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Register a new userFoodRepo: An Open Food Repository of Barcoded Food Products
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In the past decade, digital technologies have started to profoundly influence healthcare systems. Digital self-tracking has facilitated more precise epidemiological studies, and in the field of nutritional epidemiology, mobile apps have the potential to alleviate a significant part of the journaling burden by, for example, allowing users to record their food intake via a simple scan of packaged products barcodes. Such studies thus rely on databases of commercialized products, their barcodes, ingredients, and nutritional values, which are not yet openly available with sufficient geographical and product coverage. In this paper, we present FoodRepo (https://www.foodrepo.org), an open food repository of barcoded food items, whose database is programmatically accessible through an application programming interface (API). Furthermore, an open source license gives the appropriate rights to anyone to share and reuse FoodRepo data, including for commercial purposes. With currently more than 21,000 items available on the Swiss market, our database represents a solid starting point for large-scale studies in the field of digital nutrition, with the aim to lead to a better understanding of the intricate connections between diets and health in general, and metabolic disorders in particular.
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The determination of the isothermal adsorption curves represents a mechanism that allows ob-taining information on the process of adsorption of water in organic and inorganic materials. In addition, it is a measure to be considered when characterizing the physicochemical and structural properties of the materials. We want to present an approach to the state of knowledge about the methods to characterize seeds and materials associated with food products physically and struc-turally, and to relate this knowledge to biophysical processes in these materials. This review considers the papers available since 2001 associated with water adsorption studies on seeds and other food products as well as the approach of different authors to to technical and experimental models and processes that are needed for the development of this topic. From these articles the applied experimental methodologies (obtaining samples, environmental conditions and labor-atory equipment) and the mathematical models used to give physical, chemical and biological meaning to the results were analyzed and discussed, concluding in the methodologies that have best adapted to the advance of the technology for obtaining isothermal curves in the last years.
Background: The deployment of various networks (e.g., Internet of Things (IoT) and mobile networks) and databases (e.g., nutrition tables and food compositional databases) in the food system generates massive information silos due to the well-known data harmonization problem. The food knowledge graph provides a unified and standardized conceptual terminology and their relationships in a structured form and thus can transform these information silos across the whole food system to a more reusable globally digitally connected Internet of Food, enabling every stage of the food system from farm-to-fork. Scope and approach: We review the evolution of food knowledge organization, from food classification, food ontology to food knowledge graphs. We then discuss the progress in food knowledge graphs from several representative applications. We finally discuss the main challenges and future directions. Key findings and conclusions: Our comprehensive summary of current research on food knowledge graphs shows that food knowledge graphs play an important role in food-oriented applications, including food search and Question Answering (QA), personalized dietary recommendation, food analysis and visualization, food traceability, and food machinery intelligent manufacturing. Future directions for food knowledge graphs cover several fields such as multimodal food knowledge graphs and food intelligence.
A key challenge in robotic food manipulation is modeling the material properties of diverse and deformable food items. We propose using a multimodal sensory approach to interact and play with food that facilitates the ability to distinguish these properties across food items. First, we use a robotic arm and an array of sensors, which are synchronized using ROS, to collect a diverse dataset consisting of 21 unique food items with varying slices and properties. Afterwards, we learn visual embedding networks that utilize a combination of proprioceptive, audio, and visual data to encode similarities among food items using a triplet loss formulation. Our evaluations show that embeddings learned through interactions can successfully increase performance in a wide range of material and shape classification tasks. We envision that these learned embeddings can be utilized as a basis for planning and selecting optimal parameters for more material-aware robotic food manipulation skills. Furthermore, we hope to stimulate further innovations in the field of food robotics by sharing this food playing dataset with the research community.
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 Singapores 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.
The automatic recognition of food on images has numerous interesting applications, including nutritional tracking in medical cohorts. The problem has received significant research attention, but an ongoing public benchmark to develop open and reproducible algorithms has been missing. Here, we report on the setup of such a benchmark using publicly available food images sourced through the mobile MyFoodRepo app. Through four rounds, the benchmark released the MyFoodRepo-273 dataset constituting 24,119 images and a total of 39,325 segmented polygons categorized in 273 different classes. Models were evaluated on private tests sets from the same platform with 5,000 images and 7,865 annotations in the final round. Top-performing models on the 273 food categories reached a mean average precision of 0.568 (round 4) and a mean average recall of 0.885 (round 3). We present experimental validation of round 4 results, and discuss implications of the benchmark setup designed to increase the size and diversity of the dataset for future rounds.
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