"Automatic Ingestion Monitor Version 2" - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images.
"Automatic Ingestion Monitor Version 2" - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images.
复制标题
“自动摄取监测器版本2”-一种用于自动食物摄取检测和被动捕获食物图像的新型可穿戴设备。
DOI：
10.1109/jbhi.2020.2995473
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发表时间：
2021-03
影响因子：
7.7
通讯作者：
Sazonov E
中科院分区：
文献类型：
--
作者：
Doulah A;Ghosh T;Hossain D;Imtiaz MH;Sazonov E
食物图像捕获和/或可穿戴传感器用于饮食评估的使用已经越来越普及。“主动”方法依赖于用户拍摄每个进食片段的图像。“被动”方法使用可持续捕获图像的可穿戴相机。大多数“被动”拍摄的图像与食物消费无关，可能会带来隐私问题。在本文中，我们提出了一种新的可穿戴传感器（自动摄取监视器，AIM-2），旨在捕捉图像，只有在自动检测进食事件。捕获方法在从社区中的30名志愿者收集的数据集上进行了验证，这些志愿者在伪自由生活环境中佩戴AIM-2 24小时，在自由生活环境中佩戴24小时。AIM-2能够检测10秒内的食物摄入量，F1评分（平均值和标准差）为81.8 ± 10.1%。进食事件检测的准确率为82.7%。在总共捕获的180，570张图像中，8，929张（4.9%）图像属于检测到的进食事件。隐私问题进行了评估，通过问卷调查的规模1-7。连续捕获的关注值为5.0 ± 1.6（关注），而仅在进食期间捕获的图像的关注值为1.9 ±1.7（不关注）。结果表明，AIM-2可以提供准确的食物摄入量检测，减少分析图像的数量，并减轻用户的隐私问题。
Use of food image capture and/or wearable sensors for dietary assessment has grown in popularity. “Active” methods rely on the user to take an image of each eating episode. “Passive” methods use wearable cameras that continuously capture images. Most of “passively” captured images are not related to food consumption and may present privacy concerns. In this paper, we propose a novel wearable sensor (Automatic Ingestion Monitor, AIM-2) designed to capture images only during automatically detected eating episodes. The capture method was validated on a dataset collected from 30 volunteers in the community wearing the AIM-2 for 24h in pseudo-free-living and 24h in a free-living environment. The AIM-2 was able to detect food intake over 10-second epochs with a (mean and standard deviation) F1-score of 81.8 ± 10.1%. The accuracy of eating episode detection was 82.7%. Out of a total of 180,570 images captured, 8,929 (4.9%) images belonged to detected eating episodes. Privacy concerns were assessed by a questionnaire on a scale 1–7. Continuous capture had concern value of 5.0 ± 1.6 (concerned) while image capture only during food intake had concern value of 1.9 ±1.7 (not concerned). Results suggest that AIM-2 can provide accurate detection of food intake, reduce the number of images for analysis and alleviate the privacy concerns of the users.