Human health real-time monitoring system and method based on hydrogel flexible sensor

Abstract

The invention discloses a human health real-time monitoring system and method based on a hydrogel flexible sensor, relates to the technical field of health monitoring, and aims to solve the problems of chronic disease patient dynamic and static state signal acquisition distortion and mode switching energy consumption caused by insufficient scene adaptability of a traditional rigid sensor. Comprising a human body motion state signal acquisition module used for acquiring various human body motion state signals by using a hydrogel flexible sensor and detecting whether the hydrogel flexible sensor is in a preset working scene state according to the human body motion state signals; the mode selection module is used for selecting an adaptive working mode from preset working modes of the hydrogel flexible sensor according to the human body motion state signal output by the human body motion state signal acquisition module; according to the hydrogel flexible sensor, the problem of signal distortion caused by scene misjudgment of a traditional sensor is directly solved through a biocompatibility curved surface fitting design of the hydrogel flexible sensor and in combination with pressure signal frequency characteristic analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring, and more particularly to a real-time human health monitoring system and method based on a hydrogel flexible sensor. Background Art

[0002] Traditional rigid sensors face numerous challenges in the long-term health monitoring of chronic diseases such as diabetes and cardiovascular disease. For example, in diabetic patients, abnormal peripheral circulation can cause an average drift of 15%-20% in the pressure perception threshold. Clinical data from a tertiary hospital showed that the misdiagnosis rate was as high as 32% when using traditional rigid pressure sensors to monitor the risk of foot ulcers. Furthermore, when patients recovering from cardiovascular disease switch between static and dynamic states, conventional accelerometers, with a fixed sampling frequency (typically 100Hz), are unable to capture sudden heart rate changes in real time (such as a sudden increase from a resting heart rate of 60 beats / minute to 120 beats / minute during exercise), resulting in the loss of key physiological signals.

[0003] The limitations of traditional sensors are particularly evident when transitioning between static (such as bed rest) and dynamic (such as gait training) scenarios. When a patient transitions from bed rest to standing and walking, the rigid sensor, lacking an adaptive adjustment mechanism, can experience pressure signal acquisition errors of up to ±25%, and the acceleration signal-to-noise ratio drops by 40%. At the same time, device energy consumption increases by more than three times. These issues make it difficult for traditional sensors to meet the real-time, accuracy, and comfort requirements of refined chronic disease management. Breakthroughs in new flexible sensing technologies are urgently needed. To address this, we propose a real-time human health monitoring system and method based on hydrogel flexible sensors. Summary of the Invention

[0004] The purpose of the present invention is to provide a real-time human health monitoring system and method based on hydrogel flexible sensors to solve the problems of distortion in dynamic and static state signal acquisition and mode switching energy consumption of chronic disease patients caused by insufficient scene adaptability of traditional rigid sensors.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a real-time human health monitoring system based on a hydrogel flexible sensor, comprising:

[0006] A human motion state signal acquisition module is used to obtain a variety of human motion state signals using a hydrogel flexible sensor, and detect whether the hydrogel flexible sensor is in a preset working scene state based on the human motion state signals;

[0007] Among them, the various human motion state signals include acceleration signals, pressure signals, temperature signals and strain signals, and the preset working scene states include static states and dynamic states;

[0008] A mode selection module is used to select an adaptive working mode from the preset working modes of the hydrogel flexible sensor according to the human motion state signal output by the human motion state signal acquisition module, and to monitor the human health state in real time through the selected working mode to obtain real-time monitoring results;

[0009] Wherein, the preset working mode includes a first preset working mode and a second preset working mode;

[0010] The mode output module is used to put the hydrogel flexible sensor into a standby state when the human motion state signal obtained by the human motion state signal acquisition module does not conform to the preset working scene state.

[0011] A method for real-time monitoring of human health based on a hydrogel flexible sensor comprises the following steps:

[0012] S1: Using hydrogel flexible sensors to obtain various human motion state signals;

[0013] S2: Detecting whether the hydrogel flexible sensor is in a preset working scene state based on the human body motion state signal;

[0014] S3: When the hydrogel flexible sensor is in a preset working scene state, the human health real-time monitoring system selects an adaptive working mode from the preset working modes of the hydrogel flexible sensor according to the human motion state signal;

[0015] S4: Monitor the health status of the human body in real time through the selected working mode to obtain real-time monitoring results;

[0016] S5: When the hydrogel flexible sensor is not in a preset working scene state, the hydrogel flexible sensor is in a standby state.

[0017] Preferably, in step S2, detecting whether the hydrogel flexible sensor is in a preset working scene state according to the human body motion state signal specifically includes the following steps:

[0018] S201: The hydrogel flexible sensor acquires an acceleration signal and a pressure signal, and calculates a first pressure change frequency and a first acceleration change frequency respectively;

[0019] The hydrogel flexible sensor further includes a temperature sensor, which obtains the current temperature and calculates the temperature change frequency based on the current temperature;

[0020] S202: The hydrogel flexible sensor calculates the common frequency component of the first pressure change frequency and the first acceleration change frequency within a preset time length as the static pressure change frequency f static ;

[0021] S203: When the range of the pressure signal output by the hydrogel flexible sensor is within the preset pressure signal value and the static pressure change frequency f static At the static judgment threshold f static_th When the φ is within the range of φ, the hydrogel flexible sensor is determined to be in a static state; otherwise, the hydrogel flexible sensor is determined to be in a dynamic state.

