CN117168623A - Medical multi-point fusion temperature measurement system and method - Google Patents
Medical multi-point fusion temperature measurement system and method Download PDFInfo
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- 238000009529 body temperature measurement Methods 0.000 title claims abstract description 175
- 230000004927 fusion Effects 0.000 title claims abstract description 112
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000012544 monitoring process Methods 0.000 claims abstract description 144
- 230000036760 body temperature Effects 0.000 claims abstract description 20
- 238000007499 fusion processing Methods 0.000 claims abstract description 14
- 238000004861 thermometry Methods 0.000 claims description 21
- 230000017531 blood circulation Effects 0.000 claims description 18
- 238000012937 correction Methods 0.000 claims description 16
- 230000005855 radiation Effects 0.000 claims description 16
- 238000001914 filtration Methods 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000011084 recovery Methods 0.000 claims description 13
- 238000012360 testing method Methods 0.000 claims description 11
- 238000005259 measurement Methods 0.000 claims description 10
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- 230000007613 environmental effect Effects 0.000 claims description 9
- 238000002310 reflectometry Methods 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 230000001575 pathological effect Effects 0.000 claims description 5
- 238000000638 solvent extraction Methods 0.000 claims description 4
- 230000036541 health Effects 0.000 claims description 2
- 238000005192 partition Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims 1
- 208000027418 Wounds and injury Diseases 0.000 description 16
- 230000001815 facial effect Effects 0.000 description 13
- 239000003570 air Substances 0.000 description 6
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- 230000005457 Black-body radiation Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000007493 shaping process Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000013316 zoning Methods 0.000 description 2
- 206010072170 Skin wound Diseases 0.000 description 1
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- 230000000694 effects Effects 0.000 description 1
- 230000017525 heat dissipation Effects 0.000 description 1
- 230000020169 heat generation Effects 0.000 description 1
- 238000002513 implantation Methods 0.000 description 1
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Abstract
The application relates to the field of medical temperature measurement, in particular to a medical multi-point fusion temperature measurement system and a medical multi-point fusion temperature measurement method, wherein the medical multi-point fusion temperature measurement system comprises the following steps: dividing the body temperature measuring areas of the patient to be measured according to the difference of the body surface reflection ratios of different temperature measuring areas; constructing a reference model conforming to standard factors according to the environment information and the human body state information; simultaneously setting a plurality of temperature measuring points in a healthy temperature measuring area and a rehabilitation monitoring area, and measuring a plurality of groups of temperature data of the whole body area of a tested patient; and carrying out data fitting and data fusion processing on multiple groups of temperature data measured from different healthy temperature measuring areas and rehabilitation monitoring areas, calculating the rehabilitation degree of the rehabilitation monitoring areas of the measured patient, and informing the patient and doctor of the rehabilitation condition monitoring results of the patient. The application solves the problems of inaccurate and incomplete temperature measurement in the prior art.
Description
Technical Field
The application relates to the field of medical temperature measurement, in particular to a medical multi-point fusion temperature measurement system and a medical multi-point fusion temperature measurement method.
Background
The body temperature of the human body is one of the important signs of human health. Under different environmental temperatures, the body emits infrared signals to the outside through heat generation and heat dissipation, so that the adaptability to the environmental temperature can be maintained, and the development possibility is provided for a non-contact infrared temperature measurement method. Meanwhile, the body temperature is kept at while the human body is healthyAbout DEG C, and will not change greatly due to the change of external temperature, but is in a certain place of human bodyWhen there is a change in function or a lesion or wound in some body area, a change in relatively constant body temperature occurs. In contemporary clinical medicine, the body temperature of the human body is an important physiological parameter, and the body temperature of the patient can provide the doctor with important information on the physiological state of the patient. Therefore, the body temperature of the patient is accurately and comprehensively measured, the occurrence of certain diseases can be diagnosed, the recovery condition of the wound can be monitored in real time when the wound appears on the body surface of the patient, the recovery condition of the wound is predicted, and a reliable auxiliary basis is provided for the follow-up diagnosis and treatment scheme.
In the prior art disclosed in the application, as disclosed in the patent application publication No. CN111473869a, an infrared human body temperature measuring method is disclosed, through which an operator can obtain a human body temperature, set a first temperature compensation coefficient, obtain an ambient temperature, set a second temperature compensation coefficient, and automatically calibrate the measured temperature.
As another example, application publication number CN111307293a discloses an infrared human body temperature measurement system and a temperature measurement method, a blackbody is introduced as a standard model, the blackbody measures the blackbody radiation surface temperature in real time through an internally arranged radiation surface temperature sensor, an infrared thermometer acquires the blackbody radiation surface temperature measured by the radiation surface temperature sensor in real time through a communication module, and the measured blackbody radiation surface temperature is used as a reference to calculate the human body temperature.
The above patent only considers the influence of the ambient temperature on the temperature measurement process, does not consider that the measured patient is different, and the measured patient is different in different body areas, and when the used model is applied to the actual environment, larger errors can occur, and the problems of inaccurate temperature measurement, incomplete consideration factors and poor applicability all occur.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The application aims to solve the technical problems that in the prior art, environmental factors can influence a temperature measurement result in the temperature measurement process and the measured patients are different, and provides a medical multi-point fusion temperature measurement system and a medical multi-point fusion temperature measurement method.
