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.

中文翻译：

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基于物联网的家庭护理健康信息感知分析与预警

针对现有家庭护理系统中老年人健康监测不足的问题，设计了一种基于物联网技术的健康信息分析预警系统，可以对老年人的生理数据进行监测。即时的。它还可以基于老年人的实时监视数据，体格检查数据和其他类型的健康数据，这些数据可以用于预测疾病，从而实现疾病的“早期发现和早期治疗”。首先，分析和设计基于物联网的家庭护理监控系统的架构和内容。其次，根据收集的老年人的心率，血压和三轴加速度信息，分析确定老年人是否有跌倒的危险，所设计的系统用于预警。最后，本文分析了家庭护理系统健康监测软件中疾病预测模块的预测算法原理。为了提高预测的准确性，DS证据理论被用于优化传统的BP神经网络（BPNN）算法并进行实验测试。测试结果表明，家庭护理系统的健康信息分析和预警软件能够满足实际需求，达到预期目标。DS证据理论用于优化传统BP神经网络（BPNN）算法并进行实验测试。测试结果表明，家庭护理系统的健康信息分析和预警软件能够满足实际需求，达到预期目标。DS证据理论用于优化传统BP神经网络（BPNN）算法并进行实验测试。测试结果表明，家庭护理系统的健康信息分析和预警软件能够满足实际需求，达到预期目标。