Research On The Key Technology Of The Intelligent Nursing Bed For Extracting Physiological Signals Without Restraint

The increasing population aging problem in China and the hidden dangers of insufficient traditional medical resources will drive a kind of intelligent care bed research that can reduce the burden of nursing staff and compensate for the shortage of nursing staff.The intelligent nursing bed with the method of extracting physiological signals came into being.This topic studies a medical care device that can be used at home and can remotely monitor the health status.The physiological signals monitored by it include breathing,heart rate,and body movement.These signal entropy changes have a one-to-one mapping with the occurrence of sleep stages.Relationship;The motion-coupling relationship between chest and abdominal breathing signals also has a potential model for the occurrence of sleep apnea hypopnea syndrome.Studying these contents has realized long-term,non-invasive,natural real-time sleep monitoring,which can further evaluate the quality of sleep,provide a basis for doctors to guide sleep rehabilitation or disease information mining.Lay a foundation for the development and application of sleep monitoring equipment for most people1.1.Developed a collection module for detecting chest and abdominal breathing signals,including XGZP6847 air pressure sensor,5K? slide rheostat,AD7705 dual 16-bit ADC data acquisition module and ARDUINO single chip microcomputer.Among them,the breathing signal is transmitted to the XGZP6847 air pressure sensor through a self-made air cushion,and then the signal is analog-to-digital converted,and then transmitted to LabView for data display and processing through the ARDUINO microcontroller board.The vibration signal of human body is extracted by three-axis acceleration sensor,and the heart impact signal is obtained by denoising algorithm.2.The respiratory signal is extracted without restraint,and the sleep quality monitoring experiment and the development of physiological information fusion algorithm are completed with it,obtained the difference of breath,body movement and turning over in the sleep state,and studied the physiological information fusion algorithm of breath,heart rate and body movement,used the physiological information entropy mutation and estimated the local sleep period points,improved the sleep period points by using the compensation algorithm,obtained the whole night sleep period map,and The results of polysomnography were basically the same.3.Realized the construction of smart nursing bed,including the development of smart pillows.After identifying sleep apnea syndrome,control the inflation and deflation of seven solenoid valves of the smart pillow to adjust the position of the head and correct the symptoms of breathing snoring;recognition of sleeping position By monitoring the sleeping time in bed position through sleeping position recognition,regular actions such as assisted turning over and getting up of the electric nursing bed can be achieved to prevent the occurrence of pressure ulcers for bedridden staff;The mobile phone APP monitors the sleep quality remotely,using the STM32F767 integrated Bluetooth module,wifi module,etc.to achieve communication with the server and remote monitoring of the mobile phone.Identify and alert to emergency situations such as sleep quality,accidental falls,and inanimate signs.