A method and system for detecting sweat single component content

Abstract

The application discloses a sweat single component content detection method and system. The application mixes graphene material into the sweat to be detected to enhance signal recognition, collects phase data of the reader as recognition features, and carries out smooth and normalized data preprocessing, then uses a widengap algorithm to process the phase data, combines the phase after feature extraction to draw a new curve, so as to recognize the content of a component of interest. The method of the application breaks through the limitation of the liquid recognition granularity of the wireless sensing method by using the characteristics of the graphene material, and realizes the sensing of the concentration change of a certain solute in a solution. Different from the traditional liquid recognition method, the application is a non-contact liquid recognition scheme based on wireless sensing, and provides a new idea for the detection and recognition of sweat components.

Description

Technical Field

[0001] This invention belongs to the field of liquid identification technology and relates to a method and system for detecting the content of a single component in sweat based on graphene materials. Background Technology

[0002] Sweat contains many important biomarkers, which can be used to monitor electrolyte imbalances, glucose levels, and more. The detection and identification of sweat components is of great significance for monitoring physiological health and diagnosing diseases. The inorganic components of sweat are mainly salts such as sodium chloride, and the main electrolytes are sodium and chloride ions. With the loss of sodium and chloride ions in sweat, the body cannot effectively regulate physiological changes in body fluids and temperature. The trace glucose levels in sweat are highly correlated with blood glucose levels, and the detection of glucose content in sweat provides a non-invasive alternative to blood glucose testing. Therefore, the identification of sodium chloride and glucose concentrations in sweat is of great importance.

[0003] In sweat detection and composition analysis research, there are three main categories: electrochemical methods, colorimetric methods, and sensor detection methods. However, these methods either have limitations in detecting organic solutes or rely on wearable sensors, which require battery power and prolonged wear. To address these issues, an effective solution is to utilize wireless devices to explore key problems and solutions in daily health monitoring. Using wireless signals for liquid identification eliminates the need for bulky equipment and causes no harm to the body, offering advantages such as natural convenience and non-invasiveness.

[0004] Traditional liquid identification typically relies on expensive, specialized equipment, such as bulky and costly spectrometers used in laboratory environments. Due to these instrumental limitations, such methods are difficult to widely apply in everyday life. In recent years, identification technologies based on wireless signals (such as RFID, Wi-Fi, and UWB signals) have significantly reduced the deployment costs of liquid identification systems. The main advantage of wireless sensing is that it does not require the target to carry or wear any devices for sensing, making it more convenient than traditional wearable or sensor-based methods.

[0005] Applying wireless sensing technology for liquid identification involves analyzing the properties of a liquid by observing changes in the way wireless signals penetrate or are reflected from it, making liquid identification more convenient. Previous work has largely focused on contact- or non-contact wireless sensing liquid identification technologies, which only identified the type of liquid. Identifying changes in the concentration of a specific solute component in a solution requires extremely fine granularity. Current wireless sensing technology is still in its early stages, and achieving fine-grained liquid identification remains a significant challenge. Summary of the Invention

[0006] In view of the defects or deficiencies of the prior art, the present invention provides a method for detecting the content of a single component in sweat.

[0007] Therefore, the method for detecting the content of a single component in sweat provided by the present invention includes:

[0008] A standard phase curve diagram of a solution with a known gradient concentration is generated, which is composed of the phase curves of multiple solutions with known concentrations located on the same coordinate system; the solution with the known gradient concentration is prepared by adding different amounts of the single component to human sweat or artificial sweat.

[0009] A phase curve of the sweat to be tested is generated under the coordinate system, and the content of the component to be tested in the sweat is determined based on the position of the phase curve of the sweat to be tested.

[0010] Phase curves for each solution of known concentration or for the sweat sample to be tested were acquired using an antenna, reader, and electronic tag, including the following methods:

[0011] (1) Add graphene powder to the solution to be tested and mix well; the electronic tag is attached to the outer surface of the container wall containing the solution to be tested or to the surface of the carrier on which the solution to be tested is adsorbed; the content of graphene powder in each solution is the same.

[0012] (2) The antenna transmits a frequency signal, which is received by the electronic tag. The electronic tag receives the signal and generates corresponding identification information, which is then transmitted to the reader. The reader generates an initial phase value at the corresponding frequency based on the received information. The frequency ranges for each solution are the same.

