspondence with the zero crossing point, there may be a certain offset. Therefore, there may be a large error in the conventional method of obtaining the impedance value based on the collected voltage and the known current.

Since the relationship between the magnitude of the impedance and the magnitude of the voltage is linear, the error in table 1 is also positively correlated. In the invention, the electric impedance is calculated by adopting a method of directly calculating according to the ADC value. The calculation formula is as follows:

Z＝K×ADC+ε

in the formula, K and epsilon are respectively a slope and an offset.

Specifically, after the acquisition, the measurement signal needs to be amplified, and the differential amplification circuit is adopted to amplify the measurement. And the differential amplifying circuit converts the sampled differential double-ended signal into a single-ended signal.

After the measurement signal is amplified, conditioning is required, and a conditioning flow chart is shown in fig. 7. The signal conditioning circuit consists of a wave trap and a band-pass filter. The input end of the wave trap forms the input end of the signal conditioning circuit, the output end of the wave trap is connected with the input end of the band-pass filter, and the output end of the band-pass filter forms the output end of the signal conditioning circuit. The purpose of the wave trap is to avoid the influence of the commercial power 50Hz, the wave trap is mainly designed to filter the frequency of 50Hz, and the frequency is obviously attenuated near the frequency of 50Hz, so that the main function of the wave trap is to ensure that only the power frequency is electrically filtered without influencing signals of other frequencies. The band-pass filter can filter high-frequency noise and some direct current offset, for the low-frequency part, the direct current component needs to be filtered, and the slight fluctuation of the signal caused by the pulsation of the blood flow needs to be considered, wherein the signal change rate of the part needs to be removed in order to obtain a more accurate and stable impedance value, and for the main part of the high-frequency part or the noise part, the external interference noise is mainly filtered.

The amplitude and phase detection part mainly detects the amplitude and phase of signals, and comprises an amplitude detection circuit and a phase detection circuit, as shown in fig. 8 and 9, amplitude and phase detection is carried out by using a logarithmic detector, two measurement signals are input through input INPA and INPB, decibel values of the ratio of two signal powers and phases between the two signal powers are output through VMAG and VPHS.

According to the logarithmic detection principle, the calculation formula of the amplitude and the phase is as follows:

V_MAG＝(R_FI_SLP/20)(P_INA-P_INB)+V_CP

V_PHS＝-R_FI_Φ(|Φ(V_INA)-Φ(V_INB)|-90°)+V_CP

in the above equation, PINA and PINB are the equivalent powers in logarithmic units of VINA and VINB at a specified reference impedance. For the gain function, use R_FI_SLPThe slope is shown as 600 mv/decade divided by 20 dB/decade to 30 mv/dB. The voltage value corresponding to-30 dB to +30dB is 0-1.8V by taking Vcp as the midpoint of 900 mV. Slope of the phase function representing R_FI_ΦRepresents 10 mV/degree, also takes 900mV as the midpoint, corresponding to 90 degrees; 0-180 degrees corresponds to 1.8V-0V; the voltage range is the same for 0 to-180 degrees, but the slopes are opposite, where φ is the degree of phase for each signal. As shown in fig. 10 and 11.

In the invention, the accuracy of measuring the impedance has important influence on the result, so that the self-correcting process is added in the invention to improve the accuracy of impedance measurement. There are many reasons for affecting the accuracy of impedance measurements: for example, the components themselves influence: due to the component manufacturing process, even devices of the same type and the same manufacturer have more or less differences. For example, the effect of measuring frequency variation: since the frequency response of the human system is different at different frequencies, the measurement of frequency changes inevitably cause changes in system parameters. Since the human body is a complex system, self-calibration is an essential process to narrow the difference of each detection. In order to ensure the accuracy of the impedance determination, the system parameters (K, epsilon) need to be corrected before the formal measurement.

In order to meet the error correction requirements of a plurality of precision resistors, an impedance network for correction needs to be additionally arranged on the basis of a signal amplification circuit, the impedance network is formed by connecting more than 2 impedance matching branches in parallel, and each impedance matching branch is formed by connecting 1 precision resistor and 1 switch in series. The two ends of the impedance network are connected in parallel with the output end of the constant current source generating circuit, the impedance network is connected with the output end of the excitation signal, and the measurement correcting network or the human body abdominal impedance is switched and measured through the analog switch. As shown in fig. 12.

