Disclosure of Invention
The invention aims to provide a human health real-time monitoring system and method based on a hydrogel flexible sensor, which are used for solving the problems of acquisition distortion of dynamic and static state signals of a chronic patient and energy consumption of mode switching caused by insufficient scene suitability of a traditional rigid sensor.
In order to solve the technical problems, the invention provides the following technical scheme that the human health real-time monitoring system based on the hydrogel flexible sensor comprises:
The human body motion state signal acquisition module is used for acquiring various human body motion state signals by using the 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;
Wherein, the various human motion state signals comprise acceleration signals, pressure signals, temperature signals and strain signals, the preset working scene state comprises a static state and a dynamic state;
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 motion state signals output by the human motion state signal acquisition module, and carrying out real-time monitoring on the health state of the human through the selected working mode to acquire a real-time monitoring result;
The preset working modes comprise a first preset working mode and a second preset working mode;
And the mode output module is used for enabling the hydrogel flexible sensor to be in a standby state when the human body motion state signal acquired by the human body motion state signal acquisition module does not accord with a preset working scene state.
A human health real-time monitoring method based on a hydrogel flexible sensor comprises the following steps:
s1, acquiring various human motion state signals by using a hydrogel flexible sensor;
S2, detecting whether the hydrogel flexible sensor is in a preset working scene state according to a 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 preset working modes of the hydrogel flexible sensor according to a human motion state signal;
s4, monitoring the health state of the human body in real time through the selected working mode to obtain a real-time monitoring result;
and S5, when the hydrogel flexible sensor is not in a preset working scene state, the hydrogel flexible sensor is in a standby state.
Preferably, in the step S2, whether the hydrogel flexible sensor is in a preset working scene state is detected according to a human motion state signal, which specifically includes the following steps:
s201, acquiring an acceleration signal and a pressure signal by a hydrogel flexible sensor, and respectively calculating a first pressure change frequency and a first acceleration change frequency;
The hydrogel flexible sensor further comprises a temperature sensor, wherein the temperature sensor acquires the current temperature and calculates the temperature change frequency according to the current temperature;
s202, calculating a common frequency component of a first pressure change frequency and a first acceleration change frequency within a preset time length by using the hydrogel flexible sensor as a static pressure change frequency fstatic;
S203, when the output pressure signal range of the hydrogel flexible sensor is within a preset pressure signal value and the static pressure change frequency fstatic is within a static judgment threshold fstatic_th, determining that the hydrogel flexible sensor is in a static state, otherwise, determining that the hydrogel flexible sensor is in a dynamic state.
Preferably, in the step S201:
The first pressure change frequency calculation formula is: Wherein, fp is the frequency of pressure change, tcurrent is the current time of the first pressure change, tprev is the last time of the first pressure change, and the difference delta tp between the two represents the minimum time interval for collecting pressure signals;
The first acceleration change frequency calculation formula is: Where fa is the frequency of acceleration change, t 'current is the current time of the first acceleration change, t'prev is the last time of the first acceleration change, Δta is the minimum time interval for acquiring the acceleration signal, and na is the number of acceleration changes.
Preferably, in the step S3, an adapted operation mode is selected from preset operation modes of the hydrogel flexible sensor according to a human motion state signal, and the method specifically includes the following steps:
S301, acquiring a preset working mode, wherein the preset working mode comprises a first preset working mode and a second preset working mode;
S302, selecting a first preset working mode when the hydrogel flexible sensor is in a dynamic state;
s303, selecting a second preset working mode when the hydrogel flexible sensor is in a static state.
Preferably, after selecting the first preset operation mode, the method further comprises the following steps:
S302a, a hydrogel flexible sensor acquires a strain signal, an acceleration signal and a pressure signal, calculates a second acceleration change frequency fa2 through the strain signal, calculates a third acceleration change frequency fa3 based on the acceleration signal, and calculates a second pressure change frequency fp2 through the pressure signal;
S302b, determining a comprehensive change frequency fcom through the second acceleration change frequency, the third acceleration change frequency and the second pressure change frequency;
S302c, when the second pressure change frequency is similar to the acceleration change frequency, namely |fp2-fa|≤δf, wherein deltaf is a frequency approximate threshold value, selecting a first preset working mode A, and otherwise, selecting a first preset working mode B.
