CN112040866B - Body temperature compensation method based on second-generation wavelet, mobile terminal and storage medium - Google Patents
Body temperature compensation method based on second-generation wavelet, mobile terminal and storage medium Download PDFInfo
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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
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14507—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue specially adapted for measuring characteristics of body fluids other than blood
- A61B5/14517—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue specially adapted for measuring characteristics of body fluids other than blood for sweat
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14546—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring analytes not otherwise provided for, e.g. ions, cytochromes
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1468—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using chemical or electrochemical methods, e.g. by polarographic means
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- 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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- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
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Abstract
The invention discloses a body temperature compensation method based on a second generation wavelet, a mobile terminal and a storage medium, wherein the method comprises the following steps: inputting an original signal to a body temperature compensation model, wherein the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; the original signal is a biomarker detection result to be calibrated and compensated; calculating a compensation factor according to the approximate signal and the detail signal; and compensating the biomarker detection result to be calibrated and compensated according to the compensation factor, and outputting the biomarker detection result after compensation by the body temperature compensation model. Real-time body temperature compensation is carried out on the electronic wearable equipment with high precision and low power consumption, and the influence of body temperature on the performance of the electronic wearable equipment is effectively eliminated.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a body temperature compensation method based on second-generation wavelets, a mobile terminal and a storage medium.
Background
Recent developments in personalized medicine have now greatly increased the need for continuous monitoring of physiological states in humans. Wearable electrochemical sensors (Wearable Electrochemical sensor, WES), which are the most central components of wearable electronic devices, are the most suitable technology for continuously monitoring physiological states of human bodies, and have been attracting attention and developing in recent years. Because sweat contains abundant clinically relevant biomarkers, sweat becomes one of the most suitable biological fluids for continuous monitoring. There are two major categories of major biomarkers in sweat: metabolites (metanolite such as blood glucose and lactic acid) and electrolytes (Electrolyte such as potassium ions and sodium ions). The sensor structure of a typical wearable electronic device is schematically shown in fig. 1, and can detect four important physiological parameters of a human body, namely blood sugar content, lactic acid content, potassium ion concentration and sodium ion concentration. The Ag/AgCI (silver/silver chloride) electrode of fig. 1 is used as a common reference electrode for the blood glucose sensor anode (GoX) and the lactate sensor anode (Lox), and an output current proportional to the blood glucose/lactate content is generated between the electrodes; potassium Ion (K+) and sodium Ion electrode (Na+) are used as Ion selective electrodes (Ion-selected Electrodes, ISE) which are connected with a reference electrode formed by polyvinyl butyral (Polyvinyl Butyral, PVB), so that a stable output voltage can be generated. Electrolyte imbalance can reflect potential danger caused by abnormal heart rate, electrolyte loss is also a main reason for body dysfunction, and monitoring key electrolyte concentration can early warn of potential heart diseases. The real-time measurement of electrolyte concentration can alert a patient, doctor, coach or athlete to the loss or dehydration status of electrolyte; also, abnormal metabolite content can affect exercise state and scientific recovery after exercise, and if the change of blood sugar and lactic acid during exercise can be accurately mastered in real time, the physical function balance can be recovered after exercise and application. Therefore, how to detect abnormal metabolite content or disturbance of electrolyte level with high performance becomes important and challenging.
