CN219070299U - Human respiration monitoring system and intelligent mask - Google Patents

Abstract

The utility model provides a human body respiration monitoring system and an intelligent mask, which belong to the technical field of respiration monitoring, wherein the human body respiration monitoring system comprises a sensor, and the sensor is of a flexible LSMO film structure and is used for responding signals for making resistance changes to human body respiration; the sensor also comprises a flexible test module or a signal processing module, wherein the flexible test module is connected with the sensor in a wired manner and is used for analyzing response signals of resistance changes; the signal processing module is connected with the sensor in a wired way and is used for converting the response signal of the resistance change into a digital signal. The intelligent mask comprises a mask body and the human body respiration monitoring system, and the sensor is arranged on the inner side of the mask body. The human body respiration monitoring system adopts the flexible LSMO film structure as the sensor, and the response signals of the LSMO film have the distinguishing capability on different respiration states, so that the health condition of a user can be reflected more accurately.

Description

Human respiration monitoring system and intelligent mask

Technical Field

The utility model belongs to the technical field of respiratory monitoring, and particularly relates to a human respiratory monitoring system.

Background

The lungs are an important organ of the human body and the respiratory status may be directly indicative of the health condition, however, in many cases respiratory parameters lack of attention, which may mean some underlying diseases. For example, respiratory rate is a valuable indicator for determining clinical deterioration, respiratory rate is higher than 27 minutes ^-1 Is a predictive signal of cardiopulmonary arrest and therefore respiratory parameters play an important role in clinical decisions. Respiratory abnormalities, including imperceptible respiratory rate change (RRV) and sudden shortness of breath, are indicative of life threatening changes in physiological status, and therefore a continuous, long-term, accurate respiratory monitoring scheme is of great value for clinical diagnosis and daily prophylaxis.

Typically, the patient's respiratory rate is determined by manual counting by medical personnel, and discrete respiratory rate values obtained by manual counting do not provide continuous, detailed respiratory information, and are subjective and biased. The traditional hospital respiration monitoring method has the defects of huge equipment, high cost, professional operation and the like, and has great defects for common people.

In recent years, the technical feasibility of monitoring the respiratory condition of the human body using an intelligent mask has become a research hotspot (CN 113576454A, CN 114569908A). The mask can realize continuous monitoring, even can foresee possible abnormality of human respiratory system, and has important significance in clinical treatment and daily prevention.

In the process of realizing the utility model, the inventor finds that the existing intelligent mask has at least the following defects:

the respiration monitoring sensor of the existing intelligent mask is a capacitive sensor, a piezoelectric sensor or a resistive sensor, wherein the response capability of the capacitive sensor and the piezoelectric sensor to respiration details is insufficient, and the resolution capability of waveforms generated by the intelligent mask prepared based on the sensor to different respiration states is still insufficient.

Disclosure of Invention

Based on the background problem, the utility model aims to provide a human body respiration monitoring system, which adopts a flexible LSMO film structure as a sensor, and response signals of the LSMO film have distinguishing capability on different respiration states, so that the health condition of a user is reflected more accurately; another object of the utility model is to provide an intelligent mask.

In order to achieve the above object, on the one hand, the technical scheme provided by the embodiment of the utility model is as follows:

a human breath monitoring system comprising:

the sensor is of a flexible LSMO film structure and is used for responding signals for making resistance change to human respiration; further comprises:

the flexible testing module is connected with the sensor in a wired way and is used for analyzing a response signal of resistance change; or alternatively, the first and second heat exchangers may be,

and the signal processing module is connected with the sensor in a wired way and is used for converting the response signal of the resistance change into a digital signal.

In one embodiment, the flexible LSMO thin film structure is composed of an inorganic substrate and La deposited on the surface of the inorganic substrate _0.7 Sr _0.3 MnO ₃ Film composition.

Further, the inorganic substrate is mica.

In one embodiment, the inorganic substrate has a thickness of 20-50 μm.

In one embodiment, the La _0.7 Sr _0.3 MnO ₃ The thickness of the film is 100-200nm.

In one embodiment, the flexible test module is AES-4SD.

In one embodiment, the signal processing module is model ES20B.

In one embodiment, the human breath monitoring system further comprises:

and the intelligent terminal is connected with the flexible test module in a wired mode or connected with the signal processing module in a wireless mode and is used for displaying and storing monitoring data.

