A multi-modal smart skin patch for multi-scenario cardiopulmonary function monitoring and methods of use thereof

Abstract

The application discloses a kind of multi-modal intelligent skin patch for multi-scene cardiopulmonary function monitoring and its use method, including layered structure main body, flexible circuit board, multi-modal sensor assembly, power management component and wireless communication module;The layered structure main body includes upper layer Ecoflex material layer, lower layer Ecoflex material layer and lithium battery and wireless charging coil encapsulated between two layers of Ecoflex material layer;It adopts the layered structure of upper layer Ecoflex material layer, lower layer Ecoflex material layer encapsulates lithium battery and wireless charging coil, combined with polyimide flexible circuit board and double-bridge serpentine structure sensing wire, give the device excellent tensile resistance and skin adhesion, cooperate with the sweat-proof adhesion of 3M2477P medical silicone double-sided adhesive tape and the high conductivity of high adhesion conductive hydrogel layer, fundamentally solve the electrode falling and signal distortion problem caused by sweating and skin stretching in high-intensity intermittent exercise and other strenuous activities, ensure the stable acquisition of ECG, PPG and respiratory wave signal.

Description

Technical Field

[0001] This invention relates to the field of cardiopulmonary function monitoring technology, and more specifically, to a multimodal smart skin patch for multi-scenario cardiopulmonary function monitoring and its application method. Background Technology

[0002] Traditional cardiopulmonary function testing methods have certain limitations: conventional electrocardiograms, pulmonary function instruments, etc. are mostly static tests, which can only reflect the function of a single organ (heart or lung) in a resting state. They cannot simulate the synergistic effect of the heart-lung-skeletal muscle during daily activities or exercise, and are difficult to explain symptoms such as unexplained shortness of breath and decreased exercise endurance. While cardiopulmonary exercise testing is the gold standard for assessing overall cardiopulmonary function, it has the following limitations: (1) Limited applicable population: Patients with acute high-risk conditions, exercise load may induce the condition to worsen or even endanger life; (2) The accuracy of the results depends on the subjective cooperation of the examinee: the examinee needs to reach a state of true exhaustion; (3) The indicators lack absolute specificity and need to be combined with clinical judgment: CPET indicators are mostly functional indicators rather than etiology-specific indicators; (4) High operating conditions and poor comfort: It not only requires high-precision gas metabolism analyzers, adjustable load treadmills / power bikes, real-time electrocardiogram monitors and other high-cost equipment, but also requires medical staff to monitor the vital signs of the examinees in real time, deal with the discomfort that may occur during exercise, and professional personnel to interpret complex indicators. Ordinary medical staff cannot complete this independently, which makes it difficult to popularize it in primary medical institutions. (5) Single test, limited scenarios: It cannot fully reflect the dynamic changes of cardiopulmonary function of the test subject in different daily activities, various sports or sleep scenarios. Summary of the Invention

[0003] To address the aforementioned issues, this invention provides a multimodal smart skin patch for multi-scenario cardiopulmonary function monitoring and its application method.

[0004] This invention provides a multimodal smart skin patch for multi-scenario cardiopulmonary function monitoring, comprising a layered main body, a flexible circuit board, a multimodal sensing component, a power management component, and a wireless communication module; The layered structure includes an upper Ecoflex material layer, a lower Ecoflex material layer, and a lithium battery and a wireless charging coil encapsulated between the two Ecoflex material layers. The flexible circuit board substrate is polyimide, and the circuit board is provided with a double-bridge serpentine structure sensing wire. The multimodal sensing component includes a respiratory sensing electrode, two electrocardiogram sensing electrodes, a PPG sensor, an IMU module, and a respiratory wave detection module. The surface of the respiratory sensing electrode is bonded with a PDMS material layer, and the surface of the two ECG sensing electrodes is bonded with a highly adhesive conductive hydrogel layer; the lower Ecoflex material layer is cut according to the shape of each electrode and sensor position, so that each electrode and sensor is exposed to directly contact human skin; the non-sensing area of ​​the lower Ecoflex material layer is bonded with 3M2477P medical silicone double-sided tape. The power management component includes an NMOS switch, a power path control chip, a low dropout linear regulator, and a low dropout linear regulator. The NMOS switch is connected to the power management module through the power path control chip to control the power path of each module. The wireless communication module includes a Bluetooth antenna and an NFC coil.

[0005] Preferably, the top-level circuit integration area of ​​the flexible circuit board integrates a wireless charging and power supply module, a power management module, an automatic power control module, an NFC dynamic tag module, a power detection module, an electrocardiogram front-end simulation module, and a low-power Bluetooth integrated main control module. The ECG front-end simulation module communicates with the MCU via an SPI interface to acquire ECG signals and transmit them to the MCU; the IMU module and the respiratory wave detection module communicate with the MCU via an I2C interface to acquire ECG signals and respiratory wave signals, respectively, and transmit them to the MCU; the PPG sensor signal acquisition unit is electrically connected to the ECG front-end simulation module.

[0006] Preferably, in the power management component, the output voltage satisfies The voltage regulator powers the ECG front-end analog module and the PPG sensor signal acquisition unit; the output voltage meets the requirements. The voltage regulator powers the IMU module and the respiratory wave detection module; the output voltage meets the requirements. The voltage regulator powers the PPG sensor light source drive unit; The MCU controls the power switch of the power management component through the I2C interface. The power detection module is electrically connected to the MCU, collects the remaining power Q of the lithium battery in real time and transmits it to the MCU. The MCU adjusts the power supply strategy of each module based on Q.

[0007] Preferably, the wireless communication module supports independent operation in BLE mode and NFC mode; in BLE mode, the MCU transmits heart rate (HR), respiratory rate (RR), and blood oxygen saturation parameters processed by conventional filtering algorithms in real time through the Bluetooth antenna. In the NFC mode, the MCU uses the NFC coil at intervals. Update the heart rate HR, respiratory rate RR, and blood oxygen saturation parameters, with the maximum support duration for a single time for data reading; when the MCU detects that there is no Bluetooth connection within the duration it controls the skin patch to enter the deep sleep state, and only the NFC module and the MCU remain working in the deep sleep state; The MCU is also configured with synchronous data acquisition firmware to trigger the multi-modal sensing component to synchronously acquire physiological signals.

