Blood Sugar Level Indication Through Chewing and Swallowing from Acoustic MEMS Sensor and Deep Learning Algorithm for Diabetic Management.

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Abstract

Diabetes, a metabolic disorder due to high blood glycemic index in the human body. The glycemic index varies in the human of improper diet and eating pattern such as junk foods, variation in the quantity of food, swallowing of food without chewing and stress. However, the diagnose of increase or decrease in the glycemic index is a challenging task. Similarly, the regulation of glycemic index without regular exercise is a major problem in day to day life. In this paper, we propose a novel SCS method to regulate glycemic index without exercise through changing the eating method. The proposed SCS eating method consists of Size of the food, Chewing style and Swallow time (SCS) of the food to regulate glycemic index. Furthermore, the proposed SCS method evaluate and validate through the acoustic signal acquired and processed with deep learning algorithm to analyze the chewing pattern of food to formulate a standard procedure for eating style and to reduce the glycemic level. The validation of diabetes done by measurement of blood glycemic through AccuChek Instant S Glucometer. Furthermore, the SCS method of eating style from 50 diabetes persons reduces the blood glucose level drastically by 85% after following the proposed method of eating style.