[0013] A frequency-phase curve S1 is generated with frequency as the horizontal axis and initial phase value as the vertical axis; the sampling period depends on the sampling frequency of the RFID reader.

[0014] (3) Smooth the frequency-phase curve to eliminate jumps on the curve and obtain the frequency-phase smooth curve S2.

[0015] (4) Normalize the phase values ​​of the frequency-phase smoothing curve so that the normalized values ​​of each phase value are in the range of 0 to 1, and obtain the normalized frequency-phase smoothing curve S3.

[0016] (5) Find the estimated derivative values ​​of each intermediate point on curve S3 except for the first and last points. The estimated derivative of any intermediate point is the average of the slope of the line containing the intermediate point and its left neighbor and the slope of the line containing the left neighbor and right neighbor.

[0017] (6) Generate the slope-intercept equation y = kx + b, where k is the average of the estimated derivative values ​​of all intermediate points, b is 20 to 30 times the value of k and the sum of the average of the normalized phase values ​​of all points in step (4); x is the frequency channel index, y is the standard phase; the slope-intercept line corresponding to the slope-intercept equation is the standard phase curve of the corresponding solution.

[0018] An alternative is that the graphene powder is multilayer graphene powder.

[0019] Alternatively, the single component may be sodium chloride or glucose.

[0020] An alternative approach is to have the graphene powder content in the test solution be 1–3 g / L.

[0021] Alternatively, the reader can be an RFID reader.

[0022] An optional approach is that the frequency range of the frequency signal in step (2) is 860MHz to 960MHz;

[0023] An optional approach is to periodically collect the initial phase value in step (2), and after repeated sampling, calculate the average value of multiple initial phase values ​​at the same frequency as the average phase value at that frequency; generate a frequency-phase curve S1 with frequency as the horizontal axis and the average phase value as the vertical axis; the sampling period depends on the sampling frequency of the RFID reader.

[0024] An optional approach is that, in step (3), the frequency-phase curve is smoothed to obtain a frequency-phase smoothed curve S2. The smoothing process includes adding π or 2π to the phase value of the vertical axis of the phase curve, or subtracting π or 2π to eliminate the jumps on the curve.

[0025] Alternatively, the antenna, container, and electronic tag are located on the same straight line, and the signal emitted by the antenna passes through the container to reach the electronic tag.

[0026] On the other hand, the present invention provides a single component content detection system for sweat. The provided detection system includes the antenna, a container or a carrier capable of absorbing sweat, an electronic tag, a reader, and a processor, wherein the electronic tag is affixed to the outer wall of the container or to the surface of the carrier;

[0027] The antenna, reader, electronic tag, and processor acquire the phase curve of each known concentration solution or the phase curve of the sweat to be detected using the method described in claim 1.

[0028] Compared with the prior art, the present invention has the following positive and beneficial effects.

[0029] (1) The artificial sweat identification method based on graphene material of the present invention is a fine-grained sensing method based on a reader. It uses a reader device to sense the target solution, and uses graphene material to enhance the recognition of the signal and a complete signal processing algorithm module to preprocess the data, which can perform fine-grained identification of artificial sweat with similar concentrations with high accuracy.

[0030] (2) This invention combines a reader to perform passive sweat identification, which is a fine-grained liquid identification method based on wireless sensing. Unlike traditional technologies such as sensors that require power supply and have high costs, this method uses an RFID reader to transmit and receive data, which facilitates scientific research and daily life needs.

[0031] (3) The signal processing algorithm module of the present invention effectively extracts the features of the phase curve through the widengap algorithm, expands the similarity of sweat with similar concentrations, and thus achieves effective identification; in the subsequent component detection, the concentration range of a single changing solute can be determined by preprocessing the phase data of the concentration to be detected and comparing it with the known concentration. Attached Figure Description

[0032] Figure 1 This is a diagram of a beaker containing artificial sweat mixed with multilayer graphene powder, affixed with an RFID tag, according to the present invention.

[0033] Figure 2 This is a diagram of the experimental environment for this invention.