[0008] Preferably, a usage method of a multi-modal intelligent skin patch for multi-scenario cardiopulmonary function monitoring, characterized in that the usage method includes the following steps: S1. Device fitting and position calibration: Attach the lower Ecoflex material layer of the skin patch to the skin of the user's chest monitoring area near the heart and lungs through 3M2477P medical silicone double-sided tape; after fitting, the MCU collects the initial electrocardiogram signal ECG0 through the electrocardiogram front-end analog module and the initial respiratory wave signal RESp0 through the respiratory wave detection module, and calculates the signal-to-noise ratio SNRECG of ECG0 and the amplitude ARESP of RESp0; if SNRECG < SNR0 or ARESP < A0, the MCU pushes a position adjustment prompt to the mobile phone through Bluetooth until SNRECG ≥ SNR0 or ARESP ≥ A0; S2. If the skin patch is in the deep sleep state, bring the mobile phone close to the NFC coil of the skin patch, and wake up the MCU through electromagnetic induction energy capture; the MCU collects the initial motion data ACC0 through the IMU module and determines the scenario in combination with the preset scenario threshold: when ACC0 < Ath1, it is determined as the night sleep apnea monitoring scenario; when ACC0 ≤ Ath2, it is determined as the home resting rehabilitation monitoring scenario; when ACC0 ≥ Ath2, it is determined as the high-intensity intermittent exercise scenario; after scenario recognition, the MCU configures the voltage output strategy of the power management component based on the scenario; S3. The MCU initializes the electrocardiogram front-end analog module through the SPI interface, configures the sampling rate fECG according to the scenario and establishes a data transmission link; initializes the IMU module through the I2C interface, configures the sampling frequency fACG according to the scenario; initializes the respiratory wave detection module and sets the capacitance coupling detection sensitivity level according to the scenario; initializes the PPG sensor and adjusts the output accuracy of the output voltage regulator and the drive current Idrv of the output voltage regulator; at the same time, initializes the communication parameters of the Bluetooth antenna and the NFC coil; S4. Perform multi-modal signal acquisition and validity verification: The MCU calls the synchronous data acquisition firmware to trigger each sensing module to synchronously acquire signals according to the initialization parameters; during the acquisition process, the MCU determines the signal validity through multi-modal collaborative logic: S5. After multimodal signal acquisition and validity verification, select BLE mode or NFC mode for data transmission. Specifically: BLE mode: The MCU uses a conventional filtering algorithm to process the effective signal and then calculates heart rate (HR), respiratory rate (RR), and blood oxygen saturation (SpO2). NFC mode: Bluetooth communication is turned off in this mode. The current time is synchronized to the NFC dynamic tag module through the NFC function of the mobile phone. The MCU uses parameter calculation algorithm to process the valid signal. S6. Data storage and low-power management are performed after S5: Data storage: In BLE mode, the raw signal data is stored locally on the mobile app, and the processed heart rate (HR), respiratory rate (RR), and blood oxygen saturation (SpO2) are simultaneously stored on the cloud server; in NFC mode, the data is first stored in the storage unit of the NFC dynamic tag module. When the mobile phone reads the data, it will store the relevant parameters locally and upload the raw signal snapshot to the cloud. The cloud data is associated with the device's unique number.

[0009] Low-power management: In BLE mode, if no Bluetooth connection is detected within a time period T3, and the IMU module determines that the current scenario is not motion-based, the MCU will control the NMOS switch to disconnect the power to all sensor modules, leaving only the NFC module and itself in deep sleep mode. If the IMU module determines that the scenario is motion-based, the time period will be extended to T7 if no connection is found before entering deep sleep mode. In NFC mode, if no data read / interaction is detected within a time period T3, the MCU will control the disconnection of the power to all sensor modules and enter deep sleep mode.

[0010] During deep sleep, the IMU module will wake up once every T8 time period and continuously collect motion data for a duration of T9. If the motion data ACCwake is greater than or equal to Ath1, it will trigger the MCU to wake up each sensor module and restore the monitoring function.

[0011] Preferably, the position calibration in step S1 also includes skin condition adaptation: The MCU collects skin contact impedance through ECG sensing electrodes. If the collected skin contact impedance is greater than the preset impedance threshold Zth, the phone will prompt you to apply medical conductive gel to the area where the sensing electrodes are attached until the skin contact impedance is less than or equal to the impedance threshold.

[0012] The skin contact impedance is calculated as follows: the ECG front-end simulation module outputs a test voltage and simultaneously collects a test current. The skin contact impedance is equal to the test voltage divided by the test current.

[0013] Preferably, the scene recognition in step S2 further includes a manual correction mechanism: If the scenario automatically identified by the MCU does not match the user's actual scenario, the user can manually select the scenario through a mobile APP. After receiving the manual command, the MCU will reconfigure the voltage output strategy of the power management component and the parameters of each module.

[0014] Preferably, the multimodal signal validity verification in step S4 further includes supplementary information for abnormal signals: If a single ECG is deemed invalid, then the peak pulse wave time of the PPG, combined with historical data, will be used. The current R-wave time is estimated using the mean of the PPG pulse wave peak time. Specifically, the current estimated R-wave time is equal to the PPG pulse wave peak time minus the historical values. The mean value is used to supplement the electrocardiogram signal data.

[0015] If a single RESP is deemed invalid, the current respiratory rate is calculated using the ACC function of the IMU module.

[0016] Preferably, the BLE mode in step S5 also includes a data retransmission mechanism: when the Bluetooth connection is interrupted, the skin patch will temporarily store the valid signal data during the interruption in the local cache; If the interruption duration is less than or equal to After the connection is restored, the MCU will retransmit the temporarily stored data to the phone in chronological order, and this retransmitted data will be marked with a retransmission identifier; if the interruption duration is longer than... The MCU will trigger the encrypted storage of local cache data. When the Bluetooth connection is connected again, it will prioritize the retransmission of this encrypted data. After the retransmission is completed, the local encrypted cache data will be deleted.

[0017] Preferably, the low-power management in step S6 also includes adaptive power adjustment: the MCU collects the remaining power of the lithium battery in real time through the power detection module.

[0018] When the remaining battery power is less than the preset battery power threshold When the remaining battery power is less than the preset battery power threshold, the MCU will automatically reduce the sampling frequency of each module; At this time, the MCU retains only the ECG front-end analog module, PPG sensor acquisition function, and NFC communication function, while shutting down the IMU module and respiratory wave detection module; when the remaining battery power is greater than or equal to the preset battery power threshold... At this time, the MCU will restore the normal sampling frequency and power supply of each module. The relationship between the three power thresholds is as follows: < < .

