A human core body temperature and inertia signal acquisition device and acquisition method

Abstract

The application discloses a human core body temperature and inertia signal collection device and a collection method. The device comprises a first collection module, a second collection module, a third collection module, a fourth collection module, a bottom packaging layer, a top packaging layer and a core circuit module layer. The method comprises the following steps: step 1, setting the collection device at a relevant position and collecting position sensor data; step 2, inputting three groups of heat conduction layer temperatures and environmental temperatures into a prediction model for processing to obtain three groups of predicted core temperatures, and normalizing the three groups of predicted core temperatures to determine the core body temperature of the subject; and step 3, taking the inertia data of the third collection module as a reference value, processing the data of the first collection module and the second collection module to obtain corrected inertia measurement data. The application can provide reliable data for subsequent detection and diagnosis of human physiological mechanisms, and can also obtain physiological and action information of the human body from the inertia signal.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of human core body temperature and inertial signal acquisition device and acquisition method. BACKGROUND

[0002] The core body temperature of human body is an important index reflecting the health degree of human body, and the core body temperature can be directly measured by inserting a thermometer at the pulmonary artery, esophagus, bladder or rectum, etc., but these invasive methods are inconvenient in operation, cannot realize continuous monitoring of body temperature, are usually invasive or minimally invasive, the measurement process is uncomfortable and interfering for users, and no corresponding device is developed to realize continuous measurement. Therefore, it is of great significance to continuously detect the core temperature of human body in a non-invasive manner.

[0003] Today, with the increasingly advanced medical detection technology, there are still many chronic diseases that cannot be accurately detected, such as hypertension, diabetes, chronic obstructive pulmonary disease, etc. Usually, these can only detect pathological characteristics when the disease occurs. In addition, the increase of social pressure leads to more young people in a sub-health state, but these chronic disease risks have not attracted the attention of young patients. Therefore, for young people in a sub-health state, long-term physiological information acquisition and monitoring of human body is of great benefit to early detection of chronic diseases. Human behavior contains rich physiological characteristic information, and human motion information can reflect the human motion condition and body function. At present, human motion information detection is to monitor the motion information of human body by using visual tools or sensors, and to process and analyze various posture information, so as to realize the judgment and recognition of human physiological behavior. It is necessary to design a core body temperature and inertial parameter detection system for personal and family health monitoring by using inertial sensors to continuously and real-timely monitor human behavior and analyze human motion physiological data. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a human core body temperature and inertial signal acquisition device and acquisition method, which can provide reliable data for subsequent detection and diagnosis of human physiological mechanism, and can also obtain physiological and motion information of human body from inertial signals.

[0005] In order to solve the above technical problems, the present application provides a human core body temperature and inertial signal acquisition device, comprising: a first acquisition module 1, a second acquisition module 2, a third acquisition module 3, a fourth acquisition module 4, a bottom packaging layer 5, a top packaging layer 6, a core circuit module layer 7; the bottom packaging layer 5 is in close contact with the skin of the subject, the first acquisition module 1, the second acquisition module 2 and the core circuit module layer 7 are in contact connection with the bottom packaging layer 5, the third acquisition module 3 and the fourth acquisition module 4 are in contact connection with the upper surface of the core circuit module layer 7, the top of the first acquisition module 1, the second acquisition module 2, the fourth acquisition module 4 and the core circuit module layer 7 is in contact connection with the top packaging layer 6, the module gap is filled by the top packaging layer 6, the fourth acquisition module 4 is a temperature sensor, which is embedded in the top packaging layer 6 at the geometric center of the device and has an exposed upper surface to collect the ambient temperature of the subject.

