A health status monitoring system and preparation method thereof

Abstract

The present invention belongs to the field of intelligent monitoring technology, specifically relating to a health monitoring system and its preparation method. The system comprises a health monitoring mask and a host computer. The health monitoring mask includes the mask and its built-in respiratory monitoring unit, heart rate monitoring unit, and health monitoring unit. The host computer includes a display interface and its built-in data processing module. The respiratory monitoring unit includes a thermoelectric device made of P-type and N-type semiconductor thermoelectric particles. The heart rate monitoring unit consists of a MAX30102 integrated on a flexible PCB, a voltage regulator circuit, a decoupling capacitor, and a pull-up resistor. The health monitoring unit is sewn into the mask and includes an ADC conversion unit I, a microcontroller chip with a built-in Bluetooth module and an integrated circuit bus. The host computer includes a display interface and a data processing module to receive monitoring data from the respiratory monitoring unit and the heart rate monitoring unit, match the data into groups, and perform multi-source fusion. Based on the multi-source fusion data, the health status of the human body can be identified and judged.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring, and in particular relates to a health status monitoring system and a preparation method thereof. Background Art

[0002] Heart rate and respiratory rate are two key physiological parameters for monitoring human health. Currently, many people are already in sub-health conditions due to stress, staying up late, and irregular diets. In addition to regular physical examinations, daily real-time monitoring of heart rate and respiratory rate is also necessary for health assessment and early warning.

[0003] Current heart rate and respiration monitoring devices suffer from numerous shortcomings. Typically, some small, portable devices lack the ability to simultaneously monitor both heart rate and respiration. Furthermore, devices that can simultaneously monitor both heart rate and respiration are often expensive, difficult to carry, and unsuitable for daily, real-time monitoring. With the increasing emphasis on health and improved living standards, there is a need for low-cost, portable, and easy-to-use devices for daily heart rate and respiration monitoring. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a health status monitoring system and a preparation method thereof.

[0005] A health monitoring system includes a health monitoring mask and a host computer. The health monitoring mask includes the mask and a respiratory monitoring unit, a heartbeat monitoring unit, and a health monitoring unit arranged therein. The host computer includes a display interface and a data processing module built into the host computer.

[0006] The health monitoring unit is sewn into the mask and includes ADC conversion unit I, a microcontroller chip with a built-in Bluetooth module and integrated circuit bus (I2C). ADC conversion unit I includes channel 0 and channel 1.

[0007] The respiratory monitoring unit includes a thermoelectric device made of P-type semiconductor thermoelectric particles and N-type semiconductor thermoelectric particles. The external positive and negative wires of the thermoelectric device are connected to channel 0 and channel 1 of the health monitoring unit, respectively. Channel 0 and channel 1 form a differential signal, which is transmitted to ADC conversion unit I. ADC conversion unit I converts the differential signal into a digital signal, which is transmitted to the microcontroller chip (model: ESP32) in the health monitoring unit. The digital signal is then uploaded to the host computer in real time via the Bluetooth module built into the microcontroller chip.

[0008] The heartbeat monitoring unit consists of a MAX30102, a voltage regulator circuit, a decoupling capacitor, and a pull-up resistor integrated on a flexible PCB. The MAX30102 converts the collected optical signal into an electrical signal via its built-in photodiode (PD). The electrical signal is then converted into a digital signal via its built-in ADC. The digital signal is then transmitted via the microcontroller chip (model: ESP32)'s built-in integrated circuit bus (I2C) to the health monitoring unit's microcontroller chip. The digital signal is then uploaded to the host computer in real time via the microcontroller chip's built-in Bluetooth module.

[0009] The data processing module included in the host computer receives the data transmitted by the respiratory monitoring unit and the heartbeat monitoring unit, matches the data of the two into groups for multi-source fusion, and identifies and judges the real-time health status of the human body based on the data after multi-source fusion.

[0010] Preferably, the data processing module includes a data recording module, a data preprocessing module, a data training module, and a data identification module that are sequentially connected by signals; the data recording module collects and stores historical data through a respiratory monitoring unit and a heartbeat monitoring unit; the data preprocessing module preprocesses the data collected by the data recording module; in the data training module, the XGBoost framework is used to establish an XGBoost model, the preprocessed data is used to train the XGBoost model, and the hyperparameters of the model are adjusted and optimized to obtain an optimized XGBoost model; the optimized XGBoost model is loaded into the data identification module to identify the health status of the human body.

[0011] Preferably, in the data preprocessing module, the specific method of preprocessing is: matching the four types of data into groups for multi-source fusion, the first type of historical data is multi-source fused and defined as positive, the second, third, and fourth types of historical data are multi-source fused and defined as negative; the data after the above processing are divided into a training set and a test set according to a ratio of 9:1, the training set is 90% and the test set is 10%; wherein, the four types of data are matched into groups for multi-source fusion, which means: matching the voltage waveform data reflecting the phrase transmitted by the respiratory monitoring unit with the waveform data reflecting the heartbeat transmitted by the heartbeat monitoring unit, and dividing them into Four types: the first type: when a person says an abnormal phrase such as "help", the voltage waveform reflecting the abnormal phrase is sent by the respiratory monitoring unit to the data recording module, and the corresponding waveform reflecting the abnormal heartbeat is sent by the heartbeat monitoring unit to the data recording module when the person is in a dangerous state such as falling and losing weight. The second type: when a person says an abnormal phrase such as "help", the voltage waveform reflecting the abnormal phrase is sent by the respiratory monitoring unit to the data recording module, and the corresponding waveform reflecting the normal heartbeat is sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state such as sitting, walking, jogging, etc. The third type: when a person says "how The fourth type: when a person says normal phrases such as "how are you", "yes", "no", etc., the voltage waveform reflecting the normal phrase is sent by the respiratory monitoring unit to the data recording module, and the corresponding waveform reflecting the abnormal heartbeat is sent by the heartbeat monitoring unit to the data recording module when the person is in a dangerous state such as falling and losing weight at that moment. The fourth type: when a person says normal phrases such as "how are you", "yes", "no", etc., the voltage waveform reflecting the normal phrase is sent by the respiratory monitoring unit to the data recording module, and the corresponding waveform reflecting the normal heartbeat is sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state such as sitting, walking, jogging, etc. at that moment.