On the other hand, the embodiment of the utility model provides an intelligent mask which comprises a mask body and the human body respiration monitoring system.

Further, the sensor is arranged on the inner side of the mask body.

Compared with the prior art, the embodiment of the utility model has at least the following effects:

1. the human body respiration monitoring system adopts the flexible LSMO film structure as the sensor, and the response signals of the LSMO film have the distinguishing capability on different respiration states, so that the health condition of a user is reflected more accurately, and the flexible LSMO film structure has high mechanical stability and good repeatability and durability in respiration monitoring.

2. The flexible LSMO thin film structure of the utility model consists of an inorganic substrate and La deposited on the surface of the inorganic substrate _0.7 Sr _0.3 MnO ₃ The inorganic substrate can realize epitaxial growth of LSMO lattice structure, thereby ensuring stability of sensor response signals; the inorganic substrate is selected from mica, so that not only can the epitaxial growth of the LSMO film be realized, but also the LSMO film has mechanical flexibility after being peeled off, and can be better attached to the curved surface of a mask and the like for working.

3. The human body respiratory monitoring system can sense respiratory abnormality possibly occurring in a respiratory system, and has important application value in clinical treatment and daily prevention.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present utility model, the drawings that are required to be used in the description of the embodiments will be briefly described below.

FIG. 1 is a schematic diagram of a human respiratory monitoring system according to embodiment 1 of the present utility model;

FIG. 2 is a schematic diagram of the LSMO/Mica film structure of example 1 of the present utility model;

FIG. 3 is a graph showing the percent resistance versus temperature for LSMO/Mica films of example 1 of this utility model under different bending conditions;

FIG. 4 is a graph showing the percent resistance versus number of bends of LSMO/Mica films of example 1 of this utility model at different temperatures;

FIG. 5 is a response behavior of the LSMO/Mica film of example 1 of this utility model in the strain range of-5% to-40%;

FIG. 6 is a graph showing the percent resistance versus time for an LSMO/Mica film according to example 1 of the present utility model under a given strain;

FIG. 7 is a graph showing the response signal detected by the human respiratory monitoring system according to embodiment 1 of the present utility model over time;

FIG. 8 is a statistical plot of breath intensity and frequency for the different breath states of FIG. 7;

FIG. 9 is an enlarged detail view of waveforms for different respiratory states of FIG. 7;

FIG. 10 is a graph showing the distribution of valley and valley positions corresponding to different respiratory states in FIG. 7;

FIG. 11 is a schematic diagram of a human respiratory monitoring system in embodiment 2 of the present utility model;

FIG. 12 is a graph showing the response signal monitored by the human respiratory monitoring system according to example 2 of the present utility model over time;

FIG. 13 is an enlarged detail view of the waveforms of days 1, 3, 5 and 7 of FIG. 12;

FIG. 14 is a statistical plot of breath intensity and frequency for days 1-7 of FIG. 12;

FIG. 15 is a graph showing response signals of the testers #1 and #2 in different states monitored by the human respiratory monitoring system according to the embodiment 2 of the present utility model;

FIG. 16 is a statistical plot of breath intensity and frequency for tester #1 and tester #2 of FIG. 5 at different conditions;

FIG. 17 is an enlarged detail view of waveforms of the tester #1 in FIG. 15 under different conditions and corresponding distribution diagrams of valley values and valley positions under different conditions;

FIG. 18 is an enlarged detail view of waveforms of the tester #2 in FIG. 15 under different conditions and corresponding distribution diagrams of valley values and valley positions under different conditions;

FIG. 19 is normal breath and breath hold data collected by a human respiratory monitoring system in example 2 of the present utility model;

FIG. 20 is normal respiration and cough data collected by the human respiratory monitoring system of example 2 of the present utility model;

fig. 21 is an external schematic view of a smart mask according to embodiment 3 of the present utility model;

fig. 22 is an internal schematic view of the intelligent mask in embodiment 3 of the present utility model.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present utility model more apparent, the technical solutions of the embodiments of the present utility model will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present utility model, and it is apparent that the described embodiments are some embodiments of the present utility model, but not all embodiments of the present utility model. All other embodiments, which can be made by those skilled in the art based on the embodiments of the utility model without making any inventive effort, are intended to be within the scope of the utility model.

