Smart Tableware-Based Meal Information Recognition by Comparing Supervised Learning and Multi-Instance Learning

Abstract

In recent years, with the improvement of health awareness, people have paid more and more attention to proper meal. Existing research has shown that a proper meal can help people prevent lifestyle diseases such as diabetes. In this research, by attaching sensors to the tableware, the information during the meal can be captured, and after processing and analyzing it, the meal information, such as time and sequence of meal, can be obtained. This paper introduces how to use supervised learning and multi-instance learning to deal with meal information and a detailed comparison is made. Three supervised learning algorithms and two multi-instance learning algorithms are used in the experiment. The experimental results showed that although the supervised learning algorithms have achieved good results in F-score, the multi-instance learning algorithms have achieved better results not only in accuracy but also in F-score.