A wearable sensor based on printed carbon material

Abstract

The application provides a wearable sensor based on printing carbon material, and relates to the technical field of motion monitoring.The wearable sensor comprises a signal calibration module, a deformation recognition module, a feature analysis module, a mode analysis module and a state monitoring module.In the application, through dynamic environment compensation and multi-dimensional feature fusion, the signal baseline stability is enhanced, the residual drift error caused by temperature and humidity gradient mutation is reduced, the anti-interference ability in complex environment is improved, the morphological correlation analysis of deformation response curve and motion artifact is adopted, the separation precision of physiological signal and interference component is optimized, the recognition accuracy of deformation feature is enhanced, multi-dimensional feature collaborative calculation is used, the dynamic change characteristics of deformation signal are quantified, the decoupling efficiency of breathing depth and motion intensity is improved, the accurate capture of breathing parameters in dynamic scenes is realized, the accuracy of motion state recognition and monitoring is enhanced by combining the evaluation of breathing mode features and real-time fluctuation parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motion monitoring, in particular to a wearable sensor based on printed carbon materials. BACKGROUND

[0002] The technical field of motion monitoring includes a technical system for dynamically collecting and calculating human motion and physiological parameters based on wearable devices. The core of this technical field involves extracting effective feature parameters from raw biomechanical signals, including acceleration, angular velocity, pressure distribution, and electromyography signals, and achieving gait analysis, heart rate variability monitoring, or respiratory rhythm calculation through time-frequency analysis and pattern recognition algorithms, including motion tracking using an inertial measurement unit combined with Kalman filtering, and physiological parameter analysis using photoplethysmography combined with adaptive threshold detection.

[0003] Among them, a wearable sensor based on printed carbon materials refers to a monitoring system integrating a multi-modal signal processing link. The core function is to map the dynamic physical signals output by the carbon material sensing unit into human physiological parameters, covering multiple links from signal collection to parameter output. First, the deformation signals of different parts are synchronously acquired through a multi-channel parallel acquisition mechanism, and the adaptive baseline calibration technology is used to eliminate signal drift caused by changes in environmental temperature and humidity. Second, the original signals are analyzed in time and frequency domains to extract feature waveforms related to heart rate, respiratory rhythm, and muscle contraction strength. Then, a multi-source data fusion strategy is adopted to establish a correlation model between different sensing channels, and the motion artifact interference is reduced through feature weight distribution. Finally, based on the dynamic threshold library trained by historical data, the mapping of physical signals and target physiological parameters is realized, including heart rate variability, apnea index, and muscle fatigue degree, completing non-invasive continuous monitoring.

[0004] The calibration process of traditional wearable sensors uses fixed thresholds or linear compensation models, which cannot adapt to sudden changes in temperature and humidity gradients or nonlinear drift scenarios in real time, resulting in incomplete signal baseline correction and residual drift errors in high-temperature and high-humidity environments. Time-frequency domain joint analysis focuses on frequency band energy distribution and ignores the dynamic correlation of deformation signals in acceleration, amplitude, and other time domain features, making it difficult to distinguish time domain overlapping signals of respiratory rhythm and limb movement, causing respiratory cycle calculation deviation, and limiting the monitoring stability and accuracy of existing technologies in dynamic environments and complex motion scenarios. SUMMARY

[0005] To solve the technical problems existing in the prior art, the embodiments of the present application provide a wearable sensor based on printed carbon materials. The technical solution is as follows:

[0006] A wearable sensor based on printed carbon material is provided, which comprises: a signal calibration module that collects an electric signal of a carbon material sensor, acquires real-time data of a temperature and humidity node on a surface of the sensor, constructs an electric signal compensation parameter table by analyzing the influence of temperature and humidity changes on the electric signal; a deformation recognition module that calls the electric signal compensation parameter table, performs baseline correction on the electric signal, identifies and separates a deformation signal in the electric signal by analyzing the relationship between carbon material stress and deformation response curves, and generates a deformation signal dataset; a feature analysis module that extracts acceleration, amplitude and deformation frequency of the deformation signal in a continuous time window based on the deformation signal dataset, calculates transition smoothness of a deformation curve, and generates deformation feature parameters; a pattern analysis module that calls the deformation feature parameters, marks an abnormal fluctuation section by the deformation frequency and smoothness parameters, separates a motion interference signal, compares the deformation features with known breathing patterns, extracts breathing period and breathing depth data, and generates breathing rhythm parameters; and a state monitoring module that inputs the breathing rhythm parameters, evaluates the motion intensity and regularity of breathing rhythm of a user according to the fluctuation and amplitude stability of the breathing period, identifies the motion state of the user, and generates a motion state classification label.

[0007] Optionally, the electric signal compensation parameter table specifically refers to a temperature and humidity gradient change rate, a compensation weight factor and a hysteresis effect correction amount, the deformation signal dataset includes a deformation response amplitude, a stress distribution time sequence and a baseline offset correction amount, the deformation feature parameters specifically refer to a deformation acceleration value, a deformation frequency fluctuation rate and a transition smoothness, the breathing rhythm parameters include a breathing period time sequence, an amplitude stability coefficient and a motion artifact separation threshold, and the motion state classification label specifically refers to a motion intensity data, a motion state matching degree and a state identification label.

[0008] Optionally, the signal calibration module comprises: a data acquisition sub-module that collects an electric signal of a carbon material sensor, acquires real-time temperature and humidity data of a temperature and humidity node on a surface of the sensor, and generates a sensor dataset; a gradient analysis sub-module that analyzes the fluctuation amplitude and duration of temperature and humidity data over time based on the sensor dataset by calculating the spatial gradient change rate of adjacent temperature and humidity nodes, and generates a temperature and humidity gradient fluctuation rate; and a compensation construction sub-module that calls the temperature and humidity gradient fluctuation rate, identifies the baseline drift mapping relationship between temperature and humidity and the electric signal by analyzing the influence of temperature and humidity changes on the electric signal, and generates an electric signal compensation parameter table.

