WO2022011595A1

WO2022011595A1 - Body temperature compensation method based on second generation wavelet, and mobile terminal and storage medium

Info

Publication number: WO2022011595A1
Application number: PCT/CN2020/102102
Authority: WO
Inventors: 华韵之
Original assignee: 华韵之
Priority date: 2020-07-15
Filing date: 2020-07-15
Publication date: 2022-01-20
Also published as: CN112040866A; CN112040866B

Abstract

A body temperature compensation method based on a second generation wavelet, and a mobile terminal and a storage medium. The method comprises: inputting an original signal into a body temperature compensation model, and splitting the original signal by means of the body temperature compensation model to obtain an approximate signal and a detail signal (S100), wherein the original signal is a biomarker measurement result to be subjected to calibration and compensation; calculating a compensation factor according to the approximate signal and the detail signal (S200); and according to the compensation factor, carrying out compensation on the biomarker measurement result to be subjected to calibration and compensation, and outputting a compensated biomarker measurement result by means of the body temperature compensation model (S300). By means of the method, real-time body temperature compensation can be performed on an electronic wearable device in a highly precise and low-power-consumption manner, thereby eliminating the effect of body temperature on the performance of the electronic wearable device.

Description

Body temperature compensation method, mobile terminal and storage medium based on second generation wavelet

technical field

The invention relates to the field of medical technology, and in particular, to a body temperature compensation method, a mobile terminal and a storage medium based on the second-generation wavelet.

Background technique

Recent developments in personalized medicine have led to a huge increase in the need for continuous monitoring of the physiological state of the human body. Wearable Electrochemical Sensor (WES), as the core component of wearable electronic devices, is the most suitable technology for continuous monitoring of the physiological state of the human body. Because sweat contains a wealth of clinically relevant biomarkers, it is one of the most suitable biological fluids for continuous monitoring. There are two main categories of major biomarkers in sweat: metabolites (eg, blood glucose and lactate) and electrolytes (eg, potassium and sodium ions). The schematic diagram of the sensor structure of a typical wearable electronic device, as shown in Figure 1, can detect four important human physiological parameters: blood sugar content, lactic acid content, potassium ion concentration and sodium ion concentration. In Figure 1, the Ag/AgCI (silver/silver chloride) electrode is used as the common reference electrode of the blood glucose sensor anode (GoX) and the lactic acid sensor anode (Lox), and an output current proportional to the blood glucose content/lactic acid content will be generated between the electrodes; Potassium ion (K+) and sodium ion electrodes (Na+), as an ion-selected electrode (Ion-selected Electrodes, ISE), are connected to a reference electrode composed of polyvinyl butyral (PVB), which can generate more stable output voltage. Electrolyte imbalance can reflect the potential danger posed by abnormal heart rate, and electrolyte loss is also a major cause of bodily dysfunction, so monitoring key electrolyte concentrations can warn of potential heart disease. The real-time measurement of electrolyte concentration can remind patients, doctors, coaches or athletes of electrolyte loss or dehydration. Similarly, abnormal metabolite levels can also affect exercise status and scientific recovery after exercise. If the blood sugar during exercise can be accurately grasped in real time and lactic acid changes, it is beneficial to restore the balance of body functions during exercise and after use. Therefore, how to detect abnormal metabolite content or electrolyte level disturbance with high performance becomes important and challenging.

To achieve high performance of biomarker detection in sweat, there are four specific requirements: 1. Multi-component detection; 2. Real-time continuity; 3. Accuracy; 4. Low power consumption. The first requirement is met by a textile-based, highly stretchable, printed voltage sensor array that can be used for multiple ionic and enzymatic sweat analysis simultaneously. Textiles are absorbent components that can provide rich elasticity, enabling close contact between the sensor and the body. Combined with graphene, which has good strength, electrical conductivity, and optical transparency, the second requirement can be better addressed. One of the difficulties in solving the third requirement is that the human body will have different resistance and impedance characteristics under different skin temperatures, thereby affecting the detection results of enzymes, electrolytes or ions. As shown in Figure 2, Figure 2 shows that although the blood sugar content (100uM) or the lactate content (5mM) in the sweat is fixed, the output current is very obvious due to the change of skin temperature (from 20°C to 40°C). s difference. In order to eliminate the influence of human skin temperature on the sensor performance, a body temperature compensation model must be introduced to calibrate the data collected by the sensor. Although the effect of body temperature on the performance of potentiometric sensors is not particularly significant, it has a significant impact on the biochemical properties of enzymes (such as blood glucose or lactate), so high-performance sensors must compensate for the effect of body temperature in real time. The fourth requirement becomes the key to the realization of the first three requirements. Although the number of detected components is proportional to the power consumption, it is obviously unacceptable to detect too few components. If the power consumption is too large, it cannot meet the real-time and continuous detection. The accuracy of the detection results will inevitably lead to an increase in power consumption. If a simple model is used for lower power consumption, the accuracy will be affected.