[0006] Preferably, the first acquisition module 1, the second acquisition module 2 and the third acquisition module 3 are arranged in a regular triangle and have the same structure layer, comprising: a heat-conducting layer 22, a U-shaped metal heat shield 23 and a detection sensor 21; the detection sensor 21 is located at the bottom center of the U-shaped metal heat shield 23 and is in contact connection with the U-shaped metal heat shield 23, and the inside of the U-shaped metal heat shield 23 is filled with a heat-conducting layer; the U-shaped opening of the U-shaped metal heat shield 23 in the first acquisition module 1 and the second acquisition module 2 faces the bottom packaging layer 5, and the U-shaped opening of the U-shaped metal heat shield 23 of the third acquisition module 3 faces the top packaging layer 6.

[0007] Preferably, the detection sensor 21 is embedded with a six-axis inertial sensor for temperature detection to collect the temperature and inertial signal of the location.

[0008] Preferably, the bottom circumference of the heat-conducting layer 22 is 12 times the height thereof.

[0009] Preferably, the thickness of the heat-conducting layer of the first acquisition module 1 is 3-5 times the thickness of the heat-conducting layer of the second acquisition module 2 and the third acquisition module 3.

[0010] Correspondingly, a human core body temperature and inertial signal acquisition method comprises the following steps:

[0011] Step 1: The acquisition device is arranged on the body surface of the subject at the two ends of the sternal handle 9 and the sternal notch 8, the first acquisition module 1 and the second acquisition module 2 are located above the body surface at the left and right ends of the sternal notch 8, the third acquisition module 3 is located above the body surface of the sternal handle 9, and the sensor data at the positions is collected; there are four original temperature values measured directly: ① the thick heat-conducting layer temperature collected by the first acquisition module 1; ② the thick heat-conducting layer temperature collected by the second acquisition module 2; ③ the thick heat-conducting layer temperature collected by the third acquisition module 3; and ④ the temperature of the fourth acquisition module 4. IMU1 IMU2 ​The temperature of the thin heat conduction layer collected by the second collection module 2, that is, the temperature of the thin heat conduction layer; ③ the ambient temperature T after passing through the heat conduction layer IMU3 The temperature of the heat conduction layer collected by the third collection module 3, that is, the temperature of the heat conduction layer; ④ the ambient temperature T amb ;

[0012] Step 2, input the three groups of heat conduction layer temperatures and ambient temperatures into the prediction model for processing to obtain three groups of predicted core temperatures, and normalize the three groups of predicted core temperatures to determine the core temperature of the subject;

[0013] Step 3, taking the inertial data of the third collection module 3 as a reference value, the noise and disturbance of the data of the first collection module 1 and the second collection module 2 are processed to obtain the corrected inertial measurement data of the first collection module 1 and the second collection module 2.

[0014] Preferably, in step 2, the three groups of heat conduction layer temperatures and ambient temperatures are input into the prediction model for processing to obtain three groups of predicted core temperatures, and the three groups of predicted core temperatures are normalized to determine the core temperature of the subject, which specifically includes the following steps:

[0015] Step 21, median bit filtering; the sampling frequency is 10 Hz, and the collected T IMU1 , T IMU2 , T IMU3 , T amb Four groups of original temperatures are filtered by the median bit filtering method to eliminate accidental pulse interference;

[0016] Step 22, core temperature prediction model calculation; the filtered four groups of temperature values are brought into the model formula T Core1 , T Core2 to calculate to obtain three groups of predicted core temperature data values T1, T2, and T3;

[0017] Step 23, normalization weighted average algorithm processing; assuming that the weighted value is i∈[1,N], the three groups of predicted core temperature data values T1, T2, and T3 are estimated according to the normalization algorithm to obtain a fusion value close to the true value of the core temperature, thereby obtaining an accurate measurement result T Core of the core temperature, eliminating the uncertainty in the measurement process.

