CN113974583B - Monitoring device based on flexible chip and monitoring insole - Google Patents
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
- A61B5/0531—Measuring skin impedance
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
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- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
- G01K13/20—Clinical contact thermometers for use with humans or animals
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- G—PHYSICS
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- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
- G01L5/0028—Force sensors associated with force applying means
- G01L5/0038—Force sensors associated with force applying means applying a pushing force
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Abstract
The application discloses a flexible chip-based monitoring device and a monitoring insole, wherein a data acquisition unit is an organic flexible chip layer: the data acquisition unit is used for acquiring a first data signal of the human body to be monitored and transmitting the first data signal to the data processing unit; the data processing unit is used for preprocessing and analyzing the first data signal and transmitting the second data signal obtained after processing to the data transmission unit; the data transmission unit is used for transmitting the second data signal to the cloud platform; the cloud platform is used for carrying out deep analysis processing on the second data and transmitting the index result of the human physiological signal obtained after the processing to the user terminal; the application has the beneficial effects that the process of monitoring various physiological data signals of the human body in real time in different time periods is realized, so that the user intuitively obtains the state of the body; the monitoring device is arranged in the insole, so that more physiological signal data can be collected after the insole is worn for a long time, and the insole can be replaced in different shoes.
Description
Technical Field
The application relates to the technical field of flexible chip management, in particular to a monitoring device based on a flexible chip and a monitoring insole.
Background
Along with the development of internet science and technology, physiological signal data of a human body can be used for analysis and processing, and processed results can be presented to a user, so that the user can intuitively judge the current state condition of the body of the user.
The physiological signals of the human body have the following characteristics: physiological signals such as heart rate have time continuity, and signals are continuously generated; physiological signals such as plantar pressure and temperature have spatial distribution, and the pressure and temperature values of different parts are greatly different; the physiological signal intensity depends on time, space and other variables, and the distribution intensity of the signal in time and space is changed along with the change of the external environment and the change of the state of the human body; the human body has various physiological signals, and the intensity and the corresponding physiological activities of the signals are different.
In the medical equipment in the prior art, only certain physiological signals of a human body can be collected and analyzed at specific time in a medical institution, although the collection and monitoring of the traditional ECG heart rate signals are mature and widely applied; but adopts a patch electrode mode, and an electrocardiograph is used for collection. The contact type can bring physical discomfort to a patient to a certain extent, and the physiological signals of the human body cannot be monitored in real time, so that a rule cannot be formed.
In view of this, the present application has been made.
Disclosure of Invention
The application aims to provide a monitoring device and a monitoring insole based on a flexible chip, which can realize real-time monitoring of physiological signals on a human body, thereby obtaining the change condition of the physiological signals of the human body in real time and timely enabling a user to know the state of the body of the user.
The application is realized by the following technical scheme:
a monitoring device based on a flexible chip, which is applied to contact with human skin to monitor physiological signals of the human body; the cloud platform comprises a data acquisition unit, a data processing unit, a data transmission unit and a cloud platform; the data acquisition unit is an organic flexible chip layer:
the data acquisition unit is used for acquiring a first data signal of a human body to be monitored and transmitting the first data signal into the data processing unit, wherein the first data signal comprises a first BCG data signal, a temperature data signal, a first pressure data signal and an electrical impedance data signal;
the data processing unit is used for preprocessing and analyzing the first data signal, transmitting a second data signal obtained after processing into the data transmission unit, wherein the second data signal is a characteristic value extracted from the waveform characteristics of the first BCG data signal and a characteristic value calculated from the first pressure data signal;
the data transmission unit is used for transmitting the second data signal to the cloud platform;
the cloud platform is used for carrying out deep analysis processing on the second data and transmitting the index result of the human physiological signal obtained after the processing to the user terminal.
