research-article

A digital technology framework to optimise the self-management of obesity

Authors:

Patrick McAllister,

Huiru Zheng,

Raymond Bond,

Anne MoorheadAuthors Info & Claims

UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

Pages 1126 - 1131

https://doi.org/10.1145/2968219.2978096

Published: 12 September 2016 Publication History

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Abstract

Obesity is increasing globally and can cause major chronic conditions. Much research has been completed in utilising digital technologies to optimise the self-management of obesity. This research proposes an obesity management framework which highlights digital technologies to promote self-management of obesity. This work discusses preliminary research using image classification to promote food logging and crowdsourcing to determine calorie content of food images through aggregating the predictions of experts and non-experts. Preliminary results from image classification show SMO classifier achieved 73.87% accuracy in classifying 15 food items, which is promising as computer vision methods could be incorporated into food logging methods. Crowdsourcing results show that aggregated expert group mode percentage error was +2.60% (SD 3.87) in predicting calories in meals and non-expert group mode percentage error was +29.07% (SD 20.48). Further analysis on the crowdsourcing dataset will be completed to ascertain how many experts or nonexperts is needed to get the most accurate calorie prediction.

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Bond RMoorhead AZheng HMcAllister P(2018)Bootstrapping analysis of crowdsourced non-expert estimates of the number of calories in photographs of meals2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2018.8621166(1465-1469)Online publication date: Dec-2018
https://doi.org/10.1109/BIBM.2018.8621166

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UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct

September 2016

1807 pages

ISBN:9781450344623

DOI:10.1145/2968219

General Chairs:
Paul Lukowicz
DFKI and University of Kaiserslautern
,
Antonio Krüger
DFKI and Saarland University
,
Program Chairs:
Andreas Bulling
Max Planck Institute for Informatics
,
Youn-Kyung Lim
KAIST
,
Shwetak N. Patel
University of Washington

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 September 2016

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UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 12 - 16, 2016

Germany, Heidelberg

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Bond RMoorhead AZheng HMcAllister P(2018)Bootstrapping analysis of crowdsourced non-expert estimates of the number of calories in photographs of meals2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2018.8621166(1465-1469)Online publication date: Dec-2018
https://doi.org/10.1109/BIBM.2018.8621166

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Treatment of children obesity and diabetes through gamification: a case of study

Specializing for predicting obesity and its co-morbidities

A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity

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