[0012] The present invention also provides a method for preparing the above-mentioned health status monitoring system, comprising the following steps:

[0013] (1.1) Mask processing

[0014] A commercial cotton mask was selected as the flexible substrate, and a hole array was prepared on one side of the mask corresponding to the area between the mouth and nostrils by laser cutting. The holes in the hole array were 1.4 mm long, 1.4 mm wide, and the spacing between them was 2.3 mm.

[0015] (1.2) Construction of health status monitoring unit

[0016] The health status monitoring unit is sewn into the mask. The health status monitoring unit includes ADC conversion unit I, a microcontroller chip with built-in Bluetooth module and integrated circuit bus. ADC conversion unit I includes channel 0 and channel 1.

[0017] (2) Preparation of respiratory monitoring unit

[0018] The respiratory monitoring unit includes a thermoelectric device, which includes P-type semiconductor thermoelectric particles and N-type semiconductor thermoelectric particles. The specific preparation process is as follows:

[0019] (2.1) Preparation of P-type and N-type semiconductor thermoelectric particles: Sb2Te3 was used as the raw material for the P-type semiconductor thermoelectric particles, and Bi2Te3 was used as the raw material for the N-type semiconductor thermoelectric particles. Both the P-type and N-type semiconductor thermoelectric particles were formed into blocks with dimensions of 1.4 mm in length, 1.4 mm in width, and 2.5 mm in height. The spacing between the N-type and P-type semiconductor thermoelectric particles was 2.3 mm.

[0020] (2.2) Electrode Preparation: Conductive fabric was cut into rectangular pieces by laser cutting to serve as electrodes. The electrode dimensions were length × width = 4.9 mm × 1.4 mm.

[0021] (2.3) Place the electrode prepared in step 2.2 on a metal steel mesh, then transfer it to a thermal release tape and apply tin paste to the electrode;

[0022] (2.4) Lay the mask obtained in step 1 flat on a heating plate. Then, alternately embed the P-type and N-type semiconductor thermoelectric particles obtained in step 2.1 into the holes of the hole array prepared in step 1, and place the electrodes prepared in step 2.3 on the outer ends of the semiconductor thermoelectric particles. After placement, press a glass plate on them to prevent the P-type and N-type semiconductor thermoelectric particles from moving during heating. Adjust the heating temperature of the heating plate to 200°C and weld at 200°C for 10 minutes to complete welding. After welding is completed, naturally cool to room temperature and then remove the glass plate.

[0023] (2.5) After completing step 2.4, turn the mask over and lay it flat on the heating plate again; then, place the electrode prepared in step 2.3 on the unwelded end of the semiconductor thermoelectric particle. After placement, press a glass plate on it to prevent the P-type and N-type semiconductor thermoelectric particles from moving during the heating process; adjust the heating temperature of the heating plate to 200°C, and weld at 200°C for 10 minutes to complete welding; after welding is completed, naturally cool to room temperature and then remove the glass plate to obtain the thermoelectric device; wherein, the P-type and N-type semiconductor thermoelectric particles form an electrically series and thermally parallel structure;

[0024] (2.6) The external positive and negative wires of the thermoelectric device are connected to channel 0 and channel 1 of the health monitoring unit respectively. Channel 0 and channel 1 form a differential signal, which is input into ADC conversion unit I. ADC conversion unit I converts the differential signal into a digital signal, which is transmitted to the microcontroller chip in the health monitoring unit and then uploaded to the host computer in real time through the Bluetooth module built into the microcontroller chip.

[0025] (3) Preparation of heartbeat monitoring unit

[0026] The MAX30102 is selected as the heartbeat sensor module. A voltage regulator circuit is designed to maintain it at 1.8V as the power supply for the MAX30102. A decoupling capacitor is added between the power supply and ground to filter out high-frequency noise. Pull-up resistors are added to the data and clock lines that communicate between the MAX30102 and the integrated circuit bus of the health monitoring unit to prevent overshoot.

[0027] The MAX30102, voltage regulator circuit, decoupling capacitor and pull-up resistor are all integrated on a flexible PCB as the base of the heartbeat monitoring unit; the size of the heartbeat monitoring unit is 2cm 2 , is sewn into the mask.

[0028] The MAX30102 converts the collected optical signals into electrical signals through its built-in photodiode, which are then converted into digital signals through its built-in ADC. The digital signals are then transmitted to the microcontroller chip in the health monitoring unit via the integrated circuit bus and then uploaded to the host computer in real time via the Bluetooth module built into the microcontroller chip.