When the physiological signals of the human body are monitored, electrode patches are stuck on the human body and are monitored in a certain time period, but when the physiological signals of the human body are monitored by adopting the method, the electrode patches stuck on the human body are connected with a circuit, only a part of the physiological signals of the human body can be collected and analyzed in a specific time, and the physiological signals of the human body cannot be monitored in real time.
Preferably, the data acquisition unit comprises a flexible heart rate sensor, a flexible temperature sensor, a flexible pressure sensor and a flexible electrical impedance sensor, and the output end of the flexible heart rate sensor, the output end of the flexible temperature sensor, the output end of the flexible pressure sensor and the output end of the flexible electrical impedance sensor are all connected with the input end of the data processing unit.
Preferably, the data processing unit includes a multiplexing module, a signal amplifying module, a digital-to-analog conversion module and a calculation processing module, wherein an input end of the multiplexing module is connected with an output end of the data acquisition unit, an output end of the multiplexing module is connected with an input end of the signal amplifying module, an output end of the signal amplifying module is connected with an input end of the digital-to-analog conversion module, an output end of the digital-to-analog conversion module is connected with an input end of the calculation processing module, an output end of the calculation processing module is connected with an input end of the data transmission unit, and the calculation processing module is used for preprocessing and analyzing the first data signal and transmitting the second data signal obtained after processing and analysis to the data transmission unit.
Preferably, the specific steps of preprocessing analysis in the data processing unit include:
extracting waveform characteristics of the first BCG data signal based on the first BCG data signal to obtain a second BCG data signal, wherein the second BCG data signal comprises J-wave amplitude a max Relative amplitude a of JK wave rel I wave start point, J wave peak time interval t min Time interval t between J wave peak and K wave peak max ；
Based on the first pressure data signal, calculating in a matrix manner to obtain a second pressure data signal, wherein the second pressure data signal comprises average pressure P ave Peak pressure P peak Center of pressure X in X direction cop Center of pressure Y in Y direction cop Center of pressure velocity V cop 。
Preferably, the step of performing depth analysis processing in the cloud platform includes:
processing and analyzing the second BCG data signal by adopting a K-means++ clustering algorithm to obtain the physiological index of the human body;
classifying and analyzing the temperature data signals by adopting a clustering algorithm to obtain the time-dependent change rule of the temperature of the human body in different states;
classifying and analyzing the second pressure data signal by adopting a Gaussian naive Bayes algorithm to obtain human physiological state information;
and analyzing the electrical impedance data signal by adopting a statistical analysis method to obtain physical physiological indexes of the human body.
Preferably, the processing analysis is performed on the second BCG data signal by adopting a K-means++ clustering algorithm to obtain a physiological index of a human body, and the specific operation method comprises the following steps:
step A: generating a 4N-dimensional feature vector f based on the obtained second BCG data signal i ；
And (B) step (B): based on 4N dimension feature vector f i Obtaining the probability D that each sample point is selected as the next cluster center M (f i ，f j )；
Step C: selecting D with the highest probability value M (f i ，f j ) And B, taking the corresponding sample points as the next clustering centers, and repeating the step A and the step B to obtain K clustering centers;
step D: and (3) calculating the classification of the BCG signal peaks obtained by the converged clustering algorithm, calculating the average value of each cluster and the cross correlation of the subsequence where the peak is and the clustering template sequence based on the classification, and obtaining the human physiological index through the average value and the cross correlation.
Preferably, the data transmission unit includes a bluetooth module, an RF radio frequency module and an antenna module,
the Bluetooth module is used for receiving the second data signal and transmitting the second data signal to the RF module according to a Bluetooth Mesh protocol transmission method;
the RF module is used for receiving the second data signal and transmitting the second data signal to the cloud platform through the antenna module.
