Research On Breathing And Fall Sensing Of Wireless Body Area Network Based On SDR

In recent years,my country’s population has grown rapidly,and the number of chronic disease patients has also increased rapidly.Mobile medical care and health monitoring are facing greater pressure.The wireless body area network is of great significance to medical care,which can monitor human health and assist in the diagnosis of diseases.Therefore,the wireless body area network will have huge application prospects and broad market demand in my country.At the same time,the wireless body area network is not only limited to the field of medical monitoring,but can also be applied to sports training,military activities and other fields through the perception of human behavior.In this paper,the researchers built a wireless body area network perception platform,and conducted a case study combined with the basic needs of symptom detection.This paper studies and analyzes the intelligent perception of wireless body area network,detects human behavior and symptoms based on channel state information,and verifies the reliability and accuracy of the platform through fine-grained and coarse-grained activities.The main work of this paper includes the following aspects:(1)The current research status of related fields and the application of body area network in different fields are summarized.The basic principle of non-contact sensing based on Channel State Information(CSI)is clarified,the behavior of the human body is modeled,and the position changes caused by motion are analyzed.These changes will disturb the wireless channel.form reflects.There is a one-to-one correspondence between the activity of the human body and the fluctuation of the received CSI data.(2)Build a wireless body area network perception platform.The USRP hardware device was used to externally connect an omnidirectional antenna to send and receive wireless signals disturbed by the subjects’ activities,and to extract CSI data.The noise and interference in the data are removed by outlier elimination and filtering algorithm,and finally the channel state information data is classified and identified by machine learning algorithm.(3)Design case studies in terms of fine-grained and coarse-grained.For small-scale finegrained activities,tidal breathing was selected for testing.For large-scale coarse-grained activities,the fall behavior is selected for experiments;the feasibility and accuracy of the scheme proposed in this paper are finally verified.In summary,the perception platform based on the wireless body area network proposed in this paper can more accurately identify human activities,and to a certain extent can assist in the detection of tidal breathing,a common disease of the elderly,and can help monitor fall behavior.