A QEPAS-based dynamic monitoring system and method for end-tidal carbon dioxide during exercise

Abstract

This invention claims protection for a dynamic monitoring system and method for end-tidal carbon dioxide during exercise based on QEPAS, relating to the field of dynamic monitoring of human physiological parameters and gas sensing technology. It includes: a breathing interface, a gas sampling flow path, an acoustic detection module, an optical excitation module, and a signal processing and control module. The breathing interface is connected to the gas sampling flow path, which is connected to the acoustic detection module. The optical excitation module is positioned facing the acoustic detection module, and the signal output terminal of the acoustic detection module is connected to the signal processing and control module. The invention aims to solve the problems of poor portability, slow response, weak resistance to moisture interference, and inability to achieve dynamic continuous monitoring in existing technologies. By optimizing the laser absorption spectrum, the core acoustic detection module, and the control algorithm, this invention achieves highly sensitive, highly stable, and real-time online monitoring of carbon dioxide concentration in the human body during exercise. This invention provides a highly promising innovative solution for fields such as sports health management, chronic disease rehabilitation, and smart healthcare.

Description

Technical Field

[0001] This invention relates to the field of dynamic monitoring of human physiological parameters and gas sensing technology, and in particular to a method for detecting end-tidal carbon dioxide based on Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) technology. This invention relates to a dynamic monitoring system and its application in sports medicine, personalized health management, and smart healthcare. Specifically, it aims to address the issue of human exhalation during exercise. The technical challenges of real-time, accurate, and portable concentration monitoring. Background Technology

[0002] End-tidal carbon dioxide (CCO2) Dynamic monitoring (DMR) is a key non-invasive physiological indicator for assessing human respiratory, circulatory, and metabolic functions. In clinical medicine, it is widely used for ventilator adjustment during surgery and anesthesia, evaluation of cardiopulmonary resuscitation effectiveness, and metabolic analysis in intensive care. In recent years, with the increasing demand for personalized health management and exercise rehabilitation, dynamic monitoring before, during, and after exercise has become increasingly important. The changes are of great value in assessing an individual's cardiopulmonary function adaptability to exercise load, optimizing training programs, and guiding the rehabilitation of chronic diseases.

[0003] However, the existing In practical applications, especially in scenarios requiring portable, real-time, and dynamic monitoring, monitoring technologies suffer from the following core shortcomings:

[0004] 1) Limitations of traditional detection methods: While the mainstream nondispersive infrared (NDIR) spectroscopy method is technically mature, its systems are bulky, power-consuming, and have relatively slow response speeds. Furthermore, it is highly susceptible to interference from water vapor in exhaled breath, leading to decreased measurement accuracy. This makes it difficult to integrate into wearable or portable devices, and thus cannot meet the needs for continuous, dynamic monitoring of individuals during movement.

[0005] 2) Lack of dynamic response analysis capabilities: Existing methods are mostly single-point or static measurements, making it difficult to capture in real time the rapid changes in the respiratory and circulatory systems under exercise stress. During high-intensity exercise, the body's metabolic products... The respiratory rate, respiratory rate, and tidal volume all change drastically, requiring a high-temporal-resolution, high-sensitivity monitoring system to characterize them. Transient fluctuation characteristics.

[0006] 3) Insufficient resistance to environmental interference: During exercise, the temperature and humidity of the exhaled air change drastically. The detection signal of traditional optical methods (such as NDIR) is easily affected by the overlapping of water vapor absorption spectral lines, resulting in large deviations in the measurement results and making it difficult to guarantee the stability of the system.

[0007] Therefore, there is an urgent need to develop a new type of instrument with high sensitivity, fast response speed, strong resistance to humidity interference, miniaturization, and low power consumption. Dynamic monitoring systems and methods are proposed to fill the current technological gap in the field of real-time monitoring of exercise physiology and personalized health management.

