Sensing device for synchronously monitoring lactic acid and cortisol in sweat and preparation method thereof

Abstract

The invention discloses a sensing device for synchronously monitoring lactic acid and cortisol in sweat and a preparation method of the sensing device. The sensing device is composed of a flexible patch substrate, a bionic sweat management unit, a multi-mode electrochemical sensing unit and an intelligent analysis early warning unit, wherein the bionic sweat management unit, the multi-mode electrochemical sensing unit and the intelligent analysis early warning unit are integrated with the flexible patch substrate. The bionic unit adopts a flower-shaped micro-channel and hydrophilic and hydrophobic wettability gradient design, and passive self-driven capture, directional enrichment and discharge of sweat are realized based on Laplace pressure difference. The sensing unit adopts a four-electrode system, and the specific synchronous detection of lactic acid and cortisol is realized through a first working electrode and a second working electrode. The intelligent unit is internally provided with an algorithm, synchronously processes double-path signals, fuses and analyzes exercise metabolism and pressure level, and generates comprehensive health assessment and early warning. The preparation method comprises the steps of constructing the flower-shaped micro-channel and the wettability gradient, processing and modifying the four electrodes and performing system integrated packaging. According to the invention, passive, stable and synchronous monitoring and intelligent early warning of two indexes are realized.

Description

Technical Field

[0001] This invention relates to the field of sweat detection technology, and in particular to a sensing device for simultaneously monitoring sweat lactic acid and cortisol, and its preparation method. Background Technology

[0002] With the increasing demand for personalized health management, wearable physiological monitoring technology has become a research hotspot. Sweat, as a biofluid rich in various physiological markers, provides an ideal medium for non-invasive and continuous monitoring of in vivo chemical information. Lactic acid and cortisol are key indicators reflecting the body's exercise metabolic load and neuroendocrine stress level, respectively. Simultaneous monitoring of these two substances is of great value for comprehensively assessing physical function, preventing overtraining, and managing stress.

[0003] However, existing wearable sweat sensing technologies still face a series of systemic technical bottlenecks in achieving reliable and simultaneous monitoring of lactic acid and cortisol, mainly in sweat management, sensing mechanisms, system integration, and data interpretation.

[0004] In sweat collection and fluid management, existing technologies mostly rely on passive capillary action or complex external driving structures, which present a trade-off between efficiency and stability. For example, embedded microfluidic channels fabricated using soft lithography or layered integrated structures based on transfer processes typically involve complex processes such as photolithography, vacuum coating, plasma bonding, or precision transfer alignment. These processes are numerous, costly, and not conducive to large-scale production. More importantly, these structures rely primarily on capillary force to drive sweat, and their flow rate is easily affected by fluctuations in sweat secretion rate, viscosity, and channel surface properties. This can lead to inconsistent detection responses, and during continuous monitoring, it is difficult to avoid the mixing of new and old sweat, making it impossible to ensure that the sensing interface is always in contact with freshly secreted samples.

[0005] In terms of physiological signal sensing mechanisms, existing technologies lack sufficient sensitivity, stability, and multi-indicator synchronization capabilities. Firstly, detection methods have limitations: while colorimetric methods are intuitive, their quantitative accuracy is low, and results are greatly affected by ambient light and imaging conditions, resulting in poor reproducibility. Traditional enzyme electrochemical sensors are limited by the fact that enzyme activity is easily affected by sweat temperature and pH fluctuations, leading to poor long-term stability and short lifespan. Secondly, for protein biomarkers such as cortisol, the current mainstream approach is antibody-based electrochemical immunosensing. However, antigen-antibody binding is usually irreversible, resulting in single-use sensors that cannot achieve continuous monitoring. Furthermore, antibodies themselves are extremely sensitive to storage conditions such as cold chain and operating environment, easily becoming inactive, further increasing costs and limiting their practical applications. Existing solutions mostly focus on the detection of single or a small number of indicators, lacking effective means for in-situ, simultaneous, and highly specific detection of biomarkers with different properties, such as lactate and cortisol.

[0006] Existing technologies also face challenges in system integration and user experience. Integrating sensors based on different principles, such as electrochemical, immunological, and complex microfluidic structures, onto the same flexible substrate presents difficulties in process compatibility and yield control. Furthermore, most devices only provide raw data or simple concentration values, lacking the ability to deeply fuse and intelligently interpret multimodal physiological information. The generated reports often require specialized knowledge to understand, failing to effectively lower the barrier to entry for ordinary users. The devices also need improvement in terms of long-term wear comfort, fit to dynamic skin, and reliability during exercise.

