CN-121971051-A - Integrated multi-parameter information acquisition sensor and information acquisition method

CN121971051ACN 121971051 ACN121971051 ACN 121971051ACN-121971051-A

Abstract

The invention relates to the technical field of biosensing and health monitoring, in particular to an integrated multi-parameter information acquisition sensor and an information acquisition method, wherein the information acquisition method comprises the following steps of carrying out noise reduction and baseline correction on an acquired original resistance signal so as to eliminate environmental noise interference; the method comprises the steps of establishing a resolving model based on temperature sensitive coefficients and pressure sensitive coefficients of a first measuring unit and a second measuring unit, determining coefficient parameters through a calibration experiment and performing cross resolving to obtain a calculation formula of body temperature and indirectly obtaining respiratory rate, obtaining respiratory rate parameters according to time sequence characteristics of piezoresistive variable quantity and the calculation formula of the piezoresistive variable quantity, and enabling acquired body temperature data and piezoresistive variable quantity to be built to form the resolving model so as to meet simultaneous acquisition requirements of body temperature and respiratory rate, and correspondingly disclosing a structure of an integrated multi-parameter information acquisition sensor, wherein the sensor comprises a flexible substrate and a data acquisition module so as to meet space occupation requirements.

Inventors

LI NAN
HU YUN
TANG TIAN
WEI DAPENG
Yi Yuanbang
XU SONG

Assignees

成都智敏芯科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260116

Claims (8)

1. The integrated multi-parameter information acquisition method is characterized by comprising the following steps of: S1, noise reduction and baseline correction are carried out on an acquired original resistance signal so as to eliminate environmental noise interference; S2, establishing a resolving model based on the temperature sensitive coefficient and the pressure sensitive coefficient of each of the first measuring unit and the second measuring unit; S3, determining coefficient parameters through a calibration experiment and performing cross resolving to obtain a calculation formula of body temperature and indirectly obtaining respiratory rate, wherein the coefficient parameters comprise the resistance of a first measuring unit, the resistance of a second measuring unit, the piezoresistive variable quantity caused by the body temperature and the pressure; s4, acquiring respiratory rate parameters according to the time sequence characteristics of the piezoresistive variable quantity and combining the calculation formulas of the body temperature and the piezoresistive variable quantity.
2. An integrated multi-parameter information collection method according to claim 1 wherein, The step S1 of denoising and baseline correction of the acquired original resistance signal to eliminate environmental interference specifically comprises the following steps: Removing power frequency interference by using a notch filter, and filtering high-frequency noise by using a 2-order Butterworth low-pass filter; Taking the average value of the signals acquired for initial 3s as a baseline value, and carrying out baseline correction on subsequent signals to eliminate zero drift errors; and smoothing the signal by adopting a sliding window, wherein the signal-to-noise ratio of the smoothed signal is more than or equal to 40dB.
3. An integrated multi-parameter information collection method according to claim 1 wherein, The expression of the established solution model in step S2 is as follows: R 1 =a 1 ·T+b 1 ·F+R 10 R 2 =a 2 ·T+b 2 ·F+R 20 Wherein R 10 is the initial resistance of the first measuring unit at 37 ℃ and 0kPa, R 20 is the initial resistance of the second measuring unit at 37 ℃ and 0kPa, a 1 is the temperature sensitive coefficient of the first measuring unit, a 2 is the temperature sensitive coefficient of the second measuring unit, b 1 is the pressure sensitive coefficient of the first measuring unit, b 2 is the pressure sensitive coefficient of the second measuring unit, T is the body temperature, F is the piezoresistance change caused by pressure, R 1 is the resistance of the first measuring unit, and R 2 is the resistance of the second measuring unit.
4. An integrated multi-parameter information collection method according to claim 3 wherein, Substituting the preprocessed R 1 、R 2 value into the solution model, and calculating the following formula by matrix inversion operation: T=[(R 1 -R 10 )•b 2 -(R 2 -R 20 )•b 1 ]/(a 1 •b 2 -a 2 •b 1 ) F=[(R 2 -R 20 )•a 1 -(R 1 -R 10 )•a 2 ]/(a 1 •b 2 -a 2 •b 1 ).
5. an integrated multi-parameter information collection method according to claim 3 wherein, The method for determining the coefficient parameters through the calibration experiment comprises the following steps: placing the sensor in a constant temperature and constant pressure environment box, and setting a temperature gradient and a pressure gradient, wherein the temperature gradient is 32-42 ℃ and the pressure gradient is 0-5 kPa; And collecting R 1 、R 2 values under various working conditions, and obtaining a temperature-sensitive coefficient a 1 、a 2 and a pressure-sensitive coefficient b 1 、b 2 through least square fitting.
6. An integrated multi-parameter information collection method according to claim 1 wherein, The step S4 of acquiring the respiratory rate parameter according to the time series characteristic of the piezoresistive variable quantity and combining the calculation formulas of the body temperature and the piezoresistive variable quantity specifically comprises the following steps: The peak value of the piezoresistive variable quantity corresponds to the inspiration process with the maximum pressure, the valley value corresponds to the expiration process with the minimum pressure, and the respiration period is identified through a peak detection algorithm; And counting the number of respiratory cycles within 1 minute, namely the respiratory rate.
7. An integrated multi-parameter information acquisition sensor is characterized in that, The device comprises a flexible substrate and a data acquisition module, wherein an interpolation electrode is arranged on the surface of the flexible substrate, a second measurement unit and a first measurement unit are printed on the interpolation electrode, the data acquisition module is respectively connected with the second measurement unit and the first measurement unit through the interpolation electrode, the first measurement unit takes carbon nano tube/PDMS composite slurry as a raw material, and the second measurement unit takes nano silver/UV cured resin composite ink as a raw material.
8. An integrated multi-parameter information acquisition sensor as set forth in claim 7 wherein, The data acquisition module comprises an excitation power supply unit, an amplifier, an ADC conversion unit and a singlechip, wherein the excitation power supply unit is used for providing direct-current constant-voltage excitation for the first measurement unit and the second measurement unit, the amplifier is connected with the first measurement unit and is used for amplifying voltage signals converted by resistance change, the ADC conversion unit is connected with the output end of the amplifier and the output end of the second measurement unit and is used for ensuring capturing of tiny fluctuation of respiratory signals, and the singlechip is used for realizing synchronous acquisition of the first measurement unit and the second measurement unit.