[0008] A search revealed that although application publication number CN121558634A falls under the same category as this patent in quartz-enhanced photoacoustic spectroscopy technology, there are substantial differences between the two in terms of technical field, detection object, technical problem solved, core technical solution, application process, and technical effect. CN121558634A focuses on the detection and tracing of gas leaks in industrial pipelines, emphasizing backend algorithm optimization such as dynamic data segmentation, feature extraction, dynamic thresholding, and leak tracing. CN121558634A does not disclose the water vapor-resistant absorption spectrum, laser and gas path-specific operating parameters, and the standardized three-stage monitoring process of resting-exercise-recovery designed for monitoring carbon dioxide at the end of respiration in active human subjects, nor does it include the algorithm for extracting cardiopulmonary function-related feature parameters. Furthermore, due to the significant differences in application scenarios and technical improvement directions between the two, it is difficult for those skilled in the art to gain inspiration for technical integration from this prior art document. Summary of the Invention

[0009] This invention aims to solve the problems of the prior art mentioned above. It proposes a dynamic monitoring system and method for end-tidal carbon dioxide during exercise based on QEPAS. The technical solution of this invention is as follows:

[0010] A dynamic end-tidal carbon dioxide monitoring system based on QEPAS (Quality-Effective Particulate Air) includes: a breathing interface, a gas sampling path, an acoustic detection module, an optical excitation module, and a signal processing and control module. The breathing interface is connected to the gas sampling path, which is connected to the acoustic detection module. The optical excitation module is positioned facing the acoustic detection module, and the signal output terminal of the acoustic detection module is connected to the signal processing and control module.

[0011] The breathing interface is used to collect the subject's exhaled air as an airway inlet, ensuring that the exhaled air is introduced into the sampling flow path without damage.

[0012] The gas sampling flow path is used to control the flow rate and drive the exhaled gas in one direction, prevent external gas backflow interference, and ensure that the airflow is smoothly delivered into the acoustic detection module at a constant flow rate.

[0013] The acoustic detection module is used to receive the photoacoustic signal generated by laser-excited CO2, and convert it into an electrical signal through the resonance of a quartz tuning fork to achieve high-sensitivity detection of weak sound waves.

[0014] The optical excitation module is used to generate a laser beam with precise wavelength and stable modulation to selectively excite target CO2 molecules and provide an excitation source for the photoacoustic effect.

[0015] The signal processing and control module is used to demodulate, acquire, and invert the concentration of electrical signals, and to control the system's operating parameters to achieve real-time dynamic monitoring and data storage and analysis.

[0016] Furthermore, the gas sampling flow path is connected in series with a one-way valve, a needle valve, and a diaphragm pump, with the constant airflow velocity set to 80 sccm. The one-way valve is used to prevent backflow of external gas. The needle valve is used to precisely adjust the gas flow rate by changing the flow cross-sectional area to control the flow velocity. The diaphragm pump is used to provide active suction power, continuously drawing exhaled gas from the breathing interface into the acoustic detection module. The needle valve and the diaphragm pump work together to ensure unidirectional and stable airflow delivery.

[0017] Furthermore, the optical excitation module includes a distributed feedback laser and a function signal generator; the distributed feedback laser has a center wavelength of 2.004 μm and a maximum continuous output power of approximately 3 mW; the function signal generator outputs a modulation signal consisting of a superimposed sine wave and a sawtooth wave, the sawtooth wave being used to scan the target absorption spectrum, and the sine wave frequency being precisely aligned with the resonance frequency of the quartz tuning fork within the acoustic detection module.

[0018] Furthermore, the operating current of the distributed feedback laser is 80 mA, the operating temperature is 25 °C, and the laser modulation depth is fixed at 11 mA; the sine wave frequency is consistent with the 32.768 kHz resonance frequency of the quartz tuning fork in the acoustic detection module.

