Non-invasive lactic acid concentration prediction system and lactic acid concentration prediction method

Abstract

The present invention relates to a non-invasive lactic acid concentration prediction system and lactic acid concentration prediction method, and may provide a lactic acid concentration prediction system and lactic acid concentration prediction method, in which a lactic acid concentration prediction value is calculated using the heart rate of a user along with lactic acid content information in a body fluid of the user, and thus lactic acid concentration prediction may be performed with high accuracy.

Description

Noninvasive lactate concentration prediction system and lactate concentration prediction method

[0001] The present invention relates to a lactate concentration prediction system and a lactate concentration prediction method for predicting lactate concentration using a non-invasive method.

[0002]

[0003] Lactic acid is used as an indicator to check an individual's exercise capacity. Under aerobic conditions with low exercise intensity, the rate of lactic acid production is very low. However, when the exercise intensity increases or an individual's exercise capacity is exceeded and breathing is insufficient, the muscles require energy even under anaerobic conditions, so the rate of lactic acid production increases as a byproduct. Therefore, the point where the concentration of lactic acid suddenly increases is called the threshold, and exercise capacity is evaluated by judging the high or low exercise capacity using the lactate threshold point.

[0004] To this end, a lactate sensor is used to determine the effect and status of exercise. However, a typical lactate sensor is a disposable sensor that uses blood as a sample and has an invasive structure, so it has the disadvantage of being difficult to use to determine an individual's exercise ability.

[0005] Disposable blood sensors can cause fear and pain in subjects due to needles, and the pain can be doubled as blood samples must be collected and analyzed multiple times or even dozens of times to determine the threshold. In addition, if the sensor is collected without sufficient washing during exercise, it can cause disease due to contamination during the process.

[0006] Additionally, in order to ultimately determine the threshold index for exercise ability, if a disposable sensor is used during exercise, there are situations where the exercise must be stopped several times, but by using a sensor that allows repeated measurements, it is possible to measure in real time, making it easier to determine the threshold point.

[0007] Korean Patent Publication No. 10-1624769 discloses a sensor that measures lactic acid concentration with high precision in a short period of time. The biosensor comprises an electrode system and a reagent layer sequentially laminated on a substrate, and the reagent layer contains lactate oxidase, a mediator, and N-(2-acetamide)-2-aminoethanesulfonic acid. However, the sensitivity range of lactic acid is low, making measurement using body fluids other than blood or plasma, such as sweat, impossible. In addition, it is practically difficult to collect blood multiple times to continuously monitor the target substance, making continuous measurement difficult using the above method. In addition, the continuous measurement sensor has a problem in that the deviation of the result value is large for each measurement, making repeated measurements difficult.

[0008] Therefore, there is a need to develop a lactate concentration prediction system and a lactate concentration prediction method that can predict lactate concentration with high accuracy and little deviation from the measured results using a non-invasive method.

[0009]

[0010] The present invention aims to provide a lactate concentration prediction system and a lactate concentration prediction method capable of predicting blood lactate concentration with high accuracy by calculating a blood lactate concentration prediction value using heart rate along with lactate content information in a user's body fluid measured by a non-invasive method.

[0011] In addition, the present invention aims to provide a lactate concentration prediction system and a lactate concentration prediction method that can further increase the accuracy of lactate concentration prediction by constructing a model for predicting lactate concentration using a previously stored data set, and calculating a lactate concentration prediction value by inputting information on the user's heart rate and lactate content in body fluid into the constructed lactate concentration prediction model.

[0012] In addition, the present invention aims to provide a lactate concentration prediction system and a lactate concentration prediction method that can further increase the accuracy of lactate concentration prediction for each individual by constructing a personalized lactate concentration prediction model using the user's own data set, and calculating a lactate concentration prediction value by inputting the user's heart rate and lactate content in body fluid into the constructed personalized lactate concentration prediction model.

[0013] In addition, the present invention aims to provide a lactate concentration prediction system and a lactate concentration prediction method that can easily determine a user's lactate concentration periodically during daily life or exercise.

[0014]

[0015] To solve the above problem, the present invention provides a lactate concentration prediction system including a body fluid lactate content measurement unit that measures information on the lactate content in body fluid secreted from a user; a heart rate measurement unit that measures the user's heart rate; and a lactate concentration analysis unit that calculates a blood lactate concentration prediction value using the measured body fluid lactate content and heart rate.

[0016] The above lactate concentration prediction system may further include an information storage unit that stores information about the lactate concentration in the measured body fluid.

[0017] The above body fluid may be any one selected from the group consisting of sweat, tears, saliva, and urine.

[0018] The information on the above lactic acid content may be spectroscopic information measured using a photometric method or electrochemical information measured using an electrochemical method.

[0019] The above lactate concentration analysis unit may construct a lactate concentration prediction model based on a plurality of data sets, and the data sets may include heart rate, lactate content information in body fluid, and actual lactate content corresponding to the lactate content information in body fluid.

[0020] The above-mentioned lactate concentration analysis unit may calculate a blood lactate concentration prediction value by inputting information about the user's heart rate and the lactate content in the measured body fluid into the constructed lactate concentration prediction model.

[0021] The above lactate concentration analysis unit may calculate a predicted blood lactate concentration value by inputting information about the user's heart rate and the lactate content in the measured body fluid into Equation 1 or Equation 2 below.

[0022] [Formula 1]

[0023] BL = 5.52 - 0.1038 HR + 0.0569 SL + 0.000504 HR 2

[0024] [Formula 2]

[0025] BL = 5.28 - 0.1230 HR + 0.684 SL + 0.000585 HR 2 - 0.0552 SL 2

[0026] In the above equations 1 and 2, BL is the blood lactate concentration (mmol / L), SL is the body fluid lactate concentration (mmol / L), and HR is the heart rate (beats / min).

[0027]

[0028] The above multiple data sets may be five or more data sets.

[0029] The above data set may be someone else's data set.

[0030] The above-mentioned data set of another person may be previously stored in the above-mentioned information storage unit.

[0031] The above data set may be the user's own data set.

[0032] Information about the lactic acid content in the body fluid secreted from the user and the heart rate may be measured at a specific point in time after the start of exercise.

[0033] The specific point in time after the start of the above exercise may be 5 minutes after the start of the exercise.

[0034] The above lactic acid concentration prediction system may further include a display unit that displays the lactic acid concentration prediction value calculated by the lactic acid concentration analysis unit.

[0035] The above lactic acid concentration prediction system may further include a communication unit for transmitting and receiving information with an external device.

[0036] In addition, the present invention provides a method for predicting lactate concentration, including a step of measuring lactate content in body fluid for measuring information on lactate content in body fluid secreted from a user; a heart rate measurement step for measuring the heart rate of the user; and a step of analyzing lactate concentration for calculating a predicted value of blood lactate concentration using the measured lactate content in body fluid and heart rate.

[0037] The above method for predicting lactic acid concentration may further include an information storage unit that stores information regarding the lactic acid content in the measured body fluid.

[0038] The above body fluid may be any one selected from the group consisting of sweat, tears, saliva, and urine.

[0039] The information on the above lactic acid content may be spectroscopic information measured using a photometric method or electrochemical information measured using an electrochemical method.

[0040] The above lactate concentration analysis step includes a step of constructing a lactate concentration prediction model based on a plurality of data sets, wherein the data sets may include heart rate, lactate content information in body fluid, and actual lactate content corresponding to the lactate content information in body fluid.

[0041] The above-described lactate concentration analysis step may include a step of calculating a blood lactate concentration prediction value by inputting information about the user's heart rate and the lactate concentration content in the measured body fluid into the constructed lactate concentration prediction model.