In order to achieve the above purpose, the technical scheme of the medical multi-point fusion temperature measurement system provided by the application is as follows:
the system comprises a body surface reflectivity partitioning module, a standard factor reference module, a multi-point temperature measurement module, a data correction module, a data characteristic fusion module, a rehabilitation situation prediction module and a temperature intelligent connection module;
the body surface reflectivity partition module is used for: transmitting infrared radiation to the measured patient, receiving the infrared signals reflected by the measured patient, and dividing the measured patient into body temperature measuring areas according to the difference of the values of the body surface reflection ratios of different temperature measuring areas;
the standard factor reference module is used for: constructing a reference model conforming to standard factors according to the environment information and the human body state information;
the multi-point temperature measurement module is used for: simultaneously setting a plurality of temperature measuring points in a healthy temperature measuring area and a rehabilitation monitoring area, and measuring a plurality of groups of temperature data of the whole body area of a measured patient, wherein the healthy temperature measuring area is an area of the body of the patient, which does not contain a wound, and the rehabilitation monitoring area is a wound of the body of the patient;
the data correction module is used for: non-linear fitting is carried out on a plurality of groups of temperature data measured from different healthy temperature measuring areas and rehabilitation monitoring areas and a standard factor reference model;
the data characteristic fusion module is used for: respectively carrying out filtering fusion treatment on temperature data corrected by the standard factor correction model in the healthy temperature measurement area and the rehabilitation monitoring area;
the rehabilitation situation prediction module is used for: comparing the temperature of the rehabilitation area after the data fusion processing with the temperature data of other healthy areas of the patient, calculating to obtain the rehabilitation degree of the rehabilitation monitoring area of the patient to be tested, and monitoring the rehabilitation condition of the patient in real time;
the temperature intelligent connection module is used for: and (3) accessing temperature comparison data obtained by fusing the temperature measurement and a rehabilitation condition monitoring result of the patient into a patient smart phone and a medical staff information station to carry out abnormal temperature voice broadcasting.
In addition, the medical multipoint fusion temperature measurement method has the following specific technical scheme that:
transmitting infrared radiation to the measured patient, receiving the infrared signals reflected by the measured patient, and dividing the measured patient into body temperature measuring areas according to the difference of the values of the body surface reflection ratios of different temperature measuring areas;
in particular, the body temperature measurement area reflection ratioThe calculation strategy of (2) is as follows:
；
wherein,to emit an amount of infrared radiation to a patient under test;
is the amount of infrared radiation detected after being reflected by the body surface of the patient to be measured.
Specifically, the method for dividing the body temperature measurement area comprises the following steps: according to the reflectanceGrading the body temperature measuring area from small to large:
；
wherein the method comprises the steps ofIndicate->Personal body temperature measurement zone grade.
Constructing a reference model conforming to standard factors according to the environment information and the human body state information;
specifically, the standard factor information of the reference model of the standard factor includes: within five minutes of completing one temperature measurement, each timeRecording 15 times of data, wherein the standard temperature value of the monitoring environment is +.>Standard humidity value->Standard air flow rate ∈>The standard blood flow rate of the patient to be tested is +.>。
Specifically, the standard temperature value of the monitoring environmentStandard humidity value->Standard air flow rate +.>Standard blood flow rate of the patient to be tested>The calculation strategy of (2) is as follows:
，/>；
，/>；
wherein,recording the number of times of data within five minutes for completing one temperature measurement;
is->The measured real-time environmental temperature of the secondary record;
is->Real-time ambient humidity of the secondary record;
is->Real-time ambient airflow rate of the secondary record;
is->The real-time blood flow velocity of the patient under test is recorded.
Simultaneously setting a plurality of temperature measuring points in a healthy temperature measuring area and a rehabilitation monitoring area, and measuring a plurality of groups of temperature data of the whole body area of a tested patient;
specifically, the rehabilitation monitoring region includes: the edge line of the wound on the patient's body surface extends outwardlyThe curved surface monitoring area is formed, and 20 temperature measuring points are uniformly distributed in the curved surface monitoring area.
Specifically, the plurality of sets of temperature data include: healthy temperature measuring areaTemperature data set of 10 reflectance grade regions in (a) and rehabilitation monitoring region +.>Temperature data of the medium curved surface monitoring area;
wherein the temperature dataset is；
In the healthy temperature measuring area A, the ith 0 Temperature data of an nth temperature measurement point in the respective reflectance level areas.
Non-linear fitting is carried out on a plurality of groups of temperature data measured from different healthy temperature measuring areas and rehabilitation monitoring areas and a standard factor reference model;
specifically, the nonlinear polynomial fitting of the plurality of sets of temperature data and the reference model of the standard factor includes:
healthy temperature measuring areaThe polynomial fit of (2) is:
；
rehabilitation monitoring areaThe polynomial fit of (2) is:
；
wherein,the time point of temperature measurement; />The temperature measurement times are within five minutes;
monitoring the lowest temperature of the environment in the period of one temperature measurement;
monitoring the highest temperature of the environment in the period of one temperature measurement;
is->Polynomial fitting parameters of the ambient humidity of the secondary temperature measurement data;
is->Polynomial fitting parameters of ambient airflow velocity of the secondary temperature measurement data;
is->Polynomial fitting parameters of patient blood flow rate for the secondary thermometry data.