[0018] Preferably, in step 22, the two core temperature prediction models formed by the first collection module 1 and the third collection module 3, and the second collection module 2 and the third collection module 3 are as follows:

[0019] T Core1 =T amb +(T IMU1(2) -T IMU3 )÷(A-B)

[0020] Wherein,

[0021]

[0022]

[0023] The core temperature prediction model composed of the first acquisition module 1 and the second acquisition module 2 is:

[0024]

[0025] Wherein, T Core1 , T Core2 all represent the core temperature calculation value of the core temperature prediction model, T amb represents the environment temperature measured by the fourth acquisition module, T IMU1(2) represents the temperature measured by the first (second) acquisition module, T IMU3 represents the temperature measured by the third acquisition module, t ts , t bS , t form , t IMU1(2)(3) , t tissue , t ele , t device1(2)(3) represent the bottom packaging layer thickness, the top packaging layer thickness, the U-shaped metal heat shield thickness, the detection sensor thickness in the first acquisition module / second acquisition module / third acquisition module, the measurement site equivalent skin tissue thickness, the core circuit module layer thickness, and the heat conduction layer thickness in the first acquisition module / second acquisition module / third acquisition module, respectively; k silicone , k form , k IMU1(2)(3) , k tissue , k ele represent the thermal conductivity coefficients of the used silica gel, heat insulation metal, detection sensor, human skin tissue, and used circuit board, respectively, and h represents the convective heat transfer coefficient.

[0026] Preferably, in step 23, the fusion value close to the true value of the core temperature is estimated according to the normalization algorithm according to the three groups of predicted core temperature data values T1, T2, and T3, so as to obtain the accurate measurement result T of the core temperature Core Specifically includes the following steps:

[0027] (a) calculating the average value of each group

[0028] (b) calculating the deviation ΔT of the predicted core temperature data value of different groups and the average value in step (a) i ,

[0029] (c) calculating the fusion value T of the core temperature close to the true value according to the deviation ΔT iSubstitute into the weight function f(T) and normalize it to obtain

[0030] (d) obtaining the weighted value from the normalized deviation i∈[1,N],

[0031] (e) obtaining the final average value, i.e. the core body temperature value T from the weighted value Core , The weight function is mainly selected by experience, and the specific expression formula is:

[0032]

[0033] Preferably, in step 3, the third acquisition module 3 acquires a group of data at rest as the zero drift value of the accelerometer, and the data acquired by the subsequent three IMU inertial sensors are all subtracted by the zero drift value of the corresponding axis, and the sampling frequency is 50Hz; the acceleration signal is preprocessed by using a second-order Butterworth band-pass filter, the mean filtering method is used to remove the acceleration direct current component, and the wavelet threshold filtering method is used to denoise the gyroscope signal.

[0034] The present application has the advantages that: the multiple sensors arranged on the sternal handle and the suprasternal notch of the human body can simultaneously collect the core body temperature non-invasively, and the motion characteristics of different parts of the human body are utilized to collect the inertial signals at multiple positions, which can provide reliable data for the subsequent detection and diagnosis of the physiological mechanism of the human body, and the physiological and motion information of the human body can also be obtained from the inertial signals. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 It is a layered front view of the detection structure of the human core body temperature and inertial signal acquisition device of the present application.

[0036] Figure 2 It is a layered side view of the detection structure of the human core body temperature and inertial signal acquisition device of the present application.

[0037] Figure 3 It is a layered schematic view of the three acquisition modules of the human core body temperature and inertial signal acquisition device of the present application.

[0038] Figure 4 It is an acquisition scheme diagram of the human core body temperature and inertial signal acquisition device of the present application.

[0039] Wherein, 1, the first acquisition module, 2, the second acquisition module, 3, the third acquisition module, 4, the fourth acquisition module, 5, the bottom encapsulation layer, 6, the top encapsulation layer, 7, the core circuit module layer, 8, the suprasternal notch, 9, the manubrium, 21, the detection sensor, 22, the heat conduction layer, 23, the U-shaped metal heat shield. DETAILED DESCRIPTION

[0040] As shown in Figure 1 and Figure 2 , a human body core temperature and inertial signal acquisition device, comprising: a first acquisition module 1, a second acquisition module 2, a third acquisition module 3, a fourth acquisition module 4, a bottom encapsulation layer 5, a top encapsulation layer 6, a core circuit module layer 7;