[0007] In summary, current technologies lack a flexible wearable system capable of simultaneously achieving efficient self-driven sweat management, stable detection of multimodal biomarkers, particularly lactic acid and cortisol, low-cost and controllable preparation, and intelligent data fusion and interpretation. Therefore, developing an integrated solution combining biomimetic fluid control, novel sensitive materials, and intelligent algorithms is of urgent significance and great value in propelling wearable sweat monitoring technology from the laboratory to large-scale applications, meeting the diverse needs from professional sports science to public health management. Summary of the Invention

[0008] To address the aforementioned shortcomings, this invention proposes a sensing device and its preparation method for simultaneously monitoring lactic acid and cortisol in sweat, achieving self-driven, stable, and synchronous monitoring and intelligent health early warning of lactic acid and cortisol in sweat.

[0009] This invention provides the following technical solution: a sensing device for simultaneously monitoring sweat lactic acid and cortisol, comprising: Flexible patch substrate; The biomimetic sweat management unit, integrated on a flexible patch substrate, is configured to achieve spontaneous capture of sweat from the skin surface, directional enrichment towards the central detection area, and continuous discharge of waste liquid after detection based on the curvature gradient-induced Laplace pressure difference. The multimodal electrochemical sensing unit is located in the central detection area of ​​the biomimetic sweat management unit. It includes a first working electrode for detecting lactic acid concentration, a second working electrode for detecting cortisol concentration, and a counter electrode and a reference electrode shared by the two. The first working electrode and the second working electrode are modified with different sensitive materials to achieve specific and simultaneous electrochemical detection of lactic acid and cortisol in sweat. The intelligent analysis and early warning unit, electrically connected to the multimodal electrochemical sensing unit, is configured as follows: The response signals from the first working electrode and the second working electrode are acquired in real time and processed synchronously to generate time-correlated lactate concentration data streams and cortisol concentration data streams. The built-in multimodal data fusion algorithm is executed, which assesses the user's exercise metabolic load and recovery status based on lactate concentration data stream and assesses the user's neuroendocrine stress level based on cortisol concentration data stream; Based on the fusion analysis of exercise metabolic load and neuroendocrine stress level, a comprehensive health status assessment and graded early warning information is generated. The biomimetic sweat management unit includes a microchannel layer with a flower-shaped topology. The stamen region of the microchannel layer forms a central detection area, and its inner surface is modified with a hydrophilic material. Multiple petal-shaped channels radiating outward from the central detection area form directional flow collection channels, and their inner surfaces are also modified with hydrophilic materials. The areas between the directional flow collection channels are modified with hydrophobic materials, thereby forming a wettability gradient together with the hydrophilic materials to drive sweat flow.

[0010] As an improvement, the surface of the first working electrode is sequentially modified with nano-nickel oxide for catalyzing the electrochemical oxidation of lactic acid and a Nafion protective layer covering it, and the surface of the second working electrode is covalently bound with an aptamer for specifically recognizing cortisol and a Nafion protective layer covering it.

[0011] As an improvement, the hydrophilic material is a composite of polyvinyl alcohol and silica, which is modified in the form of a film layer on the inner surface of the stamen region and the directional flow channels; the hydrophobic material is a self-assembled monolayer of octadecyltrichlorosilane, which is modified in the region between the directional flow channels; the hydrophilic film layer and the hydrophobic monolayer together form a wettability gradient pattern that drives the directional flow of sweat.

[0012] As an improvement, the biomimetic sweat management unit also includes a contact layer bonded to the microchannel layer. The contact layer has a liquid inlet at its center. A directional flow collection channel is arranged circumferentially between the microchannel layer and the contact layer. The ratio of the diameter of the central detection area to the flow channel diameter of the directional flow collection channel is (14-17):1. The height of the directional flow collection channel is 1.5-2.5 times its flow channel diameter to match the rate of sweating during human exercise, thereby achieving continuous directional enrichment and renewal of sweat.

[0013] As an improvement, the intelligent analysis and early warning unit includes: A low-power acquisition circuit is used to synchronously acquire, amplify, and filter the response signals of the first working electrode and the second working electrode. The microprocessor has an embedded multimodal data fusion algorithm for processing conditioned bimodal sensor data, analyzing it through a built-in lactate and cortisol threshold model, and generating preliminary assessment results of metabolic state and stress level. The wireless communication module is used to output comprehensive health status assessment and graded early warning information generated based on the preliminary assessment results.