[0029] (4) Construction of data processing module

[0030] The data processing module is built into the host computer and includes a data recording module, a data preprocessing module, a data training module, and a data recognition module which are sequentially connected by signals;

[0031] (4.1) The data recording module collects historical data through the respiratory monitoring unit and the heartbeat monitoring unit, and the historical data include the first type: when a person says an abnormal phrase such as "help", the voltage waveform reflecting the abnormal phrase is sent to the data recording module by the respiratory monitoring unit, and the corresponding waveform reflecting the abnormal heartbeat is sent to the data recording module by the heartbeat monitoring unit when the person is in a dangerous state such as falling and losing weight at that moment; the second type: when a person says an abnormal phrase such as "help", the voltage waveform reflecting the abnormal phrase is sent to the data recording module by the respiratory monitoring unit, and the corresponding waveform reflecting the normal heartbeat is sent to the data recording module by the heartbeat monitoring unit when the person is in a safe state such as sitting, walking, jogging, etc. at that moment; the third type: when a person says normal phrases such as "how are you", "yes", "no", the voltage waveform reflecting the normal phrase is sent to the data recording module by the respiratory monitoring unit, and the corresponding waveform reflecting the abnormal heartbeat is sent to the data recording module by the heartbeat monitoring unit when the person is in a dangerous state such as falling and losing weight at that moment; the fourth type: when a person says "how are The voltage waveform reflecting the normal phrases sent by the respiratory monitoring unit to the data recording module when the person says "you", "yes", "no" and the corresponding waveform reflecting the normal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state such as sitting, walking, jogging, etc. at that moment;

[0032] (4.2) The data preprocessing module preprocesses the data collected by the data recording module in step 4.1. The specific preprocessing method is as follows: the four types of data in step 4.1 are matched into groups for multi-source fusion, the first type of historical data is multi-source fused and defined as positive, and the second, third, and fourth types of historical data are multi-source fused and defined as negative; the processed data is divided into a training set and a test set in a ratio of 9:1, with the training set accounting for 90% and the test set accounting for 10%;

[0033] (4.3) In the data training module, the XGBoost framework is used to build an XGBoost model. The training set from step 4.2 is used to train the XGBoost model, and the model's hyperparameters are adjusted and optimized based on the training results. The trained and fine-tuned XGBoost model is evaluated using the test set from step 4.2, and the recognition results of the XGBoost model are judged based on the evaluation results. The model's accuracy and generalization ability are evaluated by comparing the results with the true values ​​from the test set to determine whether the model achieves the expected results. Assuming that the evaluation is performed using a test set, which contains a set of sample data with known true values, these sample data can be input into the trained and fine-tuned XGBoost model and then compared with the true values. These recognition values ​​are compared with the true values, and the model's performance is measured using evaluation metrics such as accuracy and precision. If the evaluation results show that the XGBoost model has a high accuracy, that is, the values ​​of "accuracy" and "precision" are high, then it can be considered that the model has good generalization ability and can accurately identify; on the contrary, if the evaluation results show that the recognition error of the XGBoost model is large, then it may be necessary to further optimize the model, adjust the model parameters or retrain the model to improve its accuracy and performance. In the XGBoost model, the number of classifiers is 100, the maximum depth of the tree is 6, the learning rate is 0.1, and the regularization coefficient is 1e -5 , the downsampling rate is 0.8.

[0034] (4.4) The optimized XGBoost model obtained in step 4.3 is loaded into the data recognition module to identify the health status of the human body, and the recognition results are displayed on the display interface of the host computer.

[0035] The host computer is a desktop computer or a mobile phone.

[0036] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0037] 1. The health monitoring mask of the present invention uses a respiratory monitoring unit and a heartbeat monitoring unit to simultaneously identify two physiological indicators: respiration and heartbeat. These two indicators are combined through a data processing module in the host computer to achieve multi-source fusion recognition of abnormal phrases and abnormal heartbeats. This allows accurate identification of the wearer's critical condition with a recognition rate of up to 98.67%, demonstrating its significant potential in medical rescue. The respiratory monitoring unit operates according to the Seebeck principle. The temperature difference between the inner and outer surfaces of the respiratory monitoring unit, based on a thermoelectric device, creates a temperature difference between the two ends of the semiconductor thermoelectric particles, which are thermally connected in parallel and electrically connected in series. The thermoelectric device then outputs a voltage. When a person breathes, they exhale and inhale air. Exhaled air removes heat from the body, causing the air temperature to rise, while inhaled air removes heat from the air, causing it to fall. Therefore, the respiratory monitoring unit can monitor changes in a person's breathing through the thermoelectric device. Similarly, speaking is accompanied by the exhalation and inhalation of air, causing the air temperature to rise and fall. In the respiratory monitoring unit of this invention, when a person speaks different phrases, the exhaled and inhaled gases differ, causing the air temperature to rise and fall by different amounts, resulting in different voltage outputs from the thermoelectric device, and thus different voltage waveforms. Therefore, the respiratory monitoring unit can recognize phrases. The heartbeat monitoring unit operates on the principle of PPG (Proton-Pulsating Pathfinder) and transmits light through the skin into blood vessels. Each heartbeat causes the blood vessels to expand and contract, resulting in changes in the intensity of the light reflected by the blood vessels. The heartbeat monitoring unit then outputs a digital signal based on this change in light intensity, thereby monitoring the heartbeat.

