Abstract
Aiming at the problem of insufficient health monitoring of the elderly in the existing home care system, this paper designs a health information analysis and early warning system based on the Internet of Things (IoT) technology, which can monitor the physiological data of the elderly in real time. It also can be based on the elderly real-time monitoring data, physical examination data, and other types of health data, which can be used to predict diseases, so as to achieve “early detection and early treatment” of diseases. First, analyse and design the architecture and content of the home care monitoring system based on the Internet of Things. Secondly, based on the collected heart rate, blood pressure, and three-axis acceleration information of the elderly, it is analysed to determine whether the elderly are in danger of falling, and the designed system is used for early warning. Finally, this paper analyses the prediction algorithm theory of the disease prediction module in the health monitoring software of the home care system. In order to improve the accuracy of prediction, the DS evidence theory is used to optimize the traditional BP neural network (BPNN) algorithm and conduct experimental tests. The test results show that the health information analysis and early warning software of the home care system meet actual needs and achieve the expected goals.
Highlights
With the increasingly obvious trend of population aging and the increasing number of “4 + 2 + 1” households, the society’s requirements for elderly care services are increasing, and traditional institutional elderly care can no longer meet the increasing demand for elderly care [1]
In order to improve the accuracy of prediction, this paper uses DS evidence theory to optimize the traditional disease prediction algorithm based on BP neural network (BPNN) to improve the accuracy of disease prediction
E whole prediction model includes three steps, the training of traditional BPNN, the prediction process of BPNN, and the use of DS evidence theory to fuse data. e multiple BPNN prediction results obtained above are normalized and converted into the basic probability distribution that meets the DS evidence theory conditions. en use the DS combination formula to fuse the data to get the unique probability distribution, and get the final prediction result according to the decision rule
Summary
With the increasingly obvious trend of population aging and the increasing number of “4 + 2 + 1” households, the society’s requirements for elderly care services are increasing, and traditional institutional elderly care can no longer meet the increasing demand for elderly care [1]. E system uses various sensors to collect human body sign data (such as heart rate, blood pressure, and blood sugar) and displays these data on the mobile terminal in real time [11] It can be based on physical data; the analysis results provide targeted health advice for the elderly. Today, when aging is becoming more and more serious, it is especially important to establish complete and economical support for the semidisabled elderly [12,13,14] These methods are not eager for the Internet of ings, and there is no advanced evidence theory to improve the accuracy of BP network prediction. Is paper is based on the background of home care for the elderly, based on the IoT technology to develop the elderly health information analysis and early warning cloud platform.
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