Modelling weightlifting “training-diet-competition” cycle ontology with domain and task ontologies

Studies in weightlifting have been characterized by unclear results, and paucity of information. This is due to the fact that enhancing the understanding of the mechanics of successful lift requires collaborative contributions of several stakeholders such as coach, nutritionist, biomechanist, and physiologist as well as the aid of technical advances in motion analysis, data acquisition, and methods of analysis. Currently, there are still a lack of knowledge sharing between these stakeholders. The knowledge owned by these experts are not captures, classified or integrated into an information system for decision-making. In this study, we propose an ontology-driven weightlifting knowledge model as a solution for promoting a better understanding of the weightlifting domain as a whole. The study aims to build a knowledge framework for Olympic weightlifting, bringing together related knowledge subdomains such as training methodology, biomechanics, and dietary while modelling the synergy among them. In so doing, terminology, semantics, and used concepts will be unified among researchers, coaches, nutritionists, and athletes to partially obviate the recognized limitations and inconsistencies. The whole weightlifting "training-diet-competition" (TDC) cycle is semantically modelled by conceiving, designing, and integrating domain and task ontologies with the latter devising reasoning capability toward an automated and tailored weightlifting TDC cycle.- (undefined

Desenvolvimento e análise de um sistema de recomendação para sugestão de artigos médicos no internamento do Hospital da Luz Lisboa

The internet advances have led to an increase of data and information availability. This overload of information tends to compromise the capacity to manage and filter the available data. In the health domain, the increasing digitalization in healthcare led to a substantial rise of the recorded data. Various recommendation systems (RS) have been developed to help healthcare professionals integrate all information and make efficient and effective decisions. Here, a preliminary RS based on collaborative filtering is proposed to reduce the time that healthcare professionals spend in registering medical items consumed during patients’ hospitalization. For that purpose, the RS was built to perform suggestions of the medical items and respective quantities needed in the first day of hospitalization of a patient. Data regarding the diagnostics, surgical procedures and medical item records associated to surgeries of inpatients during a period of one year in Hospital da Luz Lisboa was filtered, restructured, and analysed (N = 5088 surgeries) for the construction of the RS. A 75-25% split of the data was considered with a 4-fold cross-validation procedure applied on the train set to tune the hyperparameters settings for the algorithm. The RS was then tested and evaluated regarding its overall performance in terms of accuracy, classification performance, and coverage. The same measures were applied to assess the quality of the recommendations for each medical specialty of the hospital. Furthermore, the trust of healthcare professionals in the RS was also assessed. A moderate overall performance was achieved (precision = 0.608, recall = 0.729, F1-Measure = 0.663, RMSE = 6.901) and the quality of the algorithm’s recommendations varied between medical specialties. Additionally, the algorithm presented higher values of precision, recall and F1-Measure in the predictions of the most frequently registered medical items in the test set, which corresponded to approximately 85% of the consumptions in the first day of hospitalization. Regarding the coverage of the RS, approximately 80% of the medical items used in the test set were never recommended by the algorithm, corresponding to only 5.57% of the consumptions. Lastly, although in the point of view of hospital’s nurses there is some trust in the RS results, several suggestions were given for further improvements of the algorithm. Despite the limitations of the RS, the observed results represent a starting point for the development of a tool that can support healthcare professionals of Hospital da Luz Lisboa in registering medical items needed during inpatients’ hospitalization.Os avanços da internet têm aumentado a quantidade de informação disponível, pelo que o seu excesso tende a dificultar a capacidade de gestão e filtragem da mesma. Este fenómeno pode ser observado no domínio da saúde onde a digitalização dos serviços médicos levou a um aumento substancial dos dados registados nos hospitais. Com o intuito de ajudar os profissionais de saúde a integrar toda a informação e, assim, realizarem decisões eficientes e efetivas, vários sistemas de recomendação (SR) foram desenvolvidos. Neste projeto, propõe-se um SR preliminar baseado em filtragem colaborativa para reduzir o tempo despendido pelos profissionais de saúde do Hospital da Luz Lisboa no registo de artigos médicos consumidos durante o período de internamento de doentes. Para isso, o SR foi desenvolvido de modo a formular recomendações relativamente aos artigos médicos e respetivas quantidades necessárias para o primeiro dia de internamento de um doente. A construção do SR teve por base os diagnósticos, procedimentos cirúrgicos e registos de consumos de artigos médicos associados a propostas cirúrgicas de doentes que foram internados no período de um ano no Hospital da Luz Lisboa. O conjunto de dados foi filtrado, reestruturado e analisado (N = 5088 propostas cirúrgicas), para posteriormente ser dividido em conjuntos de treino e de teste (75-25%). Foi aplicada uma 4-fold cross-validation sobre o conjunto de treino para a afinação dos hiperparâmetros do algoritmo, sendo o SR foi testado e avaliado relativamente às suas recomendações a nível global e em cada especialidade médica do hospital em termos de accuracy, classification performance e coverage. Foi igualmente avaliado o grau de confiança no SR por parte dos profissionais de saúde do hospital. O SR apresentou uma performance global razoável (precisão = 0.608, sensibilidade = 0.729, F1 = 0.663, RMSE = 6.901) e demonstrou diferentes níveis de qualidade de recomendações dependendo da especialidade médica. Os melhores valores de precisão, sensibilidade e F1 foram observados nas previsões dos artigos médicos mais frequentemente registados, que correspondem a cerca de 85% dos consumos feitos no primeiro dia de internamento dos doentes do conjunto de teste. O algoritmo nunca sugeriu aproximadamente 80% dos artigos médicos utilizados no conjunto de teste, no entanto, estes apenas correspondiam 5.57% dos consumos totais. Por fim, e embora do ponto de vista dos enfermeiros do hospital haja alguma confiança nos resultados do SR, foram dadas sugestões para futuros ajustes do algoritmo. Não obstante as limitações do SR, os resultados obtidos representam um ponto de partida para o desenvolvimento de uma ferramenta de apoio aos profissionais de saúde do Hospital da Luz nos registos dos artigos médicos necessários durante o internamento de doentes.Mestrado em Estatística Médic

