491 research outputs found

    DDSS: Dynamic decision support system for elderly

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    To provide robust healthcare services and personalized recommendations details relating to a patient’s daily life activities, profile information, and personal experience is of vital importance. This paper focuses on improvement in general health status of elderly patients through the use of an innovative service which align dietary intake with activity information. Personalized healthcare services based on the patient’s activities of daily living and their shared experience, are provided as outputs. A knowledge driven approach has been used where all the daily life activities, social interactions, and profile information are modeled in an ontology. The semantic context is exploited that enables fine-grained situation analysis for recommendation of personalized services and decision support. Preliminary experimental results for the dynamic nature of the systems and its corresponding personalized recommendations have been found to be encouraging

    Expert System for Nutrition Care Process of Older Adults

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    This paper presents an expert system for a nutrition care process tailored for the specific needs of elders. Dietary knowledge is defined by nutritionists and encoded as Nutrition Care Process Ontology, and then used as underlining base and standardized model for the nutrition care planning. An inference engine is developed on top of the ontology, providing semantic reasoning infrastructure and mechanisms for evaluating the rules defined for assessing short and long term elders’ self-feeding behaviours, to identify unhealthy dietary patterns and detect the early instauration of malnutrition. Our expert system provides personalized intervention plans covering nutrition education, diet prescription and food ordering adapted to the older adult’s specific nutritional needs, health conditions and food preferences. In-lab evaluation results are presented proving the usefulness and quality of the expert system as well as the computational efficiency, coupling and cohesion of the defined ontology

    Ontology-Based Personalized Dietary Recommendation For Travelers

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    Tourism is fun that allows a person to know different cultural aspects of the world. In the tour plan, food searching according to nutritional value also plays an important role. However it is a challenging task to facilitate the tourists to be healthy and fit while traveling by selecting the food according to their requirement. This paper aims to develop an ontology based solution that will help the tourists / travelers to plan for a healthy food with personalized option of diet with food safety recommendation according to the region of interest. The proposed approach is tested on a sample data based on a traveler visiting Asian region

    A Linear General Type-2 Fuzzy Logic Based Computing With Words Approach for Realising an Ambient Intelligent Platform for Cooking Recipes Recommendation

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    This paper addresses the need to enhance transparency in ambient intelligent environments by developing more natural ways of interaction, which allow the users to communicate easily with the hidden networked devices rather than embedding obtrusive tablets and computing equipment throughout their surroundings. Ambient intelligence vision aims to realize digital environments that adapt to users in a responsive, transparent, and context-aware manner in order to enhance users' comfort. It is, therefore, appropriate to employ the paradigm of “computing with words” (CWWs), which aims to mimic the ability of humans to communicate transparently and manipulate perceptions via words. One of the daily activities that would increase the comfort levels of the users (especially people with disabilities) is cooking and performing tasks in the kitchen. Existing approaches on food preparation, cooking, and recipe recommendation stress on healthy eating and balanced meal choices while providing limited personalization features through the use of intrusive user interfaces. Herein, we present an application, which transparently interacts with users based on a novel CWWs approach in order to predict the recipe's difficulty level and to recommend an appropriate recipe depending on the user's mood, appetite, and spare time. The proposed CWWs framework is based on linear general type-2 (LGT2) fuzzy sets, which linearly quantify the linguistic modifiers in the third dimension in order to better represent the user perceptions while avoiding the drawbacks of type-1 and interval type-2 fuzzy sets. The LGT2-based CWWs framework can learn from user experiences and adapt to them in order to establish more natural human-machine interaction. We have carried numerous real-world experiments with various users in the University of Essex intelligent flat. The comparison analysis between interval type-2 fuzzy sets and LGT2 fuzzy sets demonstrates up to 55.43% improvement when general type-2 fuzzy sets are used than when interval type-2 fuzzy sets are used instead. The quantitative and qualitative analysis both show the success of the system in providing a natural interaction with the users for recommending food recipes where the quantitative analysis shows the high statistical correlation between the system output and the users' feedback; the qualitative analysis presents social scienc

    Ontology-based personalized dietary recommendation for weightlifting

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    As pointed at LIVESTRONG.COM, Olympic weightlifters are quite possibly the strongest and most skilled lifters on earth. The ability to put nearly 300 kg over head or clean and jerk three times their bodyweight is feat of strength unmatched in other sports. While this takes years of dedicated training, diet is also critical as optimal nutrition is essential for peak performance. Nutritional misinformation can do as much harm to the ambitious athlete as good nutrition can help. In this study, we propose ontology-based personalized dietary recommendation for weightlifting to assist athletes meet their requirements. This paper describes a food and nutrition ontology working with a rule-based knowledge framework to provide specific menus for different times of the day and different training phases for the athlete's diary nutritional needs and personal preferences. The main components of this system are the food and nutrition ontology, the athletes' profiles and nutritional rules for sports athletes.This research is funded by Sports Science Centre, Sports Authority of Thailand

    Формирование персонализированного рациона питания с использованием структурной оптимизации

