Feasibility of identifying eating moments from first-person images leveraging human computation
Feasibility of identifying eating moments from first-person images leveraging human computation
复制标题
利用人类计算从第一人称图像中识别进食时刻的可行性
DOI：
10.1145/2526667.2526672
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
G. Abowd
中科院分区：
文献类型：
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作者：
Edison Thomaz;Aman Parnami;Irfan Essa;G. Abowd
医学研究界普遍认为，需要更有效的饮食评估和食物日志机制来对抗肥胖和其他营养相关疾病。然而，目前不可能自动捕获和客观地评估个体的饮食行为。目前使用的饮食评估和日志方法有几个局限性;他们对个人造成了重大负担，往往不够详细或准确。在本文中，我们描述了一种方法，我们利用人类计算来识别可穿戴相机拍摄的第一人称视点图像中的进食时刻。识别进食时刻是自动化饮食评估和建立帮助个人反思饮食的系统的关键第一步。在一项为期3天的可行性研究中，共有5名参与者收集了17，575张图像，我们的方法能够以89.68%的准确率识别进食时刻。
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.
影响因子：
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作者：
Sun M;Fernstrom JD;Jia W;Hackworth SA;Yao N;Li Y;Li C;Fernstrom MH;Sclabassi RJ
通讯作者：
Sclabassi RJ