Respectively carrying out filtering fusion treatment on temperature data corrected by the standard factor correction model in the healthy temperature measurement area and the rehabilitation monitoring area;
specifically, the filtering fusion of the temperature data of the healthy temperature measurement area and the rehabilitation monitoring area corrected by the standard factor correction model respectively comprises the following specific steps:
s91: predicting the fused temperature data to obtain the first healthy temperature measuring areaPredicted temperature data of subspecies>First->Predicted temperature data of subspecies>；
Wherein->The total fusion times of the healthy temperature measurement area is that m is an integer greater than 1;
wherein->N is an integer greater than 1 for the total number of fusion times of the rehabilitation monitoring area;
s92: obtaining the prediction uncertainty of each fusion of the healthy temperature measurement area for the prediction temperature data obtained in the step S91Predictive uncertainty of each fusion of rehabilitation monitoring area +.>；
；
Wherein,is->Temperature measurement white noise of secondary fusion; />A time period for completing one temperature measurement;
is at->Go up to->Integrating; lnt the logarithm of t;
is->During secondary fusion, the uncertainty of prediction of the healthy temperature measurement area;
is->During secondary fusion, the uncertainty of prediction of the healthy temperature measurement area;
；
wherein,is->Temperature measurement white noise of secondary fusion; />A time period for completing one temperature measurement;
is->During secondary fusion, the uncertainty of prediction of the rehabilitation monitoring area;
is->During secondary fusion, the uncertainty of prediction of the rehabilitation monitoring area;
s93: according to the measurement noise of the whole measurement process, noise filtering is carried out to obtain the healthy temperature measurement areaNoise gain of sub-data fusion->First->Noise gain of sub-data fusion->；
；
；
S94: calculating the temperature data of the healthy temperature measuring area after data fusion processing through the actually measured temperature value and the predicted temperature dataTemperature data of rehabilitation monitoring area +.>；
；
；
Wherein,in the time period t for completing one temperature measurement, the healthy temperature measurement area is +>Predicted temperature data after secondary fusion;
in the time period t for completing one-time temperature measurement, the first part of the rehabilitation temperature measurement area is>Predicted temperature data after secondary fusion;
the time point for temperature measurement is t 0 In the healthy temperature measuring area A, the predicted temperature data before the mth fusion is performed;
the time point for temperature measurement is t 0 And the predicted temperature data before nth fusion in the rehabilitation temperature measuring region B.
Comparing the temperature of the rehabilitation area after the data fusion processing with the temperature data of other healthy areas of the patient, calculating to obtain the rehabilitation degree of the rehabilitation monitoring area of the patient to be tested, and monitoring the rehabilitation condition of the patient in real time;
specifically, the rehabilitation degree of the rehabilitation monitoring area of the patient to be testedThe calculation strategy of (2) is as follows:
；
is->Is the average value of (2); />Is->Is a mean value of (c).
And (3) accessing temperature comparison data obtained by fusing the temperature measurement and a rehabilitation condition monitoring result of the patient into a patient smart phone and a medical staff information station to carry out abnormal temperature voice broadcasting.
Specifically, the rehabilitation monitoring result includes:
if it isA doctor diagnoses and judges pathological reasons of slower rehabilitation in the monitored area;
if it isThen suggesting to continue hospitalization monitoring;
if it isThe patient to be tested can voluntarily discharge his or her home.
Compared with the prior art, the application has the following technical effects:
1. in the application, in addition to the influence of environmental factors, the temperature measurement error is caused by different body surface reflectances of the measured patient, the infrared radiation quantity reflected by the measured patient is recorded, the body surface reflection ratio of the measured patient is calculated, the temperature measurement areas are divided, and meanwhile, different temperature measurement modes are adopted for different temperature measurement areas, so that the temperature measurement error caused by different body surface reflectances of the measured patient is reduced, and the temperature measurement data is more comprehensive and accurate.
2. According to the method, the multi-point temperature measurement module is arranged according to the zoning result of the body surface reflectivity zoning module, and the temperature fitting correction is carried out through the standard factor reference module under the standard environment, so that the measurement error caused by the environment and the state factors of the measured patient in the measurement process is reduced, and the accuracy of the obtained temperature data is greatly improved.
3. According to the application, compared with a simple mean value calculation algorithm, the data robustness is enhanced by adopting a filtering fusion algorithm through the data feature fusion module, the more comprehensive features of the data are fused, and the multiple visual angles and the data sources are integrated, so that the output temperature data is more comprehensive and more accurate.
4. According to the application, the real-time temperature data of the healthy temperature measuring area and the rehabilitation monitoring area of the patient to be measured are compared, and the calculated wound rehabilitation degree of the patient is used for measuring the wound rehabilitation condition of the patient, so that the wound rehabilitation process of the patient is more scientific and visual, the purpose of updating and broadcasting the wound rehabilitation condition of the patient in real time is realized, and the universality of the application is enhanced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a schematic diagram of a medical multi-point fusion temperature measurement system according to embodiment 1 of the present application;
FIG. 2 is a flow chart of a medical multi-point fusion temperature measurement method according to embodiment 2 of the present application;
FIG. 3 is a grading chart of the body temperature measurement area of a patient under test according to embodiment 1 of the present application;
FIG. 4 is a diagram showing a healthy temperature measurement region according to embodiment 2 of the present applicationIs a polynomial fit to the graph.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Further, in describing the embodiments of the present application in detail, the cross-sectional view of the device structure is not partially enlarged to a general scale for convenience of description, and the schematic is only an example, which should not limit the scope of protection of the present application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Embodiment one:
referring to fig. 1 and 3, the present embodiment provides a medical multi-point fusion thermometry system.