[0038] 2. The health monitoring mask device of the present invention has high integration, wireless transmission, and a flexible PCB of only 2cm. 2 , easy to wear.

[0039] 3. The thermoelectric device in the health monitoring mask of the present invention has excellent thermoelectric performance and fast response speed.

[0040] 4. People often release distress signals and emergency reactions in critical situations. For example, in a critical scenario where weightlessness is accompanied by pain, people will call for help (say phrases such as "Help") and have an abnormal heartbeat (heartbeat intensity suddenly increases). In order to help people deal with such critical situations and facilitate rescue, the health monitoring mask of the present invention is combined with deep learning to realize abnormal phrases (people say different phrases, corresponding to different exhaled gases and inhaled gases, different breathing brings different heat changes, different heat changes cause different temperature differences, and different voltage waveforms are output through thermoelectric devices) and abnormal heartbeat multi-source fusion abnormal situation recognition and alarm. When people say abnormal phrases accompanied by abnormal heartbeat waveforms, our system will identify the abnormal situation, the host computer will display that it is in an abnormal situation, and use a loudspeaker to issue an alarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a physical diagram of the health status monitoring system of the present invention.

[0042] Figure 2 This is the relationship diagram between the output voltage and power of the respiratory monitoring unit.

[0043] Figure 3 It is a graph of the response speed of the respiratory monitoring unit.

[0044] Figure 4 It is a respiratory and heart rate monitoring diagram of the health status monitoring system of the present invention under different motion states of the human body.

[0045] Figure 5 It is a construction diagram of the data processing module in the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments. The present invention is not limited to the following embodiments, and specific implementation methods can be determined based on the technical solutions of the present invention and actual conditions.

[0047] Example 1

[0048] like Figure 1 As shown, a health status monitoring system includes a health status monitoring mask and a host computer. The health status monitoring mask includes the mask and a respiratory monitoring unit, a heartbeat monitoring unit, and a health status monitoring unit arranged therein. The host computer includes a display interface and a built-in data processing module of the host computer.

[0049] The health monitoring unit is sewn into the mask and includes ADC conversion unit I, a microcontroller chip with a built-in Bluetooth module and an integrated circuit bus. ADC conversion unit I includes channel 0 and channel 1.

[0050] The respiratory monitoring unit includes a thermoelectric device made of P-type semiconductor thermoelectric particles and N-type semiconductor thermoelectric particles. The external positive and negative wires of the thermoelectric device are connected to channel 0 and channel 1 of the health monitoring unit, respectively. Channel 0 and channel 1 form a differential signal, which is input into ADC conversion unit I. ADC conversion unit I converts the differential signal into a digital signal. The digital signal is transmitted to the microcontroller chip (model: ESP32) in the health monitoring unit and then uploaded to the host computer in real time via the Bluetooth module built into the microcontroller chip.

[0051] The heartbeat monitoring unit consists of a MAX30102, a voltage regulator circuit, a decoupling capacitor, and a pull-up resistor, all integrated on a flexible PCB (printed circuit board) as a substrate. The MAX30102 converts the collected optical signal into an electrical signal via its built-in photodiode (PD). The electrical signal is then converted into a digital signal via its built-in ADC. The digital signal is then transmitted to the microcontroller chip (model: ESP32) in the health monitoring unit via its built-in integrated circuit bus (I2C). The digital signal is then uploaded to the host computer in real time via the microcontroller chip's built-in Bluetooth module.

[0052] The host computer has a built-in data processing module, which receives data transmitted by the respiratory monitoring unit and the heartbeat monitoring unit to identify and judge the real-time health status of the human body; wherein, the data processing module includes a data recording module, a data preprocessing module, a data training module, and a data recognition module which are sequentially connected by signals; the data recording module collects and stores historical data through the respiratory monitoring unit and the heartbeat monitoring unit; the data preprocessing module preprocesses the data collected by the data recording module; in the data training module, the XGBoost framework is used to establish an XGBoost model, the preprocessed data is used to train the XGBoost model, and the hyperparameters of the model are adjusted and optimized to obtain an optimized XGBoost model; the optimized XGBoost model is loaded into the data recognition module to identify the health status of the human body.

[0053] In the data preprocessing module, the specific method of preprocessing is: matching the four types of data into groups for multi-source fusion, the first type of historical data is multi-source fused and defined as positive, the second, third, and fourth types of historical data are multi-source fused and defined as negative; the data after the above processing are divided into training set and test set according to the ratio of 9:1, the training set is 90% and the test set is 10%; wherein, the four types of data are matched into groups for multi-source fusion, which means: matching the voltage waveform data reflecting the phrase transmitted by the respiratory monitoring unit with the voltage waveform data reflecting the heartbeat transmitted by the heartbeat monitoring unit, and dividing them into four types :The first type: when a person says an abnormal phrase such as "help", the voltage waveform reflecting the abnormal phrase is sent by the respiratory monitoring unit to the data recording module, and the corresponding waveform reflecting the abnormal heartbeat is sent by the heartbeat monitoring unit to the data recording module when the person is in a dangerous state such as falling and losing weight at that moment; the second type: when a person says an abnormal phrase such as "help", the voltage waveform reflecting the abnormal phrase is sent by the respiratory monitoring unit to the data recording module, and the corresponding waveform reflecting the normal heartbeat is sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state such as sitting, walking, jogging, etc.; the third type: when a person says "how The fourth type: when a person says normal phrases such as "how are you", "yes", "no", etc., the voltage waveform reflecting the normal phrase is sent by the respiratory monitoring unit to the data recording module, and the corresponding waveform reflecting the abnormal heartbeat is sent by the heartbeat monitoring unit to the data recording module when the person is in a dangerous state such as falling and losing weight at that moment. The fourth type: when a person says normal phrases such as "how are you", "yes", "no", etc., the voltage waveform reflecting the normal phrase is sent by the respiratory monitoring unit to the data recording module, and the corresponding waveform reflecting the normal heartbeat is sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state such as sitting, walking, jogging, etc. at that moment.

