Title: A smart tableware-based meal information collection system using machine learning

Authors: Liyang Zhang; Kohei Kaiya; Hiroyuki Suzuki; Akio Koyama

Addresses: Department of Informatics, Graduate School of Science and Engineering, Yamagata University, Yonezawa, Yamagata, Japan ' Department of Informatics, Graduate School of Science and Engineering, Yamagata University, Yonezawa, Yamagata, Japan ' Department of Informatics, Graduate School of Science and Engineering, Yamagata University, Yonezawa, Yamagata, Japan ' Department of Informatics, Graduate School of Science and Engineering, Yamagata University, Yonezawa, Yamagata, Japan

Abstract: In recent years, due to lifestyle-related diseases, people have paid more and more attention to the management of healthy meals. Some meal management systems are entering people's lives gradually. Existing studies have found that the proper meal habits, such as a correct meal sequence, can help prevent disease to a certain extent. In this paper, we introduce the smart tableware consisting of an acceleration sensor and a pressure sensor to obtain meal information such as meal sequence and meal content automatically. Moreover, feature extraction is performed on the meal information captured by sensors, and the machine learning algorithms are used to analyse and process the information. Finally, the meal content and meal sequence are fed back to the user to help people prevent diseases affected by lifestyle habits such as obesity and diabetes. In the experiment, we compare a variety of different machine learning algorithms and analyse the experimental results.

Keywords: meal information; smart tableware; internet of things; machine learning algorithms; support vector machine; multilayer perceptron; lifestyle-related diseases; meal management systems; meal sequence; meal content; meal time.

DOI: 10.1504/IJWGS.2019.099564

International Journal of Web and Grid Services, 2019 Vol.15 No.2, pp.206 - 218

Received: 24 Sep 2018
Accepted: 04 Feb 2019
Published online: 09 May 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article