Multi-source Data Sensing And Analysis For Health Monitoring

The development of economy and society provokes higher requirements of the public for the quality of medical service.Health monitoring has become an important means of disease prevention and early diagnosis and treatment.High effective health monitoring can effectively reduce the risk of disease,thus reduce the patient’s pain,improve the cure rate and loose the burden of medical treatment.For a long time,there has been a lack of effective monitoring methods to realize the universal and continuous health monitoring of the population.The lack of monitoring means causes the current medical model to remain mainly disease treatment,rather than monitoring and prevention.Intelligent equipment,robot technology,mobile communication technology and machine learning technology have shown great application prospects in technology innovation,economic development,disease treatment and improvement of life quality.Profound changes have taken place in the field of health care,and the changes in medical services will accelerate the development of the medical industry and benefit the public.In this thesis,health monitoring for the indoor user is taken as the research scene,to explore the method of multi-source health data sensing and analysis for the user health,and to explore the new medical method based on monitoring and prevention with the help of the current mainstream technologies.From the practical point of view,this thesis studies and explores the multisource data sensing and analysis for health monitoring,which has important theoretical significance and practical application value.This thesis explores how these technologies can be used in the field of intelligent medical treatment,including two aspects of physiological disease and mental health,which are of theoretical and practical value.This research content includes the design and implementation of the architecture of indoor health monitoring system,multi-source medical data sensing based on intelligent equipment,and multi-source data analysis based on machine learning.In the architecture design of health monitoring system,this thesis designs a three layerarchitecture of intelligent medical system that supports high concurrent business,including the user perception interaction layer with intelligent devices,high performance business layer supporting high concurrent business processing,and data service layer that supports the efficient management of heterogeneous medical data.The system architecture is the infrastructure and important guarantee for the realization of the technical plan.In the aspect of multi-source medical data sensing,this thesis designs intelligent perception equipment to realize intelligent perception of environmental health data in smart home environment,designs smart clothing to achieve comfortable and efficient collection of user physiological data and designs interactive robot to intelligent perception of user behavior data in indoor environment.Medical data is the source of information for smart healthcare system,and has great significance.In the design of multi-source medical data analysis,this thesis builds the corresponding analysis model based on the multi-source medical data with the help of machine learning,analyzes the overall health status of the users from many aspects,such as environmental health,physiological disease,physiological state and mental health,and provides important auxiliary means for the diagnosis and treatment of physiological diseases or psychological problems.Finally,this thesis shows some achievements of intelligent medical treatment,and tests the performance of the smart medical prototype system based on emotion recognition application.