[0022] Preferably, in step S201:

[0023] The calculation formula for the first pressure change frequency is: Where, f p is the pressure variation frequency, t current is the current moment of the first pressure change, t prev is the last moment of the first pressure change, and the difference between the two is Δt p Represents the minimum time interval for collecting pressure signals; n p is the number of pressure changes;

[0024] The calculation formula for the first acceleration change frequency is: Where, f a is the frequency of acceleration change, t′ current is the current moment of the first acceleration change, t′ prev is the last time the first acceleration changed, Δt a is the minimum time interval for collecting acceleration signals; n a is the number of acceleration changes.

[0025] Preferably, in step S3, selecting an adapted working mode from preset working modes of the hydrogel flexible sensor according to the human body motion state signal specifically includes the following steps:

[0026] S301: Acquire a preset working mode, where the preset working mode includes a first preset working mode and a second preset working mode;

[0027] S302: When the hydrogel flexible sensor is in a dynamic state, selecting a first preset working mode;

[0028] S303: When the hydrogel flexible sensor is in a static state, selecting a second preset operating mode.

[0029] Preferably, after selecting the first preset working mode, the following steps are further included:

[0030] S302a: The hydrogel flexible sensor obtains a strain signal, an acceleration signal, and a pressure signal, and calculates a second acceleration change frequency f through the strain signal. a2 , calculate the third acceleration change frequency f based on the acceleration signal a3, calculate the second pressure change frequency f through the pressure signal p2 ;

[0031] S302b: Determine the comprehensive change frequency f by using the second acceleration change frequency, the third acceleration change frequency, and the second pressure change frequency com ;

[0032] S302c: When the second pressure change frequency is similar to the acceleration change frequency, that is, |f p2 -f a |≤δ f , where δ f is the frequency approximation threshold, the first preset working mode A is selected, otherwise, the first preset working mode B is selected.

[0033] Preferably, after selecting the second preset working mode, the method further includes:

[0034] S303a: When the acceleration signal is at the second acceleration threshold a th2 When , the hydrogel flexible sensor outputs a first pressure signal;

[0035] S303b: Determine whether the acceleration signal is within the third acceleration threshold a th3 ,If not, the hydrogel flexible sensor performs Fourier transform on the pressure signal;

[0036] S303c: Determine the Fourier transform result. If the Fourier transform result is an amplitude, determine whether the pressure signal is higher than the second pressure threshold p. th2 , if yes, the hydrogel flexible sensor outputs a first pressure signal;

[0037] S303d: When the Fourier transform result is frequency, determine whether the amplitude of the pressure signal in the preset frequency band in the pressure signal is higher than the third pressure threshold p th3 If yes, the hydrogel flexible sensor outputs a second pressure signal.

[0038] Preferably, after selecting the first preset working mode A, the following steps are included:

[0039] A1: The human body's motion patterns are acquired based on acceleration signals, and the hydrogel flexible sensor acquires strain and pressure signals;

[0040] A2: Determine whether the acceleration signal is lower than the first acceleration threshold a for transition from dynamic to static th1 ,If yes, the hydrogel flexible sensor performs Fourier transform on the pressure signal;

[0041] A3: Determine the Fourier transform result. If the Fourier transform result is an amplitude, determine whether the pressure signal is higher than the second pressure threshold p. th2If yes, the hydrogel flexible sensor outputs a first pressure signal;

[0042] A4: When the Fourier transform result is frequency, determine whether the amplitude of the pressure signal in the preset frequency band in the pressure signal is higher than the third pressure threshold p th3 If yes, the hydrogel flexible sensor outputs a second pressure signal.

[0043] Preferably, after selecting the first preset working mode B, the method further includes:

[0044] B1: The human body's motion patterns are acquired based on acceleration signals, and the hydrogel flexible sensor acquires strain and pressure signals;

[0045] B2: Determine whether the acceleration signal is lower than the first acceleration threshold a for dynamic to static transition th1 , if yes, the hydrogel flexible sensor outputs a third pressure signal;

[0046] B3: When the acceleration signal is higher than the first acceleration threshold a th1 When the acceleration is high, the hydrogel flexible sensor performs Fourier transform on the pressure signal, and when the acceleration is high, the pressure signal is transformed in the frequency domain to explore the characteristics of the pressure signal;

[0047] B4: Determine the Fourier transform result. If it is an amplitude, determine whether the pressure signal is higher than the second pressure threshold p th2 ,If so, the hydrogel flexible sensor outputs a first pressure signal;

[0048] B5: When the Fourier transform result is frequency, determine whether the amplitude of the pressure signal in the preset frequency band in the pressure signal is higher than the third pressure threshold p th3 If yes, the hydrogel flexible sensor outputs a second pressure signal.