The high performance of biomarker detection in sweat is achieved, and the requirements of four aspects are specifically: 1. detecting multiple components; 2. real-time continuity; 3. accuracy; 4. low power consumption. The textile-based highly stretchable printed voltage sensor array can be used for sweat analysis of various ions and enzymes at the same time, thus being capable of meeting the first requirement. The textile is an assembly with adsorption force, can provide abundant elasticity, can be in close contact with a sensor and a body, combines graphene with good strength, conductivity and optical transparency, and can better solve the second requirement. One of the difficulties in solving the third requirement is that the human body has different resistance and impedance characteristics under different skin temperatures, thereby affecting the detection result of enzymes, electrolytes or ions. As shown in FIG. 2, FIG. 2 shows that although the blood sugar content (100 uM) or the lactic acid content (5 mM) in sweat is fixed, the output current is very significantly different due to the variation of skin temperature (from 20℃to 40 ℃). In order to eliminate the effect of human skin temperature on sensor performance, a body temperature compensation model must be introduced to calibrate the data acquired by the sensor. Although the effect of body temperature on the performance of the voltage sensor (potentiometric sensor) is not particularly pronounced, it has a significant impact on the biochemical performance of enzymes such as blood glucose or lactic acid, so that high performance sensors must compensate for the effects of body temperature in real time. The fourth requirement becomes critical to whether the first three requirements can be fulfilled. Although the detection component is directly proportional to the power consumption, the detection of too few components is obviously unacceptable, if the power consumption is too large, real-time and continuous detection cannot be satisfied, if calculation and calibration of a complex model are adopted for detection data, although the accuracy of a detection result can be improved, the power consumption is necessarily improved, and if a simple model is adopted for lower power consumption, the accuracy is affected.
Accordingly, the prior art is still in need of improvement and development.
Disclosure of Invention
In view of the shortcomings of the prior art, the invention aims to provide a body temperature compensation method, a mobile terminal and a storage medium based on second-generation wavelets, so as to realize real-time body temperature compensation of electronic wearable equipment with high precision and low power consumption, and effectively eliminate the influence of body temperature on the performance of the electronic wearable equipment.
The technical scheme of the invention is as follows:
a method of body temperature compensation based on a second generation wavelet, the method comprising the steps of:
inputting an original signal to a body temperature compensation model, wherein the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; the original signal is a biomarker detection result to be calibrated and compensated;
calculating a compensation factor according to the approximate signal and the detail signal;
and compensating the biomarker detection result to be calibrated and compensated according to the compensation factor, and outputting the biomarker detection result after compensation by the body temperature compensation model.
According to a further arrangement of the invention, the step of inputting the original signal to a body temperature compensation model, the body temperature compensation model splitting the original signal to obtain an approximation signal and a detail signal comprises:
let the original signal be x [ n ] (n=0, 1, obtain original signal x [ n ] and split the original signal x [ n ] into approximation signal a [ n ] and detail signal d [ n ];
wherein a [ n ] =x [2n ], d [ n ] =x [2n+1].
According to a further arrangement of the invention, the step of calculating the compensation factor from the approximation signal and the detail signal comprises:
based on the detail signal d [ n ]]The information in (a) is to the approximation signal a [ n ]]Updating to obtain updated approximate signal a' [ n ]]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the updated approximation signal a' [ n ]]The expression of (2) is:
representing a smoothing operation, defined as: />
Wherein U (d [ n ]]) For an adaptive update operator, it is defined as:
wherein delta is L ＝|a[n]-d[n-1]|，Δ R ＝|a[n]-d[n]|。
According to a further arrangement of the present invention, the step of calculating the compensation factor from the approximation signal and the detail signal further comprises:
predicting the detail signal d [ n ] according to the information contained in the updated approximate signal a '[ n ] to obtain a predicted detail signal d' [ n ];
wherein the expression of the predicted detail signal d' [ n ] is: d '[ n ] =dn ] -P (a' [ n ]);
wherein P represents a predictor defined as:
according to a further arrangement of the present invention, the step of calculating the compensation factor from the approximation signal and the detail signal further comprises:
calculating a compensation factor c [ n ] according to the updated approximate signal a '[ n ] and the updated detail signal d' [ n ]; wherein, the expression of the compensation factor c [ n ] is:
In a further arrangement of the present invention, the step of inputting the original signal to a body temperature compensation model, and the body temperature compensation model splitting the original signal to obtain the approximate signal and the detail signal further includes:
initializing the compensation times n, the second-order norm of the updated approximate signal a' n and a compensation factor c n; where n=0, ||a' |0 ] |=0, and c [0] =0.