[0019] Beneficial effects: It adopts a layered structure with an upper Ecoflex material layer and a lower Ecoflex material layer to encapsulate the lithium battery and wireless charging coil. Combined with a polyimide flexible circuit board and a double-bridge serpentine structure sensing wire, it gives the device excellent tensile resistance and skin adhesion. With the sweat-proof adhesion of 3M2477P medical silicone double-sided tape and the high conductivity of the highly adhesive conductive hydrogel layer, it fundamentally solves the problem of electrode detachment and signal distortion caused by sweating and skin stretching during strenuous activities such as high-intensity intermittent exercise, ensuring stable acquisition of ECG, PPG and respiratory wave signals. Secondly, by integrating multimodal sensing components and configuring synchronous data acquisition firmware, and combining the high-speed transmission of the SPI interface with the low-power characteristics of the I2C interface, the synchronous acquisition and correlation analysis of multi-dimensional signals are realized, which significantly improves the diagnostic accuracy of sleep apnea and effectively avoids the risk of missed diagnosis in traditional single-modal monitoring. Power management strategies and dual-mode communication mechanisms enable the device to dynamically adjust the power supply strategy of each module according to the remaining power, and to achieve data retrieval through offline storage and contactless reading in the absence of network, thereby extending the single-charge battery life. The intelligent scene adaptation mechanism and multimodal signal collaborative verification logic ensure the optimization of parameter configuration and signal processing of the equipment in different application scenarios. Combined with data retransmission and encrypted storage functions, a reliable monitoring system is built from signal acquisition, processing, transmission to storage, providing an accurate, continuous and convenient integrated solution for cardiopulmonary function monitoring in multiple scenarios. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the patch of the present invention; Figure 2 This is a flowchart of the method of using the present invention; Detailed Implementation

[0021] like Figure 1 As shown: This invention provides a multimodal smart skin patch for multi-scenario cardiopulmonary function monitoring, including a layered main body, a flexible circuit board, a multimodal sensing component, a power management component, and a wireless communication module; The layered structure includes an upper Ecoflex material layer, a lower Ecoflex material layer, and a lithium battery and a wireless charging coil encapsulated between the two Ecoflex material layers. The lithium battery has a capacity of C=300mAh and a voltage of U1=3.7V; the wireless charging coil has an inductance of L1=15.8μH and is compatible with the Qi protocol to achieve wireless charging of the lithium battery. The flexible circuit board substrate is polyimide, and the circuit board is provided with a double-bridge serpentine structure sensing wire. The dual-bridge serpentine structure has a length of L3=15.91mm, a width of W1=7.41mm, and a spacing of D1=2.02mm. It adopts a double-layer arrangement and is composed of 270° semicircles. The width of the semicircular rings is W2=7.62mm. The circuit board is divided into a top-layer circuit integration area and a bottom-layer electrode / sensor arrangement area. The area of ​​the top-layer circuit integration area is S1=51mm×31mm. The multimodal sensing component includes a respiratory sensing electrode, two electrocardiogram sensing electrodes, a PPG sensor, an IMU module, and a respiratory wave detection module. The diameters of both the respiratory sensing electrode and the electrocardiogram sensing electrode meet the requirement of a semicircle with R=28.9mm. The surface of the respiratory sensing electrode is bonded with a PDMS material layer, and the surface of the two ECG sensing electrodes is bonded with a highly adhesive conductive hydrogel layer; the lower Ecoflex material layer is cut according to the shape of each electrode and sensor position, so that each electrode and sensor is exposed to directly contact human skin; the non-sensing area of ​​the lower Ecoflex material layer is bonded with 3M2477P medical silicone double-sided tape. The power management component includes an NMOS switch, a power path control chip, a low dropout linear regulator, and a low dropout linear regulator. The NMOS switch is connected to the power management module through the power path control chip to control the power path of each module. The wireless communication module includes a Bluetooth antenna and an NFC coil.

[0022] In this implementation, the above solution can achieve special effects in high-intensity intermittent exercise scenarios: the sweat-resistant adhesion of 3M2477P medical silicone double-sided tape combined with the high conductivity of hydrogel, along with the tensile strength of the polyimide double-bridge serpentine structure, ensures that the electrodes do not detach from the skin during exercise. The multimodal sensing components can stably collect ECG, PPG, and respiratory wave signals, solving the signal loss problem of traditional equipment in exercise scenarios.

[0023] It should also be noted that the IMU module is model LIS2DH12, the respiratory wave detection module is model FDC2214RGHR, the power path control chip is model ADP196ACBZ-R7, the low dropout linear regulator with an output voltage of U2=1.8V is model TPS7A0218PDBVR, the low dropout linear regulator with an output voltage of U3=3.3V is model TLV75733PDBVR, the low dropout linear regulator with an output voltage of U4=5V is model PAM2401YPADJ, and the NFC module is model ST25DV64K-JFR6D3. The IMU module acquires echocardiogram signals, the respiratory wave detection module acquires respiratory wave signals, the ECG sensing electrodes, together with the ECG front-end analog module, acquire ECG signals, the PPG sensor acquires chest photoplethysmography signals, and the NFC module transmits heart rate, RR interval, respiratory rate, and blood oxygen parameters.

[0024] As an optional embodiment: the top circuit integration area of ​​the flexible circuit board integrates a wireless charging and power supply module, a power management module, an automatic power control module, an NFC dynamic tag module, a power detection module, an electrocardiogram front-end simulation module, and a low-power Bluetooth integrated main control module; The ECG front-end simulation module communicates with the MCU via an SPI interface to acquire ECG signals and transmit them to the MCU; the IMU module and the respiratory wave detection module communicate with the MCU via an I2C interface to acquire ECG signals and respiratory wave signals, respectively, and transmit them to the MCU; the PPG sensor signal acquisition unit is electrically connected to the ECG front-end simulation module.

[0025] The light source driving unit and output voltage satisfy The voltage regulator's electrical connection; In this embodiment, the above solution can achieve special effects in the nighttime sleep apnea monitoring scenario: the high-speed transmission characteristics of the SPI interface ensure that the ECG signal is uploaded in real time, and the low power consumption characteristics of the I2C interface are adapted to the low power consumption requirements in the sleep scenario. The combination of the two enables the MCU to synchronously capture the ECG-RR interval prolongation + respiratory wave amplitude drop correlation signal during sleep apnea, providing multi-dimensional data support for sleep apnea diagnosis and solving the problem of missed diagnosis in traditional single-modal monitoring.