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    The design of a human personalized diet considering a variety of different factors is associated with system analysis and formalization of data and knowledge, as well as with the development of digital technologies. The paper presents the methodology of optimization and formation of personalized diets based on structural-parametric modeling. The proposed approach allows solving the following tasks: 1)  to analyze the daily diet or individual meals (breakfast, lunch, afternoon snack, dinner, additional meals or snacks) with a known quantitative set of finished products in terms of energy value and chemical composition in order to reveal dietary disorders; 2)  to calculate quantity of products optimal for a meal from the fixed list, thereby composing an individual reference diet with regard to the mental and physical activities, nutritive status of a consumer and economic aspects; 3) to optimize a diet depending on the task at hand by selecting a group of finished products from a complete or selected list of archival data, equally taking into account all the necessary parameters; 4) to adjust the diet taking into account dietary deviations in certain parameters of the chemical composition and energy value by additional introduction of special purpose products with the increased biological value, multivitamin and multivitamin-mineral supplements, as well as natural bioactive substances.Конструирование персонализированного рациона питания человека с учетом многообразия различных факторов связано с системным анализом и формализацией накопленных данных и знаний, а также с развитием цифровых технологий. В работе представлена методология оптимизации и формирования персонализированных рационов питания на основе структурно-параметрического моделирования. Предлагаемый подход позволяет решать следующие задачи: 1) анализировать суточный рацион или отдельные приемы пищи (завтрак, обед, полдник, ужин, дополнительные приемы пищи (перекус)) с известным количественным набором готовых продуктов по энергетической ценности и химическому составу с целью выявления диетических нарушений; 2) рассчитывать оптимальное для приема пищи количество продуктов из фиксированного перечня, тем самым составляя индивидуальный эталонный рацион с учетом умственной и физической нагрузки, нутритивного статуса потребителя, а также экономических аспектов; 3) оптимизировать рацион в зависимости от поставленной задачи путем подбора группы готовых продуктов из полного или избранного перечня архивных данных, равнозначно учитывая при этом все необходимые параметры; 4) корректировать рацион питания с учетом диетических отклонений по отдельным параметрам химического состава и энергетической ценности за счет дополнительного введения продуктов повышенной биологической ценности специального назначения, поливитаминных и поливитаминно-минеральных препаратов, а также природных биологически активных веществ

    Using conceptual graphs for clinical guidelines representation and knowledge visualization

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    The intrinsic complexity of the medical domain requires the building of some tools to assist the clinician and improve the patient’s health care. Clinical practice guidelines and protocols (CGPs) are documents with the aim of guiding decisions and criteria in specific areas of healthcare and they have been represented using several languages, but these are difficult to understand without a formal background. This paper uses conceptual graph formalism to represent CGPs. The originality here is the use of a graph-based approach in which reasoning is based on graph-theory operations to support sound logical reasoning in a visual manner. It allows users to have a maximal understanding and control over each step of the knowledge reasoning process in the CGPs exploitation. The application example concentrates on a protocol for the management of adult patients with hyperosmolar hyperglycemic state in the Intensive Care Unit

    A Survey on Automated Food Monitoring and Dietary Management Systems

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    Healthy diet with balanced nutrition is key to the prevention of life-threatening diseases such as obesity, cardiovascular disease, and cancer. Recent advances in smartphone and wearable sensor technologies have led to a proliferation of food monitoring applications based on automated food image processing and eating episode detection, with the goal to conquer drawbacks of the traditional manual food journaling that is time consuming, inaccurate, underreporting, and low adherent. In order to provide users feedback with nutritional information accompanied by insightful dietary advice, various techniques in light of the key computational learning principles have been explored. This survey presents a variety of methodologies and resources on this topic, along with unsolved problems, and closes with a perspective and boarder implications of this field

    Using argumentation to manage users' preferences

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    Argumentation has provided a means to deal with inconsistent knowledge. We explore the potential of argumentation to handle conflicting user preferences. Classical preference handling methods in Artificial Intelligence (AI) lack the ability to handle ambiguity and the evolution of preferences over time. Previous experiments conducted by the authors indicate the usefulness of argumentation systems to handle Ambient Intelligence (AmI) examples with the aforementioned characteristics. This paper explores a generalized framework that can be applied to handle user preferences in AmI. The paper provides an overall preference handling architecture which can be used to extend current argumentation systems. We show how the proposed system can handle multiple users with the introduction of personalised preference functions. We illustrate how user preferences can be handled in realistic ways in AmI environments (such as smart homes), by showing how the system can make decisions based on inhabitants’ preferences on lighting, healthy eating and leisure

    Ontologies in medicinal chemistry: current status and future challenges

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    [Abstract] Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal chemistry, making it possible to achieve significant advances in fields such as the design, synthesis and biological evaluation of compounds. However, with this data explosion, the storage, management and analysis of available data to extract relevant information has become even a more complex task that offers challenging research issues to Artificial Intelligence (AI) scientists. Ontologies have emerged in AI as a key tool to formally represent and semantically organize aspects of the real world. Beyond glossaries or thesauri, ontologies facilitate communication between experts and allow the application of computational techniques to extract useful information from available data. In medicinal chemistry, multiple ontologies have been developed during the last years which contain knowledge about chemical compounds and processes of synthesis of pharmaceutical products. This article reviews the principal standards and ontologies in medicinal chemistry, analyzes their main applications and suggests future directions.Instituto de Salud Carlos III; FIS-PI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/217Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2011/034Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/21
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