Bootstrapping analysis of crowdsourced non-expert estimates of the number of calories in photographs of meals

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A total of 1800 estimates from non-expert dietitians were collected online. This comprised of 120 subjects each providing an estimate per image (120 subjects * 15 images). The overall aim of this study was to use crowdsourcing to determine the accuracy of non-experts in estimating calories in a meal as shown in a photograph. We analyzed percentiles and used bootstrapping with replacement to determine the typical percentile rank estimate that is in proximity to the ground truth and to determine the least number of sample estimates from a crowd of non-experts that can provide a convenient and accurate estimate of the number of calories in a meal.
Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Subtitle of host publicationConference Proceedings
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Place of PublicationMadrid, Spain
PublisherIEEE
Pages1465-1469
Number of pages5
ISBN (Electronic)978-1-5386-5488-0
DOIs
Publication statusPublished (in print/issue) - 29 Jan 2019
EventIEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Madrid, Spain
Duration: 3 Dec 20186 Dec 2018
http://orienta.ugr.es/bibm2018/

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Country/TerritorySpain
CityMadrid
Period3/12/186/12/18
Internet address

Keywords

  • crowdsourcing
  • food
  • digital health
  • food data analytics
  • data analytics
  • bootstrapping
  • calories
  • obesity

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