Preferably, the organic flexible chip includes a package layer, an electrode, a conductive layer, an insulating layer, and a substrate, and the multilayer structure of the organic flexible chip is manufactured through a lamination process;
the packaging layer is a 3M Tegaderm dressing;
the electrode is a silver nanowire;
the conductive layer is a conductive porous nano-composite formed by uniformly mixing foamed aluminum, graphene, carbon nano-tube and Ecoflex high polymer;
the insulating layer is polymethyl methacrylate or polyethylene terephthalate.
The application also provides a monitoring insole based on the flexible chip, which comprises an insole body and the monitoring device based on the flexible chip,
the insole body comprises a sensing chip layer, an insulating protection layer and a calculation transmission basal layer; the data acquisition unit is arranged on the perception chip layer, and the data processing unit and the data transmission unit are arranged on the calculation transmission substrate layer; the insulation protection layer is arranged between the sensing chip layer and the calculation transmission substrate layer, and the insole body is obtained through multi-layer process pressing.
Preferably, the insole is provided with at least two insole bodies when monitoring the electrical impedance data signal of the human body.
Compared with the prior art, the application has the following advantages and beneficial effects:
1. the monitoring device and the monitoring insole based on the flexible chip realize the process of monitoring, analyzing and calculating various physiological data signals of a human body in real time in different time periods, and can enable a user to intuitively obtain the state of the body through the data signals;
2. the monitoring device based on the flexible chip and the monitoring insole provided by the embodiment of the application are arranged in the insole, the monitoring device can collect more physiological signal data after being worn for a long time, and the stretchable organic flexible chip has various detection and calculation functions and can be replaced in different shoes.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present application, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present application and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a monitoring device
FIG. 2 is a flow chart of a monitoring process
FIG. 3 is a schematic view of a monitoring insole
FIG. 4 is a schematic view of a monitoring device with specific arrangement
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present application, the present application will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present application and the descriptions thereof are for illustrating the present application only and are not to be construed as limiting the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one of ordinary skill in the art that: no such specific details are necessary to practice the application. In other instances, well-known structures, circuits, materials, or methods have not been described in detail in order not to obscure the application.
Throughout the specification, references to "one embodiment," "an embodiment," "one example," or "an example" mean: a particular feature, structure, or characteristic described in connection with the embodiment or example is included within at least one embodiment of the application. Thus, the appearances of the phrases "in one embodiment," "in an example," or "in an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and that the illustrations are not necessarily drawn to scale. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In the description of the present application, the terms "front", "rear", "left", "right", "upper", "lower", "vertical", "horizontal", "high", "low", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, merely to facilitate description of the present application and simplify description, and do not indicate or imply that the insole or component referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the scope of protection of the present application.
Example 1
The embodiment discloses a monitoring device based on a flexible chip, as shown in fig. 1 and 2, the monitoring device is applied to contact with human skin to monitor physiological signals of the human body, the monitoring device of the embodiment can be used for measuring physiological signals on any part of the human body, and the monitoring device is not limited to any part which is specifically arranged, can be used in scenes such as hands, cushions and the like, and comprises a data acquisition unit, a data processing unit, a data transmission unit and a cloud platform; the data acquisition unit is an organic flexible chip layer, and in the embodiment, the organic flexible chip is combined with the monitoring device, so that the monitoring device formed by the organic flexible chip can be applied to each part of a human body, and the process of monitoring physiological signals of the human body in real time can be realized.
In this embodiment, the organic flexible chip is a flexible chip with skin-friendly signals, and the organic flexible chip includes a packaging layer, an electrode, a conductive layer, an insulating layer and a substrate, and the multilayer structure of the organic flexible chip is manufactured by a lamination process; the packaging layer is a 3M Tegaderm dressing; the electrode is a silver nanowire; the conductive layer is a conductive porous nano-composite formed by uniformly mixing foamed aluminum, graphene, carbon nano-tube and Ecoflex high polymer; the insulating layer is polymethyl methacrylate or polyethylene terephthalate.
