Cloud-Supported Cyber–Physical Localization Framework for Patients Monitoring

TLDR

Cloud‑supported cyber‑physical systems integrate physical devices with cyberspace, enabling patient monitoring that requires accurate location tracking, but face challenges in positioning, ubiquitous access, large‑scale computation, and communication. The study aims to develop an infrastructure that delivers scalable, ubiquitous real‑time data processing and communication for patient monitoring. The authors propose a cloud‑supported cyber‑physical localization system that uses smartphones to capture voice and EEG signals and applies Gaussian mixture modeling for real‑time, efficient patient localization. The Gaussian mixture modeling approach outperforms comparable methods in localization error estimation.

Abstract

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.