Description

Integrated multi-parameter information acquisition sensor and information acquisition method Technical Field The invention relates to the technical field of biosensing and health monitoring, in particular to an integrated multi-parameter information acquisition sensor and an information acquisition method. Background In the current biological physiological parameter monitoring field, the respiration rate and the body surface temperature are used as core indexes for reflecting the health state of a human body, and the accurate acquisition of the respiration rate and the body surface temperature has important significance for early disease screening and dynamic disease monitoring. In the traditional technology, the respiratory rate monitoring is mostly dependent on independent devices such as chest and abdomen strain sensors, airflow sensors and the like, the body temperature monitoring is usually carried out by adopting special temperature sensing elements such as thermocouples, thermistors and the like, the series of traditional sensors are mostly made of rigid substrates and conductive materials, the degree of fit with the body surface of a human body is insufficient, the sensors are easily interfered by the activity of the human body, larger errors of monitoring data are caused, and further the problems of low integration level of the devices, poor wearing comfort, complex acquisition system and the like are caused. In the prior art, although the wearing problem is solved by part of flexible sensors, only single parameter acquisition is often realized, and if respiration and body temperature information are required to be acquired simultaneously, a multi-sensor combination system is required to be built. Disclosure of Invention The invention aims to provide an integrated multi-parameter information acquisition sensor and an information acquisition method, which meet the simultaneous acquisition requirement of body temperature and respiratory rate data without larger space occupation. In order to achieve the above object, the present invention provides an integrated multi-parameter information acquisition method, comprising the following steps: S1, noise reduction and baseline correction are carried out on an acquired original resistance signal so as to eliminate environmental noise interference; S2, establishing a resolving model based on the temperature sensitive coefficient and the pressure sensitive coefficient of each of the first measuring unit and the second measuring unit; S3, determining coefficient parameters through a calibration experiment and performing cross resolving to obtain a calculation formula of body temperature and indirectly obtaining respiratory rate, wherein the coefficient parameters comprise the resistance of a first measuring unit, the resistance of a second measuring unit, the piezoresistive variable quantity caused by the body temperature and the pressure; s4, acquiring respiratory rate parameters according to the time sequence characteristics of the piezoresistive variable quantity and combining the calculation formulas of the body temperature and the piezoresistive variable quantity. Under the condition that the respiration rate and the body temperature are required to be collected simultaneously, the technical scheme adopted by the invention is that firstly, two conductive materials with different temperature-sensitive coefficients and pressure-sensitive coefficients are selected, wherein the temperature-sensitive dominant conductive material has the characteristics of sensitivity to temperature change and weak piezoresistance response, the pressure-sensitive dominant conductive material needs to present remarkable pressure-dependent resistance change and has low temperature interference coefficient, a binary once equation set is established as a solution model based on the temperature-sensitive coefficients and the pressure-sensitive coefficients of the two materials, coefficient parameters in the solution model are determined through calibration experiments, a calculation formula capable of solving the body temperature and the piezoresistance change is constructed, and then the respiration rate is indirectly obtained through the piezoresistance change, so that the requirement for simultaneous measurement of the respiration rate and the body temperature is met, and meanwhile, the space occupation is small. The step S1 of denoising and baseline correction of the acquired original resistance signal to eliminate environmental interference specifically includes the following steps: Removing power frequency interference by using a notch filter, and filtering high-frequency noise by using a 2-order Butterworth low-pass filter; Taking the average value of the signals acquired for initial 3s as a baseline value, and carrying out baseline correction on subsequent signals to eliminate zero drift errors; and smoothing the signal by adopting a sliding window, wherein the signal-to-noi