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Automatic food detection in egocentric images using artificial intelligence technology.
- Source :
-
Public Health Nutrition . May2019, Vol. 22 Issue 7, p1168-1179. 12p. - Publication Year :
- 2019
-
Abstract
- <bold>Objective: </bold>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.<bold>Design: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13689800
- Volume :
- 22
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Public Health Nutrition
- Publication Type :
- Academic Journal
- Accession number :
- 136116856
- Full Text :
- https://doi.org/10.1017/S1368980018000538