After the signal excitation, the signal acquisition and the signal processing, the measured impedance value of the human abdomen can be read out through the microprocessor. However, for the complexity of the human body, the data read directly has some problems, and the human body has frequency response even if the resistance value of the human body is regarded as a system. Therefore, at the moment of applying the excitation, the obtained current impedance value cannot directly reflect the real situation of the human body due to the self frequency response of the human body, and the read impedance value is meaningful only when the frequency response of the human body reaches a steady state.

As shown in fig. 13, the impedance response curve is obtained by the microprocessor sending the impedance response curve to the upper computer, i.e. the data processing platform, through the serial port. Where the solid line is the ADC value measured by the circuit, the short dashed line is the value after digital low-pass filtering, and the long dashed line is the calculated impedance value. As can be seen from the figure, the value read out only makes sense when the system has reached a stable state, otherwise the value read out during the dynamic response period is not representative and does not satisfy consistency.

It now appears that it is critical how to determine whether the response curve enters steady state. Here we implement the principle of a low-pass filter by means of a digital algorithm.

The transfer function of a first-order passive low-pass filter in an analog signal is known as:

this is a transfer function in the S domain that must be translated into discrete signals to be applied to the computer. Converting the continuous signal into the discrete signal can be realized by Z conversion, and the continuous signal can be obtained by the Z conversion:

where T is the sampling period. This translates the above equation into a difference equation:

wherein, Y (n) is the output quantity of the filter of this time, x (n) is the sampling value of this time, and Y (n-1) is the output value of the filter of the last time. As can be seen from the above equation, T/(T + RC) and RC/(T + RC) are in a complementary relationship, the digital filter algorithm can be simplified as follows:

Y(n)＝a*X(n)+(1-a)*Y(n-1)

where a is the filter coefficient, adjusting this parameter to an appropriate value can result in the result shown in fig. 14. It can be observed that the dashed line is the result after digital low pass filtering, which is better to determine when the signal enters steady state than when the solid line does not pass the digital low pass filter.

The sampled data remains substantially unchanged when steady state is entered. According to the idea, a linear queue chain table is created through a data structure algorithm. And judging that the data is in a steady state period when the error successive approximation is zero. As shown in fig. 15, the process of data cycle entry into the queue.

The equation for determining the steady state can be defined by a least mean square error (LMS), and for the convenience of calculation, a steady state determination function is defined by a minimum absolute error (LAD), and the conditions for determining the steady state are as follows:

in the formula, n is the number of data in the queue, ξ is the minimum steady-state error defined, and the precision of steady-state judgment is adjusted by changing n and ξ.

The voltage of the abdomen of the human body is respectively measured through the impedance network and is corrected by using a least square method. Referring to fig. 16, the specific correction method is as follows:

the values obtained by ADC sampling the voltage of the measuring resistor are respectively ADC₁、ADC₂… …, obtaining the slope relation between the voltage and the resistance and the offset thereof according to the maximum likelihood estimation:

the difficulty in ensuring the accuracy of measuring the body resistance is how to adapt the above variables to the complexity of the body impedance. During the measurement process, a queue is established, error signals are detected in real time through the queue, and errors in the queue are calculated, so that the difference of the measurement can be detected in real time during the measurement. If the measurement error is detected to be higher than the set threshold value, recalculating and correcting the system parameters to adapt to the change caused by the current human body complexity, stacking the new corrected system parameters, and recalculating a more accurate human body impedance value.

The impedance values of the impedance network at four different frequencies were measured using an impedance network such as that of fig. 12, and the error results before and after correction were compared, as shown in table 2.

TABLE 2 comparison of results before and after correction

Compared with the table above, the error results before and after correction, and the introduction of the self-correcting method has obvious improvement effect on the measurement accuracy.

An abdominal component analysis model of the MAP estimation (maximum a posteriori estimation) method was established. The MAP estimate is a more deep estimate than the ML estimate, and considering the a posteriori density pi (w | d, x), the MAP estimate of the parameter vector w can be defined by:

w_MAP＝argmaxπ(w|d,x)

wherein w is a weight vector, d is a parameter represented by an output expected value, wherein the parameter represents abdominal fat (fat), water content (water), trunk muscle (muscle), inorganic salt (inSalt) or protein (protein), and the data is measured by a standard medical instrument; x is an input vector representing the height (height), weight (weight), BMI (BMI: weight (kilogram) divided by height (meter) squared), and impedance values measured at different frequencies (immedence 1, immedence 2, immedence 3).

