Research On Nursing Service Robot And Its ECG Compressed Sensing Of Body Sensor Network

The aged tendency of population and the empty nest families,and a large number of disabled patients make medical,social insurance system and the family face enormous pressure.How to provide service for the elderly and disabled such as medical rehabilitation training,personal care,remote health care is an important problem to be solved currently.In this paper,aimed at these problems,a kind of nursing service robot is designed by modular design thought.The robot,which based on the multifunctional nursing bed in the laboratory,is integrated intelligent health care device with physiological parameter remote monitoring subsystem.At the same time,aimed at the problem of signal reconstruction accuracy in the remote monitoring center and low power of physiological parameteror remote monitoring subsystem,ECG signal is taken as the research object by using compressed sensing theoryappeared in recent years,an ECG compressed sensing method of low power body area network and a method of ECG reconstruction of body area network based on overcomplete dictionary is propsed respectively.To solve the problem of personal health care and bedsore of the elderly and disabled,after introducing the structure and function of the nursing service robot system,an intelligent sanitation nursing instrument,which is made up of mechanical structure and control system,is designed for helping patients who stay in bed turn over and do automatic processing excrement and urine.This paper introduces the design of the main body of excrement and urine processing,turning over mechanism and the control box and the design of control system based on the Atmegal28 MCU in detail.The test result showed this robot has the functions of excrement and urine automatic reaction,washing,human body cleaning and drying,and it also can prevent bedsores effectively through turning over.The result proves this kind of Robots have good reliability and comfort.To realize the function of remote health monitoring effectively,the physiological parameteror remote monitoring subsystem,which include four modular such as data acquisition,data communication,data processing and power management,is designed after introducing the related knowledge of body area network in detail.The hardware circuit and software system of the four modular are designed,then the reliability,stability and real-time of the subsystem are tested and analysed.The test results show that the data packet error rate,received signal strength indication and the data transfer time of the system can meet the requirement of the physiological parameteror remote monitoring subsystem.After introducing ECG and compressed sensing theory,aimed at low power problem in body area network,an ECG compressed sensing method of low power body area network based on the compressed sensing theory is proposed.Random binary matrices is used as the sensing matrix to measure ECG signals on the sensor nodes.After measured value is transmitted to remote monitoring center,ECG signal sparse representation under the discrete cosine transform and block sparse Bayesian learning reconstruction algorithm is used to reconstructed the ECG signals.The simulation results show that the 30%of overall signal can get reconstruction signal which's SNR is more than 60dB,each numbers in each rank of sensing matrix can be controlled below 5,which reduces the power of sensor node sampling,calculation and transmission.The method has the advantages of low power,high accuracy of signal reconstruction and easy to hardware implementation.And then,aimed at the problem of demanding high accuracy reconstruction ECG signal in remote monitoring center of body area network,this paper proposes a method of ECG reconstruction of body area network using compressed sensing based on overcomplete dictionary.The proposed method uses compressed sensing theory and then uses random binary matrices as the sensing matrix to measure ECG signal on the sensor nodes.After measured value is transmitted to remote monitoring center,the overcomplete dictionary based on K-SVD algorithm training and block sparse Bayesian learning reconstruction algorithm are used to reconstruct the ECG signal.The simulation results show that the SNR performance of compressed sensing reconstruction ECG based on K-SVD overcomplete dictionary method is 5-22 dB higher than that of using discrete cosine transform method when the ECG signal compression rate is at 70%-95%.The method has the advantages of high accuracy of signal reconstruction and easy to hardware implementation.Nursing service robot designed in this paper has realized the function of medical rehabilitation training,personal care,remote health monitoring,it can improve the quality of life of the elderly and disabled,detection and prevention of sudden illness,alleviate the burden of the nursing personnel.it also has important significance to ensure that social stability development of china.At the same time,the research results has important reference value in practical application and popularizing of the compressed sensing theory on the body area network platform.