In the description of the present utility model, it should be noted that the terms "center," "top," "bottom," "left," "right," "vertical," "horizontal," "inner," "outer," "front," "rear," and the like indicate an azimuth or a positional relationship based on that shown in the drawings of the specification, and are merely for convenience of description and to simplify the description, but do not indicate or imply that the apparatus or elements to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus should not be construed as limiting the present utility model.

The technical scheme of the utility model is described in detail through specific embodiments.

Example 1

The human body respiration monitoring system, as shown in fig. 1, comprises a sensor 1, a flexible test module 2 and an intelligent terminal 3.

In clinical situations, in addition to requiring real-time and continuous monitoring of the respiration of a patient, the method also needs to meet the requirement of stable and reliable signal transmission, so in order to prevent the emergency caused by the disconnection of a link and the loss of a signal due to network fluctuation, the embodiment connects the sensor 1 with the flexible test module 2 in a wired manner, specifically connects the sensor 1 with the flexible test module 2 in a two-wire manner, and connects the flexible test module 2 with the intelligent terminal 3 in a wired manner, namely the embodiment provides a system for monitoring the respiration of the patient in clinical situations.

In this embodiment, the sensor 1 is a flexible LSMO/Mica thin film structure for responding to human breath.

Specifically, as shown in FIG. 2, the flexible LSMO/Mica thin film structure consists of a Mica Mica substrate 101 and La deposited on the surface of the Mica Mica substrate 101 _0.7 Sr _0.3 MnO ₃ A film 102 composition; the Mica Mica substrate 101 of this example has a thickness of 20-50 μm, la _0.7 Sr _0.3 MnO ₃ The thickness of the film is 100-200nm.

The flexible LSMO/Mica film structure of the embodiment can be prepared by adopting a pulse laser deposition method, specifically, high-purity nitrogen (99.9%) is adopted as a deposition atmosphere, and an epitaxial seed layer with a certain thickness is deposited on a Mica Mica substrate; then nitrogen is pumped out, high-purity oxygen (99.9%) is introduced, and the epitaxial LSMO/Mica film with excellent properties can be realized by continuous deposition; then, mechanically stripping and shape cutting are carried out on the two-dimensional material Mica Mica substrate by using a surgical knife, and when the thickness of the Mica Mica substrate is reduced to 20-50 mu m, a flexible LSMO/Mica film can be obtained; finally, the LSMO/Mica film is connected with the flexible test module 2 by using conductive silver paste and wires.

The specific technological parameters of the pulse laser deposition process are as follows: substrate temperature 700 ℃ and laser flux 1.2 J.cm ^-2 The laser frequency is 4Hz; the gas pressure was 13.3Pa.

The variation of the resistance percentage of the prepared flexible LSMO/Mica thin film structure in different bending states was tested, and the test structure is shown in fig. 3, it can be seen that MIT points of the thin film in the flat state, the bent-down state and the bent-up state are 270K, 245K and 230K, respectively, i.e., the resistance of the bent LSMO/Mica thin film as a whole remains unchanged compared to the flat state. It can also be seen from fig. 3 that the resistance of the LSMO/Mica film varies with temperature, indicating that the LSMO/Mica film has potential for use in a variable resistance sensor.

The mechanical stability of the flexible LSMO/Mica film was then tested by: the flexible LSMO/Mica film is fixed on a flexible test module 2 (AES-4 SD of a flexible device tester) for mechanical bending, the bending curvature radius is 5mm, the bending up and bending down times are equal, the mechanical bending times are 0-50000 times, the film is taken down after the designed bending times are reached, the resistance value of the LSMO/Mica film at 20 ℃, 25 ℃, 30 ℃ and 35 ℃ is tested by using a KEYSIGHT B2985A electrometer/high resistance meter, so as to characterize the mechanical stability of the LSMO/Mica film, and the test temperature is set for simulating the breathing environment temperature of a human body.

The test structure is shown in fig. 4, and it can be seen from fig. 4 that in the original state of no bending, the resistance change effect gradually increases with the increase of temperature, and the resistivity of the flexible LSMO/Mica film changes regularly with the change of temperature in the mechanical bending process for 50000 times, so that the mechanical stability of the LSMO/Mica film is verified.