[0054] Example 2

[0055] like Figure 1-5 A method for preparing a health status monitoring system comprises the following steps:

[0056] (1.1) Mask processing

[0057] A commercial cotton mask was selected as the flexible substrate, and a hole array was prepared on one side of the mask corresponding to the area between the mouth and nostrils by laser cutting. The holes in the hole array were 1.4 mm long, 1.4 mm wide, and the spacing between them was 2.3 mm.

[0058] (1.2) Construction of health status monitoring unit

[0059] The health status monitoring unit is sewn into the mask. The health status monitoring unit includes ADC conversion unit I, a microcontroller chip with built-in Bluetooth module and integrated circuit bus. ADC conversion unit I includes channel 0 and channel 1.

[0060] (2) Preparation of respiratory monitoring unit

[0061] The respiratory monitoring unit includes a thermoelectric device, which includes P-type semiconductor thermoelectric particles and N-type semiconductor thermoelectric particles. The specific preparation process is as follows:

[0062] (2.1) Preparation of P-type and N-type semiconductor thermoelectric particles: Sb2Te3 was used as the raw material for the P-type semiconductor thermoelectric particles, and Bi2Te3 was used as the raw material for the N-type semiconductor thermoelectric particles. Both the P-type and N-type semiconductor thermoelectric particles were formed into blocks with dimensions of 1.4 mm in length, 1.4 mm in width, and 2.5 mm in height. The spacing between the N-type and P-type semiconductor thermoelectric particles was 2.3 mm.

[0063] (2.2) Electrode Preparation: Conductive fabric was cut into rectangular pieces by laser cutting to serve as electrodes. The electrode dimensions were length × width = 4.9 mm × 1.4 mm.

[0064] (2.3) Place the electrode prepared in step 2.2 on a metal steel mesh, then transfer it to a thermal release tape and apply tin paste to the electrode;

[0065] (2.4) Lay the mask obtained in step 1 flat on a heating plate. Then, alternately embed the P-type and N-type semiconductor thermoelectric particles obtained in step 2.1 into the holes of the hole array prepared in step 1, and place the electrodes prepared in step 2.3 on the outer ends of the semiconductor thermoelectric particles. After placement, press a glass plate on them to prevent the P-type and N-type semiconductor thermoelectric particles from moving during heating. Adjust the heating temperature of the heating plate to 200°C and weld at 200°C for 10 minutes to complete welding. After welding is completed, naturally cool to room temperature and then remove the glass plate.

[0066] (2.5) After completing step 2.4, turn the mask over and lay it flat on the heating plate again; then, place the electrode prepared in step 2.3 on the unwelded end of the semiconductor thermoelectric particle. After placement, press a glass plate on it to prevent the P-type and N-type semiconductor thermoelectric particles from moving during the heating process; adjust the heating temperature of the heating plate to 200°C, and weld at 200°C for 10 minutes to complete welding; after welding is completed, naturally cool to room temperature and then remove the glass plate to obtain the thermoelectric device; wherein, the P-type and N-type semiconductor thermoelectric particles form an electrically series and thermally parallel structure;

[0067] (2.6) The external positive and negative wires of the thermoelectric device are connected to channel 0 and channel 1 of the health monitoring unit, respectively. Channel 0 and channel 1 form a differential signal, which is input into ADC conversion unit I. ADC conversion unit I converts the differential signal into a digital signal, which is transmitted to the microcontroller chip (model: ESP32) in the health monitoring unit and then uploaded to the host computer in real time via the Bluetooth module built into the microcontroller chip.

[0068] (3) Preparation of heartbeat monitoring unit

[0069] The MAX30102 is selected as the heartbeat sensor module. A voltage regulator circuit is designed to maintain 1.8V as the power supply for the MAX30102. Decoupling capacitors are added between the power supply and ground to filter out high-frequency noise. Pull-up resistors are added to the data line (SDA) and clock line (SCL) that communicate between the MAX30102 and the integrated circuit bus (I2C) of the health monitoring unit to prevent overshoot.

[0070] The MAX30102, voltage regulator circuit, decoupling capacitor and pull-up resistor are all integrated on a flexible PCB as the base of the heartbeat monitoring unit; the size of the heartbeat monitoring unit is 2cm 2 , is sewn into the mask.

[0071] The MAX30102 converts the collected optical signals into electrical signals through its built-in photodiode (PD), which are then converted into digital signals through its built-in ADC. The digital signals are then transmitted to the microcontroller chip (model: ESP32) in the health monitoring unit via the integrated circuit bus (IIC), and then uploaded to the host computer in real time via the Bluetooth module built into the microcontroller chip.