According to a further arrangement of the present invention, the step of compensating the biomarker detection result to be compensated according to the compensation factor, and the step of outputting the compensated biomarker detection result by the body temperature compensation model further includes:
when n=n+1 times of compensation are carried out, updating the compensation factors according to the biomarker detection result to be calibrated and compensated input for n=n+1 times, and obtaining updated compensation factors;
and compensating the biomarker detection result to be calibrated and compensated of the n=n+1th input according to the updated compensation factor, wherein the expression is as follows:
x '[ n+1] =x [ n+1] -c [ n+1] wherein x [ n+1] represents the biomarker detection result to be corrected for compensation of the n=n+1 th input, c [ n+1] represents the updated compensation factor, and x' [ n+1] represents the biomarker detection result after the n=n+1 th compensation.
According to the further arrangement of the invention, the biomarker detection result to be calibrated and compensated is any one of the four results of blood sugar content, lactic acid content, potassium ion concentration and sodium ion concentration.
A mobile terminal comprising a memory storing a computer program and a processor implementing the steps of the second generation wavelet based body temperature compensation method when the computer program is executed.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a second generation wavelet based body temperature compensation method.
The invention provides a body temperature compensation method based on a second generation wavelet, a mobile terminal and a storage medium, wherein the method comprises the following steps: inputting an original signal to a body temperature compensation model, wherein the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; the original signal is a biomarker detection result to be calibrated and compensated; calculating a compensation factor according to the approximate signal and the detail signal; and compensating the biomarker detection result to be calibrated and compensated according to the compensation factor, and outputting the biomarker detection result after compensation by the body temperature compensation model. Real-time body temperature compensation is carried out on the electronic wearable equipment with high precision and low power consumption, and the influence of body temperature on the performance of the electronic wearable equipment is effectively eliminated.
Drawings
Fig. 1 is a schematic diagram of a sensor structure of a typical wearable electronic device.
FIG. 2 is a graph showing the effect of human skin temperature on the detection of blood glucose or lactic acid.
Fig. 3 is a flow chart of a second generation wavelet based body temperature compensation method in one embodiment.
Fig. 4 is a schematic diagram of the structure of a lifting frame of a second generation wavelet in one embodiment.
Fig. 5 is a flow chart of a body temperature compensation model of a lifting framework based on a second generation wavelet in one embodiment.
Fig. 6 is a logic diagram of a low power body temperature compensation circuit based on an MSP430 platform in one embodiment.
Fig. 7 is a schematic diagram of the results of body temperature compensation in one embodiment.
Detailed Description
The inventors found that a simple and widely used body temperature compensation model is from the university of california berkeley division, which model is simply considered: the current or voltage increases by 0.18% for every one degree of body temperature rise, based on 20 ℃. The model is simple in calculation, so that the power consumption of the wearable electronic equipment is low, but the model does not fully consider the individual difference of each person, the temperature of 20 ℃ is not necessarily reasonable as a reference, a temperature sensor for accurately measuring the skin temperature is required to be added on the hardware design, and the complexity and the power consumption of a hardware system are inevitably increased. In addition, in the aspect of low power consumption, three links of signal operational amplification (amplification), analog-to-digital (a/D) conversion (analog-digital converter) and digital signal processing (digital signal processing) cannot be highly integrated, so that the implementation of low power consumption is affected. The invention provides a body temperature compensation method, a mobile terminal and a storage medium based on a second generation wavelet, wherein the method uses a lifting frame in the second generation wavelet to establish a body temperature compensation model with flexibility and universality, and completes the highly integrated design of signal operational amplification, A/D conversion and body temperature compensation processing on the hardware system design, thereby realizing real-time body temperature compensation of an electronic wearable device with high precision and low power consumption and effectively eliminating the influence of body temperature on the performance of the electronic wearable device.
In order to make the objects, technical solutions and effects of the present invention clearer and more obvious, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description and claims, unless the context clearly dictates otherwise, the terms "a" and "an" and "the" may refer to either a single or a plurality.