Finally, the mechanical bending resistance response of the flexible LSMO/Mica film is tested, and particularly the flexible test module 2 is adopted to test the regular change of the resistance of the LSMO/Mica film along with the bending, wherein the definition of the resistance change rate is delta R/R ₀ X 100%, where ΔR and R ₀ The variable resistance and the resistance in the flat state are respectively expressed, and the strain is defined as delta L/L ₀ X 100%, where ΔL and L ₀ The values of the LSMO/Mica length change and the length in a flat state are respectively, the strain values are set to be-5%, -10%, -20%, -40%, and the compression, holding, recovery and rest are respectively 30, 60, 30 and 60 seconds every 180 seconds of bending period.

The test results are shown in fig. 5, and it can be seen from fig. 5 that in the test of 60min, it takes 3 minutes to complete each bending cycle, and compression, holding, recovery, and rest are respectively 30s, 60s, 30s, and 60s to quantify the mechanical bending of the film. It can also be seen from fig. 5 that the response signal of the LSMO/Mica film is regular and recoverable at each bending cycle, the maximum absolute response value increases with increasing strain, -5%, -10%, -20% strain corresponding resistance changes are-5%, -10%, -20%, -40% strain resulting in a resistance decrease of about-32.5%, i.e. at larger strains the resistance change effect tends to saturate.

Mechanical strain (-25%) was applied during the bending cycle, and the remaining experimental conditions were the same as above, and long-term mechanical bending test was performed, and as shown in fig. 6, it can be seen that the flexible LSMO/Mica film had good mechanical stability and durability.

In this embodiment, the flexible test module 2 is an AES-4SD flexible equipment analysis system of the SINO AGGTECH, and the AES-4SD is an existing product, and the structure and principle of this embodiment are not described in detail.

In this embodiment, as shown in fig. 1, the intelligent terminal 3 is specifically a computer, and is connected with the flexible test module 2 in a wired manner, so as to display, record, store and so on the respiration monitoring result.

The human respiration monitoring system of this embodiment can be set up in oxygen mask department or gauze mask department as required, carries out long-term test to it to verify response and stability to actual breathing, and response signal is as shown in fig. 7 with the relation of time, can see: the flexible LSMO/Mica film has good repeatability in continuous respiration test for at least 3 hours, and although the response signal of the later normal respiration cannot be restored to the initial state to a certain extent, which is probably caused by the fact that the humidity of the lung is accumulated on the surface of the LSMO/Mica film, the waveform and the respiration frequency can still be used for monitoring and analyzing the lung condition of a human body.

Fig. 8 statistically analyzes the respiration intensity and frequency (BPM) of each state, it can be seen that: the respiration intensity of the cough, the normal respiration, the deep respiration and the recovered normal respiration are respectively 9.70+/-2.84, 21.09+/-0.77, 34.22+/-1.57 and 25.28+/-0.40 percent, the respiration intensity of the cough is smaller than that of the other states, and the respiration intensity of the deep respiration is highest. The respiratory frequency is 40.62+/-17.38, 19.30+/-2.88, 17.45+/-2.09 and 19.86+/-2.98 BPM respectively, which are all in a reasonable range, especially the resting respiratory frequency. The respiratory rate of coughing is the lowest, but the respiratory rate of normal and deep breathing is not apparent, since the tester breathes only deeper and not faster. Taken together, the results show that the breathing frequency of the front normal breath is substantially comparable to the breathing frequency of the back normal breath, and that the LSMO/Mica film is seen to be well reproducible in respiratory monitoring.

The details of each breathing state are exaggerated in fig. 9, showing that the LSMO/Mica film has excellent perceptibility of human breathing.

The corresponding valley (- ΔR/R) in FIG. 10 ₀ ) And valley (time) profiles more clearly reflect respiration intensity and rhythm, it can be seen that: different respiratory states exhibit unique shapes and different response signal ranges. Notably, the cough waveform appears as a bimodal waveform during each cycle, with a large difference from other cycles, the cough frequency is higher than in other states, but the intensity is much lower because the lungs need to contract twice during each period and cannot recover to the original level.

It can be seen from the above test that the human respiratory monitoring system of the present embodiment can be used for monitoring and analyzing the lung condition of a human body, and has good repeatability.

Example 2

Unlike embodiment 1, as shown in fig. 11, the human breath monitoring system of the present embodiment includes: sensor 1, intelligent terminal 3 and signal processing module 4, signal processing module 4 and sensor 1 wired connection, intelligent terminal 3 and signal processing module 4 wireless connection, this embodiment provides a portable monitoring system promptly, is applicable to daily respiration monitoring.