[0019] Furthermore, the acoustic detection module integrates a quartz tuning fork and a pair of resonant tubes. The quartz tuning fork converts the weak acoustic wave signal generated by the photoacoustic effect into an electrical signal, and uses its ultra-high quality factor to amplify and enhance the resonant frequency signal, thereby improving detection sensitivity and signal-to-noise ratio. The pair of resonant tubes form an acoustic resonant cavity on both sides of the quartz tuning fork, enhancing the accumulation of standing wave energy of the photoacoustic signal, improving the coupling efficiency between the acoustic wave and the tuning fork, and further amplifying the weak signal. The system selects the carbon dioxide absorption line with a wavenumber of 4989.967 cm⁻¹ as the detection target spectral line based on the HITRAN spectral database. This spectral line has moderate intensity and is not affected by nearby water vapor absorption peaks.

[0020] Furthermore, the signal processing and control module sequentially includes a preamplifier, a lock-in amplifier, a data acquisition card, and a main control computer. The preamplifier is used to perform primary amplification and impedance matching on the weak electrical signal output by the quartz tuning fork to improve the signal amplitude. The lock-in amplifier is used to demodulate the signal using the second harmonic (2f) to extract the target frequency component and improve the signal-to-noise ratio. The data acquisition card is used to convert the analog signal demodulated by the lock-in amplifier into a digital signal and transmit it to the main control computer. The main control computer is used to run the control software to realize laser parameter setting, concentration inversion calculation, real-time dynamic curve plotting, and data storage and management.

[0021] A method for dynamic monitoring of end-tidal carbon dioxide during exercise using any of the systems described above, comprising the following steps:

[0022] (1) The subject exhales through the breathing interface, and the exhaled gas is sent into the acoustic detection module at a constant flow rate of 80 sccm through the gas sampling flow path;

[0023] (2) The optical excitation module outputs a modulated laser with a center wavelength of 2.004 μm. The laser interacts with CO2 molecules in the acoustic detection module to generate a photoacoustic signal, which is converted into an electrical signal by a quartz tuning fork.

[0024] (3) The electrical signal is amplified by the preamplifier, demodulated by the lock-in amplifier, and acquired by the data acquisition card before being transmitted to the main control computer. The carbon dioxide concentration is then converted in real time according to the preset linear relationship.

[0025] Furthermore, the monitoring process is divided into three stages: the first stage involves collecting carbon dioxide concentration at rest for 300 seconds to establish a resting baseline; the second stage involves collecting carbon dioxide concentration during moderate-intensity exercise for 600 seconds; and the third stage involves collecting carbon dioxide concentration during the recovery period after exercise for 600 seconds, forming a complete dynamic monitoring process of "resting-exercise-recovery".

[0026] Furthermore, the main control computer converts the demodulated signal into carbon dioxide concentration based on a preset linear relationship: concentration = k × 2f signal amplitude + b, where k is a sensitivity coefficient obtained from concentration calibration experiments, reflecting the concentration change per unit signal; 2f signal amplitude is the intensity of the second harmonic signal demodulated by the lock-in amplifier; b is the baseline intercept, representing the background signal offset at zero concentration; a dynamic curve is generated in real time with time as the horizontal axis and concentration as the vertical axis; the main control computer has a built-in feature extraction algorithm module that performs the following processing: first, the time-domain concentration signal is filtered and denoised using a moving average; second, the monitoring period is automatically divided into resting period, activity period, and recovery period based on preset time axis labels; then, an algorithm combining segmented statistics and adaptive threshold detection is used—during the resting period, the concentration values ​​within the window are arithmetically averaged to output the resting EtCO2 baseline value; during the activity period, a sliding window peak detection is used to track the maximum concentration value and its occurrence time; during the recovery period, the curve is located by threshold comparison and linear interpolation with the resting baseline as a reference, decreasing to "baseline value + 10% × (peak value − The time point corresponding to the "baseline value" is calculated, and the time required to recover to 90% of the baseline is recorded as the time required. Finally, three types of characteristic parameters are output: the average resting concentration, the peak concentration during exercise, and the time required to recover to 90% of the baseline, thus completing the quantitative analysis of physiological indicators.