Uniformly mixing aluminum foam, graphene, carbon Nano Tube (CNT) and Ecoflex high polymer to form a conductive layer by using a conductive porous nano composite with high porosity; the electrode is a silver nanowire and can be printed on the composite layer; the packaging layer is a 3M Tegaderm dressing; the insulating layer may be polymethyl methacrylate (PMMA) or polyethylene terephthalate (PET). The multilayer structure is made by a lamination process. Due to the porous fiber structure, the organic flexible chip film shows good permeability to hot air and water vapor, thereby ensuring the drying of the microenvironment between the skin surface and the wearable sensor and having good comfort.
The data acquisition unit is used for acquiring a first data signal of a human body to be monitored and transmitting the first data signal into the data processing unit, wherein the first data signal comprises a first BCG data signal, a temperature data signal, a first pressure data signal and an electrical impedance data signal;
in this embodiment, the thin film itself of the organic flexible chip has the ability to detect pressure in a wide range while maintaining high sensitivity, and thus can be used as a flexible pressure sensor; after other sensors are embedded, the multi-dimensional signal detection and signal transmission functions can be realized by one layer. The multi-layer structure is close to the skin of the human body, the data acquisition unit is provided with a plurality of flexible sensor chips at specific positions according to different shapes of different products, and the chips can sense physiological signals such as human body electrical impedance, surface temperature, heart rate, pressure and the like through contacting or approaching the skin of the human body
Further, the data acquisition unit is arranged on a skin-friendly organic flexible chip, can be arranged close to the skin and is used for directly measuring various data signals of a human body, and comprises a flexible heart rate sensor, a flexible temperature sensor, a flexible pressure sensor and a flexible electrical impedance sensor, wherein the output end of the flexible heart rate sensor, the output end of the flexible temperature sensor, the output end of the flexible pressure sensor and the output end of the flexible electrical impedance sensor are all connected with the input end of the data processing unit;
in this embodiment, the data processing unit is configured to perform preprocessing analysis on the first data signal, and transmit a second data signal obtained after processing to the data transmission unit;
when the acquired first data signal is transmitted to the data processing unit, the data processing unit can perform simple analysis processing on the first data signal, can obtain related variable signals after the analysis processing, and transmits the obtained related variable signals to the cloud platform for related algorithm processing through the data transmission unit;
the data processing unit comprises a multipath switching module, a signal amplifying module, a digital-to-analog conversion module and a calculation processing module, wherein the input end of the multipath switching module is connected with the output end of the data acquisition unit, the output end of the multipath switching module is connected with the input end of the signal amplifying module, the output end of the signal amplifying module is connected with the input end of the digital-to-analog conversion module, the output end of the digital-to-analog conversion module is connected with the input end of the calculation processing module, the output end of the calculation processing module is connected with the input end of the data transmission unit, and the calculation processing module is used for carrying out preprocessing analysis on a first data signal and transmitting a second data signal obtained after processing analysis into the data transmission unit.
The pretreatment analysis specifically comprises the following steps:
extracting waveform characteristics of the first BCG data signal based on the first BCG data signal to obtain a second BCG data signal, wherein the second BCG data signal comprises J-wave amplitude a max Relative amplitude a of JK wave rel I wave start point, J wave peak time interval t min Time interval t between J wave peak and K wave peak max ；
Based on the first pressure data signal, calculating in a matrix manner to obtain a second pressure data signal, wherein the second pressure data signal comprises average pressure P ave Peak pressure P peak Center of pressure X in X direction cop Center of pressure Y in Y direction cop Center of pressure velocity V cop 。
When the monitoring device collects a plurality of pressure data signals, the first pressure data signals are based on the second pressure data signals, and the second pressure data signals are obtained by calculating in a matrix modeA pressure characteristic value calculated by a pressure data signal, wherein the second pressure data signal comprises an average pressure P ave Peak pressure P peak Center of pressure X in X direction cop Center of pressure Y in Y direction cop Center of pressure velocity V cop ；
Average pressure:
peak pressure: p (P) peak ＝Max(P 10 …P mn …)
Center of pressure in X direction:
center of pressure in Y direction:
center of pressure velocity:
when pressure is transmitted to the cloud platform for advanced treatment, the method specifically comprises the following steps:
due to sensor errors, abnormal data may occur in the sample set, so that data preprocessing and data feature processing are required. Normalization processing is carried out on each pressure variable, and all data are classified into a distribution with the mean value of 0 and the variance of 1 by using mean variance normalization:
where μ is the sample mean and S is the standard deviation.