[0042] The above lactate concentration analysis step may be to calculate a predicted blood lactate concentration value by inputting information about the user's heart rate and the lactate content in the measured body fluid into Equation 1 or Equation 2 below.

[0043] [Formula 1]

[0044] BL = 5.52 - 0.1038 HR + 0.0569 SL + 0.000504 HR 2

[0045] [Formula 2]

[0046] BL = 5.28 - 0.1230 HR + 0.684 SL + 0.000585 HR 2 - 0.0552 SL 2

[0047] In the above equations 1 and 2, BL is the blood lactate concentration (mmol / L), SL is the body fluid lactate concentration (mmol / L), and HR is the heart rate (beats / min).

[0048]

[0049] The above multiple data sets may be five or more data sets.

[0050] The data set used in the step of constructing the above lactic acid concentration prediction model may be a previously stored data set of another person.

[0051] The data set used in the step of building the above lactate concentration prediction model may be the user's own data set.

[0052] Information about the lactic acid content in the body fluid secreted from the user may be measured at a specific point in time after the start of exercise.

[0053] The specific point in time after the start of the above exercise may be 5 minutes after the start of the exercise.

[0054] The above method for predicting lactate concentration may further include a step of transmitting and receiving information with an external device.

[0055]

[0056] The present invention can provide a lactate concentration prediction system and a lactate concentration prediction method capable of predicting blood lactate concentration with high accuracy by calculating a blood lactate concentration prediction value using heart rate along with lactate content information in a user's body fluid measured by a non-invasive method.

[0057] In addition, the present invention can provide a lactate concentration prediction system and a lactate concentration prediction method that can further increase the accuracy of lactate concentration prediction by constructing a model for predicting lactate concentration using a previously stored data set, and calculating a lactate concentration prediction value by inputting information on the user's heart rate and lactate content in body fluid into the constructed lactate concentration prediction model.

[0058] In addition, the present invention can provide a lactate concentration prediction system and a lactate concentration prediction method that can further increase the accuracy of lactate concentration prediction for each individual by constructing a personalized lactate concentration prediction model using the user's own data set and calculating a lactate concentration prediction value by inputting the user's heart rate and lactate content in body fluid into the constructed personalized lactate concentration prediction model.

[0059] In addition, the present invention can provide a lactate concentration prediction system and a lactate concentration prediction method that can easily determine a user's lactate concentration periodically during daily life or exercise.

[0060]

[0061] Figure 1 is a block diagram of a lactic acid concentration prediction system according to a first embodiment of the present invention.

[0062] Figure 2 is a block diagram of a lactic acid concentration prediction system according to a second embodiment of the present invention.

[0063] Figure 3 is a block diagram of a lactic acid concentration prediction system according to a third embodiment of the present invention.

[0064] Figure 4 is a plan view of a lactate sensor according to one embodiment of the present invention.

[0065] Fig. 5 is a cross-sectional view showing a surface cut along line A-A' of Fig. 4.

[0066] Figure 6 is a flowchart of a method for predicting lactic acid concentration according to the first embodiment of the present invention.

[0067] Figure 7 is a flowchart of a method for predicting lactic acid concentration according to a second embodiment of the present invention.

[0068] Figure 8 is a flowchart of a method for predicting lactic acid concentration according to a third embodiment of the present invention.

[0069] Figure 9 is a diagram showing the results of evaluating the accuracy of a predicted lactic acid concentration value calculated using the lactic acid concentration prediction system / lactic acid concentration prediction method of the present invention.

[0070]

[0071] What each symbol represents is as follows:

[0072] 10: Lactic acid concentration prediction system

[0073] 110: Description

[0074] 120: Sensor section

[0075] 130: Working electrode

[0076] 131: Electrode section

[0077] 132: Electronic transmission unit

[0078] 133: Reaction section

[0079] 134: Filter section

[0080] 140: Reference electrode

[0081] 150: Wiring section

[0082]

[0083] The present invention relates to a non-invasive lactate concentration prediction system and a lactate concentration prediction method, and provides a lactate concentration prediction system and a lactate concentration prediction method capable of predicting lactate concentration with high accuracy by calculating a lactate concentration measurement value using the user's heart rate as well as information on the lactate content in the user's body fluid.

[0084] More specifically, the present invention provides a lactate concentration prediction system including a body fluid lactate content measurement unit that measures information on the lactate content in body fluid secreted from a user; a heart rate measurement unit that measures the user's heart rate; and a lactate concentration analysis unit that calculates a blood lactate concentration prediction value using the measured body fluid lactate content and heart rate.

[0085] In addition, the present invention provides a method for predicting a lactate concentration, including a step of measuring a lactate content in a body fluid secreted from a user; a step of measuring a heart rate of the user; and a step of analyzing a lactate concentration for calculating a predicted blood lactate concentration value using the measured lactate content in the body fluid and the heart rate.

[0086]

[0087] Hereinafter, embodiments of the present invention will be described in more detail with reference to the drawings. However, the following drawings attached to this specification illustrate preferred embodiments of the present invention and, together with the contents of the invention described above, serve to further understand the technical concept of the present invention. Therefore, the present invention should not be interpreted as being limited to the matters described in such drawings.

[0088] In this specification, singular forms also include plural forms, unless otherwise specified in the text. Like reference numerals refer to like elements throughout the specification.

[0089] As used herein, the terms “comprises” and / or “comprising” are used to mean that they do not exclude the presence or addition of one or more other components, steps, operations and / or devices other than the components, steps, operations and / or devices mentioned.

[0090] As used herein, the term "connection" is used to mean both indirectly connecting and directly connecting multiple components, and to mean both physically connecting and electrically connecting.

[0091]

[0092] Lactic Acid Concentration Prediction System

[0093] Figures 1 to 3 are block diagrams schematically showing a lactic acid concentration prediction system according to the first to third embodiments of the present invention.

[0094]

[0095] Referring to FIG. 1, a lactate concentration prediction system (10) according to a first embodiment of the present invention includes a body fluid lactate content measurement unit (100) that measures information on the lactate content in body fluid secreted from a user, a heart rate measurement unit (200) that measures the user's heart rate, and a blood lactate concentration analysis unit (300) that calculates a blood lactate concentration prediction value using the measured body fluid lactate content and heart rate.

[0096]

[0097] The lactic acid content measuring unit (100) in the above body fluid can measure information about the lactic acid content in the body fluid secreted from the user, and the body fluid secreted from the user can be any one selected from the group consisting of sweat, tears, saliva, and urine.

[0098] The information on the above lactic acid content may be spectroscopic information measured using a photometric method or electrochemical information measured using an electrochemical method.

[0099] Both the photometric and electrochemical methods basically utilize an oxidizing enzyme that can react with lactate to oxidize it. The photometric method uses a photometer to obtain spectroscopic information such as light reflectance or transmittance when lactate in body fluid is oxidized by the oxidizing enzyme, and quantifies this to measure the lactate content. The electrochemical method obtains electrochemical information such as the current generated when the oxygen or oxidized mediator formed when lactate in body fluid is oxidized by the oxidizing enzyme is converted to hydrogen peroxide or a reduced mediator and then oxidized again to return to its original oxidized form, and quantifies this to measure the lactate content.

[0100]

[0101] The lactate concentration prediction system of the present invention includes a heart rate measurement unit (200) that measures the user's heart rate.

[0102] The user's heart rate and the lactate content can be measured at a specific point in time after the start of exercise. For example, if 5 minutes of exercise and 30 seconds of rest are one set, the heart rate and the lactate content in the body fluid can be measured at the end of the exercise. In this case, the time point may be 5 minutes after the start of exercise.