Preferably, the selecting the second preset operation mode further includes:
S303a, outputting a first pressure signal by the hydrogel flexible sensor when the acceleration signal is at a second acceleration threshold value ath2;
S303b, judging whether the acceleration signal is at a third acceleration threshold value ath3, if not, carrying out Fourier transform on the pressure signal by the hydrogel flexible sensor;
s303c, judging a Fourier transform result, if the Fourier transform result is the amplitude, judging whether the pressure signal is higher than a second pressure threshold pth2, and if so, outputting a first pressure signal by the hydrogel flexible sensor;
And S303d, when the Fourier transform result is frequency, judging whether the amplitude of a pressure signal in a preset frequency band in the pressure signal is higher than a third pressure threshold pth3, and if so, outputting a second pressure signal by the hydrogel flexible sensor.
Preferably, the selection of the first preset operation mode a includes the following steps:
A1, acquiring a motion rule of a human body according to an acceleration signal, and acquiring a strain signal and a pressure signal by a hydrogel flexible sensor;
A2, judging whether the acceleration signal is lower than a first acceleration threshold value ath1 from dynamic to static, if so, carrying out Fourier transform on the pressure signal by the hydrogel flexible sensor;
A3, judging a Fourier transform result, and if the Fourier transform result is an amplitude value, judging whether the pressure signal is higher than a second pressure threshold pth2 or not;
And A4, judging whether the amplitude of a pressure signal in a preset frequency band in the pressure signal is higher than a third pressure threshold pth3 or not when the Fourier transform result is frequency, and outputting a second pressure signal by the hydrogel flexible sensor if the amplitude of the pressure signal is higher than the third pressure threshold pth3.
Preferably, the selecting the first preset operation mode B further includes:
B1, acquiring a motion rule of a human body according to an acceleration signal, and acquiring a strain signal and a pressure signal by a hydrogel flexible sensor;
B2, judging whether the acceleration signal is lower than a first acceleration threshold value ath1 from dynamic to static, and if so, outputting a third pressure signal by the hydrogel flexible sensor;
B3, when the acceleration signal is higher than a first acceleration threshold value ath1, the hydrogel flexible sensor carries out Fourier transform on the pressure signal, and when the acceleration is higher, carries out frequency domain transform on the pressure signal, and excavates the pressure signal characteristics;
B4, judging a Fourier transform result, if the result is the amplitude, judging whether the pressure signal is higher than a second pressure threshold pth2, and if so, outputting a first pressure signal by the hydrogel flexible sensor;
And B5, when the Fourier transform result is frequency, judging whether the amplitude of a pressure signal in a preset frequency band in the pressure signal is higher than a third pressure threshold pth3, and if so, outputting a second pressure signal by the hydrogel flexible sensor.
Preferably, in step S4, the real-time monitoring is performed on the health status of the human body through the selected working mode to obtain a real-time monitoring result, which includes the following steps:
s401, acquiring various human body motion state signals of a hydrogel flexible sensor acquired before target monitoring time;
s402, acquiring strain characteristics of a strain signal based on the strain signal acquired before the target monitoring time;
s403, based on acceleration data in acceleration signals acquired before target monitoring time, counting the occurrence times of the same acceleration data, and acquiring acceleration characteristics of different acceleration data;
s404, acquiring pressure data characteristics based on pressure signals acquired before target monitoring time;
And S405, taking the strain characteristic, the acceleration characteristic and the pressure data characteristic as detection results.
Compared with the prior art, the invention has the beneficial effects that:
1. The invention realizes millisecond recognition of static and dynamic scenes of chronic patients by combining with pressure signal frequency characteristic analysis through the biocompatible curved surface fitting design of the hydrogel flexible sensor, automatically switches the working mode, statically enables a second preset mode of low-frequency sampling, dynamically activates a first preset mode of multi-mode fusion, directly solves the signal distortion problem caused by scene misjudgment of the traditional sensor, and reduces the pressure signal acquisition error from +/-15% to +/-3% through dynamic mode switching in the walking scene of diabetics.
2. The invention further carries out frequency domain-time domain joint analysis on the multi-mode signals through deep decoupling and early warning of the characteristic parameters of the chronic diseases based on a strain-acceleration conversion algorithm and a Fourier transform technology, can deeply mine the characteristic parameters related to the chronic diseases, further solves the problem that the traditional monitoring means has single analysis dimension on the physiological characteristics of the chronic diseases, and provides data support for personalized rehabilitation scheme formulation.