[0072] (4) Construction of data processing module

[0073] The data processing module is loaded into the host computer, and the data processing module includes a data recording module, a data preprocessing module, a data training module, and a data recognition module which are sequentially connected by signals;

[0074] (4.1) The data recording module collects historical data through the respiratory monitoring unit and the heartbeat monitoring unit, and the historical data include the first type: when a person says an abnormal phrase such as "help", the voltage waveform reflecting the abnormal phrase is sent to the data recording module by the respiratory monitoring unit, and the corresponding waveform reflecting the abnormal heartbeat is sent to the data recording module by the heartbeat monitoring unit when the person is in a dangerous state such as falling and losing weight at that moment; the second type: when a person says an abnormal phrase such as "help", the voltage waveform reflecting the abnormal phrase is sent to the data recording module by the respiratory monitoring unit, and the corresponding waveform reflecting the normal heartbeat is sent to the data recording module by the heartbeat monitoring unit when the person is in a safe state such as sitting, walking, jogging, etc. at that moment; the third type: when a person says normal phrases such as "how are you", "yes", "no", the voltage waveform reflecting the normal phrase is sent to the data recording module by the respiratory monitoring unit, and the corresponding waveform reflecting the abnormal heartbeat is sent to the data recording module by the heartbeat monitoring unit when the person is in a dangerous state such as falling and losing weight at that moment; the fourth type: when a person says "how are The voltage waveform reflecting the normal phrases sent by the respiratory monitoring unit to the data recording module when the person says "you", "yes", "no" and the corresponding waveform reflecting the normal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state such as sitting, walking, jogging, etc. at that moment;

[0075] (4.2) The data preprocessing module preprocesses the data collected by the data recording module in step 4.1. The specific preprocessing method is as follows: the four types of data in step 4.1 are matched into groups for multi-source fusion, the first type of historical data is multi-source fused and defined as positive, and the second, third, and fourth types of historical data are multi-source fused and defined as negative; the processed data is divided into a training set and a test set in a ratio of 9:1, with the training set accounting for 90% and the test set accounting for 10%;

[0076] (4.3) In the data training module, the XGBoost framework is used to build an XGBoost model. The training set from step 4.2 is used to train the XGBoost model, and the model's hyperparameters are adjusted based on the training results. The trained and fine-tuned XGBoost model is evaluated using the test set from step 4.2, and the recognition results of the XGBoost model are judged based on the evaluation results. The model's accuracy and generalization ability are evaluated by comparing the results with the true values ​​from the test set to determine whether the model achieves the expected results. Assuming that the test set is used for evaluation, the test set contains a set of sample data with known true values. This sample data can be input into the trained and fine-tuned XGBoost model and then compared with the true values. These recognition values ​​are compared with the true values, and the model's performance is measured using evaluation metrics such as accuracy and precision. If the evaluation results show that the XGBoost model has a high accuracy, that is, the values ​​of "accuracy" and "precision" are high, then it can be considered that the model has good generalization ability and can accurately identify; on the contrary, if the evaluation results show that the XGBoost model has a low accuracy, then it may be necessary to further optimize the model, optimize the model parameters or retrain the model to improve its accuracy and performance. In the XGBoost model, the number of classifiers is 100, the maximum depth of the tree is 6, the learning rate is 0.1, and the regularization coefficient is 1e -5 , the downsampling rate is 0.8.

[0077] (4.4) The optimized XGBoost model obtained in step 4.3 is loaded into the data recognition module to identify the health status of the human body, and the recognition results are displayed on the display interface of the host computer.

[0078] After actual operation, the recognition accuracy of the above data processing module is 98.67%.

[0079] Performance test: Test the performance of the prepared health monitoring system. Place the prepared thermoelectric device on the test bench, apply different temperatures to the upper and lower surfaces of the thermoelectric device to form a temperature difference, and use Keithley 2400 to measure the voltage generated under different temperature differences. The measured data is as follows Figure 2 As shown. When the temperature difference is 30K, the open circuit voltage of the thermoelectric device is 140mV. An airflow speed of 3.2m / s is applied to one side of the thermoelectric device to simulate human breathing and test its reaction rate. Figure 3 As shown in FIG, the response time of the thermoelectric device is 1.4s. The respiratory and heart rate monitoring of the present invention tests the respiratory and heart rate under different motion states, such as Figure 4 shown.