[0103] The heart rate measuring device can be any known device without limitation, for example, a wristwatch capable of measuring heart rate can be used.

[0104] When a large amount of information on heart rate and lactate in body fluids is collected at a specific point after the start of exercise, it becomes possible to predict the lactate content trend from the start of exercise to a specific point after exercise and provide it to the user.

[0105] Conventional invasive lactate concentration measurement devices require blood sampling, limiting their ability to monitor lactate levels periodically during exercise or in daily life. However, the lactate concentration prediction system of the present invention non-invasively measures lactate content in body fluids secreted by the user and uses this information to provide highly accurate lactate concentration prediction values, thereby facilitating the periodic monitoring of the user's lactate status during daily life or exercise.

[0106]

[0107] The lactate concentration prediction system of the present invention may further include an information storage unit (400) and store user information regarding the measured lactate content and heart rate.

[0108] The above information storage unit (400) includes both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data sets, program modules or other data.

[0109]

[0110] The above lactic acid concentration analysis unit (300) can calculate a predicted value of the user's lactic acid concentration using information about the user's heart rate and the lactic acid content in the measured body fluid secreted from the user.

[0111] When predicting a user's lactate concentration based on information about the lactate content in the user's body fluids, even with identical lactate content, the lactate content in the body fluids may vary depending on each user's individual physical characteristics. Furthermore, by applying the user's heart rate, along with information about the lactate content in the user's body fluids, to the calculation of the predicted lactate concentration and correcting for errors, a higher accuracy prediction of the user's lactate concentration is possible.

[0112] The above lactic acid concentration analysis unit (300) may construct a lactic acid concentration prediction model based on multiple data sets.

[0113] The above data set may include heart rate, information on lactate content in body fluid, and actual lactate content corresponding to information on lactate content in body fluid.

[0114] The actual lactate content corresponding to the above-mentioned information on lactate content in body fluid refers to the lactate content actually measured at substantially the same time as the time at which the information on lactate content in body fluid was acquired. The substantially same time may be within 10 minutes from the time at which the information on lactate content in body fluid was acquired, preferably within 5 minutes, and more preferably within 3 minutes. The above-mentioned actually measured lactate content is measured through blood collection and may be measured using a known method.

[0115] The above lactate concentration prediction model may include at least one selected from the group consisting of a coefficient representing a correlation between information about a user's heart rate and a lactate content in body fluid secreted from the user, a coefficient representing a correlation between a user's heart rate and an actual lactate content, and a coefficient representing a correlation between information about a lactate content in body fluid secreted from the user and an actual lactate content.

[0116] The above-mentioned lactate concentration analysis unit (300) may input information regarding the user's heart rate and the measured lactate content in the body fluid into the above-mentioned lactate concentration prediction model to calculate a lactate concentration prediction value. By applying a coefficient regarding the correlation between each variable, the accuracy of lactate concentration prediction based on information regarding the lactate content in the user's body fluid can be further improved.

[0117] The above lactate concentration analysis unit may calculate a predicted blood lactate concentration value by inputting information about the user's heart rate and the lactate content in the measured body fluid into Equation 1 or Equation 2 below.

[0118] [Formula 1]

[0119] BL = 5.52 - 0.1038 HR + 0.0569 SL + 0.000504 HR 2

[0120] [Formula 2]

[0121] BL = 5.28 - 0.1230 HR + 0.684 SL + 0.000585 HR 2 - 0.0552 SL 2

[0122] In the above equations 1 and 2, BL is the blood lactate concentration (mmol / L), SL is the body fluid lactate concentration (mmol / L), and HR is the heart rate (beats / min).

[0123]

[0124] The data set used to build the above lactic acid concentration prediction model may be another person's data set, and the other person's data set may be pre-stored in the information storage unit (400).

[0125] In addition, the data set used to build the lactate concentration prediction model may be the user's own data set. Although at least five or more data sets are required to build the lactate concentration prediction model, a sufficient number of data sets may not be collected when each user initially uses the lactate concentration prediction system according to the present invention. Therefore, before the user's own data set is sufficiently collected, a lactate concentration prediction model may be built based on multiple data sets of others previously stored in the information storage unit (400) and used to calculate the lactate concentration prediction value. Thereafter, when the user's own data set is sufficiently stored through repeated use by the user, a new personalized lactate concentration prediction model may be built based on the user's multiple data sets, and the newly built personalized lactate concentration prediction model may be used to calculate the lactate concentration prediction value.

[0126] In this way, by building a personalized lactate concentration prediction model optimized for each user based on the user's own data set and inputting information about the user's heart rate and lactate content in body fluids into the model to calculate a lactate concentration prediction value, the accuracy of lactate concentration prediction for each individual user can be further improved.

[0127] The above lactic acid concentration analysis unit (300) can also build a lactic acid concentration prediction model through machine learning based on the multiple data sets.

[0128] The above machine learning may utilize one or more selected from the group consisting of linear regression analysis, nonlinear regression analysis, partial least squares (PLS), Bayesian network, hidden markov model, decision tree, boosting, bagging, support vector machine, convolutional neural network, deep neural network, and recursive neural network (RNN), and may utilize one or more selected from the group consisting of boosting, bagging, support vector machine, and deep neural network, and it is more preferable to utilize linear regression analysis.

[0129] Multiple data sets may be used to build the lactate concentration prediction model using the above machine learning. To improve the accuracy of the constructed lactate concentration prediction model, it is preferable to use at least five data sets. For example, the lactate concentration prediction model may be built using machine learning based on five to twenty data sets.

[0130]

[0131] Referring to FIG. 2, the lactic acid concentration prediction system (10) according to the second embodiment of the present invention may further include a display unit (500) that displays the lactic acid concentration prediction value calculated by the lactic acid concentration analysis unit (300). The description given for the lactic acid concentration prediction system according to FIG. 1 may be equally applied to the lactic acid concentration prediction system illustrated in FIG. 2, and detailed descriptions of substantially identical or similar components are omitted.

[0132]

[0133] The display unit (500) may include a display panel and a controller that controls the operation of the display panel. The display panel may be implemented in various forms, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), an active-matrix organic light-emitting diode (AM-OLED), and a plasma display panel (PDP). The display unit (500) may be implemented in a flexible manner or in a wearable manner, and may be provided as an integrated touch screen that is combined with a touch panel of a body information input unit described below in a laminated structure.

[0134]

[0135] Referring to FIG. 3, the lactate concentration prediction system (10) according to the third embodiment of the present invention may further include a communication unit (600) for transmitting and receiving information with an external device. Accordingly, the description described for the lactate concentration prediction system according to FIGS. 1 and 2 may be equally applied to the lactate concentration prediction system illustrated in FIG. 3, and detailed descriptions of substantially identical or similar components are omitted.

[0136]

[0137] The above communication unit (600) can transmit and receive information with external devices according to various wired and wireless communication methods. The communication unit (600) may perform communication using at least one wireless communication method among Wi-Fi, Bluetooth, wireless communication, and NFC.

[0138] The external device may be a user's personal mobile terminal. For example, the communication unit (600) may transmit information about the user's heart rate, the lactic acid content in body fluid, and / or a predicted lactic acid concentration value to the user's personal mobile terminal, or may receive a data set of the user or another person stored in the user's personal mobile terminal through the communication unit (600).

[0139] The external device may be a cloud server. For example, the device may transmit the user's heart rate, information on the lactic acid content in body fluid, and / or a predicted lactic acid concentration value to the cloud server through the communication unit (600), or may receive the user's own or another person's data set stored in the cloud server through the communication unit (600).