[0009] Optionally, the specific formula of the influence of temperature and humidity changes on the electric signal is:

[0010]

[0011] The correlation coefficient of temperature and humidity and the electric signal is calculated to identify the baseline drift mapping relationship between temperature and humidity and the electric signal; wherein R XThe correlation coefficient representing the influence of temperature and humidity changes on the electric signal, X i' The temperature value or humidity value measured at the i'th time point, V The average value of the temperature or humidity data at all time points, V i The electric signal value measured at the i'th time point, V The average value of the electric signal data at all time points, i' represents the i'th time point in the data acquisition process, and n' represents the total number of time sampling points.

[0012] Optionally, the deformation recognition module includes: a baseline correction submodule that calls the electric signal compensation parameter table, corrects the original electric signal, calculates the compensated electric signal amplitude and timing characteristics, and generates a corrected electric signal waveform; a response analysis submodule that analyzes the corresponding relationship between the carbon material stress and deformation response curve based on the corrected electric signal waveform, analyzes the influence of carbon material deformation on the electric signal, calculates the sensitivity parameter of the electric signal to deformation, and generates a deformation response characteristic parameter; and a signal separation submodule that calls the deformation response characteristic parameter, recognizes and separates the deformation signal in the electric signal, and generates a deformation signal dataset.

[0013] Optionally, the specific formula for the influence of carbon material deformation on the electric signal is:

[0014]

[0015] The sensitivity parameter of the electric signal to deformation; wherein SD represents the sensitivity parameter of the electric signal to deformation, VDc q The measured value of the corrected electric signal corresponding to the q'th deformation variable, VDc0 represents the initial value of the corrected electric signal when the deformation variable is zero, Xε q The q'th deformation variable, Xε0 represents the deformation variable without external force, p represents the data sampling point number, and q represents the deformation variable serial number currently calculated.

[0016] Optionally, the feature analysis module includes: an acceleration calculation submodule that calls the deformation signal dataset, calculates the change speed of the deformation signal in a continuous time window, recognizes the deformation acceleration, and generates a deformation acceleration value; an amplitude frequency identification submodule that uses the deformation acceleration value to record the fluctuation amplitude of the deformation signal, calculates the deformation frequency, and generates a deformation amplitude frequency parameter; and a smoothness calculation submodule that calls the deformation amplitude frequency parameter, analyzes the transition smoothness of the deformation curve, calculates a smoothness score, and generates a deformation feature parameter in combination with the acceleration, amplitude, and deformation frequency of the signal.

[0017] Optionally, the specific formula for calculating the smoothness score is:

[0018]

[0019] a smoothness score is calculated; wherein, S ∈ representing the smoothness score, J ∈i representing the deformation signal change rate at the i th time point, representing the mean value of the deformation signal change rate, N representing the total number of time points in the deformation signal data set, a ∈i representing the deformation acceleration at the i th time point, representing the mean value of the deformation acceleration, f ∈ representing the deformation signal frequency, representing the mean value of the deformation signal frequency, i representing the index of the time point currently calculated.

[0020] Optionally, the mode analysis module comprises: an abnormality marking submodule that calls the deformation feature parameters, uses the deformation frequency fluctuation rate and the smoothness score, identifies and marks the abnormal fluctuation section by calculating the statistical characteristics of the deformation frequency, and generates an abnormal fluctuation interval mark; a signal screening submodule that extracts the amplitude data in the deformation signal based on the abnormal fluctuation interval mark, compares the amplitude threshold interval in the preset motion interference database, screens and separates the motion interference signal, and generates an interference signal separation result; and a breathing extraction submodule that calls the interference signal separation result, identifies the breathing mode by comparing the processed deformation signal with the breathing mode in the breathing mode database, records the corresponding breathing period and breathing depth data, and generates a breathing rhythm parameter.

[0021] Optionally, the state monitoring module comprises: a fluctuation evaluation submodule that calls the breathing rhythm parameter, extracts the breathing period timing data, evaluates the change trend and fluctuation of the breathing period, and generates a breathing period fluctuation coefficient; an intensity analysis submodule that extracts the breathing amplitude stability data based on the breathing period fluctuation coefficient, identifies the user's exercise intensity, and generates an exercise intensity index; and a state classification submodule that calls the exercise intensity index, combines the change characteristics of the breathing period, compares with the data characteristics of multiple exercise states in the preset exercise mode database, identifies the user's exercise state, and generates an exercise state classification label.

[0022] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:

[0023] Through dynamic environment compensation and multi-dimensional feature fusion, the signal baseline stability is enhanced, the residual drift error caused by temperature and humidity gradient mutation is reduced, the anti-interference ability in complex environment is improved, the shape correlation analysis of deformation response curve and motion artifact is adopted, the separation precision of physiological signal and interference component is optimized, the recognition accuracy of deformation feature is enhanced, the multi-dimensional feature collaborative calculation is used, the dynamic change characteristics of deformation signal are quantified, the decoupling efficiency of breathing depth and exercise intensity is improved, the accurate capture of respiratory parameters in dynamic scene is realized, the motion state recognition and monitoring accuracy is enhanced by combining the evaluation of respiratory mode feature and real-time fluctuation parameter. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0025] Figure 1 The sensor flowchart of the present application;

[0026] Figure 2 The sensor frame schematic diagram of the present application;

[0027] Figure 3 The signal calibration module flowchart of the present application;

[0028] Figure 4 The deformation recognition module flowchart of the present application;

[0029] Figure 5 The feature analysis module flowchart of the present application;

[0030] Figure 6 The mode analysis module flowchart of the present application;

[0031] Figure 7 The state monitoring module flowchart of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the present application will be described below in combination with the drawings.

[0033] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. In fact, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0034] In the embodiments of the present application, sometimes the subscript such as W1 may be written in the form of non-subscript such as W1, and the meanings expressed thereby are consistent when the difference is not emphasized.

[0035] To make the technical problems, technical solutions and advantages to be solved by the present application clearer, specific embodiments will be described in detail below with reference to the drawings.

