Community Monitoring System Based On Wearable Devices

With the significant improvement of people's living standards,the aging of the population has become one of the important problems facing China's development.Chronic diseases and accidents are more likely to cause irreparable harm to the elderly,especially to special groups such as empty nesters and people living alone.Therefore,by combining the monitoring system with the Internet of Things technology to realize remote monitoring of a variety of physiological parameters of the elderly,and at the same time to identify the fall behavior of the elderly,it can effectively meet a variety of monitoring needs of the elderly in their daily life,so that the elderly can get monitoring services at home.At present,the distribution of medical resources is not balanced enough in China,and the medical model relying on community hospitals is not mature,so the research on community monitoring system has certain economic value and social significance.In this context,this paper designs a community monitoring system based on wearable devices.The hardware of the system includes self-developed monitoring devices,data receiving devices and intelligent devices required by the monitoring platform.Wearable monitoring devices can continuously measure various physical signs such as heart rate,blood oxygen,blood pressure,body temperature and other physiological parameters,as well as detect falling behavior.In order to get rid of the bondage of traditional mobile phones as the data receiving equipment,the monitoring device forms the multi-sign parameter information and the fall information into the data packet,and transmits it to the data receiving device through the LoRa wireless communication technology.The data receiving equipment receiving data packets over a long distance and real-time uploaded to the system server,server for data storage and management system.Finally,the management of the basic information and medical data of the elderly and the acquisition of real-time monitoring data and medical information are realized through the application software.In the research and development stage of monitoring equipment,according to the fall detection algorithm and considering the comfort of wearing equipment,fall behavior was studied by analyzing the wrist motion and posture characteristics.Firstly,the SVM method is used to obtain the optimal observation window and eigenvalue.Then,a fall detection algorithm based on multilevel threshold is designed by combining the effective feature judgment with the attitude judgment.The effect is good in practical application.In order to improve the comfort level,PPG signals were collected by multi-channel photoelectric sensors,and pulse wave signals were preprocessed by smooth filtering and cubic spline interpolation.In the aspect of blood pressure measurement,an individualized parameter calibration method is used to improve the accuracy of blood pressure measurement.Secondly,in terms of body temperature detection,in order to obtain medical body temperature more accurately and faster,a body temperature detection algorithm based on multiple sensors is designed through data collection and fitting.During the testing of the monitoring system,the various functions and wireless transmission performance of the monitoring equipment were tested,and the test data were recorded and analyzed.Through experimental verification,the monitoring equipment meets the design requirements and monitoring requirements,and the monitoring platform application software meets the basic application functions.Therefore,the system is suitable for the community,pension institutions and other scenarios to promote the use.