[0140]

[0141] The lactate concentration prediction system of the present invention may further include a body information input unit, and the actual lactate content may be input through the body information input unit.

[0142] The above body information input unit may include at least one of a mechanical button, a touch panel, and a digitizer.

[0143] The above mechanical buttons may include various types of buttons, such as mechanical key buttons, wheel buttons, etc.

[0144] The above touch panel detects the user's touch input and calculates touch coordinates corresponding to the detected touch signal. When the touch panel is combined with the display unit (500) described below to form a touch screen, the touch screen can be implemented as various types of touch screens, such as electrostatic, pressure-sensitive, or piezoelectric. The electrostatic type uses a dielectric coated on the surface of the touch screen to detect the micro-electricity generated by the user's body when a part of the user's body touches the touch screen surface, thereby calculating touch coordinates. The pressure-sensitive type includes two electrode plates built into the touch screen, and when the user touches the screen, the upper and lower plates of the touched point come into contact, causing current to flow, thereby calculating touch coordinates. The touch signal generated on the touch screen can be mainly generated by a human finger, but can also be generated by an object made of a conductive material that can apply a change in electrostatic capacity.

[0145] The above digitizer detects proximity input or touch input of a user's touch pen and calculates a position corresponding to the detected proximity input or touch input signal.

[0146] The digitizer can detect touch or proximity input, for example, by using electromagnetic resonance (ERM) based on changes in the intensity of an electromagnetic field caused by the proximity or touch of a pen. The digitizer can be provided to have a certain area below the display panel of the display unit (500).

[0147] The user's body information may be directly input by the user by operating a mechanical button, touch panel, or digitizer provided in the body information input unit, or may be transmitted from an external device through a communication unit (600) described below.

[0148] The above user's physical information may include at least one selected from the group consisting of age, height, weight, and body fat percentage in addition to the actual lactate content and heart rate, and preferably includes at least two selected from the group consisting of age, height, weight, and body fat percentage, and more preferably includes all of age, height, weight, and body fat percentage.

[0149] The above body fat percentage refers to the ratio (%) of body fat weight to the user's body weight.

[0150]

[0151] In one embodiment of the present invention, the lactic acid content measurement unit (100) in the body fluid may include a lactate sensor that measures the lactic acid content in the body fluid using an electrochemical method.

[0152] FIG. 4 is a plan view of a lactate sensor according to one embodiment of the present invention, and FIG. 5 is a cross-sectional view showing a surface cut along line A-A' of FIG. 4.

[0153] Referring to FIG. 4, the lactate sensor may include a substrate (110), a sensor unit (120), and a wiring unit (150), and the sensor unit (120) may include a working electrode (130) and a reference electrode (140) provided spaced apart from the working electrode (130).

[0154]

[0155] The above substrate (110) functions to provide a structural base for the components constituting the lactate sensor. For example, the substrate (110) may be implemented in the form of a substrate film having a rigid material such as glass or having flexible properties.

[0156] Examples of specific materials that can be applied to the substrate film when the substrate (110) is implemented flexibly include polyester resins such as polyethylene terephthalate, polyethylene isophthalate, polyethylene naphthalate, and polybutylene terephthalate; cellulose resins such as diacetyl cellulose and triacetyl cellulose; polycarbonate resins; acrylic resins such as polymethyl (meth) acrylate and polyethyl (meth) acrylate; styrene resins such as polystyrene and acrylonitrile-styrene copolymers; polyolefin resins such as polyethylene, polypropylene, polyolefins having a cyclo- or norbornene structure, and ethylene-propylene copolymers; vinyl chloride resins; amide resins such as nylon and aromatic polyamides; imide resins; polyethersulfone resins; sulfone resins; polyetheretherketone resins; sulfated polyphenylene resins; vinyl alcohol resins; Examples of the transparent optical film include films composed of thermoplastic resins such as vinylidene chloride resins; vinyl butyral resins; allylate resins; polyoxymethylene resins; and epoxy resins. Films composed of blends of the above thermoplastic resins can also be used. In addition, films composed of thermosetting resins such as (meth)acrylic, urethane, acrylic urethane, epoxy, and silicone resins or ultraviolet-curable resins can also be used. The thickness of such a transparent optical film can be appropriately determined, but is generally determined to be 1 to 500 μm in consideration of workability such as strength and handleability, thin layer properties, etc., and is particularly preferably 1 to 300 μm, and more preferably 5 to 200 μm.

[0157] Such a base film may contain one or more suitable additives. Examples of the additives include ultraviolet absorbers, antioxidants, lubricants, plasticizers, release agents, anti-coloring agents, flame retardants, nucleating agents, antistatic agents, pigments, and colorants. The base film may have a structure including various functional layers, such as a hard coating layer, an antireflection layer, and a gas barrier layer, on one or both sides of the film. The functional layers are not limited to those described above, and may include various functional layers depending on the intended use.

[0158] Additionally, the substrate film may be surface-treated, if necessary. Such surface treatments include dry treatments such as plasma treatment, corona treatment, and primer treatment, and chemical treatments such as alkaline treatment including saponification treatment.

[0159]

[0160] Referring to FIG. 5, the working electrode (130) may include an electrode portion (131), an electron transfer portion (132), and a reaction portion (133) formed on a substrate (110), and may further include a filter portion (134).

[0161] The above electrode unit (131) is composed of a plurality of electrodes formed on a substrate (110). This electrode unit (131) detects an electrical signal generated by a reaction between a material constituting the reaction unit (133) described below and lactic acid contained in the user's body fluid.

[0162] The electrode portion (131) is not particularly limited as long as it is a conductive material. The electrode portion (131) may include, for example, one or more selected from the group consisting of gold (Au), silver (Ag), copper (Cu), platinum (Pt), titanium (Ti), nickel (Ni), tin (Ni), molybdenum (Mo), cobalt (Co), and APC, or may be an alloy thereof. APC is an Ag-Pd-Cu alloy.

[0163] The electrode portion (131) can be formed on the substrate (110) by screen printing, physical vapor deposition, etching, or attachment of a conductive tape.

[0164] The sensor unit (120) may be composed of a plurality of electrodes. The plurality of electrodes may include at least one working electrode (130) and at least one reference electrode (140). In addition, the working electrodes and the reference electrodes may be arranged to correspond to each other.

[0165] The above plurality of electrodes may be connected to the information storage unit (400) and / or the lactate concentration analysis unit (300) through the wiring unit (150).

[0166]

[0167] The above electron transfer unit (132) is formed on the electrode unit (131). The electron transfer unit (132) reacts with lactic acid and undergoes a redox reaction with the reduced enzyme, and the electron transfer medium in a reduced state thus formed generates a current at the electrode surface to which an oxidation potential is applied.

[0168] The electron transfer unit (132) may include an electron transfer mediator, for example, the electron transfer mediator may be hexaammineruthenium(III) chloride, potassium ferricyanide, potassium ferrocyanide, dimethylferrocene (DMF), ferricinium, ferrocene monocarboxylic acid (FCOOH), 7,7,8,8-tetracyanoquino-dimethane (TCNQ), tetrathia fulvalene (TTF), nickelocene (Nc), N-methyl acidinium (NMA+), Tetrathiatetracene (TTT), N-methylphenazinium (NMP+), hydroquinone, 3-dimethylaminobenzoic acid (MBTHDMAB), 3-methyl2-benzothiozolinone hydrazone, 2-methoxy-4-allylphenol, 4-aminoantipyrin (AAP), dimethylaniline, 4-aminoantipyrene, 4-methoxynaphthol, 3,3',5,5'-tetramethyl benzidine (TMB), 2,2-azino-di-[3-ethyl-benzthiazoline sulfonate], o-dianisidine, o-toluidine, 2,4-dichlorophenol (2,4-dichlorophenol), 4-aminophenazone, benzidine, Prussian blue, bipyridine-osmium complex compounds, etc. can be used.