3. The invention also discloses a system for automatically switching from a working state to a standby state through setting a non-physiological activity triggering threshold value by scene perception driven low-power consumption intelligent management, and the compliance of chronic patients in wearing for a long time is further optimized on the basis of solving the problem of high power consumption of traditional equipment. The standby mechanism can prolong the equipment endurance from 2 days to 24 days in the traditional scheme by monitoring for 12 hours per day of diabetics, so that the charging frequency is obviously reduced, and the method is particularly suitable for chronic disease groups with inconvenient actions.
Detailed Description
The invention relates to a human health real-time monitoring system based on a hydrogel flexible sensor, which comprises a human motion state signal acquisition module, a mode selection module and a mode output module:
The human body motion state signal acquisition module is used for acquiring various human body motion state signals by using the 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;
In the embodiment of the invention, the various human body motion state signals comprise acceleration signals, pressure signals, temperature signals and strain signals, and the preset working scene states comprise a static state and a dynamic state;
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 motion state signals output by the human motion state signal acquisition module, and carrying out real-time monitoring on the health state of the human through the selected working mode to acquire a real-time monitoring result;
In an embodiment of the present invention, the preset operation modes include a first preset operation mode and a second preset operation mode;
And the mode output module is used for enabling the hydrogel flexible sensor to be in a standby state when the human body motion state signal acquired by the human body motion state signal acquisition module does not accord with a preset working scene state.
In a second embodiment, as shown in fig. 1, a method for monitoring human health in real time based on a hydrogel flexible sensor comprises the following steps:
s1, acquiring various human motion state signals by using a hydrogel flexible sensor;
S2, detecting whether the hydrogel flexible sensor is in a preset working scene state according to a human body motion state signal;
In another embodiment of the present invention, in the step S2, whether the hydrogel flexible sensor is in a preset working scene state is detected according to a human motion state signal, which specifically includes the following steps:
s201, acquiring an acceleration signal and a pressure signal by a hydrogel flexible sensor, and respectively calculating a first pressure change frequency and a first acceleration change frequency;
as another embodiment of the present invention, the first pressure change frequency calculation formula in step S201 is:
Wherein fp is the frequency of pressure change, which intuitively reflects the frequency of fluctuation of the pressure signal, tcurrent is the current time of the first pressure change, tprev is the last time of the first pressure change, the difference value deltatp between the two represents the minimum time interval for collecting the pressure signal, and np is the number of pressure changes and records the number of pressure changes in deltatp. The formula is used for calculating the change times of the pressure signal in unit time, and the dynamic change characteristics of the pressure signal can be quantified through the formula to assist in judging the motion state of the human body;
as another embodiment of the present invention, the first acceleration change frequency calculation formula in step S201 is:
Where fa is the frequency of acceleration change reflecting the frequency of change of the acceleration signal, t 'current is the current time of the first acceleration change, t'prev is the last time of the first acceleration change, Δta is the minimum time interval for collecting the acceleration signal, and na is the number of acceleration changes. The formula is similar to the pressure change frequency calculation logic and is used for calculating the change times of the acceleration signal in unit time. By the formula, the dynamic change of acceleration during human body movement can be analyzed, and information such as movement intensity and the like can be judged;
As another embodiment of the present invention, the hydrogel flexible sensor further includes a temperature sensor, the temperature sensor obtains a current temperature, and calculates a temperature change frequency according to the current temperature, where the temperature change frequency calculation formula is:
Where fT is the frequency of temperature change reflecting the frequency of temperature fluctuation, t″current is the current time of temperature change, t″prev is the last time of temperature change, ΔtT is the minimum time interval for acquiring temperature signals, and nT is the number of temperature changes. The formula is used for calculating the change times of the temperature signal in unit time, and although the weight of the temperature change in judging the static state and the dynamic state is relatively smaller, the temperature change frequency is calculated through the formula, so that the conditions of heat change and the like generated by human body due to movement can be assisted to be analyzed;
S202, the hydrogel flexible sensor calculates a common frequency component of the first pressure change frequency and the first acceleration change frequency within a preset time length as a static pressure change frequency fstatic, namely, the common frequency component of the first pressure change frequency and the first acceleration change frequency within the preset time length is taken. The operation is to screen out the common stable frequency characteristics of the pressure and the acceleration in the static state from the change frequencies of the pressure and the acceleration, eliminate the possibly random change frequencies of the pressure and the acceleration in the dynamic movement, and reflect the signal characteristics in the static state more accurately;
S203, when the range of the output pressure signal of the hydrogel flexible sensor is within a preset pressure signal value (Pmin≤P≤Pmax) and the static pressure change frequency fstatic is within a static judgment threshold fstatic_th, determining that the hydrogel flexible sensor is in a static state, otherwise, determining that the hydrogel flexible sensor is in a dynamic state, wherein P is the pressure signal, Pmin is a pressure minimum threshold, Pmax is a pressure maximum threshold, and when the formula is met, indicating that the range of the output pressure signal of the hydrogel flexible sensor is within the preset pressure signal value, indicating that the pressure is in a relatively stable state;
