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Automated adjustment of crowdsourced calorie estimations for accurate food image logging

McAllister, Patrick, Moorhead, Anne, Bond, Raymond and Zheng, Huiru (2017) Automated adjustment of crowdsourced calorie estimations for accurate food image logging. In: BHI workshop at 2017 IEEE International Conference on Bioinformatics and Biomedicine, Kansas City, MO, USA. IEEE. 8 pp. [Conference contribution]

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[img] Text - Accepted Version

URL: http://dx.doi.org/10.1109/BIBM.2017.8217803

DOI: 10.1109/BIBM.2017.8217803


Obesity is increasing globally and is a risk factor for many chronic conditions such as such as heart disease, sleep apnea, type-2 diabetes, and some cancers. Research shows that food logging is beneficial in promoting weight loss. Crowdsourcing has also been used in promoting dietary feedback for food logging. This work investigates the feasibility of crowdsourcing to provide support in accurately determining calories in meal images. Two groups, 1. experts and 2. non-experts, completed a calorie estimation survey consisting of 15 meal images. Descriptive statistics were used to analyse the performance of each group. Collectively, non- experts could determine which meals had larger amounts of calories and analysis showed that meals with greater calories resulted in greater standard deviations of non-expert estimates. Secondary experiments were completed that used crowdsourcing to adjust user calorie estimations using non-expert calorie estimations. Five-fold cross validation was used and results from the calorie adjustment process show a reduced overall mean calorie difference in each fold and the mean error percentage decreased from 40.85% to 25.52% in comparing original mean estimations against adjusted mean estimations. As such, there is credibility in adjusting calorie estimates from a crowd as opposed to simply taking a central measure such as the mean.

Item Type:Conference contribution (Paper)
Keywords:crowd sourcing, carlorie estimation, food image logging
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Social Sciences > School of Communication
Faculty of Computing & Engineering > School of Computing and Mathematics
Faculty of Social Sciences
Research Institutes and Groups:Institute of Nursing and Health Research
Institute of Nursing and Health Research > Managing Chronic Illness Research Centre
Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:39623
Deposited By: Dr Huiru Zheng
Deposited On:17 Apr 2018 11:30
Last Modified:17 Apr 2018 11:30

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