[0169] The above electron transfer unit (132) may also play a role in protecting the electrode unit (131). For example, the electron transfer unit (132) may have a structure in which an electrode protector that protects the electrode unit (131) and an electron transfer medium that performs the function of electron transfer are mixed. Here, in order to protect the electrode, the electron transfer unit (132) may further include an electrode protection material.

[0170] For example, Prussian blue included in the electron transfer unit (132) is a component that performs the function of electron transport, and is a blue pigment whose main component is potassium hexacyanoferrate (II) iron (III), and has high oxidation properties. When the electron transfer unit (132) including Prussian blue is formed between the electrode unit (131) and the reaction unit (133), the sensitivity of the electrode can be improved, but the metallic electrode unit (131) located below the Prussian blue can be oxidized and corroded. Therefore, in an additional embodiment of the present invention, in order to prevent oxidation of the electrode unit (131) and protect the electrode, the electron transfer unit (132) may further include an electrode protection material together with the electron transfer medium. In a specific example, the electrode protection material may be carbon.

[0171]

[0172] The above reaction unit (133) is formed on the electron transfer unit (132) and may include an oxidation-reduction enzyme that reacts with lactic acid contained in the user's body fluid and is reduced. Here, the reduced enzyme reacts with the electron transfer mediator of the electron transfer unit (132) to quantify glucose.

[0173] For example, the reaction unit (133) may include lactate oxidase and / or lactate dehydronase.

[0174]

[0175] The above reaction unit (133) may further include a separate fixative layer on the outside. The fixative layer may serve to protect the reaction unit (133) by preventing the penetration of impurities and ionic components.

[0176] The above-mentioned fixative layer may include a hygroscopic polymer as a fixative, and has the effect of protecting the enzyme from external temperature changes, while drying and shrinking at low humidity and expanding at high humidity, thereby providing tension to the outside of the enzyme and blocking the active site of the enzyme, thereby facilitating the storage of the electrochemical sensor, especially under normal atmospheric humidity conditions, and reducing the effect of humidity influence. If the fixative layer is not included as a separate layer, and the fixative is included together with the mediator and / or enzyme in the reaction section (133), the enzyme reaction layer may partially coagulate due to high acidity, making the electrochemical sensor inoperable.

[0177] In addition, the above-mentioned fixative layer may include a filtered fixative. When a cross-linking agent is included in the reaction section (133) and the fixative is applied to the upper surface of the reaction section (133), the fixative may have the effect of protecting the enzyme from external temperature changes. On the other hand, when the enzyme is dried and shrunk at low humidity and greatly expanded at high humidity, tension is applied to the outside of the enzyme, blocking the active site (binding sector), thereby increasing the possibility of inactivation. Accordingly, the current value may fluctuate greatly depending on the storage or measurement humidity, which may reduce accuracy and precision, thereby reducing its role as a biosensor.

[0178] Accordingly, by including the filtered fixative in the fixative layer included as a separate layer on the upper surface of the reaction unit (133), the influence of ambient humidity is greatly reduced, thereby improving accuracy and precision.

[0179] The above-mentioned fixative layer may be manufactured from a fixative layer composition. Specifically, the fixative layer of the present invention may be formed by applying the fixative layer composition to the upper surface of the reaction unit (133). The fixative layer composition may include at least one selected from the group consisting of a solvent and a fixative, and preferably, the fixative may be a filtered fixative.

[0180] The above-mentioned fixative may include a hygroscopic polymer having a three-dimensional network structure by cross-linking, and preferably includes an amine-based hygroscopic polymer. In one or more embodiments, the fixative may be at least one selected from the group consisting of agarose, cellulose, chitosan, polyvinyl alcohol, and polyvinyl chloride, and may be a copolymer derived from polyaniline or tetrafluoroethylene, and may include a functional group derived from sulfonic acid in the structure. In terms of maintaining the properties of the enzyme and convenience in the manufacturing method, it is preferable to include chitosan.

[0181] According to one embodiment of the present invention, it is preferable that the fixative undergo a filtration process using a filter, and the filter used in the filtration process may have a diameter of 6㎛ or less, and a commercially available product includes LCF-11100 6㎛ (Pall Corporation). When the fixative undergoes a filtration process, the fixative that is not dissolved in the solvent is filtered out, thereby improving the uniformity of the fixative layer. If the fixative that is not dissolved remains in the fixative layer, there is a problem in that the reliability of the electric signal is reduced due to hydrogen bonding with external water molecules. Through a filtration process for the fixative, the uniformity of the fixative layer can be improved and the influence of humidity can be reduced.

[0182] The above-mentioned fixing agent is essential for fixing the reaction part (133) and protecting the enzyme, but as the content thereof increases, the surface roughness of the reaction part (133) increases. An increase in surface roughness leads to an increase in current noise, which increases the dispersion of the measured current amount. Therefore, it is important to include the fixing agent in an appropriate content range in terms of reliability of the continuous measurement type electrochemical sensor according to the present invention. For example, the fixing agent may be included in an amount of 0.01 to 5 wt% or less, and preferably 0.01 to 3 wt% or less, based on the total weight of the fixing agent layer composition. If it is out of the above range, a problem may occur in which current noise increases, resulting in a decrease in the reliability of the measured current value.

[0183] The solvent is not particularly limited as long as it dissolves the fixative, but may be water, and preferably deionized water (DI water) or purified water. The solvent may be included in an amount of 60 to 99.5 wt% based on the total weight of the fixative layer composition.

[0184] Additionally, the above-described fixative layer composition may further include a small amount of a weakly acidic substance to lower the pH. For example, by including acetic acid in an amount of less than 3 wt% based on the total weight of the fixative layer composition, the pH of the composition may be lowered from neutral to weakly acidic, thereby increasing the solubility of the fixative in the solvent.

[0185]

[0186] The above filter unit (134) is formed on the reaction unit (133). For example, it can directly cover the upper surface of the reaction unit (133). The filter unit (134) can protect the reaction unit (133) from external physical force. In addition, it can prevent the oxidizing enzyme or dehydrogenase of the reaction unit (133) from being exposed to the external environment.

[0187] The above filter unit (134) can only allow the substance to be detected to pass through the sample. Therefore, the reaction unit (133) can be prevented from being denatured or damaged by substances other than the substance to be detected.

[0188] If the filter unit (134) allows the detection target substance to pass through, an ion exchange membrane commonly used in the art may be used. The ion exchange membrane may include a cation exchange resin such as a perfluorosulfonic acid resin. For example, the ion exchange membrane may include Nafion.

[0189]

[0190] The above reference electrode (140) may be placed on the substrate (110). For example, the reference electrode (140) may be placed on the same surface as the surface on which the working electrode (130) of the substrate (110) is placed. For example, the reference electrode (140) may be placed spaced apart from the working electrode (130), and the reference electrode (140) and the working electrode (130) may be electrically disconnected.

[0191] The reference electrode (140) can provide a reference value for the voltage or potential value measured at the working electrode (130) when measuring a sample. Using the potential value of the reference electrode (140) as a reference value, the concentration of the target substance selectively permeated into the working electrode (130) can be specified.

[0192] For example, by comparing the potential value at the reference electrode (140) and the potential value measured at the working electrode (130), the voltage generated purely by the concentration difference of the target substance (e.g., lactic acid) inside / outside the working electrode (140) can be calculated, and the concentration of the target substance can be derived from the voltage.