In this embodiment, further, as shown in fig. 4, the flexible computing chip layer analyzes and processes the original physiological signal data through statistics and machine learning algorithm to realize an edge computing function, other parts of the insole circuit are ultrathin circuit modules placed in the heel part, and the core chip is a TICC2652 wireless MCU; CC2652 is a low power, ultra-small, high integration, single chip solution tailored for wearable devices; the chip is internally provided with 8 paths of analog input signal interfaces, an analog signal change-over switch, a 12bit ADC sampler, an ARM Cortex-M4 low-power consumption CPU, a BLE Bluetooth modem, an SRAM and a Flash; after being powered up, TI CC2652 can complete the data acquisition, signal processing and analysis functions of the intelligent insole sensor in the chip,
the data transmission unit is used for transmitting the second data signal to the cloud platform;
in this embodiment, the data transmission unit mainly adopts a bluetooth protocol mode, packages the obtained data signals, sends the data signals to the cloud platform, and adopts a wireless connection mode to transmit the data, so that the method is not limited to the place and time of use of the user, and can be hidden at the position of the measured physiological data signals.
The data transmission unit comprises a Bluetooth module, an RF module and an antenna module,
the bluetooth module is configured to receive the second data signal, and transmit the second data signal to the RF radio frequency module according to a bluetooth Mesh protocol transmission method, where in this embodiment, the bluetooth module is a bluetooth low energy 5.2 module and a bluetooth low energy modem, and data is transmitted according to the bluetooth Mesh protocol;
the RF module is used for receiving the second data signal and transmitting the second data signal to the cloud platform through the antenna module.
In this embodiment, the cloud platform is configured to perform deep analysis processing on the second data, and transmit the processed physiological signal index result of the human body to the user terminal, where the cloud platform is an algorithm for processing various physiological data signals, and each physiological signal can calculate, through a corresponding algorithm, a process of obtaining fluctuation of the physiological signal and a physical state condition of the human body corresponding to the physiological signal, and the terminal is a client for receiving a final processing result, and may be a mobile phone, a tablet, or other objects that are not limited to specific receiving objects.
In this embodiment, the depth analysis processing step includes:
processing and analyzing the second BCG data signal by adopting a K-means++ clustering algorithm to obtain the physiological index of the human body;
the method for processing and analyzing the second BCG data signal by adopting the K-means++ clustering algorithm to obtain the physiological index of the human body comprises the following steps:
step A: generating a 4N-dimensional feature vector f based on the obtained second BCG data signal i ；
f i ＝(a max，i ，t max，i ，a rel，i ，t min，i ，…，a max，i+N-1 ，t max，i+N-1 ，a rel，i+N-1 ，t min，i+N-1 ) T ；
And (B) step (B): based on 4N dimension feature vector f i Obtaining the probability D that each sample point is selected as the next cluster center M (f i ，f j )；
Step C: selecting D with the highest probability value M (f i ，f j ) And B, taking the corresponding sample points as the next clustering centers, and repeating the step A and the step B to obtain K clustering centers;
step D: and (3) calculating the classification of the BCG signal peaks obtained by the converged clustering algorithm, calculating the average value of each cluster and the cross correlation of the subsequence where the peak is and the clustering template sequence based on the classification, and obtaining the human physiological index through the average value and the cross correlation.