[0036] The present application provides a wearable sensor based on printed carbon materials, please refer to Figures 1 to 2 The present application provides a wearable sensor based on printed carbon materials, which comprises: a signal calibration module for collecting carbon material sensor electrical signals, acquiring real-time data of temperature and humidity nodes on the surface of the sensor, constructing an electrical signal compensation parameter table by analyzing the influence of temperature and humidity changes on the electrical signals; a deformation recognition module for calling the electrical signal compensation parameter table, performing baseline correction on the electrical signals, identifying and separating the deformation signals in the electrical signals by analyzing the relationship between the stress of the carbon material and the deformation response curve, and generating a deformation signal dataset; a feature analysis module for extracting the acceleration, amplitude and deformation frequency of the deformation signals in the continuous time window based on the deformation signal dataset, calculating the transition smoothness of the deformation curve, and generating deformation feature parameters; a pattern analysis module for calling the deformation feature parameters, marking abnormal fluctuation sections by the deformation frequency and smoothness parameters, separating motion interference signals, comparing the deformation features with known breathing patterns, extracting breathing period and breathing depth data, and generating breathing rhythm parameters; and a state monitoring module for inputting the breathing rhythm parameters, evaluating the user's motion intensity and the regularity of the breathing rhythm according to the fluctuation and amplitude stability of the breathing period, identifying the user's motion state, and generating a motion state classification label. The electrical signal compensation parameter table specifically includes temperature and humidity gradient change rate, compensation weight factor and hysteresis effect correction amount, the deformation signal dataset includes deformation response amplitude, stress distribution time sequence and baseline offset correction amount, the deformation feature parameters specifically refer to deformation acceleration value, deformation frequency fluctuation rate and transition smoothness, the breathing rhythm parameters include breathing period time sequence, amplitude stability coefficient and motion artifact separation threshold, and the motion state classification label specifically refers to motion intensity data, motion state matching degree and state identification label.

[0037] Please refer to Figure 2 and Figure 3 The signal calibration module comprises:

[0038] The data acquisition submodule collects carbon material sensor electrical signals, acquires real-time temperature and humidity data of temperature and humidity nodes on the surface of the sensor in real time, and generates a sensor dataset;

[0039] The data acquisition sub-module collects the electrical signal of the carbon material sensor. First, a plurality of carbon material sensors are deployed in the monitoring area, each sensor having an independent electrical signal acquisition unit and a temperature and humidity sensing end. To ensure data integrity, the sensor layout needs to consider environmental factors, such as deploying denser sensors in areas with rapid temperature and humidity changes, and appropriately reducing the number of sensors in stable environment areas to optimize data collection efficiency and cost. The sensor electrical signal acquisition unit uses a high-precision analog-to-digital conversion chip to convert the sensor resistance signal into a voltage signal. The main control unit periodically reads the data and adds a timestamp to each sampling point to ensure the timing of the data. Once the collection is complete, the data needs to be stored in the EEPRM or SD card to prevent data loss. At the same time, to ensure real-time data transmission, the system uses wireless communication technologies such as LRa or Wi-Fi to regularly send data to the server. On the server side, data storage uses the CSV format, with each row of data containing a timestamp, electrical signal value, temperature value, humidity value, and other fields. Subsequently, the server analyzes the raw voltage values output by the sensor and calculates the temperature value based on the calibration coefficients provided by the sensor manufacturer. The temperature calculation method is as follows: T = A * V + B.

[0040] where T is the current measured temperature (°C), V is the voltage output by the sensor (V), and A and B are the calibration coefficients provided by the sensor manufacturer.

[0041] Calculation example: Assuming that the sensor calibration coefficients are A = 50 and B = -25, and the current collected voltage signal is V = 0.7V, the calculated temperature is: T = 50 * 0.7 - 25 = 10°C.

[0042] After all the collected data is stored and analyzed, it enters the data preprocessing stage, including abnormal data rejection and data interpolation completion, to ensure the integrity of the sensor data set. The gradient analysis sub-module calculates the spatial gradient change rate of adjacent temperature and humidity nodes based on the sensor data set, analyzes the fluctuation amplitude and duration of temperature and humidity data over time, and generates the temperature and humidity gradient fluctuation rate.

[0043] The gradient analysis sub-module calculates the temperature and humidity gradient change rate of adjacent sensors at the same time point based on the sensor data set. First, the data of all measurement points is sorted in chronological order to construct a time series data matrix, and a spatial temperature and humidity distribution matrix is established based on the sensor layout. Each matrix element represents the temperature or humidity value at a specific location. When calculating the temperature gradient, the finite difference method is used to calculate the data of adjacent measurement points. For example, the temperature change rate between adjacent measurement points is calculated as follows:

[0044]

[0045] where G TTemperature gradient (°C / m), T2 is the temperature measurement value of the adjacent sensor (°C), T1 is the temperature measurement value of the previous sensor (°C), d is the distance between the two sensors (m).

[0046] Calculation example: assuming that the temperature measurement values of the adjacent sensors in a certain area are T2 = 30°C and T1 = 25°C, and the sensor spacing d = 2m, then the calculated temperature gradient is:

[0047] In addition, in order to analyze the time change rate of temperature and humidity, a sliding window technique is used on the time series, the window length is set to 10 minutes, the difference between the maximum and minimum values of temperature and humidity in the window is calculated, and the duration of the change exceeding the threshold value is counted, for example, when the temperature change exceeds 2°C and the duration is greater than 5 minutes, the time period is marked as a significant temperature fluctuation area, finally, the gradient analysis submodule combines the spatial gradient and the time fluctuation rate to generate a temperature and humidity gradient fluctuation rate data table.

[0048] The compensation construction submodule calls the temperature and humidity gradient fluctuation rate, identifies the baseline drift mapping relationship between temperature and humidity and electrical signal by analyzing the influence of temperature and humidity change on electrical signal, and generates an electrical signal compensation parameter table;

[0049] The specific formula of the influence of temperature and humidity change on electrical signal is:

[0050]

[0051] Calculate the correlation coefficient of temperature and humidity and electrical signal, identify the baseline drift mapping relationship between temperature and humidity and electrical signal;

[0052] Wherein, R X represents the correlation coefficient of the influence of temperature and humidity change on electrical signal, X i' represents the temperature value or humidity value measured at the i'th time point, represents the average value of temperature or humidity data at all time points, V i represents the electrical signal value measured at the i'th time point, represents the average value of electrical signal data at all time points, i' represents the i'th time point in the data acquisition process, and n' represents the number of time points in the data acquisition process.

