Local fat layer thickness analyzer based on multi-electrode bioimpedance measurement

Abstract

The present invention discloses a local fat layer thickness analyzer based on multi-electrode biological impedance measurement, comprising the following four parts: a current excitation source, a voltage readout circuit, electrodes and data processing; the current excitation source is used to apply current to human tissue, the voltage readout circuit is used to measure the voltage on the surface of human tissue, the electrodes are used to connect the current excitation source, the voltage readout circuit and human tissue, and a current excitation source is set on the surface of the human body to apply current to the human tissue, and the voltage difference at different distances on the surface of the human tissue is read out by the voltage readout circuit respectively; in the data processing, the multi-electrode impedance measurement algorithm obtained by measurement fitting is used, and the voltage difference measured by the voltage readout circuit is substituted to obtain the local fat layer thickness of the biological to be measured. Therefore, the present invention can reduce the problems caused by the electrode contact impedance and improve the accuracy of local analysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of wearable health equipment, and in particular relates to a local fat layer thickness analyzer based on multi-electrode biological impedance measurement. Background Art

[0002] Bioimpedance is one of the important parameters that reflect the physiological condition of the human body and is an important indicator for many physiological parameter detection devices. In the past two decades, the overweight and obesity rates of Chinese residents have increased year by year, increasing the incidence of many chronic diseases such as hypertension, hyperlipidemia, and diabetes, becoming a serious social health problem; people pay more and more attention to their own health and body shape, and the number of people doing fitness is also increasing; the body fat rate of the human body is an important indicator of the health and physical condition of the human body and is widely studied.

[0003] The methods on the market for measuring human body fat percentage include CT method and MRI method. CT method and MRI method cannot be used in daily life because of the large instruments and high prices. Although the research on measuring body fat percentage through bioimpedance is still on the surface, it has broad prospects because of its characteristics of being non-invasive, harmless, cheap, simple to operate and rich in functional information. It is mostly used in home diagnosis and treatment and daily monitoring scenarios.

[0004] Most current bioimpedance methods measure whole body fat, or use two-electrode and four-electrode measurement methods. For example, in the literature [Hermann Scharfetter, Patricia Brunner, Fat and Hydration Monitoring by Abdominal Bioimpedance Analysis: Data Interpretation by Hierarchical Electrical Modeling [M]. IEEE Transactions on Biomedical Engineering, 2005, 52 (6): 975-982], finite element modeling was used to model the human abdomen as a three-layer model, and fat and hydration tests were performed on abdominal bioimpedance analysis. The effects of each layer of the abdominal structure on abdominal impedance were studied. This technology provides the idea of ​​finite element decomposition, but uses a two-electrode measurement method, which has low accuracy in actual measurements.

[0005] The literature [Jingna Mao, A Five-Tissue-Layer Human Body Communication Circuit Model Tunable to Individual Characteristics [M]. IEEE Transactions on Biomedical Circuits and Systems, 2018, 12(2): 303-312] proposed a five-layer model of the human body to measure the path loss of the human body. However, the use of global path measurement results in a large loss error. These bioimpedance measurement methods have low accuracy. For example, the global measurement has a large error, the two-electrode measurement method has a relatively large interface impedance and motion artifacts, and the four-electrode measurement method has inaccurate current measurement. Summary of the invention

[0006] In view of the above, the present invention provides a local fat layer thickness analyzer based on multi-electrode bioimpedance measurement, which can better improve the shortcomings of existing methods for measuring fat layer thickness and bioimpedance, such as complex operation, bulky instruments, and low accuracy.

[0007] A local fat layer thickness analyzer based on multi-electrode biological impedance measurement, comprising:

[0008] A multi-electrode module, comprising a pair of excitation electrodes and M pairs of test electrodes, where M is a natural number greater than 1; these electrodes are regularly arranged on the surface of human tissue, the excitation electrodes are placed outside the test electrodes, and the two test electrodes of each pair are equidistant from the outer excitation electrodes;

[0009] A current source module, used for applying an alternating current excitation to human tissue via an excitation electrode;

[0010] A voltage reading module is used to collect and obtain the voltage signal generated on the test electrode after the surface of the human tissue is stimulated;

[0011] The data processing module integrates the local fat layer thickness algorithm program, which is used to convert the multiple voltage signals collected by the voltage reading module into And the distance between the corresponding test electrode pairs Substitute it into the algorithm program to calculate the local fat layer thickness of human tissue.

