Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping.

Yasmine Probst,Duc Nguyen,Wanqing Li,Minh Tran

doi:10.3390/nu7085274

Yasmine Probst, Duc Nguyen + Show 2 more

Open Access

https://doi.org/10.3390/nu7085274

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Abstract

Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.

Highlights

Assessment of dietary intake is a process vital to dietetic care across different disciplines and specialties of practice
This paper describes the development of a baseline prototype of an automated food record by using image processing and pattern recognition algorithms
Applying image processing and pattern recognition techniques on a mobile device has allowed for the development of an automated food record via the use of a Smartphone

Summary

Introduction

Assessment of dietary intake is a process vital to dietetic care across different disciplines and specialties of practice. Nutrients 2015, 7 of conducting a dietary assessment with a participant is inherently flawed due to various forms of bias depending on the method of assessment being applied [1] in addition to the format of the assessment. The time burden of the process and the literacy of the target group for which dietary intake data is required are factors that affect the data being recorded. This is evident in assessing the intakes of children where the age of the child plays an important role on the method employed. With the combination of these challenges and technological advances, many dietary assessment methods have been partially or fully automated in an attempt to reduce bias, ease the burden for the participants and streamline the steps applied within each method [1]

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