Taking a right leg burn patient with the age of 55 years as an example, the temperature measuring environment is autumn, and after skin implantation surgery, the newly implanted skin wound area is monitored in real time during hospitalization, and the specific steps are as follows:
the body surface reflectivity partitioning module transmits infrared radiation to the tested patient and receives the infrared signals reflected by the tested patient, and the tested patient is partitioned into body temperature measuring areas according to the difference of the body surface reflectivity values of different temperature measuring areas;
wherein the body temperature measurement area reflectanceThe calculation strategy of (2) is as follows:
；
to emit an amount of infrared radiation to a patient under test;
is the infrared radiation quantity detected after the body surface of the tested patient reflects;
the reflectance of the entire body thermometry region of the burn patient is calculated as follows:
；
according to the reflectanceFrom small to smallTo greatly grade the body temperature measuring area:
；
wherein,indicate->Personal body temperature measurement zone grade.
As shown in fig. 3, the body temperature measurement region of the burn patient can be classified as follows:
。
the standard factor reference module builds a reference model conforming to standard factors according to the environmental information and the person;
the standard factor information of the standard factor reference model includes: within five minutes of completing one temperature measurement, each timeRecording 15 times of data, wherein the standard temperature value of the monitoring environment is +.>Standard humidity value->Standard airflow rate +.>The standard blood flow rate of the patient to be tested is +.>；
The standard temperature value of the monitoring environmentStandard ofHumidity value->Standard air flow rate->Standard blood flow rate of the patient to be tested>The calculation strategy of (2) is as follows:
，/>；
，/>；
wherein,recording the number of times of data within five minutes for completing one temperature measurement;
is->The measured real-time environmental temperature of the secondary record;
is->Real-time ambient humidity of the secondary record;
is->Real-time ambient airflow rate of the secondary record;
is->Real-time blood flow velocity of the patient under test for the secondary record;
calculation can be obtained that the burn patient has the following characteristics in the whole temperature measurement process:
monitoring standard temperature values of an environmentIs->；
Standard humidity valueIs->；
Standard air flow rateIs->；
Standard blood flow rate of a patient under testIs->。
The multi-point temperature measurement module is used for simultaneously setting a plurality of temperature measurement points in a healthy temperature measurement area and a rehabilitation monitoring area, and measuring a plurality of groups of temperature data of the whole body area of a measured patient, wherein the healthy temperature measurement area is an area of the body of the patient, which does not contain a wound, and the rehabilitation monitoring area is a wound of the body of the patient;
the edge line of the wound on the patient's body surface extends outwardlyThe curved surface monitoring area is formed, and 20 temperature measuring points are uniformly distributed in the curved surface monitoring area.
The plurality of sets of temperature data includes: healthy temperature measuring areaTemperature data set of 10 reflectance grade regions in (a) and rehabilitation monitoring region +.>Temperature data of the medium curved surface monitoring area;
wherein the temperature dataset is。
In the healthy temperature measuring area A, the ith 0 Temperature data of an nth temperature measurement point in the respective reflectance level areas.
The temperature data set of the healthy temperature measuring area of the burn patient is as follows:
the temperature data set of the reflectance grade area of the healthy temperature measurement area is:
；
the data correction module carries out nonlinear fitting on a plurality of groups of temperature data measured from different healthy temperature measuring areas and rehabilitation monitoring areas and a standard factor reference model;
the nonlinear polynomial fit of the sets of temperature data to the standard factor reference model includes:
healthy temperature measuring areaThe polynomial fit of (2) is:
rehabilitation monitoring areaThe polynomial fit of (2) is:
wherein,the time point of temperature measurement; />The temperature measurement times are within five minutes;
monitoring the lowest temperature of the environment in the period of one temperature measurement;
monitoring the highest temperature of the environment in the period of one temperature measurement;
is->Polynomial fitting parameters of the ambient humidity of the secondary temperature measurement data;
is->Ambient air flow rate of secondary temperature measurement dataPolynomial fitting parameters of the degree;
is->Polynomial fit parameters of patient blood flow rate for the secondary thermometry data;
the polynomial fit of the healthy and rehabilitated temperature measurement areas of the burn patient is as follows:
healthy temperature measuring areaThe polynomial fit of (2) is:
rehabilitation monitoring areaThe polynomial fit of (2) is:
the data characteristic fusion module respectively carries out filtering fusion processing on the temperature data of the healthy temperature measurement area and the rehabilitation monitoring area corrected by the standard factor correction model;
the filtering fusion of the temperature data of the healthy temperature measuring area and the rehabilitation monitoring area corrected by the standard factor correction model comprises the following specific steps:
s91: predicting the fused temperature data to obtain the first healthy temperature measuring areaPredicted temperature data of subspecies>First->Predicted temperature data of subspecies>；
Wherein->The total fusion times of the healthy temperature measurement area is that m is an integer greater than 1;
wherein->N is an integer greater than 1 for the total number of fusion times of the rehabilitation monitoring area;
total number of fusions of healthy thermometry regions of burn patients；
Total number of fusions of the rehabilitation monitoring area of the burn patient；
S92: obtaining the prediction uncertainty of each fusion of the healthy temperature measurement area for the prediction temperature data obtained in the step S91Predictive uncertainty of each fusion of rehabilitation monitoring area +.>；
；
Wherein,is->Temperature measurement white noise of secondary fusion; />A time period for completing one temperature measurement;
is at->Go up to->Integrating; lnt the logarithm of t;
is->During secondary fusion, the uncertainty of prediction of the healthy temperature measurement area;
is->During secondary fusion, the uncertainty of prediction of the healthy temperature measurement area;
wherein,is->Temperature measurement white noise of secondary fusion; />A time period for completing one temperature measurement; />Is->During secondary fusion, the uncertainty of prediction of the rehabilitation monitoring area;
is->During secondary fusion, the uncertainty of prediction of the rehabilitation monitoring area;
taking the 10 th data fusion as an example: time period for completing one-time temperature measurement；
The uncertainty of the healthy temperature measurement area and the rehabilitation monitoring area of the burn patient is as follows:
prediction uncertainty of each fusion of healthy temperature measurement areas：
；
Prediction uncertainty of each fusion of rehabilitation monitoring area：
。
S93: according to the measurement noise of the whole measurement process, noise filtering is carried out to obtain the healthy temperature measurement areaNoise gain of sub-data fusion->First->Noise gain of sub-data fusion->；
；
；
Taking the 10 th data fusion as an example, the burn patient was obtained:
healthy temperature measurement region NoNoise gain of sub-data fusion->：
；
Rehabilitation monitoring region NoNoise gain of sub-data fusion->：/>
。
S94: calculating the number of passes by using the actually measured temperature value and the predicted temperature dataAccording to the temperature data of the healthy temperature measuring area after the fusion treatmentTemperature data of rehabilitation monitoring area +.>；
；
；
Wherein,in the time period t for completing one temperature measurement, the healthy temperature measurement area is +>Predicted temperature data after secondary fusion;
in the time period t for completing one-time temperature measurement, the first part of the rehabilitation temperature measurement area is>Predicted temperature data after secondary fusion;
the time point for temperature measurement is t 0 In the healthy temperature measuring area A, the predicted temperature data before the mth fusion is performed;
the time point for temperature measurement is t 0 And the predicted temperature data before nth fusion in the rehabilitation temperature measuring region B.