[0014] As an improvement, the microprocessor integrates a lactate state assessment module, which includes: The intensity determination function is used to analyze real-time lactate level data. Compared with the preset resting threshold Medium intensity threshold and high intensity threshold Compare the results and output the corresponding exercise intensity level. The metabolic assessment function is used to calculate lactate clearance rate based on continuous time series lactate level data. And based on the clearance rate The metabolic efficiency level is output by comparing the result with a preset efficiency threshold. The risk warning function is used to identify lactic acid levels exceeding a high-intensity threshold in a recently scheduled number of measurements. The frequency of training is determined, and when the frequency reaches a preset risk threshold, an overtraining risk warning signal is generated.

[0015] As an improvement, the microprocessor integrates a cortisol state assessment module, which is configured as follows: The basic state determination function is used to determine the cortisol level data acquired in real time. With the preset exhaustion threshold Lower limit of normal range and upper limit Compare and output the baseline pressure level status; The stress type discrimination function is used to calculate the historical variance of recent cortisol level data. And based on real-time cortisol level data With historical variance The combination of these relationships helps distinguish between acute and chronic stress states.

[0016] As an improvement, the microprocessor also integrates a comprehensive evaluation and early warning module, which is configured as follows: The scoring mapping function is used to assign lactate exercise metabolic levels. and cortisol stress levels Each is mapped to a predefined risk score; The risk calculation function is used to calculate the total risk score. = + ; The early warning decision-making function is used to adjust the overall risk score. It compares the data with multiple preset threshold ranges and triggers corresponding health warning signals and intervention suggestions based on the comparison results.

[0017] As an improvement, the counter electrode contains a platinum nanoparticle modification layer to form a stable electrochemical counter electrode; the reference electrode contains an Ag / AgCl structure to provide a stable reference potential.

[0018] A method for preparing a sensing device for simultaneously monitoring sweat lactic acid and cortisol, used to prepare any of the sensing devices for simultaneously monitoring sweat lactic acid and cortisol as described above, specifically includes the following steps: S1. A microchannel layer with a flower-like topological structure is prepared as the main body of the biomimetic sweat management unit, and wettability gradient patterns are constructed on its surface by selectively modifying hydrophilic and hydrophobic materials. S2. In the flower-like region of the microfluidic layer, the substrates for the first working electrode, the second working electrode, the counter electrode, and the reference electrode are prepared by microfabrication technology. S3. Functional modification of the electrode substrate: A nano-nickel oxide mixed solution is drop-coated onto the first working electrode and dried to form a modification layer; the second working electrode is aminated and then a cortisol-specific aptamer is fixed; platinum nanoparticles are deposited on the counter electrode by sputtering; a silver layer is electrochemically deposited on the reference electrode and then chlorinated to form an Ag / AgCl system. S4. The functionalized microfluidic layer is combined with a flexible patch substrate, and a microprocessor circuit integrating a multimodal data fusion algorithm is electrically connected to it, and then packaged to obtain a sensing device.

[0019] Compared with the prior art, the advantages of the present invention are as follows: First, the device utilizes a biomimetic flower-like topology combined with a wettability gradient formed by hydrophilic and hydrophobic materials. By leveraging the Laplace pressure difference induced by the curvature gradient, it achieves pump-free, unidirectional sweat transport without external force. This design rapidly and directionally concentrates trace amounts of sweat into the central detection area, effectively preventing backflow or evaporation. This ensures continuous and accurate detection even at low sweating rates, laying a reliable foundation for long-term in-situ monitoring through fluid management.

[0020] Secondly, the device integrates an innovative multimodal electrochemical sensing unit. A four-electrode system enables simultaneous, in-situ detection of lactic acid and cortisol in the same sweat sample, eliminating errors and delays from multiple detections and ensuring data consistency and comparability. The selection of core sensing materials brings breakthroughs in stability and cost: the first working electrode uses nano-nickel oxide instead of traditional lactate oxidase, giving it extremely strong tolerance to temperature and pH fluctuations in the sweat environment, significantly extending its theoretical shelf life. Furthermore, the raw material cost of nano-nickel oxide is only about 1 / 10 of that of high-purity lactate oxidase. Simultaneously, both nano-nickel oxide and the aptamer can be modified using a simple deposition method, avoiding the complex bioimmobilization process required for traditional enzymes or antibodies, greatly reducing production steps and time, and laying a crucial technological foundation for the large-scale popularization of consumer-grade health products. The second working electrode uses a reversibly binding cortisol-specific aptamer, overcoming the bottleneck of single-use traditional antibody sensors, enabling continuous and repeatable monitoring of cortisol levels and supporting long-term dynamic tracking. The shared, stabilized counter electrode and reference electrode together form a robust and reliable electrochemical detection environment.