[0034] Figure 3 This is an intermediate result of Example 1, wherein, Figure 3 (a) is the frequency-phase curve S1 of graphene-artificial sweat mixture solution with different concentrations of sodium chloride before the jump in Example 1 of the present invention; Figure 3 (b) is the frequency-phase smoothing curve S2 of graphene-artificial sweat mixture solution with different concentrations of sodium chloride after smoothing treatment in Example 1 of the present invention; Figure 3 (c) is the frequency-phase smoothing curve S3 of graphene-artificial sweat mixture solutions of different concentrations of sodium chloride after normalization in Example 1 of the present invention.

[0035] Figure 4 The results are from Example 1, wherein, Figure 4 (a) is a standard phase curve of graphene-artificial sweat mixture solution with different concentrations of sodium chloride in Example 1 of the present invention; Figure 4 (b) is a partial magnified view of the standard phase curves of graphene-artificial sweat mixtures with different concentrations of sodium chloride in Example 1 of the present invention;

[0036] Figure 5This is a standard phase curve of the sodium chloride concentration to be measured in Example 1 of the present invention.

[0037] Figure 6 For the comparative results, where, Figure 6 (a) Frequency-phase curves of artificial sweat solutions with different concentrations of sodium chloride before the jump were shown in Figure S1, which was a comparative treatment. Figure 6 (b) is the frequency-phase smoothing curve S2 of artificial sweat solutions with different concentrations of sodium chloride after comparative smoothing treatment; Figure 6 (c) is the frequency-phase smoothing curve S3 of artificial sweat solutions with different concentrations of sodium chloride after comparative normalization; Figure 6 (d) is a standard phase curve of the sodium chloride concentration to be tested in the comparative example.

[0038] Figure 7 This is an intermediate result of Example 2, wherein, Figure 7 (a) is the frequency-phase curve S1 of graphene-artificial sweat mixture solutions with different concentrations of glucose before the jump in this invention; Figure 7 (b) is the frequency-phase smoothing curve S2 of graphene-artificial sweat mixture solution with different concentrations of glucose after the treatment of the jump in this invention; Figure 7 (b) is the normalized frequency-phase smoothing curve S3 of graphene-artificial sweat mixture solution with different concentrations of glucose according to the present invention.

[0039] Figure 8 The results are from Example 2, where, Figure 8 (a) is a standard phase curve of graphene-artificial sweat mixture solutions of different concentrations of glucose according to the present invention; Figure 8 (b) is a partially enlarged view of the standard phase curves of graphene-artificial sweat mixtures of different concentrations of glucose according to the present invention.

[0040] Figure 9 This is a standard phase curve of the glucose concentration to be measured in Example 2 of the present invention. Detailed Implementation

[0041] Unless otherwise specified, the scientific and technical terms used in this article are intended for understanding by those skilled in the art.

[0042] This invention is based on the principle that when radio electromagnetic signals pass through liquids, the signals slow down and attenuate, which is reflected in the phase and intensity of the output signal. The degree of slowdown and attenuation varies depending on the characteristics of the target. Research has found that in sweat composition identification, the phase and amplitude of the signal differ slightly due to variations in component concentration, with the phase change being more variable and fine-grained. The mechanism by which graphene, such as multilayer graphene, acts in signal identification is that when radio electromagnetic signals pass through liquids via RFID tags, the graphene in the liquid acts as an absorbing material, altering the coupling magnetic field between the liquid and the antenna. This change in the coupling magnetic field alters the refraction and attenuation of the signal as it passes through the mixed solution, translating into changes in phase and amplitude, thereby enhancing signal recognition accuracy.

[0043] This invention preferentially uses multilayer graphene as the signal transmission medium, mainly based on the high conductivity, small mass, thin layers, and multilayer characteristics of multilayer graphene, as well as its good attenuation ability. During the incident electrical signal, most of the electromagnetic waves can enter the interior of the graphene material, and the signal waves that enter the interior can be greatly reduced or even eliminated. Since each carbon sheet in multilayer graphene has high conductivity, but each carbon atom is stacked and separated from each other, the frequency band of electromagnetic waves is separated. Through frequency division absorption, broadband absorption of electromagnetic waves is achieved. Multilayer graphene achieves frequency band absorption due to its multilayer and thin characteristics.

[0044] Studies have found that the amplitude of the test solution curve does not change significantly for different component concentrations, but the phase changes are relatively large. Therefore, this invention selects phase as the identification feature. After plotting the phase as an image with a changing trend, the jump positions of the phase data are smoothed. Specifically, the phase change trend is unified by adding or subtracting π (or 2π), eliminating jumps on the curve and achieving curve smoothing.