In this embodiment, the signal processing module 4 is configured to convert a resistance change analog signal formed by the response of the sensor 1 to human breath into a digital signal, and transmit the digital signal to the intelligent terminal 3 through bluetooth, where the intelligent terminal 3 in this embodiment is a smart phone.

Specifically, the model of the signal processing module 4 in this embodiment is ES20B, that is, the signal processing module 4 is an existing product, so the circuit diagram of this embodiment will not be described again.

The human body respiration monitoring system of the embodiment further comprises a power supply, the power supply is a lithium battery with the power of 300mAh, and the working current of the signal processing module 4 is about 10mA, and the working current of the sensor 1 is lower than 0.1mA, so that the power supply of the embodiment can meet continuous operation for tens of hours under the condition of full charge, and can meet daily use requirements.

The human body respiration monitoring system of the embodiment is researched by adopting endurance testThe long-term response stability is recorded, the change of the human respiratory response signal along with time is recorded, the respiratory monitoring is carried out for 1 hour every day in a sitting state within a week, and the result is shown in figure 12, and the waveform result and the valley (-delta R/R) are amplified ₀ ) And valley (time) profiles are shown in fig. 13.

As can be seen from fig. 12 and 13: 40 second amplified waveform on day 1, day 3, day 5, day 7 and corresponding trough (- ΔR/R) ₀ ) And valley (time) profiles show that the response signal of the LSMO/Mica membrane is relatively stable and recoverable, indicating that the respiration monitoring module has good sensing and sampling capabilities.

Furthermore, the respiration data from day 1 to day 7 are summarized in fig. 14 by means of statistical histograms with error bars, as can be seen: the stable respiration intensity and respiration rate indicate that the wireless respiration monitoring module has reliability for long-term operation, and the stability of such sensing behavior is attributed to the excellent electrical transmission characteristics and mechanical bending stability of the LSMO/Mica film.

Different health conditions and different populations produce complex respiratory parameters including respiratory rate, relative intensity and waveform. The respiratory condition reflects the health condition of the respiratory system, and respiratory parameters may not appear abnormal in the respiratory system within a few seconds, and long-term observation is needed to distinguish the respiratory parameters. Traditional respiratory rate measurements are observed by medical personnel for 15 seconds or 30 seconds of chest relief and the count is multiplied by four times/two times to estimate the respiratory rate of the patient. The method has the defects of long-term and quantitative monitoring of human breath, short period and subjective effect.

To verify the ability of the human respiratory monitoring system of this embodiment to monitor different conditions and different populations, we collected data of different respiratory conditions generated by different exercise rates of two volunteers (tester #1, male, 26 years; tester #2, female, 22 years) on the treadmill. 3KPH (kilometers per hour), 6KPH and 9KPH are defined as walking, jogging and running, respectively (0 KPH means sitting).

In fig. 15, the result of recording different respiration states is shown, and it should be noted that, in order to display the waveform of the respiration state of the user in real time, corresponding application software needs to be installed on the intelligent terminal 3 (such as a smart phone), where the application software is an existing product, and the principle of this embodiment is not described again.

As can be seen from fig. 15: under different respiratory states, the response signals of two testers show stable and recoverable curves, which shows that the human respiration monitoring system based on the LSMO/Mica film has good reliability and durability in practical application.

Fig. 16 is a statistical histogram of error bars for breath intensity and frequency for different testers and different conditions to further demonstrate the stability of the respiratory monitoring system of this embodiment and study differences in breath at a statistical level. Generally, as the intensity of exercise increases, the intensity and frequency of respiration also increases. The respiratory intensities of walking, jogging and running of the test person 1 were 6.70+ -0.27, 13.34+ -1.08 and 14.09+ -1.10%, respectively, and the corresponding respiratory frequencies were 20.40+ -4.54, 25.45+ -5.84 and 27.17+ -4.97 BPM, respectively, and the experimental results support the above-mentioned points. It will be appreciated that tester #1 increases the rate of oxygen inhalation by greatly increasing the intensity of respiration and slightly increasing the frequency to accommodate the intensity of exercise, that is, deeper breaths may be more acceptable to tester # 1. The respiration intensity and frequency of the tester #2 in the above three exercise states were 7.28±0.57, 12.22±1.20, 10.97±0.76% and 18.64±3.00, 21.28±4.03, 39.87 ±2.78BPM, respectively, which were the same as the tendency of the tester #1 in the low exercise intensity (+.6kph).

