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WO2022011595A1 - Procédé de compensation de température corporelle basé sur une ondelette de deuxième génération et terminal mobile et support de stockage - Google Patents

Procédé de compensation de température corporelle basé sur une ondelette de deuxième génération et terminal mobile et support de stockage Download PDF

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WO2022011595A1
WO2022011595A1 PCT/CN2020/102102 CN2020102102W WO2022011595A1 WO 2022011595 A1 WO2022011595 A1 WO 2022011595A1 CN 2020102102 W CN2020102102 W CN 2020102102W WO 2022011595 A1 WO2022011595 A1 WO 2022011595A1
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signal
body temperature
temperature compensation
compensation
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Chinese (zh)
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华韵之
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Priority to CN202080001250.0A priority patent/CN112040866B/zh
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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 or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14507—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or 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 or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue specially adapted for measuring characteristics of body fluids other than blood for sweat
    • 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 or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • 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 or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14546—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring analytes not otherwise provided for, e.g. ions, cytochromes
    • 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 or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/1468—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using chemical or electrochemical methods, e.g. by polarographic means
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

Definitions

  • 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.
  • Wearable Electrochemical Sensor 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).
  • metabolites eg, blood glucose and lactate
  • 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.
  • 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.
  • PVB polyvinyl butyral
  • 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.
  • 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.
  • 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.
  • 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.
  • a body temperature compensation model must be introduced to calibrate the data collected by the sensor.
  • 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.
  • 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.
  • 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.
  • a body temperature compensation method based on the second generation wavelet comprises the steps of:
  • 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 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 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:
  • 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 represents the smoothing operation, which is defined as: Among them, U(d[n]) is the adaptive update operator, which is defined as:
  • ⁇ L
  • ⁇ R
  • the step of calculating the compensation factor according to the approximate signal and the detail signal further includes:
  • the step of calculating the compensation factor according to the approximate signal and the detail signal further includes:
  • 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:
  • the inputting the original signal to the body temperature compensation model further includes before the step of splitting the original signal to obtain the approximate signal and the detail signal:
  • 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:
  • the compensation factor is updated to obtain the updated compensation factor
  • 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.
  • 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.
  • 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.
  • the temperature sensor inevitably increases the complexity and power consumption of the hardware system.
  • 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.
  • the second-generation wavelet-based body temperature compensation method provided in this application can be applied to a terminal.
  • 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.
  • 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.
  • the body temperature compensation model is established based on the lifting framework in the second-generation wavelet.
  • 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.
  • 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:
  • 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
  • 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] ]
  • U() represents the smoothing operation, which is defined as: Among them, U(d[n]) is the adaptive update operator, which is defined as:
  • ⁇ L
  • ⁇ R
  • ⁇ L ⁇ R represents the enhancement of the fluctuation
  • ⁇ L ⁇ R represents the fluctuation unchanged
  • ⁇ L > ⁇ R means that the fluctuation becomes smaller.
  • 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.
  • 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].
  • 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 ⁇ L ⁇ 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].
  • 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;
  • P represents the prediction operator, which represents the contour information (low-frequency information) after the smoothing operation, which is defined as:
  • 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.
  • 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: :
  • is the second-order norm of the updated approximate signal a'[n]
  • 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.
  • the biomarker detection result to be calibrated and compensated for the nth input is compensated according to the updated compensation factor
  • c[n] will be fine-tuned on the basis of c[n-1], and the fine-tuning part will be fine-tuned.
  • 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.
  • 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:
  • 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:
  • 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.
  • 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:
  • 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.
  • 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.
  • 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].
  • FIG. 7 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.
  • 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.
  • 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.
  • 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:
  • 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.
  • the reference voltage Vref+ can adapt to 0v ⁇ 1.5v, and meet the maximum dynamic range of the sensor analog signal. .
  • 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.
  • a watchdog circuit is added between the 1st and 16th pins of the MSP430 chip.
  • 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.
  • 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.
  • 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.
  • 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.
  • the compensated signal processing result can perform data communication with peripheral slaves through the serial communication interface circuit (pins 7, 14, and 15).
  • 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.
  • a computer-readable storage medium 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.
  • the second-generation wavelet-based body temperature compensation method, mobile terminal and storage medium 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.
  • 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.
  • 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.