[0045] Further research revealed that the smoothed curves still had a problem: the curves were very similar, requiring magnification of the image lines to observe the differences between the data from different test solution groups or standard solution groups.

[0046] Because the phase curves are very similar, this invention aims to determine their different trends by observing changes in slope to obtain shape information, regularize the curves, and widen the differences to highlight their respective characteristics. Specifically, this invention utilizes the derivative estimation method in the DDTW algorithm (Derivative Dynamic Time Warping algorithm). By using the estimated derivative value, the differences between phase curves with different solute concentrations are amplified while ensuring the generalization ability of the data processing method. This method is more robust to outliers than using only two data points. The use of a simple derivative estimation method greatly increases the generalization ability of this invention, making the curve slope more stable.

[0047] This invention utilizes the properties of graphene to overcome the limitations of wireless sensing methods in terms of liquid identification granularity, achieving robust fine-grained identification of sweat component concentrations with high accuracy. Unlike traditional liquid identification methods, this invention is a contactless liquid identification solution, providing new ideas and methods for sweat detection and identification.

[0048] The antenna used below is model EN-9028P, with an operating frequency range of 902MHz to 928MHz; the RFID tag model is IMPINJ M4QT, with an operating frequency range of 860MHz to 960MHz, and the following examples use an operating frequency of 902.75MHz to 927.25MHz; the RFID reader model is Impinj Speedway R420, using a sampling frequency of once every 0.5MHz. To facilitate the representation of the standard phase curve equation, its horizontal axis is represented by the count values ​​of 50 frequency channels.

[0049] The known gradient concentration solution described in this invention is a gradient concentration solution prepared by adding the test component to a base solution of human sweat or artificial sweat. Artificial sweat is a simulated test solution developed through scientific research and strictly based on the composition of real human sweat. It is a chemically formulated solution with a composition similar to human sweat, and its chemical composition and content are close to 99.99% of that of healthy human sweat. Since human sweat is difficult to collect and conduct large-scale experiments with, artificial sweat, which is similar to human sweat, is chosen for experimental research. Donghee Son et al. used artificial sweat to simulate human sweat and verified the self-healing properties of their proposed electronic skin system under actual skin sweating conditions. Qiuyue Yang et al. proposed a microfluidic nanosensor for detecting sweating rate, conductivity, etc., using artificial sweat with a certain amount of copper added to obtain peak current values, thereby correcting for copper ion interference when measuring human sweating rate. This demonstrates that when large quantities of human sweat are difficult to obtain for experiments, artificial sweat can effectively simulate human sweat. The existing artificial sweat formula is as follows: sodium chloride (NaCl) 20 g / L; ammonium chloride (NH4Cl) 17.5 g / L; urea (CH4N2O) 5 g / L; acetic acid (CH3COOH) 2.5 g / L; lactic acid (C3H6O3) 15 g / L; then sodium hydroxide (NaOH) is added until the solution pH reaches 4.0. Based on this, the artificial sweat solvent used in the following embodiments of the present invention is water, and the formula is: sodium chloride (NaCl) 20 g / L, ammonium chloride (NH4Cl) 17.5 g / L, urea (CH4N2O) 5 g / L, acetic acid (CH3COOH) 2.5 g / L, lactic acid (C3H6O3) 15 g / L, then sodium hydroxide (NaOH) is added until the solution pH reaches 4.0; each embodiment adds an ingredient of interest (sodium chloride or glucose) to this formula to explain the present invention.

[0050] Example 1:

[0051] In this embodiment, four groups of test samples were set up. In each group, 0.05g of multilayer graphene was added to 25ml of artificial sweat (the concentration of multilayer graphene was 2g / L). 0.1g, 0.3g, 0.5g, and 0.7g of sodium chloride were added to each group, with the concentration of added sodium chloride ranging from 4g / L to 28g / L. After adding sodium chloride, the concentration of sodium chloride in the test samples ranged from 24g / L to 48g / L.