The calculation formula of the human body composition is as follows:

wherein fat represents abdominal fat content, water represents abdominal moisture content, protein represents abdominal protein content, inSalt represents abdominal inorganic salt content, muscle represents abdominal muscle content, light represents height, weight represents body weight, BMI represents body mass index, impedence 1, impedence 2, and impedence 3 represent impedance values measured at three different frequencies, respectively,

as a weight vector, the weight vector is,

is the expected error vector of the model.

Because the impedance value is only used for estimating the content of each component of the human body and the content of the component at the measured part can be completely reflected, the complexity and the integrity of the human body are caused. The BMI of the height and the weight of a person not only reflects important indexes of the health condition of the human body, but also is influenced by the whole human body when the human body tissue is stimulated, the impedance value of the measured part cannot be accurately reflected by the impedance value obtained by the tested part, and is related to the whole human body, so the height, the weight and the BMI of the person are used as indispensable input variables when a model is established. The use of these variables as input variables can greatly improve the accuracy of the measurement.

The calculation formula of the human body components is developed as follows:

abdominal fat ═ ω₁₁Height + ω₁₂Body weight + ω₁₈*BMI+ω₁₄Impedance 1+ omega₁₈Impedance 2+ omega₁₆Impedance 3+ delta₁

Water content of abdomen being omega₂₁Height + ω₂₂Body weight + ω₂₈*BMI+ω₂₄Impedance 1+ omega₂₈Impedance 2+ omega₂₆Impedance 3+ delta₂

Abdominal protein omega₃₁Height + ω₃₂Body weight + ω₃₈*BMI+ω₃₄Impedance 1+ omega₃₈Impedance 2+ omega₃₆Impedance 3+ delta₃

Abdomen inorganic salt ═ omega₄₁Height + ω₄₂Body weight + ω₄₈*BMI+ω₄₄Impedance 1+ omega₄₈Impedance 2+ omega₄₆Impedance 3+ delta₄

Trunk muscle is omega₅₁Height + ω₅₂Body weight + ω₅₈*BMI+ω₅₄Impedance 1+ omega₅₈Impedance 2+ omega₅₆Impedance 3+ delta₅。

Written in the form of the general formula:

where δ represents the expected error of the model.

In the above model, the difficulty is how to select an appropriate weight, and the content information of each component of the abdomen can be more accurately calculated only on the basis of the correct weight. In order to ensure that the measurement error is as small as possible, the weight is determined by a supervised learning method, wherein a learning sample is a data result obtained by measurement of a standard medical instrument. According to the method, the model coefficient is determined through MAP estimation, and the estimation algorithm comprises an adjustable regularization parameter lambda, so that the accuracy of the model can be improved. The specific determination process of the parameters is as follows:

the training samples for parameter estimation are assumed to conform to statistical independence and synchronization distribution, gaussian, and stability. The posterior density can be derived from the gaussian distribution function and the MAP estimate is expressed as:

where λ is the regularization parameter.

Defining a quadratic function:

it is clear that maximizing the parameter of w is equivalent to minimizing the quadratic function ξ (w). It will be readily appreciated that the most efficient estimate w can be obtained by differentiating the quadratic function ξ (w) and making the result 0_MAP. From this, a MAP estimate of the following parameter vector can be obtained:

wherein, R is an autocorrelation matrix, R is a cross-correlation matrix, and I is a unit matrix.

And after the specific model weight and the expected error are solved, a trained model can be obtained. According to the abdominal impedance obtained by detection, the abdominal component can be obtained according to the abdominal impedance acquisition method and the abdominal model.

Because the temperature and humidity of the equipment operating environment influence, the voltage values of the same impedance collected by the system under different environments can be different, so the power-on self-test is added in the invention, and the system is initialized to correct the system parameters. In addition, as the usage time of the device increases, errors of the circuit system are gradually accumulated, and a correction option is added in the invention at the same time, so that a user can correct the data when finding that the data is abnormal, wherein the overall design flow chart is shown in fig. 17, and the specific steps are as follows:

in the formula, fat represents abdominal fat content, water represents abdominal moisture content, protein represents abdominal protein content, inSalt represents abdominal inorganic salt content, muscle represents abdominal muscle content, light represents height, weight represents body weight, BMI represents body mass index, impedence 1, impedence 2 and impedence 3 represent abdominal impedance values measured at three different frequencies, w is a weight vector, and δ is an expected error vector of the model.

