A Wearable Infrared Fast Response Real-time Temperature Monitoring System

In recent years,New Coronavirus pneumonia has been raging,and human body temperature is one of the important criteria for the initial judgement of the disease.It has important reference significance in the prevention,control,quarantine and health monitoring.In hospital emergency departments,intensive care units and other occasions,it is often necessary to carry out real-time continuous monitoring of body temperature,such as wearing temperature measuring terminals at the patient’s armpit,anus and other positions for real-time continuous body temperature monitoring;In ICU cardiopulmonary surgery,temperature measuring equipment can be implanted in patients’ bodies,and the human circulatory system can be used for real-time continuous measurement of core temperature.The traditional temperature measurement method mentioned above not only has the disadvantages of expensive and scarce equipment,but also has certain invasiveness to the human body,which may cause some patients to have resistance.In addition,this kind of temperature measuring equipment usually adopts the principle of thermal conductivity for temperature measurement,and its temperature detection response rate is relatively slow.According to these problems,this thesis shows a wearable infrared fast response real-time temperature monitoring system.In order to meet the requirements of wearable and fast response,this thesis designs a temperature measurement hardware module that can meet these two requirements,the module includes body temperature monitoring terminal and heterogeneous network.The body temperature monitoring terminal uses two MLX90614＿DCC infrared temperature sensors to form the temperature measurement core module.The temperature array is composed of the ambient temperature and the infrared temperature in the MLX90614＿DCC.The system uses a classification algorithm to obtain the relationship between the two temperatures to achieve the purpose of rapid response temperature measurement.The data transmission heterogeneous network module includes two parts,Zigbee and GPRS,which are used to realize the functions of communication protocol conversion,body temperature data package processing and wireless transmission.The establishment of a heterogeneous network can realize the purpose of transmitting the data from the internal network to the external network,and supply it to the subsequent server,so as to realize the related functions of online real-time processing of body temperature data.In order to meet the real-time online monitoring requirements of the system,the system develops a background data server and a Web service monitoring platform based on Java language.The system has embedded algorithms in the server to process data online in real time and save the data to the background database,and realize the real-time query function of temperature data and predicted temperature through the Web server.The embedded algorithm adopts the abnormal data filtering algorithm based on KNN,and the abnormal temperature is filtered and integrated through the value of the training parameter K,so as to improve the reliability of the temperature data;the system uses two sensors to form a temperature measurement array to achieve the purpose of multi-point measurement and applies classification algorithm training to improve the accuracy of temperature data;the system uses the ARIMA algorithm based on the time series prediction model to train the user’s body temperature data,estimates the order of the model through the autocorrelation function and the partial correlation function,and uses the AIC and BIC criteria to test the order.After determining the p,q,d order of the model,according to the stability characteristics of the body temperature sequence,the algorithm uses the method of moment estimation to solve the p+q+d unknown parameters,so as to obtain the algorithm model function.When predicting body temperature,the algorithm model sets the confidence interval of the prediction sequence and the prediction step size to predict the change trend of the user’s body temperature in real time;the system develops a web service monitoring platform based on the enterprise-level SSM framework,which can not only correspond interface information with database information one-to-one,but also realize real-time online query of body temperature,historical record query and other functions.Finally,the system performs functional tests on the overall functions of the infrared wearable body temperature collection terminal,the gateway,the data server and the backend management system,and also checks the functionality and accuracy of the KNN anomaly data filtering algorithm and the time series prediction model ARIMA algorithm embedded in the data server to test the feasibility of the system as well as the measurement effect.