[0041] Wherein, the bottom encapsulation layer 5 is in close contact with the skin of the subject; the first acquisition module 1, the second acquisition module 2 and the core circuit module layer 7 are all in contact with the bottom encapsulation layer 5; the third acquisition module 3 and the fourth acquisition module 4 are both in contact with the upper surface of the core circuit module layer 7; the top of the first acquisition module 1, the second acquisition module 2, the fourth acquisition module 4 and the core circuit module layer 7 are all in contact with the top encapsulation layer 6, and the module gap is filled by the top encapsulation layer 6; wherein the fourth acquisition module 4 is a temperature sensor with an exposed upper surface to collect the environmental temperature of the subject;

[0042] As shown in Figure 3 and Figure 4 , the first acquisition module 1, the second acquisition module 2 and the third acquisition module 3 are arranged in an equilateral triangle and have the same structure layer, comprising: a heat conduction layer 22, a U-shaped metal heat shield 23 and a detection sensor 21; the detection sensor 21 is located at the bottom center of the U-shaped metal heat shield 23 and is in contact with the U-shaped metal heat shield 23, and the inside of the U-shaped metal heat shield 23 is filled with the heat conduction layer 22; the bottom circumference of the heat conduction layer 22 is 1 / 2 times the height thereof, and the thickness of the heat conduction layer 22 of the first acquisition module 1 is 3-5 times the thickness of the heat conduction layer 22 of the second acquisition module 2 and the third acquisition module 3; the detection sensor 21 is a six-axis inertial sensor (IMU) with temperature detection embedded therein to collect the temperature and inertial signal of the heat conduction layer 22 at the location;

[0043] As shown in Figure 3 , the U-shaped openings of the U-shaped metal heat shields 23 in the first acquisition module 1 and the second acquisition module 2 both face the bottom encapsulation layer 5, and the U-shaped opening of the U-shaped metal heat shield 23 of the third acquisition module 3 faces the top encapsulation layer 6.

[0044] The core circuit module layer includes a core processor control module, a wireless transmission module, a power supply module and necessary peripheral circuit modules such as data storage.

[0045] The material of each heat-conducting layer and encapsulating layer is selected from silicon glue; the material of the heat-insulating metal is selected from any one of gold, silver, nickel, aluminum foil, and metal-plated polyester.

[0046] Taking a user as an example, the specific implementation process of the present application is as follows:

[0047] Step (a): The collection device is arranged on the body surface of the sternal manubrium and both ends of the sternal notch. The first collection module and the second collection module are located above the body surface of the left and right ends of the sternal notch, and the third collection module is located above the body surface of the sternal manubrium. The position sensor data is collected.

[0048] Step (b): The three groups of heat-conducting layer temperatures and the ambient temperature are respectively input into the prediction model for processing to obtain three groups of predicted core temperatures. The core temperature of the subject is determined by normalizing the three groups of predicted core temperatures.

[0049] Step (c): The inertial detection sensor acquires inertial signals such as acceleration, angular velocity, and angle at the collection position, and transmits the inertial signals to the core processing system through the communication interface. The embedded control module analyzes the information, takes the inertial data of the third collection module as the reference value, and processes the data of the first collection module and the second collection module for noise and disturbance to obtain the corrected inertial measurement data of the first collection module and the second collection module.

[0050] The two core temperature prediction models formed by the first collection module and the third collection module, and the second collection module and the third collection module are:

[0051] T Core1 = T amb + (T IMU1(2) -T IMU3 ) ÷ (A-B)

[0052] Wherein,

[0053]

[0054]

[0055] The core temperature prediction model formed by the first collection module and the second collection module is:

[0056]

[0057] Wherein, T Core1 , T Core2 represent the predicted core temperature, T amb represents the ambient temperature measured by the fourth collection module, T IMU1(2) represents the temperature measured by the first (second) collection module, and TIMU3 t represents the temperature measured by the third acquisition module ts t bS t form t IMU1(2) t tissue t ele t device1(2) t represent the bottom encapsulation layer thickness, the top encapsulation layer thickness, the U-shaped metal heat shield thickness, the detection sensor thickness in the first / second acquisition module, the measurement site equivalent skin tissue thickness, the core circuit module layer thickness, the heat conduction layer thickness in the first / second acquisition module, respectively; k silicone k form k IMU1(2)(3) k tissue k ele k represent the thermal conductivity of the used silica gel, metal, detection sensor, human skin tissue, and used circuit board, respectively, and h represents the convective heat transfer coefficient.