Therefore, the existing technology still needs to be improved and developed.

SUMMARY OF THE INVENTION

In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a body temperature compensation method, mobile terminal and storage medium based on the second generation wavelet, so as to achieve real-time body temperature compensation for electronic wearable devices with high precision and low power consumption , which effectively eliminates the influence of body temperature on the performance of electronic wearable devices.

The technical scheme of the present invention is as follows:

A body temperature compensation method based on the second generation wavelet, the method comprises the steps of:

inputting the original signal to the body temperature compensation model, and the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; wherein, the original signal is the detection result of the biomarker to be calibrated and compensated;

Calculate the compensation factor according to the approximate signal and the detail signal;

The detection result of the biomarker to be calibrated and compensated is compensated according to the compensation factor, and the body temperature compensation model outputs the detection result of the biomarker after compensation.

In a further arrangement of the present invention, the original signal is input to the body temperature compensation model, and the steps of the body temperature compensation model splitting the original signal to obtain an approximate signal and a detail signal include:

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 approximate signals a[n] and detail signal d[n];

Among them, a[n]=x[2n], d[n]=x[2n+1].

In a further arrangement of the present invention, the step of calculating the compensation factor according to the approximate signal and the detail signal includes:

The approximate signal a[n] is updated according to the information in the detail signal d[n] to obtain the updated approximate signal a'[n]; wherein, the expression of the updated approximate signal a'[n] The formula is:

represents the smoothing operation, which is defined as:

Among them, U(d[n]) is the adaptive update operator, which is defined as:

Among them, Δ _L =|a[n]-d[n-1]|, Δ _R =|a[n]-d[n]|.

In a further arrangement of the present invention, the step of calculating the compensation factor according to the approximate signal and the detail signal further includes:

Predict 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];

The expression of the predicted detail signal d'[n] is: d'[n]=d[n]-P(a'[n]);

where P represents the prediction operator, which is defined as:

The compensation factor c[n] is calculated 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:

Where ||a'[n]|| is the second-order norm of the updated approximate signal a'[n], and its expression is:

In a further arrangement of the present invention, the inputting the original signal to the body temperature compensation model, the body temperature compensation model further includes before the step of splitting the original signal to obtain the approximate signal and the detail signal:

Initialize the number of compensation n, the second-order norm ||a'[n]|| of the updated approximate signal, and the compensation factor c[n]; where n=0, ||a'[0]||=0, c[0]=0.

In a further arrangement of the present invention, the step of compensating the detection result of the biomarker to be calibrated and compensated according to the compensation factor, and outputting the compensated detection result of the biomarker by the body temperature compensation model further includes:

When performing the n=n+1th compensation, according to the n=n+1th input of the biomarker detection result to be calibrated and compensated, the compensation factor is updated to obtain the updated compensation factor;

The biomarker detection result to be calibrated and compensated for the n=n+1th input is compensated according to the updated compensation factor, and its expression is:

x'[n+1]=x[n+1]-c[n+1], where x[n+1] represents the biomarker detection result to be calibrated and compensated for the n=n+1th input, c [n+1] represents the updated compensation factor, and x'[n+1] represents the biomarker detection result after the n=n+1th compensation.

In a further arrangement of the present invention, the detection result of the biomarker to be calibrated and compensated is any one of four results: blood sugar content, lactic acid content, potassium ion concentration and sodium ion concentration.

A mobile terminal includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the second-generation wavelet-based body temperature compensation method when the processor executes the computer program.

A computer-readable storage medium on which a computer program is stored, the computer program implementing the steps of a second-generation wavelet-based body temperature compensation method when executed by a processor.

