Design Of Cloud Service System For Home Remote Health Monitoring

With the development of society and the accelerated pace of life,the incidence of chronic diseases represented by hypertension is increasing.Chronic diseases have the characteristic of complicated etiology and long disease course,which results in long-term or even lifelong treatment.These diseases would make negetive impact on the health of people,imposing a heavy burden on the family.Furthermore,they severely consumed medical and social resources and seriously engulfed the fruits of Chinese economic development.Thus,chronic diseases become a major social problem.Hypertension is the most common chronic disease and the main risk factor of cardiovascular and cerebrovascular diseases.At present,there are about 200 million hypertensive patients in China,accounting for about 1/5 of the total number of hypertensive patients in the world.It is estimated that the direct and indirect costs caused by hypertension and related diseases amount to RMB 300 billion annually.At present,the family telemedicine monitoring system is a key measure to prevent and cure the chronic diseases.However,the existing systems have some weaknesses.First,the home monitoring terminal have complicated installation and use process,along with poor user experience.Second,the communication nodes based on the Bluetooth need to be paired with the intelligent terminals,which limits the mobility of the home monitoring terminals and increases the system costs.Third,the existing diagnostic criteria are too simple and the characteristics of the patient were not considered,which will result in misdiagnosis and missed diagnosis as well as increase the time and money costs of doctors and patients.Fourth,it can not provide timely and accurate prediction and early warning services of diseases.This study designs a cloud service system for home remote health monitoring which is represented by blood pressure monitoring.The system is composed of health monitoring terminal based on IoT,home monitoring gateway,cloud server,and information service terminal.The health monitoring terminal(IoT-sphygmomanometer)is designed to collect the patient’s blood pressure data,WiFi network module embedded as transmission media.The home monitoring gateway is designed to receive the patient’s blood pressure data form IoTsphygmomanometer and sends the data to the cloud server.Then,the classification model for hypertension classification was established using machine learning algorithms such as CART decision tree,random forest,gradient elevation,and K-nearest neighbor.Evaluate the model according to model evaluation indicators such as precision,recall,f1-score,AUC and accuracy.For non-linear and non-stationary blood pressure data,the prediction model of blood pressure is established using wavelet analysis and neural network.Last,the software is designed to provide blood pressure data insertion,data search,hypertension diagnosis and blood pressure prediction and other functions.