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Abstract

L'invention concerne un procédé de compensation de température corporelle basé sur une ondelette de deuxième génération, et un terminal mobile et un support de stockage. Le procédé consiste à : entrer un signal d'origine dans un modèle de compensation de température corporelle et diviser le signal d'origine au moyen du modèle de compensation de température corporelle pour obtenir un signal approximatif et un signal de détail (S100), le signal d'origine étant un résultat de mesure de biomarqueur devant être soumis à un étalonnage et à une compensation ; calculer un facteur de compensation en fonction du signal approximatif et du signal de détail (S200) ; et en fonction du facteur de compensation, effectuer une compensation sur le résultat de mesure de biomarqueur devant être soumis à un étalonnage et à une compensation et émettre un résultat de mesure de biomarqueur compensé au moyen du modèle de compensation de température corporelle (S300). Le procédé permet de réaliser une compensation de température corporelle en temps réel sur un dispositif portable électronique d'une manière hautement précise et à faible consommation d'énergie, ce qui permet d'éliminer l'effet de la température corporelle sur les performances du dispositif portable électronique.
PCT/CN2020/102102 2020-07-15 2020-07-15 Procédé de compensation de température corporelle basé sur une ondelette de deuxième génération et terminal mobile et support de stockage Ceased WO2022011595A1 (fr)

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Application Number Priority Date Filing Date Title
PCT/CN2020/102102 WO2022011595A1 (fr) 2020-07-15 2020-07-15 Procédé de compensation de température corporelle basé sur une ondelette de deuxième génération et terminal mobile et support de stockage
CN202080001250.0A CN112040866B (zh) 2020-07-15 2020-07-15 基于第二代小波的体温补偿方法、移动终端及存储介质

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104434064A (zh) * 2014-11-26 2015-03-25 中国科学院计算技术研究所 一种心率和呼吸率信号处理与跟踪方法及其系统
US20160089086A1 (en) * 2014-09-26 2016-03-31 Pixart Imaging Inc. Heart rate detection module, and detection and denoising method thereof
CN110146201A (zh) * 2019-04-13 2019-08-20 复旦大学 一种具有温度补偿的传感信号调理系统及温度补偿方法
CN110177502A (zh) * 2017-09-13 2019-08-27 美敦力泌力美公司 用于校准和优化葡萄糖传感器和传感器输出的方法、系统和设备

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7398173B2 (en) * 2005-05-04 2008-07-08 Ami Semiconductor, Inc. Providing nonlinear temperature compensation for sensing means by use of Padé approximant function emulators
CN101596554B (zh) * 2009-07-01 2011-06-01 燕山大学 基于波函数的冷带轧机板厚控制有限频域前馈补偿方法
CN102999884B (zh) * 2011-09-16 2015-11-25 株式会社东芝 图像处理设备和方法
CN105004278B (zh) * 2015-07-10 2018-03-16 东南大学 基于分布传感和小波解析技术的实时基线及去噪处理方法
CN108828301A (zh) * 2018-03-07 2018-11-16 江苏大学 一种基于复小波变换与Teager能量算子的电压跌落检测方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160089086A1 (en) * 2014-09-26 2016-03-31 Pixart Imaging Inc. Heart rate detection module, and detection and denoising method thereof
CN104434064A (zh) * 2014-11-26 2015-03-25 中国科学院计算技术研究所 一种心率和呼吸率信号处理与跟踪方法及其系统
CN110177502A (zh) * 2017-09-13 2019-08-27 美敦力泌力美公司 用于校准和优化葡萄糖传感器和传感器输出的方法、系统和设备
CN110146201A (zh) * 2019-04-13 2019-08-20 复旦大学 一种具有温度补偿的传感信号调理系统及温度补偿方法

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