And 2, measuring basic parameters, abdominal component content and abdominal impedance values of each sample for sample sets consisting of different crowds to obtain sample data sets corresponding to the sample sets.

And 3, training the abdominal component analysis model constructed in the step 1 by using the sample data set obtained in the step 2, and determining a weight vector of the abdominal component analysis model and an expected error vector of the model by using a maximum posterior estimation method to obtain the trained abdominal component analysis model.

And 4, measuring the basic parameters and the abdominal impedance value of the tested person.

And 5, sending the basic parameters and the abdominal impedance values of the tested person obtained in the step 5 into the abdominal component analysis model trained in the step 3 to obtain the abdominal component content of the tested person.

The basic parameters include height, weight and body mass index. The abdominal component content includes abdominal fat content, abdominal moisture content, abdominal protein content, abdominal inorganic salt content, and abdominal muscle content. The impedance values of the abdomen include impedance values of the abdomen measured at three different frequencies.

The method for measuring the impedance value of the abdomen is as follows:

It should be noted that, although the above-mentioned embodiments of the present invention are illustrative, the present invention is not limited thereto, and thus the present invention is not limited to the above-mentioned embodiments. Other embodiments, which can be made by those skilled in the art in light of the teachings of the present invention, are considered to be within the scope of the present invention without departing from its principles.

Claims

1. An abdominal component analysis method is characterized by comprising the following steps:

2. The abdominal composition analysis method according to claim 1, wherein the impedance value of the abdomen is measured in the steps 2 and 4 as follows:

3. The method for analyzing abdominal components of claim 2, wherein in the step a, the sinusoidal excitation signals of three frequencies are sinusoidal excitation signals in a frequency range of 1KHz to 1 MHz.

4. An abdominal component analyzer for realizing the abdominal component analysis method of claim 1, which is characterized by comprising a constant current source generating circuit, a signal acquisition circuit, a signal amplifying circuit, a signal conditioning circuit, an amplitude and phase detection circuit, an AD data acquisition circuit, a microprocessor and a data processing platform; the control end of the microprocessor is connected with the constant current source generating circuit; the output end of the constant current source generating circuit is contacted with the abdomen of the human body, the input end of the signal acquisition circuit is contacted with the abdomen of the human body, and the output end of the constant current source generating circuit is separated from the input end of the signal acquisition circuit by a certain distance; the output end of the signal acquisition circuit is connected with the input end of the microprocessor after sequentially passing through the signal amplification circuit, the signal conditioning circuit, the amplitude-phase detection circuit and the AD data acquisition circuit, and the output end of the microprocessor is connected with the data processing platform.

5. The abdominal component analyzer according to claim 4, wherein the constant current source generating circuit outputs three sinusoidal excitation signals in a frequency range of 1KHz to 1MHz at the output terminal.

6. The abdominal component analyzer according to claim 4, wherein the constant current source generating circuit is composed of a DSS signal generator, an amplifying filter circuit and a voltage-controlled constant current source circuit; the DSS signal generator further comprises a phase accumulator, a waveform memory, a digital-to-analog converter and a low-pass filter;

the control end of the microprocessor is connected with the input end of the phase accumulator, and the clock control ends of the phase accumulator and the digital-to-analog converter; the output end of the phase accumulator is connected with the input end of the waveform memory, the output end of the waveform memory is connected with the input end of the digital-to-analog converter, and the output end of the digital-to-analog converter is connected with the input end of the low-pass filter; the output end of the low-pass filter is connected with the input end of the amplifying and filtering circuit, the output end of the amplifying and filtering circuit is connected with the input end of the voltage-controlled constant current source circuit, and the output end of the voltage-controlled constant current source circuit forms the output end of the constant current source generating circuit.

7. The abdominal composition analyzer of claim 4, wherein the signal conditioning circuit comprises a wave trap and a band pass filter; the input end of the wave trap forms the input end of the signal conditioning circuit, the output end of the wave trap is connected with the input end of the band-pass filter, and the output end of the band-pass filter forms the output end of the signal conditioning circuit.

8. The abdominal composition analyzer of claim 4, further comprising an impedance network for calibration; the impedance network is formed by connecting more than 2 impedance matching branches in parallel, and each impedance matching branch is formed by connecting 1 precision resistor and 1 switch in series; two ends of the impedance network are connected in parallel with the output end of the constant current source generating circuit.