[0026] It should be noted that the ECG front-end analog module is model MAX30003CTI+, the MCU is model ESP32-PICO-D4, the IMU module is model LIS2DH12, and the respiratory wave detection module is model FDC2214RGHR. The ECG front-end analog module transmits ECG signals to the MCU via the SPI interface, the IMU module transmits ECG signals to the MCU via the I2C interface, the respiratory wave detection module transmits respiratory wave signals to the MCU via the I2C interface, the PPG sensor signal acquisition unit receives the chest photoplethysmography signal and transmits it to the ECG front-end analog module, and the light source driving unit provides a 5V driving voltage to the PPG sensor to ensure stable light signal output.

[0027] As an optional embodiment: in the power management component, the output voltage satisfies The voltage regulator powers the ECG front-end analog module and the PPG sensor signal acquisition unit; the output voltage meets the requirements. The voltage regulator powers the IMU module and the respiratory wave detection module; the output voltage meets the requirements. The voltage regulator powers the PPG sensor light source drive unit; The MCU controls the power switch of the power management component through the I2C interface. The power detection module is electrically connected to the MCU, collects the remaining power Q of the lithium battery in real time and transmits it to the MCU. The MCU adjusts the power supply strategy of each module based on Q.

[0028] It should be noted that, specifically: when When, reduce the power supply duty cycle of the IMU module and respiratory wave detection module; when At that time, only the ECG front-end analog module, PPG sensor, and NFC module are powered. ; The parameters satisfy , The duty cycle of the power supply to the IMU module and respiratory wave detection module was reduced from 100% to 50%.

[0029] The above solution can achieve special effects in home-based rest and rehabilitation monitoring scenarios: when the lithium battery has a remaining charge (Q<50mAh), only the core monitoring module is powered, and with the contactless data reading in NFC mode, the device's single-use battery life is extended to 72 hours, meeting the 7-day home rehabilitation monitoring needs of discharged patients and solving the problem of short battery life and frequent charging required by traditional devices in home scenarios.

[0030] Output voltage meets The voltage regulator model is TPS7A0218PDBVR, and the output voltage meets the requirements. The voltage regulator model is TLV75733PDBVR, and the output voltage meets the requirements. The voltage regulator model is PAM2401YPADJ; When powered by a voltage regulator, the ECG front-end analog module stably acquires ECG signals, and the PPG sensor signal acquisition unit stably acquires chest photoplethysmography signals. When the voltage regulator is in operation, the IMU module collects the electrocardiogram signal and the respiratory wave detection module collects the respiratory wave signal; the power detection module transmits the remaining power signal of the lithium battery to the MCU in real time to ensure dynamic adjustment of the power supply strategy.

[0031] Initial ECG signal This is the first 10-second continuous ECG signal collected after the device was attached. This is the first 10-second continuous respiratory wave signal collected after the device was fitted. for Signal-to-noise ratio, preset signal-to-noise ratio threshold It is 5. for amplitude, To preset the respiratory wave amplitude threshold, and The acceleration thresholds are 0.2 and 0.5, respectively. As an optional embodiment: the wireless communication module supports independent operation in BLE mode and NFC mode; in BLE mode, the MCU transmits heart rate (HR), respiratory rate (RR), and blood oxygen saturation parameters processed by conventional filtering algorithms in real time through the Bluetooth antenna; In the NFC mode, the MCU uses the NFC coil at intervals. Update heart rate (HR), respiratory rate (RR), and blood oxygen saturation parameters; maximum duration of single test. Data reading; the MCU detects the duration When there is no Bluetooth connection, the control skin patch enters a deep sleep state, in which only the NFC module and MCU remain working. MCU deep sleep current meets ; The MCU is also configured with synchronous data acquisition firmware to trigger the multimodal sensing components to synchronously acquire physiological signals.

[0032] The parameters satisfy ( =10s), =7h), =30s), the frequency of the physiological signal synchronously acquired by the multimodal sensing components meets (f=250Hz), and the conventional filtering algorithm is 50Hz notch filter + 0.5-100Hz bandpass filter (that is, the filtering function satisfies (H(f)=1), (0.5Hz≤f≤100Hz) and (f≠50Hz); (H(f)=0), other f); The above solution can achieve special effects in outdoor emergency monitoring scenarios: the real-time transmission in BLE mode (delay ≤ 100 ms) can be used for instant health warnings during outdoor sports, and the offline storage + contactless reading in NFC mode (without the need for mobile phone Bluetooth connection) can, in a network-free environment, read historical data within 7 hours by approaching the patch with a mobile phone, providing key cardiopulmonary function information for outdoor first aid and solving the limitation of traditional devices relying on network / Bluetooth connection.

[0033] The NFC module model is ST25DV64K-JFR6D3, and the MCU model is ESP32-PICO-D4; after processing electrocardiogram signals, respiratory wave signals, and chest photoplethysmography signals with conventional filtering algorithms, the heart rate HR, respiratory rate RR, and blood oxygen saturation (SpO2) are calculated; when the synchronous acquisition frequency (f = 250 Hz), the multimodal sensing component synchronously acquires electrocardiogram signals, ballistocardiogram signals, respiratory wave signals, and chest photoplethysmography signals; the parameters updated at intervals (T1 = 10 s) in NFC mode include the RR interval (RR{int}) derived from electrocardiogram signals, the blood oxygen saturation (SpO2) derived from chest photoplethysmography signals, and the respiratory rate RR derived from respiratory wave signals.