In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The body temperature compensation method based on the second generation wavelet can be applied to a terminal. The wearable electronic device is particularly applied to a wearable electronic device, and the wearable electronic device is provided with a wearable electrochemical sensor for continuously monitoring the physiological state of a human body. The wearable electronic equipment adopts a singlechip as a processor, wherein a single instruction cycle (RISC) chip of an MSP430 model is adopted.
The following describes an embodiment in which a second generation wavelet based body temperature compensation method is applied to a wearable electronic device having a wearable electrochemical sensor.
Referring to fig. 3 in combination with fig. 4, fig. 3 is a flow chart of a body temperature compensation method based on a second generation wavelet, and fig. 4 is a structural diagram of a lifting frame of the second generation wavelet in an embodiment, as shown in fig. 3 and fig. 4, the method includes the steps of:
step S100, inputting an original signal to a body temperature compensation model, wherein the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; the original signal is a biomarker detection result to be calibrated and compensated; the biomarker detection result to be calibrated and compensated in the present invention may be any one of four results of blood glucose content, lactic acid content, potassium ion concentration and sodium ion concentration.
The body temperature compensation model is built based on a lifting framework in a second generation wavelet, compared with the existing first generation wavelet analysis method, the recently-occurring lifting framework can be used as the second generation wavelet to show more flexibility and universality, one universal lifting framework comprises two lifting steps of updating and predicting, and the adaptivity is introduced, so that the system in the application does not need to adjust algorithm parameters in the whole operation period, and can adapt to various changes of signal characteristics. The body temperature compensation model can update and predict the formed approximate components and detail components according to the signals, calculate compensation factors and compensate input signals in real time.
In a further implementation of one embodiment, the step of inputting the raw signal to a body temperature compensation model, the body temperature compensation model splitting the raw signal to obtain an approximation signal and a detail signal includes:
step S101, let the original signal be x [ N ] (n=0, 1..once., N), obtaining an original signal x [ N ] and splitting the original signal x [ N ] into an approximate signal a [ N ] and a detail signal d [ N ];
wherein a [ n ] =x [2n ], d [ n ] =x [2n+1].
Specifically, the original signal x [ N ] (n=0, 1,..n.) is first Split (Split) into two data streams: approximation signal a [ n ] =x [2n ] and detail signal d [ n ] =x [2n+1]. The approximate signal contains a low-frequency part of the original signal, which is also called a signal profile, the detail signal contains a high-frequency part of the original signal, namely detail or mutation of the signal, and the compensation of the skin temperature of the human body is completed on the basis of the approximate signal and the detail signal, namely unnecessary fluctuation of the skin temperature of the human body on the biomarker detection result is eliminated.
in a further implementation of an embodiment, the step of calculating a compensation factor from the approximation signal and the detail signal comprises:
step S201, according to the detail signal d [ n ]]The information in (a) is to the approximation signal a [ n ]]Updating to obtain updated approximate signal a' [ n ]]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the updated approximation signal a' [ n ]]The expression U () of (a) is:
representing a smoothing operation, defined as: />
Wherein U (d [ n ]]) For an adaptive update operator, it is defined as:
wherein delta is L ＝|a[n]-d[n-1]|，Δ R ＝|a[n]-d[n]|，Δ L ＜Δ R Indicating an increase in ripple, delta L ＝Δ R Indicating that the fluctuation is unchanged, delta L ＞Δ R Indicating that the fluctuation becomes small. Wherein the smoothing operation can retain the low frequency part (contour part) of the signal, can retain the most important slowly varying information of the signal, and takes part in the smoothing operation requiring two signal sequences, one of which is denoted as x and the other as y, y [ n ]]Is the nth point of the signal sequence y.