The present invention provides a body temperature compensation method, a mobile terminal and a storage medium based on the second generation wavelet. The method includes the steps of: inputting an original signal into a body temperature compensation model, and the body temperature compensation model splits the original signal to obtain an approximate signal and detail signal; wherein, the original signal is the detection result of the biomarker to be calibrated and compensated; a compensation factor is calculated according to the approximate signal and the detail signal; the biological marker to be calibrated and compensated is compensated according to the compensation factor Marker detection results, the body temperature compensation model outputs the compensated biomarker detection results. Real-time body temperature compensation for electronic wearable devices with high precision and low power consumption is realized, and the influence of body temperature on the performance of electronic wearable devices is effectively eliminated.

Description of drawings

Figure 1 is a schematic diagram of the sensor structure of a typical wearable electronic device.

Figure 2 shows the effect of human skin temperature on the detection of blood sugar or lactate.

FIG. 3 is a schematic flowchart of a body temperature compensation method based on the second generation wavelet in one embodiment.

FIG. 4 is a schematic structural diagram of the lifting framework of the second generation wavelet in one embodiment.

FIG. 5 is a flow chart of a body temperature compensation model based on the second-generation wavelet-based lifting framework in one embodiment.

FIG. 6 is a logic diagram of a low-power body temperature compensation circuit based on the MSP430 platform in one embodiment.

Figure 7 is a schematic diagram of body temperature compensation results in one embodiment.

detailed description

The inventor found that the current simple and widely used body temperature compensation model is from the University of California, Berkeley. This model is simple to calculate, so the power consumption of wearable electronic devices is low, but this model does not fully consider the individual differences of each person, and 20 °C as a benchmark may not be reasonable, and it is necessary to add an accurate measurement of skin temperature in the hardware design. The temperature sensor inevitably increases the complexity and power consumption of the hardware system. In addition, in terms of low power consumption, the three links of signal op amp (amplification), A/D conversion (analogue-digital converter, analog-to-digital conversion) and digital signal processing (digital signal processing) cannot be highly integrated, which affects the Implementation of low power consumption. The present invention provides a body temperature compensation method, a mobile terminal and a storage medium based on the second-generation wavelet. The method uses the lifting framework in the second-generation wavelet to establish a flexible and universal body temperature compensation model. Complete the highly integrated design of signal op amp, A/D conversion and body temperature compensation processing, so as to realize real-time body temperature compensation of electronic wearable devices with high precision and low power consumption, and effectively eliminate the effect of body temperature on the performance of electronic wearable devices. Influence.

In order to make the objectives, technical solutions and effects of the present invention clearer and clearer, the present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

In the embodiments and the scope of the patent application, unless the context has a special limitation on the articles, "a" and "the" can generally refer to a single or plural.

In addition, the technical solutions between the various embodiments can be combined with each other, but must be based on the realization by those of ordinary skill in the art. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that the combination of such technical solutions does not exist. , is not within the scope of protection required by the present invention.

The second-generation wavelet-based body temperature compensation method provided in this application can be applied to a terminal. Among them, it is especially used in a wearable electronic device, the wearable electronic device has a wearable electrochemical sensor for continuously monitoring the physiological state of the human body. The wearable electronic device uses a single-chip microcomputer as a processor, wherein a single-instruction-cycle RISC chip of the MSP430 model is used.

The following describes an example in which a second-generation wavelet-based body temperature compensation method is applied to a wearable electronic device with a wearable electrochemical sensor.

Please refer to FIG. 3 in conjunction with FIG. 4. FIG. 3 is a schematic flowchart of a body temperature compensation method based on the second-generation wavelet. 4, the method includes the steps:

Step S100, input the original signal to the body temperature compensation model, and the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; wherein, the original signal is the detection result of the biomarker to be calibrated and compensated; in the present invention The biomarker detection results to be calibrated and compensated can be any of the four results of blood glucose content, lactic acid content, potassium ion concentration and sodium ion concentration.

Among them, the body temperature compensation model is established based on the lifting framework in the second-generation wavelet. Compared with the existing first-generation wavelet analysis methods, the recently emerging lifting framework as the second-generation wavelet can show more flexibility and versatility A general improvement framework includes two improvement steps of update (Update) and prediction (Prediction), and introduces adaptability, so that the system in this application does not need to adjust algorithm parameters during the entire operation cycle, and can adapt to the signal Various changes in characteristics. The body temperature compensation model can calculate the compensation factor according to the approximate and detailed components formed after signal update and prediction, and compensate the input signal in real time.