[0021] Furthermore, the device's built-in intelligent analysis and early warning unit deeply integrates data acquisition and health insights. The unit simultaneously acquires, amplifies, and filters dual-channel sensor signals, and utilizes embedded algorithms to professionally analyze time-correlated lactate and cortisol data streams. The algorithm can not only independently assess exercise intensity, metabolic recovery efficiency, overtraining risk, and stress levels and types, but also fuse these two types of physiological information through a comprehensive assessment module. Based on a preset risk scoring model, it generates a comprehensive health status assessment and graded early warning information, transforming complex physiological signals into easily understandable reports of stress indices and fatigue levels. This enhances the user experience and significantly lowers the technical barrier to health monitoring.

[0022] Finally, the fabrication method provided by this invention clarifies the complete process path from microfluidic structure fabrication, hydrophilic-hydrophobic patterning modification, multifunctional electrode fabrication and modification to system integration. This method is highly operable and can reliably fabricate sensing devices with intricate structures and high functional integration, providing a clear technical solution for the industrialization of this innovative design.

[0023] In summary, this invention, through the deep integration of biomimetic microfluidic technology, multimodal electrochemical sensing technology, intelligent data analysis technology, and material innovation, overcomes key challenges such as spontaneous sweat management, simultaneous and stable detection of dual biomarkers, and intelligent data interpretation. Ultimately, it creates a flexible wearable monitoring system with significant advantages in detection efficiency, long-term stability, manufacturing cost, wearing comfort, and user experience. It is not only suitable for monitoring the extreme conditions of professional athletes but can also be extended to the broad market of mass fitness and daily health management, possessing significant practical value and market potential. Attached Figure Description

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Figure 1 An exploded view of the structure of a sensor device for simultaneously monitoring lactic acid and cortisol in sweat; Figure 2 This is a structural diagram of a sensing device for simultaneously monitoring lactic acid and cortisol in sweat.

[0025] The markings in the above figure are as follows: 1. Flexible patch substrate; 2. Bionic sweat management unit; 2.1. Microfluidic layer; 2.1.1. Central detection area; 2.1.2. Directional collection channel; 2.2. Contact layer; 2.2.1. Liquid inlet; 3. Multimodal electrochemical sensing unit; 3.1. First working electrode; 3.2. Second working electrode; 3.3. Counter electrode; 3.4. Reference electrode; 4. Intelligent analysis and early warning unit. Detailed Implementation

[0026] like Figures 1 to 2As shown, a sensing device for simultaneously monitoring sweat lactic acid and cortisol includes a flexible patch substrate, a biomimetic sweat management unit integrated on the flexible patch substrate, a multimodal electrochemical sensing unit disposed in the central detection area of ​​the biomimetic sweat management unit, and an intelligent analysis and early warning unit electrically connected to the multimodal electrochemical sensing unit. The biomimetic sweat management unit is configured to achieve spontaneous capture of sweat from the skin surface, directional enrichment towards the central detection area, and continuous discharge of waste liquid after detection based on the Laplace pressure difference induced by curvature gradient. The biomimetic sweat management unit includes a microchannel layer with a flower-shaped topology. The stamen region of the microchannel layer constitutes the central detection area, and its inner surface is modified with a hydrophilic material. Multiple petal-shaped channels radiating outward from the central detection area constitute directional flow collection channels, and their inner surfaces are also modified with hydrophilic materials. The area between the directional flow collection channels is modified with a hydrophobic material, thereby forming a wettability gradient with the hydrophilic material to drive sweat flow. The sensing unit includes a first working electrode for detecting lactic acid concentration, a second working electrode for detecting cortisol concentration, and a counter electrode and a reference electrode shared by both. The first and second working electrodes are modified with different sensitive materials to achieve specific and synchronous electrochemical detection of lactic acid and cortisol in sweat. The intelligent analysis and early warning unit is configured to: acquire and process response signals from the first and second working electrodes in real time, generating time-correlated lactic acid concentration data streams and cortisol concentration data streams; execute a built-in multimodal data fusion algorithm, which assesses the user's exercise metabolic load and recovery status based on the lactic acid concentration data stream and assesses the user's neuroendocrine stress level based on the cortisol concentration data stream; and generate comprehensive health status assessment and graded early warning information based on the fusion analysis results of exercise metabolic load and neuroendocrine stress level. Preferably, the flexible patch substrate is made of polydimethylsiloxane, i.e., PDMS.

[0027] The surface of the first working electrode is sequentially modified with nano-nickel oxide for catalyzing the electrochemical oxidation of lactic acid and a Nafion protective layer covering it. The surface of the second working electrode is covalently bound with an aptamer for specifically recognizing cortisol and a Nafion protective layer covering it.

