Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
8-2019
Abstract
An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones and propose a smart-food logging system: FoodAI, which offers state-of-the-art deep-learning based image recognition capabilities. FoodAI has been developed in Singapore and is particularly focused on food items commonly consumed in Singapore. FoodAI models were trained on a corpus of 400,000 food images from 756 different classes.In this paper we present extensive analysis and insights into the development of this system. FoodAI has been deployed as an API service and is one of the components powering Healthy 365, a mobile app developed by Singapore's Heath Promotion Board. We have over 100 registered organizations (universities, companies, start-ups) subscribing to this service and actively receive several API requests a day. FoodAI has made food logging convenient, aiding smart consumption and a healthy lifestyle.
Keywords
Food Computing, Image Recognition, Smart Food Logging
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems | Health Information Technology
Research Areas
Data Science and Engineering; Intelligent Systems and Optimization
Publication
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, AK, August 4-9
First Page
2260
Last Page
2268
ISBN
9781450362016
Identifier
10.1145/3292500.3330734
Publisher
ACM
City or Country
New York
Citation
SAHOO, Doyen; WANG, Hao; SHU, Ke; WU, Xiongwei; LE, Hung; ACHANANUPARP, Palakorn; LIM, Ee-peng; and HOI, Steven C. H..
FoodAI: Food image recognition via deep learning for smart food logging. (2019). KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, AK, August 4-9. 2260-2268.
Available at: https://ink.library.smu.edu.sg/sis_research/4427
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3292500.3330734
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Health Information Technology Commons