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 preset working modes of the hydrogel flexible sensor according to a human motion state signal;
as another embodiment of the present invention, in the step S3, according to the human motion state signal, an adapted operation mode is selected from preset operation modes of the hydrogel flexible sensor, and the method specifically includes the following steps:
S301, acquiring a preset working mode, wherein the preset working mode comprises a first preset working mode and a second preset working mode;
S302, selecting a first preset working mode when the hydrogel flexible sensor is in a dynamic state;
as another embodiment of the present invention, after selecting the first preset operation mode, the method further includes the following steps:
S302a, a hydrogel flexible sensor acquires a strain signal, an acceleration signal and a pressure signal, calculates a second acceleration change frequency fa2 through the strain signal, calculates a third acceleration change frequency fa3 based on the acceleration signal, and calculates a second pressure change frequency fp2 through the pressure signal;
A second acceleration change frequency calculation formula: Wherein, delta epsilon is the change value of the strain signal and reflects the change of the deformation degree of the sensor caused by the motion of the human body, delta tε is the time interval for collecting the strain signal, ka is the conversion coefficient of strain and acceleration and is used for converting the strain change into the equivalent acceleration change. The equation calculates the equivalent acceleration change frequency from the strain signal. By the formula, the motion information related to acceleration can be mined from the strain signal, and the motion state analysis dimension is enriched;
s302b, determining the comprehensive change frequency through the second acceleration change frequency, the third acceleration change frequency and the second pressure change frequency, setting the comprehensive change frequency as fcom, and adopting a calculation formula: the formula carries out weighted average (here, simple arithmetic average) on the change frequency of strain, acceleration and pressure signals to obtain a frequency value which comprehensively reflects the change characteristics of various signals when the human body moves dynamically. Through the comprehensive change frequency, the information such as the intensity, rhythm and the like of the dynamic motion of the human body can be more comprehensively estimated, and a basis is provided for the subsequent selection of a proper sub-mode;
S302c, when the second pressure change frequency is similar to the acceleration change frequency, i.e. the frequency of the first pressure change frequency is i.e. fp2-fa|≤δf(δf is a frequency approximate threshold value), selecting a first preset working mode A, otherwise, selecting a first preset working mode B;
As another embodiment of the present invention, the selecting the first preset operation mode a includes the following steps:
A1, acquiring a motion rule of a human body according to an acceleration signal, and acquiring a strain signal and a pressure signal by a hydrogel flexible sensor;
A2, judging whether the acceleration signal is lower than a first acceleration threshold value ath1 from dynamic to static, and if so, carrying out Fourier transformation on the pressure signal by the hydrogel flexible sensor. According to the method, the condition of small acceleration (low possible motion strength) is screened out by setting an acceleration threshold value, the pressure signal is subjected to Fourier transformation, and the time-domain pressure signal is converted into a frequency-domain signal, so that the frequency components and characteristics of the pressure signal can be analyzed conveniently;
A3, judging the Fourier transform result, if the Fourier transform result is the amplitude, judging whether the pressure signal is higher than a second pressure threshold pth2, and if so, outputting a first pressure signal by the hydrogel flexible sensor. In the frequency domain amplitude dimension, the pressure signals are screened, and the pressure signals meeting the specific amplitude condition are extracted for subsequent health state analysis;
And A4, judging whether the amplitude of a pressure signal in a preset frequency band in the pressure signal is higher than a third pressure threshold pth3 or not when the Fourier transform result is frequency, and outputting a second pressure signal by the hydrogel flexible sensor if the amplitude of the pressure signal is higher than the third pressure threshold pth3. In the frequency domain, the step further screens pressure signals with specific frequency bands and the amplitudes meeting the conditions, and more accurately extracts useful pressure signal characteristics;
as another embodiment of the present invention, after selecting the first preset operation mode B, further includes:
B1, acquiring a motion rule of a human body according to an acceleration signal, and acquiring a strain signal and a pressure signal by a hydrogel flexible sensor;
And B2, judging whether the acceleration signal is lower than a first acceleration threshold value ath1 from dynamic to static, and if so, outputting a third pressure signal by the hydrogel flexible sensor. When the acceleration is low (the motion strength is possibly low), the step directly outputs a pressure signal, so that the processing flow is simplified, and the calculated amount is reduced;
B3, when the acceleration signal is higher than the first acceleration threshold value ath1, the hydrogel flexible sensor carries out Fourier transformation on the pressure signal. When the acceleration is high (the movement intensity is high), carrying out frequency domain transformation on the pressure signal, and excavating the pressure signal characteristics under more complex movement states;
Judging the Fourier transform result, if the Fourier transform result is the amplitude, judging whether the pressure signal is higher than a second pressure threshold pth2, and if so, outputting a first pressure signal by the hydrogel flexible sensor;
b5, when the Fourier transform result is frequency, judging whether the amplitude of a pressure signal in a preset frequency band in the pressure signal is higher than a third pressure threshold pth3, and if so, outputting a second pressure signal by the hydrogel flexible sensor;
S303, selecting a second preset working mode when the hydrogel flexible sensor is in a static state;
As another embodiment of the present invention, the selecting the second preset operation mode further includes:
And S303a, outputting a first pressure signal by the hydrogel flexible sensor when the acceleration signal is at a second acceleration threshold value ath2. This step outputs a pressure signal for a specific acceleration condition (possibly representing a slight motion of the human body) in a static state for monitoring the body pressure change in the static state.