[0012] Furthermore, the data processing module is arranged in the smart mobile device, and the other modules are arranged on the wearable device and transmit the voltage signal to the data processing module in the smart mobile device through the wireless Bluetooth module.

[0013] Furthermore, the local fat layer thickness algorithm program in the data processing module is designed through the following process;

[0014] (1) Establish a human tissue layered model and set the parameters of each layer, including conductivity and relative permittivity;

[0015] (2) applying alternating current excitation to the human tissue layered model through an excitation electrode;

[0016] (3) For any pair of test electrodes, the electrode voltage (U) corresponding to different electrode spacings (D) was obtained under a fixed fat layer thickness, and the linear equation between the electrode spacing and the electrode voltage was fitted using the least squares method, and the slope (a) and intercept (b) of the linear equation were obtained;

[0017] (4) Change the fat layer thickness and repeat step (3) to obtain N groups of different fat layer thicknesses Different electrode spacing Corresponding to the collected voltage signal Thus, N groups of linear equations and the slopes and intercepts of N groups of linear equations are obtained, where N is a natural number greater than 1;

[0018] (5) The least squares method is used to fit the above N groups of linear equations to obtain the functional relationship between the fat layer thickness and the slope and intercept of the linear equation, which are a(x) and b(x) respectively. The measured voltage is then linked to the local fat layer thickness to generate a local fat layer thickness algorithm program.

[0019] Furthermore, the human tissue layered model consists of a skin layer, a fat layer, a muscle layer, a cortical bone layer and a bone marrow layer from top to bottom.

[0020] Furthermore, the linear equation obtained by fitting in step (3) is as follows:

[0021] U=aD+b

[0022] Where: U is the voltage between the two test electrodes, D is the distance between the two test electrodes, a is the slope of the linear equation, and b is the intercept of the linear equation.

[0023] Furthermore, in step (5), according to the functional relationship a(x) between the fat layer thickness x and the slope of the linear equation and the functional relationship b(x) between the fat layer thickness x and the intercept of the linear equation, the following functional relationship can be further obtained:

[0024] U=a(x)D+b(x)

[0025] a(x)=k2x 2 +k1x+k o

[0026] b(x)=l2x 2 +l1x+lo

[0027] Wherein: k0~k2 and l0~l2 are the polynomial coefficients obtained by least squares fitting, and x is the thickness of the fat layer.

[0028] Furthermore, for M pairs of test electrodes, the voltage reading module will correspondingly collect M groups of voltage signals, and the data processing module will substitute these M groups of voltage signals into the local fat layer thickness algorithm program to calculate the fat layer thickness analysis results, and then obtain the local fat layer thickness calculation results.

[0029] Compared with the prior art, the present invention has the following beneficial technical effects:

[0030] 1. The present invention is a wearable instrument with a multi-channel chip, which reduces the space and cost of the equipment. It adopts the method of placing electrodes on the surface of the skin, which has the characteristics of being non-invasive, harmless and easy to operate. It is convenient to measure the thickness of the local fat layer at a specific location, which is conducive to the targeted evaluation of fitness effects and health conditions.

[0031] 2. The present invention adopts a multi-electrode biological body impedance measurement algorithm and a local measurement method to reduce the influence of interface impedance and motion artifacts and increase the accuracy of calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the application structure of the fat layer thickness analyzer of the present invention and the intelligent mobile device.

[0033] Figure 2 It is a schematic diagram of the application structure of the fat layer thickness analyzer and the wearable device of the present invention.

[0034] Figure 3 It is a schematic diagram of the connection structure of each functional module in the fat layer thickness analyzer of the present invention.

[0035] Figure 4 It is a schematic diagram of the flow chart of the multi-electrode human body impedance measurement method of the present invention.

[0036] Figure 5 Schematic diagram of electrode placement when fitting the human body impedance measurement algorithm of the present invention.

[0037] Figure 6 Schematic diagram of the fitting process of the human body impedance measurement algorithm of the present invention.

[0038] Figure 7 The figure is a schematic diagram of the steps of the least square method used in the present invention.

[0039] Figure 8 This is a schematic diagram of the human tissue layering model used in the present invention.