Wherein the signal data stream x n is subjected to a splitting step prior to the splitting step]The data in (a) being sampled successively, i.e. the signal data stream x [ n ]]The three consecutive samples in (a) are d [ n-1 ]]，a[n]And d [ n ]]. In all cases a [ n ]]Will adapt its neighbors, i.e. d [ n-1 ]]Or d [ n ]]Will replace an]Thereby minimizing unwanted or unreasonable mutations in the signal. If delta L ＜Δ R This means that the signal gets stronger over time. Due to the updated data a' [ n ]]Reflects the steady-state component of the signal, then a [ n ]]It should be used with its smooth neighbors, i.e. d n-1 is used]Substitution is performed.
In a further implementation of an embodiment, the step of calculating a compensation factor from the approximation signal and the detail signal further comprises the steps of:
and step S202, predicting the detail signal d [ n ] according to the information contained in the updated approximate signal a '[ n ] to obtain a predicted detail signal d' [ n ]. That is, when predicting the detail signal d n, the information contained in the updated approximation signal a' n is used;
wherein the expression of the predicted detail signal d' [ n ] is: d '[ n ] =dn ] -P (a' [ n ]);
where P represents a predictor, representing contour information (low frequency information) after a smoothing operation, which is defined as:
so that the steady-state component in the predicted detail signal is removed, the updated detail signal d' n only contains the high-frequency component in the detail signal obtained by splitting the original signal, and thus the method can be effectively used for extracting the transient state information of the signal.
In a further implementation of an embodiment, the step of calculating a compensation factor from the approximation signal and the detail signal further comprises:
step S203, calculating a compensation factor c [ n ] according to the updated approximate signal a '[ n ] and the updated detail signal d' [ n ]; wherein, the expression of the compensation factor c [ n ] is:
And step 300, compensating the biomarker detection result to be calibrated and compensated according to the compensation factor, and outputting the biomarker detection result after compensation by the body temperature compensation model. And compensating the biomarker detection result to be calibrated and compensated according to the updated compensation factor, wherein the expression of the compensated biomarker detection result is as follows: x's'[n]＝x[n]-c[n]Wherein x [ n ]]Representing the n-th input biomarker detection result to be calibrated and compensated, c [ n ]]Representing the updated compensation factor, x' [ n ]]The biomarker detection results after the nth compensation were shown. Since the compensation has continuity, c [ n ]]Will be at c [ n-1 ]]Fine-tuning based on the basis of (a) fine-tuning part
Depending on the high-frequency signal d' [ n ]]Is a variation of (c). If the signal becomes weak, the compensation formula c [ n ]]Will decrease, the compensation force will decrease, if the variation is 0, i.e. c [ n ]]Equal to 0, no compensation is required, c [ n ] if the change is enhanced]And when the compensation force is increased, the compensation force is increased.
In a further implementation of an embodiment, the step of inputting the original signal to a body temperature compensation model, the body temperature compensation model splitting the original signal to obtain an approximation signal and a detail signal further comprises:
initializing the compensation times n, the second-order norm of the updated approximate signal a' n and a compensation factor c n; where n=0, ||a' |0 ] |=0, and c [0] =0.
In a further implementation of an embodiment, the step of compensating the biomarker detection result to be compensated for calibration according to the compensation factor, and the step of outputting the compensated biomarker detection result by the body temperature compensation model further includes:
step 301, when n=n+1 times of compensation are performed, updating the compensation factor according to the biomarker detection result to be calibrated and compensated input for n=n+1 times, and obtaining an updated compensation factor;
step S302, compensating the biomarker detection result to be calibrated and compensated input for the n=n+1th time according to the updated compensation factor, where the expression is:
x '[ n+1] =x [ n+1] -c [ n+1], wherein x [ n+1] represents the biomarker detection result to be calibrated for compensation inputted n=n+1, c [ n+1] represents the updated compensation factor, and x' [ n+1] represents the biomarker detection result after n=n+1-th compensation.