[0058] In addition, the collected temperature and inertia information is transmitted to a data application platform through wireless communication technology and analyzed and processed again by the data application platform.

Claims

1. A device for collecting human core body temperature and inertial signals, characterized in that, Comprise: The first acquisition module (1), second acquisition module (2), third acquisition module (3), fourth acquisition module (4), bottom encapsulation layer (5), top encapsulation layer (6), core circuit module layer (7);The bottom encapsulation layer (5) is close to the skin of the subject, the first acquisition module (1), second acquisition module (2) and core circuit module layer (7) are all in contact with the bottom encapsulation layer (5), the third acquisition module (3), fourth acquisition module (4) are all in contact with the upper surface of the core circuit module layer (7), the top of the first acquisition module (1), second acquisition module (2), third acquisition module (3) and core circuit module layer (7) is in contact with the top encapsulation layer (6), the module gap is filled by the top encapsulation layer (6), the fourth acquisition module (4) is a temperature sensor, embedded in the top encapsulation layer (6) of the device geometric center, the upper surface is exposed, and the temperature of the environment where the subject is located is collected; The first acquisition module (1), second acquisition module (2) and third acquisition module (3) are arranged in a regular triangle and have the same structure layer, comprising: a heat conducting layer (22), a U-shaped metal heat shield (23), and a detection sensor (21);The detection sensor (21) is located at the bottom center of the U-shaped metal heat shield (23) and is in contact with the U-shaped metal heat shield (23), and the inside of the U-shaped metal heat shield (23) is filled with a heat conducting layer;The U-shaped opening of the U-shaped metal heat shield (23) in the first acquisition module (1) and the second acquisition module (2) faces the bottom encapsulation layer (5), and the U-shaped opening of the U-shaped metal heat shield (23) of the third acquisition module (3) faces the top encapsulation layer (6).

2. The human core body temperature and inertial signal acquisition device of claim 1, wherein, The detection sensor (21) is embedded with a six-axis inertial sensor for temperature detection, which collects the temperature of the heat conducting layer and the inertial signal at the location.

3. The human core body temperature and inertial signal acquisition device of claim 1, wherein, The bottom circumference of the heat conducting layer (22) is 12 times the height thereof.

4. The human core body temperature and inertial signal acquisition device of claim 1, wherein, The thickness of the heat conducting layer of the first acquisition module (1) is 3-5 times the thickness of the heat conducting layer of the second acquisition module (2) and the third acquisition module (3).

5. A collection method using the collection device of the human core body temperature and inertial signal according to claim 1, characterized by, The method comprises the following steps: Step 1: Set the acquisition device on the body surface at both ends of the subject's sternal manubrium (9) and suprasternal notch (8). The first acquisition module (1) and the second acquisition module (2) are located above the body surface at both ends of the suprasternal notch (8), and the third acquisition module (3) is located above the body surface at the sternal manubrium (9). Collect position sensor data; There are 4 directly measured raw temperature values: ① Body temperature T after passing through the heat-conducting layer. IMU1 ① The temperature of the thick heat-conducting layer collected by the first acquisition module (1); ② The body temperature T after passing through the heat-conducting layer. IMU2 ③ The ambient temperature T after passing through the heat-conducting layer, which is the temperature of the thin heat-conducting layer collected by the second acquisition module (2); IMU3 That is, the temperature of the heat-conducting layer collected by the third acquisition module (3); ④ Ambient temperature T amb ; Step 2, input the three groups of heat conducting layer temperature and environmental temperature into the prediction model respectively, obtain three groups of predicted core temperature, and normalize the three groups of predicted core temperature to determine the core temperature of the subject; Step 3, taking the inertial data of the third acquisition module (3) as the reference value, the noise and disturbance of the first acquisition module (1) and the second acquisition module (2) are processed, and the corrected inertial measurement data of the first acquisition module (1) and the second acquisition module (2) are obtained.