[0080] The above are only preferred embodiments of the present invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A health monitoring system, comprising a health monitoring mask and a host computer, wherein the health monitoring mask comprises a respiratory monitoring unit, a heartbeat monitoring unit, and a health monitoring unit provided therein, and the host computer comprises a display interface and a data processing module built into the host computer; The health monitoring unit is sewn into the mask and includes a microcontroller chip and an ADC conversion unit I. The microcontroller chip has a built-in Bluetooth module and an integrated circuit bus. The ADC conversion unit I includes channel 0 and channel 1. The respiratory monitoring unit includes a thermoelectric device made of P-type semiconductor thermoelectric particles and N-type semiconductor thermoelectric particles. The external positive and negative wires of the thermoelectric device are connected to channel 0 and channel 1 of the health monitoring unit respectively. Channel 0 and channel 1 form a differential signal, which is transmitted to ADC conversion unit I. ADC conversion unit I converts the differential signal into a digital signal, which is transmitted to the microcontroller chip in the health monitoring unit. The digital signal is then uploaded to the host computer in real time via the Bluetooth module built into the microcontroller chip. The heartbeat monitoring unit consists of a MAX30102 integrated on a flexible PCB substrate, along with a voltage regulator circuit, a decoupling capacitor, and a pull-up resistor. The MAX30102 converts the collected optical signal into an electrical signal via a built-in photodiode. The electrical signal is then converted into a digital signal via a built-in ADC. The digital signal is then transmitted via an integrated circuit bus to the microcontroller chip in the health monitoring unit. The digital signal is then uploaded to the host computer in real time via the microcontroller chip's built-in Bluetooth module. The data processing module included in the host computer receives the data transmitted by the respiratory monitoring unit and the heartbeat monitoring unit, matches the data of the two into groups for multi-source fusion, and determines the real-time health status of the human body based on the data recognition after multi-source fusion; the data processing module includes a data recording module, a data preprocessing module, a data training module, and a data recognition module that are sequentially connected by signals; the data recording module collects and stores historical data through the respiratory monitoring unit and the heartbeat monitoring unit; The data preprocessing module preprocesses the data collected by the data recording module. In the data training module, the XGBoost framework is used to establish an XGBoost model. The preprocessed data is used to train and test the XGBoost model. The model's hyperparameters are adjusted and optimized to obtain an optimized XGBoost model. The optimized XGBoost model is loaded into the data recognition module to identify human health conditions. The process of obtaining the optimized XGBoost model is as follows: Step 1. The data recording module collects historical data through the respiratory monitoring unit and the heartbeat monitoring unit. The historical data includes the first type: a voltage waveform reflecting the abnormal phrase sent by the respiratory monitoring unit to the data recording module when a person utters an abnormal phrase, and a corresponding waveform reflecting the abnormal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a dangerous state at that moment; the second type: a voltage waveform reflecting the abnormal phrase sent by the respiratory monitoring unit to the data recording module when the person utters the abnormal phrase, and a corresponding waveform reflecting the normal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state at that moment; the third type: a voltage waveform reflecting the normal phrase sent by the respiratory monitoring unit to the data recording module when the person utters a normal phrase, and a corresponding waveform reflecting the abnormal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a dangerous state at that moment; the fourth type: a voltage waveform reflecting the normal phrase sent by the respiratory monitoring unit to the data recording module when the person utters a normal phrase, and a corresponding waveform reflecting the normal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state at that moment; Step 2: The data preprocessing module preprocesses the data collected by the data recording module in step 1. The specific preprocessing method is as follows: the four types of data in step 1 are matched into groups for multi-source fusion, the first type of historical data is multi-source fused and defined as positive, and the second, third, and fourth types of historical data are multi-source fused and defined as negative; the processed data is divided into a training set and a test set in a ratio of 9:1, with the training set accounting for 90% and the test set accounting for 10%. Step 3. In the data training module, the XGBoost framework is used to establish an XGBoost model. The training set in step 2 is used to train the XGBoost model, and the hyperparameters of the model are adjusted and optimized according to the training results. The trained and tuned XGBoost model is evaluated using the test set in step 2, and the recognition results of the XGBoost model are judged based on the evaluation results to determine whether the model achieves the expected effect. The recognition value is compared with the true value, and "accuracy" and "precision" are used as evaluation indicators to measure the performance of the model. The health monitoring mask simultaneously identifies two physiological indicators, breathing and heartbeat, through a breathing monitoring unit and a heartbeat monitoring unit. The two are combined through a data processing module in a host computer to achieve multi-source fusion recognition of abnormal phrases and abnormal heartbeats, thereby identifying the critical situation of the wearer of the health monitoring mask; the recognition result of the data recognition module is displayed on the display interface of the host computer.

