Browsing by Author "Samir, Mohamed Amr"

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Applying Deep Learning to Track Food Consumption and Human Activity for Non-intrusive Blood Glucose Monitoring
(Institute of Electrical and Electronics Engineers, 04/12/2021) Samir, Mohamed Amr; Mohamed, Zeinab A; Hussein, Mona Abdelmotaleb A; Atia, Ayman
Blood glucose monitoring is a wide area of research as it plays a huge part in controlling diabetes and many of its symptoms. A common human disease 'Diabetes Mellitus' (DM), which is characterized by hyperglycemia, has a number of harmful complications. In addition, the low glucose level in blood caused by hypoglycemia is correlated to fatal brain failure and death. In this paper, we explore a variety of related research to have a grasp on some of the systems and concepts that can assist in forming an autonomous system for glucose monitoring, including deep learning techniques. The proposed system in this paper utilizes non-intrusive Continuous Glucose Monitoring (CGM) devices for tracking glucose levels, combined with food classification and Human Activity Recognition (HAR) using deep learning. We relate the preprandial and peak postprandial glucose levels extracted from CGM with the Glycimc Load (GL) present in food, which makes it possible to form an estimation of blood sugar increase as well as predict hyperglycemia. The system also relates human activity with decrease in blood glucose to warn against possible signs of hypoglycemia before it occurs. We have conducted 3 different experiments; two of which are comparison between deep learning models for food classification and HAR with good results achieved, as well as an experimental result that we obtained by testing hyperglycemia prediction on real data of diabetic patients. The system was able to predict hyperglycemia with an accuracy percentage of 93.2%. © 2021 IEEE.
Exam Cheating Detection System with Multiple-Human Pose Estimation
(Institute of Electrical and Electronics Engineers, 19/11/2021) Samir, Mohamed Amr; Maged, Youssef; Atia, Ayman
Cheating in exams is a persistent problem that contributes to academic dishonesty. In this paper we explore a variety of related work proposed as a solution for exam cheating, then we propose an exam cheating detection system that works for both on-site and online examinations. The proposed system applies Human Pose Estimation that includes both single-user and multiple-user tracking algorithms. Based on video footage, the system can detect whether or not a student is cheating by continuously validating their head posture and hand movement conditions during the exam. The system doesn't fully imply a student is cheating, instead, we use the term 'warning' for the output to indicate that the student has met an abnormal condition that is similar to cheating behavior. At last, we validate the system usage in real-life examination environments through two different experiments that resulted in accuracy numbers of 92%-97% in cheating detection.