The burn patient was obtained:
temperature data of healthy temperature measuring area after data fusion processing：
；
Temperature data of rehabilitation monitoring area after data fusion processing：
。
The rehabilitation condition prediction module compares the temperature of the rehabilitation region after the data fusion processing with the temperature data of other healthy regions of the patient, calculates and obtains the rehabilitation degree of the rehabilitation monitoring region of the patient to be tested, and monitors the rehabilitation condition of the patient in real time;
recovery degree of recovery monitoring area of patient to be testedThe calculation strategy of (2) is as follows:
；
is->Is the average value of (2); />Is->Is a mean value of (c).
Obtaining the recovery degree of the recovery monitoring area of the burn patient:
；
the rehabilitation monitoring result comprises:
the rehabilitation condition of the rehabilitation monitoring area is poor, doctors are recommended to diagnose, and pathological reasons of slower rehabilitation of the monitoring area are judged;
the rehabilitation condition of the rehabilitation monitoring area is general, and the hospitalization monitoring is recommended to be continued;
the rehabilitation monitoring area has better rehabilitation condition, and the tested patient can voluntarily discharge from the hospital for rehabilitation.
According to the rehabilitation degree of the rehabilitation monitoring area of the burn patientJudging that the recovery condition of the recovery monitoring area of the burn patient is poor, suggesting doctors to diagnose and judge the pathological reasons of slower recovery of the monitoring area;
the temperature intelligent connection module is used for carrying out voice broadcast on abnormal temperature by connecting temperature comparison data obtained by fusing temperature measurement and a rehabilitation condition monitoring result of a patient into a patient intelligent mobile phone and a medical staff information station.
Embodiment two:
as shown in fig. 2, the application provides a medical multi-point fusion temperature measurement method, which comprises the following specific steps:
taking a patient to be tested as a facial plastic patient with 25 years of age, taking summer as an example, and carrying out real-time temperature monitoring on an operation suture wound area of a facial medical plastic area during hospitalization after facial plastic operation, wherein the specific steps are as follows:
a1: transmitting infrared radiation to the measured patient, receiving the infrared signals reflected by the measured patient, and dividing the measured patient into body temperature measuring areas according to the difference of the values of the body surface reflection ratios of different temperature measuring areas;
wherein the body temperature measurement area reflectanceThe calculation strategy of (2) is as follows:
to emit an amount of infrared radiation to a patient under test;
is the infrared radiation quantity detected after the body surface of the tested patient reflects;
the reflectance of the entire body thermometry region of the facial plastic patient is calculated as follows:
；
according to the reflectanceGrading the body temperature measuring area from small to large:
；
wherein,indicate->A personal body temperature measurement zone grade;
the body thermometry region of the facial plastic patient may be classified as follows:
。
a2: constructing a reference model conforming to standard factors according to the environment information and the human body state information;
the standard factor information of the standard factor reference model includes: within five minutes of completing one temperature measurement, each timeRecording 15 times of data, wherein the standard temperature value of the monitoring environment is +.>Standard humidity value->Standard airflow rate +.>The standard blood flow rate of the patient to be tested is +.>；
Monitoring standard temperature values of an environmentStandard humidity value->Standard air flow rate->Standard blood flow rate of the patient to be tested>The calculation strategy of (2) is as follows:
，/>；
，/>；
wherein,recording the number of times of data within five minutes for completing one temperature measurement;
is->The measured real-time environmental temperature of the secondary record;
is->Real-time ambient humidity of the secondary record;
is->Real-time ambient airflow rate of the secondary record;
is->Real-time blood flow velocity of the patient under test for the secondary record;
the face shaping patient is calculated and obtained in the whole temperature measurement process:
monitoring standard temperature values of an environment：/>；
Standard humidity value：/>；
Standard air flow rate：/>；
Standard blood flow rate of a patient under test：/>。
A3: simultaneously setting a plurality of temperature measuring points in a healthy temperature measuring area and a rehabilitation monitoring area, and measuring a plurality of groups of temperature data of the whole body area of a tested patient;
the edge line of the wound on the patient's body surface extends outwardlyThe curved surface monitoring area is formed, and 20 temperature measuring points are uniformly distributed in the curved surface monitoring area.
