Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges

Muhammad Waleed*, Tariq Kamal, Tai Won Um*, Abdul Hafeez, Bilal Habib, Knud Erik Skouby

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

14 Citations (Scopus)
15 Downloads (Pure)

Abstract

The remote monitoring of patients using the internet of things (IoT) is essential for ensuring continuous observation, improving healthcare, and decreasing the associated costs (i.e., reducing hospital admissions and emergency visits). There has been much emphasis on developing methods and approaches for remote patient monitoring using IoT. Most existing frameworks cover parts or sub-parts of the overall system but fail to provide a detailed and well-integrated model that covers different layers. The leverage of remote monitoring tools and their coupling with health services requires an architecture that handles data flow and enables significant interventions. This paper proposes a cloud-based patient monitoring model that enables IoT-generated data collection, storage, processing, and visualization. The system has three main parts: sensing (IoT-enabled data collection), network (processing functions and storage), and application (interface for health workers and caretakers). In order to handle the large IoT data, the sensing module employs filtering and variable sampling. This pre-processing helps reduce the data received from IoT devices and enables the observation of four times more patients compared to not using edge processing. We also discuss the flow of data and processing, thus enabling the deployment of data visualization services and intelligent applications.

Original languageEnglish
Article number6760
JournalSensors
Volume23
Issue number15
ISSN1424-8220
DOIs
Publication statusPublished - Aug 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • cloud computing
  • edge processing
  • internet of things
  • remote patient monitoring

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