An Integrated System for Mobile Image-Based Dietary Assessment

TL;DR

This paper introduces a mobile image-based dietary assessment system that captures high-quality food images and provides annotated datasets, aiming to improve accuracy and convenience in dietary intake measurement.

Contribution

It presents a novel integrated system that combines mobile image capture with groundtruth annotation for dietary assessment, addressing dataset quality challenges.

Findings

System successfully captures high-quality food images in natural settings.

Provides annotated datasets for developing machine learning models.

Deployed in controlled and community dietary studies.

Abstract

Accurate assessment of dietary intake requires improved tools to overcome limitations of current methods including user burden and measurement error. Emerging technologies such as image-based approaches using advanced machine learning techniques coupled with widely available mobile devices present new opportunities to improve the accuracy of dietary assessment that is cost-effective, convenient and timely. However, the quality and quantity of datasets are essential for achieving good performance for automated image analysis. Building a large image dataset with high quality groundtruth annotation is a challenging problem, especially for food images as the associated nutrition information needs to be provided or verified by trained dietitians with domain knowledge. In this paper, we present the design and development of a mobile, image-based dietary assessment system to capture and analyze dietary intake, which has been deployed in both controlled-feeding and community-dwelling dietary studies. Our system is capable of collecting high quality food images in naturalistic settings and provides groundtruth annotations for developing new computational approaches.