[0027] The advantages and beneficial effects of this invention are as follows:

[0028] 1) High sensitivity and low detection limit: Utilizing the high quality factor of the QTF (experimentally measured Q≈10240), weak photoacoustic signals are resonantly amplified and detected, achieving high sensitivity and low detection limit. Extremely high sensitivity for concentration detection, with a detection limit as low as 49.46 ppm, sufficient to accurately capture moving particles. To achieve precise quantitative analysis of minute changes.

[0029] 2) Excellent resistance to water vapor interference: Through careful selection of the location at 2.004μm (corresponding to wavenumber 4989.967) )of The absorption spectrum effectively avoids the strong absorption peaks of water vapor, the main interfering component in exhaled gas. This allows the system to maintain high measurement accuracy and long-term stability even in complex environments with drastic humidity changes before and after exercise, which is difficult to achieve with traditional NDIR methods.

[0030] 3) Rapid dynamic response and real-time monitoring capabilities: QEPAS technology is essentially a spectroscopic method. Combined with an optimized gas flow path (80 sccm), the system has a fast response speed, enabling real-time tracking in milliseconds to seconds (reflected in: response time: the system can complete concentration response within seconds (combined with an 80 sccm flow rate); humidity resistance: the measurement deviation is less than 1% when the humidity of exhaled gas changes by 30%; detection limit: 49.46 ppm (far lower than the typical exhaled CO2 concentration of 3-5%)). This allows it to perfectly capture the effects of exercise stress. Rapid dynamic changes in concentration (such as the rise from resting to exercise state) provide a high-temporal-resolution analytical tool for exercise physiology research.

[0031] 4) System miniaturization and low power consumption potential: QEPAS technology eliminates the need for long-path absorption cells required by traditional spectrometers, and the core detector ADM is compact in size. Combined with DFB lasers and integrated circuits, this system can be easily developed into portable and even wearable devices, providing key technical support for personalized health management in scenarios such as home, sports venues, and outdoors.

[0032] 5) Improved Dynamic Monitoring and Evaluation Methods: This invention not only provides a high-performance sensor but also proposes a complete three-stage dynamic monitoring process and data analysis method based on "resting-movement-recovery." Through this method, data can be obtained... By analyzing the changes throughout the entire exercise process and extracting core indicators for quantitatively assessing an individual's cardiopulmonary function, metabolic intensity, and exercise recovery ability, we provide a new data dimension and basis for scientifically formulating exercise prescriptions, evaluating rehabilitation effects, and providing early warnings of exercise risks.

[0033] 6) Excellent linearity: The system exhibits excellent linearity. The concentration response exhibits a high degree of linearity (R²=0.999), simplifying the concentration calibration and calculation process and ensuring the accuracy and reliability of the measurement results.

[0034] In summary, this invention successfully solves the problem of portable [devices] in the prior art. This monitoring addresses key pain points and provides a promising and innovative solution for fields such as sports and health management, chronic disease rehabilitation, and smart healthcare. Attached Figure Description

[0035] Figure 1 The preferred embodiment of this invention is based on QEPAS technology. Schematic diagram of dynamic monitoring system structure;

[0036] Figure 2 This is the frequency response curve of a quartz tuning fork (QTF).

[0037] Figure 3 yes and Simulated absorption spectrum (4989.0 - 4991.0) );

[0038] Figure 4 It is a graph showing the relationship between the system signal and the modulation depth;

[0039] Figure 5 This is a graph showing the test results of the system concentration response linearity versus the detection limit;

[0040] Figure 6 It is a dynamic curve of exercise-induced respiration. Detailed Implementation

[0041] The technical solutions of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings of the embodiments. The described embodiments are merely some embodiments of the present invention.

[0042] The technical solution of the present invention to solve the above-mentioned technical problems is:

[0043] The purpose of this invention is to provide a dynamic monitoring system and method for end-tidal carbon dioxide during exercise based on quartz-enhanced photoacoustic spectroscopy (QEPAS), aiming to solve the problems of poor portability, slow response, weak resistance to moisture interference, and inability to achieve dynamic continuous monitoring in existing technologies. This invention achieves dynamic monitoring of the human body during movement by optimizing the laser absorption spectrum, the core acoustic detection module, and the control algorithm. Highly sensitive, highly stable, and real-time online monitoring of concentration.