It should be understood that, although the steps in the flowchart of fig. 3 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 3 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
For better understanding of the present invention, the present invention further provides a specific application embodiment of a body temperature compensation method based on a second generation wavelet, as shown in fig. 5, fig. 5 is a flow chart of a body temperature compensation model based on a lifting frame of the second generation wavelet, which includes the steps of:
initializing n=0, ||a' |0 ] |=0, c [0] =0;
splitting (split) the original signal a [ n ] into an approximation signal a [ n ] and a detail signal d [ n ];
updating (update) the approximation signal a [ n ] to obtain a' [ n ];
predicting (prediction) the detail signal d n to obtain d' [ n ];
updating
Updating compensation factors
Body temperature compensation x' [ n ] =x [ n ] -c [ n ];
let n=n+1;
judging whether the system stops working or not; if not, carrying out n+1th compensation, splitting the input original signal a [ n ], and cycling the steps; if the system is judged to stop working, the system stops running.
By the technical scheme, the model does not need the parameter of the skin temperature of the human body, so that a high-precision temperature sensor can be omitted in hardware, and meanwhile, the power consumption is effectively reduced. Compared with a simple body temperature compensation model, the lifting frame is taken as a second generation wavelet to comprehensively consider the local change of signals, so that the problem that the performance of the first generation wavelet is not very ideal when discontinuous signals are processed is solved, the body temperature compensation model does not need to accurately measure the body temperature, and the requirement on hardware design is greatly reduced. In addition, the body temperature compensation model in the invention only involves the n-1 data in the adaptive update operator U (d [ n ]), the predictor P (a' [ n ])andthe compensation factor c [ n ] when processing the n-th data, so the model is only a first-order digital filter, and the required data storage and calculation are minimized. Therefore, referring to fig. 7, fig. 7 is a schematic diagram of a body temperature compensation result, the invention establishes a body temperature compensation model with flexibility and universality by using a lifting frame in the second generation wavelet, thereby realizing real-time body temperature compensation for the electronic wearable device with high precision and low power consumption and effectively eliminating the influence of body temperature on the performance of the electronic wearable device.
In one embodiment, the present application further provides a mobile terminal, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the second-generation wavelet-based body temperature compensation method when executing the computer program. The mobile terminal uses an MSP430 singlechip as a processor, and the MSP430 chip (such as MSP430G 2553) is a 16-bit single instruction cycle RISC (reduced instruction set) singlechip proposed by a company in the United states. Since the working voltage of the MSP430 chip can be as low as 1.5v, the system can supply power by adopting a 1.5v micro lithium ion polymer rechargeable battery (Li-ion Polymer Rechargeable Battery), and a 3.7v battery which is common to other systems at present is not needed. As shown in fig. 6, fig. 6 is a logic diagram of a low power body temperature compensation circuit based on an MSP430 platform in one embodiment, the MSP430 platform including an MSP430 control chip, analog-to-digital conversion setting circuitry, system clock setting circuitry, watchdog circuitry and serial communications circuitry and memory. Referring to fig. 1, the output of any one analog signal of the 4 sensors of the wearable electronic device may be used as the signal input of the MSP430 control chip and connected to the 8 th corner of the MSP430 control chip. Wherein:
in the system clock setting circuit, a 14kHz vibrator is integrated in the MSP430, so that an external crystal oscillator is not needed. The clock frequency of the Master Clock (MCLK) is realized by programming according to the requirement, so that a good compromise is made between high speed (from 14kHz to 1.12 MHz) and low power consumption, and the performance of the singlechip is exerted to the greatest extent.
The analog-to-digital conversion circuit adopts a 10bit A/D (10 bit analog-to-digital conversion) interface built in an MSP430 chip, and the reference voltage Vref+ can adapt to 0 v-1.5 v by adjusting the variable resistor R1, so that the maximum dynamic range of the analog signal of the sensor is met.
Wherein, the MSP430 chip can set the analog-to-digital conversion rate, and the 10bit A/D analog-to-digital conversion rate of the MSP430 chip can be dynamically set within 200 ksps. The system sampling rate may be set to 4Hz considering that the bandwidth of the analog signal is on the order of 1 Hz. The analog-to-digital conversion rate is correspondingly set to 40sps, so that the power consumption of the system can be greatly reduced.