2. The method for preparing the health monitoring system according to claim 1, comprising the following steps: (1.1) Mask processing A cotton mask was selected as the flexible substrate, and according to the design requirements, a hole array was prepared on one side of the mask between the mouth and nostrils by laser cutting; (1.2) Construction of health status monitoring unit The health monitoring unit is sewn into the mask and includes a microcontroller chip and an ADC conversion unit I. The microcontroller chip has a built-in Bluetooth module and an integrated circuit bus. The ADC conversion unit I includes channel 0 and channel 1. (2) Preparation of respiratory monitoring unit The respiratory monitoring unit includes a thermoelectric device, which includes P-type semiconductor thermoelectric particles and N-type semiconductor thermoelectric particles. The specific preparation process is as follows: (2.1) Preparation of P-type and N-type semiconductor thermoelectric particles: Sb2Te3 is used as the raw material for P-type semiconductor thermoelectric particles, and Bi2Te3 is used as the raw material for N-type semiconductor thermoelectric particles. Both P-type and N-type semiconductor thermoelectric particles are formed into blocks, and their length, width, and spacing between the semiconductor thermoelectric particles match the length and width of the holes in the hole array and the hole spacing; (2.2) Electrode preparation: Cut the conductive fabric into rectangular pieces using laser cutting to serve as electrodes. (2.3) Place the electrodes prepared in step 2.2 on a metal steel mesh for alignment, then transfer them to a thermal release tape and apply tin paste to the electrodes; (2.4) Lay the mask obtained in step 1.2 flat on a heating plate. Then, alternately embed the P-type and N-type semiconductor thermoelectric particles obtained in step 2.1 into the holes of the hole array prepared in step 1.1, and place the electrodes prepared in step 2.3 on the outer ends of the semiconductor thermoelectric particles. After placement, press a glass plate on them to prevent the P-type and N-type semiconductor thermoelectric particles from moving during heating; heat the heating plate to a certain temperature for welding; after welding, naturally cool to room temperature and then remove the glass plate; (2.5) Turn the mask after completing step 2.4 over and lay it flat on the heating plate again; then, place the electrode prepared in step 2.3 on the unsoldered end of the semiconductor thermoelectric particle. After placement, press a glass plate on it to prevent the P-type and N-type semiconductor thermoelectric particles from moving during heating; heat the heating plate to a certain temperature for welding; after welding, naturally cool to room temperature and then remove the glass plate to obtain the thermoelectric device; wherein, the P-type and N-type semiconductor thermoelectric particles form an electrically series and thermally parallel structure; (2.6) Connecting the external positive and negative leads of the thermoelectric device to channel 0 and channel 1 of the health monitoring unit, respectively. Channel 0 and channel 1 form a differential signal, which is input into ADC conversion unit I. ADC conversion unit I converts the differential signal into a digital signal, which is transmitted to the microcontroller chip included in the health monitoring unit and then uploaded to the host computer in real time via the Bluetooth module built into the microcontroller chip. (3) Preparation of heartbeat monitoring unit The MAX30102 was selected as the heartbeat sensor module. A voltage regulator circuit was designed to maintain a 1.8V voltage as the MAX30102 power supply. A decoupling capacitor was added between the power supply and ground to filter out high-frequency noise. Pull-up resistors were added to the SDA data line and SCL clock line, which communicate between the MAX30102 and the integrated circuit bus of the health monitoring unit, to prevent overshoot. The MAX30102, voltage regulator circuit, decoupling capacitor and pull-up resistor are all integrated on a flexible PCB serving as the base of the heartbeat monitoring unit; the heartbeat monitoring unit is sewn into the mask; The MAX30102 converts the collected optical signals into electrical signals through its built-in photodiode. The electrical signals are then converted into digital signals through its built-in ADC. The digital signals are then transmitted to the microcontroller chip in the health monitoring unit via the integrated circuit bus I2C. The digital signals are then uploaded to the host computer in real time via the microcontroller chip's built-in Bluetooth module. (4) Construction of data processing module The data processing module is loaded into the host computer, and the data processing module includes a data recording module, a data preprocessing module, a data training module, and a data recognition module which are sequentially connected by signals; (4.1) The data recording module collects historical data through the respiratory monitoring unit and the heartbeat monitoring unit. The historical data include the first type: the voltage waveform reflecting the abnormal phrase sent by the respiratory monitoring unit to the data recording module when a person speaks an abnormal phrase, and the corresponding waveform reflecting the abnormal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a dangerous state at that moment; the second type: the voltage waveform reflecting the abnormal phrase sent by the respiratory monitoring unit to the data recording module when a person speaks an abnormal phrase, and the corresponding waveform reflecting the normal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state at that moment; the third type: the voltage waveform reflecting the normal phrase sent by the respiratory monitoring unit to the data recording module when a person speaks a normal phrase, and the corresponding waveform reflecting the abnormal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a dangerous state at that moment; the fourth type: the voltage waveform reflecting the normal phrase sent by the respiratory monitoring unit to the data recording module when a person speaks a normal phrase The voltage waveform reflecting the normal phrase of the recording module and the corresponding waveform reflecting the normal heartbeat sent by the heartbeat monitoring unit to the data recording module when the person is in a safe state at that moment; (4.2) The data preprocessing module preprocesses the data collected by the data recording module in step 4.

1. The specific preprocessing method is as follows: the four types of data in step 4.1 are matched into groups and multi-source fusion is performed. The first type of historical data is multi-source fused and defined as positive, and the second, third, and fourth types of historical data are multi-source fused and defined as negative. The processed data is divided into a training set and a test set in a ratio of 9:1, with the training set accounting for 90% and the test set accounting for 10%. (4.3) In the data training module, use the XGBoost framework to build an XGBoost model. Train the XGBoost model using the training set from step 4.2, and adjust and optimize the model's hyperparameters based on the training results. Evaluate the trained and tuned XGBoost model using the test set from step 4.2, and judge the recognition results of the XGBoost model based on the evaluation results to determine whether the model achieves the expected results. Compare the recognition values ​​with the true values, using "accuracy" and "precision" as evaluation metrics to measure the model's performance. (4.4) The optimized XGBoost model obtained in step 4.3 is loaded into the data recognition module to identify the health status of the human body. The recognition results are displayed on the display interface of the host computer.

3. The preparation method according to claim 2, characterized in that In step (2), the P-type and N-type semiconductor thermoelectric particles are both formed into blocks, and their dimensions are 1.4 mm in length, 1.4 mm in width, and 2.5 mm in height. The spacing between the N-type semiconductor thermoelectric particles and the P-type semiconductor thermoelectric particles is 2.3 mm; the dimensions of the electrode are length × width = 4.9 mm × 1.4 mm.

4. The preparation method according to claim 2, characterized in that In steps (2.4) and (2.5), the step of heating the heating plate to a certain temperature for welding is specifically as follows: adjusting the heating temperature of the heating plate to 200°C, and welding at 200°C for 10 minutes to complete the welding.

5. The preparation method according to claim 2, characterized in that In step (3), the size of the heartbeat monitoring unit is 2cm 2 .

6. The preparation method according to claim 2, characterized in that The host computer is a desktop computer or a mobile phone including a display interface.

7. The preparation method according to claim 2, characterized in that The XGBoost model described in step (3) has 100 classifiers, a maximum tree depth of 6, a learning rate of 0.1, and a regularization coefficient of 1e -5 , the downsampling rate is 0.8.

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