The plurality of sets of temperature data includes: healthy temperature measuring areaTemperature data set of 10 reflectance grade regions in (a) and rehabilitation monitoring region +.>Temperature data of the medium curved surface monitoring area;
wherein the temperature dataset is。
In the healthy temperature measuring area A, the ith 0 Temperature data of an nth temperature measurement point in the respective reflectance level areas.
The temperature data set of the reflectance grade area of the healthy temperature measurement area is:
；
rehabilitation monitoring areaTemperature data of medium curved surface monitoring area +.>。
A4: non-linear fitting is carried out on a plurality of groups of temperature data measured from different healthy temperature measuring areas and rehabilitation monitoring areas and a standard factor reference model;
the nonlinear polynomial fit of the sets of temperature data to the standard factor reference model includes:
healthy temperature measuring areaThe polynomial fit of (2) is:
；
rehabilitation monitoring areaThe polynomial fit of (2) is:
；
wherein,the time point of temperature measurement; />The temperature measurement times are within five minutes;
monitoring the lowest temperature of the environment in the period of one temperature measurement;
monitoring the highest temperature of the environment in the period of one temperature measurement;
is->Polynomial fitting parameters of the ambient humidity of the secondary temperature measurement data;
is->Polynomial fitting parameters of ambient airflow velocity of the secondary temperature measurement data;
is->Polynomial fit parameters of patient blood flow rate for the secondary thermometry data;
the polynomial fit of the healthy and rehabilitated regions of the facial plastic patient is as follows:
healthy temperature measuring areaThe polynomial fit of (2) is:
；
rehabilitation monitoring areaThe polynomial fit of (2) is:
；
a5: respectively carrying out filtering fusion treatment on temperature data corrected by the standard factor correction model in the healthy temperature measurement area and the rehabilitation monitoring area;
the filtering fusion of the temperature data of the healthy temperature measuring area and the rehabilitation monitoring area corrected by the standard factor correction model comprises the following specific steps:
s91: predicting the fused temperature data to obtain the first healthy temperature measuring areaPredicted temperature data of subspecies>First->Predicted temperature data of subspecies>；
Wherein->For measuring temperature for healthThe total fusion times of the regions, m is an integer greater than 1;
wherein->N is an integer greater than 1 for the total number of fusion times of the rehabilitation monitoring area;
total number of fusions of healthy thermometry regions of the facial plastic patient；
Total number of fusions of rehabilitation monitoring areas of the facial plastic patient；
S92: obtaining the prediction uncertainty of each fusion of the healthy temperature measurement area for the prediction temperature data obtained in the step S91Predictive uncertainty of each fusion of rehabilitation monitoring area +.>；
；
Wherein,is->Temperature measurement white noise of secondary fusion; />A time period for completing one temperature measurement;
is at->Go up to->Integrating; lnt the logarithm of t;
is->During secondary fusion, the uncertainty of prediction of the healthy temperature measurement area;
is->During secondary fusion, the uncertainty of prediction of the healthy temperature measurement area;
wherein,is->Temperature measurement white noise of secondary fusion; />A time period for completing one temperature measurement; />Is->During secondary fusion, the uncertainty of prediction of the rehabilitation monitoring area;
is->During secondary fusion, the uncertainty of prediction of the rehabilitation monitoring area;
taking the 15 th data fusion as an example: time period for completing one-time temperature measurement；
Obtaining the facial plastic patient:
prediction uncertainty of each fusion of healthy temperature measurement areas：
；
Prediction uncertainty of each fusion of rehabilitation monitoring area：/>
。
S93: according to the measurement noise of the whole measurement process, noise filtering is carried out to obtain the healthy temperature measurement areaNoise gain of sub-data fusion->First->Noise gain of sub-data fusion->；
；
；
Taking the 15 th time fusion as an example, the facial plastic patient was obtained:
healthy temperature measurement region NoNoise gain of sub-data fusion->：
；
Rehabilitation monitoring region NoNoise gain of sub-data fusion->：
。
S94: calculating the temperature data of the healthy temperature measuring area after data fusion processing through the actually measured temperature value and the predicted temperature dataTemperature data of rehabilitation monitoring area +.>；
；
；
Wherein,in the time period t for completing one temperature measurement, the healthy temperature measurement area is +>Predicted temperature data after secondary fusion;
in the time period t for completing one-time temperature measurement, the first part of the rehabilitation temperature measurement area is>Predicted temperature data after secondary fusion;
the time point for temperature measurement is t 0 In the healthy temperature measuring area A, the predicted temperature data before the mth fusion is performed; />
The time point for temperature measurement is t 0 And the predicted temperature data before nth fusion in the rehabilitation temperature measuring region B.
Obtaining the facial plastic patient:
temperature data of healthy temperature measuring area after data fusion processing：
；
Rehabilitation monitoring area after data fusion processingTemperature data of a domain：
。
A6: comparing the temperature of the rehabilitation area after the data fusion processing with the temperature data of other healthy areas of the patient, calculating to obtain the rehabilitation degree of the rehabilitation monitoring area of the patient to be tested, and monitoring the rehabilitation condition of the patient in real time;
recovery degree of recovery monitoring area of patient to be testedThe calculation strategy of (2) is as follows:
；
is->Is the average value of (2); />Is->Is a mean value of (c).
