LAUSR

Abstract The development of the technology creates the lot of changes in food processing which leads to alter human life style. Due to the change of their food habits and lack of attention to the dietary habits there is an increase in the prevalence of diseases such as obesity, heart attack, diabetics and so on. Hence it is important to monitor the food consumption which will help to avoid health issues associated with it. For this purpose, IoT medical tooth chip is placed in the teeth via filling or bonding process, which is one of the electrochemical sensors that collect information of the consumed food such as salt, sugar, fat etc. The gathered information can be analyzed to assess the quality of food intake. The collected information is processed by using the bacterial optimization along with adaptive deep learning neural network that examines the collected IoT information using self-learning process. The IoT device is embedded in teeth that reduce the difficulties during mastication. The excellence of IoT device based food quality level prediction system is analyzed using MATLAB implementation setup in which data were collected from 53 patients and 15 patients was used to construct the testing model to assess the efficiency of the food quality examined using IoT device up to 99.23% of accuracy process.