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Technological advancements are opening new opportunities for managing chronic diseases such as diabetes. Current systems enable real-time glucose monitoring, but recent studies show that incorporating physiological and activity variables can improve glucose prediction. This work presents the architecture and implementation of an IoT infrastructure that combines glucose monitoring with physiological data from a sports wristband, together with contextual events such as carbohydrate intake and insulin administration. Glucose values are obtained through the FreeStyle Libre 2 sensor and the DiaBox app, while a custom Android application collects wristband data. All information is transmitted in real time via MQTT for structured storage. This infrastructure provides a unified dataset of glucose, physiological, and contextual variables, laying the foundation for the development of AI-based predictive systems to improve diabetes management.

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IoT Architecture for Real-Time Glucose and Physical Activity Data Management

  • 会议论文

  • 发表日期：

    2026年

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  • E. Lupión-Lorente,
  • M. Lupión,
  • P. M. Ortigosa,
  • E. M. Ortigosa,
  • N. C. Cruz

摘要

Technological advancements are opening new opportunities for managing chronic diseases such as diabetes. Current systems enable real-time glucose monitoring, but recent studies show that incorporating physiological and activity variables can improve glucose prediction. This work presents the architecture and implementation of an IoT infrastructure that combines glucose monitoring with physiological data from a sports wristband, together with contextual events such as carbohydrate intake and insulin administration. Glucose values are obtained through the FreeStyle Libre 2 sensor and the DiaBox app, while a custom Android application collects wristband data. All information is transmitted in real time via MQTT for structured storage. This infrastructure provides a unified dataset of glucose, physiological, and contextual variables, laying the foundation for the development of AI-based predictive systems to improve diabetes management.