[0028] The hydrophilic material is a composite of polyvinyl alcohol and silica, which is modified in the form of a film layer on the inner surface of the stamen region and the directional flow channels; the hydrophobic material is a self-assembled monolayer of octadecyltrichlorosilane, which is modified in the region between the directional flow channels; the hydrophilic film layer and the hydrophobic monolayer together form a wettability gradient pattern that drives the directional flow of sweat.

[0029] Preferably, the hydrophilic material is prepared by adding 0.5g of polyvinyl alcohol (PVA) to deionized water to make a 6g mixed solution, and then magnetically stirring it in a 90°C water bath until a transparent solution is obtained. Separately, 0.18g of silica is dissolved in 1.8g of anhydrous ethanol and ultrasonically treated until there are no large particles dispersed. Then, the silica mixed solution is added dropwise to the PVA solution and magnetically stirred at room temperature until uniformly dispersed. The hydrophobic material is prepared by mixing 0.4g of octadecyltrichlorosilane (ODTS) with anhydrous ethanol to a final volume of 9.6g, and then adding one drop of 4% (w / w) glacial acetic acid. The special material is coated onto specific areas of PDMS and then slowly dried at low temperature for molding.

[0030] The biomimetic sweat management unit also includes a contact layer bonded to the microchannel layer. The contact layer has a liquid inlet at its center. A directional flow collection channel is arranged circumferentially between the microchannel layer and the contact layer. The ratio of the diameter of the central detection area to the diameter of the directional flow collection channel is (14-17):1. The height of the directional flow collection channel is 1.5-2.5 times its diameter to match the rate of sweating during human exercise, thereby achieving continuous directional enrichment and renewal of sweat. Preferably, the diameter of the central detection area is a circular sweat storage cavity and electrode integration site with a diameter of 5 mm. The height of the directional flow collection channel is 0.6 mm, the diameter of the directional flow collection channel is 0.3 mm, and the diameter of the microchannel layer is 2 cm. Preferably, the material of the contact layer is PDMS.

[0031] The intelligent analysis and early warning unit includes: A low-power acquisition circuit is used to synchronously acquire, amplify, and filter the response signals of the first working electrode and the second working electrode. The microprocessor has an embedded multimodal data fusion algorithm for processing conditioned bimodal sensor data, analyzing it through a built-in lactate and cortisol threshold model, and generating preliminary assessment results of metabolic state and stress level. The wireless communication module is used to output comprehensive health status assessment and graded early warning information generated based on the preliminary assessment results.

[0032] The microprocessor integrates a lactate state assessment module, which includes: The intensity determination function is used to analyze real-time lactate level data. Compared with the preset resting threshold Medium intensity threshold and high intensity threshold Compare the results and output the corresponding exercise intensity level. The metabolic assessment function is used to calculate lactate clearance rate based on continuous time series lactate level data. And based on the clearance rate The metabolic efficiency level is output by comparing the result with a preset efficiency threshold. The risk warning function is used to identify lactic acid levels exceeding a high-intensity threshold in a recently scheduled number of measurements. The frequency of training is determined, and when the frequency reaches a preset risk threshold, an overtraining risk warning signal is generated.

[0033] The microprocessor integrates a cortisol state assessment module, which is configured as follows: The basic state determination function is used to determine the cortisol level data acquired in real time. With the preset exhaustion threshold Lower limit of normal range and upper limit Compare and output the baseline pressure level status; The stress type discrimination function is used to calculate the historical variance of recent cortisol level data. And based on real-time cortisol level data With historical variance The combination of these relationships helps distinguish between acute and chronic stress states.

[0034] The microprocessor also integrates a comprehensive evaluation and early warning module, which is configured as follows: The scoring mapping function is used to assign lactate exercise metabolic levels. and cortisol stress levels Each is mapped to a predefined risk score; The risk calculation function is used to calculate the total risk score. = + ; The early warning decision-making function is used to adjust the overall risk score. It compares the data with multiple preset threshold ranges and triggers corresponding health warning signals and intervention suggestions based on the comparison results.

[0035] The counter electrode contains a platinum nanoparticle modification layer to form a stable electrochemical counter electrode; the reference electrode contains an Ag / AgCl structure to provide a stable reference potential.