Finally, selecting a sample point (a wheel disc method) corresponding to the maximum probability value as a next clustering center; repeating the above steps selects K total cluster centers, where we can take k=4 or k=5. After the convergence is calculated, the classification of BCG signal peaks obtained according to a clustering algorithm can be obtained, then the average value of each cluster and the cross correlation between the subsequence where the peak is and the clustering template sequence are calculated, and the standard for calculating the heart rate can be obtained through the two indexes.
Classifying and analyzing the second temperature data signal by adopting a clustering algorithm to obtain the time-dependent temperature change rule of the human body in different states;
classifying and analyzing the second pressure data signal by adopting a Gaussian naive Bayes algorithm to obtain human physiological state information;
the classifying analysis is carried out on the second pressure data signal by adopting a naive Bayes algorithm to obtain the physiological state information of the human body, and the specific substeps comprise:
the total dataset was split into training and test sets at 67% and 33%. Preparing training sample set, and establishing disease and motion label class C i : diabetes C 1 Varicose vein C 2 Degenerative joint disease C 3 Meniscus injury C 4 The method comprises the steps of carrying out a first treatment on the surface of the Motion label: walk C 5 Basketball C 6 Running C 7 Dancing C 8 Etc. Calculating a priori probability P (C) i ) The dataset is classified with labels so that we calculate the mean and standard deviation for each class. The mathematical expectation and standard deviation of each class are then determined.
The likelihood of each category is found using a gaussian function:
where μ is the sample mean, σ 2 Is the variance. Likelihood-continuous-multiplication (taking natural logarithm):
calculating the conditional probability of each category:
training a model and establishing a Gaussian naive Bayes classifier.
Using test set data { x } j Predicting, finding the maximum probability, wherein the maximum probability is the category of the label on the corresponding label category:
when the received signal is a temperature signal, classification is performed using a Support Vector Machine (SVM) algorithm. And cleaning a large amount of historical temperature data in a period of time, mapping the data to a separable high-dimensional space, such as converting the data into a 5-dimensional feature vector, dividing the sample into a training set and a testing set, labeling the processed sample with labels, and setting several labels of interference data, normal body temperature, slight heating and severe heating. And calculating errors by using a training set and training models, and selecting a classification model with the smallest error by adjusting training parameters.
When the received signal is an electrical impedance signal, the algorithm with smaller error is selected for classification analysis through the K-means++ clustering algorithm, gaussian naive Bayes, support vector machine and other algorithms.
When the temperature data signal, the second pressure data signal, the electrical impedance data signal and the second BCG data signal are processed on the cloud platform, the state of the human body corresponding to the human body can be directly obtained through an algorithm for processing various physiological data signals contained on the cloud platform.
By adopting the monitoring device based on the flexible chip, the process of monitoring, analyzing and calculating various physiological data signals of a human body in real time in different time periods is realized, and a user can intuitively obtain the state of the body through the data signals.
Example two
The embodiment discloses a monitoring insole based on a flexible chip, as shown in fig. 3, comprising an insole body and a monitoring device based on the flexible chip in the embodiment I,
the insole body comprises a sensing chip layer 8, an insulating protection layer 9 and a calculation transmission substrate layer 10; the data acquisition unit is arranged on the perception chip layer 8, and the data processing unit 6 and the data transmission unit 5 are arranged on the calculation transmission substrate layer; the insulating protection layer 9 is arranged between the sensing chip layer 8 and the calculation transmission substrate layer, and the insole body is obtained by multi-layer process pressing.