[0049] Preferably, in step S4, performing real-time monitoring of the human health status through the selected working mode to obtain real-time monitoring results includes the following steps:

[0050] S401: Acquire multiple human motion state signals of the hydrogel flexible sensor collected before the target monitoring time;

[0051] S402: Acquire strain characteristics of the strain signal based on the strain signal collected before the target monitoring time;

[0052] S403: Based on the acceleration data in the acceleration signal collected before the target monitoring time, counting the number of times the same acceleration data appears, and obtaining acceleration characteristics of different acceleration data;

[0053] S404: Obtaining pressure data features based on the pressure signal collected before the target monitoring time;

[0054] S405: Detection results are based on the strain characteristics, acceleration characteristics, and pressure data characteristics.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The present invention uses the biocompatible curved surface fitting design of the hydrogel flexible sensor, combined with pressure signal frequency characteristic analysis, to achieve millisecond-level recognition of static and dynamic scenes of chronic disease patients, and automatically switch working modes, statically enabling the second preset mode of low-frequency sampling and dynamically activating the first preset mode of multimodal fusion. This directly solves the signal distortion problem caused by scene misjudgment of traditional sensors. In the walking scene of diabetic patients, the pressure signal acquisition error is reduced from ±15% to ±3% through dynamic mode switching.

[0057] 2. The present invention also conducts frequency domain-time domain joint analysis of multimodal signals based on the strain-acceleration conversion algorithm and Fourier transform technology through deep decoupling and early warning of chronic disease characteristic parameters, which can deeply mine the characteristic parameters related to chronic diseases, further solve the problem of traditional monitoring methods having a single dimension in analyzing the physiological characteristics of chronic diseases, and provide data support for the formulation of personalized rehabilitation plans.

[0058] 3. This invention also utilizes scenario-driven, low-power intelligent management. By setting a non-physiological activity trigger threshold, the system automatically switches from active to standby mode. This not only addresses the high power consumption of traditional devices, but also further optimizes the long-term wear compliance of patients with chronic diseases. For a diabetic patient monitoring 12 hours daily, the standby mechanism can extend the device's battery life from the traditional two days to 24 days, significantly reducing charging frequency. This is particularly suitable for patients with chronic diseases who have limited mobility. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0060] Example 1: The present invention relates to a real-time human health monitoring system based on a hydrogel flexible sensor, comprising a human motion state signal acquisition module, a mode selection module, and a mode output module:

[0061] A human motion state signal acquisition module is used to obtain a variety of human motion state signals using a hydrogel flexible sensor, and detect whether the hydrogel flexible sensor is in a preset working scene state based on the human motion state signals;

[0062] In an embodiment of the present invention, the multiple human motion state signals include acceleration signals, pressure signals, temperature signals, and strain signals, and the preset working scene states include static states and dynamic states;

[0063] A mode selection module is used to select an adaptive working mode from the preset working modes of the hydrogel flexible sensor according to the human motion state signal output by the human motion state signal acquisition module, and to monitor the human health state in real time through the selected working mode to obtain real-time monitoring results;

[0064] In an embodiment of the present invention, the preset working mode includes a first preset working mode and a second preset working mode;

[0065] The mode output module is used to put the hydrogel flexible sensor into a standby state when the human motion state signal obtained by the human motion state signal acquisition module does not conform to the preset working scene state.

[0066] Example 2: Figure 1 As shown, a real-time human health monitoring method based on a hydrogel flexible sensor includes the following steps:

[0067] S1: Using hydrogel flexible sensors to obtain various human motion state signals;

[0068] S2: Detecting whether the hydrogel flexible sensor is in a preset working scene state based on the human body motion state signal;

[0069] As another embodiment of the present invention, in step S2, detecting whether the hydrogel flexible sensor is in a preset working scene state according to the human body motion state signal specifically includes the following steps:

[0070] S201: The hydrogel flexible sensor acquires an acceleration signal and a pressure signal, and calculates a first pressure change frequency and a first acceleration change frequency respectively;

[0071] As another embodiment of the present invention, the calculation formula for the first pressure change frequency in step S201 is:

[0072]

[0073] Where, f p is the pressure change frequency, which directly reflects the frequency of pressure signal fluctuations; t current is the current moment of the first pressure change, t prev is the last moment of the first pressure change, and the difference between the two is Δt p Represents the minimum time interval for collecting pressure signals; n p is the number of pressure changes, recorded in Δt p The number of internal pressure changes. This formula is used to calculate the number of pressure signal changes per unit time. Through this formula, the dynamic change characteristics of the pressure signal can be quantified to assist in determining the body's movement status;

[0074] As another embodiment of the present invention, the calculation formula for the first acceleration change frequency in step S201 is:

[0075]