[0044] The technical solution of the present invention specifically includes the following steps:

[0045] 1. System Construction: Building a system based on QEPAS technology Miniaturized dynamic monitoring system

[0046] The system mainly includes: a breathing interface, a gas sampling flow path, an acoustic detection module (ADM), an optical excitation module, and a signal processing and control module. Its core connections and workflow are as follows:

[0047] 1) Breathing Interface and Gas Flow Path: The subject exhales through the breathing interface (such as a face mask or mouthpiece). The exhaled air is delivered to the ADM at a constant flow rate (e.g., 80 sccm, or standard milliliters per minute) via a one-way valve, needle valve, and diaphragm pump. The one-way valve prevents backflow of external gas, and the needle valve and pump work together to ensure stable airflow.

[0048] 2) Optical Excitation Module: A distributed feedback (DFB) laser with a center wavelength of 2.004 μm is used as the excitation source. This laser is controlled by a high-precision drive module, with the operating current and temperature stably controlled at preset values ​​(e.g., 80 mA and 25 °C) to ensure the stability of the output wavelength. A function signal generator produces a modulation signal consisting of a superposition of a sine wave and a sawtooth wave to drive the laser. The sawtooth wave is used to scan the target absorption spectrum, and the sine wave frequency is precisely aligned with the resonant frequency (e.g., 32.768 kHz) of the subsequent quartz tuning fork (QTF).

[0049] 3) Acoustic Detection Module (ADM): The ADM is the core of the system, integrating a quartz tuning fork (QTF) with a high quality factor (Q value) and a pair of resonant tubes. The modulated laser beam passes through the ADM and interacts with the incoming gas inlet. Molecular interactions. When the laser wavelength is aligned... At the absorption peak, gas molecules absorb light energy and generate a sound wave of the same frequency through non-radiative relaxation. This sound wave drives the QTF to produce a piezoelectric effect at its resonant frequency, converting the weak sound wave signal into an electrical signal.

[0050] 4) Signal Processing and Control Module: The weak electrical signal output by the QTF is amplified by a preamplifier and then sent to a lock-in amplifier for second harmonic (2f) demodulation to extract a signal proportional to the gas concentration. The demodulated signal is then sent to the main control computer via a data acquisition card (DAQ). The main control computer runs LabVIEW or similar control software to perform laser parameter setting, signal acquisition, real-time concentration inversion, dynamic curve plotting, and data storage.

[0051] 2. Optimized selection of absorption lines to eliminate water vapor interference.

[0052] To address the high humidity of exhaled breath during exercise, this invention, based on the HITRAN spectral database, simulates typical exhaled breath components (e.g., 4%). 6.2% In this context, The absorption spectrum in the 2.0 μm band. Through comparative analysis, the value at 4989.967 was selected. place The absorption line serves as the target monitoring spectral line. This line has moderate intensity and does not overlap with the absorption peaks of nearby water vapor, thus minimizing the interference of humidity changes on measurement accuracy from the source of spectral selection and enhancing the system's stability in real-world, complex respiratory environments.

[0053] 3. Optimize key system parameters to improve signal-to-noise ratio.

[0054] To achieve optimal detection performance, this invention systematically optimized the core modulation parameter—laser modulation depth. Under standard atmospheric pressure, a concentration of 500 ppm... Using nitrogen as a standard gas, the amplitude of the sinusoidal modulation signal was gradually increased from 8 mA to 13 mA in 0.5 mA increments. The 2f signal amplitude and noise level (1σ) at each modulation depth were recorded, and the signal-to-noise ratio (SNR) was calculated. Experimental results show that the 2f signal amplitude first increases and then decreases as the modulation depth increases. At a modulation depth of 11 mA, the system achieves the largest 2f signal response and the optimal SNR. Therefore, 11 mA is determined as the optimal operating modulation depth for the system of this invention.

