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IoT-Based Continuous Glucose Monitoring System: A Feasibility Study

Tuan Nguyen Gia, Mai Ali, Imed Ben Dhaou, Amir-Mohammad Rahmani, Tomi Westerlund, Pasi Liljeberg, Hannu Tenhunen, IoT-Based Continuous Glucose Monitoring System: A Feasibility Study. Procedia Computer Science (109), 327–334, 2017.



Health monitoring systems based on Internet-of-things (IoT) have been recently introduced to improve the quality of health care services. However, the number of advanced IoT-based continuous glucose monitoring systems is small and the existing systems have several limitations. In this paper we study feasibility of invasive and continuous glucose monitoring (CGM) system utilizing IoT based approach. We designed an IoT-based system architecture from a sensor device to a back-end system for presenting real-time glucose, body temperature and contextual data (i.e. environmental temperature) in graphical and human-readable forms to end-users such as patients and doctors. In addition, nRF communication protocol is customized for suiting to the glucose monitoring system and achieving a high level of energy efficiency. Furthermore, we investigate energy consumption of the sensor device and design energy harvesting units for the device. Finally, the work provides many advanced services at a gateway level such as a push notification service for notifying patient and doctors in case of abnormal situations (i.e. too low or too high glucose level). The results show that our system is able to achieve continuous glucose monitoring remotely in real-time. In addition, the results reveal that a high level of energy efficiency can be achieved by applying the customized nRF component, the power management unit and the energy harvesting unit altogether in the sensor device.

BibTeX entry:

  title = {IoT-Based Continuous Glucose Monitoring System: A Feasibility Study},
  author = {Nguyen Gia, Tuan and Ali, Mai and Ben Dhaou, Imed and Rahmani, Amir-Mohammad and Westerlund, Tomi and Liljeberg, Pasi and Tenhunen, Hannu},
  journal = {Procedia Computer Science},
  number = {109},
  publisher = {Elsevier},
  pages = { 327–334},
  year = {2017},
  ISSN = {1877-0509},

Belongs to TUCS Research Unit(s): Embedded Computer and Electronic Systems (ECES)

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