When the monitoring device measures plantar pressure signals and the pressure signals are collected, in the embodiment, the collected pressure signals are a matrix formed by a plurality of pressure signals, and the pressure signals P i Monitoring process of (t): based on the mechanical property of the organic flexible chip, a plurality of (20) pressure monitoring points are arranged on the sensing layer: the dense grid position woven by the carbon nano-tube lines or the gold wires in the flexible chip film is the pressure monitoring point. The sensor can set different sampling rates, and the change process of plantar pressure distribution is acquired with high precision in a complete gait process. The pressure values at different positions and different times form a matrix P mn Wherein m=20, representing 20 pressure points; pressure data is collected from the time the foot contacts the ground, from the start of heel strike to a complete gait prior to the next heel strike: for example, the gait standing period has high sampling frequency, more data points can be acquired, and the foot is separated from the ground in the middle and end phases of the swing phase, so that the purpose of saving electricity can be achieved without acquiring data. Assuming n=20, sampling at 20 time points during one gait is represented, and finally the following pressure matrix is obtained in one complete gait:
at sampling zero time, P 10 Near maximum, representing the heel just touching the ground, followingWith the center of gravity of the human body shifted, P 10 Can reach the maximum value quickly, and P is separated from the ground along with the heel 10 The value of (2) is zero until the next heel strike (end of the single foot step).
And when the data algorithm in the cloud platform is processed, the data algorithm is processed based on each signal in the obtained matrix, the temperature data signal is acquired through the device, the first BCG signal is acquired by a plurality of signals, and different algorithms are adopted for processing a plurality of signals of the same category.
When monitoring human body electrical impedance data signals, the insole body is provided with at least two insoles, when the insoles are provided with two insoles, the two insoles are respectively placed in different shoes, signals are transmitted through the flexible electrical impedance sensor 4 of one insole, signals are received by the flexible electrical impedance sensor 4 in the other shoe, the signals measured between the signals are electrical impedance signals, in the embodiment, the two insoles are arranged in the shoes, a plurality of different electrical impedance data signals are measured, and the physiological index of human body constitution under a long time scale can be obtained through the processing result.
Adopt the monitoring shoe-pad based on flexible chip that this embodiment provided, set up in the shoe-pad through monitoring devices, wear for a long time can collect more physiological signal data, stretchable organic flexible chip possess multiple detection calculation function, and can change in different shoes.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the application, and is not meant to limit the scope of the application, but to limit the application to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (7)
1. The monitoring device based on the flexible chip is characterized by being applied to contact with human skin to monitor physiological signals of a human body; the cloud platform comprises a data acquisition unit, a data processing unit, a data transmission unit and a cloud platform; the data acquisition unit is an organic flexible chip layer:
the data acquisition unit is used for acquiring a first data signal of a human body to be monitored and transmitting the first data signal into the data processing unit, wherein the first data signal comprises a first BCG data signal, a temperature data signal, a first pressure data signal and an electrical impedance data signal;
the data processing unit is used for preprocessing and analyzing the first data signal, transmitting a second data signal obtained after processing into the data transmission unit, wherein the second data signal is a characteristic value extracted from the waveform characteristics of the first BCG data signal and a characteristic value calculated from the first pressure data signal;
the step of preprocessing analysis in the data processing unit comprises the following steps:
extracting waveform characteristics of the first BCG data signal based on the first BCG data signal to obtain a second BCG data signal, wherein the second BCG data signal comprises J-wave amplitude alpha max Relative amplitude a of JK wave rel I wave start point, J wave peak time interval t min Time interval t between J wave peak and K wave peak max ；
Based on the first pressure data signal, calculating in a matrix manner to obtain a second pressure data signal, wherein the second pressure data signal comprises average pressure P ave Peak pressure P peak Center of pressure X in X direction cop Center of pressure Y in Y direction cop Center of pressure velocity V cop ；
The data transmission unit is used for transmitting the second data signal to the cloud platform;
the cloud platform is used for carrying out deep analysis processing on the second data signal and transmitting the processed human physiological signal index result to a user terminal;
the step of performing depth analysis processing in the cloud platform comprises the following steps:
processing and analyzing the second BCG data signal by adopting a K-means++ clustering algorithm to obtain the physiological index of the human body;
the method for processing and analyzing the second BCG data signal by adopting the K-means++ clustering algorithm to obtain the physiological index of the human body comprises the following steps:
step A: generating a 4N-dimensional feature vector f based on the obtained second BCG data signal i ；
And (B) step (B): based on 4N dimension feature vector f i Obtaining the probability D that each sample point is selected as the next cluster center M (f i ，f j )；
Step C: selecting D with the highest probability value M (f i ，f j ) And B, taking the corresponding sample points as the next clustering centers, and repeating the step A and the step B to obtain K clustering centers;
step D: the classification of BCG signal peaks obtained by the clustering algorithm after convergence is calculated, the average value of each cluster and the cross correlation between the subsequence where the peak is and the clustering template sequence are calculated based on the classification, and the human physiological index is obtained through the average value and the cross correlation;
classifying and analyzing the temperature data signals by adopting a clustering algorithm to obtain the time-dependent change rule of the temperature of the human body in different states;
classifying and analyzing the second pressure data signal by adopting a Gaussian naive Bayes algorithm to obtain human physiological state information;
and analyzing the electrical impedance data signal by adopting a statistical analysis method to obtain physical physiological indexes of the human body.