[0040] Fig. 9 This is a schematic diagram of the placement of electrodes on the surface of human tissue during testing of the present invention. DETAILED DESCRIPTION

[0041] In order to describe the present invention more specifically, the technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0042] like Figure 1 and Figure 2 As shown, the local fat layer thickness analyzer based on multi-electrode biological impedance measurement of the present invention comprises: a current excitation module, a voltage readout module, a multi-electrode module and a data processing module. Figure 3 Shown is the connection method between various modules.

[0043] The current excitation module consists of a current excitation source, which is used to output the current excitation signal and apply it to the current excitation electrode on the surface of the human tissue to be tested, that is, connected to the current excitation electrode 1 and the current excitation electrode 2 in the multi-electrode module. One end injects the excitation current signal into the human body, and the other end receives the current signal.

[0044] The voltage readout module consists of a voltage readout circuit, which is used to measure the voltage value at the electrode on the surface of human tissue. Figure 1 The medium voltage readout circuit includes two voltage readout meters, voltage readout meter 1 is connected to voltage test electrode 1 and voltage test electrode 4 in the multi-electrode module, and voltage readout meter 2 is connected to voltage test electrode 2 and voltage test electrode 3 in the multi-electrode module.

[0045] The multi-electrode module consists of six electrodes, which are regularly arranged on the surface of human tissue and are used to connect the current excitation module with the surface of human tissue and the voltage readout module with the surface of human tissue; two electrodes form a pair, and there are three pairs of electrodes, namely a pair of current excitation electrodes and two pairs of voltage test electrodes. The current excitation electrodes are placed outside the two pairs of voltage test electrodes, and the two pairs of voltage test electrodes are equidistant from the current excitation electrodes; current excitation electrode 1 is also a transmitting electrode, which injects the excitation current signal in the current excitation module into the human body, and current excitation electrode 2 is a receiving electrode. Between current excitation electrode 1 and current excitation electrode 2 is the equivalent impedance of the human body, and a voltage drop is formed after the excitation current signal flows through it; voltage test electrode 1 and voltage test electrode 4 are connected to voltage readout meter 1, and voltage test electrode 2 and voltage test electrode 3 are connected to voltage readout meter 2.

[0046] The data processing module is used to apply algorithms, including a local fat layer thickness algorithm for multi-electrode biological impedance measurement, and substitute the voltage difference measured by the two voltage readout modules into the multi-electrode impedance measurement algorithm to obtain the local fat layer thickness of the biological being to be measured. The data processing module can be set on a smart mobile device to calculate the fat layer thickness on the smart mobile device by processing the received wireless data, or set on a wearable device to directly calculate the fat layer thickness from the processed voltage signal.

[0047] The method of using the wearable local fat layer thickness analyzer is as follows: Figure 4 As shown, it mainly includes the following steps:

[0048] Step 1: Place six electrodes on the human body surface, two in a pair. There are three pairs of electrodes, namely a pair of current excitation electrodes and two pairs of voltage test electrodes. One pair of current excitation electrodes is placed on the outside, and the two pairs of voltage test electrodes are placed in the middle of the pair of current excitation electrodes. The distance between each pair of voltage test electrodes and the current excitation electrodes is equal.

[0049] In this embodiment, the six electrodes placed on the surface of the human body group are composed of silver chloride solid and conductive gel, and are used to connect the current excitation module and the human tissue surface, and the voltage test module and the human tissue surface; taking the leftmost electrode, i.e., the current excitation electrode 1, as a reference, at 0 scale, the second electrode, i.e., the voltage test electrode 1, is placed 3 cm away from the leftmost electrode, the third electrode, i.e., the voltage test electrode 2, is placed 4 cm away, the fourth electrode, i.e., the voltage test electrode 3, is placed 6 cm away, the fifth electrode, i.e., the voltage test electrode 4, is placed 7 cm away, and the sixth electrode, i.e., the current excitation electrode 2, is placed 10 cm away, as shown in FIG. Fig. 9 shown.

[0050] Step 2: The current excitation module generates a sine wave signal through a sine signal generator, which is converted into a sine current as an excitation signal i(t) after high-pass filtering and current limiting. The signal to be tested is applied to the current excitation electrode on the surface of the human tissue to be tested, that is, connected to the multi-electrode module.

