DANES: Diet and Nutrition Expert System for Meal Management and Nutrition Counseling

“Your body is your temple” As people across the globe are becoming more health conscious, eating more healthy food and avoiding junk food, a system that can measure calories and nutrition in every day meals can be very useful for maintaining one’s health. Food calorie and nutrition measurement system is very beneficial for dieticians and patients to measure and manage their daily food intake. We also know that it’s difficult to find an affordable nutritionist or a dietician across the street; therefore, we have proposed a system – DIET AND NUTRITION EXPERT SYSTEM. The proposed system is a responsive android application which contains the knowledge and data regarding the fitness of a person and nutrition content values. This application consists of the user interface which will be publicly displayed on the application i.e. the basic information regarding the fitness and nutrition values such as how to maintain good health by adapting healthy eating habits which includes the intake of calories, proteins and carbohydrates etc. in proper proportion. A dietician consults a person based on his schedule, body type, height and weight. The system too asks all this data from the user and processes it. It asks about how many hours the user works, his height, weight, age etc. The system stores and processes this data and then calculates the nutrient value needed to fill up users’ needs

Building a Persuasive Virtual Dietitian

This paper describes the Multimedia Application for Diet Management (MADiMan), a system that supports users in managing their diets while admitting diet transgressions. MADiMan consists of a numerical reasoner that takes into account users’ dietary constraints and automatically adapts the users’ diet, and of a natural language generation (NLG) system that automatically creates textual messages for explaining the results provided by the reasoner with the aim of persuading users to stick to a healthy diet. In the first part of the paper, we introduce the MADiMan system and, in particular, the basic mechanisms related to reasoning, data interpretation and content selection for a numeric data-to-text NLG system. We also discuss a number of factors influencing the design of the textual messages produced. In particular, we describe in detail the design of the sentence-aggregation procedure, which determines the compactness of the final message by applying two aggregation strategies. In the second part of the paper, we present the app that we developed, CheckYourMeal!, and the results of two human-based quantitative evaluations of the NLG module conducted using CheckYourMeal! in a simulation. The first evaluation, conducted with twenty users, ascertained both the perceived usefulness of graphics/text and the appeal, easiness and persuasiveness of the textual messages. The second evaluation, conducted with thirty-nine users, ascertained their persuasive power. The evaluations were based on the analysis of questionnaires and of logged data of users’ behaviour. Both evaluations showed significant results

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 816303 and from the Plan Propio de Investigación y Transferencia of the University of Granada under the program “Intensificación de la Investigación, modalidad B”.Acknowledgments: This work is part of the doctoral thesis of Daniel Hinojosa-Nogueira conducted within the context of the “Program of Nutrition and Food Sciences” at the University of Granada and part of the doctoral thesis of Bartolome Ortiz-Viso conducted within the context of the “Program of Information and Communication technologies” at the University of Granada.Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user’s needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.European Union’s Horizon 2020 research and innovation programme under grant agreement No 816303Plan Propio de Investigación y Transferencia of the University of Granada under the program “Intensificación de la Investigación, modalidad B

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Towards persuasive social recommendation: knowledge model

[EN] The exponential growth of social networks makes fingerprint let by users on the Internet a great source of information, with data about their preferences, needs, goals, profile and social environment. These data are distributed across di↵erent sources of information (social networks, blogs, databases, etc.) that may contain inconsistencies and their accuracy is uncertain. Paradoxically, this unprecedented availability of heterogeneous data has meant that users have more information available than they actually are able to process and understand to extract useful knowledge from it. Therefore, new tools that help users in their decision-making processes within the network (e.g. which friends to contact with or which products to consume) are needed. In this paper, we show how we have used a graph-based model to extract and model data and transform it in valuable knowledge to develop a persuasive social recommendation system1.This work was partially supported by the project MINE-CO/FEDER TIN2012-365686-C03-01 of the Spanish government and by the Spanish Ministry of Education, Culture and Sports under the Program for R&D Valorisation and Joint Resources VLC/CAMPUS, as part of the Campus of International Excellence Program (Ref. SP20140788).Palanca Cámara, J.; Heras Barberá, SM.; Jorge Cano, J.; Julian Inglada, VJ. (2015). Towards persuasive social recommendation: knowledge model. ACM SIGAPP Applied Computing Review. 15(2):41-49. https://doi.org/10.1145/2815169.2815173S4149152Desel, J., Pernici, B., Weske, M. 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