Health Status Detection Based On Intelligent Health Mouse

With the popularity of network informatization,computer have become an indispensable hardware equipment for work and entertainment in all walks of life.However,contemporary psychocognitive research has indicated that human body will cause different degrees of mental fatigue symptoms when doing long and intense mental work with computer.Mental fatigue is a form of physical sub-health.,easy to make the body immunity decline,leading to the invasion of various physiological diseases.The pulse signal could reflect the changes of blood flow pressure of the heart and contains rich information on human health status in all physiological signals controlled by the human body.Therefore,the detection and analysis of pulse signal is an effective method to detect the state of physical fatigue.In order to effectively detect and prevent mental fatigue,this paper implements a health status detection system based on the smart health mouse.This system can detect and calculate the relative changes of health status in real time,and intelligently identify the changes of the health status of the two periods then explain its physiological meanings.Meanwhile,provide the query services for the intelligent mouse users Conveniently.This article has the following main research contents:(1)Realizing the client health status detection application to real-time monitoring the health status change of the users,and calculating and displaying the relative change of the health status.This smart health mouse runs on the PC client,its major function include:perform zero-phase Butterworth low-pass filter preprocessing on the pulse data;calculates health parameters including heart rate and fatigue values;pulse data storage,upload and health parameter display.(2)Achieving a health status change intelligent recognition algorithm for a large amount of historical pulse data stored on the server,the algorithm aims to identify whether the health status changes between two periods.Firstly,use the nonlinear support vector regression algorithm to fit the single-cycle pulse data for a period of time,then extract 10 physiologically significant time domain eigenvalues.Finally,identify health status changes intelligently through the Attractive Propagation Clustering Algorithm which is based on principal component analysis.(3)Providing convenient data query function for smart health mouse users,implementing a mobile WeChat applet and a health status query website based on the Web server.The server uses the MySQL network database to store the basic information of each smart health mouse user,use the Django framework work for the front-end to achieve a user registration login interface,a historical data query interface and a health status change query interface.In order to verify the accuracy of the health status detection system,10 volunteers with no history of health were selected in this paper,and they were divided into two groups using the smart health mouse and keep working with different intensity for 4 hours.The pulse data of 30 minutes before and after the collection were performed.The health status change identification,get the correct classification results,and the health status detection application and the health status inquiry system can work normally.