Title:
Feasibility of Identifying Eating Moments from First-Person Images Leveraging Human Computation
Feasibility of Identifying Eating Moments from First-Person Images Leveraging Human Computation
dc.contributor.author | Thomaz, Edison | |
dc.contributor.author | Parnami, Aman | |
dc.contributor.author | Essa, Irfan | |
dc.contributor.author | Abowd, Gregory D. | |
dc.contributor.corporatename | Georgia Institute of Technology. School of Interactive Computing | en_US |
dc.contributor.corporatename | Georgia Institute of Technology. Institute for Robotics and Intelligent Machines | en_US |
dc.date.accessioned | 2014-03-03T18:02:52Z | |
dc.date.available | 2014-03-03T18:02:52Z | |
dc.date.issued | 2013-11 | |
dc.description | Copyright ©2013 ACM | |
dc.description | Presented at the 2013 4th International SenseCam and Pervasive Imaging Conference (SenseCam ’13), November 18-19, 2013, La Jolla, CA. | |
dc.description | DOI: 10.1145/2526667.2526672 | |
dc.description.abstract | There is widespread agreement in the medical research community that more effective mechanisms for dietary assessment and food journaling are needed to fight back against obesity and other nutrition-related diseases. However, it is presently not possible to automatically capture and objectively assess an individual’s eating behavior. Currently used dietary assessment and journaling approaches have several limitations; they pose a significant burden on individuals and are often not detailed or accurate enough. In this paper, we describe an approach where we leverage human computation to identify eating moments in first-person point-of-view images taken with wearable cameras. Recognizing eating moments is a key first step both in terms of automating dietary assessment and building systems that help individuals reflect on their diet. In a feasibility study with 5 participants over 3 days, where 17,575 images were collected in total, our method was able to recognize eating moments with 89.68% accuracy. | en_US |
dc.embargo.terms | null | en_US |
dc.identifier.citation | E. Thomaz, A. Parnami, I. Essa, and G. D. Abowd (2013), “Feasibility of Identifying Eating Moments from First-Person Images Leveraging Human Computation,” in Proceedings of ACM 4th International SenseCam and Pervasive Imaging (SenseCam ’13), 2013. | en_US |
dc.identifier.doi | 10.1145/2526667.2526672 | |
dc.identifier.uri | http://hdl.handle.net/1853/51304 | |
dc.language.iso | en_US | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.publisher.original | Association for Computing Machinery | |
dc.subject | Crowdsourcing | en_US |
dc.subject | Diet | en_US |
dc.subject | Egocentric photos | en_US |
dc.subject | Health | en_US |
dc.subject | Human computation | en_US |
dc.subject | Lifestyle | en_US |
dc.subject | Mechanical Turk | en_US |
dc.subject | Wearable | en_US |
dc.title | Feasibility of Identifying Eating Moments from First-Person Images Leveraging Human Computation | en_US |
dc.type | Text | |
dc.type.genre | Post-print | |
dc.type.genre | Proceedings | |
dspace.entity.type | Publication | |
local.contributor.author | Essa, Irfan | |
local.contributor.author | Abowd, Gregory D. | |
local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
local.contributor.corporatename | College of Computing | |
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