In a further implementation of an embodiment, the inputting the original signal to the body temperature compensation model, and the step of the body temperature compensation model splitting the original signal to obtain an approximate signal and a detail signal includes:

Step S101: 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 approximate signal a[ n] and the detail signal d[n];

Among them, a[n]=x[2n], d[n]=x[2n+1].

Specifically, the original signal x[n] (n=0, 1, . . . , N) is first split into two data streams: the approximate signal a[n]=x[2n] and the detail signal d [n]=x[2n+1]. Among them, the approximate signal contains the low-frequency part of the original signal, also known as the signal contour, and the detail signal contains the high-frequency part of the original signal, that is, the details or sudden changes of the signal. Compensation of human skin temperature is to eliminate unnecessary fluctuations caused by human skin temperature to the detection results of biomarkers.

Step S200, calculating a compensation factor according to the approximate signal and the detail signal;

In a further implementation of an embodiment, the step of calculating the compensation factor according to the approximation signal and the detail signal includes:

Step S201: Update the approximate signal a[n] according to the information in the detail signal d[n] to obtain an updated approximate signal a'[n]; wherein, the updated approximate signal a'[n] ] The expression U() is:

represents the smoothing operation, which is defined as:

Among them, U(d[n]) is the adaptive update operator, which is defined as:

Among them, Δ _L =|a[n]-d[n-1]|, Δ _R =|a[n]-d[n]|, Δ _L <Δ _R represents the enhancement of the fluctuation, and Δ _L =Δ _R represents the fluctuation unchanged, Δ _L >Δ _R means that the fluctuation becomes smaller. Among them, the smoothing operation can retain the low-frequency part (the contour part) of the signal, and can retain the most important slowly changing information of the signal. Participating in the smoothing operation requires two signal sequences, one of which is denoted as x, and the other is denoted as y, y[n] is the nth point of the signal sequence y.

Among them, before the splitting step is performed on the original signal, the data in the signal data stream x[n] is continuously sampled, that is, the three consecutive samples in the signal data stream x[n] are d[n-1], a [n] and d[n]. In all cases, a[n] will adaptively use its neighbors, that is, d[n-1] or d[n] will replace a[n], thereby trying to eliminate unwanted or unreasonable mutations in the signal. If Δ _L <Δ _R, which means that the signal gets stronger over time. Since the updated data a'[n] reflects the steady-state component of the signal, then a[n] should be replaced by its smooth neighbor, that is, d[n-1].

In a further implementation of an embodiment, the step of calculating the compensation factor according to the approximation signal and the detail signal further includes the steps of:

Step S202: Predict 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 to say, when predicting the detail signal d[n], the information contained in the updated approximate signal a'[n] will be used;

The expression of the predicted detail signal d'[n] is: d'[n]=d[n]-P(a'[n]);

Among them, P represents the prediction operator, which represents the contour information (low-frequency information) after the smoothing operation, which is defined as:

In order to remove the steady-state components in the predicted detail signal, the updated detail signal d'[n] only contains the high-frequency components in the detail signal obtained by splitting the original signal, which can be effectively used to extract the Transient information.

In a further implementation of an embodiment, the step of calculating the compensation factor according to the approximation signal and the detail signal further includes:

Step S203: Calculate and obtain 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: :

Among them, ||a'[n]|| is the second-order norm of the updated approximate signal a'[n], and its expression is:

The second-order norm is used to calculate the energy of the low-frequency part of the signal, which contains most of the energy (generally above 98%), and the fine-tuning part

It needs to be divided by the second-order norm, and the purpose is to normalize the processing to avoid overcompensation.

Step S300: Compensate the biomarker detection result to be calibrated and compensated according to the compensation factor, and the body temperature compensation model outputs the compensated biomarker detection result. Wherein, the biomarker detection result to be calibrated and compensated for the nth input is compensated according to the updated compensation factor, and the expression of the compensated biomarker detection result is: x'[n]=x[ n]-c[n], where x[n] represents the biomarker detection result to be calibrated and compensated for the nth input, c[n] represents the updated compensation factor, and x'[n] represents the nth input Biomarker detection results after subcompensation. Because the compensation is continuous, c[n] will be fine-tuned on the basis of c[n-1], and the fine-tuning part will be fine-tuned.