And S303b, judging whether the acceleration signal is at a third acceleration threshold value ath3, and if not, carrying out Fourier transformation on the pressure signal by the hydrogel flexible sensor. And when the acceleration does not meet the specific condition, carrying out frequency domain analysis on the pressure signal, and excavating potential characteristics of the pressure signal in a static state.
And S303c, judging the Fourier transform result, if the Fourier transform result is the amplitude, judging whether the pressure signal is higher than a second pressure threshold pth2, and if so, outputting a first pressure signal by the hydrogel flexible sensor.
And S303d, when the Fourier transform result is frequency, judging whether the amplitude of a pressure signal in a preset frequency band in the pressure signal is higher than a third pressure threshold pth3, and if so, outputting a second pressure signal by the hydrogel flexible sensor.
S4, monitoring the health state of the human body in real time through the selected working mode to obtain a real-time monitoring result;
in another embodiment of the present invention, in step S4, the real-time monitoring is performed on the health status of the human body through the selected working mode to obtain the real-time monitoring result, which includes the following steps:
s401, acquiring various human body motion state signals, including strain signals, acceleration signals and pressure signals, of a hydrogel flexible sensor acquired before target monitoring time;
s402, strain characteristics of the strain signal, such as strain mean muε and strain variance, are obtained based on the strain signal acquired before the target monitoring timeAnd the like, wherein the calculation formula is as follows:
Wherein N is the number of strain signal acquisition points, and epsiloni is the ith strain signal value. The strain mean muε reflects the average level of the strain signal, reflects the average deformation degree of the sensor in a period of time, and changes the strain varianceReflecting the discrete degree of the strain signal, the larger the variance is, the larger the fluctuation of the strain signal is, the more severe or complex the human body movement is possible, and the deformation rule of the human body during movement can be analyzed through the two characteristics to assist in judging the health state;
S403, based on the acceleration data in the acceleration signals collected before the target monitoring time, counting the occurrence times of the same acceleration data, and obtaining acceleration characteristics of different acceleration data, such as acceleration mode amode and the like. The acceleration mode amode represents the acceleration value with the largest occurrence number in the acquisition time period, reflects the acceleration state which is most frequently generated when the human body moves, and can analyze the habit intensity or common movement mode of the human body movement through the characteristics;
And S404, acquiring pressure data characteristics, such as a pressure maximum value Pmax, a pressure minimum value Pmin and the like, based on the pressure signals acquired before the target monitoring time. The maximum value and the minimum value of the pressure intuitively reflect the fluctuation range of the pressure signal in the monitoring time period, and the change condition of the pressure of the human body in a motion or static state can be comprehensively estimated by combining other signal characteristics, so that data support is provided for health monitoring;
s405, taking strain characteristics, acceleration characteristics and pressure data characteristics as detection results;
and S5, when the hydrogel flexible sensor is not in a preset working scene state, the hydrogel flexible sensor is in a standby state.