[0193]

[0194] The wiring section (150) is formed by a plurality of electrical wires extending from the electrodes constituting the electrode section (131). This wiring section (150) can be connected to a detection and analysis means that performs a current analysis function via an electrical connection medium such as an FPCB (Flexible Printed Circuit Board). A pad area can be provided at the end of the wiring section (150), and the FPCB can be electrically connected by being adhered to this pad area.

[0195]

[0196] The lactate concentration prediction system according to the present invention can be implemented flexibly, transparently, or wearably.

[0197]

[0198] <Method for predicting lactic acid concentration>

[0199] Figures 6 to 8 are flowcharts schematically showing a method for predicting lactic acid concentration according to the first to third embodiments of the present invention.

[0200]

[0201] Referring to FIG. 6, the method for predicting lactate concentration of the present invention includes a step (S100) of measuring lactate content in body fluid for measuring information on lactate content in body fluid secreted from a user; a heart rate measurement step (S200) of measuring the heart rate of the user; and a step (S300) of analyzing lactate concentration for calculating a blood lactate concentration prediction value using the measured lactate content in body fluid and heart rate.

[0202]

[0203] The method for predicting lactic acid concentration of the present invention may be to calculate a predicted lactic acid concentration value using the above-described lactic acid concentration prediction system, and the contents of the above-described <lactic acid concentration prediction system> may be applied without limitation.

[0204]

[0205] The method for predicting lactate concentration of the present invention includes a step (S100) of measuring lactate content in body fluid, which measures information on the lactate content in body fluid secreted from a user. The information on the lactate content in body fluid secreted from the user can be measured through a body fluid lactate content measuring unit (100). The information on the lactate content can be spectroscopic information measured using a photometric method or electrochemical information measured using an electrochemical method. The body fluid secreted from the user can be any one selected from the group consisting of sweat, tears, saliva, and urine.

[0206]

[0207] The method for predicting lactate concentration of the present invention includes a heart rate measurement step (S200) of measuring the user's heart rate.

[0208] The lactic acid content in the body fluid and the heart rate measured in the step (S100) of measuring the lactic acid content in the body fluid may be measured at a specific point in time after the start of exercise. The specific point in time may mean, for example, the end point of exercise when 5 minutes of exercise and 30 seconds of rest are performed as one set, i.e., in this case, it may be 5 minutes after the start of exercise.

[0209] The method for predicting lactate concentration according to the present invention measures information on the lactate content in body fluid secreted from a user by a non-invasive method, and uses the information to provide a highly accurate lactate concentration prediction value, thereby enabling the user's lactate concentration status to be easily identified periodically during daily life or exercise.

[0210]

[0211] The method for predicting lactate concentration of the present invention includes a lactate concentration analysis step (S300) for calculating a blood lactate concentration prediction value using the user's heart rate and information about the lactate content in the measured body fluid. When predicting the user's lactate concentration based on information about the lactate content in the body fluid secreted from the user, there may be differences in the lactate content in the body fluid depending on the physical characteristics of each individual user even if the lactate content is the same. Therefore, by applying the user's heart rate in addition to the information about the lactate content in the body fluid secreted from the user to the calculation of the lactate concentration prediction value to correct for errors, it is possible to predict the user's lactate concentration with higher accuracy.

[0212] The above-mentioned lactate concentration analysis step (S300) may include a step of constructing a lactate concentration prediction model based on multiple data sets. The data sets may include heart rate, lactate information in body fluid, and actual lactate content corresponding to the lactate information in body fluid.

[0213] In addition, the lactate concentration analysis step (S300) may include a step of calculating a lactate concentration prediction value by inputting information about the user's heart rate and the lactate content in the measured body fluid into the constructed lactate concentration prediction model. The constructed lactate concentration prediction model may include at least one selected from the group consisting of a coefficient indicating a correlation between the user's heart rate and information about the lactate content in the body fluid secreted from the user, a coefficient indicating a correlation between the user's heart rate and the actual lactate content, and a coefficient indicating a correlation between information about the lactate content in the body fluid secreted from the user and the actual lactate content, and by applying the coefficient regarding the correlation between each of the obtained variables, the accuracy of lactate concentration prediction based on information about the lactate content in the user's body fluid can be further increased.

[0214] The above lactic acid concentration analysis step (S300) can calculate a predicted blood lactic acid concentration value by inputting information about the user's heart rate and the measured lactic acid content in the body fluid into Equation 1 or Equation 2 below.

[0215] [Formula 1]

[0216] BL = 5.52 - 0.1038 HR + 0.0569 SL + 0.000504 HR 2

[0217] [Formula 2]

[0218] BL = 5.28 - 0.1230 HR + 0.684 SL + 0.000585 HR 2 - 0.0552 SL 2

[0219] In the above equations 1 and 2, BL is the blood lactate concentration (mmol / L), SL is the body fluid lactate concentration (mmol / L), and HR is the heart rate (beats / min).

[0220]

[0221] The method for predicting lactate concentration of the present invention may further include a step of transmitting and receiving information with an external device. The transmission and reception of the information may be via at least one of Wi-Fi, Bluetooth, wireless communication, and NFC.

[0222] The external device may be a user's personal mobile terminal. For example, the device may transmit information about the user's heart rate, the lactate content in body fluid, and / or a predicted lactate concentration value to the user's personal mobile terminal in real time, or may receive a data set of the user or another person stored in the user's personal mobile terminal.

[0223] The external device may be a cloud server, and may transmit, for example, information about the user's heart rate, the lactate content in body fluid, and / or a predicted lactate concentration value to the cloud server in real time, or may receive a data set of the user or another person stored on the cloud server.

[0224]

[0225] The above lactic acid concentration analysis step (S300) can also build a lactic acid concentration prediction model through machine learning based on multiple data sets.

[0226] The above machine learning may use at least one selected from the group consisting of linear regression analysis, nonlinear regression analysis, partial least squares (PLS), Bayesian network, hidden markov model, decision tree, boosting, bagging, support vector machine, convolutional neural network, deep neural network, and recursive neural network (RNN), and it is preferable to use at least one selected from the group consisting of boosting, bagging, support vector machine, and deep neural network.

[0227] Multiple data sets may be used to build the lactate concentration prediction model using the above machine learning. To improve the accuracy of the constructed lactate concentration prediction model, it is preferable to use at least five data sets. For example, the lactate concentration prediction model may be built using machine learning based on 5 to 20 data sets.

[0228]

[0229] Referring to FIG. 7, the method for predicting lactate concentration according to the second embodiment of the present invention may further include an information storage step (S250) for storing information regarding the user's heart rate and the lactate content in the measured body fluid. The information regarding the user's heart rate and the lactate content in the body fluid may be stored in the information storage unit (400).

[0230] In addition, the lactate concentration prediction method of the present invention includes a step (S310a) of constructing a lactate concentration prediction model based on a plurality of previously stored data sets of others, and a step (S320) of calculating a lactate concentration prediction value by inputting information about the user's heart rate and the lactate content in the measured body fluid into the constructed lactate concentration prediction model. The description described for the lactate concentration prediction method according to FIG. 6 can be equally applied to the lactate concentration prediction method illustrated in FIG. 7, and detailed descriptions of steps, methods, and configurations that are substantially the same or similar are omitted.

[0231] Although at least five data sets are required to build the above-described lactate concentration prediction model, a sufficient number of data sets are not collected when each user initially uses the lactate concentration prediction system according to the present invention. Therefore, before the user's own data set is sufficiently collected, a lactate concentration prediction model can be built based on multiple data sets of others previously stored in the information storage unit (400) and used to calculate the lactate concentration prediction value, thereby further increasing the accuracy of lactate concentration prediction.