[0036] A method for preparing a sensing device for simultaneously monitoring sweat lactic acid and cortisol specifically includes the following steps: S1. A microchannel layer with a flower-like topological structure is prepared as the main body of the biomimetic sweat management unit, and wettability gradient patterns are constructed on its surface by selectively modifying hydrophilic and hydrophobic materials. S2. In the flower-like region of the microfluidic layer, the substrates for the first working electrode, the second working electrode, the counter electrode, and the reference electrode are prepared by microfabrication technology. S3. Functional modification of the electrode substrate: A nano-nickel oxide mixed solution is drop-coated onto the first working electrode and dried to form a modification layer; the second working electrode is aminated and then a cortisol-specific aptamer is fixed; platinum nanoparticles are deposited on the counter electrode by sputtering; a silver layer is electrochemically deposited on the reference electrode and then chlorinated to form an Ag / AgCl system. S4. The functionalized microfluidic layer is combined with a flexible patch substrate, and a microprocessor circuit integrating a multimodal data fusion algorithm is electrically connected to it, and then packaged to obtain a sensing device.

[0037] In use, the sensing device is attached to the surface of human skin, with the contact layer of its biomimetic sweat management unit in direct contact with the skin. Sweat secreted by the skin, driven by a Laplace pressure difference induced by the curvature gradient, spontaneously enters the device through the inlet at the center of the contact layer. Under the action of the wettability gradient formed by the hydrophilic material composed of polyvinyl alcohol and silica composite and the hydrophobic material composed of an octadecyltrichlorosilane self-assembled monolayer, the sweat is rapidly transported directionally along the flower-shaped microchannel layer: first, the sweat is efficiently collected through the petal-shaped directional collection channels; then, it is continuously and stably enriched in the central detection area formed by the stamen region. The specific diameter and channel size ratio of this area ensures that it can maintain an effective detection volume and achieve continuous updating under the rate of sweating during human exercise; the waste liquid after detection is continuously discharged through the circumferential gap between the microchannel layer and the contact layer, thereby avoiding sample accumulation and providing a stable fluid environment for long-term continuous monitoring.

[0038] The sweat sample concentrated in the central detection area comes into direct contact with the multimodal electrochemical sensing unit. The first working electrode, surface-modified with nano-nickel oxide, catalyzes the electrochemical oxidation of lactic acid in the sweat. Simultaneously, the second working electrode, with a cortisol-specific aptamer covalently immobilized on its surface, specifically recognizes and binds to cortisol molecules in the sweat. These electrochemical reactions and biorecognition events modulate the current or potential signals of the first and second working electrodes, respectively. A shared counter electrode modified with platinum nanoparticles and a reference electrode based on an Ag / AgCl system provide a stable and consistent electrochemical environment for these two independent electrochemical detection processes, thus enabling specific, synchronous, and in-situ electrochemical detection of lactic acid and cortisol concentrations in the same sweat sample.

[0039] The intelligent analysis and early warning unit is then activated. Its low-power acquisition circuit simultaneously captures the weak response signals from the first and second working electrodes, and amplifies and filters them. The conditioned dual-modal sensing data is transmitted to the microprocessor, where the embedded multimodal data fusion algorithm begins to run. This algorithm operates based on a preset mathematical model and threshold parameters, through the lactate state assessment module, the cortisol state assessment module, and the comprehensive assessment and early warning module.

[0040] First, the algorithm processes the time-correlated lactate concentration data stream. The lactate status assessment module performs a motion intensity assessment: it evaluates the real-time lactate level. With resting threshold =1.0 mmol / L, moderate intensity threshold =2.0 mmol / L and high intensity threshold When compared with 4.0 mmol / L, if Exercise levels < 2.0 mmol / L are considered low-intensity exercise; if 2.0 mmol / L < <4.0 mmol / L is considered moderate-intensity exercise; if A value >4.0 mmol / L is considered high-intensity exercise. The module then performs an overtraining risk assessment: it counts the frequency of values ​​exceeding 4.0 mmol / L in the last five measurements; if this frequency is greater than or equal to three, an overtraining warning is triggered. Simultaneously, the module performs a metabolic efficiency assessment: it calculates lactate clearance rate based on continuous time-series lactate data. The calculation formula is as follows: in, This represents the change in concentration. For time intervals. If A value < -0.1 mmol / L / min indicates excellent metabolic efficiency; a value < -0.1 mmol / L / min indicates poor metabolic efficiency. A value < -0.05 mmol / L / min is considered good; otherwise, it is considered that improvement is needed.

[0041] Simultaneously, the algorithm performs synchronous processing on the time-correlated cortisol concentration data stream. The cortisol state assessment module will provide real-time cortisol levels. With exhaustion threshold =96.6 nmol / L, lower limit of normal range =138 nmol / L and upper limit =690 nmol / L for comparison. If... <96.6 nmol / L is considered as HPA axis function depletion; if If the concentration is >690 nmol / L, then the historical variance of recent data should be calculated further. ,like >10000 and If the concentration is >897 nmol / L, it is considered acute stress; otherwise, it is considered chronic stress. If the concentration is <138 nmol / L, it is considered chronic stress. A concentration of <690 nmol / L is considered normal.