[0034] As an optional embodiment, it also includes a usage method for a multimodal intelligent skin patch for multi-scenario cardiopulmonary function monitoring; The usage method includes the following steps: S1. Device fitting and position calibration: Attach the lower Ecoflex material layer of the skin patch to the skin of the user's chest monitoring area near the heart and lungs through 3M2477P medical silicone double-sided tape; after fitting, the MCU collects the initial electrocardiogram signal ECG0 through the electrocardiogram front-end analog module and the initial respiratory wave signal RESp0 through the respiratory wave detection module, and calculates the signal-to-noise ratio SNRECG of ECG0 and the amplitude ARESP of RESp0; if SNRECG < SNR0 or ARESP < A0, the MCU pushes a position adjustment prompt to the mobile phone through Bluetooth until SNRECG ≥ SNR0 or ARESP ≥ A0; Ensure that the respiratory sensing electrode, electrocardiogram sensing electrode, and PPG sensor are in close contact with the skin and there are no air bubbles; S2. If the skin patch is in the deep sleep state, approach the mobile phone to the NFC coil of the skin patch, and wake up the MCU through electromagnetic induction energy capture; the MCU collects the initial motion data ACC0 through the IMU module and determines the scenario in combination with the preset scenario threshold: when ACC0 < Ath1, it is determined as the night sleep apnea monitoring scenario; when ACC0 ≤ Ath2, it is determined as the home rest rehabilitation monitoring scenario; when ACC0 ≥ Ath2, it is determined as the high-intensity interval exercise scenario; after scenario recognition, the MCU configures the voltage output strategy of the power management component based on the scenario; S3, the MCU initializes the ECG front-end simulation module through the SPI interface, configures the sampling rate fECG according to the scenario and establishes a data transmission link; initializes the IMU module through the I2C interface, configures the sampling frequency fACG according to the scenario; initializes the respiratory wave detection module, sets the capacitive coupling detection sensitivity level according to the scenario; initializes the PPG sensor, adjusts the output accuracy of the output voltage regulator and the drive current Idrv of the output voltage regulator; and initializes the communication parameters of the Bluetooth antenna and NFC coil. It should be noted that in the sleep scenario, the sampling rate of the ECG front-end simulation module is 125Hz, the sampling frequency of the IMU module is 50Hz, and the capacitive coupling detection sensitivity level of the respiratory wave detection module is 3; in the resting scenario, the sampling rate of the ECG front-end simulation module is 200Hz, the sampling frequency of the IMU module is 80Hz, and the capacitive coupling detection sensitivity level of the respiratory wave detection module is 4; in the HIIT scenario, the sampling rate of the ECG front-end simulation module is 250Hz, the sampling frequency of the IMU module is 100Hz, and the capacitive coupling detection sensitivity level of the respiratory wave detection module is 5; the Bluetooth antenna communication baud rate is 9600bps, and the NFC coil communication distance is ≤5cm. S4. Perform multimodal signal acquisition and validity verification: The MCU calls the synchronous data acquisition firmware to trigger each sensor module to synchronously acquire signals according to the initialization parameters; during the acquisition process, the MCU determines the validity of the signal through multimodal collaborative logic: The specific calculation steps are as follows: First, determine the validity of the ECG and PPG signals. Locate the peak value of the R wave in the ECG signal and the peak value of the pulse wave in the PPG signal. Then calculate the time difference between these two times. This time difference is equal to the PPG pulse wave peak value minus the ECG R wave peak value. If the calculated time difference falls within the pre-set range of minimum to maximum permissible time difference, both the ECG and PPG signals are considered valid.

[0035] Next, the validity of the RESP and ACC signals is determined. The periodic variation trend of the RESP signal and the chest rise and fall movement trend reflected by the ACC signal are analyzed separately, and then the consistency of these two trends is calculated. If the calculated trend consistency reaches or exceeds a preset threshold P0, both the RESP and ACC signals are determined to be valid.

[0036] Finally, there is the invalid signal handling mechanism. In the above judgment process, if any signal (ECG, PPG, RESP, ACC) is judged as invalid a preset number of times, the corresponding module will be triggered to perform a re-initialization operation. S5. After multimodal signal acquisition and validity verification, select BLE mode or NFC mode for data transmission. Specifically: BLE mode: The MCU uses a conventional filtering algorithm to process the effective signal and then calculates heart rate (HR), respiratory rate (RR), and blood oxygen saturation (SpO2). The heart rate is calculated by dividing 60 by the average of 5 consecutive RR intervals; the respiratory rate is calculated by dividing 60 by the average of 3 consecutive respiratory cycles; and the blood oxygen saturation is calculated based on the amplitude ratio of red light to infrared light in PPG. The processed data is transmitted to the mobile phone via Bluetooth antenna. The transmission format is to send one data packet every T4 time interval. Each data packet contains a timestamp, scene identifier, and signal validity mark. NFC mode: Bluetooth communication is turned off in this mode. The current time is synchronized to the NFC dynamic tag module through the NFC function of the mobile phone. The MCU uses parameter calculation algorithm to process the valid signal. Heart rate, RR interval, respiratory rate, and blood oxygen saturation are updated every T1 time interval, and heart rate scatter plot and RR scatter plot are generated. The data is stored in the storage unit of the NFC dynamic tag module as a segment every T5 time interval. S6. Data storage and low-power management are performed after S5: Data storage: In BLE mode, the raw signal data is stored locally on the mobile app, and the processed heart rate (HR), respiratory rate (RR), and blood oxygen saturation (SpO2) are simultaneously stored on the cloud server; in NFC mode, the data is first stored in the storage unit of the NFC dynamic tag module. When the mobile phone reads the data, it will store the relevant parameters locally and upload the raw signal snapshot to the cloud. The cloud data is associated with the device's unique number.

[0037] Low-power management: In BLE mode, if no Bluetooth connection is detected within a time period T3, and the IMU module determines that the current scenario is not motion-based, the MCU will control the NMOS switch to disconnect the power to all sensor modules, leaving only the NFC module and itself in deep sleep mode. If the IMU module determines that the scenario is motion-based, the time period will be extended to T7 if no connection is found before entering deep sleep mode. In NFC mode, if no data read / interaction is detected within a time period T3, the MCU will control the disconnection of the power to all sensor modules and enter deep sleep mode.

[0038] During deep sleep, the IMU module will wake up once every T8 time period and continuously collect motion data for a duration of T9. If the motion data ACCwake is greater than or equal to Ath1, it will trigger the MCU to wake up each sensor module and restore the monitoring function.

[0039] The relevant parameters are as follows: signal-to-noise ratio (SNR) SNR0 is 5 dB, A0 is 0.1 V, Ath1 is 0.2 g (g is the acceleration due to gravity), Ath2 is 0.5 g, voltage deviation ΔU is ±1%, drive current Idrv is 20 mA, Δtmin is 0.1 s, Δtmax is 0.5 s, P0 is 0.8, N1 is 3, T1 is 10 s, T4 is 1 s, T5 is 10 s, T3 is 30 s, T7 is 60 s, T8 is 5 min, and T9 is 10 s. It should also be noted that it produces unique effects in three special scenarios: In the HIIT scenario, the flexible circuit board of the device and the high sampling rate initialization of the method achieve motion artifact suppression (signal-to-noise ratio is improved to more than 8dB). In sleep scenarios, the device's low-power power management and intermittent power supply methods enable 7 hours of continuous monitoring with a power consumption of ≤80mAh. In resting rehabilitation scenarios, the device's dual-mode communication and cloud storage enable doctors to remotely view patients' cardiopulmonary function trends in real time, solving the problems of poor adaptability and data isolation in traditional monitoring scenarios. The ECG front-end analog module is model MAX30003CTI+, the IMU module is model LIS2DH12, the respiratory wave detection module is model FDC2214RGHR, the NFC module is model ST25DV64K-JFR6D3, and the MCU is model ESP32-PICO-D4. In the multimodal collaborative logic, trend consistency is calculated using the Pearson correlation coefficient. The PPG red light wavelength is 660nm and the infrared light wavelength is 940nm. The NFC dynamic tag module storage unit capacity is 64KB, and the device unique number is the MCU built-in UUID. The initial electrocardiogram signal (ECG0) and initial respiratory wave signal (RESP0) collected in step S1 are used to calibrate the fitting position. The signals collected synchronously in step S4 include the electrocardiogram signal ECG, acceleration signal ACC, respiratory wave signal RESP, and PPG signal. The parameter calculation algorithm in step S5 is consistent with the logic of the conventional filtering algorithm (both are calculated after 50Hz notch filtering + 0.5~100Hz bandpass filtering).

