Intelligent Internet of Things (I2oT) for biomedical applications. Case study: Intelligent Pancreas

Abstract

The Internet of Things (IoT) has revolutionized the healthcare industry and has the potential to connect physical and virtual objects through communication capabilities, providing data collection, management, and other services. Particularly, research has been conducted on the use of IoT in mHealth applications, with a focus on diabetes self-management. This thesis proposes a novel architecture for an IoMT health system for diabetes self-management, particularly an artificial pancreas. The system is composed of three different parts: a novel approach for continuous glucose monitoring based on ECG signal, an intelligent algorithm (model predictive controller) to predict the insulin rate required for maintaining the blood glucose in the normal range, and an IoMT-platform architecture based on a smartphone application to connect the different devices and permit remote monitoring. The proposed system is designed to ensure that the blood glucose level is always within the normal range, providing real-time BG monitoring using a non-invasive, affordable device, an insulin rate calculator coupled with an autonomous injection system, and alert and advisory services to prevent potentially life-threatening scenarios. The system is remotely monitored by healthcare administrators, making it an indispensable aspect of diabetes management. The proposed system is reliable, scalable, and user-friendly, and the intelligent algorithms used for ECG data analysis and insulin rate calculation are suitable for the specific requirements and characteristics of the IoT project. Remote health monitoring technologies are revolutionizing the healthcare business and enhancing people's lives, and this thesis contributes to that revolution by proposing a novel architecture for an IoMT health system for diabetes self-management.