[0042] After completing the independent bimodal analysis, the comprehensive assessment and early warning module is activated. This module first performs the scoring mapping function: mapping lactate exercise metabolism levels... The risk score is mapped, with "high-intensity exercise triggering an overtraining warning" mapped to 2 points, "high-intensity exercise" mapped to 1 point, and all other cases mapped to 0 points; cortisol stress levels are also mapped. The risk score is mapped to each level, with "functional exhaustion" mapped to 3 points, "chronic stress" to 2 points, "acute stress" to 1 point, and "normal level" to 0 points. Subsequently, the risk calculation function is activated to calculate the total risk score. = + Finally, the early warning decision function triggers the corresponding early warning based on the value of R_total: if ≥3 triggers a red alert, and it is recommended to immediately arrange rest and seek professional help; if =2 triggers an orange alert and recommends establishing a regular sleep schedule and 150 minutes of moderate exercise per week; if =1 triggers a yellow alert and recommends resting for 24 to 48 hours and replenishing fluids and electrolytes; if If the value is 0, there will be no warning, and relaxation activities such as deep breathing exercises will be recommended.

[0043] Ultimately, the comprehensive health status assessment conclusions and graded early warning information generated by the microprocessor are sent to the user's mobile terminal device in real time via the wireless communication module. They are presented in the form of intuitive stress index, fatigue level, and clear risk level and intervention suggestions, thus completing the entire monitoring process from in-situ sweat collection and simultaneous detection of dual biomarkers to intelligent health early warning.

[0044] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0045] The units described in some embodiments of this disclosure can be implemented in software or in hardware. The described units can also be located in a processor, and the functions described above can be performed at least in part by one or more hardware logic components.

[0046] The above description only illustrates the preferred embodiments of the present invention and should not be construed as limiting the scope of the claims. The present invention is not limited to the above embodiments, and variations in its specific structure are permitted. All modifications made within the scope of the independent claims of this invention are also within the scope of protection of this invention.

Claims

1. A sensing device for simultaneously monitoring sweat lactic acid and cortisol, characterized in that, include: Flexible patch substrate; A biomimetic sweat management unit, integrated on the flexible patch substrate, is configured to achieve spontaneous capture of sweat from the skin surface, directional enrichment towards the central detection area, and continuous discharge of waste liquid after detection based on the curvature gradient-induced Laplace pressure difference. A multimodal electrochemical sensing unit is disposed in the central detection area of ​​the biomimetic sweat management unit. It includes a first working electrode for detecting lactic acid concentration, a second working electrode for detecting cortisol concentration, and a counter electrode and a reference electrode shared by the two. The first working electrode and the second working electrode are modified with different sensitive materials to achieve specific and simultaneous electrochemical detection of lactic acid and cortisol in sweat. The intelligent analysis and early warning unit, electrically connected to the multimodal electrochemical sensing unit, is configured as follows: The response signals from the first working electrode and the second working electrode are collected and processed in real time and synchronously to generate time-correlated lactate concentration data streams and cortisol concentration data streams. The built-in multimodal data fusion algorithm is executed, which assesses the user's exercise metabolic load and recovery status based on the lactate concentration data stream, and assesses the user's neuroendocrine stress level based on the cortisol concentration data stream; Based on the fusion analysis results of the exercise metabolic load and neuroendocrine stress level, a comprehensive health status assessment and graded early warning information is generated. The biomimetic sweat management unit includes a microchannel layer with a flower-shaped topology, the stamen region of which constitutes the central detection area, and its inner surface is modified with a hydrophilic material; multiple petal-shaped channels radiating outward from the central detection area constitute directional flow collection channels, and their inner surfaces are also modified with the hydrophilic material. The area between the directional flow channels is modified with a hydrophobic material, thereby forming a wettability gradient together with the hydrophilic material to drive sweat flow.

2. The sensing device for simultaneously monitoring sweat lactic acid and cortisol according to claim 1, characterized in that, The surface of the first working electrode is sequentially modified with nano-nickel oxide for catalyzing the electrochemical oxidation of lactic acid and a Nafion protective layer covering it. The surface of the second working electrode is covalently bound with an aptamer for specifically recognizing cortisol and a Nafion protective layer covering it.

3. The sensing device for simultaneously monitoring sweat lactic acid and cortisol according to claim 1, characterized in that, The hydrophilic material is a composite of polyvinyl alcohol and silica, which is modified in the form of a film layer on the inner surface of the stamen region and the directional flow channels; the hydrophobic material is a self-assembled monolayer of octadecyltrichlorosilane, which is modified in the region between the directional flow channels; the hydrophilic film layer and the hydrophobic monolayer together constitute a wettability gradient pattern that drives the directional flow of sweat.