[0040] As an optional embodiment: the position calibration in step S1 also includes skin condition adaptation: The MCU collects skin contact impedance through ECG sensing electrodes. If the collected skin contact impedance is greater than the preset impedance threshold Zth, the phone will prompt you to apply medical conductive gel to the area where the sensing electrodes are attached until the skin contact impedance is less than or equal to the impedance threshold.

[0041] The skin contact impedance is calculated as follows: the ECG front-end simulation module outputs a test voltage and simultaneously collects a test current. The skin contact impedance is equal to the test voltage divided by the test current.

[0042] The relevant parameters are as follows: impedance threshold Zth is 5000 ohms, and test voltage is 0.5 volts.

[0043] It should be noted that by using conductive gel to reduce skin contact impedance to below 5kΩ, the ECG signal amplitude is increased to above 0.2V, solving the problem of weak signal caused by dry skin and ensuring stable monitoring even when the skin is in poor condition. The ECG front-end analog module is model MAX30003CTI+, and the MCU is model ESP32-PICO-D4. When the ECG sensing electrode collects skin contact impedance, it relies on the test voltage output by the ECG front-end simulation module to collect the test current. By accurately calculating the impedance value, it ensures the accuracy of skin condition adaptation. After applying medical conductive gel, the conductivity between the ECG sensing electrode and the skin is enhanced, and the signal-to-noise ratio of ECG signal acquisition is improved by ≥3dB.

[0044] As an optional embodiment: the scene recognition in step S2 also includes a manual correction mechanism: If the scenario automatically identified by the MCU does not match the user's actual scenario, the user can manually select the scenario through a mobile APP. After receiving the manual command, the MCU will reconfigure the voltage output strategy of the power management component and the parameters of each module.

[0045] It should be noted that manual correction avoids scene misjudgment caused by fluctuations in motion data, enabling module parameters to quickly adapt to the actual scene, ensuring the continuity and accuracy of monitoring data in transitional scenarios, and solving the lag problem of traditional automatic scene recognition. The MCU model is ESP32-PICO-D4, and the wireless communication module includes a Bluetooth antenna and an NFC coil (NFC module model ST25DV64K-JFR6D3). The manual correction command is transmitted to the MCU via the Bluetooth antenna through the mobile APP. After receiving the command, the MCU reconfigures the voltage output strategy of the power management component to be consistent with the power supply logic of the automatic scene recognition (e.g., intermittent power supply of the regulator (U4) for a resting scene, and continuous power supply of the regulator (U4) for a moving scene). The reconfigured module parameters are consistent with the scene-specific parameters in step S3.

[0046] As an optional embodiment: the multimodal signal validity verification in step S4 further includes supplementary information for abnormal signals: If a single ECG is deemed invalid, then the peak pulse wave time of the PPG, combined with historical data, will be used. The current R-wave time is estimated using the mean of the PPG pulse wave peak time. Specifically, the current estimated R-wave time is equal to the PPG pulse wave peak time minus the historical values. The mean value is used to supplement the electrocardiogram signal data.

[0047] If a single RESP is deemed invalid, the current respiratory rate is calculated using the ACC function of the IMU module.

[0048] The specific process is as follows: perform low-pass filtering on the ACC signal to obtain the filtered ACC signal, extract the mean value of the interval between adjacent peaks of the filtered signal, and then divide 60 by this mean value to obtain the estimated respiratory rate.

[0049] Explanation of relevant parameters: The average value of historical Δt is the average value of the previous 5 valid Δt values.

[0050] It should be noted that by cross-supplementing multimodal signals, the accuracy of filling invalid signals is improved, monitoring interruption caused by the loss of a single signal is avoided, and the problem of poor anti-interference capability of traditional single-mode monitoring is solved.

[0051] The IMU module is model LIS2DH12, the ECG front-end analog module is model MAX30003CTI+, and the MCU is model ESP32-PICO-D4. When the ECG is invalid, the peak pulse wave time (tP) of the PPG comes from the chest photoplethysmography signal collected by the PPG sensor.

[0052] As an optional embodiment: the BLE mode in step S5 also sets a data retransmission mechanism: when the Bluetooth connection is interrupted, the skin patch will temporarily store the valid signal data during the interruption in the local cache; If the interruption duration is less than or equal to After the connection is restored, the MCU will retransmit the temporarily stored data to the phone in chronological order, and this retransmitted data will be marked with a retransmission identifier; if the interruption duration is longer than... The MCU will trigger the encrypted storage of local cache data. When the Bluetooth connection is connected again, it will prioritize the retransmission of this encrypted data. After the retransmission is completed, the local encrypted cache data will be deleted.

[0053] The relevant parameters are: It lasts for 5 minutes.

[0054] It should be noted that the MCU model is ESP32-PICO-D4, and the wireless communication module includes a Bluetooth antenna; the local cache capacity is 16KB, which can store valid signal data during the interruption (including physiological parameters derived from ECG signals, PPG signals, and respiratory wave signals); the encrypted storage uses the AES-128 algorithm to ensure that the local cache data cannot be read illegally; after the retransmission data is marked with a retransmission identifier, the mobile APP can distinguish between real-time data and retransmission data, and integrate them according to timestamps to form a complete monitoring record.

[0055] As an optional embodiment: the low power management in step S6 also includes adaptive power adjustment: the MCU collects the remaining power of the lithium battery in real time through the power detection module.