Obtaining the rehabilitation degree of the rehabilitation monitoring area of the facial plastic patient:
；
the rehabilitation monitoring result comprises:
a.the rehabilitation condition of the rehabilitation monitoring area is poor, doctors are recommended to diagnose, and the monitoring area is judgedRecovering the pathological cause of slower;
b.the rehabilitation condition of the rehabilitation monitoring area is general, and the hospitalization monitoring is recommended to be continued;
c.the rehabilitation monitoring area has better rehabilitation condition, and the tested patient is recommended to be willing to discharge from the hospital for rehabilitation.
According to the rehabilitation degree of the rehabilitation monitoring area of the facial plastic patientThe face shaping patient is judged to have better rehabilitation condition in the rehabilitation monitoring area, and the patient to be tested is recommended to be willing to take care of nursing home.
A7: and (3) accessing temperature comparison data obtained by fusing the temperature measurement and a rehabilitation condition monitoring result of the patient into a patient smart phone and a medical staff information station to carry out abnormal temperature voice broadcasting.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Claims (12)
1. A medical multipoint fusion temperature measurement method is characterized in that: the method comprises the following specific steps:
dividing the body temperature measuring area of the tested patient according to the values with different body surface reflection ratios;
constructing a reference model conforming to standard factors according to the environment information and the human body state information;
measuring a plurality of sets of temperature data of the whole body area of the patient to be measured;
non-linear fitting is carried out on a plurality of groups of temperature data measured from different healthy temperature measuring areas and rehabilitation monitoring areas and a standard factor reference model;
respectively carrying out data processing on the temperature data corrected by the standard factor correction model in the healthy temperature measuring area and the rehabilitation monitoring area;
calculating to obtain the rehabilitation degree of the rehabilitation monitoring area of the patient to be tested; and accessing the rehabilitation condition monitoring result of the patient into the intelligent mobile phone of the patient and the information station of the medical staff to carry out abnormal temperature voice broadcasting.
2. The medical multi-point fusion thermometry method of claim 1, wherein the body thermometry region reflectance ratioThe calculation strategy of (2) is as follows:
；
wherein,to emit an amount of infrared radiation to a patient under test;
is the amount of infrared radiation detected after being reflected by the body surface of the patient to be measured.
3. The medical multi-point fusion thermometry method according to claim 2, wherein the dividing method of the body thermometry region comprises: according to the reflectanceGrading the body temperature measuring area from small to large:
；
wherein the method comprises the steps ofIndicate->Personal body temperature measurement zone grade.
4. A medical multi-point fusion thermometry method according to claim 3, wherein the standard factor information of the reference model of the standard factor comprises: within five minutes of completing one temperature measurement, each timeRecording 15 times of data, wherein the standard temperature value of the monitoring environment is +.>Standard humidity value->Standard air flow rate ∈>The standard blood flow rate of the patient to be tested is +.>。
5. The medical multi-point fusion temperature measurement method according to claim 4, wherein the standard temperature value of the monitoring environmentStandard humidity value->Standard air flow rate +.>Standard blood flow rate of the patient to be tested>The calculation strategy of (2) is as follows:
，/>；
，/>；
wherein,recording the number of times of data within five minutes for completing one temperature measurement;
is->The measured real-time environmental temperature of the secondary record;
is->Real-time ambient humidity of the secondary record;
is->Real-time ambient airflow rate of the secondary record;
is->The real-time blood flow velocity of the patient under test is recorded.
6. The medical multi-point fusion thermometry method of claim 5, wherein the rehabilitation monitoring region comprises: the edge line of the wound on the patient's body surface extends outwardlyThe curved surface monitoring area is formed, and 20 temperature measuring points are uniformly distributed in the curved surface monitoring area.
7. The medical multi-point fusion thermometry method of claim 6, wherein the plurality of sets of temperature data comprises: healthy temperature measuring areaTemperature data set of 10 reflectance grade regions in (a) and rehabilitation monitoring region +.>Temperature data of medium curved surface monitoring area +.>；
Wherein the temperature dataset is；
In the healthy temperature measuring area A, the ith 0 Temperature data of an nth temperature measurement point in the respective reflectance level areas.
8. The medical multi-point fusion thermometry method of claim 7, wherein the nonlinear polynomial fit of the plurality of sets of temperature data to the reference model of the standard factor comprises:
healthy temperature measuring areaThe polynomial fit of (2) is:
；
rehabilitation monitoring areaThe polynomial fit of (2) is:
；
wherein,the time point of temperature measurement; />The temperature measurement times are within five minutes;
monitoring the lowest temperature of the environment in the period of one temperature measurement;
in the period of one temperature measurement, the highest temperature of the environment is monitoredA degree;
is->Polynomial fitting parameters of the ambient humidity of the secondary temperature measurement data;
is->Polynomial fitting parameters of ambient airflow velocity of the secondary temperature measurement data;
is->Polynomial fitting parameters of patient blood flow rate for the secondary thermometry data.