According to the method, combining physiological characteristics of a diabetic patient, aiming at special conditions such as skin conductivity change caused by blood sugar fluctuation of the diabetic patient, pressure perception difference caused by peripheral circulation abnormality and the like, the human health monitoring based on the hydrogel flexible sensor is generated:
Scene setting
Test subjects recruit 10 adult subjects diagnosed with type 2 diabetes (age 45-60 years, BMI25-32kg/m2), were asked to fasted 12 hours before testing, sit still in a sitting position for natural relaxation, walk at a normal pace of 0.8-1.2 m/s
Sensor parameters:
The measuring range of the pressure sensor is 0-50Pa (resolution is 0.1 Pa);
Acceleration sensor measuring range is 0-10m/s2 (triaxial, sampling rate 100 Hz);
Strain sensor sensitivity ka =10 (strain-acceleration conversion coefficient);
a frequency approximation threshold δf =1 Hz;
a static pressure threshold value Pmin＝10Pa、Pmax = 20Pa;
A static judgment threshold value of fstatic_th =0.5 Hz;
Ath1＝1.5m/s2 is a dynamic-static acceleration threshold value;
1. static state monitoring (sitting scenario);
S1, acquiring a human body motion state signal;
monitoring time t=60 s (actual clinical monitoring duration);
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);
Acceleration signal:
ax＝[0.12,0.08,0.15,0.10,0.09,0.11,0.13,0.10,0.12,0.11]m/s2;
ay＝[0.05,0.03,0.06,0.04,0.03,0.05,0.04,0.03,0.05,0.04]m/s2;
az＝[0.02,0.01,0.03,0.02,0.01,0.02,0.02,0.01,0.02,0.02]m/s2;
Temperature signal, t= [36.4,36.5,36.4,36.4,36.5] °c (underarm measurement);
S2, detecting the state of a working scene;
s201, calculating a change frequency;
Calculating the pressure change frequency by adopting a sliding window method (window length is 5 seconds), wherein fp = 0.42Hz;
Acceleration change frequency, analysis after three-axis data synthesis, fa =0.28 Hz;
the temperature change frequency is not calculated because of long sampling interval;
S202, calculating static pressure change frequency:
S203, judging the state that the average value of the pressure signal is 15.0Pa epsilon 10,20 Pa, fstatic<fstatic_th, and the state is in a static state;
s3, selecting a working mode;
starting a second preset working mode, and starting low-frequency sampling (10 Hz);
performing pressure signal filtering processing to remove baseline wander;
S4, monitoring results in real time;
The pressure characteristic is that the standard deviation is 0.32Pa, and the variation coefficient is 2.1%;
physiological correlation-in combination with fingertip blood glucose data collected simultaneously (6.8 mmol/L), pressure fluctuations show a weak correlation with subcutaneous tissue water content changes (r=0.23);
2. Dynamic state monitoring (walking scene);
S1, acquiring a human body motion state signal;
Monitoring time t=30s;
pressure signal p= [22.5,20.3,24.1,23.6,21.8,25.2,22.9,24.5,23.1,24.8] pa;
Acceleration signal ax peak reaches 3.8m/s2, gait cycle frequency is 1.1Hz;
strain signal ∈= [0.028,0.045,0.036,0.052,0.041];
S2, detecting the state of a working scene;
S201, calculating a change frequency:
the pressure signal adopts short-time fourier transform, fp =1.75 Hz;
acceleration dominant frequency fa =1.08 Hz;
S203, judging the state and the pressure average valueJudging as a dynamic state;
s3, selecting a working mode;
starting a first preset working mode A, and activating a signal fusion algorithm;
wavelet denoising is carried out on the acceleration signals;
S4, monitoring results in real time;
Gait parameters, namely 105 steps/min of step frequency and 0.72 meter of step length;
physiological correlation, namely blood glucose drops by 0.6mmol/L immediately after exercise and is positively correlated with the fluctuation amplitude of the pressure signal (r=0.41);
3. Standby state (non-working scenario);
Triggering conditions that the continuous 30 seconds pressure is less than 1Pa and the triaxial acceleration is less than 0.05m/s2;
power consumption data, namely, standby current is reduced to 0.5mA from 12mA in an operating state;
key parameter comparison table:
Summary of embodiments in this embodiment, cross-validation is performed by synchronously acquiring 12 multiple physiological signals such as an electrocardiogram, a respiratory rate, etc., and core sensor data is selected for analysis and display. All subjects have signed an informed consent form, and the data are subjected to desensitization treatment, so that the scientificity and safety of the test data are ensured.
The embodiments of the present invention are disclosed as preferred embodiments, but not limited thereto, and those skilled in the art will readily appreciate from the foregoing description that various modifications and variations can be made without departing from the spirit of the present invention.