[0053] Formula:

[0054]

[0055] Formula explanation and formula calculation derivation process: the formula is used to calculate the correlation coefficient of the influence of temperature and humidity change on electrical signal, and the result is used to judge the influence degree of temperature or humidity on sensor electrical signal;

[0056] Parameter meaning and setting value: Xi' Temperature value (℃) or humidity value (%RH) measured at the i'th time point, assuming that the temperature is selected as the parameter, 24.1℃, 24.3℃, 24.5℃, 24.7℃, 25.0℃;

[0057] Temperature data at all time points, V i Electric signal value (V) measured at the i'th time point, assuming that it is 1.12V, 1.18V, 1.20V, 1.25V, 1.30V; Electric signal data at all time points, n' is the total number of time sampling points, assuming that it is 5;

[0058] Substitute the parameters into the formula for calculation:

[0059]

[0060]

[0061] Correlation coefficient R X = 0.215 indicates that there is a certain positive correlation between temperature and electric signal, and the result is used for compensation correction of electric signal.

[0062] Please refer to Figure 2 and Figure 4 , the deformation recognition module comprises:

[0063] The baseline correction sub-module calls the electric signal compensation parameter table to correct the original electric signal, calculates the compensated electric signal amplitude and timing characteristics, and generates the corrected electric signal waveform; the baseline correction sub-module calls the electric signal compensation parameter table to correct the original electric signal, first, the system loads the compensation parameter table, and performs time sequence analysis on the original electric signal obtained by the sensor to extract the electric signal amplitude at the corresponding time point. According to the temperature and humidity gradient correction term in the compensation parameter table, the original signal is adjusted, the compensation calculation adopts a linear correction method, the corrected electric signal value is calculated, and the specific calculation formula is as follows: V c = V r -ΔV;

[0064] Where, V c is the corrected electric signal amplitude (V), V r is the original electric signal amplitude (V), and ΔV is the compensation amount (V) calculated based on the temperature and humidity gradient. The compensation amount is obtained from the lookup result of the compensation parameter table. The compensation parameter is set according to historical data statistics. The drift value under each temperature and humidity gradient interval is measured by experiment calibration, for example, when the temperature gradient G T= 2.5℃ / m, humidity gradient G H = 3, the measured electrical signal drift ΔV = 0.014V, then the corrected electrical signal calculation as follows:

[0065] V c = 2.0V-0.014V = 1.986V;

[0066] The corrected electrical signal amplitude is calculated, and the system sorts the signal data according to the time stamp to ensure the time sequence continuity of the data, and then the baseline correction sub-module uses the sliding window method to process the signal curve, calculates the change rate of the corrected signal in each window, and eliminates abnormal signal points, for example, in a 10ms window, the change rate R between adjacent sampling points is calculated:

[0067] Where, V c2 and V c1 are the corrected electrical signal values (V) at adjacent time points, and Δt is the time interval (s). If the calculation result exceeds the set threshold, for example, |R|>0.05V / ms, it is considered that the point is an abnormal value, interpolation correction is performed, and all corrected electrical signal data are stored in the database to finally generate the corrected electrical signal waveform.

[0068] The response analysis sub-module analyzes the corresponding relationship between the carbon material stress and deformation response curve based on the corrected electrical signal waveform, analyzes the influence of the carbon material deformation on the electrical signal, calculates the sensitivity parameter of the electrical signal to the deformation, and generates the deformation response characteristic parameter;

[0069] The specific formula for analyzing the influence of the carbon material deformation on the electrical signal is:

[0070] The sensitivity parameter of the electrical signal to the deformation is calculated; wherein SD represents the sensitivity parameter of the electrical signal to the deformation, VDc q represents the corrected electrical signal measurement value corresponding to the qth deformation variable, VDc0 represents the initial value of the corrected electrical signal when the deformation variable is zero, Xε q represents the qth deformation variable, Xε0 represents the deformation variable without external force, p represents the number of data sampling points, and q represents the deformation variable sequence number currently calculated.

[0071] Formula:

[0072] Formula explanation and formula calculation derivation process: the formula is used to calculate the sensitivity parameter of the electrical signal to the deformation, and the result is used to represent the change amplitude of the electrical signal of the carbon material under different deformation variables, and is used in the subsequent signal separation process.

[0073] Parameter meaning and set value: SD is the sensitivity parameter of the electrical signal to the deformation, representing the influence intensity of the deformation of the carbon material on the electrical signal; VDc q To correct the measured value of the electrical signal under the qth deformation variable, assuming that the corrected electrical signal value under the 1st deformation variable is 1.95V, the corrected electrical signal value under the 2nd deformation variable is 1.92V, the corrected electrical signal value under the 3rd deformation variable is 1.89V, the corrected electrical signal value under the 4th deformation variable is 1.85V, the corrected electrical signal value under the 5th deformation variable is 1.82V, the corrected electrical signal value under the 6th deformation variable is 1.78V, the corrected electrical signal value under the 7th deformation variable is 1.74V, the corrected electrical signal value under the 8th deformation variable is 1.71V, the corrected electrical signal value under the 9th deformation variable is 1.67V, and the corrected electrical signal value under the 10th deformation variable is 1.63V; VDc0 is the initial value of the corrected electrical signal when the deformation variable is zero, and is assumed to be 2.0V; Xε q The qth deformation variable is 0.001ε, the 1st deformation variable is 0.002ε, the 2nd deformation variable is 0.003ε, the 3rd deformation variable is 0.004ε, the 4th deformation variable is 0.005ε, the 5th deformation variable is 0.006ε, the 6th deformation variable is 0.007ε, the 7th deformation variable is 0.008ε, the 8th deformation variable is 0.009ε, the 9th deformation variable is 0.01ε, and the 10th deformation variable is 0.01ε; Xε0 is the deformation variable under no external force, and is assumed to be 0, indicating the strain of the material in a static state; p is the total number of data sampling points; q is the serial number of the deformation variable currently calculated;

[0074] The parameters are substituted into the formula for calculation:

[0075]

[0076] The results show that the sensitivity parameter of the electrical signal to the deformation is SD = 37.09V / ε, indicating that when the carbon material produces a unit deformation variable of 1ε, the electrical signal changes by an average of 37.09V. This value is used for subsequent deformation signal separation calculations to identify the electrical signal characteristics of the carbon material under different loads and can be used to distinguish the signal change trend caused by different deformation variables.

