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This study introduces a cloud-based model for remote patient monitoring using the Internet of Things (IoT). The integrated framework enhances data management and enables efficient patient observation, reducing healthcare costs.

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Area of Science:

  • Health Informatics
  • Internet of Things (IoT)
  • Cloud Computing

Background:

  • Remote patient monitoring is crucial for continuous healthcare observation and cost reduction.
  • Existing IoT frameworks for patient monitoring are often fragmented, lacking a comprehensive, integrated model.
  • A robust architecture is needed to manage data flow and facilitate interventions in remote healthcare.

Purpose of the Study:

  • To propose a cloud-based patient monitoring model integrating IoT data collection, storage, processing, and visualization.
  • To present a detailed, well-integrated system architecture addressing limitations of existing frameworks.
  • To enhance the efficiency of remote patient monitoring systems.

Main Methods:

  • Developed a three-part cloud-based model: sensing (IoT data collection), network (processing and storage), and application (user interface).
  • Implemented data pre-processing techniques like filtering and variable sampling in the sensing module to manage large IoT data volumes.
  • Discussed data flow and processing for visualization and intelligent application deployment.

Main Results:

  • The proposed model effectively handles IoT-generated data collection, storage, processing, and visualization.
  • Edge processing via filtering and variable sampling reduced data load, enabling observation of four times more patients.
  • The integrated architecture supports data visualization services and intelligent healthcare applications.

Conclusions:

  • The cloud-based IoT patient monitoring model provides a comprehensive solution for remote healthcare.
  • The system's architecture and data processing capabilities improve efficiency and scalability in patient monitoring.
  • This integrated approach facilitates better healthcare delivery and reduces hospital admission rates.