[0055] 4. System Calibration and Performance Evaluation

[0056] 1) Linearity calibration: Introduce different concentrations (0 to several thousand ppm) of [unspecified substance] into the system. Standard gas (background gas is high purity) Record the stable output signal at each concentration. For example... Figure 5 As shown, the system response and... are obtained through linear fitting. The concentrations showed a highly linear relationship, with a linear correlation coefficient R² ≈ 0.999. This provides a reliable basis for subsequent accurate quantitative calculations of concentrations.

[0057] 2) Detection limit determination: Introduce 550 ppm into the system. / Using standard gas, the system output signal was measured to be 41.2 μV, with a noise level of 3.4 μV, resulting in a calculated signal-to-noise ratio (SNR) of 11.12. Extrapolating from the 1σ noise level, the detection limit of this system is 49.46 ppm. This is significantly lower than that of human exhaled air. The typical concentration (~3-5%) fully demonstrates that the system possesses extremely high sensitivity and can accurately capture... Tiny fluctuations.

[0058] 5. Methods for dynamic monitoring of respiration during exercise

[0059] This invention proposes a standardized dynamic monitoring procedure for exercise-induced respiration, used to acquire and analyze the respiratory rate of subjects throughout the entire cycle of rest, exercise, and recovery. Pattern of change:

[0060] Phase 1: Resting baseline measurement: Subjects maintained steady breathing at rest, and the system continuously collected their exhaled breaths. Concentration data is continuously collected for a first preset time period (e.g., 300 seconds) to establish the individual's resting state. Baseline value (e.g., approximately 3.7%).

[0061] Phase Two: Real-time Monitoring During Exercise: Subjects begin exercise of a preset intensity (e.g., moderate intensity) (e.g., running). The system continuously collects data during the exercise. Data is recorded continuously for a second preset time period (e.g., 600 seconds). Real-time recording is performed to track the effects of increased metabolism and enhanced respiratory drive. The concentration increases dynamically (e.g., from 3.8% to 4.8%).

[0062] Phase 3: Post-exercise recovery monitoring: After exercise, the subject immediately returned to a resting state, and the system continued to continuously collect their exhaled breaths. Data is recorded continuously for a third preset time period (e.g., 600 seconds). The concentration slowly decreases from its peak and eventually approaches the resting baseline, completing the recovery process.

[0063] 6. Data Processing and Analysis Algorithms

[0064] The main control computer has built-in analysis algorithms that can process the collected dynamic data in real time.

[0065] 1) Real-time concentration inversion: Based on a pre-calibrated linear relationship (concentration = k * 2f signal amplitude + b), the 2f signal demodulated by the lock-in amplifier is converted in real time into... Concentration value.

[0066] 2) Dynamic curve plotting: with time as the horizontal axis, Concentration is used as the ordinate, generated in real time. The dynamic change curve visually displays the entire process from rest, exercise to recovery.

[0067] Feature parameter extraction: The algorithm can automatically identify and extract key physiological feature parameters, including but not limited to: resting position. Average value, peak motion The time required to recover to 90% of baseline after exercise (recovery rate index) and other metrics provide quantitative evidence for assessing individual cardiopulmonary function and exercise load.

[0068] Among them, the appendix Figure 1 For QEPAS-based technology Schematic diagram of dynamic monitoring system structure;

[0069] Figure 1 The diagram clearly shows the breathing interface, gas sampling flow path (including one-way valve, needle valve, and pump), core component ADM (containing QTF), optical excitation module (DFB laser, signal generator), signal processing module (preamplifier, lock-in amplifier, DAQ card), and main control computer (including control and data analysis software). Arrows indicate the direction of gas flow and electrical signal transmission.

[0070] Appendix Figure 2 The frequency response curve of a quartz tuning fork (QTF);

[0071] Figure 2 In the graph: the horizontal axis represents the modulation frequency (kHz), and the vertical axis represents the QTF response signal amplitude. The curve shows the center resonant frequency f0 = 32.764 kHz, the full width at half maximum (FWHM) = 3.2 Hz, and the formula for calculating the quality factor Q is indicated.