[0077] The signal separation submodule calls the deformation response characteristic parameters to identify and separate the deformation signal in the electrical signal and generate a deformation signal dataset. The signal separation submodule calls the deformation response characteristic parameters to identify and separate the deformation signal in the electrical signal. First, the corrected electrical signal waveform is subjected to frequency spectrum analysis, and the low-frequency component is extracted as the deformation signal characteristic value. The deformation response characteristic parameters are used for matching to distinguish the electrical signal changes under different deformation variables, and the deformation contribution value in the electrical signal component is calculated as follows: ∈ = S·∈;

[0078] wherein V ∈ is the deformation signal amplitude (V), S is the sensitivity of the electrical signal to deformation (V / ε), ∈ is the measured deformation (ε), if the deformation ∈ = 0.003ε, the sensitivity S = 4V / ε, then the deformation signal amplitude is calculated as follows: V ∈ = 4 x 0.003 = 0.012V;

[0079] After the calculation, the deformation component in the electrical signal is separated from other interference signals, the frequency domain filtering method is used to remove high frequency noise, and pure deformation signal data is obtained, and finally the deformation signal data set is generated.

[0080] Please refer to Figure 2 and Figure 5 , the feature analysis module comprises:

[0081] The acceleration calculation sub-module calls the deformation signal data set, calculates the change speed of the deformation signal in the continuous time window, identifies the deformation acceleration, and generates the deformation acceleration value; the acceleration calculation sub-module calls the deformation signal data set, calculates the change speed of the deformation signal in the continuous time window, first, the system extracts the deformation of the continuous time points from the deformation signal data set, arranges them in time sequence, and then calculates the deformation of the adjacent time points, to obtain the change rate of the deformation, i.e. the deformation speed, the deformation speed calculation method is as follows:

[0082] wherein v ∈ is the deformation speed (ε / s), ∈ t2 is the deformation at time t2 (ε), ∈ t1 is the deformation at time t1 (ε), and Δt is the interval between the two time points (s). For example, when t1 = 1s, the deformation ∈ t1 = 0.002ε, when t2 = 2s, the deformation ∈ t2 = 0.006ε, then the deformation speed is calculated as follows:

[0083]

[0084] Subsequently, in the entire deformation signal data set, the deformation speed of different time periods is calculated by using the sliding window method, the window length is set to 10ms, the deformation speed average in each window is calculated to reduce the influence of instantaneous noise, and based on the calculated deformation speed data, the deformation acceleration, i.e. the change rate of the deformation speed, is calculated, and the calculation method is as follows:

[0085] wherein a ∈ is the deformation acceleration (ε / s2), v ∈2 is the deformation speed at time t2 (ε / s), and v ∈1is the deformation velocity (ε / s) at time t1, for example, if v ∈1 = 0.004 ε / s, v ∈2 = 0.008 ε / s, and the time interval Δt = 2 s, then the deformation acceleration is calculated as follows:

[0086] Finally, the system traverses the entire deformation signal data set, calculates the deformation acceleration values at all time points, stores them in the database, and generates the deformation acceleration values.

[0087] The amplitude frequency identification submodule uses the deformation acceleration values to record the fluctuation amplitude of the deformation signal, calculates the deformation frequency, and generates the deformation amplitude frequency parameters; the amplitude frequency identification submodule uses the deformation acceleration values to record the fluctuation amplitude of the deformation signal, calculates the deformation frequency, and first, the system extracts time series data from the deformation acceleration data set, calculates the maximum and minimum values of the deformation acceleration, obtains the complete fluctuation amplitude, and the calculation method is as follows: A ∈ = |a ∈max -a ∈min |;

[0088] Where A ∈ is the deformation acceleration fluctuation amplitude (ε / s 2 ), a ∈max is the maximum deformation acceleration (ε / s 2 ), and a ∈min is the minimum deformation acceleration (ε / s 2 ), for example, in a certain period of time, the maximum deformation acceleration measured is a ∈max = 0.008 ε / s 2 , and the minimum deformation acceleration is a ∈min = -0.004 ε / s 2 , then the fluctuation amplitude is calculated as follows: A ∈ = |0.008 - (-0.004)| = 0.012 ε / s 2 ;

[0089] Next, the system calculates the frequency of the deformation signal by the zero-crossing point detection method, that is, it counts the number of periodic changes of the deformation signal in unit time, counts a zero-crossing point every time the deformation acceleration signal changes from negative to positive (or vice versa), and calculates the average time interval T ∈ (s) between adjacent zero-crossing points, and the deformation frequency is calculated as follows:

[0090] Where f ∈ is the deformation frequency (Hz), and T ∈ is the time interval of a complete cycle (s), for example, in the measured data, the average period T ∈= 0.5s, the deformation frequency is calculated as follows:

[0091]

[0092] All the calculated fluctuation amplitudes and deformation frequency data are stored in a database and used for subsequent deformation feature analysis to finally generate deformation amplitude frequency parameters. The smoothness calculation submodule calls the deformation amplitude frequency parameters, analyzes the transition smoothness of the deformation curve, calculates the smoothness score, and combines the acceleration, amplitude, and deformation frequency of the signal to generate deformation feature parameters;

[0093] The specific formula for calculating the smoothness score is:

[0094]

[0095] The smoothness score is calculated; wherein S ∈ represents the smoothness score, J ∈i represents the deformation signal change rate at the i-th time point, represents the average deformation signal change rate, N represents the total number of time points in the deformation signal data set, a ∈i represents the deformation acceleration at the i-th time point, represents the average deformation acceleration, f ∈ represents the deformation signal frequency, represents the average deformation signal frequency, and i represents the current calculation time point index.

[0096] Formula:

[0097] Formula details and formula calculation derivation process: the formula is used to calculate the smoothness score of the deformation signal, and the result is used to measure the degree of change of the signal in the time series, providing a reference for further analysis of the deformation signal features.