[0051] In this embodiment, the current excitation module includes a sine signal generator to generate a current signal i(t)=Asin(ω0t). The current is limited to a safe value of 500μA, where A is the amplitude of the sine wave, ω0 is the angular frequency, and the signal frequency is 100Hz~100kHz.

[0052] Step 3: After the voltage measurement module detects the voltage drop signal on the voltage test electrode, it performs filtering and other processing and then sends it to the ADC to convert the analog signal into a digital signal, thereby obtaining two sets of voltage signals, namely U1 and U2.

[0053] In this embodiment, the voltage measurement module includes two voltmeters, and a low-drift low-dropout linear regulator is integrated inside the voltmeter, with four gears to choose from, namely 2.35V / 2.45V / 2.8V / 3.0V, output voltage; the voltmeter also includes an internal reference voltage source, the output is 1.225V, which is mainly used as a reference voltage for measurement to provide to an external capacitor to improve accuracy or as an internal reference voltage. The voltmeter measures the voltage signal at the voltage test electrode 1 and the voltage test electrode 4 in step 1, and the voltage difference is U1, and the distance between the voltage test electrode 1 and the voltage test electrode 4 is D1; ​​similarly, the voltage at the voltage test electrode 2 and the voltage test electrode 3 is obtained, and the voltage difference is U2, and the distance between the voltage test electrode 1 and the voltage test electrode 4 is D2.

[0054] Step 4: The voltage signal obtained in step 3 is used to calculate the thickness of the fat layer using a multi-electrode biological impedance measurement algorithm.

[0055] In this embodiment, the obtained voltage difference signals U1 and U2 are wirelessly transmitted to a smart mobile device and respectively introduced into a multi-electrode biological impedance measurement algorithm to obtain the thickness of the fat layer, and then the local body fat percentage of the human body is calculated.

[0056] like Figure 6 As shown, the multi-electrode biological impedance measurement algorithm in the present invention mainly includes the following steps:

[0057] (1) Establish a human tissue layered model and set the parameters of each layer, namely, conductivity and relative permittivity.

[0058] The five-layer model of the human body established in this embodiment is as follows: Figure 8 As shown, from top to bottom they are the skin layer, fat layer, muscle layer, cortical bone layer and bone marrow layer.

[0059] (2) Place electrodes on the human tissue layer model and apply excitation current.

[0060] In this embodiment, four electrodes are placed on the surface of the human body layered model, and the conductivity and dielectric constant are set. Figure 5 As shown, the distance between the two outermost electrodes is 10 cm, and the distance between the two inner electrodes is d; a current source is set to generate a current signal i(t) as an excitation signal connected to the electrode, wherein the sinusoidal signal i(t) = Asin(ω0t), and the signal frequency is 100 Hz to 100 kHz.

[0061] (3) The voltage values ​​between electrodes at different distances under a fixed fat layer thickness are obtained, and a set of linear equations are obtained by fitting the measured voltage and the electrode distance using the least squares method.

[0062] Specifically: the thickness of the fat layer is fixed, and the distances between the two inner electrodes are set to 2cm, 3cm, 4cm, 5cm and 6cm respectively, and five sets of voltage differences between the five sets of electrodes are obtained. The five voltage differences and electrode spacing As a parameter, the least squares method is used for fitting to obtain a set of polynomial equations. The algorithm flow of the least squares method fitting is as follows: Figure 7 As shown, first assume that the fitting polynomial equation is The coefficient a can be solved i , we get the polynomial equation a for different electrode spacing D under fixed fat layer thickness i (D).

[0063] (4) The thickness of the fat layer is changed, and step (3) is repeated until the voltage values ​​between the electrodes at different distances under six fat layer thicknesses are obtained, thereby obtaining six sets of linear equations.

[0064] In this embodiment, the thickness of the fat layer is changed to 1 mm, 2 mm, 3 mm, 4 mm, 5 mm and 6 mm respectively, and step (3) is repeated to obtain six different sets of polynomial equations.

[0065] (5) The coefficients of the six sets of linear equations are fitted by the least squares method to obtain a multi-electrode human body impedance measurement algorithm.

[0066] a i (x) = k o +k1x+…+k5x 5

[0067] In this embodiment, the fat layer thickness and each coefficient in the polynomial equation are fitted by the least squares method to obtain a polynomial equation about the coefficients in the polynomial equation in step (3); combining the polynomial equations in step (3) and step (4) can obtain a multi-electrode biological impedance measurement algorithm.