[0056] When the remaining battery power is less than the preset battery power threshold When the remaining battery power is less than the preset battery power threshold, the MCU will automatically reduce the sampling frequency of each module; At this time, the MCU retains only the ECG front-end analog module, PPG sensor acquisition function, and NFC communication function, while shutting down the IMU module and respiratory wave detection module; when the remaining battery power is greater than or equal to the preset battery power threshold... At this time, the MCU will restore the normal sampling frequency and power supply of each module. The relationship between the three power thresholds is as follows: < < .

[0057] The relevant parameters meet the following requirements: power threshold. 100 mA 50 mA, It has a capacity of 200 mA / h.

[0058] It should also be noted that the supplementary test subjects for the scenario threshold experiment were 100 people, including 60 healthy individuals aged 20 to 40 years old and 40 patients with cardiopulmonary diseases aged 50 to 80 years old, including patients with hypertension and coronary heart disease. The test method was 24-hour dynamic data collection, covering 8 hours of sleep, 8 hours of rest at home, 1 hour of high-intensity exercise, and 7 hours of other activities, with the average IMU acceleration recorded every 10 seconds. The threshold was determined through K-means cluster analysis. The average acceleration in the sleep scenario was concentrated between 0 and 0.2g, so the threshold Ath1 was set at 0.2g; the average acceleration in the rest at home was concentrated between 0.2 and 0.5g, so the threshold Ath2 was set at 0.5g; and the average acceleration in the high-intensity exercise scenario was concentrated between 0.5 and 2g, with Ath2 at 0.5g as the dividing line. Regarding population suitability, the threshold was lowered by 10% for children (6 to 12 years old, 20 people tested) due to their smaller range of motion; and the threshold was raised by 10% for the elderly (65 years old and above, 20 people tested) due to their weaker acceleration signal caused by muscle relaxation.

[0059] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions that fall within the scope of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of this template.

Claims

1. A multimodal smart skin patch for multi-scenario cardiopulmonary function monitoring, characterized in that, It includes a layered main body, a flexible circuit board, multimodal sensing components, a power management component, and a wireless communication module; The layered structure includes an upper Ecoflex material layer, a lower Ecoflex material layer, and a lithium battery and a wireless charging coil encapsulated between the two Ecoflex material layers. The substrate of the flexible circuit board is polyimide, and a double-bridge serpentine structure sensing wire is provided on the flexible circuit board. The multimodal sensing component includes a respiratory sensing electrode, two electrocardiogram sensing electrodes, a PPG sensor, an IMU module, and a respiratory wave detection module. The surface of the respiratory sensing electrode is bonded with a PDMS material layer, and the surface of the two electrocardiogram sensing electrodes is bonded with a highly adhesive conductive hydrogel layer. The lower Ecoflex material layer is cut according to the shape of each electrode and sensor, corresponding to the positions of each electrode and sensor, so that each electrode and sensor is exposed to directly contact human skin. The non-sensing areas on the surface of the lower Ecoflex material layer are bonded with 3M2477P medical-grade silicone double-sided adhesive. The power management component includes an NMOS switch, a power path control chip, a low dropout linear regulator, and a power management module. The NMOS switch is connected to the power management module through the power path control chip to control the power path of each module. The wireless communication module includes a Bluetooth antenna and an NFC coil. The top circuit integration area of ​​the flexible circuit board integrates a wireless charging and power supply module, a power management module, an automatic power control module, an NFC dynamic tag module, a power detection module, an electrocardiogram front-end simulation module, and an MCU. The ECG front-end simulation module communicates with the MCU via an SPI interface to acquire ECG signals and transmit them to the MCU; the IMU module and the respiratory wave detection module communicate with the MCU via I2C interfaces to acquire acceleration signals and respiratory wave signals, respectively, and transmit them to the MCU; the PPG sensor is electrically connected to the ECG front-end simulation module. In the power management component, the output voltage meets the following requirements. The low-dropout linear regulator powers the ECG front-end analog module and PPG sensor; the output voltage meets the requirements. The low-dropout linear regulator powers the IMU module and the respiratory wave detection module; the output voltage meets the requirements. The low-dropout linear regulator powers the PPG sensor light source drive unit; The MCU controls the NMOS switch of the power management component through the I2C interface. The power detection module is electrically connected to the MCU, collects the remaining power of the lithium battery in real time and transmits it to the MCU. The MCU adjusts the power supply strategy of each module based on the remaining power. The power supply strategy includes: when the remaining power is less than a preset power threshold... When the remaining battery power is less than the preset battery power threshold, the MCU will automatically reduce the sampling frequency of each module; At this time, the MCU retains only the ECG front-end analog module, PPG sensor acquisition function, and NFC communication function, while shutting down the IMU module and respiratory wave detection module; when the remaining battery power is greater than or equal to the preset battery power threshold... At this time, the MCU will restore the normal sampling frequency and power supply of each module, among which Less than Less than ; The wireless communication module supports independent operation in BLE mode and NFC mode; in BLE mode, the MCU transmits heart rate, respiratory rate and blood oxygen saturation processed by conventional filtering algorithm in real time through Bluetooth antenna. In the NFC mode, the MCU uses the NFC coil at intervals. Update heart rate, respiratory rate, blood oxygen saturation, and maximum support duration per session. Data reading; the MCU detects the duration When there is no Bluetooth connection, the control multimodal smart skin patch enters a deep sleep state. In the deep sleep state, the NFC dynamic tag module, MCU and IMU module continue to work intermittently. The MCU also calls the synchronous data acquisition firmware to trigger the multimodal sensing components to synchronously acquire physiological signals. During the acquisition process, the MCU determines the validity of the signal through multimodal collaborative logic. If a single ECG signal is deemed invalid, then the peak time of the pulse wave from the PPG (Pulse Wave Gyre) is used in conjunction with historical time differences. The current R-wave time is estimated using the mean of the pulse wave values. Specifically, the estimated R-wave time is equal to the peak pulse wave time of the PPG minus the historical time difference. The average value of these values ​​is used to supplement the electrocardiogram signal data. If a respiratory wave signal is deemed invalid in a single instance, the current respiratory rate is calculated using the acceleration signal acquired by the IMU module.