[0098] Parameter meaning and setting value: J ∈i is the deformation signal change rate at the i-th time point, and the setting value is 0.002, 0.004, 0.005, 0.003, 0.004ε / s 3 ; is the average deformation signal change rate; N is the total number of time points in the deformation signal data set, and the setting value is 5; a ∈i is the deformation acceleration at the i-th time point, and the setting value is 0.006, 0.008, 0.007, 0.005, 0.006ε / s 2 ; is the average deformation acceleration; f ∈ is the deformation signal frequency, and the setting value is 2.5Hz; is the average deformation signal frequency, and the setting value is 2.4Hz.

[0099] Substitute the parameters into the formula to calculate:

[0100]

[0101]

[0102] The calculation result 0.1019 reflects the smoothness score of the deformation signal, and the lower the value, the higher the smoothness of the signal. The result shows that the current signal has a certain degree of volatility, but is generally stable. Further combined with the acceleration, amplitude and frequency of the deformation signal, it can be used for deformation feature analysis.

[0103] See Figure 2 and Figure 6 , the mode analysis module comprises:

[0104] The abnormality marking sub-module calls the deformation feature parameters, uses the deformation frequency fluctuation rate and the smoothness score, calculates the statistical characteristics of the deformation frequency, identifies and marks the abnormal fluctuation section, and generates the abnormal fluctuation interval mark. The abnormality marking sub-module calls the deformation feature parameters, uses the deformation frequency fluctuation rate and the smoothness score, calculates the statistical characteristics of the deformation frequency, identifies and marks the abnormal fluctuation section. First, the system extracts the deformation frequency fluctuation rate data from the deformation feature parameter set, arranges them in time sequence, calculates the deformation frequency mean value in each time window, and analyzes the change with time. The deformation frequency mean value is calculated as follows:

[0105] Where F avg is the deformation frequency mean value (Hz), F j is the deformation frequency at the jth time point (Hz), and N f is the number of sampling points in the time window. For example, the deformation frequency data F = 1.8, 2.0, 2.2, 1.9, 2.1] Hz is measured in 5 time points, and the deformation frequency mean value is calculated as follows:

[0106] After the calculation is completed, the system further calculates the standard deviation of the deformation frequency to measure the fluctuation degree. The standard deviation is calculated as follows:

[0107] Where σ F is the deformation frequency fluctuation standard deviation (Hz). If σ F exceeds the set threshold, for example, σ F > 0.5 Hz, then the time interval is marked as an abnormal fluctuation interval and stored in the database, and finally the abnormal fluctuation interval mark is generated.

[0108] The signal screening submodule extracts the amplitude data in the deformation signal based on the abnormal fluctuation interval mark, compares the amplitude threshold interval in the preset motion interference database, screens and separates the motion interference signal, and generates an interference signal separation result. The signal screening submodule extracts the amplitude data in the deformation signal based on the abnormal fluctuation interval mark, compares the amplitude threshold interval in the preset motion interference database, screens and separates the motion interference signal, first, the system extracts the deformation signal amplitude data from the abnormal fluctuation interval, and calculates the maximum and minimum value range, the deformation signal amplitude calculation method is as follows: def = |D max -D min |;

[0109] Wherein, A def is the amplitude of the deformation signal (ε), D max is the maximum deformation of the deformation signal (ε), D min is the minimum deformation of the deformation signal (ε), for example, in a certain period of time, the maximum deformation is D max = 0.006ε, the minimum deformation is D min = 0.002ε, then the amplitude is calculated as follows:

[0110] A def = |0.006-0.002| = 0.004ε;

[0111] Subsequently, the system extracts the amplitude threshold interval of different types of motion interference from the preset motion interference database, and compares it with the amplitude calculated at present, for example, if the amplitude range of running interference is set to 0.003≤A def ≤0.007ε in the database, and the calculated deformation signal amplitude A def = 0.004ε, it is determined that the deformation signal may be caused by motion interference, and is marked and separated, all screened motion interference signals are stored in the database, and finally the interference signal separation result is generated.

[0112] The breathing extraction submodule calls the interference signal separation result, compares the processed deformation signal with the breathing pattern in the breathing pattern database, identifies the breathing pattern, records the corresponding breathing period and breathing depth data, and generates the breathing rhythm parameter; The breathing extraction submodule calls the interference signal separation result, compares the processed deformation signal with the breathing pattern in the breathing pattern database, identifies the breathing pattern, records the corresponding breathing period and breathing depth data, first, the system extracts the periodic signal segment from the deformation signal data set after the interference signal is separated, and calculates the average breathing period, the calculation method is as follows:

[0113] Wherein, T bris the average respiratory period (s), T k is the length of the kth respiratory period (s), N T is the measured respiratory period number, for example, if the measured length of 5 respiratory periods is 4.8, 5.0, 5.2, 4.9, 5.1]s, then the average respiratory period is calculated as follows:

[0114] Next, the system calculates the respiratory depth data, that is, the amplitude variation range of the deformation signal during the respiratory process, and the calculation method is as follows: A br = |D br,max -D br,min |;

[0115] Where, A br is the amplitude of the respiratory deformation signal (ε), D br,max is the maximum deformation of the respiratory signal (ε), D br,min is the minimum deformation of the respiratory signal (ε), if the measured maximum deformation in a respiratory period is D br,max = 0.005ε, and the minimum deformation is D br,min = 0.002ε, then the respiratory depth is calculated as follows: A br = |0.005-0.002| = 0.003ε;

[0116] After the calculation is completed, the system compares the current measured respiratory period and the respiratory depth data with different modes in the respiratory mode database, for example, the amplitude threshold of the deep breathing mode in the database is A br > 0.004ε, and the threshold of the shallow breathing mode is A br <0.002ε, if the calculated respiratory amplitude A br = 0.003ε, then the respiratory mode does not belong to deep or shallow breathing, the system marks it as normal breathing mode and stores it in the database, and finally generates the respiratory rhythm parameters.