[0076] Where, f a is the acceleration change frequency, which reflects the frequency of changes in the acceleration signal; t′ current is the current moment of the first acceleration change, t′ prev is the last time the first acceleration changed, Δt a is the minimum time interval for collecting acceleration signals; n a is the number of acceleration changes. This formula is similar to the calculation logic of pressure change frequency and is used to calculate the number of changes in the acceleration signal per unit time. This formula can be used to analyze the dynamic changes in acceleration during human movement and determine information such as exercise intensity;

[0077] As another embodiment of the present invention, the hydrogel flexible sensor further includes a temperature sensor, which obtains the current temperature and calculates the temperature change frequency based on the current temperature. The temperature change frequency calculation formula is:

[0078]

[0079] Where, f T is the temperature change frequency, reflecting the frequency of temperature fluctuations; t″ current is the current moment of temperature change, t″ prev is the last time the temperature changed, Δt T is the minimum time interval for collecting temperature signals; n T is the number of temperature changes. This formula is used to calculate the number of changes in the temperature signal per unit time. Although temperature changes have a relatively small weight in determining static and dynamic states, calculating the temperature change frequency using this formula can assist in analyzing conditions such as heat changes generated by human body movement.

[0080] S202: The hydrogel flexible sensor calculates the common frequency component of the first pressure change frequency and the first acceleration change frequency within a preset time length as the static pressure change frequency f static , that is, taking the common frequency components of the two within a preset time length. This operation is to filter out the common stable frequency characteristics of the two in the static state from the pressure and acceleration change frequencies, exclude the chaotic change frequencies that may appear in the dynamic movement, and more accurately reflect the signal characteristics in the static state;

[0081] S203: When the range of the pressure signal output by the hydrogel flexible sensor is within the preset pressure signal value (Pmin ≤P≤P max ), and the static pressure change frequency f static At the static judgment threshold f static_th When the pressure is within 0.05, the hydrogel flexible sensor is determined to be in a static state; otherwise, the hydrogel flexible sensor is determined to be in a dynamic state. Where, P is the pressure signal, P min is the minimum pressure threshold, P max is the maximum pressure threshold. When this formula is satisfied, it means that the range of the pressure signal output by the hydrogel flexible sensor is within the preset pressure signal value, indicating that the pressure is in a relatively stable state; otherwise, it means that the pressure signal fluctuates greatly. Combined with the comparison of the static pressure change frequency and the threshold, it is jointly judged whether the human body is in a static state (such as sitting still, the pressure is stable and the static pressure change frequency is low) or a dynamic state (such as walking, the pressure fluctuation is large and the static pressure change frequency is high);

[0082] S3: When the hydrogel flexible sensor is in a preset working scene state, the human health real-time monitoring system selects an adaptive working mode from the preset working modes of the hydrogel flexible sensor according to the human motion state signal;

[0083] As another embodiment of the present invention, in step S3, selecting an adapted operating mode from preset operating modes of the hydrogel flexible sensor according to the human motion state signal specifically includes the following steps:

[0084] S301: Acquire a preset working mode, where the preset working mode includes a first preset working mode and a second preset working mode;

[0085] S302: When the hydrogel flexible sensor is in a dynamic state, selecting a first preset working mode;

[0086] As another embodiment of the present invention, after selecting the first preset operating mode, the following steps are further included:

[0087] S302a: The hydrogel flexible sensor obtains a strain signal, an acceleration signal, and a pressure signal, and calculates a second acceleration change frequency f through the strain signal. a2 , calculate the third acceleration change frequency f based on the acceleration signal a3 , calculate the second pressure change frequency f through the pressure signal p2 ;

[0088] The calculation formula for the second acceleration change frequency is: Where Δε is the change in strain signal, reflecting the change in the degree of deformation of the sensor caused by human body movement; Δt ε is the time interval for strain signal acquisition; k ais the conversion coefficient between strain and acceleration, used to convert strain changes into equivalent acceleration changes. This formula calculates the equivalent acceleration change frequency from the strain signal. This formula can be used to extract acceleration-related motion information from the strain signal, enriching the dimension of motion state analysis.

[0089] S302b: Determine the comprehensive change frequency by using the second acceleration change frequency, the third acceleration change frequency, and the second pressure change frequency, and set it as f com , the calculation formula is: This formula takes a weighted average (here, a simple arithmetic average) of the changing frequencies of strain, acceleration, and pressure signals to produce a frequency value that comprehensively reflects the changing characteristics of multiple signals during human dynamic motion. This combined frequency value allows for a more comprehensive assessment of the intensity and rhythm of human dynamic motion, providing a basis for subsequent selection of appropriate sub-modes.