4. The sensing device for simultaneously monitoring sweat lactic acid and cortisol according to claim 1, characterized in that, The biomimetic sweat management unit also includes a contact layer bonded to the microchannel layer. The contact layer has a liquid inlet at its center. The directional flow collection channel is arranged circumferentially between the microchannel layer and the contact layer. The ratio of the diameter of the central detection area to the flow channel diameter of the directional flow collection channel is (14-17):

1. The height of the directional flow collection channel is 1.5-2.5 times its flow channel diameter to match the rate of sweating during human exercise, thereby achieving continuous directional enrichment and renewal of sweat.

5. The sensing device for simultaneously monitoring sweat lactic acid and cortisol according to claim 1, characterized in that, The intelligent analysis and early warning unit includes: A low-power acquisition circuit is used to synchronously acquire, amplify, and filter the response signals of the first working electrode and the second working electrode. The microprocessor has the multimodal data fusion algorithm embedded in it for processing conditioned bimodal sensor data, analyzing it through a built-in lactate and cortisol threshold model, and generating preliminary assessment results of metabolic state and stress level. The wireless communication module is used to output the comprehensive health status assessment and graded early warning information generated based on the preliminary assessment results.

6. The sensing device for simultaneously monitoring sweat lactic acid and cortisol according to claim 5, characterized in that, The microprocessor integrates a lactate state assessment module, which includes: The intensity determination function is used to analyze real-time lactate level data. Compared with the preset resting threshold Medium intensity threshold and high intensity threshold Compare the results and output the corresponding exercise intensity level. The metabolic assessment function is used to calculate lactate clearance rate based on continuous time series lactate level data. And based on the said clearance rate The metabolic efficiency level is output by comparing the result with a preset efficiency threshold. The risk warning function is used to count lactic acid measurements exceeding the high-intensity threshold in a recently predetermined number of measurements. The frequency of training is such that when the frequency reaches a preset risk threshold, an overtraining risk warning signal is generated.

7. The sensing device for simultaneously monitoring sweat lactic acid and cortisol according to claim 6, characterized in that, The microprocessor integrates a cortisol state assessment module, which is configured to: The basic state determination function is used to determine the cortisol level data acquired in real time. With the preset exhaustion threshold Lower limit of normal range and upper limit Compare and output the baseline pressure level status; The stress type discrimination function is used to calculate the historical variance of recent cortisol level data. And based on real-time cortisol level data With historical variance The combination of these relationships helps distinguish between acute and chronic stress states.

8. The sensing device for simultaneously monitoring sweat lactic acid and cortisol according to claim 7, characterized in that, The microprocessor also integrates a comprehensive evaluation and early warning module, which is configured as follows: The scoring mapping function is used to assign lactate exercise metabolic levels. and cortisol stress levels Each is mapped to a predefined risk score; The risk calculation function is used to calculate the total risk score. = + ; The early warning decision-making function is used to apply the total risk score. It compares the data with multiple preset threshold ranges and triggers corresponding health warning signals and intervention suggestions based on the comparison results.

9. The sensing device for simultaneously monitoring sweat lactic acid and cortisol according to claim 1, characterized in that, The counter electrode comprises a platinum nanoparticle modification layer to form a stable electrochemical counter electrode; the reference electrode comprises an Ag / AgCl structure to provide a stable reference potential.

10. A method for preparing a sensing device for simultaneously monitoring sweat lactic acid and cortisol, characterized in that, The preparation of a sensing device for simultaneously monitoring sweat lactic acid and cortisol as described in any one of claims 1-9 specifically includes the following steps: S1. A microchannel layer with a flower-like topological structure is prepared as the main body of the biomimetic sweat management unit, and wettability gradient patterns are constructed on its surface by selectively modifying hydrophilic and hydrophobic materials. S2. In the stamen region of the microchannel layer, a substrate for the first working electrode, the second working electrode, the counter electrode, and the reference electrode is prepared by microfabrication technology. S3. Functional modification of the electrode substrate: drop-coating a nano-nickel oxide mixed solution onto the first working electrode and drying it to form a modification layer; amidizing the second working electrode and immobilizing the cortisol-specific aptamer; depositing platinum nanoparticles on the counter electrode by sputtering; electrochemically depositing a silver layer on the reference electrode and chlorinating it to form an Ag / AgCl system; S4. The microchannel layer with completed functional modification is combined with a flexible patch substrate, and the microprocessor circuit integrating the multimodal data fusion algorithm is electrically connected to it, and the sensing device is encapsulated.

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