Design And Realization Of Physical Sign Monitoring And Analysis System Based On Wireless Body Area Network

With the development of the times,a series of problems arising from the explosive growth of the world population and the aging of the population have gradually attracted people's attention.In particular,the health care problems of the middle-aged and elderly people and other special groups urgently need to be solved.In some scenarios,real-time physiological data monitoring of the elderly population or patients is required.However,the traditional centralized medical monitoring method has many shortcomings in patient monitoring,and even imposes additional psychological burden on patients.WBAN technology is a combination of wireless communication,data cloud storage,mobile applications,etc.It can monitor and record the physical data of the human body for a long time without interruption,and overcome the disadvantages of large size and unportability in the implementation of traditional monitoring,and meet people's requirements on efficiency,wearable,comfort and safety of daily medical monitoring.Therefore,WBAN technology is not only developed in the field of medical care,but also applied in fire safety,military counter-terrorism,physical education,entertainment and other fields.Its great practical significance and value will play a huge role in the development of society and technology in the future.This paper mainly designed and realized a medical monitoring system based on WBAN,and combined with deep neural network,proposed a human body signs analysis and evaluation model.The main work and innovations are summarized as follows:1.This paper studies WBAN technology and analyzes the overall network structure and key technologies of the system,and designs and implements a sign monitoring system based on WBAN architecture and theoretical technologies.This system combines WBAN technology,low power wireless data transmission technology,biological detection technology,Android mobile application development technology and the remote server technology.Based on the design and construction of hardware and software parts of the system,the collection,data storage and analysis of human body temperature,heart rate,blood oxygen and respiratory parameters are realized.The experimental results show that the system has good performance and can collect physiological data of human body accurately and effectively,and realize the data interaction between Android client software and remote server,basically completing the requirements of system design.2.In the process of continuous change of human body signs,it is necessary to associate the data with time series when judging and predicting human body signs in a period of time,and combine the physical signs at a certain moment with the data before and after the moment for analysis.In view of the characteristics of the above physical changes,this paper based on the realization of monitoring several basic human signs proposes a human physiological status assessment and trend prediction model,which utilizes the function of deep neural network mode classification to conduct real-time multi-parameter fusion analysis of human physiological datas collected by the system.The experimental results show that this model has achieved good performance and usability,and the accuracy of human body state classification can reach over 96.5%,and the prediction results are basically close to the judgment results,with a good prediction classification accuracy,meeting the requirements of the initial design.