[0052] See Figure 1 As shown, the RFID tag is affixed to the beaker containing the sample to be tested. During the test, the beaker is placed in a location where the antenna signal can be received. See [link to documentation]. Figure 2As shown, in this embodiment, the container is preferably placed directly in front of the antenna, with the container positioned between the antenna and the electronic tag. The RFID reader transmits a signal of a certain frequency through the antenna. After receiving the signal, the RFID tag transmits the stored identification information back to the RFID reader. The RFID reader receives and identifies the information sent back by the RFID tag and detects the solution concentration based on the phase information received by the RFID reader. The calculations for each step in this embodiment are performed in MATLAB software.

[0053] In step (2) of this embodiment, the initial phase value is periodically collected. After repeated sampling, the average value of multiple initial phase values ​​at the same frequency is calculated as the average phase value at that frequency. A frequency-phase curve S1 is generated with frequency as the horizontal axis and the average phase value as the vertical axis. The sampling period depends on the sampling frequency of the RFID reader.

[0054] The frequency-phase curve S1 obtained in this embodiment is as follows: Figure 3 As shown in (a);

[0055] In step (3), the frequency-phase curve is smoothed to obtain the frequency-phase smoothed curve S2. The smoothing process includes adding or subtracting π or 2π to the phase value of the vertical axis of the phase curve to eliminate jumps on the curve. The smoothed frequency-phase smoothed curve S2 is shown in Figure 3(b). The normalized frequency-phase smoothed curve S3 is shown in Figure 3(b). Figure 3 As shown in (c);

[0056] The slope-intercept equations corresponding to each test group are as follows: y = -0.0184x + 0.9799; y = -0.0188x + 0.9647; y = -0.0187x + 0.9435; y = -0.0185x + 0.9148. The standard phase curve of this embodiment is plotted as follows. Figure 4 As shown in (a), a magnified view of the standard phase curve is as follows: Figure 4 As shown in (b).

[0057] The standard curve of this embodiment was further used to test the test solution with a sodium chloride concentration of 36 g / L. Figure 4 (a) shows the phase curve of the solution to be tested generated using the method of this invention at the coordinates indicated in (a). See [reference needed]. Figure 5 As shown in the figure, the thickest curve is the phase curve of the solution to be tested. Based on the position of the phase curve of the solution to be tested in the figure, the concentration range of the solution to be tested is determined to be 32 g / L to 40 g / L.

[0058] Comparative example:

[0059] The difference between this comparative example and Example 1 is that graphene was not added to the samples in each group. The results obtained are as follows: Frequency-phase curve S1 is shown in Figure 1. Figure 6As shown in (a); the smoothed frequency-phase smoothed curve S2 is shown in 6(b); the normalized frequency-phase smoothed curve S3 is shown in... Figure 6 As shown in (c); the slope-intercept equations corresponding to each test group are: y = -0.0179*x + 0.9688; y = -0.0181*x + 0.9781;

[0060] y = -0.0180*x + 0.9692; y = -0.0180*x + 0.9601, standard phase curve diagram as follows Figure 6 As shown in (d).

[0061] Example 2:

[0062] In this embodiment, four groups of test samples were set up. In each group, 0.05g of graphene was added to 25ml of artificial sweat, and the graphene concentration was 2g / L. 0.1g, 0.3g, 0.5g, and 0.7g of glucose were added to each group, with the glucose concentration ranging from 4g / L to 28g / L. After the addition, the glucose concentration in the solution ranged from 4g / L to 28g / L.

[0063] The frequency-phase curve S1 obtained in this embodiment is as follows: Figure 7 As shown in (a); the smoothing process yields the frequency-phase smoothed curve S2 as follows. Figure 7 (b) shows the normalized frequency-phase smoothing curve S3 as follows: Figure 7 As shown in (c).

[0064] The slope-intercept equations corresponding to each test group are as follows: y = -0.0185x + 0.9254; y = -0.0183x + 0.9147; y = -0.0186x + 0.9145; y = -0.0191x + 0.8976. The standard phase curve of this embodiment is plotted as follows. Figure 8 As shown in (a), a magnified view of the standard phase curve is as follows: Figure 8 As shown in (b).

[0065] The standard curve of this embodiment was further used to test the test solution with a glucose concentration of 8 g / L. A phase curve of the test solution was generated under the aforementioned coordinates. See [link to relevant documentation]. Figure 9 As shown, the thickest curve is the phase curve of the test solution. Based on the position of the phase curve of the test solution, the concentration range of the test solution is determined to be 4 g / L to 12 g / L.