Depends on the variation of the high frequency signal d'[n]. If the signal becomes weak, the compensation formula c[n] will decrease, and the compensation strength will be weakened. If the change is 0, that is, c[n] is equal to 0, no compensation is needed. If the change is stronger and c[n] increases, then Compensation is increased.

In a further implementation of an embodiment, the inputting the original signal to the body temperature compensation model, before the step of splitting the original signal to obtain the approximate signal and the detail signal, the body temperature compensation model further includes:

In a further implementation of an embodiment, the step of compensating the biomarker detection result to be calibrated and compensated according to the compensation factor, and outputting the compensated biomarker detection result by the body temperature compensation model further includes:

Step S301, when performing the n=n+1th compensation, according to the n=n+1th input of the biomarker detection result to be calibrated and compensated, update the compensation factor and obtain the updated compensation factor;

Step S302: Compensate the biomarker detection result to be calibrated and compensated for the n=n+1th input according to the updated compensation factor, and its expression is:

It should be understood that, although the steps in the flowchart of FIG. 3 are sequentially displayed according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order, and these steps may be performed in other orders. Moreover, at least a part of the steps in FIG. 3 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution of these sub-steps or stages The sequence is also not necessarily sequential, but may be performed alternately or alternately with other steps or sub-steps of other steps or at least a portion of a phase.

For better understanding of the present invention, the present invention also provides a specific application example of a body temperature compensation method based on the second generation wavelet, as shown in FIG. 5 , which is the body temperature compensation of the lifting frame based on the second generation wavelet. A flow chart of the model, which includes the steps:

Initialize n=0, ||a'[0]||=0, c[0]=0;

Split the original signal a[n] into an approximate signal a[n] and a detail signal d[n];

Update the approximate signal a[n] to get a'[n];

Predict the detail signal d[n] to get d'[n];

renew

Update compensation factor

Perform body temperature compensation x'[n]=x[n]-c[n];

Let n=n+1;

Determine whether the system has stopped working; if not, perform the n+1th compensation, and then split the input original signal a[n], and repeat the above steps; if it is determined that the system has stopped working, stop running the system.

Through the above technical solution, the parameter of human skin temperature is not needed in this model, so a high-precision temperature sensor can be omitted from the hardware, and the power consumption can be effectively reduced. Compared with the simple body temperature compensation model, the lifting frame as the second generation wavelet comprehensively considers the local changes of the signal, which better solves the problem that the performance of the first generation wavelet is not very ideal when dealing with discontinuous signals. The compensation model does not require accurate body temperature measurement, which greatly reduces the requirements for hardware design. In addition, when the body temperature compensation model in the present invention processes the nth data, there are only the adaptive update operator U(d[n]), the prediction operator P(a'[n]) and the compensation factor c[n]. The n-1th data will be involved, so this model is only a first-order digital filter, and the required data storage and calculation have been reduced to a minimum. Therefore, please refer to FIG. 7, which is a schematic diagram of the body temperature compensation result. The present invention establishes a more flexible and versatile body temperature compensation model by using the lifting framework in the second generation wavelet, and realizes high precision and low power consumption. Real-time body temperature compensation for electronic wearable devices can effectively eliminate the impact of body temperature on the performance of electronic wearable devices.

In an embodiment, the present application further provides a mobile terminal, including a memory and a processor, the memory stores a computer program, and the processor implements the second-generation wavelet-based method when the processor executes the computer program The steps of the body temperature compensation method. The mobile terminal uses an MSP430 microcontroller as a processor, and the MSP430 chip (such as MSP430G2553) is a 16-bit single instruction cycle RISC (reduced instruction set) microcontroller launched by a company in the United States. Since the working voltage of the MSP430 chip can be as low as 1.5v, the system power supply can be powered by a 1.5v micro Li-ion Polymer Rechargeable Battery (Li-ion Polymer Rechargeable Battery), instead of the 3.7v battery commonly used by other systems today. As shown in FIG. 6 , FIG. 6 is a logic diagram of a low-power body temperature compensation circuit based on the MSP430 platform in one embodiment. The MSP430 platform includes an MSP430 control chip, an analog-to-digital conversion setting circuit, a system clock setting circuit, a watchdog circuit and Serial communication circuits and memories. Among them, please refer to Figure 1 at the same time, any one of the analog signal outputs of the 4-way sensors of the wearable electronic device can be used as the signal input of the MSP430 control chip and connected to the No. 8 tube corner of the MSP430 control chip. in:

In the system clock setting circuit, the MSP430 integrates a 14kHz vibrator, so no external crystal is required. The master clock (MCLK) clock frequency is programmed as needed to make a good compromise between high speed (from 14kHz to 1.12MHz) and low power consumption to maximize the performance of the microcontroller.

