Analysis And Application Of Nursing Monitoring Data Based Of Smart Mattress

The problem of aging population is getting more and more serious,and the accompanying problem of old-age care has become a hot spot of social concern.The application level of information technology is relatively low.Due to the current lack of health and old-age resources,and the growing health and pension needs of the society are becoming more and more difficult to meet.How to apply intelligent technology in the smart old-age industry,using advanced science and technology to solve the social pension pressure has become a very valuable research topic.In this paper,the elderly monitoring data of intelligent mattress in nursing homes are used to analyze and mine the data in depth,combining with the health and life needs of the elderly in nursing homes.The sleep evaluation model and the hidden abnormal behavior mining algorithm model are proposed.The monitoring system of nursing homes based on the data of intelligent mattress is designed by integrating them with other monitoring data of intelligent mattress,The main work of this paper is as follows:Firstly,because the sleep quality of the elderly in nursing homes is difficult to evaluate,the paper design a sub-index sleep evaluation model for the elderly in nursing homes based on monitoring data.The method is based on the data mining method and the sleep characteristics of the elderly,combined with the knowledge of authoritative literature,the analytic hierarchy process is used to establish a sleep evaluation model.Firstly,sleep indicators are extracted based on monitoring data,then the indicators are stratified and weighted,after the weight vector passes the consistency test,the sleep evaluation model of stratified indicators for the elderly in nursing homes is constructed.Next,this paper uses this model to evaluate the sleep of the elderly,and then compares the results with those of the professional sleep evaluation scale.Pearson correlation coefficient and Spearman correlation coefficient are 0.930 and 0.956.It shows that there is a strong correlation and consistency between the two,which proves the reliability of the evaluation model.Then,this paper studies the algorithm of the hidden anomaly behavior recognition of the elderly in the nursing home.Firstly,it analyzes the living characteristics of the elderly in the nursing home,and provides the basis for the subsequent data,next,this paper combines the application scenario to pre-process the monitoring data collected by the smart mattress,and uses a method to search the peak of the periodic spectrum to calculate the non-sample sequence hidden period value.Then the missing data is estimated based on hidden periodic values.The processed time series can not only maintain the original data characteristics,but also calculate the missing values according to the periodicity of the original sequence,which will have a better filling effect.Then,through the comparative analysis of several time series models,the adaptive filtering method is chosen as the prediction model,and its modeling process and improvement scheme are introduced,and the prediction effect of the model is judged by using MAPE(Mean Absolute Percentage Error).Finally,according to the prediction results of the activity time of the elderly,this paper defines the criteria for distinguishing the abnormal behavior of the elderly in nursing homes,which provides a basis for identifying the abnormal behavior of the elderly in nursing homes.Finally,according to the actual needs,combined with the monitoring data of intelligent mattress,this paper designs and implements a monitoring platform for the elderly in nursing homes.Firstly,the system realizes real-time and comprehensive monitoring of the vital signs and daily life of the elderly.