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

McAllister, Patrick, Zheng, Huiru, Bond, Raymond and Moorhead, Anne (2016) A digital technology framework to optimise the self-management of obesity. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing, Germany. ACM. 5 pp. [Conference contribution]

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URL: http://dl.acm.org/citation.cfm?id=2978096

DOI: http://dx.doi.org/10.1145/2968219.2978096

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.

Item Type:Conference contribution (Paper)
Keywords:Obesity, Self-Management, Connected Health, Mobile Computing, Machine Learning, Machine Vision, Crowdsourcing
Faculties and Schools:Faculty of Social Sciences > School of Communication
Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Social Sciences
Research Institutes and Groups:Institute for Research in Social Sciences > Communication
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Institute for Research in Social Sciences
ID Code:35978
Deposited By: Dr Raymond Bond
Deposited On:27 Sep 2016 09:33
Last Modified:17 Oct 2017 16:25

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