A novel vision-based approach for dietary assessment using deep learning view synthesis
Author(s)
Lo, Frank P-W
Sun, Yingnan
Qiu, Jianing
Lo, Benny
Type
Conference Paper
Abstract
Dietary assessment system has proven as an effective tool to evaluate the eating behavior of patients suffering from diabetes and obesity. To assess the dietary intake, the traditional method is to carry out a 24-hour dietary recall (24HR), a structured interview aimed at capturing information on food items and portion size consumed by participants. However, unconscious biases are developed easily due to individual's subjective perception in this self-reporting technique which may lead to inaccuracy. Thus, this paper proposed a novel vision-based approach for estimating the volume of food items based on deep learning view synthesis and depth sensing techniques. In this paper, a point completion network is applied to perform 3D reconstruction of food items using a single depth image captured from any convenient viewing angle. Compared to previous approaches, the proposed method has addressed several key challenges in vision-based dietary assessment, such as view occlusion and scale ambiguity. Experiments have been carried out to examine this approach and showed the feasibility of the algorithm in accurate estimation of food volume.
Date Issued
2019-07-25
Date Acceptance
2019-07-01
Citation
2019 IEEE 16TH INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN), 2019
ISSN
2376-8886
Publisher
IEEE
Journal / Book Title
2019 IEEE 16TH INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN)
Copyright Statement
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Bill and Melinda Gates Foundation
Bill & Melinda Gates Foundation
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000492872400029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
OPP1171395
OPP1171395
Source
IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Subjects
Science & Technology
Technology
Computer Science, Interdisciplinary Applications
Engineering, Electrical & Electronic
Computer Science
Engineering
Deep learning
Dietary assessment
Publication Status
Published
Start Date
2019-05-19
Finish Date
2019-05-22
Coverage Spatial
Univ Illinois Chicago, Chicago, IL
Date Publish Online
2019-07-25