[0068] The wearable device and multi-electrode bioimpedance measurement algorithm of the present invention have improved the shortcomings of existing methods for measuring fat layer thickness and bioimpedance, such as complex operation, bulky instruments, and low precision, reduced the problems caused by electrode contact impedance, and improved the accuracy of local analysis.

[0069] The above description of the embodiments is to facilitate the understanding and application of the present invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments. Improvements and modifications made by those skilled in the art to the present invention based on the disclosure of the present invention should be within the protection scope of the present invention.

Claims

1. A local fat layer thickness analyzer based on multi-electrode biological impedance measurement, characterized in that: include: A multi-electrode module, comprising a pair of excitation electrodes and M pairs of test electrodes, where M is a natural number greater than 1; These electrodes are regularly arranged on the surface of human tissue, the excitation electrode is placed outside the test electrode, and the two test electrodes of each pair are equidistant from the excitation electrode outside; A current source module, used for applying an alternating current excitation to human tissue through an excitation electrode; A voltage reading module is used to collect and obtain the voltage signal generated on the test electrode after the surface of the human tissue is stimulated; The data processing module integrates a local fat layer thickness algorithm program, which is used to substitute multiple groups of voltage signals collected by the voltage readout module and the spacing of their corresponding test electrode pairs into the algorithm program, so as to calculate the local fat layer thickness of human tissue.

2. The local fat layer thickness analyzer according to claim 1, characterized in that: The data processing module is arranged in the smart mobile device, and the other modules are arranged on the wearable device and transmit the voltage signal to the data processing module in the smart mobile device through the wireless Bluetooth module.

3. The local fat layer thickness analyzer according to claim 1, characterized in that: The local fat layer thickness algorithm program in the data processing module is designed through the following process steps: (1) Establish a human tissue layered model and set the parameters of each layer, including conductivity and relative permittivity; (2) applying an alternating current excitation to the human tissue layered model through an excitation electrode; (3) For any pair of test electrodes, the electrode voltage corresponding to different electrode spacings is obtained under a fixed fat layer thickness, and the linear equation between the electrode spacing and the electrode voltage is fitted using the least squares method, and the slope and intercept of the linear equation are obtained; (4) changing the thickness of the fat layer, and repeating step (3) to obtain N groups of voltage signals corresponding to different electrode spacings under different fat layer thicknesses, thereby obtaining N groups of linear equations and slopes and intercepts of the N groups of linear equations, where N is a natural number greater than 1; (5) The least squares method is used to fit the above N groups of linear equations to obtain the functional relationship between the fat layer thickness and the slope and intercept of the linear equation, which are a(x) and b(x) respectively. The measured voltage is then linked to the local fat layer thickness to generate a local fat layer thickness algorithm program.

4. The local fat layer thickness analyzer according to claim 3, characterized in that: The human body tissue layered model consists of a skin layer, a fat layer, a muscle layer, a cortical bone layer and a bone marrow layer from top to bottom.

5. The local fat layer thickness analyzer according to claim 3, characterized in that: The linear equation expression obtained by fitting in step (3) is as follows: U=aD+b Where: U is the voltage between the two test electrodes, D is the distance between the two test electrodes, a is the slope of the linear equation, and b is the intercept of the linear equation.

6. The local fat layer thickness analyzer according to claim 5, characterized in that: In the step (5), according to the functional relationship a(x) between the fat layer thickness and the slope of the linear equation and the functional relationship b(x) between the fat layer thickness and the intercept of the linear equation, the following functional relationship can be further obtained: U=a(x)D+b(x) a(x)=k2x 2 +k1x+k o b(x)=l2x 2 +l1x+l o Wherein: k0~k2 and l0~l2 are the polynomial coefficients obtained by least squares fitting, and x is the thickness of the fat layer.

7. The local fat layer thickness analyzer according to claim 1, characterized in that: For M pairs of test electrodes, the voltage readout module will correspondingly collect M groups of voltage signals. The data processing module substitutes these M groups of voltage signals into the local fat layer thickness algorithm program to calculate the fat layer thickness analysis results, and then obtain the local fat layer thickness calculation results.

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