Research On Monitoring Technology Of Elderly Health Based On ECG And Acceleration Signal

Today,urbanization is accelerating and the population is aging,the number of elderly living alone without their children has increased significantly,so the safety of elderly people living alone has attracted widespread attention.Chronic heart disease is one of the three major causes of death in the elderly.And cardiovascular disease has become the first killer that endangers the life and health of the elderly.Falling is one of the dangerous accidents that threaten the health of middle-aged and elderly people,as well as one of the important causes of elderly casualties.Therefore,in view of the health and safety of the elderly living alone,this paper studies the elderly health monitoring technology based on ECG and acceleration signals.In order to extract effective ECG signals,the Mallat algorithm is used to decompose and reconstruct the ECG signals,and the wavelet threshold method is used to denoise.Biorthogonal quadratic B-spline wavelet transform is adopted to locate the peak value of R wave by using the singularity detection principle of wavelet transform.Q wave and S wave are located back and forth on the basis of R wave to complete the feature extraction of QRS wave group.The principal component analysis method is used to reduce the dimension of the ECG signal characteristics and perform data compression,and the top 10 beats were selected according to the contribution rate.It is proposed to use particle swarm optimization to optimize the BP neural network,make up for its defects,and improve the classification and recognition effect.The MIT-BIH ECG database was used to test the effects of five ECG classifications on different algorithms,and a comparative analysis was conducted.In the research of fall detection technology,the three-axis combined acceleration and human body inclination are used as the feature quantity,and the multi-threshold fall detection algorithm is designed in combination with the feature quantity,and the combined acceleration threshold and the inclination angle threshold are set to perform the fall detection.Volunteers are used to simulate the fall experiment and verify the feasibility of the algorithm.This paper proposes a solution for ECG and fall detection of "physiological signal acquisition front-end + mobile terminal user APP".The physiological signal acquisition front end realizes the collection and data transmission of the human body’s electrocardiogram and acceleration signals.At the same time,the Android-based Xiaozhi APP was developed for use on the mobile terminal.Xiaozhi APP processes the front-end data and realized the functions of ECG waveform display,data storage and abnormal alarm.In this paper,the elderly health monitoring technology based on ECG and acceleration signals is studied,and the elderly health status monitoring is realized through the research of ECG and acceleration signal acquisition,signal preprocessing,ECG signal classification and fall detection algorithm.The monitoring system avoids the patient’s fall due to syncope caused by abnormal ECG to cause secondary injury and the occurrence of abnormal ECG caused by the fall behavior and cause danger.The application based on ECG and fall detection has certain practical significance And social significance.