6. The method of claim 5, wherein, In step 2, the three groups of heat conducting layer temperature and environmental temperature are input into the prediction model respectively, three groups of predicted core temperature are obtained, and the three groups of predicted core temperature are normalized to determine the core temperature of the subject, which comprises the following steps: Step 21, median filter; sampling frequency 10 Hz, T IMU1 , T IMU2 , T IMU3 , T amb Four groups of original temperature using median filter to eliminate occasional pulse interference; Step 22, core body temperature prediction model calculation; the filtered four sets of temperature values are brought into the model formula T Core1 , Core2 calculation, three sets of predicted core body temperature data values T1, T2, T3 are obtained; Step 23, normalization weighted average algorithm processing; assuming the weighted value is The three groups of predicted core body temperature data values T1, T2, T3 are estimated according to the normalization algorithm to obtain the fusion value close to the true value of the core body temperature, so as to obtain the accurate measurement result T of the core body temperature Core , eliminating the uncertainty in the measurement process.

7. The method of claim 5, wherein the step of collecting is performed by a user. In step 2, the two core temperature prediction models composed of the first acquisition module (1) and the third acquisition module (3), and the second acquisition module (2) and the third acquisition module (3) are: T Core1 = T amb + (T IMU1(2) - T IMU3 ) ÷ (A - B) Wherein, The core temperature prediction model composed of the first acquisition module (1) and the second acquisition module (2) is: wherein, T Core1 , T Core2 all represent the core temperature calculation value of the core temperature prediction model, T amb represents the environment temperature measured by the fourth acquisition module, T IMU1(2) represents the temperature measured by the first (second) acquisition module, T IMU3 represents the temperature measured by the third acquisition module, t ts , t bS , t form , t IMU1(2)(3) , t tissue , t ele , t device1(2)(3) represent, in sequence, the bottom packaging layer thickness, the top packaging layer thickness, the U-shaped metal heat shield thickness, the detection sensor thickness in the first acquisition module / second acquisition module / third acquisition module, the measurement site equivalent skin tissue thickness, the core circuit module layer thickness, and the heat conduction layer thickness in the first acquisition module / second acquisition module / third acquisition module; k silicone , k form , k IMU1(2)(3) , k tissue , k ele represent, in sequence, the thermal conductivity of the used silica gel, heat insulation metal, detection sensor, human skin tissue, and used circuit board, and h represents the convective heat transfer coefficient.

8. The data acquisition method as described in claim 5, characterized in that, In step 23, the three sets of predicted core body temperature data values T1, T2, T3 are estimated according to a normalization algorithm to obtain a fused value close to the true value of the core body temperature, thereby obtaining an accurate measurement result T of the core body temperature Core Specifically includes the following steps: (a) calculating the mean value for each group (b) calculating a deviation amount ΔT of both the different group predicted core body temperature data value and the average value in step (a) i , (c) the deviation amount ΔT i Substituting into the weight function f(T) and normalizing it, we obtain (d) deriving a weighting value from the normalized bias (e) obtaining a final average value, i.e. the core temperature value T, from the weighted values Core , wherein the weight function is chosen empirically and the specific expression is:

9. The data acquisition method as described in claim 5, characterized in that, In step 3, the third acquisition module (3) acquires a set of data as the zero drift value of the accelerometer at rest, and the data acquired by the subsequent three IMU inertial sensors are all reduced by the zero drift value of the corresponding axis, and the sampling frequency is 50 Hz; the acceleration signal is preprocessed by using the second-order Butterworth band-pass filter, the mean filtering method is used to remove the direct current component of the acceleration, and the wavelet threshold filtering method is used for noise removal processing of the gyro signal.

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