The analog-to-digital conversion circuit adopts the built-in 10bitA/D (10-bit analog-to-digital conversion) interface of the MSP430 chip. By adjusting the variable resistor R1, the reference voltage Vref+ can adapt to 0v ~ 1.5v, and meet the maximum dynamic range of the sensor analog signal. .

Among them, the MSP430 chip can set the analog-to-digital conversion rate, and the 10bitA/D analog-to-digital conversion rate of the MSP430 chip can be dynamically set within 200ksps. Considering that the bandwidth of the analog signal is in the order of 1Hz, the system sampling rate can be set to 4Hz. The analog-to-digital conversion rate is correspondingly set to 40sps, which can greatly reduce the power consumption of the system.

In the watchdog circuit, a watchdog circuit is added between the 1st and 16th pins of the MSP430 chip. When a problem occurs in the system program, a system reset signal will be generated to help the system reset and restart the program.

The memory implements a body temperature compensation method based on the second-generation wavelet by adopting the body temperature compensation processing program in assembly language, and is loaded into the non-volatile memory Flash of the MSP430 chip.

Because the body temperature compensation model is relatively simple, in order to reduce power consumption and better utilize the low power consumption characteristics of the MSP430 chip, the unnecessary data memory can be turned off. The data memory RAM has only 512 bytes, which is mainly used to store variables and intermediate results of operations. The storage required by this system is within 100 bytes, so about 80% of the data memory can be closed.

It should be noted that those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile In the computer-readable storage medium, when the computer program is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database or other medium used in the various embodiments provided in this application may include non-volatile and/or volatile memory. Nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Road (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

In the serial communication interface circuit, the compensated signal processing result can perform data communication with peripheral slaves through the serial communication interface circuit (pins 7, 14, and 15). Among them, because the result processed by the wearable sensor needs to be transmitted to the mobile phone in real time, the peripheral slave may be a low-power Bluetooth module. Wireless communication with the smartphone is carried out through this peripheral slave.

The present invention adopts a single instruction cycle RISC chip such as MSP430. The core processing part of each data in this model requires less than 100 single instructions of addition or multiplication in total, and the data processing burden is very light, ensuring low theoretical and algorithmic requirements. Realizability of power consumption; when the hardware of the system is implemented, the signal op amp, A/D conversion and digital signal processing are highly integrated in one chip, the integration is high and the signal processing algorithm can be dynamically adjusted, thereby improving the self-adaptation of the system Therefore, the hardware design ensures the low power consumption of the system again.

In one embodiment, a computer-readable storage medium is provided on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

Step S100, input the original signal to the body temperature compensation model, and the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; wherein, the original signal is the detection result of the biomarker to be calibrated and compensated; The body temperature compensation method of the second generation wavelet is described, and will not be repeated here.

Step S200: Calculate and obtain a compensation factor according to the approximate signal and the detail signal; the details are as described in a body temperature compensation method based on the second-generation wavelet, which will not be repeated here.

Step S300: Compensate the detection result of the biomarker to be calibrated and compensated according to the compensation factor, and output the compensated detection result of the biomarker by the body temperature compensation model. The details are as described in a body temperature compensation method based on the second-generation wavelet, which will not be repeated here.

To sum up, the second-generation wavelet-based body temperature compensation method, mobile terminal and storage medium provided by the present invention can establish a more flexible and versatile body temperature by using the lifting frame in the second-generation wavelet. Compensation model, compared with the simple body temperature compensation model, the lifting frame as the second generation wavelet comprehensively considers the local changes of the signal, and better solves the problem that the performance of the first generation wavelet is not very ideal when dealing with discontinuous signals; this model There is no need to accurately measure body temperature, which greatly reduces the requirements for hardware design; using a single-instruction-cycle RISC chip such as MSP430, the core processing part of each data in this model requires a total of less than 100 additions or multiplications. Single instruction, data processing burden Very light, theoretically and algorithmically ensure the achievability of low power consumption; when the system is implemented in hardware, the signal op amp, A/D conversion and digital signal processing are highly integrated into one chip, with high integration and dynamic Adjust the signal processing algorithm to ensure the low power consumption of the system again from the hardware design. The core of the MSP430 single-chip microcomputer platform is a 16-bit single-instruction cycle RISC processor. Compared with many other single-chip microcomputers, the most significant advantages are strong computing power and low overall power consumption. The system can realize the clock frequency of the main clock of the system through programming according to actual needs, so as to make a good compromise between high speed and low power consumption, and maximize the performance of the microcontroller. In order to make better use of the low power consumption of MSP430, the unnecessary memory will be turned off when the system is working. Therefore, the present application realizes real-time body temperature compensation for electronic wearable devices with high precision and low power consumption, and effectively eliminates the influence of body temperature on the performance of electronic wearable devices.

