The Design And Implementation Of Low Power Vital Signs Monitoring System

In recent years,the proportion of China's elderly population is getting higher and higher,among which the elderly living alone account for a large proportion.The health problem of the elderly living alone has become the focus of public attention.And now the pace of life is accelerating,and many young people are also in a state of sub-health.The monitoring of human health has become the focus of people's attention.Wearable medical monitoring system,which has developed rapidly recently,provides a practical solution to solve the problem of health status monitoring.Major technology companies have launched their own wearable devices to monitor the parameters of human vital signs.However,the physiological indicators monitored by these products are relatively single,and the basic physiological parameters such as heart rate,ECG,pulse,body temperature,blood oxygen saturation concentration and blood pressure cannot be monitored at the same time.The medical multi parameter monitoring devices launched by some medical companies do not meet the wearable requirements,and also have great defects in system power consumption.In addition,although some scholars have studied the fusion reasoning of vital signs parameters,the research on the fusion of these signs parameters is to infer the health state of the fusion of body signs parameters in the static state of the human body,and there is no research on the fusion of signs parameters in the moving state of the human body,so it can not fully characterize the state of the human body.In order to enable the system to monitor a number of physiological parameters,it has low power consumption and wearability.In terms of data fusion,it can fuse a variety of vital sign parameters collected by the system in static and moving states,and then infer the health state of human body.The following research work has been carried out in this subject:Firstly,this paper designs a low-power scheme of vital signs monitoring system,which is divided into four parts: wrist,chest,gateway and mobile terminal The low-power scheme includes two aspects: hardware circuit design and software scheduling strategy design.Secondly the low-power devices are selected,and the wrist,chest and gateway circuits are designed.The circuit structure adopts the form of "rigid flexible combination board" to facilitate the wearable design of the circuit.The rigid part is used to place electronic devices,and the flexible part is used for the connection between the rigid parts.Thirdly,the corresponding software of wrist,chest,gateway circuit and mobile terminal is designed to realize the establishment of body area network,the monitoring and transmission of vital signs data,the recognition of human motion state,the display of mobile terminal data and the transmission of signs data to the server for storage.Then,the low-power software scheduling strategy is designed to control the working mode of MCU and sensors of each part of the system according to the motion states of human body,such as static,walking,running and going upstairs and downstairs,dynamically adjust the system working time and sleep time,and reduce the system power consumption from the aspect of software scheduling.Finally,the fuzzy neural network is used to fuse and judge the motion state and various vital sign parameters collected by the system.In this part of the research,firstly,the neural network and fuzzy theory are compared,and then the structure of fuzzy neural network and parameter learning algorithm are analyzed in detail.Fuzzy neural network is used to complete data fusion and judge the health state of human body under different motion states.Through the research and design of low-power life monitoring system,the system can monitor a variety of vital signs signals,and has low-power characteristics and wearability.It can meet the monitoring of vital signs in daily life,and has practical significance for caring for the health of the elderly and the management of human sub-health.It also has a certain reference significance for vital sign data fusion under exercise state.