[0090] S302c: When the second pressure change frequency is similar to the acceleration change frequency, that is, |f p2 -f a |≤δ f (δ f is the frequency approximate threshold), select the first preset working mode A, otherwise, select the first preset working mode B;

[0091] As another embodiment of the present invention, after selecting the first preset operating mode A, the following steps are included:

[0092] A1: The human body's motion patterns are acquired based on acceleration signals, and the hydrogel flexible sensor acquires strain and pressure signals;

[0093] A2: Determine whether the acceleration signal is lower than the first acceleration threshold a for transition from dynamic to static th1 If yes, the hydrogel flexible sensor performs a Fourier transform on the pressure signal. This step sets an acceleration threshold to filter out situations with low acceleration (possibly low exercise intensity), and performs a Fourier transform on the pressure signal to convert the time domain pressure signal into a frequency domain signal, making it easier to analyze the frequency components and characteristics of the pressure signal.

[0094] A3: Determine the Fourier transform result. If the Fourier transform result is an amplitude, determine whether the pressure signal is higher than the second pressure threshold p. th2 If yes, the hydrogel flexible sensor outputs a first pressure signal. This step filters the pressure signals in the frequency domain amplitude dimension and extracts the pressure signals that meet the specific amplitude conditions for subsequent health status analysis.

[0095] A4: When the Fourier transform result is frequency, determine whether the amplitude of the pressure signal in the preset frequency band in the pressure signal is higher than the third pressure threshold pth3 If yes, the hydrogel flexible sensor outputs a second pressure signal. This step further screens pressure signals with specific frequency bands and amplitudes that meet the conditions in the frequency domain, and more accurately extracts useful pressure signal features.

[0096] As another embodiment of the present invention, after selecting the first preset operating mode B, the method further includes:

[0097] B1: The human body's motion patterns are acquired based on acceleration signals, and the hydrogel flexible sensor acquires strain and pressure signals;

[0098] B2: Determine whether the acceleration signal is lower than the first acceleration threshold a for dynamic to static transition th1 If yes, the hydrogel flexible sensor outputs a third pressure signal. This step directly outputs a pressure signal when the acceleration is low (the exercise intensity may be low), which simplifies the processing flow and reduces the amount of calculation.

[0099] B3: When the acceleration signal is higher than the first acceleration threshold a th1 When the acceleration is high (the intensity of the movement is high), the pressure signal is transformed into the frequency domain to explore the pressure signal characteristics under more complex movement states.

[0100] B4: Determine the Fourier transform result. If the Fourier transform result is an amplitude, determine whether the pressure signal is higher than the second pressure threshold p. th2 , if yes, the hydrogel flexible sensor outputs a first pressure signal;

[0101] B5: When the Fourier transform result is frequency, determine whether the amplitude of the pressure signal in the preset frequency band in the pressure signal is higher than the third pressure threshold p th3 , if yes, the hydrogel flexible sensor outputs a second pressure signal;

[0102] S303: When the hydrogel flexible sensor is in a static state, selecting a second preset working mode;

[0103] As another embodiment of the present invention, after selecting the second preset operating mode, the method further includes:

[0104] S303a: When the acceleration signal is at the second acceleration threshold a th2 When the hydrogel flexible sensor outputs a first pressure signal, the hydrogel flexible sensor outputs a first pressure signal. In this step, under a static state, the pressure signal is output for a specific acceleration condition (which may indicate a slight human body movement) to monitor the body pressure changes under static conditions.

[0105] S303b: Determine whether the acceleration signal is within the third acceleration threshold a th3If not, the hydrogel flexible sensor performs Fourier transform on the pressure signal. When the acceleration does not meet the specific conditions, the pressure signal is analyzed in the frequency domain to explore the potential characteristics of the pressure signal in the static state.

[0106] S303c: Determine the Fourier transform result. If the Fourier transform result is an amplitude, determine whether the pressure signal is higher than the second pressure threshold p. th2 If yes, the hydrogel flexible sensor outputs a first pressure signal.

[0107] S303d: When the Fourier transform result is frequency, determine whether the amplitude of the pressure signal in the preset frequency band in the pressure signal is higher than the third pressure threshold p th3 If yes, the hydrogel flexible sensor outputs a second pressure signal.

[0108] S4: Monitor the health status of the human body in real time through the selected working mode to obtain real-time monitoring results;

[0109] As another embodiment of the present invention, in step S4, performing real-time monitoring of the human health status through the selected working mode to obtain real-time monitoring results includes the following steps:

[0110] S401: Acquire multiple human motion state signals of the hydrogel flexible sensor collected before the target monitoring time, including strain signals, acceleration signals, and pressure signals;

[0111] S402: Obtain the strain characteristics of the strain signal based on the strain signal collected before the target monitoring time, such as the strain mean μ ε , strain variance Etc., the calculation formula is:

[0112]

[0113] Where N is the number of strain signal acquisition points, ε i is the value of the i-th strain signal. The mean strain value μ ε Reflects the average level of the strain signal and the average deformation degree of the sensor over a period of time; strain variance Reflects the degree of dispersion of the strain signal. The larger the variance, the greater the fluctuation of the strain signal, and the more intense or complex the human body movement may be. These two characteristics can be used to analyze the deformation pattern of the human body during movement and assist in judging the health status.