It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or transformations can be made according to the above descriptions, and all these improvements and transformations should belong to the protection scope of the appended claims of the present invention.

Claims

The second-generation wavelet-based body temperature compensation method is characterized in that it includes the steps of:

inputting the original signal to the body temperature compensation model, and the body temperature compensation model splits the original signal to obtain an approximate signal and a detail signal; wherein, the original signal is the detection result of the biomarker to be calibrated and compensated;

A compensation factor is calculated according to the approximate signal and the detail signal;

The detection result of the biomarker to be calibrated and compensated is compensated according to the compensation factor, and the body temperature compensation model outputs the detection result of the biomarker after compensation.
The body temperature compensation method based on the second generation wavelet according to claim 1, characterized in that, inputting the original signal to the body temperature compensation model, and the step of dividing the original signal by the body temperature compensation model to obtain the approximate signal and the 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 approximate signals a[n] and detail signal d[n];

Among them, a[n]=x[2n], d[n]=x[2n+1].
The body temperature compensation method based on the second generation wavelet according to claim 2, wherein the step of calculating the compensation factor according to the approximate signal and the detail signal comprises:

The approximate signal a[n] is updated according to the information in the detail signal d[n] to obtain the updated approximate signal a'[n]; wherein, the expression of the updated approximate signal a'[n] The formula is:
represents the smoothing operation, which is defined as:
Among them, U(d[n]) is the adaptive update operator, which is defined as:

Among them, Δ L =|a[n]-d[n-1]|, Δ R =|a[n]-d[n]|.
The body temperature compensation method based on the second generation wavelet according to claim 3, wherein the step of calculating the compensation factor according to the approximate signal and the detail signal further comprises:

Predict 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];

The expression of the predicted detail signal d'[n] is: d'n]=d[n]-P(a'[n]);

where P represents the prediction operator, which is defined as:
The body temperature compensation method based on the second generation wavelet according to claim 4, wherein the step of calculating the compensation factor according to the two data streams further comprises:

The compensation factor c[n] is calculated 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:

Where ||a'[n]|| is the second-order norm of the updated approximate signal a'[n], and its expression is:
The body temperature compensation method based on the second generation wavelet according to claim 1, wherein the inputting the original signal to the body temperature compensation model, and the body temperature compensation model splits the original signal to obtain the approximate signal and the detail signal. Also included before:

Initialize the number of compensation n, the second-order norm ||a'[n]|| of the updated approximate signal, and the compensation factor c[n]; where n=0, ||a'[0]||=0, c[0]=0.
The body temperature compensation method based on the second generation wavelet according to claim 1, characterized in that, according to the compensation factor to compensate the biomarker detection result to be calibrated and compensated, the body temperature compensation model outputs the compensated Steps for biomarker test results also include:

When performing the n=n+1th compensation, according to the n=n+1th input of the biomarker detection result to be calibrated and compensated, the compensation factor is updated to obtain the updated compensation factor;

The biomarker detection result to be calibrated and compensated for the n=n+1th input is compensated according to the updated compensation factor, and its expression is:

x'[n+1]=x[n+1]-c[n+1], where x[n+1] represents the biomarker detection result to be calibrated and compensated for the n=n+1th input, c [n+1] represents the updated compensation factor, and x'[n+1] represents the biomarker detection result after the n=n+1th compensation.
The method for body temperature compensation based on the second generation wavelet according to claim 1, wherein the detection result of the biomarker to be calibrated and compensated is one of four results: blood glucose content, lactic acid content, potassium ion concentration and sodium ion concentration. any of the .
A mobile terminal, comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 8 when the processor executes the computer program.
A computer-readable storage medium on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.