Claims

1. A method for detecting the content of a single component in sweat, the method comprising: generating a standard phase curve of known gradient concentration solutions, the standard phase curve being composed of phase curves of a plurality of known concentration solutions located in the same coordinate; the known gradient concentration solutions being prepared by adding different amounts of the single component into human sweat or artificial sweat; generating a phase curve of the sweat to be detected in the coordinate, and determining the content of the measured component in the sweat to be detected according to the location of the phase curve of the sweat to be detected; the phase curve of each known concentration solution or the phase curve of the sweat to be detected being obtained by using an antenna, a reader and an electronic tag, the method comprising: (1) adding graphene powder into the solution to be detected and mixing; the electronic tag being attached to the outer surface of the container wall containing the solution to be detected or the surface of the carrier adsorbing the solution to be detected; the content of the graphene powder in each solution being the same; (2) the antenna emits a frequency signal, the frequency signal being received by the electronic tag, the electronic tag generating corresponding identification information according to the received signal and transmitting the identification information to the reader; the reader generating an initial phase value at the corresponding frequency according to the received information; the corresponding frequency range of each solution being the same; generating a frequency-phase curve S1 with the frequency as the horizontal coordinate and the initial phase value as the vertical coordinate; the sampling period being determined by the sampling frequency of the RFID reader; (3) performing smoothing processing on the frequency-phase curve to eliminate the jumps on the curve, and obtaining a frequency-phase smooth curve S2; (4) performing normalization processing on the phase value of the frequency-phase smooth curve, so that the normalized value range of each phase value is 0-1, and obtaining a normalized frequency-phase smooth curve S3; (5) calculating the estimated derivative value of each intermediate point on the curve S3 except the first and last points, the estimated derivative of any intermediate point being the average of the slope of the straight line passing through the intermediate point and its left neighboring point and the slope of the straight line passing through the left neighboring point and the right neighboring point of the intermediate point; (6) generating a slope-intercept equation y=kx+b, wherein k takes the average of all intermediate point estimated derivative values, b takes the sum of 20-30 times of the value of k and the average of all point normalized phase values in step (4); x is the frequency channel index, and y is the standard phase; the slope-intercept line corresponding to the slope-intercept equation is the standard phase curve of the corresponding solution. The graphene powder is a multi-layer graphene powder. The single component is sodium chloride or glucose. The content of the graphene powder in the solution to be detected is 1-3 g / L. The reader is an RFID reader. The frequency range of the frequency signal in step (2) is 860-960 MHz. In step (2), the initial phase values are periodically collected, and after repeated sampling for multiple times, the average of the initial phase values at the same frequency is taken as the phase average at the frequency; a frequency-phase curve S1 is generated with the frequency as the horizontal coordinate and the phase average as the vertical coordinate; the sampling period is determined by the sampling frequency of the RFID reader. In step (3), the frequency-phase curve is smoothed to obtain a frequency-phase smooth curve S2, and the smoothing processing includes adding π or 2π to or subtracting π or 2π from the vertical coordinate phase value of the phase curve to eliminate the jumps on the curve. ​ ​ ​ 2. The method of claim 1, wherein the method is used to detect the sweat single constituent content. ​ 3. The method of claim 1, wherein the method is used for detecting a single component content of sweat. ​ 4. The method of claim 1, wherein the method is used for detecting a single component content of sweat. ​ 5. The method of claim 1, wherein the method is used for detecting a single component content of sweat. ​ 6. The method of claim 1, wherein the method is used to detect the sweat single constituent content. ​ 7. The method of claim 1, wherein the method is used to detect the sweat single constituent content. ​ 8. The method of claim 1, wherein the method is used to detect the sweat single constituent content. ​ 9. The method of claim 1, wherein the method is used to detect the sweat single constituent content. The antenna, the container and the electronic tag are located on the same line, and the signal emitted by the antenna passes through the container to the electronic tag.

10. A sweat single component content detection system characterized by, The antenna, the container or the sweat-absorbable carrier, the electronic tag, the reader and the processor, the electronic tag is attached to the outer wall of the container or attached to the surface of the carrier. The antenna, the reader, the electronic tag and the processor adopt the method of claim 1 to obtain the phase curve of each known concentration solution or the phase curve of the sweat to be detected.

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