[0072] Appendix Figure 3 for and Simulated absorption spectrum (4989.0 - 4991.0) );

[0073] Figure 3 In the middle: the horizontal axis represents the wave number ( The vertical axis represents absorption intensity. The value at 4989.967 in the figure is... A clear view is displayed there. It has an independent absorption peak, with no obvious water vapor absorption peaks nearby interfering.

[0074] Appendix Figure 4 A graph showing the relationship between system signal and modulation depth;

[0075] Figure 4 In the graph: the horizontal axis represents modulation depth (mA), and the vertical axis represents QEPAS signal amplitude (au). The curve shows that the signal first increases and then decreases with modulation depth, reaching a peak at 11 mA, marking the optimal modulation depth point.

[0076] Appendix Figure 5 The graph shows the test results of the system concentration response linearity versus the detection limit.

[0077] Figure 5 The image contains two sub-figures. (a) The figure shows different concentrations. The system's real-time response curve is shown below; (b) The figure shows the average signal amplitude and... The linear fitting curve of the concentration is shown, and the fitting equation and R² value are labeled.

[0078] Appendix Figure 6 Dynamic curve of exercise-induced respiration

[0079] Figure 6 In the middle: the horizontal axis represents time (seconds), and the vertical axis represents... Concentration (%). The curve is clearly divided into three stages: 0-300s resting period (concentration stabilizes at about 3.7%), 300-900s activity period (concentration rises from 3.8% to 4.8%), and 900-1500s recovery period (concentration slowly decreases).

[0080] It should be noted that the user information (including but not limited to user device information, personal user information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the laws, regulations and standards of relevant countries and regions.

[0081] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions.

[0082] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0083] The above embodiments should be understood as illustrative only and not as limiting the scope of protection of the present invention. After reading the description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A dynamic monitoring system for end-tidal carbon dioxide during exercise based on QEPAS, characterized in that, include: Breathing interface, gas sampling flow path, acoustic detection module, optical excitation module, signal processing and control module; The breathing interface connects to the gas sampling flow path, which in turn connects to the acoustic detection module. The optical excitation module is positioned facing the acoustic detection module, and the signal output of the acoustic detection module is connected to the signal processing and control module. The breathing interface collects the subject's exhaled gas as an inlet, ensuring the exhaled gas is introduced into the sampling flow path without damage or contamination. It is a key connection between the system and the human body. The gas sampling flow path controls the flow rate and drives the exhaled gas in one direction, preventing backflow interference from external gases and ensuring a constant flow rate to the acoustic detection module, guaranteeing consistent detection. The acoustic detection module receives the photoacoustic signal generated by laser-excited CO2, converting it into an electrical signal through quartz tuning fork resonance, achieving high sensitivity and high signal-to-noise ratio detection of weak sound waves. The optical excitation module generates a precisely wavelength-precise and stable-modulated laser beam to selectively excite target CO2 molecules, providing a light source for the photoacoustic effect and serving as the excitation source for quantitative concentration measurement. The signal processing and control module demodulates, collects, and inverts the concentration of the electrical signal, and controls system operating parameters to achieve real-time dynamic monitoring, data visualization, and storage analysis.

2. The system according to claim 1, characterized in that, The gas sampling flow path is connected in series with a one-way valve, a needle valve, and a diaphragm pump. The constant airflow velocity is set to 80 sccm. The one-way valve is used to prevent backflow of external gas. The needle valve is used to precisely adjust the gas flow rate by changing the flow cross-sectional area to control the flow rate and achieve flow setting in conjunction with pressure monitoring. The diaphragm pump is used to provide active suction power to continuously draw exhaled gas from the interface into the detection module, ensuring unidirectional and stable airflow delivery. The needle valve and diaphragm pump work together to ensure stable airflow.