[0232]

[0233] Referring to FIG. 8, the method for predicting lactate concentration according to the third embodiment of the present invention includes a step (S310b) of constructing a lactate concentration prediction model based on a plurality of data sets of the user, and a step (S320) of calculating a lactate concentration prediction value by inputting information about the user's heart rate and the lactate content in the measured body fluid into the constructed lactate concentration prediction model. Accordingly, the description described for the lactate concentration prediction method according to FIGS. 6 and 7 can be equally applied to the lactate concentration prediction method illustrated in FIG. 8, and detailed descriptions of steps, methods, and configurations that are substantially the same or similar are omitted.

[0234] When a user repeatedly uses the lactate concentration prediction method according to the present invention and the user's own data set is sufficiently stored, a personalized lactate concentration prediction model can be newly constructed based on the user's multiple data sets, and the newly constructed personalized lactate concentration prediction model can be used to calculate a lactate concentration prediction value.

[0235] In this way, by building a personalized lactate concentration prediction model optimized for each user based on the user's own data set and inputting information about the user's heart rate and lactate content in body fluids into the model to calculate a lactate concentration prediction value, the accuracy of lactate concentration prediction for each individual user can be further improved.

[0236]

[0237] Hereinafter, the present invention will be described in more detail through examples. However, the following examples are intended to further illustrate the present invention, and the scope of the present invention is not limited by the following examples.

[0238]

[0239]

[0240] Manufacturing Example 1: Manufacturing of a reaction composition

[0241] Stock Solution 1 was prepared by stirring 200 IU / mL of lactate oxidase (LCO-301, TOYOBO) and 10 μL of phosphate buffered saline (PBS) until well mixed. Then, Stock Solution 2 was prepared by stirring 23.4 mg of 1-methoxy-5-methylphenaziniummethylsulfate (1-m-PMS (M8640, Sigma-Aldrich)) and 696 μL of phosphate buffered saline (PBS) until well mixed. Afterwards, 45 mg of 98% Hexaammineruthenium(Ⅲ) chloride (C36334, ACROS) and 3 ml of phosphate buffered saline (PBS) were stirred well to prepare Stock Solution 3. Afterwards, 80 μl of glutaraldehyde (G5882, Sigma-Aldrich) and 920 μl of purified water (DI) were stirred well to prepare Stock Solution 4. Afterwards, Stock Solution 1, Stock Solution 2, Stock Solution 3, and Stock Solution 4 were mixed in a ratio of 4:2:1:3 and left at room temperature for 30 minutes to prepare a reaction composition.

[0242]

[0243] Manufacturing Example 2: Manufacturing of a fixed layer composition

[0244] 100 mg of chitosan (448869, Sigma-Aldrich) and 10 ml of purified water (DI) were mixed, and then 100 mg of acetic acid (A2035, TCI) was mixed with the mixed solution of chitosan and purified water, and left at 50°C for 1 hour. Thereafter, the mixed solution was cooled to room temperature and filtered through a 0.6 μm particle size filter (LCF-11100 6 μm, Pall Corporation) to prepare a fixed layer composition A.

[0245]

[0246] Manufacturing Example 3: Manufacturing of a lactate sensor

[0247] A 180㎛ thick PET substrate is prepared, and APC alloy (Ag-Pd-Cu alloy) and IZO (Indium Zinc Oxide) are printed on the upper surface of the substrate using photolithography to form a metal electrode layer and a wiring portion.

[0248] Except for the areas where the wiring section is not formed and the reference electrode and working electrode areas, an insulating layer is formed by printing DW-LT09 (Dongwoo Finechem Co., Ltd.) using photolithography.

[0249] In the working electrode region where the IZO layer is not formed, a working electrode layer is first formed by screen-printing carbon paste. 2 μl of the reaction composition 1 manufactured through Manufacturing Example 1 is dropped onto the upper surface of the working electrode layer, and then dried at room temperature to form an enzyme reaction layer using a drop-casting method. 2 μl of the fixing agent layer composition according to Manufacturing Example 2 is dropped onto the upper surface of the working electrode layer using a drop-casting method, and then dried to form a fixing agent layer.

[0250] Afterwards, PBS was dropped onto the working electrode area, and after 3 minutes, the working electrode was cleaned through an air-blowing process.

[0251]

[0252] Example 1

[0253] For 7 test subjects, 5 minutes of exercise and 30 seconds of rest were repeated. Immediately after 5 minutes of exercise, sweat was collected from the forehead and chest, and the lactic acid content was measured using the lactate sensor manufactured in Manufacturing Example 3. Immediately after 5 minutes of exercise, the heart rate was measured using a wristwatch (Amazfit 4 mini). In addition, blood was collected from the earlobes of the test subjects using an invasive method, and the blood lactic acid concentration was measured. The results are shown in Table 1 below.

[0254]

[0255] Subject MeasurementsPredicted ValuesBL(mmol / L)HR(beats / min)SL(mmol / L)BL(mmol / L)ForeheadChestForehead(Formula 1)Chest(Formula 2)A1.41243.71-0.61-1.31243.703.830.610.831.5141-6.63-1.681.81385.176.151.091.572.81414.304.471.151.524.11605.234.802.112.59B1. 3126-3.01-0.632146-6.89-1.883.6159-6.38-2.635176-5.5-3.85C1.71588.639.412.192.001.71729.5211.233 .122.153.51869.438.474.194.475.72009.048.245.435.97D11306.55.470.911.272.11308.847.261.051.232.61 649.647.452.602.87518410.579.34.094.04E0.9107-5.23-0.880.81203.359.440.510.481.3131-4.95-1.241.8 1382.049.160.911.0811546.169.881.841.582.91655.827.682.452.914.31858.336.524.044.66F0.695-5.93-0 .991108-5.62-0.921.4144-5.64-1.803164-6.95-2.934.4175-7.2-3.734.71979.0410.715.154.75G1.111811.6 58.120.950.830.81289.348.921.020.831.91469.0881.631.731.61637.097.732.392.763.81778.658.13.433.76

[0256]

[0257] Based on the data set in Table 1 above, a lactate concentration prediction model was constructed through linear regression analysis. The relationship derived from the lactate concentration measured in sweat collected from the forehead is as shown in Equation 1 below, and the relationship derived from the lactate concentration measured in sweat collected from the chest is as shown in Equation 2. The relationship between each constant and coefficient value is shown in Tables 2 and 3, and the corresponding graph is as shown in Fig. 9.