2. The flexible chip-based monitoring device of claim 1, wherein the data acquisition unit comprises a flexible heart rate sensor, a flexible temperature sensor, a flexible pressure sensor and a flexible electrical impedance sensor, and the output end of the flexible heart rate sensor, the output end of the flexible temperature sensor, the output end of the flexible pressure sensor and the output end of the flexible electrical impedance sensor are all connected with the input end of the data processing unit.
3. The flexible chip-based monitoring device according to claim 1, wherein the data processing unit comprises a multiplexing module, a signal amplifying module, a digital-to-analog conversion module and a calculation processing module, wherein an input end of the multiplexing module is connected with an output end of the data acquisition unit, an output end of the multiplexing module is connected with an input end of the signal amplifying module, an output end of the signal amplifying module is connected with an input end of the digital-to-analog conversion module, an output end of the digital-to-analog conversion module is connected with an input end of the calculation processing module, an output end of the calculation processing module is connected with an input end of the data transmission unit, and the calculation processing module is used for preprocessing and analyzing the first data signal and transmitting the second data signal obtained after the processing analysis to the data transmission unit.
4. The flexible chip-based monitoring device of claim 1, wherein the data transmission unit comprises a Bluetooth module, an RF module, and an antenna module,
the Bluetooth module is used for receiving the second data signal and transmitting the second data signal to the RF module according to a Bluetooth Mesh protocol transmission method;
the RF module is used for receiving the second data signal and transmitting the second data signal to the cloud platform through the antenna module.
5. The flexible chip-based monitoring device of claim 1, wherein the organic flexible chip comprises a packaging layer, an electrode, a conductive layer, an insulating layer, and a substrate, and the multilayer structure of the organic flexible chip is manufactured by a lamination process;
the packaging layer is a 3M Tegaderm dressing;
the electrode is a silver nanowire;
the conductive layer is a conductive porous nano-composite formed by uniformly mixing foamed aluminum, graphene, carbon nano-tube and Ecoflex high polymer;
the insulating layer is polymethyl methacrylate or polyethylene terephthalate.
6. A monitoring insole based on a flexible chip is characterized by comprising an insole body and a monitoring device based on the flexible chip as claimed in any one of claims 1 to 5,
the insole body comprises a sensing chip layer, an insulating protection layer and a calculation transmission basal layer; the data acquisition unit is arranged on the perception chip layer, and the data processing unit and the data transmission unit are arranged on the calculation transmission substrate layer; the insulation protection layer is arranged between the sensing chip layer and the calculation transmission substrate layer, and the insole body is obtained through multi-layer process pressing.
7. The flexible chip-based monitoring insole of claim 6, wherein said insole is provided with at least two insole bodies when monitoring electrical impedance data signals of a human body.
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