[0117] Please refer to Figure 2 and Figure 7 , the state monitoring module comprises:

[0118] The fluctuation evaluation submodule calls the respiratory rhythm parameters, extracts the respiratory period timing data, evaluates the change trend and fluctuation of the respiratory period, and generates the respiratory period fluctuation coefficient; the fluctuation evaluation submodule calls the respiratory rhythm parameters, extracts the respiratory period timing data, evaluates the change trend and fluctuation of the respiratory period, first, the system extracts the respiratory period data of the continuous time points from the respiratory rhythm parameter set, and arranges them in time sequence, then calculates the mean of the respiratory period in multiple time windows, and the calculation method is as follows:

[0119]

[0120] wherein T mean is the average respiratory period (s), T bp,m is the mth respiratory period (s), N bp is the total number of periods, for example, if 6 respiratory periods are measured as [4.8, 5.1, 4.9, 5.0, 5.2, 4.7] s, the average respiratory period is calculated as follows:

[0121] After the calculation is completed, the system calculates the respiratory period fluctuation, that is, the degree of deviation of each respiratory period from the mean value, and the fluctuation coefficient is calculated as follows:

[0122] wherein C bp is the respiratory period fluctuation coefficient, σ bp is the respiratory period standard deviation (s), and the standard deviation is calculated as follows:

[0123] For example, if σ bp = 0.18 s and T mean = 4.95 s, the fluctuation coefficient is calculated as follows:

[0124]

[0125] The system divides the interval according to the range of the fluctuation coefficient, for example, C bp <0.05 is stable, 0.05≤C bp <0.1 is moderate fluctuation, and C bp ≥0.1 is high fluctuation, and finally the respiratory period fluctuation coefficient is generated. The intensity analysis submodule extracts the respiratory amplitude stability data based on the respiratory period fluctuation coefficient, identifies the user's exercise intensity, and generates an exercise intensity index; the intensity analysis submodule extracts the respiratory amplitude stability data based on the respiratory period fluctuation coefficient, identifies the user's exercise intensity, and first, the system extracts the respiratory amplitude in multiple time windows from the respiratory signal data set and calculates the amplitude mean, and the calculation method is as follows:

[0126]

[0127] wherein A mean is the average respiratory amplitude (ε), A br,n is the respiratory amplitude in the nth time window (ε), N amp is the total number of time windows, for example, if the respiratory amplitudes in 5 time windows are measured as [0.0035, 0.0038, 0.0040, 0.0036, 0.0037] ε, the average respiratory amplitude is calculated as follows:

[0128]

[0129] After the calculation is completed, the system calculates the respiratory amplitude stability, i.e. the amplitude deviation degree in each time window, in the following manner:

[0130] wherein S amp is the respiratory amplitude stability coefficient (dimensionless), and σ amp is the amplitude standard deviation (ε), which is calculated in the following manner:

[0131] For example, if σ amp = 0.0002ε and A mean = 0.00372ε are calculated, then the amplitude stability is calculated as follows:

[0132] The system calculates the exercise intensity index according to the amplitude stability and the respiratory cycle fluctuation coefficient in the following manner: I act = w1C bp + w2S amp .

[0133] wherein I act is the exercise intensity index (dimensionless), and w1 and w2 are weight coefficients, for example, if w1 = 0.6 and w2 = 0.4 are set, then I act is calculated as follows: I act = 0.6 x 0.036 + 0.4 x 0.0538 = 0.0436.

[0134] The system divides the intervals according to the exercise intensity index, for example, I act < 0.05 is light exercise, 0.05 ≤ I act < 0.15 is moderate exercise, and I act ≥ 0.15 is high-intensity exercise, and finally the exercise intensity index is generated.

[0135] The state classification submodule calls the exercise intensity index, combines the respiratory cycle change characteristics, compares with the data characteristics of multiple exercise states in the preset exercise mode database, identifies the exercise state of the user, and generates an exercise state classification label; the state classification submodule calls the exercise intensity index, combines the respiratory cycle change characteristics, compares with the data characteristics of multiple exercise states in the preset exercise mode database, identifies the exercise state of the user, and first, the system extracts the respiratory rhythm parameters corresponding to different exercise states from the exercise mode database, including the respiratory cycle, the amplitude mean value, the fluctuation coefficient, and the exercise intensity index, and matches the current measured data, for example, the exercise state characteristics defined in the database are as follows:

[0136] Table 1 correspondence between motion state and respiratory rhythm parameters

[0137] Motion state Respiratory cycle Mean amplitude Coefficient of variation Motion intensity index Rest 5.0-6.5 0.0025-0.004 <0.05 <0.05 Light exercise 4.0-5.0 0.003-0.005 0.05-0.1 0.05-0.15 Moderate exercise 3.0-4.0 0.004-0.006 0.1-0.2 0.15-0.3 High intensity exercise 2.0-3.0 >0.005 >0.2 >0.3

[0138] Referring to Table 1, if the current measured T mean = 4.95s, A mean = 0.00372ε, C bp = 0.036, I act = 0.0436, the matching result falls into the mild motion state, the system is stored in the database, and finally a motion state classification label is generated.

[0139] The above-described embodiments can be implemented in whole or in part by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented using software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through wired (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0140] It should be understood that the size of the sequence number of each process described above in various embodiments of the present application does not mean the order of execution, and the execution order of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0141] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0142] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other manners. For example, the embodiments of the apparatus described above are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0143] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0144] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit.

[0145] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art, or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0146] The above description is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A wearable sensor based on printing carbon materials, characterized by, The sensor comprises: The signal calibration module collects the carbon material sensor electric signal, obtains real-time data of the temperature and humidity nodes on the surface of the sensor, constructs an electric signal compensation parameter table by analyzing the influence of temperature and humidity changes on the electric signal; The deformation recognition module calls the electric signal compensation parameter table, corrects the baseline of the electric signal, recognizes and separates the deformation signal in the electric signal by analyzing the relationship between the carbon material stress and the deformation response curve, and generates a deformation signal data set; The feature analysis module extracts the acceleration, amplitude and deformation frequency of the deformation signal in a continuous time window based on the deformation signal data set, calculates the transition smoothness of the deformation curve, and generates deformation feature parameters; The mode analysis module calls the deformation feature parameters, marks abnormal fluctuation sections through the deformation frequency and smoothness parameters, separates motion interference signals, compares the deformation features with known breathing patterns, extracts breathing period and breathing depth data, and generates breathing rhythm parameters; The state monitoring module inputs the breathing rhythm parameters, evaluates the user's motion intensity and breathing rhythm regularity according to the fluctuation and amplitude stability of the breathing period, identifies the user's motion state, and generates a motion state classification label; The mode analysis module comprises: The abnormal marking sub-module calls the deformation feature parameters, uses the deformation frequency fluctuation rate and smoothness score, identifies and marks abnormal fluctuation sections by calculating the statistical characteristics of the deformation frequency, and generates abnormal fluctuation interval markers; The signal screening sub-module extracts the amplitude data in the deformation signal based on the abnormal fluctuation interval markers, compares the amplitude threshold interval in the preset motion interference database, screens and separates the motion interference signals, and generates interference signal separation results; The breathing extraction sub-module calls the interference signal separation results, compares the processed deformation signal with the breathing patterns in the breathing pattern database, identifies the breathing pattern, records the corresponding breathing period and breathing depth data, and generates breathing rhythm parameters.

