Comparison of Different Classifiers for Early Meal Detection Using Abdominal Sounds (2022)
IEEE SAM
Abstract
One of the challenges for the diabetic patients is to regulate the amount of glucose in the blood. Early and reliable meal detection represents one relevant issue to develop more effective treatments. This paper presents a comparison of different classifiers for early meal detection using abdominal sounds. The data presented in the paper is obtained from two different equipment and the classifiers are trained and tested on twelve recordings. The results show that neural networks and convolutional neural networks provide better average detection time (2.875 min and 2.791 min, respectively) than alternative methods recently proposed, and no false positives are observed during testing. Early and reliable meal detection eases the mental burden of the diabetic patients from documenting every meal in the controller and also reduces the risk of hypoglycemia.