Automatic diet monitoring: a review of computer vision and wearable sensor-based methods.

Auteur(s) :
Hassannejad H., Matrella G., Ciampolini P., De Munari I., Mordonini M., Cagnoni S.
Date :
Jan, 2017
Source(s) :
International journal of food sciences and nutrition. # p1-15
Adresse :
a Dipartimento di Ingegneria dell'Informazione , Università degli Studi di Parma , Parma , Italy.

Sommaire de l'article

Food intake and eating habits have a significant impact on people's health. Widespread diseases, such as diabetes and obesity, are directly related to eating habits. Therefore, monitoring diet can be a substantial base for developing methods and services to promote healthy lifestyle and improve personal and national health economy. Studies have demonstrated that manual reporting of food intake is inaccurate and often impractical. Thus, several methods have been proposed to automate the process. This article reviews the most relevant and recent researches on automatic diet monitoring, discussing their strengths and weaknesses. In particular, the article reviews two approaches to this problem, accounting for most of the work in the area. The first approach is based on image analysis and aims at extracting information about food content automatically from food images. The second one relies on wearable sensors and has the detection of eating behaviours as its main goal.

Source : Pubmed