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 language | English |
---|---|
Title of host publication | Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 |
Subtitle of host publication | Conference Proceedings |
Editors | Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang |
Place of Publication | Madrid, Spain |
Publisher | IEEE |
Pages | 1465-1469 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-5386-5488-0 |
DOIs | |
Publication status | Published (in print/issue) - 29 Jan 2019 |
Event | IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Madrid, Spain Duration: 3 Dec 2018 → 6 Dec 2018 http://orienta.ugr.es/bibm2018/ |
Conference
Conference | IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
---|---|
Country/Territory | Spain |
City | Madrid |
Period | 3/12/18 → 6/12/18 |
Internet address |
Keywords
- crowdsourcing
- food
- digital health
- food data analytics
- data analytics
- bootstrapping
- calories
- obesity