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首页> 外文期刊>International Journal of Intelligent Enterprise >A smart architecture design for health remote monitoring systems and heterogeneous wireless sensor network technologies: a machine learning breathlessness prediction prototype
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A smart architecture design for health remote monitoring systems and heterogeneous wireless sensor network technologies: a machine learning breathlessness prediction prototype

机译：健康远程监控系统和异构无线传感器网络技术的智能架构设计：机器学习呼吸困难预测原型

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摘要

In this paper, we propose a remote patient monitoring architecture based on WBAN wireless body sensor network for breathlessness prediction using machine learning mechanism, we develop a new gateway architecture able to interconnect heterogeneous sensor networks not equipped with the HTTP/TCP/UDP stack. To ensure interoperability and facilitate seamless access to data from different types of body sensors that communicates via multiple technologies. We have designed an application-layer approach for a web service gateway to interact with heterogeneous WSN. The gateway manages the service consumption and communicates with the server via the SOAP protocol. The proposed platform targeted to monitor and process patient health data. An improved machine learning algorithm is used for patient health status prediction to perform patient self-training models based on k-means algorithm. For our platform evaluation we study the gateway power consumption then we investigate the communication delay between the gateway and the server over three communication scenarios (3G, ADSL, LOCAL).
机译：在本文中，我们提出了一种基于WBAN无线体传感器网络的远程患者监控架构，用于使用机器学习机制的呼吸困难预测，我们开发了一种能够互连不配备HTTP / TCP / UDP堆栈的异构传感器网络的新网关体系结构。为了确保互操作性并促进通过多种技术通信的不同类型的身体传感器的无缝访问数据。我们设计了一种用于Web服务网关的应用层方法，以与异构WSN交互。网关管理服务消耗并通过SOAP协议与服务器通信。所提出的平台，用于监测和处理患者健康数据。一种改进的机器学习算法用于患者健康状态预测，以执行基于K均值算法的患者自训练模型。对于我们的平台评估，我们研究了网关功耗，然后我们研究了网关和服务器之间的通信延迟超过三种通信场景（3G，ADSL，本地）。

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