The watchdog circuit is added between pins 1 and 16 of the MSP430 chip, and when a system program has a problem, a system reset signal is generated to help the system reset and restart the program.
The memory realizes a body temperature compensation method based on a second generation wavelet by adopting assembly language to the body temperature compensation processing program and loads the body temperature compensation method into a nonvolatile memory Flash of the MSP430 chip.
Because the body temperature compensation model is simpler, in order to reduce the power consumption, the low power consumption characteristic of the MSP430 chip can be better exerted, and the unnecessary data memory can be closed. The data memory RAM is only 512 bytes, and is mainly used for storing variables and intermediate results of operation, and the memory required by the system is less than 100 bytes, so that about 80% of the data memory can be closed.
It should be noted that, it will be understood by those skilled in the art that all or part of the procedures in the methods of the above embodiments may be implemented by a computer program, which may be stored in a non-volatile computer readable storage medium, and the computer program may include the procedures of the embodiments of the methods. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The serial communication interface circuit can carry out data communication with peripheral slaves through the serial communication interface circuit (pins 7, 14 and 15). The result processed by the wearable sensor needs to be transmitted to the mobile phone in real time, so that the peripheral slave can be a low-power-consumption Bluetooth module. Through which peripheral slaves communicate wirelessly with the smart phone.
The invention adopts the single instruction cycle RISC chip such as MSP430, the core processing part of each data in the model needs less than 100 times of addition or multiplication single instructions, the data processing burden is very light, and the realizability of low power consumption is ensured in theory and algorithm; when the hardware of the system is realized, the signal operational amplifier, the A/D conversion and the digital signal processing are highly integrated in one chip, the integration level is high, and the signal processing algorithm can be dynamically adjusted, so that the self-adaptability and the expansibility of the system are improved, and the low power consumption of the system is ensured again in the aspect of hardware design.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
step S100, inputting an original signal to a body temperature compensation model, wherein the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; the original signal is a biomarker detection result to be calibrated and compensated; specifically, the method for compensating the body temperature based on the second-generation wavelet is described, and will not be described herein.
And step 300, compensating the biomarker detection result to be calibrated and compensated according to the compensation factor, and outputting the biomarker detection result after compensation by the body temperature compensation model. Specifically, the method for compensating the body temperature based on the second-generation wavelet is described, and will not be described herein.
In summary, according to the body temperature compensation method, the mobile terminal and the storage medium based on the second-generation wavelet provided by the invention, the lifting framework in the second-generation wavelet is used for establishing the body temperature compensation model with more flexibility and universality, and compared with a simple body temperature compensation model, the lifting framework is used for comprehensively considering local changes of signals as the second-generation wavelet, so that the problem that the performance of the first-generation wavelet is not ideal when discontinuous signals are processed is better solved; the model does not need to accurately measure the body temperature, so that the requirement on hardware design is greatly reduced; the MSP430 single instruction cycle RISC chip is adopted, the core processing part of each data in the model needs less than 100 times of addition or multiplication single instructions in total, the data processing burden is very light, and the realizability of low power consumption is ensured in theory and algorithm; when the hardware of the system is realized, the signal operational amplifier, the A/D conversion and the digital signal processing are highly integrated in one chip, the integration level is high, the signal processing algorithm can be dynamically adjusted, and the low power consumption of the system is ensured again from the aspect of hardware design. The MSP430 singlechip platform has the core of a 16-bit single instruction cycle RISC processor, and has the most remarkable advantages of strong operation capability and low overall power consumption compared with other numerous singlechips. The system can realize the clock frequency of the system master clock through programming according to actual needs so as to make good compromise between high speed and low power consumption and exert the performance of the singlechip to the maximum extent. To better exploit the low power consumption characteristics of MSP430, the system may shut down unnecessary memory while operating. Therefore, the real-time body temperature compensation is carried out on the electronic wearable equipment with high precision and low power consumption, and the influence of the body temperature on the performance of the electronic wearable equipment is effectively eliminated.