[0258] [Formula 1]

[0259] BL = 5.52 - 0.1038 HR + 0.0569 SL + 0.000504 HR 2

[0260] Coefficient SE Coefficient T Value P Value Constant 5.52 2.77 2.00 0.060 HR -0.10 38 0.038 1 -2.72 0.013 S.L 0.05 69 0.05 45 1.04 0.309 HR 2 0.0005040.0001283.930.001

[0261] R 2 : 0.9063, R 2 (adj.): 0.8922

[0262] [Formula 2]

[0263] BL = 5.28 - 0.1230 HR + 0.684 SL + 0.000585 HR 2 - 0.0552 SL 2

[0264]

[0265] Coefficient SE Coefficient T Value P Value Constant 5.28 2.43 2.17 0.04 1 HR -0.12 30 0.03 47 -3.54 0.00 2 S.L. 0.68 40.45 0 1.52 0.143 HR 2 0.0005850.0001155.100.000S.L. 2 -0.05520.0278-1.990.060

[0266] R 2 : 0.9188, R 2 (adj.): 0.9069

[0267] In the above equation and table, BL is the blood lactate concentration (mmol / L), SL is the body fluid lactate concentration (mmol / L), HR is the heart rate (beats / min), and in Tables 2 and 3, the SE coefficient means the standard error of the estimated coefficient, the T value means the t-statistic, which is the value obtained by dividing the regression coefficient estimate by the standard error, and the P value represents the probability of rejecting the null hypothesis indicating that the variable has no explanatory power when the t-statistic is given, and if the P value is less than 0.05, it means that the null hypothesis is rejected and the alternative hypothesis is adopted, and the regression coefficient is judged to be significant, and R 2 The coefficient of determination is a value from 0 to 1. The higher the value, the closer it is to the estimated regression equation, which means that the predictive power of the regression model is high. R 2 (adj.) is R 2 It is a value applied to the coefficient of determination with the number of variables, and the value tends to increase as the number of variables increases, but if the value does not increase even when the number of variables increases, it is judged to be explained by the existing variables alone, and it is a value that adjusts the coefficient of determination by considering the variables and sample size, and R 2 The larger the value, the closer it is to the straight line in Fig. 9, which means higher accuracy.

[0268]

[0269] As a result of the above measurement, referring to FIG. 9, it can be seen that the lactate concentration predicted according to the lactate concentration prediction system and method of the present invention is close to a straight line, and in particular, it can be confirmed that the lactate concentration in the body fluid collected from the chest is closer to a straight line, so it can be seen that the accuracy of the lactate concentration prediction is excellent because the error with the lactate concentration measured from the blood is not large.

[0270]

[0271] A non-invasive lactate concentration prediction system and lactate concentration prediction method according to one embodiment of the present invention can predict blood lactate concentration with high accuracy and little deviation from the measurement result value using a non-invasive method, thereby making it easy to periodically determine the user's lactate concentration during daily life or exercise.

Claims

1. A body fluid lactate content measuring unit that measures information on the lactic acid content in body fluid secreted from a user; A heart rate measuring unit that measures the user's heart rate; and A lactate concentration prediction system, comprising a lactate concentration analysis unit that calculates a blood lactate concentration prediction value using the lactate content and heart rate in the measured body fluid.

2. In claim 1, A lactate concentration prediction system further comprising an information storage unit that stores information on the lactate concentration in the measured body fluid.

3. In claim 1, A lactate concentration prediction system, wherein the above body fluid is any one selected from the group consisting of sweat, tears, saliva and urine.

4. In claim 1, A lactate concentration prediction system, wherein the information on the above lactate content is spectroscopic information measured using a photometric method or electrochemical information measured using an electrochemical method.

5. In claim 1, The above lactic acid concentration analysis unit builds a lactic acid concentration prediction model based on multiple data sets, A lactate concentration prediction system, wherein the above data set includes heart rate, information on lactate content in body fluid, and actual lactate content corresponding to information on lactate content in the body fluid.

6. In claim 5, The above-mentioned lactate concentration analysis unit is a lactate concentration prediction system that calculates a blood lactate concentration prediction value by inputting information about the user's heart rate and the lactate content in the measured body fluid into the above-mentioned constructed lactate concentration prediction model.

7. In claim 6, The above lactate concentration analysis unit is a lactate concentration prediction system that calculates a blood lactate concentration prediction value by inputting information about the user's heart rate and the lactate content in the measured body fluid into Equation 1 or Equation 2 below. [Formula 1] B.L. = 5.52 - 0.1038 HR + 0.0569 S.L. + 0.000504 HR 2 [Formula 2] B.L. = 5.28 - 0.1230 HR + 0.684 S.L. + 0.000585 HR 2 - 0.0552 S.L 2 (In the above equations 1 and 2, BL is the blood lactate concentration (mmol / L), SL is the body fluid lactate concentration (mmol / L), and HR is the heart rate (beats / min).) 8. In claim 5, A lactate concentration prediction system, wherein the above multiple data sets are five or more data sets.

9. In claim 5, The above data set is a lactate concentration prediction system that is a data set of another person.

10. In claim 9, A lactate concentration prediction system, wherein the above-mentioned data set of another person is pre-stored in the above-mentioned information storage unit.

11. In claim 5, The above data set is a lactate concentration prediction system that is a user's own data set.

12. In claim 1, A lactate concentration prediction system, wherein information on the lactate content in body fluid secreted from a user and heart rate are measured at a specific point after the start of exercise.

13. In claim 12, A lactate concentration prediction system, wherein a specific point in time after the start of the above exercise is 5 minutes after the start of the exercise.

14. In claim 1, A lactate concentration prediction system further comprising a display unit that displays the lactate concentration prediction value calculated by the lactate concentration analysis unit.

15. In claim 1, A lactate concentration prediction system further comprising a communication unit for transmitting and receiving information with an external device.

16. A step for measuring lactate content in body fluid, which measures information on the lactate content in body fluid secreted from the user; A heart rate measurement step for measuring the user's heart rate; and A method for predicting lactate concentration, comprising a lactate concentration analysis step of calculating a predicted blood lactate concentration value using the lactate content and heart rate in the measured body fluid.

17. In claim 16, A method for predicting lactate concentration, further comprising an information storage unit that stores information on the lactate content in the measured body fluid.

18. In claim 16, A method for predicting lactate concentration, wherein the above body fluid is any one selected from the group consisting of sweat, tears, saliva, and urine.

19. In claim 16, A method for predicting lactate concentration, wherein the information on the above lactate content is spectroscopic information measured using a photometric method or electrochemical information measured using an electrochemical method.

20. In claim 16, The above lactic acid concentration analysis step includes a step of building a lactic acid concentration prediction model based on multiple data sets, A method for predicting lactate concentration, wherein the above data set includes information on heart rate, lactate content in body fluid, and actual lactate content corresponding to the lactate content information in body fluid.

21. In claim 20, A method for predicting lactate concentration, wherein the above-mentioned lactate concentration analysis step includes a step of calculating a blood lactate concentration prediction value by inputting information about the user's heart rate and the lactate content in the measured body fluid into the constructed lactate concentration prediction model.

22. In claim 20, The above lactate concentration analysis step is a method for predicting lactate concentration by inputting information about the user's heart rate and the lactate content in the measured body fluid into Equation 1 or Equation 2 below to calculate a predicted blood lactate concentration value. [Formula 1] B.L. = 5.52 - 0.1038 HR + 0.0569 S.L. + 0.000504 HR 2 [Formula 2] B.L. = 5.28 - 0.1230 HR + 0.684 S.L. + 0.000585 HR 2 - 0.0552 S.L 2 (In the above equations 1 and 2, BL is the blood lactate concentration (mmol / L), SL is the body fluid lactate concentration (mmol / L), and HR is the heart rate (beats / min).) 23. In claim 20, A method for predicting lactate concentration, wherein the above multiple data sets are five or more data sets.

24. In claim 20, A method for predicting lactate concentration, wherein the data set used in the step of constructing the above lactate concentration prediction model is a previously stored data set of another person.

25. In claim 20, A method for predicting lactate concentration, wherein the data set used in the step of constructing the above lactate concentration prediction model is the user's own data set.

26. In claim 16, A method for predicting lactate concentration, wherein information on the lactate content in body fluid secreted from a user and heart rate are measured at a specific point in time after the start of exercise.

27. In claim 26, A method for predicting lactate concentration, wherein a specific point in time after the start of the above exercise is 5 minutes after the start of the exercise.

28. In claim 16, A method for predicting lactate concentration, further comprising a step of transmitting and receiving information with an external device.

Images