[0114] S403: Based on the acceleration data in the acceleration signal collected before the target monitoring time, count the number of times the same acceleration data appears, and obtain the acceleration characteristics of different acceleration data, such as the acceleration mode a mode etc. Acceleration mode a modeIndicates the acceleration value that occurs most frequently during the acquisition period, reflecting the most common acceleration state during human movement. This feature can be used to analyze the habitual intensity or common movement patterns of human movement.

[0115] S404: Obtain pressure data features based on the pressure signal collected before the target monitoring time, such as the maximum pressure P max , minimum pressure P min The maximum and minimum pressure values directly reflect the fluctuation range of the pressure signal during the monitoring period. Combined with other signal characteristics, they can comprehensively evaluate the changes in pressure on the human body during movement or static state, providing data support for health monitoring.

[0116] S405: Detection results based on the strain feature, acceleration feature, and pressure data feature;

[0117] S5: When the hydrogel flexible sensor is not in a preset working scene state, the hydrogel flexible sensor is in a standby state.

[0118] Example 3: Based on the method content and combined with the physiological characteristics of diabetic patients, a human health monitoring system based on hydrogel flexible sensors is generated for special situations such as changes in skin conductivity caused by blood sugar fluctuations in diabetic patients and differences in pressure perception caused by abnormal peripheral circulation:

[0119] Scene Setting

[0120] Test subjects: 10 adult subjects diagnosed with type 2 diabetes (aged 45-60 years, BMI 25-32 kg / m 2 ), fasting for 12 hours before the test, sitting in a natural and relaxed posture in the sitting scene, and walking at a normal speed of 0.8-1.2m / s in the walking scene

[0121] Sensor parameters:

[0122] Pressure sensor range: 0-50Pa (resolution 0.1Pa);

[0123] Acceleration sensor range: 0-10m / s 2 (three axes, sampling rate 100Hz);

[0124] Strain sensor sensitivity: k a =10 (strain-acceleration conversion coefficient);

[0125] Frequency approximation threshold: δ f =1Hz;

[0126] Static pressure threshold: P min =10Pa, P max =20Pa;

[0127] Static judgment threshold: f static_th =0.5Hz;

[0128] Dynamic to static acceleration threshold: a th1 =1.5m / s 2 ;

[0129] 1. Static state monitoring (sitting scene);

[0130] S1: Acquire human motion state signal;

[0131] Monitoring time: t = 60s (actual clinical monitoring time);

[0132] Pressure signal: P = [14.2, 15.3, 15.0, 14.8, 15.1, 15.5, 14.9, 15.2, 15.0, 14.7] Pa (average every 6 seconds);

[0133] Acceleration signal:

[0134] a x =[0.12,0.08,0.15,0.10,0.09,0.11,0.13,0.10,0.12,0.11]m / s 2 ;

[0135] a y =[0.05,0.03,0.06,0.04,0.03,0.05,0.04,0.03,0.05,0.04]m / s 2 ;

[0136] a z =[0.02,0.01,0.03,0.02,0.01,0.02,0.02,0.01,0.02,0.02]m / s 2 ;

[0137] Temperature signal: T = [36.4, 36.5, 36.4, 36.4, 36.5]°C (axillary measurement);

[0138] S2: Detect the working scene status;

[0139] S201: Calculate the change frequency;

[0140] Pressure change frequency: calculated using the sliding window method (window length 5 seconds), f p =0.42Hz;

[0141] Acceleration change frequency: After analyzing the three-axis data, f a =0.28Hz;

[0142] Temperature change frequency: Due to the long sampling interval, the frequency was not calculated;

[0143] S202: Calculate static pressure change frequency:

[0144] S203: Status judgment: pressure signal average 15.0Pa∈[10,20]Pa, f static <f static_th ,Conclusion: in a static state;

[0145] S3: Select working mode;

[0146] Enable the second preset working mode and turn on low-frequency sampling (10Hz);

[0147] Perform pressure signal filtering to remove baseline drift;

[0148] S4: real-time monitoring results;

[0149] Pressure characteristics: standard deviation 0.32 Pa, coefficient of variation 2.1%;

[0150] Physiological correlation: Combined with the simultaneously collected fingertip blood glucose data (6.8 mmol / L), pressure fluctuations showed a weak correlation with changes in subcutaneous tissue water content (r = 0.23);

[0151] 2. Dynamic state monitoring (walking scene);

[0152] S1: Acquire human motion state signal;

[0153] Monitoring time: t = 30s;

[0154] Pressure signal: P = [22.5, 20.3, 24.1, 23.6, 21.8, 25.2, 22.9, 24.5, 23.1, 24.8] Pa;

[0155] Acceleration signal: a x Peak value reaches 3.8m / s 2 , gait cycle frequency 1.1Hz;

[0156] Strain signal: ε = [0.028, 0.045, 0.036, 0.052, 0.041];

[0157] S2: Detect the working scene status;

[0158] S201: Calculate the frequency of change:

[0159] The pressure signal is transformed by short-time Fourier transform, f p =1.75Hz;

[0160] Acceleration frequency fa =1.08Hz;

[0161] S203: Status judgment: pressure average Determined to be in dynamic state;

[0162] S3: Select working mode;

[0163] Enable the first preset working mode A and activate the signal fusion algorithm;

[0164] Perform wavelet denoising on the acceleration signal;

[0165] S4: real-time monitoring results;

[0166] Gait parameters: cadence 105 steps / min, stride length 0.72 m;

[0167] Physiological correlation: Blood glucose dropped by 0.6 mmol / L immediately after exercise, which was positively correlated with the amplitude of pressure signal fluctuation (r = 0.41);

[0168] 3. Standby state (non-working scenario);

[0169] Trigger conditions: Pressure < 1Pa for 30 consecutive seconds and acceleration in all three axes < 0.05m / s 2 ;

[0170] Power consumption data: standby current is reduced from 12mA in working state to 0.5mA;

[0171] Key parameter comparison table:

[0172]

[0173] Example Summary: This example uses cross-validation to simultaneously collect multiple physiological signals, including a 12-lead electrocardiogram and respiratory rate, and selects core sensor data for analysis and presentation. All subjects signed informed consent forms, and the data were desensitized, ensuring the scientific and safe nature of the experimental data.

[0174] The embodiments disclosed in the present invention are preferred embodiments, but are not limited to them. Ordinary technicians in this field can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. As long as they do not deviate from the spirit of the present invention, they are all within the scope of protection of the present invention.

Claims

1. A real-time human health monitoring system based on hydrogel flexible sensors, characterized in that: include: A human motion state signal acquisition module is used to obtain a variety of human motion state signals using a hydrogel flexible sensor, and detect whether the hydrogel flexible sensor is in a preset working scene state based on the human motion state signals; Among them, the various human motion state signals include acceleration signals, pressure signals, temperature signals and strain signals, and the preset working scene states include static states and dynamic states; A mode selection module is used to select an adaptive working mode from the preset working modes of the hydrogel flexible sensor according to the human motion state signal output by the human motion state signal acquisition module, and to monitor the human health state in real time through the selected working mode to obtain real-time monitoring results; Wherein, the preset working mode includes a first preset working mode and a second preset working mode; The mode output module is used to put the hydrogel flexible sensor into a standby state when the human motion state signal obtained by the human motion state signal acquisition module does not conform to the preset working scene state.

2. A method for real-time monitoring of human health based on a hydrogel flexible sensor, which is applicable to the real-time monitoring system for human health based on a hydrogel flexible sensor according to claim 1, characterized in that: The following steps are involved: S1: Using hydrogel flexible sensors to obtain various human motion state signals; S2: Detecting whether the hydrogel flexible sensor is in a preset working scene state based on the human body motion state signal; S3: When the hydrogel flexible sensor is in a preset working scene state, the human health real-time monitoring system selects an adaptive working mode from the preset working modes of the hydrogel flexible sensor according to the human motion state signal; S4: Monitor the health status of the human body in real time through the selected working mode to obtain real-time monitoring results; S5: When the hydrogel flexible sensor is not in a preset working scene state, the hydrogel flexible sensor is in a standby state.

3. The method for real-time monitoring of human health based on a hydrogel flexible sensor according to claim 2, characterized in that: In step S2, detecting whether the hydrogel flexible sensor is in a preset working scene state according to the human body motion state signal specifically includes the following steps: S201: The hydrogel flexible sensor acquires an acceleration signal and a pressure signal, and calculates a first pressure change frequency and a first acceleration change frequency respectively; The hydrogel flexible sensor further includes a temperature sensor, which obtains the current temperature and calculates the temperature change frequency based on the current temperature; S202: The hydrogel flexible sensor calculates the common frequency component of the first pressure change frequency and the first acceleration change frequency within a preset time length as the static pressure change frequency f static ; S203: When the range of the pressure signal output by the hydrogel flexible sensor is within the preset pressure signal value and the static pressure change frequency f static At the static judgment threshold f static_th When the φ is within the range of φ, the hydrogel flexible sensor is determined to be in a static state; otherwise, the hydrogel flexible sensor is determined to be in a dynamic state.

4. The method for real-time monitoring of human health based on a hydrogel flexible sensor according to claim 3, characterized in that: In step S201: The calculation formula for the first pressure change frequency is: Where, f p is the pressure variation frequency, t current is the current moment of the first pressure change, t prev is the last moment of the first pressure change, and the difference between the two is Δt p Represents the minimum time interval for collecting pressure signals; n p is the number of pressure changes; The calculation formula for the first acceleration change frequency is: Where, f a is the frequency of acceleration change, t′ current is the current moment of the first acceleration change, t′ prev is the last time the first acceleration changed, Δt a is the minimum time interval for collecting acceleration signals; n a is the number of acceleration changes.