3. The system according to claim 1, characterized in that, The optical excitation module includes a distributed feedback laser and a function signal generator. The center wavelength of the distributed feedback laser is 2.004 μm. The function signal generator outputs a modulation signal consisting of a superimposed sine wave and a sawtooth wave. The sawtooth wave is used to scan the target absorption spectrum, and the frequency of the sine wave is precisely aligned with the resonance frequency of the subsequent quartz tuning fork (QTF).

4. The system according to claim 3, characterized in that, The distributed feedback laser operates at a current of 80 mA and a temperature of 25 °C, with a fixed modulation depth of 11 mA. The sine wave frequency is consistent with the 32.768 kHz resonant frequency of the quartz tuning fork in the acoustic detection module.

5. The system according to claim 1, characterized in that, The acoustic detection module integrates a quartz tuning fork and a pair of resonant tubes. The quartz tuning fork is used to convert the weak acoustic wave signal generated by the photoacoustic effect into an electrical signal, and to amplify and enhance the resonant frequency signal using its ultra-high quality factor (Q≈10240), thereby improving detection sensitivity and signal-to-noise ratio. The pair of resonant tubes are used to form an acoustic resonant cavity on both sides of the quartz tuning fork, enhancing the accumulation of standing wave energy of the photoacoustic signal, improving the coupling efficiency between the sound wave and the tuning fork, and further amplifying the weak signal. The system selects a wavenumber of 4989.

967. The carbon dioxide absorption spectrum is used as the target spectrum for detection.

6. The system according to claim 1, characterized in that, The signal processing and control module includes a preamplifier, a lock-in amplifier, a data acquisition card, and a main control computer. The preamplifier is used to amplify and impedance match the weak electrical signal output by the quartz tuning fork to increase the signal amplitude and facilitate subsequent circuit processing. The lock-in amplifier is used to demodulate the second harmonic of the signal. The data acquisition card is used to convert the analog signal demodulated by the lock-in amplifier into a digital signal and transmit it to the main control computer. The main control computer is used to run the control software to realize laser parameter setting, concentration inversion calculation, real-time dynamic curve plotting, and data storage and management.

7. A method for dynamic monitoring of end-tidal carbon dioxide during exercise using the system described in any one of claims 1-6, characterized in that, Includes the following steps: (1) The subject exhales through the breathing interface, and the exhaled gas is sent into the acoustic detection module at a constant flow rate through the gas sampling flow path; (2) The optical excitation module outputs modulated laser, which interacts with the gas to generate a photoacoustic signal, which is then converted into an electrical signal by the acoustic detection module; (3) The electrical signal is amplified, demodulated and acquired and then transmitted to the main control computer to complete the real-time acquisition of carbon dioxide concentration.

8. The method according to claim 7, characterized in that, The monitoring process was divided into three stages: the first stage involved collecting carbon dioxide concentration data for 300 seconds at rest to establish a resting baseline; the second stage involved collecting carbon dioxide concentration data for 600 seconds during exercise; and the third stage involved collecting carbon dioxide concentration data for 600 seconds during the recovery period after exercise.

9. The method according to claim 7, characterized in that, The main control computer converts the demodulated signal into carbon dioxide concentration based on a preset linear relationship: concentration = k * 2f signal amplitude + b, where k is a sensitivity coefficient obtained from concentration calibration experiments, reflecting the concentration change per unit signal; 2f signal amplitude is the intensity of the second harmonic signal demodulated by the lock-in amplifier; and b is the baseline intercept, representing the background signal offset at zero concentration, plotted on the x-axis as time. Concentration is used as the ordinate, generated in real time. The dynamic change curve employs feature extraction and threshold detection algorithms to calculate the mean of the resting segment signal, find the peak of the sliding window during the movement segment, and locate the time point when the recovery segment signal drops to 90% of the baseline value. This enables the automatic quantitative extraction of physiological indicators, extracting three types of feature parameters: the average resting concentration, the peak movement concentration, and the duration corresponding to the recovery to 90% of the baseline, thus completing the quantitative analysis of physiological indicators.

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