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Cloud-Supported Cyber–Physical Localization Framework for Patients Monitoring

机译：云支持的网络-物理本地化框架，用于患者监测

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The potential of cloud-supported cyber–physical systems (CCPSs) has drawn a great deal of interest from academia and industry. CCPSs facilitate the seamless integration of devices in the physical world (e.g., sensors, cameras, microphones, speakers, and GPS devices) with cyberspace. This enables a range of emerging applications or systems such as patient or health monitoring, which require patient locations to be tracked. These systems integrate a large number of physical devices such as sensors with localization technologies (e.g., GPS and wireless local area networks) to generate, sense, analyze, and share huge quantities of medical and user-location data for complex processing. However, there are a number of challenges regarding these systems in terms of the positioning of patients, ubiquitous access, large-scale computation, and communication. Hence, there is a need for an infrastructure or system that can provide scalability and ubiquity in terms of huge real-time data processing and communications in the cyber or cloud space. To this end, this paper proposes a cloud-supported cyber–physical localization system for patient monitoring using smartphones to acquire voice and electroencephalogram signals in a scalable, real-time, and efficient manner. The proposed approach uses Gaussian mixture modeling for localization and is shown to outperform other similar methods in terms of error estimation.
机译：云支持的网络物理系统（CCPS）的潜力引起了学术界和行业的极大兴趣。 CCPS有助于将物理世界中的设备（例如传感器，照相机，麦克风，扬声器和GPS设备）与网络空间无缝集成。这实现了一系列新兴的应用程序或系统，例如患者或健康监测，需要跟踪患者的位置。这些系统将大量传感器等物理设备与本地化技术（例如GPS和无线局域网）集成在一起，以生成，感知，分析和共享大量医疗和用户位置数据以进行复杂处理。然而，就患者的位置，无处不在的访问，大规模的计算和通信而言，关于这些系统存在许多挑战。因此，需要一种可以在网络或云空间中的巨大实时数据处理和通信方面提供可伸缩性和普遍性的基础架构或系统。为此，本文提出了一种云支持的网络物理本地化系统，用于使用智能手机以可扩展，实时和有效的方式获取语音和脑电图信号的患者监护。所提出的方法使用高斯混合模型进行定位，并在误差估计方面优于其他类似方法。

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