5. The method for real-time monitoring of human health based on a hydrogel flexible sensor according to claim 2, characterized in that: In step S3, selecting an adapted working mode from the preset working modes of the hydrogel flexible sensor according to the human body motion state signal specifically includes the following steps: S301: Acquire a preset working mode, where the preset working mode includes a first preset working mode and a second preset working mode; S302: When the hydrogel flexible sensor is in a dynamic state, selecting a first preset working mode; S303: When the hydrogel flexible sensor is in a static state, selecting a second preset operating mode.

6. The method for real-time monitoring of human health based on a hydrogel flexible sensor according to claim 5, characterized in that: After selecting the first preset working mode, the following steps are also included: S302a: The hydrogel flexible sensor obtains a strain signal, an acceleration signal, and a pressure signal, and calculates a second acceleration change frequency f through the strain signal. a2 , calculate the third acceleration change frequency f based on the acceleration signal a3 , calculate the second pressure change frequency f through the pressure signal p2 ; S302b: Determine the comprehensive change frequency f by using the second acceleration change frequency, the third acceleration change frequency, and the second pressure change frequency com ; S302c: When the second pressure change frequency is similar to the acceleration change frequency, that is, |f p2 -f a |≤δ f , where δ f is the frequency approximation threshold, the first preset working mode A is selected, otherwise, the first preset working mode B is selected.

7. The method for real-time monitoring of human health based on a hydrogel flexible sensor according to claim 5, characterized in that: After selecting the second preset working mode, it also includes: S303a: When the acceleration signal is at the second acceleration threshold a th2 When , the hydrogel flexible sensor outputs a first pressure signal; S303b: Determine whether the acceleration signal is within the third acceleration threshold a th3 ,If not, the hydrogel flexible sensor performs Fourier transform on the pressure signal; S303c: Determine the Fourier transform result. If the Fourier transform result is an amplitude, determine whether the pressure signal is higher than the second pressure threshold p. th2 , if yes, the hydrogel flexible sensor outputs a first pressure signal; S303d: When the Fourier transform result is frequency, determine whether the amplitude of the pressure signal in the preset frequency band in the pressure signal is higher than the third pressure threshold p th3 If yes, the hydrogel flexible sensor outputs a second pressure signal.

8. The method for real-time monitoring of human health based on a hydrogel flexible sensor according to claim 6, characterized in that: After selecting the first preset working mode A, the following steps are included: A1: The human body's motion patterns are acquired based on acceleration signals, and the hydrogel flexible sensor acquires strain and pressure signals; A2: Determine whether the acceleration signal is lower than the first acceleration threshold a for transition from dynamic to static th1 ,If yes, the hydrogel flexible sensor performs Fourier transform on the pressure signal; A3: Determine the Fourier transform result. If the Fourier transform result is an amplitude, determine whether the pressure signal is higher than the second pressure threshold p. th2 If yes, the hydrogel flexible sensor outputs a first pressure signal; A4: When the Fourier transform result is frequency, determine whether the amplitude of the pressure signal in the preset frequency band in the pressure signal is higher than the third pressure threshold p th3 If yes, the hydrogel flexible sensor outputs a second pressure signal.

9. The method for real-time monitoring of human health based on a hydrogel flexible sensor according to claim 6, characterized in that: After selecting the first preset working mode B, the following further includes: B1: The human body's motion patterns are acquired based on acceleration signals, and the hydrogel flexible sensor acquires strain and pressure signals; B2: Determine whether the acceleration signal is lower than the first acceleration threshold a for dynamic to static transition th1 , if yes, the hydrogel flexible sensor outputs a third pressure signal; B3: When the acceleration signal is higher than the first acceleration threshold a th1 When the acceleration is high, the hydrogel flexible sensor performs Fourier transform on the pressure signal, and when the acceleration is high, the pressure signal is transformed in the frequency domain to explore the characteristics of the pressure signal; B4: Determine the Fourier transform result. If it is an amplitude, determine whether the pressure signal is higher than the second pressure threshold p th2 ,If so, the hydrogel flexible sensor outputs a first pressure signal; B5: When the Fourier transform result is frequency, determine whether the amplitude of the pressure signal in the preset frequency band in the pressure signal is higher than the third pressure threshold p th3 If yes, the hydrogel flexible sensor outputs a second pressure signal.

10. The method for real-time monitoring of human health based on a hydrogel flexible sensor according to claim 2, characterized in that: In step S4, real-time monitoring of the human health status is performed through the selected working mode to obtain real-time monitoring results, including the following steps: S401: Acquire multiple human motion state signals of the hydrogel flexible sensor collected before the target monitoring time; S402: Acquire strain characteristics of the strain signal based on the strain signal collected before the target monitoring time; S403: Based on the acceleration data in the acceleration signal collected before the target monitoring time, counting the number of times the same acceleration data appears, and obtaining acceleration characteristics of different acceleration data; S404: Obtaining pressure data features based on the pressure signal collected before the target monitoring time; S405: Detection results are based on the strain characteristics, acceleration characteristics, and pressure data characteristics.

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