2. The wearable sensor based on printed carbon material of claim 1, wherein, The electric signal compensation parameter table specifically refers to the temperature and humidity gradient change rate, the compensation weight factor and the hysteresis effect correction amount, the deformation signal data set includes the deformation response amplitude, the stress distribution time sequence and the baseline offset correction amount, the deformation feature parameters specifically refer to the deformation acceleration value, the deformation frequency fluctuation rate and the transition smoothness, the breathing rhythm parameters include the breathing period time sequence, the amplitude stability coefficient and the motion artifact separation threshold, and the motion state classification label specifically refers to the motion intensity data, the motion state matching degree and the state identification label.

3. The printed carbon material-based wearable sensor of claim 1, wherein, The signal calibration module comprises: The data acquisition sub-module collects the carbon material sensor electric signal, and obtains real-time temperature and humidity data of the temperature and humidity nodes on the surface of the sensor in real time, and generates a sensor data set; The gradient analysis sub-module analyzes the fluctuation amplitude and duration of the temperature and humidity data over time by calculating the spatial gradient change rate of adjacent temperature and humidity nodes based on the sensor data set, and generates a temperature and humidity gradient fluctuation rate; The compensation construction sub-module calls the temperature and humidity gradient fluctuation rate, analyzes the influence of temperature and humidity changes on the electric signal, identifies the baseline drift mapping relationship between temperature and humidity and the electric signal, and generates an electric signal compensation parameter table.

4. The printed carbon material-based wearable sensor of claim 3, wherein, A specific formula of the influence of temperature and humidity change on the electrical signal is: A correlation coefficient of temperature and humidity and the electrical signal is calculated, and a baseline drift mapping relationship of temperature and humidity and the electrical signal is identified; wherein R X represents the correlation coefficient of the temperature or humidity data at the i'th time point, X i′ represents the temperature or humidity value measured at the i'th time point, represents the average value of the temperature or humidity data at all time points, V i' represents the electric signal value measured at the i'th time point, represents the average value of the electric signal data at all time points, i' represents the i'th time point in the data collection process, and n' represents the total number of time points in the data collection process.

5. The printed carbon material-based wearable sensor of claim 1, wherein, The deformation identification module comprises: A baseline correction submodule calls the electrical signal compensation parameter table, corrects the original electrical signal, calculates the amplitude and timing characteristics of the compensated electrical signal, and generates a corrected electrical signal waveform; A response analysis submodule analyzes the corresponding relationship of the carbon material stress and deformation response curve based on the corrected electrical signal waveform, analyzes the influence of carbon material deformation on the electrical signal, calculates the sensitivity parameter of the electrical signal to deformation, and generates a deformation response characteristic parameter; A signal separation submodule calls the deformation response characteristic parameter, identifies and separates the deformation signal in the electrical signal, and generates a deformation signal data set.

6. The wearable sensor based on printed carbon material of claim 5, wherein, A specific formula of the influence of carbon material deformation on the electrical signal is: A sensitivity parameter of the electrical signal to deformation is calculated; wherein SD represents a sensitivity parameter of the electrical signal to the deformation, VDc q represents the measured value of the corrected electrical signal corresponding to the qth deformation variable, VDc0 represents the initial value of the corrected electrical signal when the deformation variable is zero, Xe q represents the qth deformation variable, Xe0 represents the deformation variable without external force, p represents the number of data sampling points, and q represents the serial number of the deformation variable currently calculated.

7. The printed carbon material-based wearable sensor of claim 6, wherein, The feature analysis module comprises: An acceleration calculation submodule calls the deformation signal data set, calculates the change speed of the deformation signal in a continuous time window, identifies the deformation acceleration, and generates a deformation acceleration value; An amplitude frequency identification submodule uses the deformation acceleration value to record the fluctuation amplitude of the deformation signal, calculates the deformation frequency, and generates a deformation amplitude frequency parameter; A smoothness calculation submodule calls the deformation amplitude frequency parameter, analyzes the transition smoothness of the deformation curve, calculates a smoothness score, and combines the acceleration, amplitude, and deformation frequency of the signal to generate a deformation feature parameter.

8. The printed carbon material-based wearable sensor of claim 7, wherein, A specific formula of the calculation of the smoothness score is: A smoothness score is calculated; where S ∈ represents the smoothness score, J ∈i represents the deformation signal change rate at the i-th time point, represents the mean of the deformation signal change rate, N represents the total number of time points in the deformation signal dataset, a ∈i represents the deformation acceleration at the i-th time point, represents the mean of the deformation acceleration, f ∈ represents the deformation signal frequency, represents the mean of the deformation signal frequency, i represents the index of the current calculated time point.

9. The printed carbon material-based wearable sensor of claim 1, wherein, The state monitoring module comprises: A fluctuation evaluation submodule calls the breathing rhythm parameter, extracts breathing cycle timing data, evaluates the change trend and fluctuation of the breathing cycle, and generates a breathing cycle fluctuation coefficient; An intensity analysis submodule extracts breathing amplitude stability data based on the breathing cycle fluctuation coefficient, identifies the user's exercise intensity, and generates an exercise intensity index; A state classification submodule calls the exercise intensity index, combines the change characteristics of the breathing cycle, compares with the data characteristics of multiple exercise states in a preset exercise mode database, identifies the user's exercise state, and generates an exercise state classification label.

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