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
Authors
Thomaz, Edison
Parnami, Aman
Essa, Irfan
Abowd, Gregory D.
Parnami, Aman
Essa, Irfan
Abowd, Gregory D.
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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.
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2013-11
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