Sistema de Fitness Inteligente: Exercícios

Os sistemas de recomendação têm valorizado e aumentado em popularidade nos últimos anos, com os utilizadores a procurarem a facilidade e simplicidade na obtenção de informações e sugestões, tendo por base as suas preferências e expectativas. Adicionalmente, o estilo de vida fitness está na moda com cada vez mais pessoas a adotá-lo. Dessa forma, os praticantes deste tipo de estilo de vida utilizam aplicações para registar as suas atividades, definir objetivos, monitorizar o progresso e evolução, entre outros. Contudo, a maioria das aplicações são específicas a um tipo de utilizador, sendo difícil encontrar uma que seja direcionada a praticantes iniciantes e, ao mesmo tempo, a experientes. Além disso, existem várias lacunas nas aplicações existentes, como falta de funcionalidades importantes ou aspetos de usabilidade. Posto isto, foi desenvolvido um sistema com uma componente móvel que permite a qualquer tipo de praticante de fitness realizar as funcionalidades fundamentais que a sua atividade requer, tais como registo das suas atividades, consulta de treinos ou exercícios fitness já existentes ou criados, personalização de planos e esclarecimento de dúvidas com um assistente pessoal. Além disso, possui um módulo de recomendação com o objetivo de recomendar treinos com base na natureza destes (p.e. nível ou músculos exercitados), nos objetivos do mesmo, e nas características do utilizador. Para validar as funcionalidades gerais, a utilidade e a usabilidade dos componentes desenvolvidos, foram elaborados inquéritos de satisfação. O resultado foi bastante positivo, onde uma elevada percentagem dos utilizadores inquiridos indicou que os módulos desenvolvidos eram bastante úteis, intuitivos e simples. Adicionalmente, as técnicas de recomendação também foram alvo de teste de forma a analisar o erro de previsão de cada uma delas. De forma geral, os resultados também foram positivos, com erros de previsão consideravelmente baixos.Recommender Systems have been increasing in popularity and appreciation over the past few years, with the users seeking ease and simplicity when obtaining information and suggestions, based on their preferences and expectations. Additionally, the fitness lifestyle is being adopted by increasingly more people. In this way, practitioners of this type of lifestyle use applications to record their activities, set goals, monitor progress and evolution, among others. However, most applications are specific to one type of user, and it is difficult to find one that is targeted at both beginner and experienced practitioners. Moreover, there are several gaps in existing applications, such as lack of important functionalities and usability aspects. With this, a mobile component system has been developed, which allows any type of fitness practitioner to perform the fundamental features that their activity requires, such as recording them, consulting existing or created fitness workouts, customizing plans and questions with a personal assistant. In addition, it has a recommendation module with the purpose of recommending workouts based on their nature (i.e. experience level or muscles exercised), and the user's characteristics and objectives. To validate the general functionality, usefulness and usability of the developed components, satisfaction inquiries were developed. The result was very positive, where a high percentage of users stated that the modules developed were very useful, intuitive and simple. Additionally, the recommendation techniques were also tested to analyze the prediction error of each one. Overall, the results were also positive, with considerably low errors

Ontology-based personalized performance evaluation and dietary recommendation for weightlifting.

Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it 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 ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology.Studies in weightlifting have been characterized by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. Becoming successful in weightlifting performance requires a unique physiological and biomechanics profile based on a distinctive combination of muscular strength, muscular power, flexibility, and lifting technique. An effective training which is carefully designed and monitored, is needed for accomplishment of consistent high performance. While it 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 ambitious athletes as good nutrition can help. In spite of several studies on nutrition guidelines for weightlifting training and competition as well as on design and implementation of weightlifting training programs, to the best of authors' knowledge, there is no attempt to semantically model the whole "training-diet-competition" cycle by integrating training, biomechanics, and nutrition domains.This study aims to conceive and design an ontology-enriched knowledge model to guide and support the implementation of "Recommender system of workout and nutrition forweightlifters". In doing so, it will propose: (i) understanding the weightlifting training system, from both qualitative and quantitative perspectives, following a modular ontology modeling, (ii) understanding the weightlifting diet following a modular ontology modeling, (iii) semantically integrating weightlifting and nutrition ontologies to mainly promote nutrition and weightlifting snatch exercises interoperability, (iv) extending modular ontology scope by mining rules while analyzing open data from the literature, and (v) devising reasoning capability toward an automated weightlifting "training-diet-competition" cycle supported by previously mined rulesTo support the above claims, two main artefacts were generated such as: (i) a weightliftingnutritional knowledge questionnaire to assess Thai weightlifting coaches' and athletes'knowledge regarding the weightlifting "training-diet-competition" cycle and (ii) a dual ontologyoriented weightlifting-nutrition knowledge model extended with mined rules and designed following a standard ontology development methodology