Automatic food detection in egocentric images using artificial intelligence technology

Author:

Jia Wenyan,Li Yuecheng,Qu Ruowei,Baranowski Thomas,Burke Lora E,Zhang Hong,Bai Yicheng,Mancino Juliet M,Xu Guizhi,Mao Zhi-Hong,Sun Mingui

Abstract

Abstract Objective To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment. Design To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network. Results A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both ‘food’ and ‘drink’ were considered as food images. Alternatively, if only ‘food’ items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively. Conclusions The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns.

Publisher

Cambridge University Press (CUP)

Subject

Public Health, Environmental and Occupational Health,Nutrition and Dietetics,Medicine (miscellaneous)

Cited by 69 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Artificial intelligence in brachytherapy;Journal of Radiation Research and Applied Sciences;2024-06

2. Food Quality Identification using Machine Learning;International Journal of Advanced Research in Science, Communication and Technology;2024-05-31

3. Fundus- Based Glaucoma Detection- Machine Learning A Design Approach;International Journal of Advanced Research in Science, Communication and Technology;2024-05-25

4. An Improved Tomato Ripeness Detection and Sorting System;2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG);2024-04-02

5. Computer vision and deep learning-based approaches for detection of food nutrients/nutrition: New insights and advances;Trends in Food Science & Technology;2024-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3