An Intelligent IoT Monitoring and Prediction System for Health Critical Conditions

Author First name, Last name, Institution

Omar Alfandi, Zayed University

Document Type


Source of Publication

Mobile Networks and Applications

Publication Date



Diabetes is considered among the major critical health conditions (chronic disease) around the world. This is due the fact that Glucose level could change drastically and lead to critical conditions reaching to death in some advance cases. To prevent this issues, diabetes patient are always advised to monitor their glucose level at least three times a day. Fingertip pricking - as the traditional method for glucose level tracking - leads patients to be distress and it might infect the skin. In some cases, tracking the glucose level might be a hard job especially if the patient is a child, senior, or even have several other health issues. In this paper, an optimum solution to this drawback by adopting the Wireless Sensor Network (WSN)-based non-invasive strategies has been proposed. Near-Infrared (NIR) -as an optical method of the non-invasive technique - has been adopted to help diabetic patients in continuously monitoring their blood without pain. The proposed solution will alert the patients’ parents or guardians of their situation when they about to reach critical conditions specially at night by sending alarms and notifications by Short Messages (SMS) along with the patients current location to up to three people. Moreover, a Machine Learning (ML) model is implemented to predict future events where the patient might have serious issues. This model prediction is best practice in this chronic health domain as it has never been implemented to predicted a future forecast of the patient chart. Multivariate Time-Series data set (i.e. AIM ’94) has been used to train the proposed ML model. The collected data shows a high level of accuracy when predicting serious critical conditions in Glucose levels.




Springer Science and Business Media LLC

First Page


Last Page



Computer Sciences


Wireless sensor network (WSN), Healthcare application, Diabetes, Non-invasive, Blood glucose, Near infrared spectroscopy (NIRS), IR sensor, Machine learning

Scopus ID


Indexed in Scopus


Open Access


Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license