Respiration amplified waveforms and corresponding valley profiles (- ΔR/R) for tester #1 and tester #2 in FIGS. 17 and 18 ₀ ) The same result is supported by the valley (time).

Unexpectedly, tester #2 had a decrease in respiratory strength at high exercise intensity (9 KPH) and a substantial increase in respiratory rate, indicating that it tended to increase respiratory rate rather than strength to promote oxygen inhalation. Of particular note, tester #1 developed Airway Hyperresponsiveness (AHR) two years ago, a condition that readily triggered bronchospasm (bronchioles or small airway contractions), which may be an independent factor in predicting decline in lung function.

From the sitting respiratory data of fig. 15, the respiratory intensity of tester #1 (7.82±0.46%) was slightly lower than tester #2 (9.77±0.87%) but the respiratory rate (23.92±2.19 BPM) was much higher than tester #2 (13.40±4.27 BPM) and higher than healthy people, which appears to be related to respiratory condition of tester No. 2.

Cough is taken as a severe respiratory state, and abnormal respiratory conditions (cough) can be easily distinguished from normal respiration by observing the shape of the untreated respiratory curve. FIG. 19 is raw normal breath and breath hold data, sampling frequency of 10Hz, cough response signal curve smoothing, response time of 0.5s, and it can be seen that LSMO/Mica film has good sampling ability and higher accuracy in breath monitoring.

Next, the ability of LSMO/Mica film to perceive some normal breathing mixed in different respiratory states, different cough states (defined as single, double, triple cough), was verified, and figure 20 is raw respiratory data. It can be seen from this: the distribution of points on the cough curve is different from the distribution of points on the normal respiration curve, which means that different respiration states are easily distinguished. The typical cough waveform is shown in fig. 20, and the original signal waveform, the statistical respiratory intensity and the statistical respiratory rate are combined to be analyzed, so that respiratory abnormality possibly occurring in the respiratory system can be perceived, and the cough waveform has important application value in the aspects of clinical treatment and daily prevention.

The human body respiration monitoring system of the embodiment adopts a wireless connection mode, and compared with a wired method, electromagnetic crosstalk and signal degradation exist in a simple wireless circuit, so that the response signal strength is reduced to a certain extent, and nevertheless, the human body respiration monitoring system based on the flexible LSMO/Mica film can still keep accurate response to human body respiration.

Example 3

The intelligent mask, as shown in fig. 21 and 22, comprises a mask body 100 and the human breath monitoring system in embodiment 2, the sensor 1 is disposed on the inner side of the mask body 100, and the signal processing module 4 is disposed on the outer side of the mask body 100.

The mask body 100 is a conventional mask structure, and the structure is not limited in this embodiment.

It should be noted that modifications and improvements can be made by those skilled in the art without departing from the inventive concept, and these are all within the scope of the present utility model.

Claims (10)

1. Human respiratory monitoring system, its characterized in that includes:

the sensor is of a flexible LSMO film structure and is used for responding signals for making resistance change to human respiration; further comprises:

the flexible testing module is connected with the sensor in a wired way and is used for analyzing a response signal of resistance change; or alternatively, the first and second heat exchangers may be,

and the signal processing module is connected with the sensor in a wired way and is used for converting the response signal of the resistance change into a digital signal.

2. The human breath monitoring system of claim 1, wherein the flexible LSMO thin film structure is composed of an inorganic substrate and La deposited on a surface of the inorganic substrate _0.7 Sr _0.3 MnO ₃ Film composition.

3. The human breath monitoring system of claim 2, wherein the inorganic substrate is mica.

4. The human breath monitoring system according to claim 2, wherein the inorganic substrate has a thickness of 20-50 μm.

5. The human breath monitoring system of claim 2, wherein the La _0.7 Sr _0.3 MnO ₃ The thickness of the film is 100-200nm.

6. The human breath monitoring system of claim 1, wherein the flexible test module is AES-4SD.

7. The human respiratory monitoring system of claim 1, wherein the signal processing module is model ES20B.

8. The human breath monitoring system of claim 1, further comprising:

and the intelligent terminal is connected with the flexible test module in a wired mode or connected with the signal processing module in a wireless mode and is used for displaying and storing monitoring data.

9. An intelligent mask, comprising a mask body and the human breath monitoring system according to any of claims 1-8.

10. The smart mask of claim 9, wherein the sensor is disposed inside the mask body.

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