9. The medical multipoint fusion temperature measurement method according to claim 8, wherein the filtering fusion of the temperature data corrected by the standard factor correction model in the health temperature measurement area and the rehabilitation monitoring area comprises the following specific steps:
s91: predicting the fused temperature data to obtain the first healthy temperature measuring areaPredicted temperature data of subspecies>First->Predicted temperature of minor fusionData->；
Wherein->The total fusion times of the healthy temperature measurement area is that m is an integer greater than 1;
wherein->N is an integer greater than 1 for the total number of fusion times of the rehabilitation monitoring area;
s92: obtaining the prediction uncertainty of each fusion of the healthy temperature measurement area for the prediction temperature data obtained in the step S91Predictive uncertainty of each fusion of rehabilitation monitoring area +.>；
；
Wherein,is->Temperature measurement white noise of secondary fusion; />To complete one-time temperature measurementIs a time period of (2);
is at->Go up to->Integrating; lnt the logarithm of t;
is->During secondary fusion, the uncertainty of prediction of the healthy temperature measurement area;
is->During secondary fusion, the uncertainty of prediction of the healthy temperature measurement area;
；
wherein,is->Temperature measurement white noise of secondary fusion; />A time period for completing one temperature measurement;
is->During secondary fusion, the uncertainty of prediction of the rehabilitation monitoring area;
is->During secondary fusion, the uncertainty of prediction of the rehabilitation monitoring area;
s93: according to the measurement noise of the whole measurement process, noise filtering is carried out to obtain the healthy temperature measurement areaNoise gain of sub-data fusion->First->Noise gain of sub-data fusion->；
；
；
S94: calculating the temperature data of the healthy temperature measuring area after data fusion processing through the actually measured temperature value and the predicted temperature dataTemperature data of rehabilitation monitoring area +.>；
；
；
Wherein,in the time period t for completing one temperature measurement, the healthy temperature measurement area is +>Predicted temperature data after secondary fusion;
in the time period t for completing one-time temperature measurement, the first part of the rehabilitation temperature measurement area is>Predicted temperature data after secondary fusion;
the time point for temperature measurement is t 0 In the healthy temperature measuring area A, the predicted temperature data before the mth fusion is performed;
the time point for temperature measurement is t 0 And the predicted temperature data before nth fusion in the rehabilitation temperature measuring region B.
10. A medical use according to claim 1The multipoint fusion temperature measurement method is characterized in that the recovery degree of the recovery monitoring area of the patient to be measuredThe calculation strategy of (2) is as follows:
；
is->Is the average value of (2); />Is->Is a mean value of (c).
11. The medical multi-point fusion thermometry method of claim 10, wherein the results of the rehabilitation monitoring comprise:
if it isA doctor diagnoses and judges pathological reasons of slower rehabilitation in the monitored area;
if it isThen suggesting to continue hospitalization monitoring;
if it isThe patient to be tested can voluntarily discharge his or her home.
12. A medical multi-point fusion thermometry system based on a medical multi-point fusion thermometry method according to any of claims 1-11, characterized in that the system comprises the following modules: the system comprises a body surface reflectivity partitioning module, a standard factor reference module, a multi-point temperature measurement module, a data correction module, a data characteristic fusion module, a rehabilitation situation prediction module and a temperature intelligent connection module;
the body surface reflectivity partitioning module transmits infrared radiation to a measured patient, receives infrared signals reflected by the measured patient, and partitions the measured patient into body temperature measuring areas according to different body surface reflectivity values of different temperature measuring areas;
the standard factor reference module constructs a reference model conforming to standard factors according to the environment information and the human body state information;
the multi-point temperature measurement module is used for simultaneously setting a plurality of temperature measurement points in a healthy temperature measurement area and a rehabilitation monitoring area, and measuring a plurality of groups of temperature data of the whole body area of a measured patient, wherein the healthy temperature measurement area is an area of the body of the patient, which does not contain a wound, and the rehabilitation monitoring area is a wound of the body of the patient;
the data correction module carries out nonlinear fitting on a plurality of groups of temperature data measured from different healthy temperature measuring areas and rehabilitation monitoring areas and a reference model of standard factors;
the data characteristic fusion module respectively carries out filtering fusion processing on the temperature data of the healthy temperature measurement area and the rehabilitation monitoring area corrected by the standard factor correction model;
the rehabilitation condition prediction module calculates and obtains the rehabilitation degree of a rehabilitation monitoring area of the patient to be tested by comparing the temperature of the rehabilitation area after the data fusion processing with the temperature data of other healthy areas of the patient, and monitors the rehabilitation condition of the patient in real time;
the temperature intelligent connection module is used for carrying out voice broadcast on abnormal temperature by connecting temperature comparison data obtained by fusing temperature measurement and rehabilitation condition monitoring results of a patient into a patient intelligent mobile phone and a medical staff information station.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106798545A (en) * | 2017-03-03 | 2017-06-06 | 董云鹏 | Thermometer System |
| US20180049677A1 (en) * | 2016-08-16 | 2018-02-22 | Sociedade Beneficente Israelita Brasileira Hospital Albert Einstein | System and method of monitoring patients in hospital beds |
| CN111053540A (en) * | 2019-12-23 | 2020-04-24 | 浙江大学 | CRRT computer-patient body temperature correction system based on machine learning |
| KR20200121067A (en) * | 2019-04-15 | 2020-10-23 | 현재호 | Patient administration device based on 2 dimension temperature control |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180049677A1 (en) * | 2016-08-16 | 2018-02-22 | Sociedade Beneficente Israelita Brasileira Hospital Albert Einstein | System and method of monitoring patients in hospital beds |
| CN106798545A (en) * | 2017-03-03 | 2017-06-06 | 董云鹏 | Thermometer System |
| KR20200121067A (en) * | 2019-04-15 | 2020-10-23 | 현재호 | Patient administration device based on 2 dimension temperature control |
| CN111053540A (en) * | 2019-12-23 | 2020-04-24 | 浙江大学 | CRRT computer-patient body temperature correction system based on machine learning |
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