From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore
This work addresses nutrient intake regulation for Singaporeans to prevent chronic diseases, but it is incremental as it builds on existing methods with localized data.
The authors tackled the problem of regulating nutrient intake in Singapore by developing the FoodSG platform and a localized food dataset FoodSG-233, and they proposed a food recognition model FoodSG-SCL that integrates supervised contrastive learning to improve accuracy, though no specific performance numbers are provided.
Singapore has been striving to improve the provision of healthcare services to her people. In this course, the government has taken note of the deficiency in regulating and supervising people's nutrient intake, which is identified as a contributing factor to the development of chronic diseases. Consequently, this issue has garnered significant attention. In this paper, we share our experience in addressing this issue and attaining medical-grade nutrient intake information to benefit Singaporeans in different aspects. To this end, we develop the FoodSG platform to incubate diverse healthcare-oriented applications as a service in Singapore, taking into account their shared requirements. We further identify the profound meaning of localized food datasets and systematically clean and curate a localized Singaporean food dataset FoodSG-233. To overcome the hurdle in recognition performance brought by Singaporean multifarious food dishes, we propose to integrate supervised contrastive learning into our food recognition model FoodSG-SCL for the intrinsic capability to mine hard positive/negative samples and therefore boost the accuracy. Through a comprehensive evaluation, we present performance results of the proposed model and insights on food-related healthcare applications. The FoodSG-233 dataset has been released in https://foodlg.comp.nus.edu.sg/.