It is to be understood that the invention is not limited in its application to the examples described above, but is capable of modification and variation in light of the above teachings by those skilled in the art, and that all such modifications and variations are intended to be included within the scope of the appended claims.
Claims (10)
1. The body temperature compensation method based on the second generation wavelet is characterized by comprising the following steps:
inputting an original signal to a body temperature compensation model, wherein the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; the original signal is a biomarker detection result to be calibrated and compensated;
calculating a compensation factor according to the approximate signal and the detail signal;
and compensating the biomarker detection result to be calibrated and compensated according to the compensation factor, and outputting the biomarker detection result after compensation by the body temperature compensation model.
2. The second generation wavelet based body temperature compensation method according to claim 1, wherein inputting an original signal to a body temperature compensation model, the body temperature compensation model splitting the original signal into an approximation signal and a detail signal comprises:
let the original signal be x [ N ] (n=0, 1,., N), obtain the original signal x [ N ] and split the original signal x [ N ] into an approximation signal a [ N ] and a detail signal d [ N ];
wherein a [ n ] =x [2n ], d [ n ] =x [2n+1].
3. The second generation wavelet based body temperature compensation method according to claim 2, wherein said step of calculating a compensation factor based on said approximation signal and said detail signal comprises:
based on the detail signal d [ n ]]The information in (a) is to the approximation signal a [ n ]]Updating to obtain updated approximate signal a' [ n ]]The method comprises the steps of carrying out a first treatment on the surface of the Wherein the updated approximation signal a' [ n ]]The expression of (2) is:
representing a smoothing operation, defined as: />
Wherein U (d [ n ]]) For an adaptive update operator, it is defined as:
wherein delta is L ＝|a[n]-d[n-1]|，Δ R ＝|a[n]-d[n]|。
4. A second generation wavelet based body temperature compensation method according to claim 3 wherein said step of calculating a compensation factor based on said approximation signal and said detail signal further comprises:
predicting the detail signal d [ n ] according to the information contained in the updated approximate signal a '[ n ] to obtain a predicted detail signal d' [ n ];
wherein the expression of the predicted detail signal d' [ n ] is: d '[ n ] =dn ] -P (a' [ n ]);
wherein P representsA predictor defined as:
5. the second generation wavelet based body temperature compensation method according to claim 4, wherein said step of calculating a compensation factor based on said approximation signal and said detail signal further comprises:
calculating a compensation factor c [ n ] according to the updated approximate signal a '[ n ] and the updated detail signal d' [ n ]; wherein, the expression of the compensation factor c [ n ] is:
6. The second generation wavelet based body temperature compensation method according to claim 1, wherein said inputting the original signal to a body temperature compensation model, said body temperature compensation model splitting said original signal to obtain an approximation signal and a detail signal further comprises, prior to the step of:
initializing the compensation times n, the second-order norm of the updated approximate signal a' n and a compensation factor c n; where n=0, ||a' |0 ] |=0, and c [0] =0.
7. The second generation wavelet based body temperature compensation method according to claim 1, wherein the step of compensating the biomarker detection result to be calibrated and compensated according to the compensation factor, and the step of outputting the compensated biomarker detection result by the body temperature compensation model further comprises:
when n=n+1 times of compensation are carried out, updating the compensation factors according to the biomarker detection result to be calibrated and compensated input for n=n+1 times, and obtaining updated compensation factors;
and compensating the biomarker detection result to be calibrated and compensated of the n=n+1th input according to the updated compensation factor, wherein the expression is as follows:
x '[ n+1] =x [ n+1] -c [ n+1], wherein x [ n+1] represents the biomarker detection result to be calibrated for compensation inputted n=n+1, c [ n+1] represents the updated compensation factor, and x' [ n+1] represents the biomarker detection result after n=n+1-th compensation.
8. The second generation wavelet based body temperature compensation method according to claim 1, wherein the biomarker detection result to be calibrated and compensated is any one of blood glucose content, lactic acid content, potassium ion concentration and sodium ion concentration.
9. A mobile terminal comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 8 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 8.
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