2. A method of using a multimodal smart skin patch for multi-scenario cardiopulmonary function monitoring based on claim 1, characterized in that, The method of use includes the following steps: S1. Device Fitting and Position Calibration: The lower Ecoflex material layer is attached to the user's chest monitoring area near the heart and lungs using 3M 2477P medical-grade silicone double-sided adhesive. After attachment, the MCU acquires the initial ECG signal through the ECG front-end analog module. The respiratory wave detection module acquires the initial respiratory wave signal. ,calculate signal-to-noise ratio and amplitude ;like or The MCU will then push a location adjustment prompt to the mobile phone via Bluetooth until... or , To preset the signal-to-noise ratio threshold, The threshold value for respiratory wave amplitude; If the calculated skin contact impedance is greater than the preset impedance threshold, the phone will prompt you to apply medical conductive gel to the contact area of ​​the ECG sensor electrode until the skin contact impedance is less than or equal to the preset impedance threshold. Once the impedance detection is complete, the MCU will send a 0x05 command to turn off the test voltage and switch to ECG acquisition mode. S2. If the multimodal smart skin patch is in deep sleep mode, bring the phone close to the NFC coil of the multimodal smart skin patch to wake up the MCU through electromagnetic induction energy capture; the MCU collects the initial acceleration signal through the IMU module. Determine the scenario by combining preset scenario thresholds: when When, it is determined to be a nocturnal sleep apnea monitoring scenario; when At that time, it was determined to be a home-based rest and rehabilitation monitoring scenario; when At that time, it was determined to be a high-intensity intermittent exercise scenario. and The acceleration threshold is used; after scene recognition, the MCU configures the power management component's voltage output strategy based on the scene. S3, the MCU initializes the ECG front-end simulation module via the SPI interface and configures the sampling frequency according to the scenario. A data transmission link is established; the MCU initializes the IMU module via the I2C interface and configures the sampling frequency according to the scenario. Initialize the respiratory wave detection module and set the capacitive coupling detection sensitivity level according to the scenario; initialize the PPG sensor and adjust the output accuracy and drive current of the low dropout linear regulator. Simultaneously initialize the communication parameters of the Bluetooth antenna and NFC coil; S4. Perform multimodal signal acquisition and validity verification: The MCU calls the synchronous data acquisition firmware to trigger the multimodal sensing component to synchronously acquire signals according to the initialization parameters; during the acquisition process, the MCU determines the validity of the signal through multimodal collaborative logic: If a single ECG signal is deemed invalid, then the peak time of the pulse wave from the PPG (Pulse Wave Gyre) is used in conjunction with historical time differences. The current R-wave time is estimated using the mean of the pulse wave values. Specifically, the estimated R-wave time is equal to the peak pulse wave time of the PPG minus the historical time difference. The average value of these values ​​is used to supplement the electrocardiogram signal data. If a respiratory wave signal is determined to be invalid in a single instance, the current respiratory rate is calculated using the acceleration signal acquired by the IMU module. S5. After multimodal signal acquisition and validity verification, select BLE mode or NFC mode for data transmission, specifically: BLE mode: The MCU uses a conventional filtering algorithm to process the effective signal, and then calculates the heart rate, respiratory rate, and blood oxygen saturation. NFC mode: Bluetooth communication is turned off in this mode. The current time is synchronized to the NFC dynamic tag module through the NFC function of the mobile phone. The MCU uses parameter calculation algorithm to process the valid signal. S6. Data storage and low-power management are performed after S5: Data storage: In BLE mode, the raw signal data is stored locally on the mobile app, and the processed heart rate, respiratory rate, and blood oxygen saturation are simultaneously stored on the cloud server; in NFC mode, the data is first stored in the storage unit of the NFC dynamic tag module. When the mobile phone reads the data, it will store the relevant parameters locally and upload the raw signal snapshot to the cloud server. The cloud data is associated with the device's unique number. Low power management: In BLE mode, if a duration of [duration missing] is detected... If there is no Bluetooth connection within a certain time and the IMU module determines that the current scenario is not a motion scenario, the MCU will control the NMOS switch to disconnect the power supply to the multimodal sensing components, leaving only the NFC dynamic tag module and the MCU in deep sleep mode; if the IMU module determines that the scenario is a motion scenario, the time will be extended to a certain duration. If no Bluetooth connection is established after a certain period, it will enter deep sleep mode; in NFC mode, if a connection is detected after a certain period... If there is no data reading interaction within a certain period of time, the MCU will control the power supply of the multimodal sensing component to be disconnected and enter a deep sleep state. During deep sleep, the IMU module will periodically... It wakes up once at a certain time and continues to collect data for a duration of [duration missing]. acceleration signal If acceleration signal Greater than or equal to This will trigger the MCU to wake up the multimodal sensing components and restore the monitoring function; The low-power management in step S6 also includes adaptive power adjustment: the MCU collects the remaining power of the lithium battery in real time through the power detection module. When the remaining battery power is less than the preset battery power threshold When the remaining battery power is less than the preset battery power threshold, the MCU will automatically reduce the sampling frequency of each module; At this time, the MCU retains only the ECG front-end analog module, PPG sensor acquisition function, and NFC communication function, while shutting down the IMU module and respiratory wave detection module; when the remaining battery power is greater than or equal to the preset battery power threshold... At this time, the MCU will restore the normal sampling frequency and power supply of each module, among which Less than Less than .

3. The method of use according to claim 2, characterized in that, The position calibration in step S1 also includes skin condition adaptation: The MCU collects skin contact impedance through ECG sensing electrodes. If the collected skin contact impedance is greater than a preset impedance threshold, the phone will prompt you to apply medical conductive gel to the contact area of ​​the ECG sensing electrodes until the skin contact impedance is less than or equal to the preset impedance threshold. The skin contact impedance is calculated as follows: the ECG front-end simulation module outputs a test voltage and simultaneously collects a test current. The skin contact impedance is equal to the test voltage divided by the test current.

4. The method of use according to claim 3, characterized in that, The scene recognition in step S2 also includes a manual correction mechanism: If the scenario automatically identified by the MCU does not match the user's actual scenario, the user can manually select the scenario through a mobile app. After receiving the manual command, the MCU will reconfigure the voltage output strategy of the power management component.

5. The method of use according to claim 2, characterized in that, Step S5's BLE mode also includes a data retransmission mechanism: when the Bluetooth connection is interrupted, the multimodal smart skin patch will temporarily store the valid signal during the interruption in the local cache; If the interruption duration is less than or equal to the duration After the Bluetooth connection is restored, the MCU will retransmit the temporarily stored valid signals to the mobile phone in chronological order, and these retransmitted valid signals will be marked with a retransmission identifier. If the interruption duration is greater than the duration The MCU will trigger the encrypted storage of valid signals in the local cache. When the Bluetooth connection is connected again, these encrypted valid signals will be retransmitted first. After the retransmission is completed, the encrypted valid signals in the local cache will be deleted.

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